ENAS_SearchSpacePoisoning.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K9A-o4b8e15i"
      },
      "source": [
        "Note that this code was adapted from the pytorch macro search implementation of ENAS, credits to https://github.com/MengTianjian/enas-pytorch"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3DPofOr_uY65"
      },
      "source": [
        "import os\n",
        "import sys\n",
        "\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import numpy as np\n",
        "import time\n",
        "from torch.autograd import Variable\n",
        "from torchvision import datasets, transforms\n",
        "from torch.utils.data.dataset import Subset\n",
        "from torch.optim.lr_scheduler import CosineAnnealingLR\n",
        "from torch.distributions.categorical import Categorical\n",
        "import torch.nn.functional as F"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aEHhOUVh0vsp"
      },
      "source": [
        "Visualization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1LyE5LUh0woF"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "% matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1ElG1A_L__ex"
      },
      "source": [
        "Arguments"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LV1oC6tdAAZi"
      },
      "source": [
        "args = {\n",
        "    'description':'ENAS',\n",
        "    'search_for':'macro',\n",
        "    'data_path':'export/data/',\n",
        "    'output_filename':'ENAS',\n",
        "    'resume':'',\n",
        "    'batch_size':128,\n",
        "    'num_epochs':30,\n",
        "    'log_every':50,\n",
        "    'eval_every_epochs':1,\n",
        "    'seed':69,\n",
        "    'cutout':0,\n",
        "    'fixed_arc':False,\n",
        "    'child_num_layers':12,\n",
        "    'child_out_filters':36,\n",
        "    'child_grad_bound':5.0,\n",
        "    'child_l2_reg':0.00025,\n",
        "    'child_num_branches':3,\n",
        "    'child_keep_prob':0.9,\n",
        "    'child_lr_max':0.05,\n",
        "    'child_lr_min':0.0005,\n",
        "    'child_lr_T':10,\n",
        "    'controller_lstm_size':64,\n",
        "    'controller_lstm_num_layers':1,\n",
        "    'controller_entropy_weight':0.0001,\n",
        "    'controller_train_every':1,\n",
        "    'controller_num_aggregate':20,\n",
        "    'controller_train_steps':50,\n",
        "    'controller_lr':0.001,\n",
        "    'controller_tanh_constant':1.5,\n",
        "    'controller_op_tanh_reduce':2.5,\n",
        "    'controller_skip_target':0.4,\n",
        "    'controller_skip_weight':0.8,\n",
        "    'controller_bl_dec':0.99\n",
        "}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uTP1BxNix2Ml"
      },
      "source": [
        "Helper Functions\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KMbT-ZH55BQC"
      },
      "source": [
        "class Cutout(object):\n",
        "    \"\"\"Randomly mask out a patche from an image.\n",
        "    Args:\n",
        "        length (int): The length (in pixels) of each square patch.\n",
        "        p (float): The probability of cutout being applied.\n",
        "    \"\"\"\n",
        "    def __init__(self, length, p=0.5):\n",
        "        self.length = length\n",
        "        self.p = p\n",
        "\n",
        "    def __call__(self, img):\n",
        "        \"\"\"\n",
        "        Args:\n",
        "            img (Tensor): Tensor image of size (C, H, W).\n",
        "        Returns:\n",
        "            Tensor: Image with n_holes of dimension length x length cut out of it.\n",
        "        \"\"\"\n",
        "\n",
        "        if np.random.rand() < self.p:\n",
        "\n",
        "            h = img.size(1)\n",
        "            w = img.size(2)\n",
        "\n",
        "            mask = np.ones((h, w), np.float32)\n",
        "\n",
        "            y = np.random.randint(h)\n",
        "            x = np.random.randint(w)\n",
        "\n",
        "            y1 = np.clip(y - self.length, 0, h)\n",
        "            y2 = np.clip(y + self.length, 0, h)\n",
        "            x1 = np.clip(x - self.length, 0, w)\n",
        "            x2 = np.clip(x + self.length, 0, w)\n",
        "\n",
        "            mask[y1: y2, x1: x2] = 0.\n",
        "\n",
        "            mask = torch.from_numpy(mask)\n",
        "            mask = mask.expand_as(img)\n",
        "            img = img * mask\n",
        "\n",
        "        return img"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qGWsO9kj5B8a"
      },
      "source": [
        "# class Logger(object):\n",
        "#     def __init__(self, filename):\n",
        "#         self.terminal = sys.stdout\n",
        "#         self.log = open(filename, 'w')\n",
        "\n",
        "#     def write(self, message):\n",
        "#         self.terminal.write(message)\n",
        "#         self.log.write(message)\n",
        "#         self.log.flush()\n",
        "\n",
        "#     def flush(self):\n",
        "#         #this flush method is needed for python 3 compatibility.\n",
        "#         #this handles the flush command by doing nothing.\n",
        "#         #you might want to specify some extra behavior here.\n",
        "#         pass\n",
        "\n",
        "class AverageMeter(object):\n",
        "    \"\"\"Computes and stores the average and current value\"\"\"\n",
        "    def __init__(self):\n",
        "        self.reset()\n",
        "\n",
        "    def reset(self):\n",
        "        self.val = 0\n",
        "        self.avg = 0\n",
        "        self.sum = 0\n",
        "        self.count = 0\n",
        "\n",
        "    def update(self, val, n=1):\n",
        "        self.val = val\n",
        "        self.sum += val * n\n",
        "        self.count += n\n",
        "        self.avg = self.sum / self.count"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0T56Pc-J6Ger"
      },
      "source": [
        "Controller"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yJNnOhoi6F9V"
      },
      "source": [
        "class Controller(nn.Module):\n",
        "    '''\n",
        "    https://github.com/melodyguan/enas/blob/master/src/cifar10/general_controller.py\n",
        "    '''\n",
        "    def __init__(self,\n",
        "                 search_for=\"macro\",\n",
        "                 search_whole_channels=True,\n",
        "                 num_layers=12,\n",
        "                 num_branches=6,\n",
        "                 out_filters=36,\n",
        "                 lstm_size=32,\n",
        "                 lstm_num_layers=2,\n",
        "                 tanh_constant=1.5,\n",
        "                 temperature=None,\n",
        "                 skip_target=0.4,\n",
        "                 skip_weight=0.8):\n",
        "        super(Controller, self).__init__()\n",
        "\n",
        "        self.search_for = search_for\n",
        "        self.search_whole_channels = search_whole_channels\n",
        "        self.num_layers = num_layers\n",
        "        self.num_branches = num_branches\n",
        "        self.out_filters = out_filters\n",
        "\n",
        "        self.lstm_size = lstm_size\n",
        "        self.lstm_num_layers = lstm_num_layers\n",
        "        self.tanh_constant = tanh_constant\n",
        "        self.temperature = temperature\n",
        "\n",
        "        self.skip_target = skip_target\n",
        "        self.skip_weight = skip_weight\n",
        "\n",
        "        self._create_params()\n",
        "\n",
        "    def _create_params(self):\n",
        "        '''\n",
        "        https://github.com/melodyguan/enas/blob/master/src/cifar10/general_controller.py#L83\n",
        "        '''\n",
        "        self.w_lstm = nn.LSTM(input_size=self.lstm_size,\n",
        "                              hidden_size=self.lstm_size,\n",
        "                              num_layers=self.lstm_num_layers)\n",
        "\n",
        "        self.g_emb = nn.Embedding(1, self.lstm_size)  # Learn the starting input\n",
        "\n",
        "        if self.search_whole_channels:\n",
        "            self.w_emb = nn.Embedding(self.num_branches, self.lstm_size)\n",
        "            self.w_soft = nn.Linear(self.lstm_size, self.num_branches, bias=False)\n",
        "        else:\n",
        "            assert False, \"Not implemented error: search_whole_channels = False\"\n",
        "\n",
        "        self.w_attn_1 = nn.Linear(self.lstm_size, self.lstm_size, bias=False)\n",
        "        self.w_attn_2 = nn.Linear(self.lstm_size, self.lstm_size, bias=False)\n",
        "        self.v_attn = nn.Linear(self.lstm_size, 1, bias=False)\n",
        "\n",
        "        self._reset_params()\n",
        "\n",
        "    def _reset_params(self):\n",
        "        for m in self.modules():\n",
        "            if isinstance(m, nn.Linear) or isinstance(m, nn.Embedding):\n",
        "                nn.init.uniform_(m.weight, -0.1, 0.1)\n",
        "\n",
        "        nn.init.uniform_(self.w_lstm.weight_hh_l0, -0.1, 0.1)\n",
        "        nn.init.uniform_(self.w_lstm.weight_ih_l0, -0.1, 0.1)\n",
        "\n",
        "    def forward(self):\n",
        "        '''\n",
        "        https://github.com/melodyguan/enas/blob/master/src/cifar10/general_controller.py#L126\n",
        "        '''\n",
        "        h0 = None  # setting h0 to None will initialize LSTM state with 0s\n",
        "\n",
        "        anchors = []\n",
        "        anchors_w_1 = []\n",
        "\n",
        "        arc_seq = {}\n",
        "        entropys = []\n",
        "        log_probs = []\n",
        "        skip_count = []\n",
        "        skip_penaltys = []\n",
        "\n",
        "        inputs = self.g_emb.weight\n",
        "        skip_targets = torch.tensor([1.0 - self.skip_target, self.skip_target]).cuda()\n",
        "\n",
        "        for layer_id in range(self.num_layers):\n",
        "            if self.search_whole_channels:\n",
        "                inputs = inputs.unsqueeze(0)\n",
        "                output, hn = self.w_lstm(inputs, h0)\n",
        "                output = output.squeeze(0)\n",
        "                h0 = hn\n",
        "\n",
        "                logit = self.w_soft(output)\n",
        "                if self.temperature is not None:\n",
        "                    logit /= self.temperature\n",
        "                if self.tanh_constant is not None:\n",
        "                    logit = self.tanh_constant * torch.tanh(logit)\n",
        "\n",
        "                branch_id_dist = Categorical(logits=logit)\n",
        "                branch_id = branch_id_dist.sample()\n",
        "\n",
        "                arc_seq[str(layer_id)] = [branch_id]\n",
        "\n",
        "                log_prob = branch_id_dist.log_prob(branch_id)\n",
        "                log_probs.append(log_prob.view(-1))\n",
        "                entropy = branch_id_dist.entropy()\n",
        "                entropys.append(entropy.view(-1))\n",
        "\n",
        "                inputs = self.w_emb(branch_id)\n",
        "                inputs = inputs.unsqueeze(0)\n",
        "            else:\n",
        "                # https://github.com/melodyguan/enas/blob/master/src/cifar10/general_controller.py#L171\n",
        "                assert False, \"Not implemented error: search_whole_channels = False\"\n",
        "\n",
        "            output, hn = self.w_lstm(inputs, h0)\n",
        "            output = output.squeeze(0)\n",
        "\n",
        "            if layer_id > 0:\n",
        "                query = torch.cat(anchors_w_1, dim=0)\n",
        "                query = torch.tanh(query + self.w_attn_2(output))\n",
        "                query = self.v_attn(query)\n",
        "                logit = torch.cat([-query, query], dim=1)\n",
        "                if self.temperature is not None:\n",
        "                    logit /= self.temperature\n",
        "                if self.tanh_constant is not None:\n",
        "                    logit = self.tanh_constant * torch.tanh(logit)\n",
        "\n",
        "                skip_dist = Categorical(logits=logit)\n",
        "                skip = skip_dist.sample()\n",
        "                skip = skip.view(layer_id)\n",
        "\n",
        "                arc_seq[str(layer_id)].append(skip)\n",
        "\n",
        "                skip_prob = torch.sigmoid(logit)\n",
        "                kl = skip_prob * torch.log(skip_prob / skip_targets)\n",
        "                kl = torch.sum(kl)\n",
        "                skip_penaltys.append(kl)\n",
        "\n",
        "                log_prob = skip_dist.log_prob(skip)\n",
        "                log_prob = torch.sum(log_prob)\n",
        "                log_probs.append(log_prob.view(-1))\n",
        "\n",
        "                entropy = skip_dist.entropy()\n",
        "                entropy = torch.sum(entropy)\n",
        "                entropys.append(entropy.view(-1))\n",
        "\n",
        "                # Calculate average hidden state of all nodes that got skips\n",
        "                # and use it as input for next step\n",
        "                skip = skip.type(torch.float)\n",
        "                skip = skip.view(1, layer_id)\n",
        "                skip_count.append(torch.sum(skip))\n",
        "                inputs = torch.matmul(skip, torch.cat(anchors, dim=0))\n",
        "                inputs /= (1.0 + torch.sum(skip))\n",
        "\n",
        "            else:\n",
        "                inputs = self.g_emb.weight\n",
        "\n",
        "            anchors.append(output)\n",
        "            anchors_w_1.append(self.w_attn_1(output))\n",
        "\n",
        "        self.sample_arc = arc_seq\n",
        "\n",
        "        entropys = torch.cat(entropys)\n",
        "        self.sample_entropy = torch.sum(entropys)\n",
        "\n",
        "        log_probs = torch.cat(log_probs)\n",
        "        self.sample_log_prob = torch.sum(log_probs)\n",
        "\n",
        "        skip_count = torch.stack(skip_count)\n",
        "        self.skip_count = torch.sum(skip_count)\n",
        "\n",
        "        skip_penaltys = torch.stack(skip_penaltys)\n",
        "        self.skip_penaltys = torch.mean(skip_penaltys)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VruBMpRx6iTj"
      },
      "source": [
        "Child"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SRTqpb_i6juS"
      },
      "source": [
        "class FactorizedReduction(nn.Module):\n",
        "    '''\n",
        "    Reduce both spatial dimensions (width and height) by a factor of 2, and \n",
        "    potentially to change the number of output filters\n",
        "    https://github.com/melodyguan/enas/blob/master/src/cifar10/general_child.py#L129\n",
        "    '''\n",
        "\n",
        "    def __init__(self, in_planes, out_planes, stride=2):\n",
        "        super(FactorizedReduction, self).__init__()\n",
        "\n",
        "        assert out_planes % 2 == 0, (\n",
        "        \"Need even number of filters when using this factorized reduction.\")\n",
        "\n",
        "        self.in_planes = in_planes\n",
        "        self.out_planes = out_planes\n",
        "        self.stride = stride\n",
        "\n",
        "        if stride == 1:\n",
        "            self.fr = nn.Sequential(\n",
        "                nn.Conv2d(in_planes, out_planes, kernel_size=1, bias=False),\n",
        "                nn.BatchNorm2d(out_planes, track_running_stats=False))\n",
        "        else:\n",
        "            self.path1 = nn.Sequential(\n",
        "                nn.AvgPool2d(1, stride=stride),\n",
        "                nn.Conv2d(in_planes, out_planes // 2, kernel_size=1, bias=False))\n",
        "\n",
        "            self.path2 = nn.Sequential(\n",
        "                nn.AvgPool2d(1, stride=stride),\n",
        "                nn.Conv2d(in_planes, out_planes // 2, kernel_size=1, bias=False))\n",
        "            self.bn = nn.BatchNorm2d(out_planes, track_running_stats=False)\n",
        "\n",
        "    def forward(self, x):\n",
        "        if self.stride == 1:\n",
        "            return self.fr(x)\n",
        "        else:\n",
        "            path1 = self.path1(x)\n",
        "\n",
        "            # pad the right and the bottom, then crop to include those pixels\n",
        "            path2 = F.pad(x, pad=(0, 1, 0, 1), mode='constant', value=0.)\n",
        "            path2 = path2[:, :, 1:, 1:]\n",
        "            path2 = self.path2(path2)\n",
        "\n",
        "            out = torch.cat([path1, path2], dim=1)\n",
        "            out = self.bn(out)\n",
        "            return out\n",
        "\n",
        "class ENASLayer(nn.Module):\n",
        "    '''\n",
        "    https://github.com/melodyguan/enas/blob/master/src/cifar10/general_child.py#L245\n",
        "    '''\n",
        "    def __init__(self, layer_id, in_planes, out_planes):\n",
        "        super(ENASLayer, self).__init__()\n",
        "\n",
        "        self.layer_id = layer_id\n",
        "        self.in_planes = in_planes\n",
        "        self.out_planes = out_planes\n",
        "#TAKE OUT 3 BY 3 CONVOLUTIONS, BOTH, TAKE OUT MAX POOL!\n",
        "        self.branch_0 = ConvBranch(in_planes, out_planes, kernel_size=3)\n",
        "        self.branch_1 = ConvBranch(in_planes, out_planes, kernel_size=3, separable=True)\n",
        "        self.branch_2 = ConvBranch(in_planes, out_planes, kernel_size=5)\n",
        "        self.branch_3 = ConvBranch(in_planes, out_planes, kernel_size=5, separable=True)\n",
        "        self.branch_4 = PoolBranch(in_planes, out_planes, 'avg')\n",
        "        self.branch_5 = PoolBranch(in_planes, out_planes, 'max')\n",
        "\n",
        "        self.bn = nn.BatchNorm2d(out_planes, track_running_stats=False)\n",
        "\n",
        "    def forward(self, x, prev_layers, sample_arc):\n",
        "        layer_type = sample_arc[0]\n",
        "        if self.layer_id > 0:\n",
        "            skip_indices = sample_arc[1]\n",
        "        else:\n",
        "            skip_indices = []\n",
        "        \n",
        "        if layer_type == 0:\n",
        "            out = self.branch_0(x)\n",
        "        elif layer_type == 1:\n",
        "            out = self.branch_1(x)\n",
        "        elif layer_type == 2:\n",
        "            out = self.branch_2(x)\n",
        "        elif layer_type == 3:\n",
        "            out = self.branch_3(x)\n",
        "        elif layer_type == 4:\n",
        "            out = self.branch_4(x)\n",
        "        elif layer_type == 5:\n",
        "            out = self.branch_5(x)\n",
        "        else:\n",
        "            raise ValueError(\"Unknown layer_type {}\".format(layer_type))\n",
        "\n",
        "        for i, skip in enumerate(skip_indices):\n",
        "            if skip == 1:\n",
        "                out += prev_layers[i]\n",
        "\n",
        "        out = self.bn(out)\n",
        "        return out\n",
        "\n",
        "class FixedLayer(nn.Module):\n",
        "    '''\n",
        "    https://github.com/melodyguan/enas/blob/master/src/cifar10/general_child.py#L245\n",
        "    '''\n",
        "    def __init__(self, layer_id, in_planes, out_planes, sample_arc):\n",
        "        super(FixedLayer, self).__init__()\n",
        "\n",
        "        self.layer_id = layer_id\n",
        "        self.in_planes = in_planes\n",
        "        self.out_planes = out_planes\n",
        "        self.sample_arc = sample_arc\n",
        "\n",
        "        self.layer_type = sample_arc[0]\n",
        "        if self.layer_id > 0:\n",
        "            self.skip_indices = sample_arc[1]\n",
        "        else:\n",
        "            self.skip_indices = torch.zeros(1)\n",
        "\n",
        "        \n",
        "        if self.layer_type == 0:\n",
        "            self.branch = ConvBranch(in_planes, out_planes, kernel_size=5)\n",
        "        elif self.layer_type == 1:\n",
        "            self.branch = ConvBranch(in_planes, out_planes, kernel_size=5, separable=True)\n",
        "        elif self.layer_type == 2:\n",
        "            self.branch = PoolBranch(in_planes, out_planes, 'avg')\n",
        "      \n",
        "        else:\n",
        "            raise ValueError(\"Unknown layer_type {}\".format(self.layer_type))\n",
        "\n",
        "        # Use concatentation instead of addition in the fixed layer for some reason\n",
        "        in_planes = int((torch.sum(self.skip_indices).item() + 1) * in_planes)\n",
        "        self.dim_reduc = nn.Sequential(\n",
        "            nn.Conv2d(in_planes, out_planes, kernel_size=1, bias=False),\n",
        "            nn.ReLU(),\n",
        "            nn.BatchNorm2d(out_planes, track_running_stats=False))\n",
        "\n",
        "    def forward(self, x, prev_layers, sample_arc):\n",
        "        out = self.branch(x)\n",
        "\n",
        "        res_layers = []\n",
        "        for i, skip in enumerate(self.skip_indices):\n",
        "            if skip == 1:\n",
        "                res_layers.append(prev_layers[i])\n",
        "        prev = res_layers + [out]\n",
        "        prev = torch.cat(prev, dim=1)\n",
        "\n",
        "        out = self.dim_reduc(prev)\n",
        "        return out\n",
        "\n",
        "class SeparableConv(nn.Module):\n",
        "    def __init__(self, in_planes, out_planes, kernel_size, bias):\n",
        "        super(SeparableConv, self).__init__()\n",
        "        padding = (kernel_size - 1) // 2\n",
        "        self.depthwise = nn.Conv2d(in_planes, in_planes, kernel_size=kernel_size,\n",
        "                                   padding=padding, groups=in_planes, bias=bias)\n",
        "        self.pointwise = nn.Conv2d(in_planes, out_planes, kernel_size=1, bias=bias)\n",
        "\n",
        "    def forward(self, x):\n",
        "        out = self.depthwise(x)\n",
        "        out = self.pointwise(out)\n",
        "        return out\n",
        "\n",
        "class ConvBranch(nn.Module):\n",
        "    '''\n",
        "    https://github.com/melodyguan/enas/blob/master/src/cifar10/general_child.py#L483\n",
        "    '''\n",
        "    def __init__(self, in_planes, out_planes, kernel_size, separable=False):\n",
        "        super(ConvBranch, self).__init__()\n",
        "        assert kernel_size in [3,5], \"Kernel size must be either 3 or 5\"\n",
        "\n",
        "        self.in_planes = in_planes\n",
        "        self.out_planes = out_planes\n",
        "        self.kernel_size = kernel_size\n",
        "        self.separable = separable\n",
        "\n",
        "        self.inp_conv1 = nn.Sequential(\n",
        "            nn.Conv2d(in_planes, out_planes, kernel_size=1, bias=False),\n",
        "            nn.BatchNorm2d(out_planes, track_running_stats=False),\n",
        "            nn.ReLU())\n",
        "\n",
        "        if separable:\n",
        "            self.out_conv = nn.Sequential(\n",
        "                SeparableConv(in_planes, out_planes, kernel_size=kernel_size, bias=False),\n",
        "                nn.BatchNorm2d(out_planes, track_running_stats=False),\n",
        "                nn.ReLU())\n",
        "        else:\n",
        "            padding = (kernel_size - 1) // 2\n",
        "            self.out_conv = nn.Sequential(\n",
        "                nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size,\n",
        "                          padding=padding, bias=False),\n",
        "                nn.BatchNorm2d(out_planes, track_running_stats=False),\n",
        "                nn.ReLU())\n",
        "\n",
        "    def forward(self, x):\n",
        "        out = self.inp_conv1(x)\n",
        "        out = self.out_conv(out)\n",
        "        return out\n",
        "\n",
        "class PoolBranch(nn.Module):\n",
        "    '''\n",
        "    https://github.com/melodyguan/enas/blob/master/src/cifar10/general_child.py#L546\n",
        "    '''\n",
        "    def __init__(self, in_planes, out_planes, avg_or_max):\n",
        "        super(PoolBranch, self).__init__()\n",
        "\n",
        "        self.in_planes = in_planes\n",
        "        self.out_planes = out_planes\n",
        "        self.avg_or_max = avg_or_max\n",
        "\n",
        "        self.conv1 = nn.Sequential(\n",
        "            nn.Conv2d(in_planes, out_planes, kernel_size=1, bias=False),\n",
        "            nn.BatchNorm2d(out_planes, track_running_stats=False),\n",
        "            nn.ReLU())\n",
        "\n",
        "        if avg_or_max == 'avg':\n",
        "            self.pool = torch.nn.AvgPool2d(kernel_size=3, stride=1, padding=1)\n",
        "        elif avg_or_max == 'max':\n",
        "            self.pool = torch.nn.MaxPool2d(kernel_size=3, stride=1, padding=1)\n",
        "        else:\n",
        "            raise ValueError(\"Unknown pool {}\".format(avg_or_max))\n",
        "\n",
        "    def forward(self, x):\n",
        "        out = self.conv1(x)\n",
        "        out = self.pool(out)\n",
        "        return out\n",
        "\n",
        "class SharedCNN(nn.Module):\n",
        "    def __init__(self,\n",
        "                 num_layers=12,\n",
        "                 num_branches=6,\n",
        "                 out_filters=24,\n",
        "                 keep_prob=1.0,\n",
        "                 fixed_arc=None\n",
        "                 ):\n",
        "        super(SharedCNN, self).__init__()\n",
        "\n",
        "        self.num_layers = num_layers\n",
        "        self.num_branches = num_branches\n",
        "        self.out_filters = out_filters\n",
        "        self.keep_prob = keep_prob\n",
        "        self.fixed_arc = fixed_arc\n",
        "\n",
        "        pool_distance = self.num_layers // 3\n",
        "        self.pool_layers = [pool_distance - 1, 2 * pool_distance - 1]\n",
        "\n",
        "        self.stem_conv = nn.Sequential(\n",
        "            nn.Conv2d(3, out_filters, kernel_size=3, padding=1, bias=False),\n",
        "            # nn.Conv2d(1, out_filters, kernel_size=3, padding=1, bias=False),\n",
        "            nn.BatchNorm2d(out_filters, track_running_stats=False))\n",
        "\n",
        "        self.layers = nn.ModuleList([])\n",
        "        self.pooled_layers = nn.ModuleList([])\n",
        "\n",
        "        for layer_id in range(self.num_layers):\n",
        "            if self.fixed_arc is None:\n",
        "                layer = ENASLayer(layer_id, self.out_filters, self.out_filters)\n",
        "            else:\n",
        "                layer = FixedLayer(layer_id, self.out_filters, self.out_filters, self.fixed_arc[str(layer_id)])\n",
        "            self.layers.append(layer)\n",
        "\n",
        "            if layer_id in self.pool_layers:\n",
        "                for i in range(len(self.layers)):\n",
        "                    if self.fixed_arc is None:\n",
        "                        self.pooled_layers.append(FactorizedReduction(self.out_filters, self.out_filters))\n",
        "                    else:\n",
        "                        self.pooled_layers.append(FactorizedReduction(self.out_filters, self.out_filters * 2))\n",
        "                if self.fixed_arc is not None:\n",
        "                    self.out_filters *= 2\n",
        "\n",
        "        self.global_avg_pool = nn.AdaptiveAvgPool2d((1, 1))\n",
        "        self.dropout = nn.Dropout(p=1. - self.keep_prob)\n",
        "        self.classify = nn.Linear(self.out_filters, 10)\n",
        "\n",
        "        for m in self.modules():\n",
        "            if isinstance(m, nn.Conv2d):\n",
        "                nn.init.kaiming_uniform_(m.weight, mode='fan_in', nonlinearity='relu')\n",
        "\n",
        "    def forward(self, x, sample_arc):\n",
        "\n",
        "        x = self.stem_conv(x)\n",
        "\n",
        "        prev_layers = []\n",
        "        pool_count = 0\n",
        "        for layer_id in range(self.num_layers):\n",
        "            x = self.layers[layer_id](x, prev_layers, sample_arc[str(layer_id)])\n",
        "            prev_layers.append(x)\n",
        "            if layer_id in self.pool_layers:\n",
        "                for i, prev_layer in enumerate(prev_layers):\n",
        "                    # Go through the outputs of all previous layers and downsample them\n",
        "                    prev_layers[i] = self.pooled_layers[pool_count](prev_layer)\n",
        "                    pool_count += 1\n",
        "                x = prev_layers[-1]\n",
        "\n",
        "        x = self.global_avg_pool(x)\n",
        "        x = x.view(x.shape[0], -1)\n",
        "        x = self.dropout(x)\n",
        "        out = self.classify(x)\n",
        "\n",
        "        return out"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sS2tNgkd7LQb"
      },
      "source": [
        "Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3-wwWDx-7K_I"
      },
      "source": [
        "def train_shared_cnn(epoch,\n",
        "                     controller,\n",
        "                     shared_cnn,\n",
        "                     data_loaders,\n",
        "                     shared_cnn_optimizer,\n",
        "                     fixed_arc=None):\n",
        "    \"\"\"Train shared_cnn by sampling architectures from the controller.\n",
        "    Args:\n",
        "        epoch: Current epoch.\n",
        "        controller: Controller module that generates architectures to be trained.\n",
        "        shared_cnn: CNN that contains all possible architectures, with shared weights.\n",
        "        data_loaders: Dict containing data loaders.\n",
        "        shared_cnn_optimizer: Optimizer for the shared_cnn.\n",
        "        fixed_arc: Architecture to train, overrides the controller sample\n",
        "        ...\n",
        "    \n",
        "    Returns: Nothing.\n",
        "    \"\"\"\n",
        "\n",
        "    controller.eval()\n",
        "\n",
        "    if fixed_arc is None:\n",
        "        # Use a subset of the training set when searching for an arhcitecture\n",
        "        train_loader = data_loaders['train_subset']\n",
        "    else:\n",
        "        # Use the full training set when training a fixed architecture\n",
        "        train_loader = data_loaders['train_dataset']\n",
        "\n",
        "    train_acc_meter = AverageMeter()\n",
        "    loss_meter = AverageMeter()\n",
        "\n",
        "    for i, (images, labels) in enumerate(train_loader):\n",
        "        start = time.time()\n",
        "        images = images.cuda()\n",
        "        labels = labels.cuda()\n",
        "\n",
        "        if fixed_arc is None:\n",
        "            with torch.no_grad():\n",
        "                controller()  # perform forward pass to generate a new architecture\n",
        "            sample_arc = controller.sample_arc\n",
        "        else:\n",
        "            sample_arc = fixed_arc\n",
        "\n",
        "        shared_cnn.zero_grad()\n",
        "        pred = shared_cnn(images, sample_arc)\n",
        "        loss = nn.CrossEntropyLoss()(pred, labels)\n",
        "        loss.backward()\n",
        "        grad_norm = torch.nn.utils.clip_grad_norm_(shared_cnn.parameters(), args['child_grad_bound'])\n",
        "        shared_cnn_optimizer.step()\n",
        "\n",
        "        train_acc = torch.mean((torch.max(pred, 1)[1] == labels).type(torch.float))\n",
        "\n",
        "        train_acc_meter.update(train_acc.item())\n",
        "        loss_meter.update(loss.item())\n",
        "\n",
        "        end = time.time()\n",
        "\n",
        "        if (i) % args['log_every'] == 0:\n",
        "            learning_rate = shared_cnn_optimizer.param_groups[0]['lr']\n",
        "            display = 'epoch=' + str(epoch) + \\\n",
        "                      '\\tch_step=' + str(i) + \\\n",
        "                      '\\tloss=%.6f' % (loss_meter.val) + \\\n",
        "                      '\\tlr=%.4f' % (learning_rate) + \\\n",
        "                      '\\t|g|=%.4f' % (grad_norm.item()) + \\\n",
        "                      '\\tacc=%.4f' % (train_acc_meter.val) + \\\n",
        "                      '\\ttime=%.2fit/s' % (1. / (end - start))\n",
        "            print(display)\n",
        "\n",
        "    \n",
        "    # vis_win['shared_cnn_acc'] = vis.line(\n",
        "    #     X=np.array([epoch]),\n",
        "    #     Y=np.array([train_acc_meter.avg]),\n",
        "    #     win=vis_win['shared_cnn_acc'],\n",
        "    #     opts=dict(title='shared_cnn_acc', xlabel='Iteration', ylabel='Accuracy'),\n",
        "    #     update='append' if epoch > 0 else None)\n",
        "\n",
        "    plt.figure()\n",
        "    plt.plot(np.array([epoch]),\n",
        "             np.array([train_acc_meter.avg]),\n",
        "             'ro-',\n",
        "             label='Shared CNN Accuracy')\n",
        "    plt.plot(np.array([epoch]),\n",
        "             np.array([loss_meter.avg]),\n",
        "             'go-',\n",
        "             label='Shared CNN Loss')\n",
        "    plt.legend()\n",
        "    plt.title('Shared CNN')\n",
        "    plt.xlabel('Iteration')\n",
        "    plt.savefig(\"child_training_curve.png\")\n",
        "    plt.show()\n",
        "\n",
        "    # vis_win['shared_cnn_loss'] = vis.line(\n",
        "    #     X=np.array([epoch]),\n",
        "    #     Y=np.array([loss_meter.avg]),\n",
        "    #     win=vis_win['shared_cnn_loss'],\n",
        "    #     opts=dict(title='shared_cnn_loss', xlabel='Iteration', ylabel='Loss'),\n",
        "    #     update='append' if epoch > 0 else None)\n",
        "\n",
        "    controller.train()\n",
        "\n",
        "def train_controller(epoch,\n",
        "                     controller,\n",
        "                     shared_cnn,\n",
        "                     data_loaders,\n",
        "                     controller_optimizer,\n",
        "                     baseline=None):\n",
        "    \"\"\"Train controller to optimizer validation accuracy using REINFORCE.\n",
        "    Args:\n",
        "        epoch: Current epoch.\n",
        "        controller: Controller module that generates architectures to be trained.\n",
        "        shared_cnn: CNN that contains all possible architectures, with shared weights.\n",
        "        data_loaders: Dict containing data loaders.\n",
        "        controller_optimizer: Optimizer for the controller.\n",
        "        baseline: The baseline score (i.e. average val_acc) from the previous epoch\n",
        "    \n",
        "    Returns: \n",
        "        baseline: The baseline score (i.e. average val_acc) for the current epoch\n",
        "    For more stable training we perform weight updates using the average of\n",
        "    many gradient estimates. controller_num_aggregate indicates how many samples\n",
        "    we want to average over (default = 20). By default PyTorch will sum gradients\n",
        "    each time .backward() is called (as long as an optimizer step is not taken),\n",
        "    so each iteration we divide the loss by controller_num_aggregate to get the \n",
        "    average.\n",
        "    https://github.com/melodyguan/enas/blob/master/src/cifar10/general_controller.py#L270\n",
        "    \"\"\"\n",
        "    print('Epoch ' + str(epoch) + ': Training controller')\n",
        "\n",
        "    shared_cnn.eval()\n",
        "    valid_loader = data_loaders['valid_subset']\n",
        "\n",
        "    reward_meter = AverageMeter()\n",
        "    baseline_meter = AverageMeter()\n",
        "    val_acc_meter = AverageMeter()\n",
        "    loss_meter = AverageMeter()\n",
        "\n",
        "    controller.zero_grad()\n",
        "    for i in range(args['controller_train_steps'] * args['controller_num_aggregate']):\n",
        "        start = time.time()\n",
        "        images, labels = next(iter(valid_loader))\n",
        "        images = images.cuda()\n",
        "        labels = labels.cuda()\n",
        "\n",
        "        controller()  # perform forward pass to generate a new architecture\n",
        "        sample_arc = controller.sample_arc\n",
        "\n",
        "        with torch.no_grad():\n",
        "            pred = shared_cnn(images, sample_arc)\n",
        "        val_acc = torch.mean((torch.max(pred, 1)[1] == labels).type(torch.float))\n",
        "\n",
        "        # detach to make sure that gradients aren't backpropped through the reward\n",
        "        reward = torch.tensor(val_acc.detach())\n",
        "        reward += args['controller_entropy_weight'] * controller.sample_entropy\n",
        "\n",
        "        if baseline is None:\n",
        "            baseline = val_acc\n",
        "        else:\n",
        "            baseline -= (1 - args['controller_bl_dec']) * (baseline - reward)\n",
        "            # detach to make sure that gradients are not backpropped through the baseline\n",
        "            baseline = baseline.detach()\n",
        "\n",
        "        loss = -1 * controller.sample_log_prob * (reward - baseline)\n",
        "\n",
        "        if args['controller_skip_weight'] is not None:\n",
        "            loss += args['controller_skip_weight'] * controller.skip_penaltys\n",
        "\n",
        "        reward_meter.update(reward.item())\n",
        "        baseline_meter.update(baseline.item())\n",
        "        val_acc_meter.update(val_acc.item())\n",
        "        loss_meter.update(loss.item())\n",
        "\n",
        "        # Average gradient over controller_num_aggregate samples\n",
        "        loss = loss / args['controller_num_aggregate']\n",
        "\n",
        "        loss.backward(retain_graph=True)\n",
        "\n",
        "        end = time.time()\n",
        "\n",
        "        # Aggregate gradients for controller_num_aggregate iterationa, then update weights\n",
        "        if (i + 1) % args['controller_num_aggregate'] == 0:\n",
        "            grad_norm = torch.nn.utils.clip_grad_norm_(controller.parameters(), args['child_grad_bound'])\n",
        "            controller_optimizer.step()\n",
        "            controller.zero_grad()\n",
        "\n",
        "            if (i + 1) % (2 * args['controller_num_aggregate']) == 0:\n",
        "                learning_rate = controller_optimizer.param_groups[0]['lr']\n",
        "                display = 'ctrl_step=' + str(i // args['controller_num_aggregate']) + \\\n",
        "                          '\\tloss=%.3f' % (loss_meter.val) + \\\n",
        "                          '\\tent=%.2f' % (controller.sample_entropy.item()) + \\\n",
        "                          '\\tlr=%.4f' % (learning_rate) + \\\n",
        "                          '\\t|g|=%.4f' % (grad_norm.item()) + \\\n",
        "                          '\\tacc=%.4f' % (val_acc_meter.val) + \\\n",
        "                          '\\tbl=%.2f' % (baseline_meter.val) + \\\n",
        "                          '\\ttime=%.2fit/s' % (1. / (end - start))\n",
        "                print(display)\n",
        "      \n",
        "    plt.figure()\n",
        "    plt.plot(np.column_stack([epoch] * 2), \n",
        "             np.column_stack([reward_meter.avg, baseline_meter.avg]), \n",
        "             'go-',\n",
        "             label='Controller Reward')\n",
        "    \n",
        "    plt.plot(np.array([epoch]),\n",
        "             np.array([val_acc_meter.avg]),\n",
        "             'ro-',\n",
        "             label='Controller Accuracy')\n",
        "    \n",
        "    plt.plot(np.array([epoch]),\n",
        "             np.array([loss_meter.avg]),\n",
        "             'bo-',\n",
        "             label='Controller Loss')\n",
        "    plt.legend()\n",
        "    plt.title('Controller')\n",
        "    plt.xlabel('Iteration')\n",
        "    plt.savefig(\"controller_training_curve.png\")\n",
        "    plt.show()\n",
        "    \n",
        "    # vis_win['controller_reward'] = vis.line(\n",
        "    #     X=np.column_stack([epoch] * 2),\n",
        "    #     Y=np.column_stack([reward_meter.avg, baseline_meter.avg]),\n",
        "    #     win=vis_win['controller_reward'],\n",
        "    #     opts=dict(title='controller_reward', xlabel='Iteration', ylabel='Reward'),\n",
        "    #     update='append' if epoch > 0 else None)\n",
        "\n",
        "    # vis_win['controller_acc'] = vis.line(\n",
        "    #     X=np.array([epoch]),\n",
        "    #     Y=np.array([val_acc_meter.avg]),\n",
        "    #     win=vis_win['controller_acc'],\n",
        "    #     opts=dict(title='controller_acc', xlabel='Iteration', ylabel='Accuracy'),\n",
        "    #     update='append' if epoch > 0 else None)\n",
        "\n",
        "    # vis_win['controller_loss'] = vis.line(\n",
        "    #     X=np.array([epoch]),\n",
        "    #     Y=np.array([loss_meter.avg]),\n",
        "    #     win=vis_win['controller_loss'],\n",
        "    #     opts=dict(title='controller_loss', xlabel='Iteration', ylabel='Loss'),\n",
        "    #     update='append' if epoch > 0 else None)\n",
        "\n",
        "    shared_cnn.train()\n",
        "    return baseline\n",
        "\n",
        "def train_enas(start_epoch,\n",
        "               controller,\n",
        "               shared_cnn,\n",
        "               data_loaders,\n",
        "               shared_cnn_optimizer,\n",
        "               controller_optimizer,\n",
        "               shared_cnn_scheduler):\n",
        "    \"\"\"Perform architecture search by training a controller and shared_cnn.\n",
        "    Args:\n",
        "        start_epoch: Epoch to begin on.\n",
        "        controller: Controller module that generates architectures to be trained.\n",
        "        shared_cnn: CNN that contains all possible architectures, with shared weights.\n",
        "        data_loaders: Dict containing data loaders.\n",
        "        shared_cnn_optimizer: Optimizer for the shared_cnn.\n",
        "        controller_optimizer: Optimizer for the controller.\n",
        "        shared_cnn_scheduler: Learning rate schedular for shared_cnn_optimizer\n",
        "    \n",
        "    Returns: Nothing.\n",
        "    \"\"\"\n",
        "\n",
        "    baseline = None\n",
        "    for epoch in range(start_epoch, args['num_epochs']):\n",
        "\n",
        "        train_shared_cnn(epoch,\n",
        "                         controller,\n",
        "                         shared_cnn,\n",
        "                         data_loaders,\n",
        "                         shared_cnn_optimizer)\n",
        "\n",
        "        baseline = train_controller(epoch,\n",
        "                                    controller,\n",
        "                                    shared_cnn,\n",
        "                                    data_loaders,\n",
        "                                    controller_optimizer,\n",
        "                                    baseline)\n",
        "\n",
        "        if epoch % args['eval_every_epochs'] == 0:\n",
        "            evaluate_model(epoch, controller, shared_cnn, data_loaders)\n",
        "\n",
        "        # shared_cnn_scheduler.step(epoch)\n",
        "        shared_cnn_scheduler.step()\n",
        "\n",
        "        state = {'epoch': epoch + 1,\n",
        "                 'args': args,\n",
        "                 'shared_cnn_state_dict': shared_cnn.state_dict(),\n",
        "                 'controller_state_dict': controller.state_dict(),\n",
        "                 'shared_cnn_optimizer': shared_cnn_optimizer.state_dict(),\n",
        "                 'controller_optimizer': controller_optimizer.state_dict()}\n",
        "        filename = args['output_filename'] + '.pth.tar'\n",
        "        torch.save(state, filename)\n",
        "\n",
        "def train_fixed(start_epoch,\n",
        "                controller,\n",
        "                shared_cnn,\n",
        "                data_loaders):\n",
        "    \"\"\"Train a fixed cnn architecture.\n",
        "    Args:\n",
        "        start_epoch: Epoch to begin on.\n",
        "        controller: Controller module that generates architectures to be trained.\n",
        "        shared_cnn: CNN that contains all possible architectures, with shared weights.\n",
        "        data_loaders: Dict containing data loaders.\n",
        "    \n",
        "    Returns: Nothing.\n",
        "    Given a fully trained controller and shared_cnn, we sample many architectures,\n",
        "    and then train a new cnn from scratch using the best architecture we found. \n",
        "    We change the number of filters in the new cnn such that the final layer \n",
        "    has 512 channels.\n",
        "    \"\"\"\n",
        "\n",
        "    best_arc, best_val_acc = get_best_arc(controller, shared_cnn, data_loaders, n_samples=100, verbose=True)\n",
        "    print('Best architecture:')\n",
        "    print_arc(best_arc)\n",
        "    print('Validation accuracy: ' + str(best_val_acc))\n",
        "\n",
        "    fixed_cnn = SharedCNN(num_layers=args['child_num_layers'],\n",
        "                          num_branches=args['child_num_branches'],\n",
        "                          out_filters=512 // 4,  # args.child_out_filters\n",
        "                          keep_prob=args['child_keep_prob'],\n",
        "                          fixed_arc=best_arc)\n",
        "    fixed_cnn = fixed_cnn.cuda()\n",
        "\n",
        "    fixed_cnn_optimizer = torch.optim.SGD(params=fixed_cnn.parameters(),\n",
        "                                          lr=args['child_lr_max'],\n",
        "                                          momentum=0.9,\n",
        "                                          nesterov=True,\n",
        "                                          weight_decay=args['child_l2_reg'])\n",
        "\n",
        "    fixed_cnn_scheduler = CosineAnnealingLR(optimizer=fixed_cnn_optimizer,\n",
        "                                            T_max=args['child_lr_T'],\n",
        "                                            eta_min=args['child_lr_min'])\n",
        "\n",
        "    test_loader = data_loaders['test_dataset']\n",
        "\n",
        "    for epoch in range(args['num_epochs']):\n",
        "\n",
        "        train_shared_cnn(epoch,\n",
        "                         controller,  # not actually used in training the fixed_cnn\n",
        "                         fixed_cnn,\n",
        "                         data_loaders,\n",
        "                         fixed_cnn_optimizer,\n",
        "                         best_arc)\n",
        "\n",
        "        if epoch % args['eval_every_epochs'] == 0:\n",
        "            test_acc = get_eval_accuracy(test_loader, fixed_cnn, best_arc)\n",
        "            print('Epoch ' + str(epoch) + ': Eval')\n",
        "            print('test_accuracy: %.4f' % (test_acc))\n",
        "\n",
        "        fixed_cnn_scheduler.step()\n",
        "\n",
        "        state = {'epoch': epoch + 1,\n",
        "                 'args': args,\n",
        "                 'best_arc': best_arc,\n",
        "                 'fixed_cnn_state_dict': shared_cnn.state_dict(),\n",
        "                 'fixed_cnn_optimizer': fixed_cnn_optimizer.state_dict()}\n",
        "        filename = args['output_filename'] + '_fixed.pth.tar'\n",
        "        torch.save(state, filename)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "haOdNc0K9d0s"
      },
      "source": [
        "Evaluation\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TgP3Fjzx9gCJ"
      },
      "source": [
        "def evaluate_model(epoch, controller, shared_cnn, data_loaders, n_samples=10):\n",
        "    \"\"\"Print the validation and test accuracy for a controller and shared_cnn.\n",
        "    Args:\n",
        "        epoch: Current epoch.\n",
        "        controller: Controller module that generates architectures to be trained.\n",
        "        shared_cnn: CNN that contains all possible architectures, with shared weights.\n",
        "        data_loaders: Dict containing data loaders.\n",
        "        n_samples: Number of architectures to test when looking for the best one.\n",
        "    \n",
        "    Returns: Nothing.\n",
        "    \"\"\"\n",
        "\n",
        "    controller.eval()\n",
        "    shared_cnn.eval()\n",
        "\n",
        "    print('Here are ' + str(n_samples) + ' architectures:')\n",
        "    best_arc, _ = get_best_arc(controller, shared_cnn, data_loaders, n_samples, verbose=True)\n",
        "\n",
        "    valid_loader = data_loaders['valid_subset']\n",
        "    test_loader = data_loaders['test_dataset']\n",
        "\n",
        "    valid_acc = get_eval_accuracy(valid_loader, shared_cnn, best_arc)\n",
        "    test_acc = get_eval_accuracy(test_loader, shared_cnn, best_arc)\n",
        "\n",
        "    print('Epoch ' + str(epoch) + ': Eval')\n",
        "    print('valid_accuracy: %.4f' % (valid_acc))\n",
        "    print('test_accuracy: %.4f' % (test_acc))\n",
        "\n",
        "    controller.train()\n",
        "    shared_cnn.train()\n",
        "\n",
        "def get_best_arc(controller, shared_cnn, data_loaders, n_samples=10, verbose=False):\n",
        "    \"\"\"Evaluate several architectures and return the best performing one.\n",
        "    Args:\n",
        "        controller: Controller module that generates architectures to be trained.\n",
        "        shared_cnn: CNN that contains all possible architectures, with shared weights.\n",
        "        data_loaders: Dict containing data loaders.\n",
        "        n_samples: Number of architectures to test when looking for the best one.\n",
        "        verbose: If True, display the architecture and resulting validation accuracy.\n",
        "    \n",
        "    Returns:\n",
        "        best_arc: The best performing architecture.\n",
        "        best_vall_acc: Accuracy achieved on the best performing architecture.\n",
        "    All architectures are evaluated on the same minibatch from the validation set.\n",
        "    \"\"\"\n",
        "\n",
        "    controller.eval()\n",
        "    shared_cnn.eval()\n",
        "\n",
        "    valid_loader = data_loaders['valid_subset']\n",
        "\n",
        "    images, labels = next(iter(valid_loader))\n",
        "    images = images.cuda()\n",
        "    labels = labels.cuda()\n",
        "\n",
        "    arcs = []\n",
        "    val_accs = []\n",
        "    for i in range(n_samples):\n",
        "        with torch.no_grad():\n",
        "            controller()  # perform forward pass to generate a new architecture\n",
        "        sample_arc = controller.sample_arc\n",
        "        arcs.append(sample_arc)\n",
        "\n",
        "        with torch.no_grad():\n",
        "            pred = shared_cnn(images, sample_arc)\n",
        "        val_acc = torch.mean((torch.max(pred, 1)[1] == labels).type(torch.float))\n",
        "        val_accs.append(val_acc.item())\n",
        "\n",
        "        if verbose:\n",
        "            print_arc(sample_arc)\n",
        "            print('val_acc=' + str(val_acc.item()))\n",
        "            print('-' * 80)\n",
        "\n",
        "    best_iter = np.argmax(val_accs)\n",
        "    best_arc = arcs[best_iter]\n",
        "    best_val_acc = val_accs[best_iter]\n",
        "\n",
        "    controller.train()\n",
        "    shared_cnn.train()\n",
        "    return best_arc, best_val_acc\n",
        "\n",
        "def get_eval_accuracy(loader, shared_cnn, sample_arc):\n",
        "    \"\"\"Evaluate a given architecture.\n",
        "    Args:\n",
        "        loader: A single data loader.\n",
        "        shared_cnn: CNN that contains all possible architectures, with shared weights.\n",
        "        sample_arc: The architecture to use for the evaluation.\n",
        "    \n",
        "    Returns:\n",
        "        acc: Average accuracy.\n",
        "    \"\"\"\n",
        "    total = 0.\n",
        "    acc_sum = 0.\n",
        "    for (images, labels) in loader:\n",
        "        images = images.cuda()\n",
        "        labels = labels.cuda()\n",
        "\n",
        "        with torch.no_grad():\n",
        "            pred = shared_cnn(images, sample_arc)\n",
        "        acc_sum += torch.sum((torch.max(pred, 1)[1] == labels).type(torch.float))\n",
        "        total += pred.shape[0]\n",
        "\n",
        "    acc = acc_sum / total\n",
        "    return acc.item()\n",
        "\n",
        "def print_arc(sample_arc):\n",
        "    \"\"\"Display a sample architecture in a readable format.\n",
        "    \n",
        "    Args: \n",
        "        sample_arc: The architecture to display.\n",
        "    Returns: Nothing.\n",
        "    \"\"\"\n",
        "    for key, value in sample_arc.items():\n",
        "        if len(value) == 1:\n",
        "            branch_type = value[0].cpu().numpy().tolist()\n",
        "            print('[' + ' '.join(str(n) for n in branch_type) + ']')\n",
        "        else:\n",
        "            branch_type = value[0].cpu().numpy().tolist()\n",
        "            skips = value[1].cpu().numpy().tolist()\n",
        "            print('[' + ' '.join(str(n) for n in (branch_type + skips)) + ']')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2FJvZFfq317c"
      },
      "source": [
        "Load MNIST data\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F7I4-p1hDWMP"
      },
      "source": [
        "def load_datasets():\n",
        "    \"\"\"Create data loaders for the CIFAR-10 dataset.\n",
        "    Returns: Dict containing data loaders.\n",
        "    \"\"\"\n",
        "    normalize = transforms.Normalize(mean=[x / 255.0 for x in [125.3, 123.0, 113.9]],\n",
        "                                     std=[x / 255.0 for x in [63.0, 62.1, 66.7]])\n",
        "\n",
        "    train_transform = transforms.Compose([\n",
        "        transforms.RandomCrop(32, padding=4),\n",
        "        transforms.RandomHorizontalFlip(),\n",
        "        transforms.ToTensor(),\n",
        "        normalize])\n",
        "\n",
        "    if args['cutout'] > 0:\n",
        "        train_transform.transforms.append(Cutout(length=args['cutout']))\n",
        "\n",
        "    valid_transform = transforms.Compose([\n",
        "        transforms.RandomCrop(32, padding=4),\n",
        "        transforms.RandomHorizontalFlip(),\n",
        "        transforms.ToTensor(),\n",
        "        normalize])\n",
        "\n",
        "    test_transform = transforms.Compose([\n",
        "        transforms.ToTensor(),\n",
        "        normalize])\n",
        "\n",
        "    train_dataset = datasets.CIFAR10(root=args['data_path'],\n",
        "                                     train=True,\n",
        "                                     transform=train_transform,\n",
        "                                     download=True)\n",
        "\n",
        "    valid_dataset = datasets.CIFAR10(root=args['data_path'],\n",
        "                                     train=True,\n",
        "                                     transform=valid_transform,\n",
        "                                     download=True)\n",
        "\n",
        "    test_dataset = datasets.CIFAR10(root=args['data_path'],\n",
        "                                    train=False,\n",
        "                                    transform=test_transform,\n",
        "                                    download=True)\n",
        "\n",
        "    train_indices = list(range(0, 45000))\n",
        "    valid_indices = list(range(45000, 50000))\n",
        "    train_subset = Subset(train_dataset, train_indices)\n",
        "    valid_subset = Subset(valid_dataset, valid_indices)\n",
        "\n",
        "    data_loaders = {}\n",
        "    data_loaders['train_subset'] = torch.utils.data.DataLoader(dataset=train_subset,\n",
        "                                                               batch_size=args['batch_size'],\n",
        "                                                               shuffle=True,\n",
        "                                                               pin_memory=True,\n",
        "                                                               num_workers=2)\n",
        "\n",
        "    data_loaders['valid_subset'] = torch.utils.data.DataLoader(dataset=valid_subset,\n",
        "                                                               batch_size=args['batch_size'],\n",
        "                                                               shuffle=True,\n",
        "                                                               pin_memory=True,\n",
        "                                                               num_workers=2,\n",
        "                                                               drop_last=True)\n",
        "\n",
        "    data_loaders['train_dataset'] = torch.utils.data.DataLoader(dataset=train_dataset,\n",
        "                                                                batch_size=args['batch_size'],\n",
        "                                                                shuffle=True,\n",
        "                                                                pin_memory=True,\n",
        "                                                                num_workers=2)\n",
        "\n",
        "    data_loaders['test_dataset'] = torch.utils.data.DataLoader(dataset=test_dataset,\n",
        "                                                               batch_size=args['batch_size'],\n",
        "                                                               shuffle=False,\n",
        "                                                               pin_memory=True,\n",
        "                                                               num_workers=2)\n",
        "\n",
        "    return data_loaders"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Bv3vVmrI43I"
      },
      "source": [
        "Set seed"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zD0-hX_1I3iR"
      },
      "source": [
        "np.random.seed(args['seed'])\n",
        "torch.cuda.manual_seed(args['seed'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PTxK1cI2JAKg",
        "outputId": "5f2d7c6b-e4dc-4312-c20f-90065707347a"
      },
      "source": [
        "# if args['fixed_arc']:\n",
        "#     sys.stdout = Logger(filename='logs/' + args['output_filename'] + '_fixed.log')\n",
        "# else:\n",
        "#     sys.stdout = Logger(filename='logs/' + args['output_filename'] + '.log')\n",
        "\n",
        "print(\"\\n\".join(\"{}\\t{}\".format(k, v) for k, v in args.items()))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "description\tENAS\n",
            "search_for\tmacro\n",
            "data_path\texport/data/\n",
            "output_filename\tENAS\n",
            "resume\t\n",
            "batch_size\t128\n",
            "num_epochs\t30\n",
            "log_every\t50\n",
            "eval_every_epochs\t1\n",
            "seed\t69\n",
            "cutout\t0\n",
            "fixed_arc\tFalse\n",
            "child_num_layers\t12\n",
            "child_out_filters\t36\n",
            "child_grad_bound\t5.0\n",
            "child_l2_reg\t0.00025\n",
            "child_num_branches\t3\n",
            "child_keep_prob\t0.9\n",
            "child_lr_max\t0.05\n",
            "child_lr_min\t0.0005\n",
            "child_lr_T\t10\n",
            "controller_lstm_size\t64\n",
            "controller_lstm_num_layers\t1\n",
            "controller_entropy_weight\t0.0001\n",
            "controller_train_every\t1\n",
            "controller_num_aggregate\t20\n",
            "controller_train_steps\t50\n",
            "controller_lr\t0.001\n",
            "controller_tanh_constant\t1.5\n",
            "controller_op_tanh_reduce\t2.5\n",
            "controller_skip_target\t0.4\n",
            "controller_skip_weight\t0.8\n",
            "controller_bl_dec\t0.99\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AfyaFTpaJNNs"
      },
      "source": [
        "Create data loaders"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 132,
          "referenced_widgets": [
            "560773ed24ef4c0783f814ac49db7e9b",
            "3b7c182516344b87b477d29c836f062c",
            "5ffdb454b1d9448b9c1b03bb59461c1f",
            "620d87d32ff24b74b5d1f506e6e91bc8",
            "e7e41727e60c4cad8cfcd50287128e6a",
            "eea0b29c4b8645a3ade8ad28aaf23e4b",
            "e6e35c3de3954fbab074e5beac2aa4c6",
            "e2388db3e10e4b2d81e80edc1fbef8e3"
          ]
        },
        "id": "3Dobnh0zJPij",
        "outputId": "0dc214dc-dd4f-45cf-8e32-22916d10f5de"
      },
      "source": [
        "data_loaders = load_datasets()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to export/data/cifar-10-python.tar.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "560773ed24ef4c0783f814ac49db7e9b",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=170498071.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Extracting export/data/cifar-10-python.tar.gz to export/data/\n",
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "avV0HGpdJYgM"
      },
      "source": [
        "Create controller"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UXiDwYg0JZc3"
      },
      "source": [
        "controller = Controller(search_for=args['search_for'],\n",
        "                        search_whole_channels=True,\n",
        "                        num_layers=args['child_num_layers'],\n",
        "                        num_branches=args['child_num_branches'],\n",
        "                        out_filters=args['child_out_filters'],\n",
        "                        lstm_size=args['controller_lstm_size'],\n",
        "                        lstm_num_layers=args['controller_lstm_num_layers'],\n",
        "                        tanh_constant=args['controller_tanh_constant'],\n",
        "                        temperature=None,\n",
        "                        skip_target=args['controller_skip_target'],\n",
        "                        skip_weight=args['controller_skip_weight'])\n",
        "controller = controller.cuda()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XhZlPRnrJkkW"
      },
      "source": [
        "Child architectures"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YmVCQs_FJmCO"
      },
      "source": [
        "shared_cnn = SharedCNN(num_layers=args['child_num_layers'],\n",
        "                        num_branches=args['child_num_branches'],\n",
        "                        out_filters=args['child_out_filters'],\n",
        "                        keep_prob=args['child_keep_prob'])\n",
        "\n",
        "shared_cnn = shared_cnn.cuda()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YOj77sM0Jqir"
      },
      "source": [
        "Adam optimizer for controller"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1asLfjtKJtgm"
      },
      "source": [
        "# https://github.com/melodyguan/enas/blob/master/src/utils.py#L218\n",
        "controller_optimizer = torch.optim.Adam(params=controller.parameters(),\n",
        "                                        lr=args['controller_lr'],\n",
        "                                        betas=(0.0, 0.999),\n",
        "                                        eps=1e-3)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NGPgqYrGJvcE"
      },
      "source": [
        "SGD optimizer for childs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LQBax3GCJwcZ"
      },
      "source": [
        "# https://github.com/melodyguan/enas/blob/master/src/utils.py#L213\n",
        "shared_cnn_optimizer = torch.optim.SGD(params=shared_cnn.parameters(),\n",
        "                                        lr=args['child_lr_max'],\n",
        "                                        momentum=0.9,\n",
        "                                        nesterov=True,\n",
        "                                        weight_decay=args['child_l2_reg'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5J0DFh-8J5k9"
      },
      "source": [
        "Child learning rate"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HvopQ46xKDgz"
      },
      "source": [
        "# https://github.com/melodyguan/enas/blob/master/src/utils.py#L154\n",
        "shared_cnn_scheduler = CosineAnnealingLR(optimizer=shared_cnn_optimizer,\n",
        "                                          T_max=args['child_lr_T'],\n",
        "                                          eta_min=args['child_lr_min'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "qV4UbRGF_VNp",
        "outputId": "32367711-9c9e-4396-ebc2-8b380116855a"
      },
      "source": [
        "if args['resume']:\n",
        "    if os.path.isfile(args['resume']):\n",
        "        print(\"Loading checkpoint '{}'\".format(args['resume']))\n",
        "        checkpoint = torch.load(args['resume'])\n",
        "        start_epoch = checkpoint['epoch']\n",
        "        # args = checkpoint['args']\n",
        "        print(checkpoint.keys())\n",
        "        shared_cnn.load_state_dict(checkpoint['shared_cnn_state_dict'])\n",
        "        controller.load_state_dict(checkpoint['controller_state_dict'])\n",
        "        shared_cnn_optimizer.load_state_dict(checkpoint['shared_cnn_optimizer'])\n",
        "        controller_optimizer.load_state_dict(checkpoint['controller_optimizer'])\n",
        "        shared_cnn_scheduler.optimizer = shared_cnn_optimizer  # Not sure if this actually works\n",
        "        print(\"Loaded checkpoint '{}' (epoch {})\"\n",
        "              .format(args['resume'], checkpoint['epoch']))\n",
        "    else:\n",
        "        raise ValueError(\"No checkpoint found at '{}'\".format(args['resume']))\n",
        "else:\n",
        "  start_epoch = 0\n",
        "\n",
        "if not args['fixed_arc']:\n",
        "    train_enas(start_epoch,\n",
        "                controller,\n",
        "                shared_cnn,\n",
        "                data_loaders,\n",
        "                shared_cnn_optimizer,\n",
        "                controller_optimizer,\n",
        "                shared_cnn_scheduler)\n",
        "else:\n",
        "    assert args['resume'] != '', 'A pretrained model should be used when training a fixed architecture.'\n",
        "    train_fixed(start_epoch,\n",
        "                controller,\n",
        "                shared_cnn,\n",
        "                data_loaders)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "epoch=0\tch_step=0\tloss=2.326638\tlr=0.0500\t|g|=0.5642\tacc=0.1172\ttime=2.27it/s\n",
            "epoch=0\tch_step=50\tloss=2.259871\tlr=0.0500\t|g|=0.6567\tacc=0.1016\ttime=5.01it/s\n",
            "epoch=0\tch_step=100\tloss=2.010729\tlr=0.0500\t|g|=0.5977\tacc=0.2422\ttime=5.11it/s\n",
            "epoch=0\tch_step=150\tloss=1.946116\tlr=0.0500\t|g|=0.5096\tacc=0.3125\ttime=4.81it/s\n",
            "epoch=0\tch_step=200\tloss=1.916308\tlr=0.0500\t|g|=0.8462\tacc=0.2578\ttime=4.97it/s\n",
            "epoch=0\tch_step=250\tloss=1.852486\tlr=0.0500\t|g|=0.8078\tacc=0.2656\ttime=5.04it/s\n",
            "epoch=0\tch_step=300\tloss=1.852606\tlr=0.0500\t|g|=0.8130\tacc=0.3125\ttime=4.71it/s\n",
            "epoch=0\tch_step=350\tloss=1.819196\tlr=0.0500\t|g|=0.8549\tacc=0.3281\ttime=5.80it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 0: Training controller\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:145: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=1\tloss=1.616\tent=58.87\tlr=0.0010\t|g|=0.3432\tacc=0.2891\tbl=0.27\ttime=3.01it/s\n",
            "ctrl_step=3\tloss=6.240\tent=58.69\tlr=0.0010\t|g|=0.2661\tacc=0.3828\tbl=0.28\ttime=3.00it/s\n",
            "ctrl_step=5\tloss=2.884\tent=58.42\tlr=0.0010\t|g|=0.2299\tacc=0.3359\tbl=0.29\ttime=3.11it/s\n",
            "ctrl_step=7\tloss=-0.677\tent=58.02\tlr=0.0010\t|g|=0.2399\tacc=0.2812\tbl=0.30\ttime=3.08it/s\n",
            "ctrl_step=9\tloss=-0.524\tent=57.39\tlr=0.0010\t|g|=0.1210\tacc=0.2812\tbl=0.30\ttime=2.92it/s\n",
            "ctrl_step=11\tloss=0.819\tent=56.96\tlr=0.0010\t|g|=0.2650\tacc=0.3047\tbl=0.30\ttime=3.01it/s\n",
            "ctrl_step=13\tloss=-3.668\tent=56.08\tlr=0.0010\t|g|=0.2454\tacc=0.2266\tbl=0.30\ttime=3.03it/s\n",
            "ctrl_step=15\tloss=-1.883\tent=55.04\tlr=0.0010\t|g|=0.1400\tacc=0.2578\tbl=0.30\ttime=2.93it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f689e5fd5f0>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1324, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1297, in _shutdown_workers\n",
            "    w.join(timeout=_utils.MP_STATUS_CHECK_INTERVAL)\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 140, in join\n",
            "    res = self._popen.wait(timeout)\n",
            "  File \"/usr/lib/python3.7/multiprocessing/popen_fork.py\", line 45, in wait\n",
            "    if not wait([self.sentinel], timeout):\n",
            "  File \"/usr/lib/python3.7/multiprocessing/connection.py\", line 921, in wait\n",
            "    ready = selector.select(timeout)\n",
            "  File \"/usr/lib/python3.7/selectors.py\", line 415, in select\n",
            "    fd_event_list = self._selector.poll(timeout)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/signal_handling.py\", line 66, in handler\n",
            "    _error_if_any_worker_fails()\n",
            "RuntimeError: DataLoader worker (pid 3532) is killed by signal: Segmentation fault. \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=17\tloss=3.141\tent=54.99\tlr=0.0010\t|g|=0.1738\tacc=0.3438\tbl=0.29\ttime=3.04it/s\n",
            "ctrl_step=19\tloss=-0.563\tent=54.07\tlr=0.0010\t|g|=0.1619\tacc=0.2734\tbl=0.29\ttime=3.01it/s\n",
            "ctrl_step=21\tloss=1.302\tent=54.21\tlr=0.0010\t|g|=0.0387\tacc=0.3047\tbl=0.29\ttime=2.91it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Traceback (most recent call last):\n",
            "  File \"/usr/lib/python3.7/multiprocessing/queues.py\", line 242, in _feed\n",
            "    send_bytes(obj)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=23\tloss=-0.195\tent=54.31\tlr=0.0010\t|g|=0.2935\tacc=0.2812\tbl=0.29\ttime=3.05it/s\n",
            "ctrl_step=25\tloss=0.660\tent=53.34\tlr=0.0010\t|g|=0.0868\tacc=0.2969\tbl=0.29\ttime=2.90it/s\n",
            "ctrl_step=27\tloss=0.969\tent=53.84\tlr=0.0010\t|g|=0.0590\tacc=0.2969\tbl=0.28\ttime=3.05it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Traceback (most recent call last):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=29\tloss=0.119\tent=53.96\tlr=0.0010\t|g|=0.0330\tacc=0.2812\tbl=0.28\ttime=2.91it/s\n",
            "ctrl_step=31\tloss=-0.628\tent=52.91\tlr=0.0010\t|g|=0.0600\tacc=0.2656\tbl=0.28\ttime=3.02it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Traceback (most recent call last):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=33\tloss=1.043\tent=53.42\tlr=0.0010\t|g|=0.0991\tacc=0.2969\tbl=0.28\ttime=2.90it/s\n",
            "ctrl_step=35\tloss=4.947\tent=53.59\tlr=0.0010\t|g|=0.0991\tacc=0.3750\tbl=0.29\ttime=3.03it/s\n",
            "ctrl_step=37\tloss=-0.358\tent=54.16\tlr=0.0010\t|g|=0.1877\tacc=0.2734\tbl=0.29\ttime=2.89it/s\n",
            "ctrl_step=39\tloss=1.612\tent=53.50\tlr=0.0010\t|g|=0.2297\tacc=0.3125\tbl=0.29\ttime=2.89it/s\n",
            "ctrl_step=41\tloss=0.026\tent=53.55\tlr=0.0010\t|g|=0.1166\tacc=0.2812\tbl=0.29\ttime=2.89it/s\n",
            "ctrl_step=43\tloss=-2.139\tent=54.20\tlr=0.0010\t|g|=0.1898\tacc=0.2422\tbl=0.29\ttime=2.91it/s\n",
            "ctrl_step=45\tloss=-0.349\tent=54.10\tlr=0.0010\t|g|=0.0417\tacc=0.2734\tbl=0.29\ttime=2.92it/s\n",
            "ctrl_step=47\tloss=3.221\tent=54.32\tlr=0.0010\t|g|=0.1567\tacc=0.3438\tbl=0.29\ttime=2.99it/s\n",
            "ctrl_step=49\tloss=-1.237\tent=53.25\tlr=0.0010\t|g|=0.0968\tacc=0.2578\tbl=0.29\ttime=2.88it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Here are 10 architectures:\n",
            "[2]\n",
            "[1 1]\n",
            "[1 1 0]\n",
            "[0 0 0 1]\n",
            "[1 0 0 0 0]\n",
            "[0 0 1 0 1 1]\n",
            "[2 0 0 0 0 1 0]\n",
            "[0 0 1 0 0 0 0 0]\n",
            "[0 0 0 0 0 1 0 1 0]\n",
            "[1 1 1 0 0 1 0 0 0 0]\n",
            "[0 0 0 1 0 1 0 1 0 0 0]\n",
            "[0 0 0 0 0 0 1 0 0 0 0 0]\n",
            "val_acc=0.15625\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[0 0]\n",
            "[2 1 1]\n",
            "[1 0 1 0]\n",
            "[2 0 1 0 0]\n",
            "[1 0 1 0 1 0]\n",
            "[2 0 1 0 0 0 1]\n",
            "[0 1 0 0 1 0 0 0]\n",
            "[0 1 0 1 0 0 0 0 0]\n",
            "[0 1 0 0 1 1 1 0 1 1]\n",
            "[1 1 0 1 0 0 0 0 0 0 0]\n",
            "[0 0 1 0 0 1 0 0 0 1 1 0]\n",
            "val_acc=0.328125\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[1 1]\n",
            "[2 0 0]\n",
            "[0 0 0 0]\n",
            "[2 0 1 0 0]\n",
            "[1 1 1 0 0 0]\n",
            "[0 0 1 0 0 0 1]\n",
            "[2 0 0 0 0 0 0 0]\n",
            "[0 0 0 0 0 1 0 1 1]\n",
            "[2 0 0 1 0 0 1 0 1 0]\n",
            "[2 0 1 0 0 1 0 1 0 1 1]\n",
            "[0 1 1 1 0 0 1 0 0 0 0 0]\n",
            "val_acc=0.3125\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 1 1]\n",
            "[2 0 0 0]\n",
            "[0 0 0 0 0]\n",
            "[2 1 0 0 0 0]\n",
            "[1 1 1 1 0 1 0]\n",
            "[2 0 0 0 0 0 0 1]\n",
            "[1 0 0 0 1 0 0 0 1]\n",
            "[0 0 0 0 1 0 1 0 0 0]\n",
            "[0 1 0 0 1 0 0 0 0 0 1]\n",
            "[1 1 0 0 0 1 1 1 1 0 0 0]\n",
            "val_acc=0.25\n",
            "--------------------------------------------------------------------------------\n",
            "[0]\n",
            "[1 0]\n",
            "[2 0 0]\n",
            "[1 0 0 1]\n",
            "[1 1 1 0 0]\n",
            "[2 1 0 0 0 0]\n",
            "[2 1 0 1 0 0 1]\n",
            "[2 0 0 0 0 0 0 0]\n",
            "[0 1 0 0 1 0 1 0 1]\n",
            "[2 0 1 1 0 0 1 1 0 0]\n",
            "[0 0 0 1 0 1 0 0 0 0 1]\n",
            "[2 0 0 1 0 0 0 0 0 0 0 0]\n",
            "val_acc=0.234375\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 1]\n",
            "[0 1 1]\n",
            "[0 0 1 0]\n",
            "[0 0 0 1 0]\n",
            "[2 0 1 0 0 0]\n",
            "[2 0 0 0 0 0 0]\n",
            "[1 1 1 1 0 1 1 0]\n",
            "[2 0 0 0 0 1 1 0 0]\n",
            "[2 1 1 1 0 0 0 1 1 1]\n",
            "[0 0 0 0 0 1 1 0 0 0 0]\n",
            "[0 0 0 0 0 0 1 0 1 0 0 0]\n",
            "val_acc=0.2421875\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[0 0]\n",
            "[2 0 1]\n",
            "[0 0 0 0]\n",
            "[2 0 0 1 0]\n",
            "[0 0 1 1 0 0]\n",
            "[0 0 0 0 0 0 0]\n",
            "[0 0 0 0 1 0 0 0]\n",
            "[0 0 0 0 0 1 0 0 1]\n",
            "[0 0 0 0 0 0 0 0 0 1]\n",
            "[1 0 1 0 0 0 0 0 0 0 0]\n",
            "[2 0 1 0 0 0 1 0 1 0 1 0]\n",
            "val_acc=0.2421875\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[1 0]\n",
            "[0 0 0]\n",
            "[1 0 0 0]\n",
            "[1 0 0 1 0]\n",
            "[2 0 0 0 0 0]\n",
            "[0 0 0 1 0 0 1]\n",
            "[1 0 0 0 0 0 0 1]\n",
            "[1 1 1 0 0 1 0 0 0]\n",
            "[1 0 1 0 0 0 0 0 0 0]\n",
            "[1 0 0 1 1 0 0 0 0 0 0]\n",
            "[0 0 0 1 0 0 0 0 0 0 0 0]\n",
            "val_acc=0.234375\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 1]\n",
            "[2 0 1]\n",
            "[1 1 1 0]\n",
            "[0 0 1 0 1]\n",
            "[0 0 0 0 1 0]\n",
            "[2 0 0 0 0 0 1]\n",
            "[2 0 1 0 1 0 0 1]\n",
            "[0 1 0 0 0 0 1 0 0]\n",
            "[0 0 0 1 0 1 0 0 0 0]\n",
            "[2 0 0 0 0 0 1 1 0 0 0]\n",
            "[2 0 0 0 0 0 0 0 1 0 1 0]\n",
            "val_acc=0.296875\n",
            "--------------------------------------------------------------------------------\n",
            "[1]\n",
            "[0 0]\n",
            "[2 0 0]\n",
            "[0 1 0 0]\n",
            "[1 0 1 1 1]\n",
            "[2 0 0 0 1 1]\n",
            "[1 1 1 1 0 0 0]\n",
            "[2 0 0 1 0 1 1 0]\n",
            "[1 0 1 0 0 1 1 0 0]\n",
            "[1 0 0 0 0 1 1 0 0 0]\n",
            "[2 0 1 0 0 0 0 1 0 0 1]\n",
            "[1 0 0 0 0 1 0 0 0 0 0 0]\n",
            "val_acc=0.234375\n",
            "--------------------------------------------------------------------------------\n",
            "Epoch 0: Eval\n",
            "valid_accuracy: 0.3201\n",
            "test_accuracy: 0.3182\n",
            "epoch=1\tch_step=0\tloss=1.904609\tlr=0.0488\t|g|=0.6621\tacc=0.2344\ttime=3.83it/s\n",
            "epoch=1\tch_step=50\tloss=1.919011\tlr=0.0488\t|g|=1.2373\tacc=0.2891\ttime=4.98it/s\n",
            "epoch=1\tch_step=100\tloss=1.882754\tlr=0.0488\t|g|=0.9157\tacc=0.2500\ttime=5.10it/s\n",
            "epoch=1\tch_step=150\tloss=1.770361\tlr=0.0488\t|g|=0.9956\tacc=0.2734\ttime=5.26it/s\n",
            "epoch=1\tch_step=200\tloss=1.799938\tlr=0.0488\t|g|=0.9613\tacc=0.2734\ttime=5.09it/s\n",
            "epoch=1\tch_step=250\tloss=1.817121\tlr=0.0488\t|g|=1.0894\tacc=0.3047\ttime=5.17it/s\n",
            "epoch=1\tch_step=300\tloss=1.804497\tlr=0.0488\t|g|=0.8553\tacc=0.3125\ttime=5.06it/s\n",
            "epoch=1\tch_step=350\tloss=1.770632\tlr=0.0488\t|g|=0.7332\tacc=0.2656\ttime=5.74it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1: Training controller\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:145: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "Traceback (most recent call last):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=1\tloss=1.392\tent=52.31\tlr=0.0010\t|g|=0.1207\tacc=0.3281\tbl=0.30\ttime=3.10it/s\n",
            "ctrl_step=3\tloss=1.711\tent=52.31\tlr=0.0010\t|g|=0.1490\tacc=0.3438\tbl=0.32\ttime=3.15it/s\n",
            "ctrl_step=5\tloss=2.069\tent=51.73\tlr=0.0010\t|g|=0.0325\tacc=0.3594\tbl=0.32\ttime=2.96it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Traceback (most recent call last):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=7\tloss=-0.622\tent=52.10\tlr=0.0010\t|g|=0.0341\tacc=0.3125\tbl=0.33\ttime=2.86it/s\n",
            "ctrl_step=9\tloss=-0.453\tent=51.87\tlr=0.0010\t|g|=0.2414\tacc=0.3203\tbl=0.33\ttime=3.19it/s\n",
            "ctrl_step=11\tloss=1.287\tent=51.14\tlr=0.0010\t|g|=0.1089\tacc=0.3516\tbl=0.33\ttime=2.81it/s\n",
            "ctrl_step=13\tloss=-3.300\tent=51.07\tlr=0.0010\t|g|=0.1158\tacc=0.2656\tbl=0.33\ttime=2.71it/s\n",
            "ctrl_step=15\tloss=0.534\tent=49.04\tlr=0.0010\t|g|=0.2538\tacc=0.3359\tbl=0.33\ttime=2.78it/s\n",
            "ctrl_step=17\tloss=-6.003\tent=50.18\tlr=0.0010\t|g|=0.2173\tacc=0.2031\tbl=0.33\ttime=3.19it/s\n",
            "ctrl_step=19\tloss=-4.231\tent=51.63\tlr=0.0010\t|g|=0.0607\tacc=0.2500\tbl=0.34\ttime=3.06it/s\n",
            "ctrl_step=21\tloss=0.113\tent=52.08\tlr=0.0010\t|g|=0.0626\tacc=0.3359\tbl=0.34\ttime=3.07it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Traceback (most recent call last):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=23\tloss=-0.291\tent=52.52\tlr=0.0010\t|g|=0.0611\tacc=0.3281\tbl=0.34\ttime=2.81it/s\n",
            "ctrl_step=25\tloss=-2.393\tent=51.91\tlr=0.0010\t|g|=0.0856\tacc=0.2891\tbl=0.34\ttime=3.06it/s\n",
            "ctrl_step=27\tloss=-0.830\tent=51.82\tlr=0.0010\t|g|=0.0547\tacc=0.3203\tbl=0.34\ttime=2.75it/s\n",
            "ctrl_step=29\tloss=-1.654\tent=52.23\tlr=0.0010\t|g|=0.1342\tacc=0.3047\tbl=0.34\ttime=2.98it/s\n",
            "ctrl_step=31\tloss=-0.788\tent=52.18\tlr=0.0010\t|g|=0.0774\tacc=0.3203\tbl=0.34\ttime=2.98it/s\n",
            "ctrl_step=33\tloss=-1.899\tent=52.32\tlr=0.0010\t|g|=0.0933\tacc=0.2969\tbl=0.34\ttime=2.69it/s\n",
            "ctrl_step=35\tloss=5.072\tent=53.39\tlr=0.0010\t|g|=0.1599\tacc=0.4297\tbl=0.34\ttime=2.95it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Traceback (most recent call last):\n",
            "  File \"/usr/lib/python3.7/multiprocessing/queues.py\", line 242, in _feed\n",
            "    send_bytes(obj)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=37\tloss=-0.833\tent=52.05\tlr=0.0010\t|g|=0.0753\tacc=0.3203\tbl=0.34\ttime=2.99it/s\n",
            "ctrl_step=39\tloss=1.532\tent=52.45\tlr=0.0010\t|g|=0.2471\tacc=0.3672\tbl=0.34\ttime=1.96it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Traceback (most recent call last):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=41\tloss=1.563\tent=51.44\tlr=0.0010\t|g|=0.1002\tacc=0.3672\tbl=0.34\ttime=2.94it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Traceback (most recent call last):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=43\tloss=-2.868\tent=51.85\tlr=0.0010\t|g|=0.0868\tacc=0.2812\tbl=0.34\ttime=2.74it/s\n",
            "ctrl_step=45\tloss=2.895\tent=51.38\tlr=0.0010\t|g|=0.1173\tacc=0.3906\tbl=0.34\ttime=3.05it/s\n",
            "ctrl_step=47\tloss=2.592\tent=52.13\tlr=0.0010\t|g|=0.1182\tacc=0.3828\tbl=0.34\ttime=2.90it/s\n",
            "ctrl_step=49\tloss=0.380\tent=52.91\tlr=0.0010\t|g|=0.1465\tacc=0.3438\tbl=0.34\ttime=2.99it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Here are 10 architectures:\n",
            "[0]\n",
            "[1 0]\n",
            "[1 0 0]\n",
            "[0 1 1 0]\n",
            "[0 0 0 1 1]\n",
            "[1 0 0 1 0 0]\n",
            "[2 1 0 1 0 1 1]\n",
            "[1 1 0 0 1 0 0 0]\n",
            "[2 0 0 0 0 0 1 0 0]\n",
            "[0 0 0 1 0 1 0 1 0 0]\n",
            "[2 1 0 0 0 0 1 0 1 0 0]\n",
            "[2 0 0 1 1 1 1 0 0 0 0 0]\n",
            "val_acc=0.3984375\n",
            "--------------------------------------------------------------------------------\n",
            "[0]\n",
            "[1 0]\n",
            "[0 0 0]\n",
            "[2 0 1 0]\n",
            "[0 0 0 1 0]\n",
            "[0 0 0 0 0 0]\n",
            "[0 1 0 1 0 0 0]\n",
            "[2 0 0 0 0 0 1 1]\n",
            "[1 0 0 0 1 0 0 0 1]\n",
            "[0 0 0 0 0 1 0 1 1 0]\n",
            "[2 0 0 0 1 0 0 1 0 0 0]\n",
            "[2 1 0 1 1 0 1 1 0 0 1 0]\n",
            "val_acc=0.375\n",
            "--------------------------------------------------------------------------------\n",
            "[0]\n",
            "[1 0]\n",
            "[0 0 0]\n",
            "[2 0 0 0]\n",
            "[2 1 0 0 1]\n",
            "[2 0 0 1 1 0]\n",
            "[2 0 0 0 0 1 0]\n",
            "[2 0 0 0 0 0 0 1]\n",
            "[0 0 0 0 0 1 0 0 0]\n",
            "[0 1 1 0 1 0 1 0 1 0]\n",
            "[2 1 0 0 1 0 0 0 0 0 1]\n",
            "[2 0 0 0 1 1 0 1 1 0 0 0]\n",
            "val_acc=0.375\n",
            "--------------------------------------------------------------------------------\n",
            "[1]\n",
            "[2 0]\n",
            "[0 0 0]\n",
            "[2 0 0 1]\n",
            "[2 0 0 0 1]\n",
            "[2 0 0 1 1 0]\n",
            "[0 1 1 0 0 0 0]\n",
            "[0 0 0 1 1 0 0 0]\n",
            "[1 0 0 0 0 0 0 0 1]\n",
            "[2 0 0 0 0 0 0 1 1 0]\n",
            "[0 0 0 0 0 1 0 0 1 0 0]\n",
            "[1 1 0 1 1 1 0 0 1 0 0 0]\n",
            "val_acc=0.3984375\n",
            "--------------------------------------------------------------------------------\n",
            "[0]\n",
            "[0 1]\n",
            "[1 1 0]\n",
            "[2 1 0 0]\n",
            "[0 0 0 1 0]\n",
            "[2 0 1 0 0 0]\n",
            "[0 1 0 0 0 0 0]\n",
            "[1 0 0 0 1 0 0 0]\n",
            "[2 1 0 0 1 0 1 1 0]\n",
            "[2 1 0 0 0 0 0 0 1 0]\n",
            "[2 1 0 0 1 1 0 1 1 0 0]\n",
            "[1 1 0 1 0 0 0 0 1 1 0 0]\n",
            "val_acc=0.359375\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[1 0]\n",
            "[0 0 0]\n",
            "[1 0 0 1]\n",
            "[1 0 1 0 1]\n",
            "[1 1 0 0 0 0]\n",
            "[0 1 1 0 0 0 0]\n",
            "[2 0 0 0 0 0 1 1]\n",
            "[2 0 0 1 0 0 0 0 0]\n",
            "[0 1 0 0 0 1 0 0 1 0]\n",
            "[0 0 0 1 1 0 0 0 0 0 0]\n",
            "[2 0 0 0 0 1 0 0 1 1 1 0]\n",
            "val_acc=0.328125\n",
            "--------------------------------------------------------------------------------\n",
            "[1]\n",
            "[0 0]\n",
            "[2 0 0]\n",
            "[2 0 0 0]\n",
            "[2 1 0 0 1]\n",
            "[0 0 1 0 0 0]\n",
            "[0 1 0 0 0 0 1]\n",
            "[1 1 0 0 0 0 0 0]\n",
            "[2 0 0 0 0 0 0 1 0]\n",
            "[1 0 0 0 0 0 0 1 1 1]\n",
            "[0 0 1 1 0 1 1 1 0 1 0]\n",
            "[0 0 1 0 0 0 1 0 1 0 1 1]\n",
            "val_acc=0.3125\n",
            "--------------------------------------------------------------------------------\n",
            "[1]\n",
            "[2 1]\n",
            "[2 0 0]\n",
            "[0 0 0 0]\n",
            "[2 1 1 0 0]\n",
            "[0 1 0 1 1 0]\n",
            "[2 0 0 0 0 1 0]\n",
            "[2 0 1 1 0 0 0 0]\n",
            "[1 0 0 0 0 0 1 0 0]\n",
            "[0 0 1 1 0 0 1 1 0 0]\n",
            "[1 0 0 0 0 0 1 0 0 1 1]\n",
            "[2 1 0 0 0 0 0 0 1 1 0 0]\n",
            "val_acc=0.3671875\n",
            "--------------------------------------------------------------------------------\n",
            "[0]\n",
            "[1 0]\n",
            "[0 1 1]\n",
            "[2 1 0 0]\n",
            "[1 1 0 1 0]\n",
            "[2 0 1 0 0 0]\n",
            "[2 0 1 0 0 0 0]\n",
            "[2 0 1 1 0 0 0 0]\n",
            "[1 0 1 0 0 0 0 1 0]\n",
            "[2 0 0 0 0 0 0 1 0 1]\n",
            "[2 0 0 0 1 1 0 0 1 0 1]\n",
            "[2 0 0 0 0 0 0 1 0 0 1 0]\n",
            "val_acc=0.3671875\n",
            "--------------------------------------------------------------------------------\n",
            "[0]\n",
            "[0 0]\n",
            "[1 0 0]\n",
            "[0 0 0 1]\n",
            "[0 0 0 1 1]\n",
            "[1 0 1 1 0 0]\n",
            "[2 0 0 0 0 0 0]\n",
            "[0 0 0 0 0 1 1 0]\n",
            "[0 0 0 0 1 0 0 0 0]\n",
            "[0 0 0 0 0 1 0 0 1 0]\n",
            "[1 1 0 1 0 0 1 1 0 0 0]\n",
            "[2 1 0 0 0 0 0 0 0 0 1 1]\n",
            "val_acc=0.40625\n",
            "--------------------------------------------------------------------------------\n",
            "Epoch 1: Eval\n",
            "valid_accuracy: 0.3522\n",
            "test_accuracy: 0.3562\n",
            "epoch=2\tch_step=0\tloss=1.618948\tlr=0.0453\t|g|=0.6228\tacc=0.4609\ttime=3.90it/s\n",
            "epoch=2\tch_step=50\tloss=1.739200\tlr=0.0453\t|g|=0.7187\tacc=0.3125\ttime=5.11it/s\n",
            "epoch=2\tch_step=100\tloss=1.711236\tlr=0.0453\t|g|=0.7644\tacc=0.3828\ttime=4.94it/s\n",
            "epoch=2\tch_step=150\tloss=1.809194\tlr=0.0453\t|g|=0.9334\tacc=0.2812\ttime=4.83it/s\n",
            "epoch=2\tch_step=200\tloss=1.612165\tlr=0.0453\t|g|=0.8383\tacc=0.3672\ttime=4.98it/s\n",
            "epoch=2\tch_step=250\tloss=1.610936\tlr=0.0453\t|g|=0.6059\tacc=0.3906\ttime=4.90it/s\n",
            "epoch=2\tch_step=300\tloss=1.605031\tlr=0.0453\t|g|=0.7477\tacc=0.4375\ttime=5.31it/s\n",
            "epoch=2\tch_step=350\tloss=1.465861\tlr=0.0453\t|g|=0.8607\tacc=0.4453\ttime=5.65it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 2: Training controller\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:145: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=1\tloss=-3.911\tent=52.04\tlr=0.0010\t|g|=0.0629\tacc=0.2734\tbl=0.35\ttime=2.69it/s\n",
            "ctrl_step=3\tloss=-1.250\tent=51.66\tlr=0.0010\t|g|=0.1617\tacc=0.3281\tbl=0.36\ttime=2.67it/s\n",
            "ctrl_step=5\tloss=-1.970\tent=52.37\tlr=0.0010\t|g|=0.0424\tacc=0.3203\tbl=0.36\ttime=2.64it/s\n",
            "ctrl_step=7\tloss=0.359\tent=52.15\tlr=0.0010\t|g|=0.1550\tacc=0.3672\tbl=0.37\ttime=2.59it/s\n",
            "ctrl_step=9\tloss=-0.517\tent=50.75\tlr=0.0010\t|g|=0.1342\tacc=0.3516\tbl=0.37\ttime=2.58it/s\n",
            "ctrl_step=11\tloss=-0.848\tent=51.03\tlr=0.0010\t|g|=0.1415\tacc=0.3438\tbl=0.37\ttime=2.83it/s\n",
            "ctrl_step=13\tloss=-1.250\tent=51.33\tlr=0.0010\t|g|=0.0803\tacc=0.3359\tbl=0.37\ttime=2.63it/s\n",
            "ctrl_step=15\tloss=-1.442\tent=51.51\tlr=0.0010\t|g|=0.1323\tacc=0.3359\tbl=0.37\ttime=2.86it/s\n",
            "ctrl_step=17\tloss=-4.582\tent=51.65\tlr=0.0010\t|g|=0.1784\tacc=0.2734\tbl=0.37\ttime=2.63it/s\n",
            "ctrl_step=19\tloss=-0.234\tent=52.29\tlr=0.0010\t|g|=0.1807\tacc=0.3594\tbl=0.37\ttime=2.78it/s\n",
            "ctrl_step=21\tloss=2.311\tent=51.47\tlr=0.0010\t|g|=0.0137\tacc=0.4141\tbl=0.37\ttime=2.91it/s\n",
            "ctrl_step=23\tloss=-2.993\tent=50.75\tlr=0.0010\t|g|=0.1352\tacc=0.3125\tbl=0.37\ttime=2.59it/s\n",
            "ctrl_step=25\tloss=1.269\tent=49.94\tlr=0.0010\t|g|=0.1196\tacc=0.3906\tbl=0.37\ttime=2.86it/s\n",
            "ctrl_step=27\tloss=0.297\tent=50.63\tlr=0.0010\t|g|=0.0987\tacc=0.3750\tbl=0.37\ttime=2.86it/s\n",
            "ctrl_step=29\tloss=-0.055\tent=50.84\tlr=0.0010\t|g|=0.0232\tacc=0.3672\tbl=0.37\ttime=2.56it/s\n",
            "ctrl_step=31\tloss=-1.652\tent=51.34\tlr=0.0010\t|g|=0.0186\tacc=0.3359\tbl=0.37\ttime=2.86it/s\n",
            "ctrl_step=33\tloss=-1.705\tent=51.10\tlr=0.0010\t|g|=0.1457\tacc=0.3359\tbl=0.37\ttime=2.82it/s\n",
            "ctrl_step=35\tloss=4.557\tent=49.86\tlr=0.0010\t|g|=0.1115\tacc=0.4688\tbl=0.37\ttime=2.81it/s\n",
            "ctrl_step=37\tloss=0.735\tent=50.17\tlr=0.0010\t|g|=0.1203\tacc=0.3828\tbl=0.37\ttime=2.76it/s\n",
            "ctrl_step=39\tloss=-0.958\tent=51.35\tlr=0.0010\t|g|=0.0597\tacc=0.3438\tbl=0.37\ttime=2.74it/s\n",
            "ctrl_step=41\tloss=2.665\tent=51.66\tlr=0.0010\t|g|=0.0977\tacc=0.4141\tbl=0.37\ttime=2.59it/s\n",
            "ctrl_step=43\tloss=3.626\tent=52.09\tlr=0.0010\t|g|=0.1630\tacc=0.4453\tbl=0.37\ttime=2.60it/s\n",
            "ctrl_step=45\tloss=-0.417\tent=50.79\tlr=0.0010\t|g|=0.0752\tacc=0.3594\tbl=0.37\ttime=2.81it/s\n",
            "ctrl_step=47\tloss=1.427\tent=51.36\tlr=0.0010\t|g|=0.0824\tacc=0.3984\tbl=0.37\ttime=2.57it/s\n",
            "ctrl_step=49\tloss=-2.122\tent=51.41\tlr=0.0010\t|g|=0.1758\tacc=0.3281\tbl=0.38\ttime=2.54it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Here are 10 architectures:\n",
            "[2]\n",
            "[2 0]\n",
            "[0 1 1]\n",
            "[0 0 0 0]\n",
            "[1 0 1 1 0]\n",
            "[1 0 0 0 0 1]\n",
            "[2 0 1 1 0 0 1]\n",
            "[0 1 0 0 0 1 0 0]\n",
            "[0 0 0 0 1 0 1 1 0]\n",
            "[2 0 0 0 0 0 0 1 1 0]\n",
            "[2 0 0 1 0 0 0 0 0 0 0]\n",
            "[2 0 0 0 1 1 0 1 0 1 0 0]\n",
            "val_acc=0.375\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 1]\n",
            "[2 1 0]\n",
            "[2 0 0 0]\n",
            "[0 0 0 0 1]\n",
            "[2 0 0 1 0 0]\n",
            "[0 1 0 0 0 0 0]\n",
            "[1 0 0 1 0 0 0 0]\n",
            "[0 1 0 0 0 0 0 0 0]\n",
            "[1 0 0 0 0 1 0 0 0 0]\n",
            "[2 0 1 0 1 1 0 0 0 1 1]\n",
            "[1 0 0 0 0 0 0 0 1 0 1 1]\n",
            "val_acc=0.3984375\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[0 0 1]\n",
            "[2 0 0 0]\n",
            "[1 0 0 0 0]\n",
            "[2 0 0 1 1 0]\n",
            "[0 0 0 0 0 1 0]\n",
            "[2 0 0 0 0 0 0 0]\n",
            "[2 0 0 0 1 0 0 0 1]\n",
            "[0 1 0 0 0 0 0 0 0 1]\n",
            "[2 0 0 1 0 1 1 0 0 0 0]\n",
            "[2 0 0 0 0 0 0 0 0 1 0 0]\n",
            "val_acc=0.375\n",
            "--------------------------------------------------------------------------------\n",
            "[0]\n",
            "[1 0]\n",
            "[2 1 0]\n",
            "[2 1 0 0]\n",
            "[2 0 1 0 0]\n",
            "[0 1 0 0 0 0]\n",
            "[1 0 0 0 0 1 0]\n",
            "[1 1 1 0 1 0 0 1]\n",
            "[1 1 1 0 0 0 0 0 0]\n",
            "[0 1 0 0 0 0 0 0 0 0]\n",
            "[1 0 0 0 0 0 0 0 1 0 0]\n",
            "[2 0 0 1 0 0 0 0 0 0 0 0]\n",
            "val_acc=0.3515625\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[1 1]\n",
            "[0 0 1]\n",
            "[1 0 1 0]\n",
            "[0 0 1 0 0]\n",
            "[2 0 0 0 0 0]\n",
            "[2 1 0 0 0 0 0]\n",
            "[2 0 0 0 0 0 0 0]\n",
            "[0 0 1 0 0 0 0 1 0]\n",
            "[2 1 0 1 0 0 0 0 0 0]\n",
            "[1 0 0 0 1 0 0 1 1 0 0]\n",
            "[2 0 0 0 0 0 1 0 0 0 0 0]\n",
            "val_acc=0.3046875\n",
            "--------------------------------------------------------------------------------\n",
            "[0]\n",
            "[1 0]\n",
            "[0 1 0]\n",
            "[2 0 1 0]\n",
            "[2 1 0 0 0]\n",
            "[1 0 0 0 0 0]\n",
            "[2 1 0 0 1 0 1]\n",
            "[1 0 0 0 0 0 0 0]\n",
            "[0 0 0 1 0 0 0 0 0]\n",
            "[0 0 0 0 0 0 1 0 0 0]\n",
            "[0 1 0 0 1 0 0 0 0 0 0]\n",
            "[2 1 0 0 0 0 0 0 0 0 1 0]\n",
            "val_acc=0.3984375\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 1]\n",
            "[0 1 0]\n",
            "[1 0 1 0]\n",
            "[2 1 1 0 0]\n",
            "[1 0 1 0 0 1]\n",
            "[2 1 0 0 0 1 1]\n",
            "[2 1 0 1 0 0 0 0]\n",
            "[0 0 1 1 0 1 1 0 0]\n",
            "[2 0 0 0 0 1 0 0 0 0]\n",
            "[0 0 0 0 0 0 1 0 0 0 0]\n",
            "[2 1 0 0 1 0 0 0 0 0 0 0]\n",
            "val_acc=0.4140625\n",
            "--------------------------------------------------------------------------------\n",
            "[0]\n",
            "[0 0]\n",
            "[1 0 0]\n",
            "[1 1 0 0]\n",
            "[0 0 0 0 0]\n",
            "[2 0 0 1 0 0]\n",
            "[0 0 0 0 1 0 0]\n",
            "[2 0 0 1 1 0 0 0]\n",
            "[0 0 0 0 1 0 0 0 0]\n",
            "[0 0 0 1 0 1 0 0 0 1]\n",
            "[0 0 1 1 0 0 0 0 0 1 0]\n",
            "[0 0 0 0 0 0 0 0 0 1 0 0]\n",
            "val_acc=0.40625\n",
            "--------------------------------------------------------------------------------\n",
            "[1]\n",
            "[0 0]\n",
            "[0 0 0]\n",
            "[2 1 0 1]\n",
            "[0 0 0 0 0]\n",
            "[2 0 1 1 0 0]\n",
            "[0 0 0 0 1 0 0]\n",
            "[2 0 0 0 1 0 0 0]\n",
            "[0 0 0 0 1 0 0 0 0]\n",
            "[2 0 1 0 0 0 1 0 0 1]\n",
            "[0 0 1 0 1 1 0 0 0 0 1]\n",
            "[1 1 1 0 1 1 0 0 0 0 1 1]\n",
            "val_acc=0.390625\n",
            "--------------------------------------------------------------------------------\n",
            "[0]\n",
            "[1 0]\n",
            "[0 0 0]\n",
            "[2 1 0 1]\n",
            "[0 0 0 1 0]\n",
            "[0 0 0 0 0 1]\n",
            "[2 0 0 0 1 0 0]\n",
            "[2 1 0 1 1 1 0 0]\n",
            "[2 0 0 0 0 0 0 1 0]\n",
            "[0 1 1 0 0 0 0 1 0 0]\n",
            "[0 1 1 0 1 0 0 0 0 0 0]\n",
            "[2 1 0 1 0 1 0 0 1 0 0 0]\n",
            "val_acc=0.390625\n",
            "--------------------------------------------------------------------------------\n",
            "Epoch 2: Eval\n",
            "valid_accuracy: 0.3750\n",
            "test_accuracy: 0.3665\n",
            "epoch=3\tch_step=0\tloss=1.549147\tlr=0.0398\t|g|=0.8379\tacc=0.3750\ttime=3.76it/s\n",
            "epoch=3\tch_step=50\tloss=1.681129\tlr=0.0398\t|g|=0.8746\tacc=0.3359\ttime=5.08it/s\n",
            "epoch=3\tch_step=100\tloss=1.677329\tlr=0.0398\t|g|=0.8209\tacc=0.3047\ttime=4.89it/s\n",
            "epoch=3\tch_step=150\tloss=1.621431\tlr=0.0398\t|g|=1.1509\tacc=0.3906\ttime=5.18it/s\n",
            "epoch=3\tch_step=200\tloss=1.540875\tlr=0.0398\t|g|=0.7705\tacc=0.4219\ttime=5.00it/s\n",
            "epoch=3\tch_step=250\tloss=1.491782\tlr=0.0398\t|g|=1.2067\tacc=0.3906\ttime=5.45it/s\n",
            "epoch=3\tch_step=300\tloss=1.492179\tlr=0.0398\t|g|=0.7123\tacc=0.4141\ttime=4.83it/s\n",
            "epoch=3\tch_step=350\tloss=1.451870\tlr=0.0398\t|g|=1.0502\tacc=0.4375\ttime=5.52it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 3: Training controller\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:145: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=1\tloss=1.532\tent=51.64\tlr=0.0010\t|g|=0.0194\tacc=0.4141\tbl=0.39\ttime=2.46it/s\n",
            "ctrl_step=3\tloss=-1.120\tent=50.36\tlr=0.0010\t|g|=0.0704\tacc=0.3672\tbl=0.39\ttime=2.41it/s\n",
            "ctrl_step=5\tloss=1.072\tent=51.58\tlr=0.0010\t|g|=0.1168\tacc=0.4141\tbl=0.40\ttime=2.50it/s\n",
            "ctrl_step=7\tloss=3.449\tent=50.62\tlr=0.0010\t|g|=0.0468\tacc=0.4688\tbl=0.40\ttime=2.71it/s\n",
            "ctrl_step=9\tloss=-1.043\tent=49.61\tlr=0.0010\t|g|=0.1343\tacc=0.3750\tbl=0.40\ttime=2.45it/s\n",
            "ctrl_step=11\tloss=-1.731\tent=49.38\tlr=0.0010\t|g|=0.1530\tacc=0.3672\tbl=0.40\ttime=2.75it/s\n",
            "ctrl_step=13\tloss=0.310\tent=51.58\tlr=0.0010\t|g|=0.0781\tacc=0.4062\tbl=0.40\ttime=2.66it/s\n",
            "ctrl_step=15\tloss=-2.982\tent=51.50\tlr=0.0010\t|g|=0.1615\tacc=0.3438\tbl=0.40\ttime=2.43it/s\n",
            "ctrl_step=17\tloss=-0.452\tent=49.00\tlr=0.0010\t|g|=0.2599\tacc=0.3906\tbl=0.40\ttime=2.63it/s\n",
            "ctrl_step=19\tloss=1.331\tent=49.38\tlr=0.0010\t|g|=0.2878\tacc=0.4219\tbl=0.40\ttime=2.59it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Traceback (most recent call last):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=21\tloss=-3.009\tent=51.28\tlr=0.0010\t|g|=0.2809\tacc=0.3438\tbl=0.41\ttime=2.45it/s\n",
            "ctrl_step=23\tloss=-0.030\tent=49.01\tlr=0.0010\t|g|=0.1564\tacc=0.3984\tbl=0.40\ttime=2.66it/s\n",
            "ctrl_step=25\tloss=0.228\tent=49.24\tlr=0.0010\t|g|=0.1717\tacc=0.4062\tbl=0.41\ttime=2.43it/s\n",
            "ctrl_step=27\tloss=3.571\tent=49.12\tlr=0.0010\t|g|=0.1665\tacc=0.4766\tbl=0.41\ttime=2.41it/s\n",
            "ctrl_step=29\tloss=1.655\tent=49.28\tlr=0.0010\t|g|=0.2209\tacc=0.4375\tbl=0.41\ttime=2.58it/s\n",
            "ctrl_step=31\tloss=0.059\tent=49.66\tlr=0.0010\t|g|=0.0619\tacc=0.4062\tbl=0.41\ttime=2.55it/s\n",
            "ctrl_step=33\tloss=-2.990\tent=49.64\tlr=0.0010\t|g|=0.1323\tacc=0.3438\tbl=0.41\ttime=2.51it/s\n",
            "ctrl_step=35\tloss=-3.123\tent=49.12\tlr=0.0010\t|g|=0.0727\tacc=0.3438\tbl=0.41\ttime=2.32it/s\n",
            "ctrl_step=37\tloss=1.334\tent=50.11\tlr=0.0010\t|g|=0.0462\tacc=0.4297\tbl=0.41\ttime=2.54it/s\n",
            "ctrl_step=39\tloss=2.931\tent=50.18\tlr=0.0010\t|g|=0.0536\tacc=0.4609\tbl=0.41\ttime=2.52it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Traceback (most recent call last):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=41\tloss=-1.122\tent=48.63\tlr=0.0010\t|g|=0.0958\tacc=0.3828\tbl=0.41\ttime=2.39it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Traceback (most recent call last):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=43\tloss=-0.034\tent=49.30\tlr=0.0010\t|g|=0.1149\tacc=0.3984\tbl=0.40\ttime=2.36it/s\n",
            "ctrl_step=45\tloss=2.590\tent=47.54\tlr=0.0010\t|g|=0.1690\tacc=0.4531\tbl=0.41\ttime=2.49it/s\n",
            "ctrl_step=47\tloss=1.206\tent=47.22\tlr=0.0010\t|g|=0.2843\tacc=0.4219\tbl=0.40\ttime=2.31it/s\n",
            "ctrl_step=49\tloss=0.188\tent=48.53\tlr=0.0010\t|g|=0.0356\tacc=0.4062\tbl=0.41\ttime=2.35it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/5cYbb2TIkCEsWLCAoqIiDhw4QMeOHbn88suZMmUKR48eZf78+bzzzjsVHiseOqMXiYrSIV9Zew2VV6YYKC5T3KxZs+IyxWUpr0xx27Zti8sUl+fll1/m0UcfpVevXmRlZbFnzx42bNhQfPzyyhTPmzeP8ePHAzB+/Pji6ZtXXnmFa665htatg8Jwp5xyCvv372fHjh2MHTsWCKptHltekSuvvLL49vvvv8+gQYPo2bMnubm5rF27FoDXXnuNG264ofjn0L59e1JSUujQoQPvvvsuL7/8Mr1796ZDhw6VHq8yOqMXaSoqmcogJSWYriktORkWL67WIaNWprioqIinnnqKZ599lhkzZuDu7Nmzp9yKmuVp3rw5R48eLb4fO/0DJcsTT5w4kWeeeYaMjAzmzp3L4koei+uuu465c+eyc+fOWvtgEp3Ri0TFjBlQ+myzdeugvZqiVqb41VdfJT09ne3bt7Nlyxa2bt3KuHHjWLBgARdeeCGPPPJI8Rz63r17adeuHZ06deKZZ54B4NChQxQWFpKcnMy6des4dOgQX375Ja+++mq5x9y/fz9nnHEGhw8fJjfmesnw4cN54IEHgOAJaN++fUBQ5fLFF19k+fLl33gSqy4FvUhUZGdDTk5wBm8WfM/JCdqrKWpliufNm1c8DXPMuHHjmDdvHqNGjWL06NFkZmbSq1cv7r77bgAee+wx7rvvPtLT0zn//PPZuXMnnTt35oorriAtLY0rrriiwn7ecccdZGVlMWDAAM4555zi9nvvvZe8vDx69uxJ3759iz/dqmXLllxwwQVcccUV1f5vpzSVKRZpxFSm+Phz9OjR4lfsdO/evcx1VKZYRKSJWrduHf/yL//C8OHDyw356tDFWBGRRiI1NZXNmzfX+n51Ri8iEnFxBb2ZjTKz9Wa20cymlrH8ejN7z8xWmdkbZpYatqeY2d/D9lVm9uA39y4iInWp0qkbM0sAZgMXAgXAcjNb6O7rYlb7i7s/GK4/GrgHGBUu2+TuvWq32yIiEq94zuj7AxvdfbO7/wOYD4yJXcHdv4q52wZoXC/lERE5jsUT9GcCsYUdCsK2EszsJ2a2CbgTuDFmUVcze9fM/s/Mynz3g5lNMrN8M8vftWtXFbovInUtamWKU1JS2L17d4320dTU2sVYd5/t7mcBvwRuC5s/Bbq4e2/gZuAvZnZiGdvmuHumu2cmJSXVVpdEjju1XKU4smWKjzfxBP0OoHPM/U5hW3nmA5cBuPshd98T3l4BbALOrl5XRaQidVClOJJlisuyZcsWhg0bRnp6OsOHD2fbtm0APPHEE6SlpZGRkVFcmXPt2rX079+fXr16kZ6eXlxIrVFz9wq/CC7Ybga6Ai2B1UCPUut0j7l9KZAf3k4CEsLb3QieIE6p6Hh9+/Z1EQmsW7eu+PZNN7kPGVL+V6tW7kHEl/xq1ar8bW66qeLj33vvvT5lypQylz355JM+YsQIP3LkiO/cudM7d+7sn3zyiefl5fmJJ57o27dv96KiIj/33HN9yZIl7u6enJzsu3btKt4H4I8//ri7u+fn53taWpofOHDA9+/f76mpqb5y5Up3d2/Tpo27u3/88cfeo0cPd3efM2eO33HHHe7ufvDgQe/bt69v3rzZ8/LyvHXr1r558+Yy+126D+7ul1xyic+dO9fd3R9++GEfM2aMu7unpaV5QUGBu7t/8cUX7u4+efJk//Of/+zu7ocOHfLCwsKKf4h1IPb34phjuVvWV6Vn9O5+BJgMvAR8APzV3dea2fTwFTYAk81srZmtIpii+UHYPhhYE7Y/CVzv7ntr8sQkImWr5yrFTbZMcVmWLl3KVVddBcD3v/993njjDQAGDBjAxIkTeeihh4qnls477zx++9vfMnPmTLZu3coJJ5wQ93EaSlzvjHX3F4AXSrXdHnP7pnK2ewp4qiYdFJFAA1QpjlyZ4qp68MEHefvtt3n++efp27cvK1as4KqrriIrK4vnn3+eiy++mDlz5jBs2LBaOV5d0TtjRSKiDqoUR65McXnOP/985s+fD0Bubm7xcTdt2kRWVhbTp08nKSmJ7du3s3nzZrp168aNN97ImDFjWLNmTbWOWZ9U60YkIo5VI542DbZtgy5dgpCvQZXi4jLFU6ZMYebMmSQmJpKSksKsWbMYOHAgS5cuJSMjAzMrLlP84Ycflru/Y2WKO3bsSF5eXollsWWKgbjKFG/ZsoU+ffrg7iQlJRXXja9Meno6zZoF57lXXHEF999/P9dccw133XUXSUlJPPLIIwDccsstbNiwAXdn+PDhZGRkMHPmTB577DFatGjB6aefzq233hrXMRuSyhSLNGIqUyxlUZliEREpQUEvIhJxCnqRRq6xTa9Kw6rO74OCXqQRS0xMZM+ePQp7AYKQ37NnD4mJiVXaTq+6EWnEOnXqREFBASr2J8ckJibSqVOnKm2joBdpxFq0aFGld3iKlEVTNyIiEaegFxGJOAW9iEjEKehFRCJOQS8iEnEKehGRiFPQi4hEnIJeRCTiFPQiIhGnoBcRiTgFvYhIxCnoRUQiTkEvIhJxCnoRkYhT0IuIRJyCXkQk4hT0IiIRp6AXEYk4Bb2ISMQp6EVEIk5BLyIScQp6EZGIiyvozWyUma03s41mNrWM5deb2XtmtsrM3jCz1Jhlvwq3W29mI2uz8yIiUrlKg97MEoDZwHeAVGBCbJCH/uLuPd29F3AncE+4bSowHugBjAL+J9yfiIjUk3jO6PsDG919s7v/A5gPjIldwd2/irnbBvDw9hhgvrsfcvePgY3h/kREpJ40j2OdM4HtMfcLgKzSK5nZT4CbgZbAsJhtl5Xa9swytp0ETALo0qVLPP0WEZE41drFWHef7e5nAb8EbqvitjnununumUlJSbXVJRERIb6g3wF0jrnfKWwrz3zgsmpuKyIitSyeoF8OdDezrmbWkuDi6sLYFcyse8zdfwU2hLcXAuPNrJWZdQW6A+/UvNsiIhKvSufo3f2ImU0GXgISgD+6+1ozmw7ku/tCYLKZjQAOA18APwi3XWtmfwXWAUeAn7h7UR2NRUREymDuXvla9SgzM9Pz8/MbuhsiIk2Kma1w98yylumdsSIiEaegFxGJOAW9iEjEKehFRCJOQS8iEnEKehGRiFPQi4hEnIJeRCTiFPQiIhGnoBcRiTgFvYhIxCnoRUQiTkEvIhJxCnoRkYhT0IuIRJyCXkQk4hT0IiIRp6AXEYk4Bb2ISMQp6EVEIk5BLyIScQp6EZGIU9CLiEScgl5EJOIU9CIiEaegFxGJOAW9iEjEKehFRCJOQS8iEnEKehGRiFPQi4hEXFxBb2ajzGy9mW00s6llLL/ZzNaZ2Roze9XMkmOWFZnZqvBrYW12XkREKte8shXMLAGYDVwIFADLzWyhu6+LWe1dINPdC83sBuBO4Mpw2d/dvVct91tEROIUzxl9f2Cju292938A84ExsSu4e567F4Z3lwGdarebIiJSXfEE/ZnA9pj7BWFbea4F/hZzP9HM8s1smZldVtYGZjYpXCd/165dcXRJRETiVenUTVWY2feATGBITHOyu+8ws27Aa2b2nrtvit3O3XOAHIDMzEyvzT6JiBzv4jmj3wF0jrnfKWwrwcxGANOA0e5+6Fi7u+8Iv28GFgO9a9BfERGponiCfjnQ3cy6mllLYDxQ4tUzZtYbmEMQ8p/HtJ9sZq3C26cCA4DYi7giIlLHKp26cfcjZjYZeAlIAP7o7mvNbDqQ7+4LgbuAtsATZgawzd1HA98G5pjZUYInld+VerWOiIjUMXNvXFPimZmZnp+f39DdEBFpUsxshbtnlrVM74wVEYk4Bb2ISMQp6EVEIk5BLyIScQp6EZGIU9CLiEScgl5EJOIU9CIiEaegFxGJOAW9iEjEKehFRCJOQS8iEnEKehGRiFPQi4hEnIJeRCTiFPQiIhGnoBcRiTgFvYhIxCnoRUQiTkEvIhJxCnoRkYhT0IuIRJyCXkQk4hT0IiIRp6AXEYk4Bb2ISMQp6EVEIk5BLyIScQp6EZGIU9CLiEScgl5EJOLiCnozG2Vm681so5lNLWP5zWa2zszWmNmrZpYcs+wHZrYh/PpBbXZeREQqV2nQm1kCMBv4DpAKTDCz1FKrvQtkuns68CRwZ7jtKcCvgSygP/BrMzu59rovIiKVieeMvj+w0d03u/s/gPnAmNgV3D3P3QvDu8uATuHtkcAid9/r7l8Ai4BRtdN1ERGJRzxBfyawPeZ+QdhWnmuBv1VlWzObZGb5Zpa/a9euOLokIiLxqtWLsWb2PSATuKsq27l7jrtnuntmUlJSbXZJROS4F0/Q7wA6x9zvFLaVYGYjgGnAaHc/VJVtRUSk7sQT9MuB7mbW1cxaAuOBhbErmFlvYA5ByH8es+gl4CIzOzm8CHtR2CYiIvWkeWUruPsRM5tMENAJwB/dfa2ZTQfy3X0hwVRNW+AJMwPY5u6j3X2vmd1B8GQBMN3d99bJSEREpEzm7g3dhxIyMzM9Pz+/obshItKkmNkKd88sa5neGSsiEnEKehGRiFPQi4hEnIJeRCTiFPQiIhGnoBcRiTgFvYhIxCnoRUQiTkEvIhJxCnoRkYhT0IuIRJyCXkQk4hT0IiIRp6AXEYk4Bb2ISMQp6EVEIk5BLyIScQp6EZGIU9CLiEScgl5EJOIU9CIiEaegFxGJOAW9iEjEKehFRCJOQS8Sh9xcSEmBZs2C77m5Dd0jkfg1b+gOiDR2ubkwaRIUFgb3t24N7gNkZzdcv0TipTN6kUpMm/bPkD+msDBoF2kKFPQildi2rWrtIo2Ngl6kEl26VK1dpLFR0ItUYsYMaN26ZFvr1kG7SFMQV9Cb2SgzW29mG81sahnLB5vZSjM7YmbfLbWsyMxWhV8La6vjIvUlOxtyciA5GcyC7zk5uhArTUelr7oxswRgNnAhUAAsN7OF7r4uZrVtwETgF2Xs4u/u3qsW+irSYLKzFezSdMXz8sr+wEZ33wxgZvOBMUBx0Lv7lnDZ0Troo4iI1EA8UzdnAttj7heEbfFKNLN8M1tmZpdVqXciIlJj9fGGqWR332Fm3YDXzOw9d98Uu4KZTQImAXTRSxlERGpVPGf0O4DOMfc7hW1xcfcd4ffNwGKgdxnr5Lh7prtnJiUlxbtrERGJQzxBvxzobmZdzawlMB6I69UzZnaymbUKb58KDCBmbl9EROqeuXvlK5ldDMwCEoA/uvsMM5sO5Lv7QjPrBywATgYOAjvdvYeZnQ/MAY4SPKnMcveHKznWLmBrDcZ0KrC7Bts3FlEZB2gsjVVUxhKVcUDNxpLs7mVOicQV9E2JmeW7e2ZD96OmojIO0Fgaq6iMJSrjgLobi94ZKyIScQp6EZGIi2LQ5zR0B2pJVMYBGktjFZWxRGUcUEdjidwcvYiIlBTFM3oREYmhoBcRibgmEfRm1tnM8sxsnZmtNbObylinvZk9Z2arw3WuiVnWaEolxzmWk81sgZmtMbN3zCwtZlmFJaPrUy2MZYuZvRc+Lvn12/tv9DMx7N+x35//LGOdVmb2ePizf9vMUmKW/SpsX29mI+uz76X6WO1xmFmKmf095m/lwfruf6l+xjOWikqk/8DMNoRfP6i/nn9TLYylZhnm7o3+CzgD6BPebgd8BKSWWudWYGZ4OwnYC7QM7x9o6DFUcSx3Ab8Ob58DvBreTgA2Ad2AlsDq0ts2lbGE97cApzb0YxL2xYC24e0WwNvAuaXW+THwYHh7PPB4eDs1fCxaAV3DxyihCY4jBXi/oR+LKo4lBUgHHgW+G9N+CrA5/H5yePvkpjiWcFmNMqxJnNG7+6fuvjK8vR/4gG9W0HSgnZkZ0JYg6I/Ua0fjEOdYUoHXwnU+BFLM7DRiSka7+z+AYyWjG0QNx9KoeOBAeLdF+FX6lQpjgD+Ft58Ehoe/b2OA+e5+yN0/BjYSPFb1robjaFTiGYu7b3H3NQTvvo81Eljk7nvd/QtgETCqrvtcnhqOpcaaRNDHCv/N7E3wjBjrv4FvA58A7wE3ufuxH1ijLJVcwVhWA5eH6/QHkgmKydW0ZHSdqcZYIPhFf9nMVlhQwbRBmVmCma0CPicIidJjKf75u/sRYB/QgUb2uAk56KcAAARUSURBVNRgHABdzexdM/s/MxtUb50uRxxjKU+jekygRmOBGmZYkwp6M2sLPAVMcfevSi0eCawCOgK9gP82sxPDZckevK34KmCWmZ1VX30uTyVj+R1wUvhL8VPgXaConrsYtxqMZaC79wG+A/zEzAbXV5/L4u5FHnwaWiegf+z1hKakBuP4FOji7r2Bm4G/xPwNNYioPCZQ47HUKMOaTNCbWQuCMMl196fLWOUa4OnwX6SNwMcEc8J4HKWS61NlY3H3r9z9mvCX4mqCaw6bqWHJ6LpQg7HEPi6fExTFa5DpjtLc/Usgj2/+q1/88zez5kB7YA+N8HGBqo8jnHraE267guBaw9n11+PyVTCW8jTKxwSqNZYaZ1iTCPpw/vBh4AN3v6ec1bYBw8P1TwO+BWy2RlYqOZ6xmNlJFpSEBrgOeD08U652yei6UJOxmFkbM2sXrtMGuAh4vz76XU4/k8zspPD2CQSfkfxhqdUWAsdevfFd4DUPrpQtBMaHr2bpCnQH3qmfnpdUk3GE2yaE23YjGMfm+un5N8U5lvK8BFwU/v2fTPD79VLd9LRyNRlLrWRYTa7k1tcXMJBgPncNwfTMKuBi4Hrg+nCdjsDLBPPz7wPfC9vPD9tWh9+vbQJjOY/gFSzrgaeJebVAuO5HBGdb05rqWAheObQ6/FrbCMaSTjCttCb8/bk9bJ8OjA5vJwJPEFxsfQfoFrP9tPAxWQ98pymOAxgXPhargJXApU3gMelHMP/+NcF/V2tjtv9hOMaNwDVNdSy1kWEqgSAiEnFNYupGRESqT0EvIhJxCnoRkYhT0IuIRJyCXkQk4hT0EmlmdiD8nmJmV9Xyvm8tdf+t2ty/SG1R0MvxIoXg7eNxC981WpESQe/u51exTyL1QkEvx4vfAYPCet4/CwtM3WVmyy2olf8jADMbamZLwprf68K2Z8LCa2uPFV8zs98BJ4T7yw3bjv33YOG+37eg3v6VMftebGZPmtmHZpbbGKtGSvRUdsYiEhVTgV+4+yUAYWDvc/d+4dvL3zSzl8N1+wBpHpQcBvihu+8N37q+3MyecvepZjbZgxo+pV1OUFgvAzg13Ob1cFlvoAdBldU3Cd7O/kbtD1fkn3RGL8eri4Crw6qabxOU6e0eLnsnJuQBbjSz1cAygkJZ3anYQGCeB9UKPwP+j+Dt7cf2XeBBCe1VBFNKInVKZ/RyvDLgp+5eotCVmQ0lqDUSe38EcJ67F5rZYoJaMdV1KOZ2EfoblHqgM3o5Xuwn+LjDY14CbgjLLGNmZ4dVNEtrD3wRhvw5wLkxyw4f276UJcCV4XWAJGAwDVTNUgR0NiHHjzVAUTgFMxe4l2DaZGV4QXQXUNYn97wIXG9mHxBUplwWsywHWGNmK909O6Z9AUHVztUE1T3/3d13hk8UIvVO1StFRCJOUzciIhGnoBcRiTgFvYhIxCnoRUQiTkEvIhJxCnoRkYhT0IuIRNz/B+4Uj80qf8+ZAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Here are 10 architectures:\n",
            "[0]\n",
            "[0 0]\n",
            "[1 0 1]\n",
            "[2 1 0 1]\n",
            "[2 1 0 0 0]\n",
            "[2 1 1 1 1 0]\n",
            "[1 0 1 0 0 1 0]\n",
            "[2 1 0 1 0 0 0 1]\n",
            "[2 1 0 0 0 0 1 0 0]\n",
            "[2 1 0 1 0 0 0 0 0 0]\n",
            "[2 0 1 0 0 0 0 0 0 0 0]\n",
            "[2 0 0 0 0 0 1 0 0 0 0 0]\n",
            "val_acc=0.4609375\n",
            "--------------------------------------------------------------------------------\n",
            "[1]\n",
            "[2 0]\n",
            "[0 1 0]\n",
            "[2 0 1 0]\n",
            "[0 0 0 1 1]\n",
            "[2 1 0 0 0 0]\n",
            "[2 0 0 0 0 0 0]\n",
            "[2 0 1 0 0 0 0 1]\n",
            "[2 0 0 0 1 1 0 0 0]\n",
            "[2 0 0 0 0 1 0 0 0 0]\n",
            "[2 0 0 1 0 0 1 0 0 0 0]\n",
            "[2 0 0 0 0 1 0 1 1 0 0 0]\n",
            "val_acc=0.40625\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[1 1]\n",
            "[2 1 1]\n",
            "[2 0 0 0]\n",
            "[2 0 0 1 0]\n",
            "[2 0 0 0 0 0]\n",
            "[0 0 0 0 1 0 0]\n",
            "[2 1 0 1 0 0 1 0]\n",
            "[2 0 0 0 0 0 0 0 0]\n",
            "[2 1 1 1 0 0 0 0 0 0]\n",
            "[2 0 0 0 1 0 1 0 1 0 0]\n",
            "[0 0 0 0 0 0 1 1 0 0 0 0]\n",
            "val_acc=0.40625\n",
            "--------------------------------------------------------------------------------\n",
            "[1]\n",
            "[2 1]\n",
            "[0 1 0]\n",
            "[0 1 0 0]\n",
            "[2 1 0 1 0]\n",
            "[2 0 1 0 0 1]\n",
            "[2 1 1 0 0 1 0]\n",
            "[0 0 0 1 0 1 1 0]\n",
            "[2 0 0 0 1 1 0 1 0]\n",
            "[2 0 0 0 1 0 0 0 0 1]\n",
            "[2 0 1 1 0 0 1 0 1 1 0]\n",
            "[2 0 0 0 1 0 1 0 0 1 0 1]\n",
            "val_acc=0.40625\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[0 1]\n",
            "[0 0 1]\n",
            "[2 0 0 0]\n",
            "[2 0 0 0 0]\n",
            "[0 0 0 1 0 0]\n",
            "[1 0 1 1 0 0 1]\n",
            "[2 1 0 0 1 0 0 0]\n",
            "[0 0 0 0 1 1 0 0 0]\n",
            "[2 0 0 0 1 1 0 1 0 1]\n",
            "[1 1 1 0 1 1 0 0 0 0 1]\n",
            "[0 0 0 0 1 0 0 0 0 0 0 0]\n",
            "val_acc=0.34375\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[0 0]\n",
            "[2 1 0]\n",
            "[2 0 1 1]\n",
            "[2 0 0 0 1]\n",
            "[2 0 0 0 0 0]\n",
            "[1 1 0 0 1 1 0]\n",
            "[2 1 0 0 1 0 0 1]\n",
            "[2 0 0 0 0 1 1 0 1]\n",
            "[0 0 0 0 0 1 0 1 0 1]\n",
            "[1 0 1 0 0 0 0 0 0 1 1]\n",
            "[2 0 0 0 0 0 0 0 0 0 1 0]\n",
            "val_acc=0.4921875\n",
            "--------------------------------------------------------------------------------\n",
            "[0]\n",
            "[2 0]\n",
            "[2 1 0]\n",
            "[0 0 1 1]\n",
            "[1 0 0 0 0]\n",
            "[2 0 0 0 0 1]\n",
            "[2 1 1 0 0 0 0]\n",
            "[2 0 0 1 0 0 0 0]\n",
            "[0 1 1 1 0 0 0 0 0]\n",
            "[2 1 0 0 0 0 0 0 0 1]\n",
            "[2 1 1 0 0 0 0 0 0 0 0]\n",
            "[2 0 0 1 0 0 0 1 1 0 1 0]\n",
            "val_acc=0.4140625\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[1 0]\n",
            "[1 1 0]\n",
            "[2 0 1 1]\n",
            "[1 0 0 1 0]\n",
            "[2 0 0 0 0 0]\n",
            "[1 1 0 0 0 0 0]\n",
            "[2 1 0 0 0 0 0 0]\n",
            "[2 0 1 0 0 0 0 0 0]\n",
            "[2 0 0 0 0 0 1 0 1 0]\n",
            "[2 1 0 1 0 0 1 1 1 0 1]\n",
            "[2 0 1 0 0 0 1 0 0 0 0 1]\n",
            "val_acc=0.4453125\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 1]\n",
            "[0 0 0]\n",
            "[2 1 0 0]\n",
            "[0 0 0 0 1]\n",
            "[1 0 0 0 0 1]\n",
            "[2 1 0 0 1 0 1]\n",
            "[2 0 0 1 0 1 1 1]\n",
            "[2 0 1 1 0 0 0 0 0]\n",
            "[2 0 0 1 0 0 1 0 0 0]\n",
            "[0 0 1 0 1 0 1 0 0 0 1]\n",
            "[2 0 1 1 1 0 0 0 1 1 0 0]\n",
            "val_acc=0.4375\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[1 0 1]\n",
            "[0 0 1 0]\n",
            "[2 0 1 0 0]\n",
            "[2 0 1 1 1 0]\n",
            "[2 1 1 0 1 0 0]\n",
            "[2 0 1 0 0 1 0 0]\n",
            "[2 0 0 0 0 1 1 0 0]\n",
            "[2 1 0 0 0 0 0 0 0 1]\n",
            "[0 0 0 1 0 1 0 0 1 0 1]\n",
            "[2 0 1 0 0 0 0 0 1 0 0 1]\n",
            "val_acc=0.4609375\n",
            "--------------------------------------------------------------------------------\n",
            "Epoch 3: Eval\n",
            "valid_accuracy: 0.4171\n",
            "test_accuracy: 0.4184\n",
            "epoch=4\tch_step=0\tloss=1.466925\tlr=0.0329\t|g|=1.1342\tacc=0.3672\ttime=3.65it/s\n",
            "epoch=4\tch_step=50\tloss=1.483652\tlr=0.0329\t|g|=0.8658\tacc=0.4141\ttime=4.95it/s\n",
            "epoch=4\tch_step=100\tloss=1.662398\tlr=0.0329\t|g|=0.7944\tacc=0.3672\ttime=4.97it/s\n",
            "epoch=4\tch_step=150\tloss=1.555576\tlr=0.0329\t|g|=1.4067\tacc=0.3828\ttime=4.77it/s\n",
            "epoch=4\tch_step=200\tloss=1.554075\tlr=0.0329\t|g|=1.2363\tacc=0.4219\ttime=4.77it/s\n",
            "epoch=4\tch_step=250\tloss=1.616082\tlr=0.0329\t|g|=1.0081\tacc=0.3594\ttime=5.12it/s\n",
            "epoch=4\tch_step=300\tloss=1.532324\tlr=0.0329\t|g|=0.9327\tacc=0.3984\ttime=4.85it/s\n",
            "epoch=4\tch_step=350\tloss=1.804074\tlr=0.0329\t|g|=0.9292\tacc=0.2812\ttime=6.13it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 4: Training controller\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:145: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=1\tloss=1.145\tent=50.03\tlr=0.0010\t|g|=0.2320\tacc=0.4375\tbl=0.42\ttime=2.32it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Traceback (most recent call last):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=3\tloss=-1.487\tent=47.27\tlr=0.0010\t|g|=0.1742\tacc=0.3828\tbl=0.42\ttime=2.26it/s\n",
            "ctrl_step=5\tloss=0.637\tent=48.59\tlr=0.0010\t|g|=0.1155\tacc=0.4297\tbl=0.42\ttime=2.46it/s\n",
            "ctrl_step=7\tloss=3.211\tent=49.27\tlr=0.0010\t|g|=0.1737\tacc=0.4844\tbl=0.43\ttime=2.29it/s\n",
            "ctrl_step=9\tloss=-1.000\tent=48.62\tlr=0.0010\t|g|=0.0606\tacc=0.4062\tbl=0.43\ttime=2.23it/s\n",
            "ctrl_step=11\tloss=3.132\tent=47.13\tlr=0.0010\t|g|=0.1247\tacc=0.4844\tbl=0.43\ttime=2.51it/s\n",
            "ctrl_step=13\tloss=-0.327\tent=48.54\tlr=0.0010\t|g|=0.0417\tacc=0.4219\tbl=0.43\ttime=2.24it/s\n",
            "ctrl_step=15\tloss=0.985\tent=47.10\tlr=0.0010\t|g|=0.1780\tacc=0.4531\tbl=0.44\ttime=2.23it/s\n",
            "ctrl_step=17\tloss=0.347\tent=49.06\tlr=0.0010\t|g|=0.2281\tacc=0.4375\tbl=0.44\ttime=2.19it/s\n",
            "ctrl_step=19\tloss=-0.404\tent=46.34\tlr=0.0010\t|g|=0.0741\tacc=0.4219\tbl=0.43\ttime=2.40it/s\n",
            "ctrl_step=21\tloss=-2.758\tent=49.08\tlr=0.0010\t|g|=0.1023\tacc=0.3750\tbl=0.44\ttime=2.42it/s\n",
            "ctrl_step=23\tloss=-1.757\tent=45.65\tlr=0.0010\t|g|=0.1327\tacc=0.3828\tbl=0.43\ttime=2.21it/s\n",
            "ctrl_step=25\tloss=0.790\tent=48.15\tlr=0.0010\t|g|=0.1100\tacc=0.4453\tbl=0.43\ttime=2.45it/s\n",
            "ctrl_step=27\tloss=-0.565\tent=48.55\tlr=0.0010\t|g|=0.1186\tacc=0.4219\tbl=0.44\ttime=2.42it/s\n",
            "ctrl_step=29\tloss=-0.142\tent=47.27\tlr=0.0010\t|g|=0.1524\tacc=0.4297\tbl=0.44\ttime=2.19it/s\n",
            "ctrl_step=31\tloss=0.130\tent=46.96\tlr=0.0010\t|g|=0.0371\tacc=0.4375\tbl=0.44\ttime=2.19it/s\n",
            "ctrl_step=33\tloss=-0.315\tent=48.73\tlr=0.0010\t|g|=0.1785\tacc=0.4297\tbl=0.44\ttime=2.39it/s\n",
            "ctrl_step=35\tloss=1.270\tent=48.11\tlr=0.0010\t|g|=0.1640\tacc=0.4609\tbl=0.44\ttime=2.34it/s\n",
            "ctrl_step=37\tloss=3.929\tent=47.92\tlr=0.0010\t|g|=0.1994\tacc=0.5078\tbl=0.44\ttime=2.13it/s\n",
            "ctrl_step=39\tloss=-2.120\tent=45.96\tlr=0.0010\t|g|=0.2699\tacc=0.3906\tbl=0.44\ttime=2.18it/s\n",
            "ctrl_step=41\tloss=-0.064\tent=47.06\tlr=0.0010\t|g|=0.2594\tacc=0.4297\tbl=0.44\ttime=2.16it/s\n",
            "ctrl_step=43\tloss=-1.188\tent=47.70\tlr=0.0010\t|g|=0.0588\tacc=0.4062\tbl=0.44\ttime=2.37it/s\n",
            "ctrl_step=45\tloss=4.549\tent=46.49\tlr=0.0010\t|g|=0.2495\tacc=0.5469\tbl=0.44\ttime=2.13it/s\n",
            "ctrl_step=47\tloss=0.070\tent=48.56\tlr=0.0010\t|g|=0.0498\tacc=0.4375\tbl=0.44\ttime=2.19it/s\n",
            "ctrl_step=49\tloss=0.685\tent=47.57\tlr=0.0010\t|g|=0.0731\tacc=0.4531\tbl=0.44\ttime=2.07it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Here are 10 architectures:\n",
            "[0]\n",
            "[2 0]\n",
            "[2 1 0]\n",
            "[2 1 0 0]\n",
            "[2 1 0 0 0]\n",
            "[2 0 1 0 1 0]\n",
            "[2 1 0 0 1 0 0]\n",
            "[2 0 0 1 0 1 1 1]\n",
            "[2 0 0 0 1 0 0 0 1]\n",
            "[2 0 0 1 1 1 1 1 0 0]\n",
            "[2 0 0 0 0 1 0 0 1 1 1]\n",
            "[0 1 1 0 0 0 1 1 1 1 0 0]\n",
            "val_acc=0.4140625\n",
            "--------------------------------------------------------------------------------\n",
            "[1]\n",
            "[2 0]\n",
            "[2 0 0]\n",
            "[2 1 0 1]\n",
            "[2 0 1 1 0]\n",
            "[2 0 1 0 0 0]\n",
            "[2 0 0 1 1 0 0]\n",
            "[2 0 0 0 0 0 0 0]\n",
            "[2 0 0 0 1 0 0 0 0]\n",
            "[2 1 0 0 0 0 0 1 0 0]\n",
            "[2 0 1 0 0 0 0 0 0 0 1]\n",
            "[1 0 1 1 1 1 0 0 1 0 0 1]\n",
            "val_acc=0.484375\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[0 0]\n",
            "[2 0 1]\n",
            "[2 0 0 1]\n",
            "[2 0 0 0 0]\n",
            "[2 0 1 1 0 0]\n",
            "[2 0 1 0 1 0 1]\n",
            "[2 1 0 0 1 0 1 0]\n",
            "[0 0 1 0 0 0 1 0 1]\n",
            "[2 0 1 0 0 0 0 0 0 0]\n",
            "[2 1 1 0 0 0 1 0 0 1 0]\n",
            "[1 1 0 0 0 0 0 0 1 0 0 1]\n",
            "val_acc=0.53125\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 1]\n",
            "[0 1 1]\n",
            "[2 0 1 1]\n",
            "[2 0 1 0 1]\n",
            "[2 1 0 0 0 0]\n",
            "[2 0 0 0 1 0 0]\n",
            "[2 0 0 1 0 0 0 0]\n",
            "[2 0 0 0 0 0 1 1 0]\n",
            "[2 0 0 0 0 0 0 0 0 1]\n",
            "[2 0 1 1 0 0 0 0 1 1 0]\n",
            "[2 0 1 0 0 0 0 1 0 0 0 1]\n",
            "val_acc=0.5234375\n",
            "--------------------------------------------------------------------------------\n",
            "[0]\n",
            "[2 0]\n",
            "[2 0 0]\n",
            "[2 0 0 0]\n",
            "[2 0 0 0 0]\n",
            "[2 0 0 0 1 0]\n",
            "[2 1 0 0 1 0 1]\n",
            "[2 0 0 0 1 0 0 1]\n",
            "[2 0 0 0 1 0 0 0 0]\n",
            "[1 1 0 0 0 0 0 0 0 0]\n",
            "[2 1 0 0 1 1 0 0 0 0 0]\n",
            "[1 0 0 1 1 0 1 0 1 0 1 1]\n",
            "val_acc=0.4453125\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 1]\n",
            "[2 0 1]\n",
            "[2 0 0 1]\n",
            "[2 0 0 1 0]\n",
            "[0 1 1 0 0 0]\n",
            "[2 0 0 0 0 0 0]\n",
            "[2 0 0 0 1 1 0 1]\n",
            "[2 0 1 0 0 0 0 0 0]\n",
            "[2 1 1 0 0 1 0 0 1 0]\n",
            "[1 0 1 0 0 0 0 0 0 0 0]\n",
            "[2 0 0 1 0 0 0 0 0 0 0 0]\n",
            "val_acc=0.484375\n",
            "--------------------------------------------------------------------------------\n",
            "[0]\n",
            "[2 0]\n",
            "[2 0 1]\n",
            "[2 1 1 1]\n",
            "[2 0 1 0 1]\n",
            "[2 1 0 0 0 0]\n",
            "[2 0 0 0 0 1 0]\n",
            "[2 0 0 0 0 0 0 1]\n",
            "[2 1 0 1 0 0 0 1 0]\n",
            "[2 0 0 0 0 0 1 0 1 1]\n",
            "[2 0 1 1 0 0 0 0 0 0 0]\n",
            "[2 1 0 0 0 0 0 0 0 1 1 1]\n",
            "val_acc=0.40625\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 0 1]\n",
            "[2 1 0 0]\n",
            "[2 0 0 0 0]\n",
            "[2 0 0 0 0 0]\n",
            "[2 0 0 0 0 1 0]\n",
            "[2 0 0 1 0 1 1 0]\n",
            "[2 0 1 0 0 0 0 0 1]\n",
            "[2 0 0 0 0 0 0 1 0 0]\n",
            "[0 0 1 0 0 0 0 0 1 0 0]\n",
            "[2 0 1 1 0 0 1 0 0 0 0 0]\n",
            "val_acc=0.4765625\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[1 0]\n",
            "[0 0 1]\n",
            "[2 0 0 0]\n",
            "[2 0 0 0 0]\n",
            "[2 0 1 0 0 1]\n",
            "[2 0 0 0 0 0 0]\n",
            "[2 0 0 1 0 0 0 0]\n",
            "[2 0 1 0 0 0 0 1 0]\n",
            "[2 1 0 0 1 0 1 1 0 0]\n",
            "[2 0 0 1 1 0 1 0 0 0 0]\n",
            "[2 1 0 0 0 1 1 1 0 0 0 0]\n",
            "val_acc=0.453125\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 0 0]\n",
            "[2 0 0 0]\n",
            "[2 1 1 0 0]\n",
            "[2 0 0 0 0 0]\n",
            "[2 0 1 0 0 0 0]\n",
            "[2 0 0 0 0 0 0 0]\n",
            "[0 1 0 1 0 1 0 0 1]\n",
            "[1 0 1 0 0 1 0 0 1 1]\n",
            "[2 0 0 0 0 0 0 1 0 0 0]\n",
            "[2 0 1 1 1 0 0 0 0 0 0 0]\n",
            "val_acc=0.5\n",
            "--------------------------------------------------------------------------------\n",
            "Epoch 4: Eval\n",
            "valid_accuracy: 0.4133\n",
            "test_accuracy: 0.4030\n",
            "epoch=5\tch_step=0\tloss=1.487141\tlr=0.0253\t|g|=1.0072\tacc=0.4375\ttime=3.47it/s\n",
            "epoch=5\tch_step=50\tloss=1.396351\tlr=0.0253\t|g|=1.0961\tacc=0.5000\ttime=4.84it/s\n",
            "epoch=5\tch_step=100\tloss=1.385532\tlr=0.0253\t|g|=0.8403\tacc=0.4688\ttime=4.99it/s\n",
            "epoch=5\tch_step=150\tloss=1.524186\tlr=0.0253\t|g|=0.9343\tacc=0.4453\ttime=4.84it/s\n",
            "epoch=5\tch_step=200\tloss=1.543827\tlr=0.0253\t|g|=1.2217\tacc=0.4062\ttime=4.74it/s\n",
            "epoch=5\tch_step=250\tloss=1.406296\tlr=0.0253\t|g|=1.0315\tacc=0.4922\ttime=4.65it/s\n",
            "epoch=5\tch_step=300\tloss=1.553160\tlr=0.0253\t|g|=1.5950\tacc=0.4609\ttime=4.71it/s\n",
            "epoch=5\tch_step=350\tloss=1.333716\tlr=0.0253\t|g|=1.1617\tacc=0.4531\ttime=4.90it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 5: Training controller\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:145: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=1\tloss=3.895\tent=46.48\tlr=0.0010\t|g|=0.1192\tacc=0.5469\tbl=0.46\ttime=2.10it/s\n",
            "ctrl_step=3\tloss=1.206\tent=46.20\tlr=0.0010\t|g|=0.2072\tacc=0.4922\tbl=0.47\ttime=2.11it/s\n",
            "ctrl_step=5\tloss=1.480\tent=46.14\tlr=0.0010\t|g|=0.0845\tacc=0.5078\tbl=0.48\ttime=2.23it/s\n",
            "ctrl_step=7\tloss=1.953\tent=45.69\tlr=0.0010\t|g|=0.1398\tacc=0.5156\tbl=0.48\ttime=2.26it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Traceback (most recent call last):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=9\tloss=-1.579\tent=44.84\tlr=0.0010\t|g|=0.2862\tacc=0.4453\tbl=0.48\ttime=2.04it/s\n",
            "ctrl_step=11\tloss=-1.508\tent=46.11\tlr=0.0010\t|g|=0.1482\tacc=0.4453\tbl=0.48\ttime=2.22it/s\n",
            "ctrl_step=13\tloss=-2.737\tent=44.09\tlr=0.0010\t|g|=0.3573\tacc=0.4141\tbl=0.48\ttime=2.23it/s\n",
            "ctrl_step=15\tloss=0.538\tent=44.55\tlr=0.0010\t|g|=0.0344\tacc=0.4922\tbl=0.49\ttime=2.09it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Traceback (most recent call last):\n",
            "  File \"/usr/lib/python3.7/multiprocessing/queues.py\", line 242, in _feed\n",
            "    send_bytes(obj)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=17\tloss=-0.748\tent=48.14\tlr=0.0010\t|g|=0.2326\tacc=0.4688\tbl=0.49\ttime=2.10it/s\n",
            "ctrl_step=19\tloss=-1.808\tent=44.54\tlr=0.0010\t|g|=0.0678\tacc=0.4453\tbl=0.49\ttime=2.06it/s\n",
            "ctrl_step=21\tloss=2.218\tent=44.34\tlr=0.0010\t|g|=0.3419\tacc=0.5312\tbl=0.49\ttime=2.08it/s\n",
            "ctrl_step=23\tloss=1.374\tent=46.42\tlr=0.0010\t|g|=0.0625\tacc=0.5078\tbl=0.49\ttime=2.18it/s\n",
            "ctrl_step=25\tloss=1.886\tent=44.58\tlr=0.0010\t|g|=0.2263\tacc=0.5234\tbl=0.49\ttime=2.22it/s\n",
            "ctrl_step=27\tloss=-1.063\tent=43.43\tlr=0.0010\t|g|=0.0597\tacc=0.4609\tbl=0.49\ttime=2.21it/s\n",
            "ctrl_step=29\tloss=-2.889\tent=46.14\tlr=0.0010\t|g|=0.1665\tacc=0.4141\tbl=0.49\ttime=2.23it/s\n",
            "ctrl_step=31\tloss=6.758\tent=45.82\tlr=0.0010\t|g|=0.0996\tacc=0.6328\tbl=0.49\ttime=2.12it/s\n",
            "ctrl_step=33\tloss=3.323\tent=44.19\tlr=0.0010\t|g|=0.0399\tacc=0.5625\tbl=0.49\ttime=2.01it/s\n",
            "ctrl_step=35\tloss=-1.767\tent=44.66\tlr=0.0010\t|g|=0.1107\tacc=0.4453\tbl=0.49\ttime=2.03it/s\n",
            "ctrl_step=37\tloss=0.967\tent=42.89\tlr=0.0010\t|g|=0.0764\tacc=0.5078\tbl=0.49\ttime=2.02it/s\n",
            "ctrl_step=39\tloss=-2.064\tent=45.87\tlr=0.0010\t|g|=0.1234\tacc=0.4453\tbl=0.49\ttime=2.00it/s\n",
            "ctrl_step=41\tloss=2.495\tent=44.09\tlr=0.0010\t|g|=0.0803\tacc=0.5469\tbl=0.49\ttime=2.11it/s\n",
            "ctrl_step=43\tloss=-2.267\tent=43.57\tlr=0.0010\t|g|=0.0414\tacc=0.4219\tbl=0.49\ttime=2.10it/s\n",
            "ctrl_step=45\tloss=-2.678\tent=43.17\tlr=0.0010\t|g|=0.2134\tacc=0.4141\tbl=0.49\ttime=1.95it/s\n",
            "ctrl_step=47\tloss=3.866\tent=44.52\tlr=0.0010\t|g|=0.2060\tacc=0.5625\tbl=0.49\ttime=2.15it/s\n",
            "ctrl_step=49\tloss=3.344\tent=44.47\tlr=0.0010\t|g|=0.0597\tacc=0.5547\tbl=0.49\ttime=2.11it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Here are 10 architectures:\n",
            "[2]\n",
            "[2 0]\n",
            "[2 0 1]\n",
            "[1 1 0 1]\n",
            "[2 0 0 1 1]\n",
            "[2 1 1 0 1 0]\n",
            "[2 1 1 0 0 0 0]\n",
            "[2 0 0 0 0 0 1 1]\n",
            "[2 0 0 1 1 0 1 0 0]\n",
            "[2 1 0 0 0 0 0 0 0 0]\n",
            "[0 0 0 0 0 0 0 0 0 0 0]\n",
            "[2 1 0 0 1 1 0 0 0 0 0 0]\n",
            "val_acc=0.453125\n",
            "--------------------------------------------------------------------------------\n",
            "[0]\n",
            "[2 0]\n",
            "[2 0 0]\n",
            "[2 0 0 1]\n",
            "[0 0 0 0 1]\n",
            "[2 0 1 0 0 0]\n",
            "[2 1 1 0 1 0 0]\n",
            "[2 0 1 0 0 0 0 0]\n",
            "[2 0 0 0 0 1 0 0 0]\n",
            "[2 0 1 0 0 0 0 0 1 0]\n",
            "[2 1 0 0 0 1 0 0 0 0 0]\n",
            "[0 0 0 0 1 0 1 1 0 0 1 1]\n",
            "val_acc=0.4921875\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 1 1]\n",
            "[2 0 0 0]\n",
            "[2 0 1 0 0]\n",
            "[1 1 0 1 0 0]\n",
            "[2 0 0 0 0 0 0]\n",
            "[2 0 1 0 0 0 0 1]\n",
            "[2 0 0 0 0 1 0 1 0]\n",
            "[2 0 0 0 0 1 0 1 0 1]\n",
            "[2 0 0 0 0 0 0 0 0 0 0]\n",
            "[2 0 1 0 0 1 1 0 0 0 1 1]\n",
            "val_acc=0.40625\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[1 0]\n",
            "[2 0 1]\n",
            "[2 0 0 0]\n",
            "[2 0 0 1 0]\n",
            "[2 0 0 0 0 1]\n",
            "[2 1 0 0 0 0 1]\n",
            "[2 1 0 1 0 0 0 0]\n",
            "[2 0 1 0 0 0 0 0 1]\n",
            "[2 1 0 1 1 1 0 0 1 0]\n",
            "[2 1 1 0 0 1 1 0 1 0 0]\n",
            "[0 0 0 1 0 0 0 0 1 1 0 0]\n",
            "val_acc=0.453125\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 1]\n",
            "[2 1 0]\n",
            "[2 1 0 0]\n",
            "[2 0 0 0 0]\n",
            "[2 0 0 0 1 1]\n",
            "[2 0 0 0 0 0 0]\n",
            "[2 0 0 0 1 0 0 0]\n",
            "[2 0 0 1 0 0 1 1 0]\n",
            "[2 0 1 0 0 0 0 0 1 1]\n",
            "[2 0 0 0 0 0 0 0 0 0 0]\n",
            "[2 0 0 1 0 0 0 1 0 0 0 0]\n",
            "val_acc=0.4453125\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 1]\n",
            "[2 0 0]\n",
            "[2 0 0 1]\n",
            "[2 0 0 0 0]\n",
            "[2 1 0 1 1 1]\n",
            "[2 1 0 0 1 1 0]\n",
            "[2 1 0 0 0 1 0 0]\n",
            "[2 0 0 0 0 0 0 0 0]\n",
            "[2 0 0 0 0 0 1 0 0 0]\n",
            "[2 1 0 1 1 0 1 0 0 0 1]\n",
            "[2 0 0 0 1 0 0 0 0 0 1 0]\n",
            "val_acc=0.5078125\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[1 0]\n",
            "[2 1 0]\n",
            "[2 1 0 0]\n",
            "[2 0 0 1 1]\n",
            "[2 0 1 0 0 0]\n",
            "[2 0 0 0 0 0 0]\n",
            "[2 0 0 0 1 0 0 0]\n",
            "[0 0 0 0 1 0 1 0 0]\n",
            "[2 0 1 0 0 1 0 0 1 1]\n",
            "[2 0 1 0 0 1 1 0 0 1 0]\n",
            "[1 0 0 0 0 1 1 1 0 0 0 0]\n",
            "val_acc=0.4609375\n",
            "--------------------------------------------------------------------------------\n",
            "[0]\n",
            "[1 0]\n",
            "[2 1 0]\n",
            "[2 0 0 1]\n",
            "[1 0 1 0 1]\n",
            "[2 1 0 0 1 0]\n",
            "[2 0 0 0 1 0 0]\n",
            "[2 0 0 0 0 0 0 1]\n",
            "[2 1 0 0 0 0 0 0 0]\n",
            "[2 0 0 0 0 0 0 0 0 0]\n",
            "[2 0 0 0 0 0 0 0 0 0 1]\n",
            "[2 0 1 0 0 1 1 0 1 0 0 0]\n",
            "val_acc=0.4296875\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[0 0]\n",
            "[2 0 0]\n",
            "[2 1 1 1]\n",
            "[2 1 1 0 0]\n",
            "[2 0 0 0 0 0]\n",
            "[2 0 0 0 0 0 0]\n",
            "[2 0 0 0 0 0 0 0]\n",
            "[2 0 0 0 1 0 0 0 0]\n",
            "[2 0 0 0 0 0 1 0 0 0]\n",
            "[2 0 0 0 0 0 0 1 0 0 0]\n",
            "[2 1 1 0 0 0 0 0 0 0 0 1]\n",
            "val_acc=0.453125\n",
            "--------------------------------------------------------------------------------\n",
            "[0]\n",
            "[2 0]\n",
            "[2 1 0]\n",
            "[2 1 1 0]\n",
            "[2 0 0 0 1]\n",
            "[2 0 1 0 0 0]\n",
            "[2 0 0 0 1 0 0]\n",
            "[2 0 0 1 1 1 0 0]\n",
            "[2 0 0 1 0 0 0 0 1]\n",
            "[2 0 0 0 1 1 0 1 0 0]\n",
            "[2 0 0 0 1 0 0 0 1 0 0]\n",
            "[1 0 0 0 1 0 0 1 0 1 0 0]\n",
            "val_acc=0.4765625\n",
            "--------------------------------------------------------------------------------\n",
            "Epoch 5: Eval\n",
            "valid_accuracy: 0.4858\n",
            "test_accuracy: 0.4568\n",
            "epoch=6\tch_step=0\tloss=1.376592\tlr=0.0176\t|g|=1.4216\tacc=0.4453\ttime=3.67it/s\n",
            "epoch=6\tch_step=50\tloss=1.267624\tlr=0.0176\t|g|=0.9798\tacc=0.5391\ttime=4.55it/s\n",
            "epoch=6\tch_step=100\tloss=1.324719\tlr=0.0176\t|g|=1.0787\tacc=0.4375\ttime=4.70it/s\n",
            "epoch=6\tch_step=150\tloss=1.207717\tlr=0.0176\t|g|=1.0284\tacc=0.5234\ttime=4.55it/s\n",
            "epoch=6\tch_step=200\tloss=1.314856\tlr=0.0176\t|g|=1.2754\tacc=0.5000\ttime=4.56it/s\n",
            "epoch=6\tch_step=250\tloss=1.337534\tlr=0.0176\t|g|=1.0591\tacc=0.5078\ttime=4.59it/s\n",
            "epoch=6\tch_step=300\tloss=1.156835\tlr=0.0176\t|g|=1.0546\tacc=0.5703\ttime=4.41it/s\n",
            "epoch=6\tch_step=350\tloss=1.392313\tlr=0.0176\t|g|=1.2003\tacc=0.5312\ttime=5.19it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 6: Training controller\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:145: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=1\tloss=1.817\tent=40.73\tlr=0.0010\t|g|=0.0860\tacc=0.5469\tbl=0.50\ttime=1.93it/s\n",
            "ctrl_step=3\tloss=0.052\tent=44.68\tlr=0.0010\t|g|=0.2512\tacc=0.5000\tbl=0.50\ttime=2.20it/s\n",
            "ctrl_step=5\tloss=0.438\tent=42.77\tlr=0.0010\t|g|=0.0530\tacc=0.5156\tbl=0.51\ttime=1.93it/s\n",
            "ctrl_step=7\tloss=-0.384\tent=41.47\tlr=0.0010\t|g|=0.0939\tacc=0.4922\tbl=0.51\ttime=2.05it/s\n",
            "ctrl_step=9\tloss=-2.481\tent=45.35\tlr=0.0010\t|g|=0.3872\tacc=0.4531\tbl=0.51\ttime=2.10it/s\n",
            "ctrl_step=11\tloss=1.171\tent=43.21\tlr=0.0010\t|g|=0.2454\tacc=0.5312\tbl=0.51\ttime=2.10it/s\n",
            "ctrl_step=13\tloss=2.695\tent=46.05\tlr=0.0010\t|g|=0.3289\tacc=0.5703\tbl=0.51\ttime=2.06it/s\n",
            "ctrl_step=15\tloss=4.922\tent=45.51\tlr=0.0010\t|g|=0.1093\tacc=0.6172\tbl=0.51\ttime=1.91it/s\n",
            "ctrl_step=17\tloss=-1.358\tent=45.09\tlr=0.0010\t|g|=0.0347\tacc=0.4766\tbl=0.52\ttime=1.92it/s\n",
            "ctrl_step=19\tloss=-0.302\tent=41.50\tlr=0.0010\t|g|=0.5053\tacc=0.5078\tbl=0.52\ttime=2.04it/s\n",
            "ctrl_step=21\tloss=1.412\tent=44.33\tlr=0.0010\t|g|=0.0374\tacc=0.5391\tbl=0.52\ttime=1.97it/s\n",
            "ctrl_step=23\tloss=0.362\tent=43.62\tlr=0.0010\t|g|=0.2562\tacc=0.5156\tbl=0.51\ttime=2.01it/s\n",
            "ctrl_step=25\tloss=-1.624\tent=46.51\tlr=0.0010\t|g|=0.3039\tacc=0.4688\tbl=0.51\ttime=2.01it/s\n",
            "ctrl_step=27\tloss=-2.407\tent=41.34\tlr=0.0010\t|g|=0.1297\tacc=0.4531\tbl=0.51\ttime=1.87it/s\n",
            "ctrl_step=29\tloss=4.116\tent=46.17\tlr=0.0010\t|g|=0.4739\tacc=0.6094\tbl=0.51\ttime=2.00it/s\n",
            "ctrl_step=31\tloss=0.254\tent=44.32\tlr=0.0010\t|g|=0.1714\tacc=0.5156\tbl=0.51\ttime=1.98it/s\n",
            "ctrl_step=33\tloss=-0.395\tent=43.63\tlr=0.0010\t|g|=0.1560\tacc=0.5000\tbl=0.51\ttime=1.89it/s\n",
            "ctrl_step=35\tloss=1.501\tent=41.81\tlr=0.0010\t|g|=0.2965\tacc=0.5469\tbl=0.52\ttime=1.34it/s\n",
            "ctrl_step=37\tloss=-0.050\tent=42.05\tlr=0.0010\t|g|=0.1928\tacc=0.5156\tbl=0.52\ttime=1.97it/s\n",
            "ctrl_step=39\tloss=-0.429\tent=43.02\tlr=0.0010\t|g|=0.1202\tacc=0.5078\tbl=0.52\ttime=1.99it/s\n",
            "ctrl_step=41\tloss=-1.898\tent=43.98\tlr=0.0010\t|g|=0.1877\tacc=0.4688\tbl=0.52\ttime=2.03it/s\n",
            "ctrl_step=43\tloss=0.120\tent=44.12\tlr=0.0010\t|g|=0.1968\tacc=0.5234\tbl=0.53\ttime=2.05it/s\n",
            "ctrl_step=45\tloss=-0.929\tent=44.02\tlr=0.0010\t|g|=0.0985\tacc=0.5000\tbl=0.52\ttime=1.82it/s\n",
            "ctrl_step=47\tloss=-0.971\tent=41.36\tlr=0.0010\t|g|=0.2089\tacc=0.4922\tbl=0.52\ttime=1.83it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Traceback (most recent call last):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=49\tloss=-3.955\tent=42.65\tlr=0.0010\t|g|=0.1186\tacc=0.4375\tbl=0.52\ttime=1.92it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Here are 10 architectures:\n",
            "[0]\n",
            "[2 1]\n",
            "[2 1 0]\n",
            "[2 0 0 1]\n",
            "[2 0 0 1 0]\n",
            "[0 0 0 1 1 0]\n",
            "[2 0 1 1 0 0 1]\n",
            "[1 1 0 0 0 0 0 0]\n",
            "[2 0 0 0 0 1 0 1 1]\n",
            "[2 0 0 1 0 0 1 0 0 1]\n",
            "[2 0 0 1 0 0 0 0 0 0 1]\n",
            "[2 0 0 0 1 0 0 0 0 1 0 1]\n",
            "val_acc=0.4609375\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 0 1]\n",
            "[2 0 1 1]\n",
            "[1 0 0 0 1]\n",
            "[2 0 0 0 0 0]\n",
            "[2 1 0 0 0 0 0]\n",
            "[2 1 0 1 0 0 1 0]\n",
            "[2 0 1 0 0 1 1 0 0]\n",
            "[2 0 0 0 1 0 0 0 0 1]\n",
            "[2 0 0 0 1 0 0 0 0 0 1]\n",
            "[0 0 1 0 1 1 0 0 1 0 1 1]\n",
            "val_acc=0.4765625\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 0 1]\n",
            "[2 1 0 0]\n",
            "[2 1 0 1 0]\n",
            "[2 0 1 0 1 1]\n",
            "[2 1 1 0 1 0 0]\n",
            "[2 1 0 0 1 1 0 0]\n",
            "[2 1 0 0 0 1 0 0 0]\n",
            "[2 0 1 0 0 1 0 1 0 1]\n",
            "[2 0 1 0 0 0 0 0 0 0 0]\n",
            "[2 0 0 0 0 1 1 0 0 0 0 0]\n",
            "val_acc=0.4921875\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 0 1]\n",
            "[2 0 1 0]\n",
            "[2 0 0 1 1]\n",
            "[2 0 1 1 0 1]\n",
            "[2 1 0 0 0 0 0]\n",
            "[2 1 1 0 1 0 0 0]\n",
            "[2 1 0 0 0 0 0 0 0]\n",
            "[2 0 0 0 0 0 0 0 0 0]\n",
            "[2 0 1 1 0 0 0 1 0 0 1]\n",
            "[2 0 0 1 0 0 1 0 0 0 0 1]\n",
            "val_acc=0.4921875\n",
            "--------------------------------------------------------------------------------\n",
            "[0]\n",
            "[2 0]\n",
            "[2 0 0]\n",
            "[1 1 0 0]\n",
            "[2 0 0 0 0]\n",
            "[2 0 0 0 0 1]\n",
            "[2 0 0 0 0 0 0]\n",
            "[2 1 0 0 0 0 0 1]\n",
            "[2 0 0 0 0 0 0 0 0]\n",
            "[2 0 0 0 1 0 0 1 0 0]\n",
            "[2 0 0 0 1 0 1 0 0 0 1]\n",
            "[2 0 0 0 1 0 0 0 1 1 0 1]\n",
            "val_acc=0.4296875\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 0 1]\n",
            "[0 0 1 0]\n",
            "[2 0 1 0 1]\n",
            "[2 0 0 0 0 1]\n",
            "[2 1 0 0 1 0 1]\n",
            "[2 0 0 0 0 0 1 1]\n",
            "[2 0 0 0 0 0 0 0 0]\n",
            "[2 0 0 0 0 0 1 0 1 0]\n",
            "[0 1 0 1 0 0 0 0 0 1 0]\n",
            "[2 0 0 0 0 0 0 0 0 0 0 1]\n",
            "val_acc=0.453125\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[0 0]\n",
            "[2 0 1]\n",
            "[2 1 0 0]\n",
            "[2 0 0 1 0]\n",
            "[2 0 0 0 0 1]\n",
            "[2 1 0 0 0 1 1]\n",
            "[2 0 0 1 0 0 0 0]\n",
            "[2 1 1 0 0 1 0 0 0]\n",
            "[2 0 0 1 0 1 0 1 0 0]\n",
            "[2 0 0 0 0 1 0 0 1 0 0]\n",
            "[2 1 0 0 0 0 1 1 0 0 0 0]\n",
            "val_acc=0.4921875\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 1 1]\n",
            "[2 1 0 0]\n",
            "[2 0 1 0 0]\n",
            "[2 1 0 0 0 0]\n",
            "[0 0 0 0 0 0 0]\n",
            "[2 0 0 0 1 1 0 0]\n",
            "[2 1 0 0 0 1 0 1 0]\n",
            "[2 0 0 0 0 0 0 0 0 0]\n",
            "[2 0 0 0 1 1 0 0 0 0 0]\n",
            "[0 0 1 0 0 0 0 0 0 0 0 1]\n",
            "val_acc=0.4296875\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 1]\n",
            "[2 1 0]\n",
            "[2 1 0 0]\n",
            "[2 1 1 0 0]\n",
            "[2 0 0 0 0 1]\n",
            "[2 0 1 1 0 0 1]\n",
            "[2 0 1 0 0 0 0 0]\n",
            "[1 1 1 0 0 1 1 1 1]\n",
            "[2 0 0 0 1 1 0 0 0 0]\n",
            "[0 0 0 1 0 0 0 0 1 0 0]\n",
            "[2 0 1 0 0 0 1 0 1 0 0 1]\n",
            "val_acc=0.453125\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[0 0]\n",
            "[2 0 0]\n",
            "[2 0 0 0]\n",
            "[2 1 0 0 0]\n",
            "[0 0 0 0 0 1]\n",
            "[2 0 1 0 1 1 0]\n",
            "[2 0 0 0 1 1 1 0]\n",
            "[0 0 1 0 0 0 0 0 0]\n",
            "[2 0 1 0 0 1 0 0 0 0]\n",
            "[2 0 0 1 0 0 1 1 0 1 1]\n",
            "[2 0 0 1 0 0 0 0 0 0 0 0]\n",
            "val_acc=0.5\n",
            "--------------------------------------------------------------------------------\n",
            "Epoch 6: Eval\n",
            "valid_accuracy: 0.5162\n",
            "test_accuracy: 0.4893\n",
            "epoch=7\tch_step=0\tloss=1.329380\tlr=0.0107\t|g|=1.7136\tacc=0.4609\ttime=3.34it/s\n",
            "epoch=7\tch_step=50\tloss=1.309363\tlr=0.0107\t|g|=1.0796\tacc=0.5000\ttime=4.39it/s\n",
            "epoch=7\tch_step=100\tloss=1.388743\tlr=0.0107\t|g|=1.2767\tacc=0.4844\ttime=4.52it/s\n",
            "epoch=7\tch_step=150\tloss=1.139456\tlr=0.0107\t|g|=0.9598\tacc=0.6250\ttime=4.48it/s\n",
            "epoch=7\tch_step=200\tloss=1.111038\tlr=0.0107\t|g|=1.3410\tacc=0.6016\ttime=4.41it/s\n",
            "epoch=7\tch_step=250\tloss=1.362547\tlr=0.0107\t|g|=1.2545\tacc=0.4922\ttime=4.58it/s\n",
            "epoch=7\tch_step=300\tloss=1.211518\tlr=0.0107\t|g|=1.2620\tacc=0.5156\ttime=4.57it/s\n",
            "epoch=7\tch_step=350\tloss=1.370500\tlr=0.0107\t|g|=1.5114\tacc=0.5312\ttime=5.21it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 7: Training controller\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:145: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=1\tloss=0.098\tent=43.88\tlr=0.0010\t|g|=0.0591\tacc=0.5234\tbl=0.53\ttime=1.96it/s\n",
            "ctrl_step=3\tloss=-2.828\tent=43.41\tlr=0.0010\t|g|=0.0914\tacc=0.4609\tbl=0.52\ttime=1.92it/s\n",
            "ctrl_step=5\tloss=-3.018\tent=45.21\tlr=0.0010\t|g|=0.0393\tacc=0.4609\tbl=0.53\ttime=1.84it/s\n",
            "ctrl_step=7\tloss=2.657\tent=42.60\tlr=0.0010\t|g|=0.0526\tacc=0.5859\tbl=0.53\ttime=1.81it/s\n",
            "ctrl_step=9\tloss=-3.358\tent=39.40\tlr=0.0010\t|g|=0.5651\tacc=0.4141\tbl=0.53\ttime=1.96it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Traceback (most recent call last):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=11\tloss=2.904\tent=43.86\tlr=0.0010\t|g|=0.2906\tacc=0.5938\tbl=0.53\ttime=1.97it/s\n",
            "ctrl_step=13\tloss=1.055\tent=42.99\tlr=0.0010\t|g|=0.1958\tacc=0.5547\tbl=0.54\ttime=1.79it/s\n",
            "ctrl_step=15\tloss=1.427\tent=45.37\tlr=0.0010\t|g|=0.2277\tacc=0.5625\tbl=0.54\ttime=1.90it/s\n",
            "ctrl_step=17\tloss=-3.618\tent=39.93\tlr=0.0010\t|g|=0.5807\tacc=0.4219\tbl=0.53\ttime=1.78it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Traceback (most recent call last):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=19\tloss=0.678\tent=45.11\tlr=0.0010\t|g|=0.1443\tacc=0.5391\tbl=0.53\ttime=1.87it/s\n",
            "ctrl_step=21\tloss=-2.290\tent=44.40\tlr=0.0010\t|g|=0.1952\tacc=0.4766\tbl=0.53\ttime=1.95it/s\n",
            "ctrl_step=23\tloss=1.291\tent=42.47\tlr=0.0010\t|g|=0.0282\tacc=0.5625\tbl=0.53\ttime=1.81it/s\n",
            "ctrl_step=25\tloss=1.205\tent=45.01\tlr=0.0010\t|g|=0.2321\tacc=0.5547\tbl=0.53\ttime=1.79it/s\n",
            "ctrl_step=27\tloss=-0.614\tent=43.50\tlr=0.0010\t|g|=0.1077\tacc=0.5156\tbl=0.53\ttime=1.92it/s\n",
            "ctrl_step=29\tloss=0.536\tent=38.07\tlr=0.0010\t|g|=0.6141\tacc=0.5469\tbl=0.53\ttime=1.84it/s\n",
            "ctrl_step=31\tloss=0.649\tent=45.19\tlr=0.0010\t|g|=0.0344\tacc=0.5469\tbl=0.54\ttime=1.92it/s\n",
            "ctrl_step=33\tloss=3.189\tent=41.98\tlr=0.0010\t|g|=0.0669\tacc=0.6172\tbl=0.54\ttime=1.80it/s\n",
            "ctrl_step=35\tloss=0.700\tent=43.07\tlr=0.0010\t|g|=0.1489\tacc=0.5469\tbl=0.54\ttime=1.74it/s\n",
            "ctrl_step=37\tloss=1.193\tent=43.71\tlr=0.0010\t|g|=0.0931\tacc=0.5625\tbl=0.54\ttime=1.86it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Traceback (most recent call last):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=39\tloss=-1.961\tent=42.56\tlr=0.0010\t|g|=0.1415\tacc=0.4844\tbl=0.53\ttime=1.78it/s\n",
            "ctrl_step=41\tloss=1.815\tent=43.30\tlr=0.0010\t|g|=0.0466\tacc=0.5703\tbl=0.53\ttime=1.81it/s\n",
            "ctrl_step=43\tloss=0.809\tent=43.04\tlr=0.0010\t|g|=0.2377\tacc=0.5469\tbl=0.53\ttime=1.86it/s\n",
            "ctrl_step=45\tloss=-0.297\tent=43.55\tlr=0.0010\t|g|=0.1859\tacc=0.5234\tbl=0.54\ttime=1.81it/s\n",
            "ctrl_step=47\tloss=2.082\tent=44.64\tlr=0.0010\t|g|=0.0990\tacc=0.5781\tbl=0.53\ttime=1.88it/s\n",
            "ctrl_step=49\tloss=-3.385\tent=43.93\tlr=0.0010\t|g|=0.2080\tacc=0.4609\tbl=0.53\ttime=1.86it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Here are 10 architectures:\n",
            "[2]\n",
            "[0 1]\n",
            "[2 1 0]\n",
            "[2 0 0 0]\n",
            "[2 0 0 0 0]\n",
            "[2 0 0 0 0 0]\n",
            "[1 0 0 1 1 0 0]\n",
            "[2 0 0 0 0 0 0 0]\n",
            "[2 0 1 0 0 1 0 0 0]\n",
            "[2 0 0 0 0 0 0 1 1 0]\n",
            "[2 0 0 0 0 0 1 0 1 1 0]\n",
            "[0 0 0 0 0 0 0 0 1 0 0 0]\n",
            "val_acc=0.484375\n",
            "--------------------------------------------------------------------------------\n",
            "[1]\n",
            "[2 1]\n",
            "[2 0 1]\n",
            "[2 1 0 1]\n",
            "[2 0 0 1 0]\n",
            "[2 0 0 1 0 0]\n",
            "[2 0 1 0 1 1 1]\n",
            "[2 0 0 0 0 0 1 0]\n",
            "[2 0 0 0 0 0 0 0 0]\n",
            "[2 0 0 0 1 0 1 1 0 1]\n",
            "[2 0 1 0 1 0 0 0 0 0 0]\n",
            "[2 0 0 0 0 1 1 0 1 0 0 0]\n",
            "val_acc=0.4765625\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 1]\n",
            "[2 1 0]\n",
            "[2 0 0 0]\n",
            "[2 0 0 1 0]\n",
            "[2 1 0 1 1 1]\n",
            "[2 1 0 0 0 1 0]\n",
            "[2 0 1 1 0 0 1 0]\n",
            "[2 0 0 1 0 0 0 0 0]\n",
            "[2 1 0 1 0 0 0 0 0 0]\n",
            "[2 0 0 0 0 0 0 0 0 0 0]\n",
            "[2 0 1 0 0 1 0 0 0 0 1 1]\n",
            "val_acc=0.609375\n",
            "--------------------------------------------------------------------------------\n",
            "[0]\n",
            "[2 0]\n",
            "[2 0 1]\n",
            "[2 0 1 0]\n",
            "[2 0 1 0 0]\n",
            "[2 1 0 0 1 0]\n",
            "[0 1 1 1 0 1 0]\n",
            "[2 1 0 1 0 0 0 0]\n",
            "[2 0 0 0 0 0 0 0 0]\n",
            "[2 0 0 0 0 0 0 1 0 0]\n",
            "[1 1 0 0 0 0 1 0 1 1 0]\n",
            "[2 1 1 0 1 1 1 0 0 0 0 1]\n",
            "val_acc=0.46875\n",
            "--------------------------------------------------------------------------------\n",
            "[0]\n",
            "[2 0]\n",
            "[2 0 1]\n",
            "[2 0 0 0]\n",
            "[2 0 0 1 1]\n",
            "[2 1 1 0 0 0]\n",
            "[2 0 0 0 0 1 0]\n",
            "[2 0 0 0 0 0 1 1]\n",
            "[2 0 0 0 0 0 0 0 1]\n",
            "[2 0 0 0 0 0 1 0 0 0]\n",
            "[2 0 0 0 1 0 1 0 1 0 0]\n",
            "[2 0 1 1 1 0 0 1 0 0 0 0]\n",
            "val_acc=0.5078125\n",
            "--------------------------------------------------------------------------------\n",
            "[1]\n",
            "[2 1]\n",
            "[2 0 0]\n",
            "[2 0 0 0]\n",
            "[2 1 0 0 1]\n",
            "[2 0 0 0 0 1]\n",
            "[2 0 0 0 0 0 0]\n",
            "[2 0 0 0 1 0 0 0]\n",
            "[2 1 0 1 0 1 0 1 0]\n",
            "[2 0 0 0 1 0 0 0 0 0]\n",
            "[2 0 0 0 0 0 0 0 0 0 0]\n",
            "[2 0 0 0 0 0 0 0 0 0 0 0]\n",
            "val_acc=0.453125\n",
            "--------------------------------------------------------------------------------\n",
            "[0]\n",
            "[1 0]\n",
            "[2 0 0]\n",
            "[2 0 1 1]\n",
            "[2 1 0 0 0]\n",
            "[2 0 0 0 1 0]\n",
            "[0 0 0 0 0 0 0]\n",
            "[2 0 0 0 0 0 0 0]\n",
            "[2 1 1 0 1 0 0 0 0]\n",
            "[2 0 0 0 1 0 1 1 0 1]\n",
            "[2 1 0 0 0 0 0 0 1 0 0]\n",
            "[2 0 0 0 0 1 1 0 0 0 0 0]\n",
            "val_acc=0.46875\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 0 1]\n",
            "[2 0 1 0]\n",
            "[2 0 0 1 0]\n",
            "[2 0 0 1 1 0]\n",
            "[2 0 1 0 1 0 0]\n",
            "[2 0 0 0 0 0 1 0]\n",
            "[2 0 0 0 0 0 1 0 0]\n",
            "[2 0 0 0 1 1 0 0 0 1]\n",
            "[2 0 0 0 0 0 1 0 0 0 0]\n",
            "[2 0 0 0 0 0 1 0 1 0 0 1]\n",
            "val_acc=0.546875\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 0 1]\n",
            "[2 1 0 0]\n",
            "[2 1 1 0 0]\n",
            "[2 0 1 0 0 1]\n",
            "[2 0 1 1 1 0 0]\n",
            "[2 0 0 0 0 0 0 0]\n",
            "[1 1 0 0 1 0 0 0 0]\n",
            "[2 1 0 0 0 0 1 0 0 0]\n",
            "[2 1 0 0 1 0 1 0 0 1 0]\n",
            "[2 0 0 1 0 0 0 0 0 0 0 1]\n",
            "val_acc=0.5703125\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[1 0 0]\n",
            "[2 1 0 0]\n",
            "[2 0 0 0 1]\n",
            "[2 0 1 0 0 0]\n",
            "[2 0 0 0 0 0 1]\n",
            "[2 1 0 1 1 1 1 0]\n",
            "[2 0 1 1 1 1 0 0 0]\n",
            "[2 0 0 0 0 0 0 0 0 0]\n",
            "[2 1 0 0 0 0 1 1 0 0 0]\n",
            "[2 0 0 0 1 1 0 0 0 0 0 0]\n",
            "val_acc=0.578125\n",
            "--------------------------------------------------------------------------------\n",
            "Epoch 7: Eval\n",
            "valid_accuracy: 0.5359\n",
            "test_accuracy: 0.5119\n",
            "epoch=8\tch_step=0\tloss=1.249996\tlr=0.0052\t|g|=1.8133\tacc=0.5234\ttime=3.42it/s\n",
            "epoch=8\tch_step=50\tloss=1.144753\tlr=0.0052\t|g|=1.0890\tacc=0.5781\ttime=4.51it/s\n",
            "epoch=8\tch_step=100\tloss=1.368612\tlr=0.0052\t|g|=1.3458\tacc=0.4766\ttime=4.56it/s\n",
            "epoch=8\tch_step=150\tloss=1.319500\tlr=0.0052\t|g|=1.7851\tacc=0.4766\ttime=4.58it/s\n",
            "epoch=8\tch_step=200\tloss=1.182060\tlr=0.0052\t|g|=1.3282\tacc=0.5625\ttime=4.48it/s\n",
            "epoch=8\tch_step=250\tloss=1.316510\tlr=0.0052\t|g|=1.8549\tacc=0.4844\ttime=4.77it/s\n",
            "epoch=8\tch_step=300\tloss=1.151716\tlr=0.0052\t|g|=1.2338\tacc=0.5938\ttime=4.48it/s\n",
            "epoch=8\tch_step=350\tloss=1.023315\tlr=0.0052\t|g|=1.1919\tacc=0.5938\ttime=5.10it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 8: Training controller\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:145: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=1\tloss=-0.607\tent=41.94\tlr=0.0010\t|g|=0.1751\tacc=0.5156\tbl=0.53\ttime=1.69it/s\n",
            "ctrl_step=3\tloss=1.133\tent=43.43\tlr=0.0010\t|g|=0.0649\tacc=0.5703\tbl=0.55\ttime=1.67it/s\n",
            "ctrl_step=5\tloss=-2.625\tent=41.34\tlr=0.0010\t|g|=0.1860\tacc=0.4844\tbl=0.55\ttime=1.84it/s\n",
            "ctrl_step=7\tloss=0.994\tent=43.70\tlr=0.0010\t|g|=0.2633\tacc=0.5781\tbl=0.56\ttime=1.75it/s\n",
            "ctrl_step=9\tloss=-1.854\tent=41.01\tlr=0.0010\t|g|=0.1284\tacc=0.5078\tbl=0.56\ttime=1.84it/s\n",
            "ctrl_step=11\tloss=2.586\tent=43.92\tlr=0.0010\t|g|=0.1289\tacc=0.6250\tbl=0.56\ttime=1.68it/s\n",
            "ctrl_step=13\tloss=-0.866\tent=42.52\tlr=0.0010\t|g|=0.0487\tacc=0.5312\tbl=0.56\ttime=1.80it/s\n",
            "ctrl_step=15\tloss=-0.091\tent=45.16\tlr=0.0010\t|g|=0.0177\tacc=0.5469\tbl=0.55\ttime=1.69it/s\n",
            "ctrl_step=17\tloss=3.567\tent=40.84\tlr=0.0010\t|g|=0.1238\tacc=0.6484\tbl=0.56\ttime=1.76it/s\n",
            "ctrl_step=19\tloss=-1.290\tent=43.46\tlr=0.0010\t|g|=0.1108\tacc=0.5234\tbl=0.56\ttime=1.81it/s\n",
            "ctrl_step=21\tloss=-0.456\tent=42.54\tlr=0.0010\t|g|=0.1542\tacc=0.5469\tbl=0.56\ttime=1.73it/s\n",
            "ctrl_step=23\tloss=1.225\tent=43.77\tlr=0.0010\t|g|=0.2330\tacc=0.5859\tbl=0.56\ttime=1.72it/s\n",
            "ctrl_step=25\tloss=1.715\tent=43.93\tlr=0.0010\t|g|=0.0300\tacc=0.5938\tbl=0.56\ttime=1.80it/s\n",
            "ctrl_step=27\tloss=1.254\tent=41.55\tlr=0.0010\t|g|=0.2836\tacc=0.5859\tbl=0.56\ttime=1.64it/s\n",
            "ctrl_step=29\tloss=3.909\tent=41.67\tlr=0.0010\t|g|=0.0836\tacc=0.6484\tbl=0.56\ttime=1.75it/s\n",
            "ctrl_step=31\tloss=-0.928\tent=40.30\tlr=0.0010\t|g|=0.2219\tacc=0.5312\tbl=0.56\ttime=1.76it/s\n",
            "ctrl_step=33\tloss=-1.397\tent=44.00\tlr=0.0010\t|g|=0.2618\tacc=0.5234\tbl=0.56\ttime=1.70it/s\n",
            "ctrl_step=35\tloss=-0.578\tent=42.62\tlr=0.0010\t|g|=0.1325\tacc=0.5391\tbl=0.56\ttime=1.71it/s\n",
            "ctrl_step=37\tloss=0.822\tent=43.10\tlr=0.0010\t|g|=0.3436\tacc=0.5781\tbl=0.56\ttime=1.69it/s\n",
            "ctrl_step=39\tloss=-0.745\tent=40.99\tlr=0.0010\t|g|=0.1526\tacc=0.5391\tbl=0.56\ttime=1.76it/s\n",
            "ctrl_step=41\tloss=1.188\tent=41.73\tlr=0.0010\t|g|=0.0344\tacc=0.5859\tbl=0.57\ttime=1.61it/s\n",
            "ctrl_step=43\tloss=-1.776\tent=42.48\tlr=0.0010\t|g|=0.0334\tacc=0.5234\tbl=0.57\ttime=1.72it/s\n",
            "ctrl_step=45\tloss=-2.523\tent=42.94\tlr=0.0010\t|g|=0.0244\tacc=0.4922\tbl=0.57\ttime=1.71it/s\n",
            "ctrl_step=47\tloss=-1.318\tent=43.43\tlr=0.0010\t|g|=0.1936\tacc=0.5312\tbl=0.57\ttime=1.76it/s\n",
            "ctrl_step=49\tloss=-0.465\tent=44.47\tlr=0.0010\t|g|=0.0497\tacc=0.5547\tbl=0.57\ttime=1.69it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Here are 10 architectures:\n",
            "[0]\n",
            "[2 0]\n",
            "[2 1 1]\n",
            "[2 0 1 0]\n",
            "[2 0 0 1 1]\n",
            "[2 0 1 0 0 0]\n",
            "[2 0 1 0 0 1 0]\n",
            "[2 1 1 0 0 1 0 0]\n",
            "[2 0 0 1 0 0 1 0 1]\n",
            "[2 0 0 0 0 0 0 0 1 0]\n",
            "[2 1 1 1 1 0 0 0 0 0 0]\n",
            "[2 0 0 0 0 1 0 0 0 0 1 0]\n",
            "val_acc=0.5625\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 1]\n",
            "[2 0 0]\n",
            "[2 0 0 0]\n",
            "[2 0 0 0 0]\n",
            "[2 0 0 0 0 1]\n",
            "[2 0 1 1 1 1 0]\n",
            "[2 0 1 0 0 0 1 0]\n",
            "[2 1 0 0 0 0 1 0 0]\n",
            "[2 0 1 0 0 1 0 0 1 0]\n",
            "[2 0 0 0 1 0 0 0 0 1 1]\n",
            "[2 0 0 0 1 0 0 1 0 0 0 1]\n",
            "val_acc=0.640625\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 1 1]\n",
            "[2 1 1 0]\n",
            "[2 0 0 1 0]\n",
            "[2 1 0 1 0 1]\n",
            "[2 0 0 0 1 1 1]\n",
            "[2 1 0 0 0 1 0 0]\n",
            "[2 1 0 1 0 0 0 1 0]\n",
            "[2 1 0 1 0 1 1 0 1 0]\n",
            "[2 1 0 0 0 0 1 0 1 1 0]\n",
            "[2 0 0 0 0 0 0 0 0 0 1 0]\n",
            "val_acc=0.640625\n",
            "--------------------------------------------------------------------------------\n",
            "[1]\n",
            "[2 1]\n",
            "[2 0 1]\n",
            "[2 0 0 0]\n",
            "[2 0 0 0 0]\n",
            "[2 1 0 0 0 1]\n",
            "[1 0 0 0 0 1 0]\n",
            "[2 0 0 1 1 1 0 1]\n",
            "[2 0 0 0 0 1 0 0 1]\n",
            "[2 1 0 0 0 1 1 1 1 1]\n",
            "[0 1 0 0 0 1 0 1 0 0 0]\n",
            "[2 0 0 0 0 0 1 0 0 1 1 0]\n",
            "val_acc=0.5078125\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 0 0]\n",
            "[2 0 1 0]\n",
            "[2 1 0 0 0]\n",
            "[2 0 0 0 0 1]\n",
            "[2 0 0 0 0 0 0]\n",
            "[2 0 0 0 1 0 0 0]\n",
            "[2 1 0 0 1 1 0 0 0]\n",
            "[2 0 0 1 0 0 0 0 0 0]\n",
            "[2 0 0 1 0 0 1 0 1 0 1]\n",
            "[2 0 0 0 0 0 0 0 0 0 0 1]\n",
            "val_acc=0.6484375\n",
            "--------------------------------------------------------------------------------\n",
            "[1]\n",
            "[2 0]\n",
            "[2 0 0]\n",
            "[2 1 1 1]\n",
            "[2 0 0 0 1]\n",
            "[2 0 0 0 0 0]\n",
            "[2 1 1 1 0 0 0]\n",
            "[2 0 1 0 0 0 0 0]\n",
            "[2 0 0 1 0 0 0 0 0]\n",
            "[2 0 0 0 0 0 1 0 1 0]\n",
            "[2 1 1 0 0 0 0 0 0 0 0]\n",
            "[2 0 0 0 1 0 0 0 0 1 1 0]\n",
            "val_acc=0.5234375\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 1 0]\n",
            "[2 1 0 0]\n",
            "[2 0 1 0 1]\n",
            "[2 0 0 1 1 0]\n",
            "[2 0 1 0 1 0 0]\n",
            "[2 0 0 0 0 0 0 0]\n",
            "[2 0 0 1 0 1 0 0 1]\n",
            "[2 0 0 0 0 0 0 0 0 0]\n",
            "[2 1 0 0 0 1 0 1 0 0 0]\n",
            "[1 1 0 0 0 0 1 0 0 0 0 0]\n",
            "val_acc=0.5703125\n",
            "--------------------------------------------------------------------------------\n",
            "[0]\n",
            "[2 0]\n",
            "[2 0 1]\n",
            "[2 0 0 1]\n",
            "[2 0 0 0 0]\n",
            "[2 0 1 0 0 0]\n",
            "[2 0 0 0 0 0 1]\n",
            "[2 0 0 0 0 0 0 0]\n",
            "[2 0 0 0 1 1 0 0 1]\n",
            "[2 0 0 1 0 1 0 1 1 0]\n",
            "[0 1 0 1 1 0 1 1 0 0 0]\n",
            "[2 0 0 0 0 0 1 0 0 0 0 0]\n",
            "val_acc=0.59375\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 1]\n",
            "[2 1 0]\n",
            "[2 0 1 0]\n",
            "[2 0 0 0 0]\n",
            "[2 0 0 0 0 0]\n",
            "[2 0 0 0 0 0 1]\n",
            "[2 0 0 1 0 1 1 1]\n",
            "[2 0 1 0 0 0 0 0 0]\n",
            "[2 1 0 1 0 0 0 0 1 1]\n",
            "[2 0 0 0 0 0 0 0 1 0 0]\n",
            "[0 1 0 0 1 0 0 0 0 1 0 0]\n",
            "val_acc=0.625\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 1]\n",
            "[2 0 1]\n",
            "[2 0 0 0]\n",
            "[2 1 1 0 0]\n",
            "[1 0 0 1 0 0]\n",
            "[2 0 0 1 0 0 1]\n",
            "[2 0 1 0 0 0 0 0]\n",
            "[0 0 1 1 1 0 1 1 0]\n",
            "[2 1 0 1 0 0 1 0 0 0]\n",
            "[2 0 1 0 0 0 0 0 0 0 1]\n",
            "[2 0 0 0 1 1 0 1 1 0 1 0]\n",
            "val_acc=0.6640625\n",
            "--------------------------------------------------------------------------------\n",
            "Epoch 8: Eval\n",
            "valid_accuracy: 0.5673\n",
            "test_accuracy: 0.5248\n",
            "epoch=9\tch_step=0\tloss=1.158067\tlr=0.0017\t|g|=1.3859\tacc=0.5859\ttime=3.33it/s\n",
            "epoch=9\tch_step=50\tloss=1.206876\tlr=0.0017\t|g|=1.3633\tacc=0.5938\ttime=4.67it/s\n",
            "epoch=9\tch_step=100\tloss=1.253051\tlr=0.0017\t|g|=1.6479\tacc=0.5781\ttime=4.44it/s\n",
            "epoch=9\tch_step=150\tloss=1.264674\tlr=0.0017\t|g|=1.4328\tacc=0.5547\ttime=4.63it/s\n",
            "epoch=9\tch_step=200\tloss=1.175560\tlr=0.0017\t|g|=1.2127\tacc=0.5625\ttime=4.60it/s\n",
            "epoch=9\tch_step=250\tloss=1.147830\tlr=0.0017\t|g|=1.4695\tacc=0.5703\ttime=4.56it/s\n",
            "epoch=9\tch_step=300\tloss=1.167702\tlr=0.0017\t|g|=1.6136\tacc=0.5703\ttime=4.68it/s\n",
            "epoch=9\tch_step=350\tloss=1.280361\tlr=0.0017\t|g|=1.3522\tacc=0.5234\ttime=5.55it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 9: Training controller\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:145: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=1\tloss=-0.407\tent=46.40\tlr=0.0010\t|g|=0.3768\tacc=0.5547\tbl=0.57\ttime=1.61it/s\n",
            "ctrl_step=3\tloss=-0.905\tent=40.87\tlr=0.0010\t|g|=0.4401\tacc=0.5469\tbl=0.57\ttime=1.66it/s\n",
            "ctrl_step=5\tloss=1.290\tent=42.00\tlr=0.0010\t|g|=0.1163\tacc=0.5938\tbl=0.57\ttime=1.60it/s\n",
            "ctrl_step=7\tloss=2.457\tent=44.45\tlr=0.0010\t|g|=0.2933\tacc=0.6172\tbl=0.57\ttime=1.74it/s\n",
            "ctrl_step=9\tloss=-1.895\tent=41.87\tlr=0.0010\t|g|=0.0872\tacc=0.5234\tbl=0.58\ttime=1.65it/s\n",
            "ctrl_step=11\tloss=-2.446\tent=41.23\tlr=0.0010\t|g|=0.3932\tacc=0.5078\tbl=0.57\ttime=1.64it/s\n",
            "ctrl_step=13\tloss=-0.303\tent=43.88\tlr=0.0010\t|g|=0.1116\tacc=0.5625\tbl=0.58\ttime=1.68it/s\n",
            "ctrl_step=15\tloss=-0.205\tent=44.44\tlr=0.0010\t|g|=0.2561\tacc=0.5625\tbl=0.57\ttime=1.58it/s\n",
            "ctrl_step=17\tloss=1.656\tent=42.99\tlr=0.0010\t|g|=0.0765\tacc=0.6016\tbl=0.57\ttime=1.57it/s\n",
            "ctrl_step=19\tloss=1.244\tent=41.96\tlr=0.0010\t|g|=0.0152\tacc=0.5938\tbl=0.57\ttime=1.60it/s\n",
            "ctrl_step=21\tloss=0.205\tent=44.28\tlr=0.0010\t|g|=0.1563\tacc=0.5703\tbl=0.57\ttime=1.59it/s\n",
            "ctrl_step=23\tloss=-1.082\tent=43.96\tlr=0.0010\t|g|=0.1387\tacc=0.5391\tbl=0.57\ttime=1.67it/s\n",
            "ctrl_step=25\tloss=-1.566\tent=40.81\tlr=0.0010\t|g|=0.4942\tacc=0.5156\tbl=0.57\ttime=1.55it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Traceback (most recent call last):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=27\tloss=2.763\tent=43.08\tlr=0.0010\t|g|=0.1072\tacc=0.6328\tbl=0.57\ttime=1.50it/s\n",
            "ctrl_step=29\tloss=-0.099\tent=43.09\tlr=0.0010\t|g|=0.0161\tacc=0.5625\tbl=0.57\ttime=1.60it/s\n",
            "ctrl_step=31\tloss=4.824\tent=42.21\tlr=0.0010\t|g|=0.1179\tacc=0.6875\tbl=0.57\ttime=1.61it/s\n",
            "ctrl_step=33\tloss=0.042\tent=40.89\tlr=0.0010\t|g|=0.2002\tacc=0.5703\tbl=0.57\ttime=1.66it/s\n",
            "ctrl_step=35\tloss=1.080\tent=43.68\tlr=0.0010\t|g|=0.0446\tacc=0.6016\tbl=0.58\ttime=1.61it/s\n",
            "ctrl_step=37\tloss=-1.528\tent=41.28\tlr=0.0010\t|g|=0.1275\tacc=0.5391\tbl=0.58\ttime=1.71it/s\n",
            "ctrl_step=39\tloss=1.536\tent=43.46\tlr=0.0010\t|g|=0.0273\tacc=0.6094\tbl=0.58\ttime=1.68it/s\n",
            "ctrl_step=41\tloss=4.154\tent=44.12\tlr=0.0010\t|g|=0.1182\tacc=0.6797\tbl=0.58\ttime=1.51it/s\n",
            "ctrl_step=43\tloss=-3.854\tent=42.51\tlr=0.0010\t|g|=0.2401\tacc=0.5000\tbl=0.58\ttime=1.54it/s\n",
            "ctrl_step=45\tloss=2.995\tent=38.49\tlr=0.0010\t|g|=0.5971\tacc=0.6562\tbl=0.58\ttime=1.62it/s\n",
            "ctrl_step=47\tloss=-0.393\tent=39.96\tlr=0.0010\t|g|=0.1730\tacc=0.5625\tbl=0.58\ttime=1.65it/s\n",
            "ctrl_step=49\tloss=-2.799\tent=42.08\tlr=0.0010\t|g|=0.2235\tacc=0.5000\tbl=0.57\ttime=1.61it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Here are 10 architectures:\n",
            "[2]\n",
            "[2 0]\n",
            "[2 0 0]\n",
            "[2 0 1 1]\n",
            "[0 0 1 0 0]\n",
            "[2 0 1 1 0 0]\n",
            "[2 1 1 0 0 0 0]\n",
            "[2 0 0 0 0 0 0 0]\n",
            "[2 1 0 0 1 0 0 0 0]\n",
            "[2 1 0 0 0 1 0 0 0 0]\n",
            "[2 1 0 1 0 0 1 1 0 1 0]\n",
            "[2 1 0 0 0 1 0 1 0 1 0 0]\n",
            "val_acc=0.609375\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 1 0]\n",
            "[2 0 0 1]\n",
            "[2 1 0 0 1]\n",
            "[2 0 0 0 0 1]\n",
            "[2 0 0 1 0 1 0]\n",
            "[2 0 0 0 1 0 0 0]\n",
            "[2 0 0 0 0 0 0 0 1]\n",
            "[2 0 0 1 0 1 1 0 0 1]\n",
            "[2 0 0 1 0 0 1 1 0 1 0]\n",
            "[2 0 0 0 0 0 1 1 1 0 0 0]\n",
            "val_acc=0.6640625\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 1]\n",
            "[2 0 0]\n",
            "[2 0 0 0]\n",
            "[0 0 0 1 0]\n",
            "[1 0 0 0 1 1]\n",
            "[2 0 0 0 1 0 0]\n",
            "[2 0 0 0 0 1 0 0]\n",
            "[0 0 0 0 1 0 0 0 0]\n",
            "[2 0 1 0 0 1 0 1 0 1]\n",
            "[2 0 1 1 1 1 0 1 0 0 0]\n",
            "[2 0 0 0 0 0 0 0 0 0 0 0]\n",
            "val_acc=0.6328125\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 1]\n",
            "[2 1 0]\n",
            "[2 1 0 1]\n",
            "[2 0 0 1 1]\n",
            "[2 0 1 1 0 1]\n",
            "[2 0 1 1 0 0 0]\n",
            "[2 1 1 1 0 0 0 0]\n",
            "[1 1 0 1 0 0 0 1 0]\n",
            "[2 1 0 0 1 0 0 0 1 0]\n",
            "[2 0 0 0 0 0 0 0 0 0 1]\n",
            "[2 1 0 1 0 0 0 0 1 0 1 0]\n",
            "val_acc=0.640625\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[0 0 0]\n",
            "[1 1 1 1]\n",
            "[1 0 0 0 0]\n",
            "[2 0 0 0 0 0]\n",
            "[2 0 0 0 0 0 0]\n",
            "[2 0 0 0 0 1 0 0]\n",
            "[2 0 0 0 0 0 1 1 0]\n",
            "[2 1 0 1 0 0 0 0 0 0]\n",
            "[2 1 0 1 1 0 0 1 0 0 0]\n",
            "[2 0 1 0 0 1 1 1 0 0 1 0]\n",
            "val_acc=0.6171875\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 0 1]\n",
            "[2 0 1 0]\n",
            "[2 1 1 1 0]\n",
            "[2 0 1 0 0 1]\n",
            "[2 0 1 0 0 1 1]\n",
            "[2 1 1 0 0 1 0 1]\n",
            "[2 0 0 0 0 0 0 1 1]\n",
            "[2 0 0 1 0 0 0 0 0 0]\n",
            "[2 1 0 0 0 1 1 0 0 0 0]\n",
            "[2 0 1 1 0 0 0 1 0 1 0 1]\n",
            "val_acc=0.640625\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 1]\n",
            "[2 1 0]\n",
            "[2 0 0 0]\n",
            "[2 0 0 0 0]\n",
            "[2 0 0 0 0 0]\n",
            "[2 0 0 0 0 0 0]\n",
            "[2 0 0 0 0 0 0 0]\n",
            "[2 1 1 0 0 0 0 0 0]\n",
            "[2 1 0 1 0 0 1 0 0 1]\n",
            "[2 0 1 0 0 0 0 0 0 1 0]\n",
            "[2 0 0 0 0 0 0 0 0 0 0 0]\n",
            "val_acc=0.6015625\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 0 1]\n",
            "[2 1 0 0]\n",
            "[2 0 0 0 0]\n",
            "[2 0 0 0 0 0]\n",
            "[2 0 0 1 1 0 0]\n",
            "[2 1 0 0 1 0 0 0]\n",
            "[2 0 1 0 1 0 1 0 1]\n",
            "[2 0 1 0 1 1 1 1 0 0]\n",
            "[2 0 0 0 0 0 1 0 1 0 1]\n",
            "[2 0 0 1 0 0 1 0 1 0 1 0]\n",
            "val_acc=0.671875\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 1]\n",
            "[2 1 0]\n",
            "[0 0 0 1]\n",
            "[2 0 1 0 0]\n",
            "[2 0 0 1 1 0]\n",
            "[2 0 0 0 0 1 0]\n",
            "[0 0 0 0 1 0 0 1]\n",
            "[1 1 1 0 1 1 1 0 0]\n",
            "[2 1 0 0 1 1 1 0 1 1]\n",
            "[2 0 1 0 0 1 0 1 1 0 1]\n",
            "[2 1 0 0 0 0 1 1 0 0 0 0]\n",
            "val_acc=0.625\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 0 0]\n",
            "[2 1 0 0]\n",
            "[2 1 0 0 0]\n",
            "[2 1 0 0 0 0]\n",
            "[2 0 0 0 0 0 1]\n",
            "[2 0 0 1 0 0 0 0]\n",
            "[2 1 0 1 0 0 1 0 0]\n",
            "[1 1 1 0 0 0 0 0 0 0]\n",
            "[2 0 0 1 0 0 1 0 0 0 0]\n",
            "[1 0 1 0 0 0 0 0 1 0 1 0]\n",
            "val_acc=0.6328125\n",
            "--------------------------------------------------------------------------------\n",
            "Epoch 9: Eval\n",
            "valid_accuracy: 0.5859\n",
            "test_accuracy: 0.5389\n",
            "epoch=10\tch_step=0\tloss=1.134626\tlr=0.0005\t|g|=1.6590\tacc=0.5781\ttime=3.94it/s\n",
            "epoch=10\tch_step=50\tloss=1.291296\tlr=0.0005\t|g|=1.8116\tacc=0.5000\ttime=4.69it/s\n",
            "epoch=10\tch_step=100\tloss=1.058839\tlr=0.0005\t|g|=1.3696\tacc=0.6328\ttime=4.63it/s\n",
            "epoch=10\tch_step=150\tloss=1.259101\tlr=0.0005\t|g|=1.2902\tacc=0.4922\ttime=4.52it/s\n",
            "epoch=10\tch_step=200\tloss=1.165392\tlr=0.0005\t|g|=1.5410\tacc=0.5391\ttime=4.73it/s\n",
            "epoch=10\tch_step=250\tloss=1.100628\tlr=0.0005\t|g|=1.4314\tacc=0.6094\ttime=4.79it/s\n",
            "epoch=10\tch_step=300\tloss=1.249807\tlr=0.0005\t|g|=1.3543\tacc=0.5156\ttime=4.48it/s\n",
            "epoch=10\tch_step=350\tloss=1.171028\tlr=0.0005\t|g|=1.1325\tacc=0.5703\ttime=5.01it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 10: Training controller\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:145: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=1\tloss=0.936\tent=42.43\tlr=0.0010\t|g|=0.0823\tacc=0.5938\tbl=0.57\ttime=1.58it/s\n",
            "ctrl_step=3\tloss=-2.301\tent=44.43\tlr=0.0010\t|g|=0.0719\tacc=0.5156\tbl=0.57\ttime=1.55it/s\n",
            "ctrl_step=5\tloss=0.212\tent=42.23\tlr=0.0010\t|g|=0.0635\tacc=0.5781\tbl=0.58\ttime=1.60it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Traceback (most recent call last):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=7\tloss=-1.936\tent=43.51\tlr=0.0010\t|g|=0.2303\tacc=0.5391\tbl=0.58\ttime=1.60it/s\n",
            "ctrl_step=9\tloss=0.729\tent=43.54\tlr=0.0010\t|g|=0.1749\tacc=0.5938\tbl=0.58\ttime=1.48it/s\n",
            "ctrl_step=11\tloss=0.715\tent=45.03\tlr=0.0010\t|g|=0.1102\tacc=0.5938\tbl=0.58\ttime=1.59it/s\n",
            "ctrl_step=13\tloss=-3.323\tent=42.01\tlr=0.0010\t|g|=0.0839\tacc=0.5000\tbl=0.58\ttime=1.57it/s\n",
            "ctrl_step=15\tloss=-0.130\tent=40.82\tlr=0.0010\t|g|=0.0967\tacc=0.5703\tbl=0.58\ttime=1.60it/s\n",
            "ctrl_step=17\tloss=-0.633\tent=44.29\tlr=0.0010\t|g|=0.3103\tacc=0.5625\tbl=0.58\ttime=1.55it/s\n",
            "ctrl_step=19\tloss=3.064\tent=42.91\tlr=0.0010\t|g|=0.0221\tacc=0.6484\tbl=0.58\ttime=1.61it/s\n",
            "ctrl_step=21\tloss=-2.269\tent=42.47\tlr=0.0010\t|g|=0.0541\tacc=0.5156\tbl=0.58\ttime=1.56it/s\n",
            "ctrl_step=23\tloss=1.968\tent=43.66\tlr=0.0010\t|g|=0.0830\tacc=0.6172\tbl=0.58\ttime=1.48it/s\n",
            "ctrl_step=25\tloss=0.758\tent=41.66\tlr=0.0010\t|g|=0.4348\tacc=0.5938\tbl=0.58\ttime=1.48it/s\n",
            "ctrl_step=27\tloss=1.924\tent=44.51\tlr=0.0010\t|g|=0.1371\tacc=0.6250\tbl=0.58\ttime=1.53it/s\n",
            "ctrl_step=29\tloss=-3.515\tent=45.14\tlr=0.0010\t|g|=0.7703\tacc=0.5000\tbl=0.57\ttime=1.46it/s\n",
            "ctrl_step=31\tloss=-1.904\tent=42.71\tlr=0.0010\t|g|=0.3152\tacc=0.5312\tbl=0.58\ttime=1.59it/s\n",
            "ctrl_step=33\tloss=-1.674\tent=44.20\tlr=0.0010\t|g|=0.3174\tacc=0.5391\tbl=0.58\ttime=1.55it/s\n",
            "ctrl_step=35\tloss=-0.897\tent=41.23\tlr=0.0010\t|g|=0.2381\tacc=0.5547\tbl=0.58\ttime=1.53it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Traceback (most recent call last):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=37\tloss=-2.341\tent=43.20\tlr=0.0010\t|g|=0.1702\tacc=0.5234\tbl=0.58\ttime=1.51it/s\n",
            "ctrl_step=39\tloss=1.178\tent=41.07\tlr=0.0010\t|g|=0.5445\tacc=0.6016\tbl=0.58\ttime=1.48it/s\n",
            "ctrl_step=41\tloss=0.653\tent=43.82\tlr=0.0010\t|g|=0.0791\tacc=0.5859\tbl=0.58\ttime=1.45it/s\n",
            "ctrl_step=43\tloss=-0.848\tent=40.89\tlr=0.0010\t|g|=0.1646\tacc=0.5469\tbl=0.58\ttime=1.53it/s\n",
            "ctrl_step=45\tloss=-1.205\tent=45.10\tlr=0.0010\t|g|=0.3224\tacc=0.5391\tbl=0.57\ttime=1.56it/s\n",
            "ctrl_step=47\tloss=3.204\tent=42.95\tlr=0.0010\t|g|=0.1027\tacc=0.6562\tbl=0.58\ttime=1.52it/s\n",
            "ctrl_step=49\tloss=-0.086\tent=41.72\tlr=0.0010\t|g|=0.2642\tacc=0.5703\tbl=0.58\ttime=1.43it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Here are 10 architectures:\n",
            "[2]\n",
            "[2 0]\n",
            "[2 0 1]\n",
            "[2 0 0 0]\n",
            "[2 0 0 0 0]\n",
            "[2 0 0 1 1 0]\n",
            "[2 1 0 0 0 0 1]\n",
            "[2 0 0 1 0 1 0 0]\n",
            "[2 0 0 0 1 0 1 0 0]\n",
            "[2 1 0 0 0 0 0 0 0 1]\n",
            "[2 1 0 0 1 0 1 0 0 0 0]\n",
            "[2 0 0 0 0 0 0 0 0 1 0 1]\n",
            "val_acc=0.65625\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[0 0 0]\n",
            "[2 0 0 1]\n",
            "[0 1 1 0 0]\n",
            "[2 0 1 0 0 1]\n",
            "[2 0 1 0 0 0 0]\n",
            "[2 0 0 0 1 0 0 1]\n",
            "[1 0 0 1 0 0 0 0 0]\n",
            "[2 0 0 0 0 1 0 1 0 0]\n",
            "[2 1 0 1 1 0 1 1 0 0 1]\n",
            "[2 1 0 1 1 0 0 1 0 0 0 1]\n",
            "val_acc=0.6484375\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 0 1]\n",
            "[2 0 0 0]\n",
            "[2 0 0 0 1]\n",
            "[2 0 0 0 0 0]\n",
            "[2 0 1 0 0 0 0]\n",
            "[2 0 1 0 0 1 1 1]\n",
            "[2 0 0 0 1 1 0 0 0]\n",
            "[2 0 0 0 0 1 0 1 0 0]\n",
            "[2 1 0 1 0 0 0 0 0 0 0]\n",
            "[0 1 0 0 0 0 0 0 0 1 0 1]\n",
            "val_acc=0.6015625\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 1 0]\n",
            "[2 1 0 1]\n",
            "[2 0 0 0 0]\n",
            "[2 0 0 1 1 0]\n",
            "[2 1 0 0 0 1 0]\n",
            "[2 1 1 0 1 0 0 0]\n",
            "[2 0 0 0 0 0 0 0 0]\n",
            "[2 0 0 1 0 0 0 0 0 0]\n",
            "[2 0 0 0 0 1 1 1 0 0 0]\n",
            "[2 0 0 0 0 1 0 1 0 0 0 0]\n",
            "val_acc=0.640625\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 0 0]\n",
            "[2 0 0 1]\n",
            "[2 0 1 0 1]\n",
            "[2 1 0 0 0 1]\n",
            "[2 1 0 0 0 0 0]\n",
            "[2 1 0 1 1 0 0 1]\n",
            "[2 0 1 0 1 1 0 0 0]\n",
            "[2 1 0 0 0 0 0 0 1 0]\n",
            "[2 0 0 1 0 0 0 0 1 0 0]\n",
            "[2 0 1 1 0 1 0 0 0 1 0 0]\n",
            "val_acc=0.65625\n",
            "--------------------------------------------------------------------------------\n",
            "[1]\n",
            "[2 0]\n",
            "[2 0 0]\n",
            "[2 0 0 0]\n",
            "[2 0 1 1 0]\n",
            "[2 1 0 0 0 0]\n",
            "[2 0 0 0 1 0 1]\n",
            "[2 0 1 0 0 0 1 0]\n",
            "[2 0 0 0 0 1 0 0 0]\n",
            "[0 0 0 0 0 0 0 0 0 0]\n",
            "[2 1 0 0 1 0 0 0 0 0 0]\n",
            "[2 0 1 1 0 0 0 0 0 0 1 0]\n",
            "val_acc=0.5625\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 0 0]\n",
            "[2 1 1 0]\n",
            "[2 0 1 0 1]\n",
            "[2 1 0 0 0 0]\n",
            "[2 0 0 0 1 1 0]\n",
            "[2 0 1 0 1 0 0 0]\n",
            "[2 1 0 0 0 1 0 1 0]\n",
            "[2 1 0 1 0 0 0 1 1 1]\n",
            "[2 0 0 1 0 0 0 0 0 1 1]\n",
            "[1 1 0 0 0 0 1 0 0 0 0 0]\n",
            "val_acc=0.5859375\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 1]\n",
            "[2 0 1]\n",
            "[1 1 0 0]\n",
            "[2 1 0 0 0]\n",
            "[2 1 0 0 1 1]\n",
            "[2 1 0 1 1 1 0]\n",
            "[2 0 0 1 1 0 0 0]\n",
            "[2 1 0 0 0 1 0 0 0]\n",
            "[2 1 0 1 0 0 0 0 1 0]\n",
            "[2 0 1 1 0 0 0 0 0 0 1]\n",
            "[2 0 0 0 0 0 0 0 1 0 0 0]\n",
            "val_acc=0.6484375\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 0 1]\n",
            "[2 0 0 0]\n",
            "[2 0 0 0 1]\n",
            "[2 1 1 0 1 0]\n",
            "[2 0 1 1 0 0 0]\n",
            "[2 1 1 1 0 0 0 0]\n",
            "[2 0 0 1 0 1 1 0 0]\n",
            "[2 0 0 0 1 0 0 0 1 0]\n",
            "[2 0 0 0 1 0 0 1 1 1 1]\n",
            "[2 1 0 1 1 1 0 0 0 0 0 0]\n",
            "val_acc=0.671875\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 1]\n",
            "[2 0 0]\n",
            "[2 1 0 1]\n",
            "[2 0 0 0 1]\n",
            "[2 0 0 0 1 1]\n",
            "[2 0 0 0 0 0 0]\n",
            "[2 0 1 0 0 0 0 0]\n",
            "[2 0 1 0 0 0 0 1 0]\n",
            "[1 0 1 0 0 0 0 0 1 0]\n",
            "[2 1 0 0 1 0 0 0 0 0 0]\n",
            "[2 0 1 1 0 0 0 1 0 1 1 0]\n",
            "val_acc=0.6015625\n",
            "--------------------------------------------------------------------------------\n",
            "Epoch 10: Eval\n",
            "valid_accuracy: 0.5893\n",
            "test_accuracy: 0.5341\n",
            "epoch=11\tch_step=0\tloss=1.214948\tlr=0.0017\t|g|=1.5541\tacc=0.5078\ttime=3.34it/s\n",
            "epoch=11\tch_step=50\tloss=1.265135\tlr=0.0017\t|g|=1.2462\tacc=0.5000\ttime=4.45it/s\n",
            "epoch=11\tch_step=100\tloss=1.044651\tlr=0.0017\t|g|=1.7138\tacc=0.6172\ttime=4.54it/s\n",
            "epoch=11\tch_step=150\tloss=1.321715\tlr=0.0017\t|g|=1.6089\tacc=0.5703\ttime=4.55it/s\n",
            "epoch=11\tch_step=200\tloss=0.934948\tlr=0.0017\t|g|=1.4213\tacc=0.6094\ttime=4.56it/s\n",
            "epoch=11\tch_step=250\tloss=1.199996\tlr=0.0017\t|g|=1.4546\tacc=0.5234\ttime=4.60it/s\n",
            "epoch=11\tch_step=300\tloss=1.060551\tlr=0.0017\t|g|=2.0219\tacc=0.6172\ttime=4.54it/s\n",
            "epoch=11\tch_step=350\tloss=1.168616\tlr=0.0017\t|g|=1.4842\tacc=0.5469\ttime=5.31it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 11: Training controller\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:145: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=1\tloss=-2.491\tent=43.09\tlr=0.0010\t|g|=0.0229\tacc=0.5156\tbl=0.58\ttime=1.50it/s\n",
            "ctrl_step=3\tloss=0.046\tent=43.01\tlr=0.0010\t|g|=0.0418\tacc=0.5703\tbl=0.57\ttime=1.42it/s\n",
            "ctrl_step=5\tloss=0.750\tent=43.76\tlr=0.0010\t|g|=0.3073\tacc=0.5859\tbl=0.57\ttime=1.50it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Traceback (most recent call last):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=7\tloss=4.646\tent=42.93\tlr=0.0010\t|g|=0.0142\tacc=0.6719\tbl=0.58\ttime=1.55it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Traceback (most recent call last):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=9\tloss=2.228\tent=43.83\tlr=0.0010\t|g|=0.1524\tacc=0.6328\tbl=0.58\ttime=1.48it/s\n",
            "ctrl_step=11\tloss=-3.792\tent=44.10\tlr=0.0010\t|g|=0.2753\tacc=0.5000\tbl=0.58\ttime=1.47it/s\n",
            "ctrl_step=13\tloss=-1.934\tent=45.54\tlr=0.0010\t|g|=0.3822\tacc=0.5312\tbl=0.58\ttime=1.50it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Traceback (most recent call last):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=15\tloss=-1.137\tent=41.83\tlr=0.0010\t|g|=0.1973\tacc=0.5547\tbl=0.59\ttime=1.50it/s\n",
            "ctrl_step=17\tloss=-1.331\tent=43.02\tlr=0.0010\t|g|=0.0537\tacc=0.5469\tbl=0.58\ttime=1.47it/s\n",
            "ctrl_step=19\tloss=-2.176\tent=42.41\tlr=0.0010\t|g|=0.1136\tacc=0.5234\tbl=0.58\ttime=1.40it/s\n",
            "ctrl_step=21\tloss=3.342\tent=43.36\tlr=0.0010\t|g|=0.0119\tacc=0.6562\tbl=0.58\ttime=1.51it/s\n",
            "ctrl_step=23\tloss=-1.654\tent=42.92\tlr=0.0010\t|g|=0.2548\tacc=0.5391\tbl=0.58\ttime=1.47it/s\n",
            "ctrl_step=25\tloss=-1.228\tent=45.20\tlr=0.0010\t|g|=0.4069\tacc=0.5547\tbl=0.58\ttime=1.54it/s\n",
            "ctrl_step=27\tloss=-1.934\tent=41.82\tlr=0.0010\t|g|=0.2700\tacc=0.5234\tbl=0.58\ttime=1.40it/s\n",
            "ctrl_step=29\tloss=-2.438\tent=41.34\tlr=0.0010\t|g|=0.0258\tacc=0.5156\tbl=0.58\ttime=1.48it/s\n",
            "ctrl_step=31\tloss=-3.301\tent=42.11\tlr=0.0010\t|g|=0.4895\tacc=0.4844\tbl=0.58\ttime=1.55it/s\n",
            "ctrl_step=33\tloss=1.891\tent=44.25\tlr=0.0010\t|g|=0.0980\tacc=0.6172\tbl=0.58\ttime=1.50it/s\n",
            "ctrl_step=35\tloss=0.437\tent=42.92\tlr=0.0010\t|g|=0.0388\tacc=0.5859\tbl=0.58\ttime=1.46it/s\n",
            "ctrl_step=37\tloss=2.702\tent=43.35\tlr=0.0010\t|g|=0.0553\tacc=0.6484\tbl=0.58\ttime=1.52it/s\n",
            "ctrl_step=39\tloss=2.118\tent=42.22\tlr=0.0010\t|g|=0.5114\tacc=0.6328\tbl=0.58\ttime=1.52it/s\n",
            "ctrl_step=41\tloss=-2.301\tent=43.77\tlr=0.0010\t|g|=0.0093\tacc=0.5312\tbl=0.59\ttime=1.48it/s\n",
            "ctrl_step=43\tloss=-1.604\tent=43.75\tlr=0.0010\t|g|=0.3953\tacc=0.5469\tbl=0.59\ttime=1.37it/s\n",
            "ctrl_step=45\tloss=0.336\tent=42.82\tlr=0.0010\t|g|=0.1628\tacc=0.5859\tbl=0.58\ttime=1.53it/s\n",
            "ctrl_step=47\tloss=-2.791\tent=43.05\tlr=0.0010\t|g|=0.1522\tacc=0.5078\tbl=0.58\ttime=1.40it/s\n",
            "ctrl_step=49\tloss=-2.211\tent=43.69\tlr=0.0010\t|g|=0.0292\tacc=0.5156\tbl=0.58\ttime=1.49it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Here are 10 architectures:\n",
            "[2]\n",
            "[2 0]\n",
            "[2 1 0]\n",
            "[2 0 0 0]\n",
            "[2 1 0 1 0]\n",
            "[2 0 0 0 0 0]\n",
            "[2 0 0 0 0 0 0]\n",
            "[2 0 0 1 1 0 0 0]\n",
            "[0 0 1 1 1 0 1 0 0]\n",
            "[2 0 1 1 1 0 0 0 0 1]\n",
            "[2 0 0 0 1 0 0 0 0 0 0]\n",
            "[2 0 0 0 0 0 1 0 0 0 1 1]\n",
            "val_acc=0.5859375\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 1]\n",
            "[2 0 1]\n",
            "[2 0 1 0]\n",
            "[2 0 0 1 0]\n",
            "[1 1 0 0 1 0]\n",
            "[2 0 0 0 0 1 0]\n",
            "[2 0 1 0 0 1 0 1]\n",
            "[2 0 0 0 1 1 0 0 0]\n",
            "[2 0 0 0 0 0 0 1 1 0]\n",
            "[2 0 0 0 0 0 0 0 0 0 0]\n",
            "[2 0 0 0 0 0 0 0 1 0 1 1]\n",
            "val_acc=0.5546875\n",
            "--------------------------------------------------------------------------------\n",
            "[1]\n",
            "[2 0]\n",
            "[2 0 0]\n",
            "[2 1 1 1]\n",
            "[2 0 0 0 0]\n",
            "[2 0 0 0 0 1]\n",
            "[2 1 0 0 1 0 0]\n",
            "[2 0 1 0 0 0 1 0]\n",
            "[2 1 0 0 1 0 1 1 0]\n",
            "[2 1 0 0 0 0 0 0 0 0]\n",
            "[2 0 0 1 0 0 1 0 0 0 0]\n",
            "[2 1 0 1 1 0 0 0 0 0 0 0]\n",
            "val_acc=0.5625\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 1 0]\n",
            "[2 1 0 0]\n",
            "[2 0 0 0 1]\n",
            "[2 1 0 0 0 0]\n",
            "[2 1 0 0 0 0 0]\n",
            "[2 0 0 1 0 0 0 0]\n",
            "[2 0 0 0 0 0 0 0 0]\n",
            "[1 0 0 0 1 0 1 0 0 0]\n",
            "[2 0 0 0 0 1 1 0 0 1 0]\n",
            "[2 0 0 0 0 0 0 0 1 1 0 1]\n",
            "val_acc=0.5703125\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 0 0]\n",
            "[2 0 1 1]\n",
            "[2 0 0 0 1]\n",
            "[2 0 0 0 1 0]\n",
            "[1 0 1 0 0 0 0]\n",
            "[2 1 0 0 1 0 0 1]\n",
            "[2 0 1 0 1 0 0 0 0]\n",
            "[2 0 0 1 0 0 0 0 1 0]\n",
            "[0 0 0 1 0 0 1 0 0 1 1]\n",
            "[2 0 0 0 0 0 1 1 1 0 1 1]\n",
            "val_acc=0.625\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 1]\n",
            "[2 0 0]\n",
            "[2 0 0 0]\n",
            "[2 0 0 0 0]\n",
            "[2 1 1 0 0 1]\n",
            "[2 0 0 1 0 0 0]\n",
            "[2 0 0 0 0 0 0 0]\n",
            "[2 0 0 0 0 0 1 0 1]\n",
            "[2 0 0 0 0 0 0 0 0 1]\n",
            "[2 0 0 0 0 0 1 0 0 0 1]\n",
            "[2 0 1 0 0 0 0 1 0 0 1 0]\n",
            "val_acc=0.5390625\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 0 0]\n",
            "[2 0 1 1]\n",
            "[2 1 0 0 0]\n",
            "[2 0 1 0 0 0]\n",
            "[2 0 0 1 0 0 0]\n",
            "[2 1 0 0 0 0 0 1]\n",
            "[2 1 1 1 0 0 0 0 0]\n",
            "[2 0 1 0 0 0 0 0 0 1]\n",
            "[0 1 1 1 0 0 1 0 0 0 1]\n",
            "[2 0 0 0 1 1 0 0 1 0 0 0]\n",
            "val_acc=0.6171875\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 0 0]\n",
            "[2 0 0 0]\n",
            "[2 1 0 0 1]\n",
            "[2 1 0 0 1 1]\n",
            "[1 0 1 0 0 0 0]\n",
            "[2 0 0 0 0 1 1 0]\n",
            "[2 0 1 0 0 1 0 1 1]\n",
            "[2 0 0 0 0 0 0 0 1 0]\n",
            "[2 0 0 0 0 1 0 0 1 0 0]\n",
            "[2 0 0 0 0 0 0 0 1 0 1 1]\n",
            "val_acc=0.5625\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 1]\n",
            "[2 0 0]\n",
            "[2 1 0 0]\n",
            "[2 0 0 0 0]\n",
            "[0 0 0 1 0 1]\n",
            "[2 1 0 0 0 0 1]\n",
            "[1 0 0 0 0 1 0 0]\n",
            "[2 1 0 0 0 0 0 1 0]\n",
            "[1 1 0 0 0 0 1 1 0 0]\n",
            "[2 1 0 0 1 0 1 0 0 0 0]\n",
            "[2 0 0 0 0 0 1 1 0 0 0 0]\n",
            "val_acc=0.578125\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[1 0]\n",
            "[2 1 1]\n",
            "[2 0 0 0]\n",
            "[2 1 1 0 0]\n",
            "[2 0 1 0 0 0]\n",
            "[2 0 1 0 0 0 0]\n",
            "[1 0 0 0 0 1 0 1]\n",
            "[2 0 1 0 0 1 1 1 0]\n",
            "[2 1 0 0 1 0 1 0 0 1]\n",
            "[2 0 0 1 0 0 1 0 0 0 1]\n",
            "[2 0 0 1 1 0 1 1 0 0 1 1]\n",
            "val_acc=0.5234375\n",
            "--------------------------------------------------------------------------------\n",
            "Epoch 11: Eval\n",
            "valid_accuracy: 0.5821\n",
            "test_accuracy: 0.5369\n",
            "epoch=12\tch_step=0\tloss=1.101791\tlr=0.0052\t|g|=1.6981\tacc=0.6562\ttime=3.71it/s\n",
            "epoch=12\tch_step=50\tloss=1.355736\tlr=0.0052\t|g|=2.0462\tacc=0.5938\ttime=5.04it/s\n",
            "epoch=12\tch_step=100\tloss=1.299406\tlr=0.0052\t|g|=1.6282\tacc=0.5000\ttime=4.48it/s\n",
            "epoch=12\tch_step=150\tloss=1.233592\tlr=0.0052\t|g|=1.9571\tacc=0.5781\ttime=4.70it/s\n",
            "epoch=12\tch_step=200\tloss=1.113778\tlr=0.0052\t|g|=1.3806\tacc=0.6562\ttime=4.71it/s\n",
            "epoch=12\tch_step=250\tloss=1.209501\tlr=0.0052\t|g|=1.9745\tacc=0.5625\ttime=4.71it/s\n",
            "epoch=12\tch_step=300\tloss=0.978417\tlr=0.0052\t|g|=1.5441\tacc=0.6328\ttime=4.52it/s\n",
            "epoch=12\tch_step=350\tloss=1.200380\tlr=0.0052\t|g|=1.9337\tacc=0.5391\ttime=5.23it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 12: Training controller\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:145: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=1\tloss=-1.632\tent=44.67\tlr=0.0010\t|g|=0.2238\tacc=0.5391\tbl=0.58\ttime=1.45it/s\n",
            "ctrl_step=3\tloss=0.137\tent=40.90\tlr=0.0010\t|g|=0.2419\tacc=0.5781\tbl=0.58\ttime=1.45it/s\n",
            "ctrl_step=5\tloss=-3.292\tent=43.56\tlr=0.0010\t|g|=0.0334\tacc=0.5078\tbl=0.58\ttime=1.46it/s\n",
            "ctrl_step=7\tloss=-3.228\tent=41.93\tlr=0.0010\t|g|=0.5318\tacc=0.4922\tbl=0.58\ttime=1.36it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Traceback (most recent call last):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=9\tloss=-2.001\tent=43.47\tlr=0.0010\t|g|=0.0493\tacc=0.5312\tbl=0.58\ttime=1.49it/s\n",
            "ctrl_step=11\tloss=-0.057\tent=41.98\tlr=0.0010\t|g|=0.1088\tacc=0.5781\tbl=0.58\ttime=1.41it/s\n",
            "ctrl_step=13\tloss=-0.222\tent=43.00\tlr=0.0010\t|g|=0.1625\tacc=0.5703\tbl=0.58\ttime=1.39it/s\n",
            "ctrl_step=15\tloss=-0.349\tent=43.01\tlr=0.0010\t|g|=0.0940\tacc=0.5625\tbl=0.58\ttime=1.42it/s\n",
            "ctrl_step=17\tloss=-0.150\tent=43.39\tlr=0.0010\t|g|=0.1434\tacc=0.5703\tbl=0.58\ttime=1.45it/s\n",
            "ctrl_step=19\tloss=-3.458\tent=44.25\tlr=0.0010\t|g|=0.1380\tacc=0.5000\tbl=0.58\ttime=1.37it/s\n",
            "ctrl_step=21\tloss=2.852\tent=44.29\tlr=0.0010\t|g|=0.0490\tacc=0.6328\tbl=0.58\ttime=1.41it/s\n",
            "ctrl_step=23\tloss=1.632\tent=43.86\tlr=0.0010\t|g|=0.0313\tacc=0.6094\tbl=0.58\ttime=1.46it/s\n",
            "ctrl_step=25\tloss=2.098\tent=42.75\tlr=0.0010\t|g|=0.3104\tacc=0.6172\tbl=0.58\ttime=1.35it/s\n",
            "ctrl_step=27\tloss=1.810\tent=43.74\tlr=0.0010\t|g|=0.0855\tacc=0.6172\tbl=0.58\ttime=1.34it/s\n",
            "ctrl_step=29\tloss=-0.506\tent=41.84\tlr=0.0010\t|g|=0.6732\tacc=0.5625\tbl=0.58\ttime=1.45it/s\n",
            "ctrl_step=31\tloss=4.085\tent=44.23\tlr=0.0010\t|g|=0.0226\tacc=0.6641\tbl=0.58\ttime=1.39it/s\n",
            "ctrl_step=33\tloss=1.143\tent=44.30\tlr=0.0010\t|g|=0.1677\tacc=0.6016\tbl=0.58\ttime=1.37it/s\n",
            "ctrl_step=35\tloss=-3.507\tent=43.34\tlr=0.0010\t|g|=0.3151\tacc=0.5078\tbl=0.58\ttime=1.34it/s\n",
            "ctrl_step=37\tloss=1.882\tent=41.93\tlr=0.0010\t|g|=0.0868\tacc=0.6172\tbl=0.58\ttime=1.43it/s\n",
            "ctrl_step=39\tloss=-0.970\tent=43.24\tlr=0.0010\t|g|=0.0686\tacc=0.5547\tbl=0.58\ttime=1.35it/s\n",
            "ctrl_step=41\tloss=-4.325\tent=44.92\tlr=0.0010\t|g|=0.3909\tacc=0.4688\tbl=0.57\ttime=1.43it/s\n",
            "ctrl_step=43\tloss=0.251\tent=43.17\tlr=0.0010\t|g|=0.0859\tacc=0.5781\tbl=0.58\ttime=1.30it/s\n",
            "ctrl_step=45\tloss=1.527\tent=43.71\tlr=0.0010\t|g|=0.0183\tacc=0.6094\tbl=0.58\ttime=1.35it/s\n",
            "ctrl_step=47\tloss=-1.591\tent=44.77\tlr=0.0010\t|g|=0.1866\tacc=0.5312\tbl=0.57\ttime=1.41it/s\n",
            "ctrl_step=49\tloss=-2.861\tent=42.89\tlr=0.0010\t|g|=0.1588\tacc=0.5078\tbl=0.57\ttime=1.42it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Here are 10 architectures:\n",
            "[2]\n",
            "[2 0]\n",
            "[2 0 1]\n",
            "[2 1 0 0]\n",
            "[2 0 0 0 0]\n",
            "[2 0 0 0 1 0]\n",
            "[2 0 0 0 0 0 0]\n",
            "[2 0 1 1 0 1 0 0]\n",
            "[2 1 0 0 0 0 1 1 0]\n",
            "[2 0 0 0 0 0 0 0 0 0]\n",
            "[2 1 0 0 0 0 0 0 1 0 0]\n",
            "[2 0 1 0 1 0 1 0 1 0 0 0]\n",
            "val_acc=0.625\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 1]\n",
            "[2 1 0]\n",
            "[2 0 1 0]\n",
            "[2 0 0 0 0]\n",
            "[2 0 1 0 1 0]\n",
            "[2 0 0 0 0 0 0]\n",
            "[2 0 0 0 0 1 0 0]\n",
            "[2 0 0 0 1 0 0 0 0]\n",
            "[2 0 0 0 1 0 0 1 1 1]\n",
            "[2 0 0 0 0 0 1 0 0 1 1]\n",
            "[2 1 1 0 0 0 0 0 0 1 0 1]\n",
            "val_acc=0.609375\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 1 0]\n",
            "[2 1 0 0]\n",
            "[2 0 0 0 1]\n",
            "[2 0 1 0 1 0]\n",
            "[2 0 1 1 0 0 0]\n",
            "[2 1 0 0 0 1 0 0]\n",
            "[2 0 1 0 1 0 0 1 1]\n",
            "[2 0 0 0 0 0 1 0 0 0]\n",
            "[2 0 0 0 0 0 0 0 0 0 0]\n",
            "[2 1 0 0 0 0 0 0 0 1 0 0]\n",
            "val_acc=0.6328125\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 0 0]\n",
            "[2 0 0 0]\n",
            "[2 0 0 0 0]\n",
            "[2 1 0 0 1 0]\n",
            "[2 1 0 0 1 1 0]\n",
            "[2 0 0 0 0 0 1 1]\n",
            "[2 0 0 1 1 0 0 0 1]\n",
            "[2 0 1 1 0 0 1 0 0 0]\n",
            "[2 1 0 0 0 0 0 0 1 0 1]\n",
            "[2 0 0 1 1 1 0 0 0 0 1 0]\n",
            "val_acc=0.6015625\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 1 0]\n",
            "[2 0 0 0]\n",
            "[2 0 0 0 0]\n",
            "[2 1 0 0 0 1]\n",
            "[2 1 1 0 1 0 0]\n",
            "[2 0 0 0 0 0 1 0]\n",
            "[2 1 0 0 0 1 1 0 1]\n",
            "[2 0 0 0 0 1 0 0 0 0]\n",
            "[1 0 0 0 0 0 0 0 0 1 0]\n",
            "[2 1 0 0 1 0 0 0 0 0 1 1]\n",
            "val_acc=0.5703125\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 1 0]\n",
            "[2 1 1 0]\n",
            "[2 0 1 0 0]\n",
            "[2 0 0 0 0 0]\n",
            "[2 0 0 0 0 1 0]\n",
            "[2 0 0 0 0 0 0 0]\n",
            "[2 0 0 0 1 0 0 0 1]\n",
            "[2 0 0 0 0 0 0 0 0 1]\n",
            "[2 0 1 0 0 0 0 0 0 0 0]\n",
            "[2 1 1 0 0 0 0 1 0 0 0 1]\n",
            "val_acc=0.5859375\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 1 0]\n",
            "[0 0 0 0]\n",
            "[2 1 0 0 0]\n",
            "[2 1 0 0 1 0]\n",
            "[2 1 0 1 0 0 1]\n",
            "[2 0 0 0 0 0 0 0]\n",
            "[2 0 0 1 0 1 0 0 0]\n",
            "[2 0 0 0 0 0 0 1 0 0]\n",
            "[2 1 0 1 1 1 0 1 0 0 0]\n",
            "[2 0 0 0 0 1 0 0 0 0 0 0]\n",
            "val_acc=0.5859375\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[0 0]\n",
            "[2 0 0]\n",
            "[2 1 0 0]\n",
            "[2 0 0 1 1]\n",
            "[2 0 0 0 0 1]\n",
            "[1 0 0 0 0 0 1]\n",
            "[2 0 0 0 0 0 0 0]\n",
            "[2 1 0 1 0 0 0 0 0]\n",
            "[2 0 0 0 0 0 0 0 1 1]\n",
            "[2 0 0 0 1 0 0 1 0 0 0]\n",
            "[2 0 1 0 0 0 0 0 0 0 0 0]\n",
            "val_acc=0.5234375\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[0 0 0]\n",
            "[2 0 1 0]\n",
            "[2 0 0 0 0]\n",
            "[2 1 0 0 0 0]\n",
            "[0 1 0 0 1 0 0]\n",
            "[2 0 0 0 0 0 0 0]\n",
            "[2 0 0 1 1 0 0 0 0]\n",
            "[2 0 0 0 0 0 0 0 0 0]\n",
            "[2 1 0 0 0 0 0 0 0 0 0]\n",
            "[2 0 0 0 0 0 1 0 0 0 0 0]\n",
            "val_acc=0.609375\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 1]\n",
            "[2 0 1]\n",
            "[2 0 0 0]\n",
            "[2 0 0 0 1]\n",
            "[2 1 0 0 0 1]\n",
            "[2 0 0 0 0 0 0]\n",
            "[2 0 0 0 0 1 0 0]\n",
            "[2 0 0 0 1 0 0 0 0]\n",
            "[0 1 0 1 0 0 0 0 0 0]\n",
            "[2 0 0 1 0 0 0 0 0 0 0]\n",
            "[2 0 0 1 0 1 1 0 0 0 0 0]\n",
            "val_acc=0.5859375\n",
            "--------------------------------------------------------------------------------\n",
            "Epoch 12: Eval\n",
            "valid_accuracy: 0.5895\n",
            "test_accuracy: 0.5552\n",
            "epoch=13\tch_step=0\tloss=1.213592\tlr=0.0107\t|g|=1.8400\tacc=0.5391\ttime=3.55it/s\n",
            "epoch=13\tch_step=50\tloss=1.373706\tlr=0.0107\t|g|=2.8294\tacc=0.5156\ttime=4.45it/s\n",
            "epoch=13\tch_step=100\tloss=1.088292\tlr=0.0107\t|g|=1.3264\tacc=0.6328\ttime=4.75it/s\n",
            "epoch=13\tch_step=150\tloss=1.168121\tlr=0.0107\t|g|=1.5962\tacc=0.6016\ttime=4.69it/s\n",
            "epoch=13\tch_step=200\tloss=1.079703\tlr=0.0107\t|g|=2.0619\tacc=0.5703\ttime=4.65it/s\n",
            "epoch=13\tch_step=250\tloss=1.264858\tlr=0.0107\t|g|=1.2526\tacc=0.5312\ttime=4.47it/s\n",
            "epoch=13\tch_step=300\tloss=1.126860\tlr=0.0107\t|g|=1.5193\tacc=0.5547\ttime=4.52it/s\n",
            "epoch=13\tch_step=350\tloss=1.034151\tlr=0.0107\t|g|=1.8472\tacc=0.6094\ttime=5.12it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 13: Training controller\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:145: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=1\tloss=4.009\tent=41.76\tlr=0.0010\t|g|=0.0749\tacc=0.6875\tbl=0.59\ttime=1.37it/s\n",
            "ctrl_step=3\tloss=1.767\tent=43.34\tlr=0.0010\t|g|=0.0430\tacc=0.6250\tbl=0.59\ttime=1.35it/s\n",
            "ctrl_step=5\tloss=-0.198\tent=42.07\tlr=0.0010\t|g|=0.0222\tacc=0.5781\tbl=0.59\ttime=1.29it/s\n",
            "ctrl_step=7\tloss=2.739\tent=41.03\tlr=0.0010\t|g|=0.3343\tacc=0.6328\tbl=0.59\ttime=1.34it/s\n",
            "ctrl_step=9\tloss=0.213\tent=43.16\tlr=0.0010\t|g|=0.1645\tacc=0.5859\tbl=0.59\ttime=1.36it/s\n",
            "ctrl_step=11\tloss=3.977\tent=43.32\tlr=0.0010\t|g|=0.1554\tacc=0.6641\tbl=0.59\ttime=1.34it/s\n",
            "ctrl_step=13\tloss=-2.245\tent=43.09\tlr=0.0010\t|g|=0.2263\tacc=0.5234\tbl=0.59\ttime=1.35it/s\n",
            "ctrl_step=15\tloss=-1.549\tent=43.75\tlr=0.0010\t|g|=0.1797\tacc=0.5469\tbl=0.59\ttime=1.40it/s\n",
            "ctrl_step=17\tloss=1.834\tent=42.31\tlr=0.0010\t|g|=0.0246\tacc=0.6328\tbl=0.59\ttime=1.29it/s\n",
            "ctrl_step=19\tloss=4.887\tent=42.96\tlr=0.0010\t|g|=0.0255\tacc=0.7109\tbl=0.60\ttime=1.35it/s\n",
            "ctrl_step=21\tloss=3.944\tent=43.49\tlr=0.0010\t|g|=0.4899\tacc=0.6719\tbl=0.59\ttime=1.36it/s\n",
            "ctrl_step=23\tloss=0.822\tent=44.91\tlr=0.0010\t|g|=0.0955\tacc=0.6094\tbl=0.59\ttime=1.34it/s\n",
            "ctrl_step=25\tloss=1.284\tent=43.59\tlr=0.0010\t|g|=0.4285\tacc=0.6172\tbl=0.59\ttime=1.34it/s\n",
            "ctrl_step=27\tloss=-0.194\tent=41.86\tlr=0.0010\t|g|=0.2631\tacc=0.5781\tbl=0.59\ttime=1.33it/s\n",
            "ctrl_step=29\tloss=-1.509\tent=43.21\tlr=0.0010\t|g|=0.0087\tacc=0.5469\tbl=0.59\ttime=1.32it/s\n",
            "ctrl_step=31\tloss=3.012\tent=43.58\tlr=0.0010\t|g|=0.1612\tacc=0.6562\tbl=0.59\ttime=1.32it/s\n",
            "ctrl_step=33\tloss=3.063\tent=44.30\tlr=0.0010\t|g|=0.1222\tacc=0.6562\tbl=0.59\ttime=1.33it/s\n",
            "ctrl_step=35\tloss=3.126\tent=43.64\tlr=0.0010\t|g|=0.0926\tacc=0.6562\tbl=0.59\ttime=1.29it/s\n",
            "ctrl_step=37\tloss=-2.074\tent=42.57\tlr=0.0010\t|g|=0.4843\tacc=0.5312\tbl=0.59\ttime=1.30it/s\n",
            "ctrl_step=39\tloss=-3.563\tent=43.23\tlr=0.0010\t|g|=0.0748\tacc=0.4922\tbl=0.59\ttime=1.36it/s\n",
            "ctrl_step=41\tloss=0.322\tent=41.56\tlr=0.0010\t|g|=0.0423\tacc=0.5938\tbl=0.59\ttime=1.33it/s\n",
            "ctrl_step=43\tloss=0.512\tent=40.55\tlr=0.0010\t|g|=0.4887\tacc=0.6016\tbl=0.59\ttime=1.24it/s\n",
            "ctrl_step=45\tloss=-1.500\tent=45.50\tlr=0.0010\t|g|=0.5011\tacc=0.5547\tbl=0.59\ttime=1.24it/s\n",
            "ctrl_step=47\tloss=0.210\tent=43.17\tlr=0.0010\t|g|=0.0853\tacc=0.5938\tbl=0.59\ttime=1.31it/s\n",
            "ctrl_step=49\tloss=-3.188\tent=41.33\tlr=0.0010\t|g|=0.0228\tacc=0.5312\tbl=0.59\ttime=1.24it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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YQcuWLdmzZw8ABQUFTJkyhZEjR3Lw4EHKysrYvr3yGGvXrh1vvvkmALt37+bb3/42AD/72c94+OGHufnmm5k4cSIDBw5k3rx5HD16lE8//ZQOHTpw1VVXccstt1BWVsbcuXP529/+VumxEpGsi6LPApnung0sAf4YayUzG29mq8xsVWlpaZIOLXKCqRjmVc2vpXjtc4Hy9rlNmjQpb58bS7z2ua1bty5vnxvP4sWLefTRR+nZsyd5eXns3r2bTZs2lR8/XvvcOXPmMGbMGADGjBlTPuzy4osvct1119GyZaTB2Re+8AU++eQTduzYwciRI4FId8hjyyvzjW98o/zxunXryM/Pp0ePHhQVFbF+/XoA/vrXv3LTTTeV/xzatm1LZmYm7dq146233mLx4sWcf/75tGvXrsrjVSWRM/QdQKeo6YxgXjl33x01+Xvgjlg7cvdCoBAivVyqVanIiaKKIQgyMyPDLBV16QLLltXokGFrn3v06FH+8pe/8MwzzzB9+nTcnd27d8ftABlPs2bNKCsrK5+OHraB49vmjhs3jvnz55OTk8Ps2bNZVsX/xY033sjs2bPZuXNn0m6AkcgZ+kqgm5l1NbMWwBhgQfQKZtY+anIEsDEp1YnI502fDhXPHlu2jMyvobC1z33ppZfIzs5m+/btFBcXs3XrVkaNGsW8efO46KKLeOSRR8rHuPfs2UObNm3IyMhg/vz5ABw6dIgDBw7QpUsXNmzYwKFDh9i7dy8vvfRS3GN+8skntG/fnsOHD1MUdT1j6NCh3H///UDkhWbfvn1ApCvj888/z8qVKz/3YlVTVQa6ux8BJgAvEAnqJ9x9vZlNM7MRwWoTzWy9mb0NTATGJaU6Efm8ggIoLIyckZtFvhcWRubXUNja586ZM6d8+OSYUaNGMWfOHIYPH86IESPo3bs3PXv25K677gLgscce49577yU7O5sLLriAnTt30qlTJ0aPHk337t0ZPXp0pXXefvvt5OXl0b9/f84999zy+ffccw9Lly6lR48e9OrVq/xuSS1atGDw4MGMHj26xn+9VKT2uSINgNrnnnjKysrK3yHTrVu3mOuofa6ISAO3YcMGvvjFLzJ06NC4YV4TuqeoiEiKZWVlsWXLlqTvV2foIiIhoUAXEQkJBbqISEgo0EVEQkKBLiJA+NrnZmZmsmvXrlrto7FRoIs0Qknunhva9rknGgW6SCNTB91zQ9k+N5bi4mKGDBlCdnY2Q4cOZdu2bQA8+eSTdO/enZycnPJOkuvXr6dv37707NmT7Ozs8oZgDZnehy7SwNRD99xQts+N5eabb+baa6/l2muv5Q9/+AMTJ05k/vz5TJs2jRdeeIGOHTuyd+9eAB544AEmTZpEQUEBn332WYO5iUVldIYu0sikuHtuo22fG8vy5cu5+uqrAfjWt77Fq6++CkD//v0ZN24cDz30UHlw9+vXj1/96lfMmDGDrVu3cvLJJyd8nPqiM3SRBqYeuueGrn1udT3wwAOsWLGChQsX0qtXL1avXs3VV19NXl4eCxcu5LLLLuPBBx9kyJAhSTleXdEZukgjUwfdc0PXPjeeCy64gLlz5wJQVFRUftwPPviAvLw8pk2bRnp6Otu3b2fLli2cddZZTJw4kSuvvJK1a9fW6JippDN0kUbmWJfcqVNh2zbo3DkS5rXonlvePveWW25hxowZpKWlkZmZycyZMxkwYADLly8nJycHMytvn/vuu+/G3d+x9rkdOnRg6dKlxy2Lbp8LJNQ+t7i4mNzcXNyd9PT08r7lVcnOzqZJk8h56+jRo7nvvvu47rrruPPOO0lPT+eRRx4BYPLkyWzatAl3Z+jQoeTk5DBjxgwee+wxmjdvzplnnslPf/rThI5Zn9Q+V6QBUPtciUXtc0VETlAKdBGRkFCgizQQ9TX8KQ1TTX4fFOgiDUBaWhq7d+9WqAsQCfPdu3eTlpZWre30LheRBiAjI4OSkhJKS0vruxRpINLS0sjIyKjWNgp0kQagefPm1frEo0gsGnIREQkJBbqISEgo0EVEQkKBLiISEgp0EZGQUKCLiISEAl1EJCQSCnQzG25m75nZZjObUsl6o8zMzSxmJzAREak7VQa6mTUFZgGXAlnAWDP73B1ZzawNMAlYkewiRUSkaomcofcFNrv7Fnf/DJgLXBljvduBGcDBJNYnIiIJSiTQOwLbo6ZLgnnlzCwX6OTuCyvbkZmNN7NVZrZKPStERJKr1hdFzawJcDdwa1Xrunuhu/d2997p6em1PbSIiERJJNB3AJ2ipjOCece0AboDy8ysGPgKsEAXRkVEUiuRQF8JdDOzrmbWAhgDLDi20N33ufvp7p7p7pnAG8AId9cNQ0VEUqjKQHf3I8AE4AVgI/CEu683s2lmNqKuCxQRkcQk1A/d3RcBiyrMuy3OuoNqX5aIiFSXPikqIhISCnQRkZBQoIuIhIQCXUQkJBToIiIhoUAXEQkJBbqISEgo0EVEQkKBLiISEgp0EZGQUKCLiISEAl1EJCQU6CIiIaFAFxEJCQW6iEhIKNBFREJCgS4iEhIKdBGRkFCgi4iEhAJdRCQkFOgiIiGhQBcRCQkFuohISCjQRURCQoEuIhISCnQRkZBQoIuIhERCgW5mw83sPTPbbGZTYiz/rpm9Y2ZrzOxVM8tKfqkiIlKZKgPdzJoCs4BLgSxgbIzA/rO793D3nsAdwN1Jr1RERCqVyBl6X2Czu29x98+AucCV0Su4+7+iJlsBnrwSRUQkEc0SWKcjsD1qugTIq7iSmf0n8AOgBTAk1o7MbDwwHqBz587VrVVERCqRtIui7j7L3c8Gfgz8LM46he7e2917p6enJ+vQIiJCYoG+A+gUNZ0RzItnLvC12hQlIiLVl0igrwS6mVlXM2sBjAEWRK9gZt2iJr8KbEpeiSIikogqx9Dd/YiZTQBeAJoCf3D39WY2DVjl7guACWY2DDgM/BO4ti6LFhGRz0vkoijuvghYVGHebVGPJyW5LhERqSZ9UlREJCQU6CIiIaFAFxEJCQW6iEhIKNBFREJCgS4iEhIKdBGRkFCgi4iEhAJdRCQkFOgiIiGhQBcRCQkFuohISCjQRURCQoEuIhISCnQRkZBQoIuIhIQCXUQkJBToIiIhoUAXEQkJBbqISEgo0EVEQkKBLiISEgp0EZGQUKCLiISEAl1EJCQU6CIiIaFAFxEJiYQC3cyGm9l7ZrbZzKbEWP4DM9tgZmvN7CUz65L8UkVEpDJVBrqZNQVmAZcCWcBYM8uqsNpbQG93zwaeAu5IdqEiIlK5RM7Q+wKb3X2Lu38GzAWujF7B3Ze6+4Fg8g0gI7lliohIVRIJ9I7A9qjpkmBePDcAz8VaYGbjzWyVma0qLS1NvEoREalSUi+Kmtk3gd7AnbGWu3uhu/d2997p6enJPLSIyAmvWQLr7AA6RU1nBPOOY2bDgKnAQHc/lJzyREQkUYmcoa8EuplZVzNrAYwBFkSvYGbnAw8CI9z94+SXKSIiVaky0N39CDABeAHYCDzh7uvNbJqZjQhWuxNoDTxpZmvMbEGc3YmISB1JZMgFd18ELKow77aox8OSXJeIiFSTPikqIhISCnQRkZBQoIuIhIQCXUQkJBToIiIhoUAXEQkJBbqISEgo0EVEQkKBLiISEgp0EZGQUKCLiISEAl1EJCQU6CIiIaFAFxEJCQW6iEhIKNBFREJCgS4iEhIKdBGRkFCgi4iEhAJdRCQkFOgiIiGhQBcRCQkFuohISCjQRURCQoEuIhISCnQRkZBQoIuIhERCgW5mw83sPTPbbGZTYiy/0MzeNLMjZvb15JcpIiJVqTLQzawpMAu4FMgCxppZVoXVtgHjgD8nu0AREUlMswTW6QtsdvctAGY2F7gS2HBsBXcvDpaV1UGNIiKSgESGXDoC26OmS4J51WZm481slZmtKi0trckuREQkjpReFHX3Qnfv7e6909PTU3loEZHQSyTQdwCdoqYzgnkiItKAJBLoK4FuZtbVzFoAY4AFdVuWiIhUV5WB7u5HgAnAC8BG4Al3X29m08xsBICZ9TGzEuA/gAfNbH1dFi0iIp+XyLtccPdFwKIK826LerySyFCMiIjUE31SVEQkJBToIiIhoUAXEQkJBbqISEgo0EVEQkKBLiISEgp0EZGQUKCLiISEAl1EJCQU6CIiIaFAFxEJCQW6SKCoCDIzoUmTyPeiovquSKR6EmrOJRJ2RUUwfjwcOBCZ3ro1Mg1QUFB/dYlUh87QRYCpU/8d5sccOBCZL9JYKNBFgG3bqjdfpCFSoIsAnTtXb75IQ6RAFwGmT4eWLY+f17JlZL5IY6FAFyFy4bOwELp0AbPI98JCXRCVxkXvchEJFBQowKVx0xm6iEhIKNBFREJCgS4iEhIKdBGRkFCgi4iEhLl7/RzYrBTYmqLDnQ7sStGxkk21p15jrRtUe31JZe1d3D091oJ6C/RUMrNV7t67vuuoCdWeeo21blDt9aWh1K4hFxGRkFCgi4iExIkS6IX1XUAtqPbUa6x1g2qvLw2i9hNiDF1E5ERwopyhi4iEngJdRCQkGnWgm9kfzOxjM1sXNe9OM3vXzNaa2TwzO7WS7Zua2Vtm9j+pqfi4Y9e4djM71cyeCtbdaGb9Uld5rWv/vpmtN7N1ZjbHzNJSV3nc2m8P6l5jZovNrEOcba81s03B17Wpq7rmdZtZTzNbHvzM15rZN1JZd1BDjX/mwbqnmFmJmf13aio+7ti1+X3pHCzfaGYbzCyzzgt290b7BVwI5ALrouZdDDQLHs8AZlSy/Q+APwP/05hqB/4I3Bg8bgGc2hhqBzoCfwdODqafAMY1gNpPiXo8EXggxnZfALYE308LHp/WCOo+B+gWPO4A/KOB/L5UWXvU8nuC5+l/p7Lu2tYOLAMuCh63BlrWdb2N+gzd3V8G9lSYt9jdjwSTbwAZsbY1swzgq8Dv67TIOGpau5m1JfJL9nCwzWfuvreOyz1ObX7uRHrwn2xmzYCWwId1VmgMcWr/V9RkKyDWOwUuAZa4+x53/yewBBheZ4VWUNO63f19d98UPP4Q+BiI+SnDulKLnzlm1gs4A1hcZwVWoqa1m1kWkROcJcE2n7r7gYrrJVvYb3BxPfB4nGUzgR8BbVJXTrXEq70rUAo8YmY5wGpgkrvvT2VxVYhZu7vvMLO7gG3A/wKL3b1enqgVmdl04BpgHzA4xiodge1R0yXBvHqVQN3R6/Yl8hfdBykorUpV1W5mTYDfAt8EhqW2usol8HM/B9hrZk8Tec6+CExx96N1WVejPkOvjJlNBY4ARTGWXQ587O6rU15YAiqrnciLcC5wv7ufD+wHpqSwvEpV8XM/DbiSyC94B6CVmX0ztRXG5u5T3b0Tkbon1Hc9iUq0bjNrDzwGXOfuZamqrzIJ1P49YJG7l6S2sqolUHszIB/4IdAHOAsYV9d1hTLQzWwccDlQ4MEAVgX9gRFmVgzMBYaY2Z9SV2F8CdReApS4+4pg+ikiAV/vEqh9GPB3dy9198PA08AFKSwxEUXAqBjzdwCdoqYzgnkNRby6MbNTgIXAVHd/I6VVJSZe7f2ACcHz9C7gGjP7TSoLS0C82kuANe6+JRiKnE8KnqehC3QzG05kKGVEvDErd/+Ju2e4eyYwBviru9f7mWKCte8EtpvZl4JZQ4ENKSoxrkRqJzLU8hUza2lmRqT2jamqMR4z6xY1eSXwbozVXgAuNrPTgr80Lg7m1ZtE6jazFsA84FF3fypVtVUlkdrdvcDdOwfP0x8S+TfU+1+jCf6+rARONbNj1yuGkIrnaSquFNfVFzCHyFX7w0ReEW8ANhMZ61wTfD0QrNuByJ9vFfcxiPp5l0uNawd6AquAtURe+VP2bosk1P4LIk+AdUSGAE5qALX/JahnLfAs0DFYtzfw+6htrw/+nZuJDF00+LqJjD8fjvp/WQP0bAy1V9jHOOrnXS61+X25KFjnHWA20KKu69VH/0VEQiJ0Qy4iIicqBbqISEgo0EVEQkKBLiISEgp0EZGQUKBLo2dmnwbfM83s6iTv+6cVpl9P5v5FkkmBLmGSCVQr0IMmYZU5LtDdvaF9slWknAJdwuQ3QH7Qp/r7Ful3f6eZrQz6VzEtRfYAAAGtSURBVH8HwMwGmdkrZraA4NN7ZjbfzFYHfcPHB/N+Q6Qz5BozKwrmHftrwIJ9rzOzd471GQ/2vcz+3a++KPhUrEidC3u3RTmxTAF+6O6XAwTBvM/d+5jZScBrZnasu2Mu0N3d/x5MX+/ue8zsZGClmf3F3aeY2QR37xnjWFcR+cRuDnB6sM3LwbLzgfOItAZ+jUjvoFeT/88VOZ7O0CXMLibS0GkNsAJoBxzrw/G3qDAHmGhmbxPp5d4par14BgBz3P2ou38E/D8iXfWO7bvEI10N1xAZChKpczpDlzAz4GZ3P66JlpkNItJ2OHp6GNDP3Q+Y2TKgNrfGOxT1+Ch6nkmK6AxdwuQTjr9hyQvATWbWHMDMzjGzVjG2awv8Mwjzc4GvRC07fGz7Cl4BvhGM06cTuYvU35LyrxCpIZ05SJisBY4GQyezidyLMhN4M7gwWQp8LcZ2zwPfNbONwHtEhl2OKQTWmtmb7l4QNX8ekX7dbxO5BdmP3H1n8IIgUi/UbVFEJCQ05CIiEhIKdBGRkFCgi4iEhAJdRCQkFOgiIiGhQBcRCQkFuohISPx/CyG2exVEPQEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Here are 10 architectures:\n",
            "[2]\n",
            "[2 0]\n",
            "[2 0 0]\n",
            "[2 0 0 1]\n",
            "[2 0 0 0 1]\n",
            "[2 1 0 0 0 0]\n",
            "[2 1 0 0 0 0 0]\n",
            "[2 0 1 0 0 0 0 0]\n",
            "[2 0 0 0 0 0 0 0 1]\n",
            "[2 1 0 1 0 0 0 0 1 0]\n",
            "[2 1 1 1 1 0 0 0 1 0 0]\n",
            "[2 0 0 0 0 1 0 0 0 0 0 0]\n",
            "val_acc=0.5625\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[1 0]\n",
            "[2 1 0]\n",
            "[1 0 0 1]\n",
            "[2 1 0 0 0]\n",
            "[2 0 1 0 1 0]\n",
            "[2 1 0 1 0 1 0]\n",
            "[2 0 0 0 0 0 0 0]\n",
            "[2 0 0 0 1 0 0 0 0]\n",
            "[2 0 0 0 1 1 1 0 1 0]\n",
            "[2 0 1 1 0 1 0 1 0 1 0]\n",
            "[2 0 0 0 1 0 0 1 0 0 0 0]\n",
            "val_acc=0.4609375\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 1]\n",
            "[2 1 0]\n",
            "[0 0 1 0]\n",
            "[2 0 0 0 0]\n",
            "[2 0 0 0 0 1]\n",
            "[2 0 0 1 0 0 0]\n",
            "[2 0 0 1 0 0 1 0]\n",
            "[0 1 0 0 0 0 0 0 0]\n",
            "[1 0 1 1 0 0 1 0 0 0]\n",
            "[2 0 1 0 0 1 0 0 1 0 0]\n",
            "[2 0 0 0 0 0 1 1 1 0 0 0]\n",
            "val_acc=0.5703125\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 1 0]\n",
            "[2 0 0 1]\n",
            "[2 0 1 0 0]\n",
            "[2 0 1 1 0 0]\n",
            "[2 1 0 0 0 0 0]\n",
            "[2 0 1 0 1 1 0 0]\n",
            "[2 0 1 1 1 0 0 0 0]\n",
            "[1 1 0 0 0 1 0 1 0 0]\n",
            "[2 0 0 0 1 0 0 1 1 0 1]\n",
            "[2 0 1 0 1 0 0 1 0 0 0 1]\n",
            "val_acc=0.5703125\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 1 0]\n",
            "[2 0 1 0]\n",
            "[2 0 0 1 0]\n",
            "[2 1 1 0 0 0]\n",
            "[2 0 1 0 0 0 0]\n",
            "[2 1 1 0 0 0 0 0]\n",
            "[2 1 0 0 1 1 0 0 0]\n",
            "[2 0 0 0 0 0 0 0 1 0]\n",
            "[2 0 1 0 0 0 0 0 0 0 0]\n",
            "[2 0 0 0 0 0 0 0 0 0 0 0]\n",
            "val_acc=0.5078125\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 0 0]\n",
            "[2 0 0 0]\n",
            "[2 0 0 0 0]\n",
            "[0 1 0 0 0 0]\n",
            "[2 0 0 1 0 0 0]\n",
            "[2 1 1 1 0 0 1 0]\n",
            "[2 0 0 0 0 0 1 0 1]\n",
            "[1 1 1 0 0 1 0 0 1 0]\n",
            "[2 0 1 0 0 1 1 0 0 1 0]\n",
            "[2 0 1 0 0 0 0 1 0 1 1 0]\n",
            "val_acc=0.5703125\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 0 0]\n",
            "[2 1 0 0]\n",
            "[2 0 1 1 0]\n",
            "[2 0 1 0 0 0]\n",
            "[2 0 0 0 0 0 0]\n",
            "[2 1 1 0 0 1 0 0]\n",
            "[2 0 0 0 0 0 0 0 0]\n",
            "[2 0 0 1 0 0 0 0 0 0]\n",
            "[2 0 0 0 0 1 0 0 0 0 0]\n",
            "[2 1 1 1 0 0 0 0 0 1 0 0]\n",
            "val_acc=0.5390625\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[0 0 0]\n",
            "[2 1 0 0]\n",
            "[2 0 0 0 0]\n",
            "[0 0 1 1 0 0]\n",
            "[2 0 1 0 1 0 0]\n",
            "[2 0 0 0 0 0 1 0]\n",
            "[2 0 0 0 1 0 1 0 1]\n",
            "[2 0 0 0 0 0 0 0 0 1]\n",
            "[2 0 1 0 0 1 0 0 0 0 0]\n",
            "[2 0 1 1 0 1 0 0 0 0 0 0]\n",
            "val_acc=0.5625\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[0 1 1]\n",
            "[2 0 0 0]\n",
            "[2 1 0 0 1]\n",
            "[2 1 0 0 1 0]\n",
            "[2 0 0 0 0 0 0]\n",
            "[2 0 1 1 1 0 0 1]\n",
            "[2 0 1 1 1 0 0 0 0]\n",
            "[2 0 0 0 0 0 0 1 0 0]\n",
            "[2 0 0 0 0 1 1 0 1 1 1]\n",
            "[2 0 0 0 0 0 0 0 1 0 0 0]\n",
            "val_acc=0.5625\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 0 1]\n",
            "[2 0 0 0]\n",
            "[2 0 0 1 0]\n",
            "[2 0 0 0 1 0]\n",
            "[2 0 0 1 0 0 0]\n",
            "[2 1 0 0 1 0 0 0]\n",
            "[2 0 0 1 0 0 0 0 0]\n",
            "[2 0 0 0 0 0 0 0 0 1]\n",
            "[2 1 1 0 1 0 0 0 0 0 0]\n",
            "[0 0 0 0 0 0 1 0 1 0 0 0]\n",
            "val_acc=0.5625\n",
            "--------------------------------------------------------------------------------\n",
            "Epoch 13: Eval\n",
            "valid_accuracy: 0.5990\n",
            "test_accuracy: 0.5570\n",
            "epoch=14\tch_step=0\tloss=1.044111\tlr=0.0176\t|g|=2.1583\tacc=0.5859\ttime=3.71it/s\n",
            "epoch=14\tch_step=50\tloss=1.120772\tlr=0.0176\t|g|=1.7941\tacc=0.5938\ttime=4.59it/s\n",
            "epoch=14\tch_step=100\tloss=1.151359\tlr=0.0176\t|g|=1.1374\tacc=0.5312\ttime=4.74it/s\n",
            "epoch=14\tch_step=150\tloss=0.939765\tlr=0.0176\t|g|=1.4700\tacc=0.6484\ttime=4.49it/s\n",
            "epoch=14\tch_step=200\tloss=1.129110\tlr=0.0176\t|g|=1.9497\tacc=0.5469\ttime=4.83it/s\n",
            "epoch=14\tch_step=250\tloss=1.158259\tlr=0.0176\t|g|=1.6922\tacc=0.5859\ttime=4.50it/s\n",
            "epoch=14\tch_step=300\tloss=1.074513\tlr=0.0176\t|g|=1.6139\tacc=0.6328\ttime=4.79it/s\n",
            "epoch=14\tch_step=350\tloss=1.256016\tlr=0.0176\t|g|=1.7921\tacc=0.5703\ttime=5.33it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 14: Training controller\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:145: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=1\tloss=2.891\tent=42.51\tlr=0.0010\t|g|=0.3861\tacc=0.6562\tbl=0.59\ttime=1.31it/s\n",
            "ctrl_step=3\tloss=-0.151\tent=45.64\tlr=0.0010\t|g|=0.5328\tacc=0.5781\tbl=0.59\ttime=1.36it/s\n",
            "ctrl_step=5\tloss=-0.063\tent=42.48\tlr=0.0010\t|g|=0.0381\tacc=0.5781\tbl=0.58\ttime=1.25it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Traceback (most recent call last):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=7\tloss=0.872\tent=44.11\tlr=0.0010\t|g|=0.1132\tacc=0.6016\tbl=0.58\ttime=1.33it/s\n",
            "ctrl_step=9\tloss=-1.494\tent=43.86\tlr=0.0010\t|g|=0.2209\tacc=0.5469\tbl=0.59\ttime=1.28it/s\n",
            "ctrl_step=11\tloss=-3.645\tent=41.52\tlr=0.0010\t|g|=0.1044\tacc=0.5000\tbl=0.59\ttime=1.28it/s\n",
            "ctrl_step=13\tloss=-0.833\tent=41.30\tlr=0.0010\t|g|=0.4374\tacc=0.5625\tbl=0.59\ttime=1.24it/s\n",
            "ctrl_step=15\tloss=1.672\tent=43.04\tlr=0.0010\t|g|=0.1106\tacc=0.6250\tbl=0.59\ttime=1.27it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Traceback (most recent call last):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=17\tloss=-1.530\tent=45.01\tlr=0.0010\t|g|=0.0126\tacc=0.5469\tbl=0.59\ttime=1.35it/s\n",
            "ctrl_step=19\tloss=0.838\tent=44.29\tlr=0.0010\t|g|=0.0115\tacc=0.5938\tbl=0.58\ttime=1.23it/s\n",
            "ctrl_step=21\tloss=3.139\tent=42.88\tlr=0.0010\t|g|=0.0922\tacc=0.6562\tbl=0.59\ttime=1.33it/s\n",
            "ctrl_step=23\tloss=0.043\tent=42.08\tlr=0.0010\t|g|=0.0228\tacc=0.5859\tbl=0.59\ttime=1.31it/s\n",
            "ctrl_step=25\tloss=1.465\tent=42.84\tlr=0.0010\t|g|=0.2327\tacc=0.6172\tbl=0.59\ttime=1.29it/s\n",
            "ctrl_step=27\tloss=3.291\tent=42.01\tlr=0.0010\t|g|=0.0643\tacc=0.6562\tbl=0.58\ttime=1.26it/s\n",
            "ctrl_step=29\tloss=-0.298\tent=43.44\tlr=0.0010\t|g|=0.0950\tacc=0.5703\tbl=0.58\ttime=1.27it/s\n",
            "ctrl_step=31\tloss=0.013\tent=43.15\tlr=0.0010\t|g|=0.1930\tacc=0.5781\tbl=0.58\ttime=1.25it/s\n",
            "ctrl_step=33\tloss=-0.331\tent=43.62\tlr=0.0010\t|g|=0.0531\tacc=0.5703\tbl=0.58\ttime=1.26it/s\n",
            "ctrl_step=35\tloss=2.122\tent=42.76\tlr=0.0010\t|g|=0.3907\tacc=0.6250\tbl=0.58\ttime=1.32it/s\n",
            "ctrl_step=37\tloss=1.404\tent=41.93\tlr=0.0010\t|g|=0.0179\tacc=0.6172\tbl=0.58\ttime=1.31it/s\n",
            "ctrl_step=39\tloss=-2.918\tent=43.30\tlr=0.0010\t|g|=0.0584\tacc=0.5234\tbl=0.58\ttime=1.28it/s\n",
            "ctrl_step=41\tloss=-1.588\tent=41.95\tlr=0.0010\t|g|=0.1727\tacc=0.5469\tbl=0.59\ttime=1.24it/s\n",
            "ctrl_step=43\tloss=-4.209\tent=44.30\tlr=0.0010\t|g|=0.1131\tacc=0.4922\tbl=0.59\ttime=1.26it/s\n",
            "ctrl_step=45\tloss=0.967\tent=44.13\tlr=0.0010\t|g|=0.0707\tacc=0.6094\tbl=0.59\ttime=1.19it/s\n",
            "ctrl_step=47\tloss=0.666\tent=44.14\tlr=0.0010\t|g|=0.4082\tacc=0.6016\tbl=0.59\ttime=1.25it/s\n",
            "ctrl_step=49\tloss=1.962\tent=43.06\tlr=0.0010\t|g|=0.0903\tacc=0.6250\tbl=0.59\ttime=1.25it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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V+2rdurW7u//tb3/zHj16uLv70aNH/bbbbvOcnBzv2bOnz5kzp2L/gwYN8ksuucS7d+8ete5u3br55s2bvWPHjv6vf/2rYt3dd9/tmZmZnpWV5T/96U/d3X3Tpk0+fPhwz8rK8t69e/vmzZu9uLjYv/Wtb1Xc70c/+pE/9thj7u6elpbmP/nJT7x3794+d+5cLyws9JycHM/KyvLLLrvMDxw44O7uu3bt8ksvvdSzsrI8KyvLX3/9df/P//zP4/7dt99+u8+YMeML/4ZovxfASo+Rq2rOJdLY3HwzrI7dPpc334TDlTorHjwI11wDj0Rvn0uvXlBF3/Egts9944036NatG2eddRZDhgxh0aJFjBkzhj//+c8899xzvPXWW7Rq1Yq9e/cCkJ+fz5QpUxg9ejSHDh2ivLycHTt2fGG7kdq3b8/bb78NwJ49e/jBD34AwM9//nMeffRRbrzxRiZNmsTgwYOZP38+ZWVlfPbZZ3Tq1InLLruMm2++mfLycubNm8df//rXKvcVD/VyEWlqKod5deMnKFb7XKCifW6zZs0q2udGE6t9bps2bSra58by4osv8sQTT9CrVy9yc3PZs2cPmzZtqth/rPa5c+fOZdy4cQCMGzeu4rTLyy+/zNVXX02rVqEGZ1/5ylf49NNP2blzJ6NHjwZC3SGPra/Kd77znYrb69atIy8vj549e1JUVMT69esB+Mtf/sL1119f8XNo164d6enptG/fnnfeeYcXX3yR3r170759+2r3Vx0doYs0NtV8gg/p6RDt8zHT0mDp0lrtMmjtc8vKyvjTn/7Ec889x7Rp03B39uzZE7MDZCzNmzenvLy8YvnQoUPHrY/c/4QJE1iwYAHZ2dk8/vjjLK3m/+Laa6/l8ccfZ9euXQn7AAwdoYs0NdOmQeWjx1atQuO1FLT2ua+88gpZWVns2LGDrVu3sm3bNsaMGcP8+fP5xje+wWOPPVZx0Xbv3r20bduW1NRUFixYAMDhw4c5ePAgaWlpbNiwgcOHD7Nv3z5eeeWVmPv89NNP6dixI0eOHKEo4iL18OHDmT17NhB6otm/fz8Q6sr4wgsvsGLFii88WdWWAl2kqcnPh8LC0BG5Weh7YWFovJaC1j537ty5FadPjhkzZgxz585lxIgRjBw5kpycHHr16sV9990HwJNPPsnMmTPJysrivPPOY9euXXTp0oWxY8eSmZnJ2LFjq6zzrrvuIjc3l4EDB3LuuedWjD/44IMUFxfTs2dP+vbtW/FpSS1btmTo0KGMHTu21n+9VKb2uSKNgNrnnnzKy8vp06cPTz/9NN27d486p07a54qISOJs2LCBr371qwwfPjxmmNeGLoqKiNSzjIwMtmzZkvDt6ghdRCQgFOgiIgGhQBcRCQgFuohIQCjQRQQIXvvc9PR0du/efULbaGoU6CJNUIK75wa2fe7JRoEu0sQUFUFBQaidi3voe0HBiYV6cXExLVq04LrrrqsYy87OJi8vD3dn8uTJZGZm0rNnT5566ikg1EtlyJAhXH755Zx77rnk5+fj7sycOZOPPvqIoUOHVrxTtE2bNtx6661kZ2ezfPlypk+fTmZmJpmZmcyopndNWVkZkydPpl+/fmRlZfHwww9X7D8vL4+RI0eSkZER179z69atDBs2jKysLIYPH8727dsBePrpp8nMzCQ7O7uik+T69evp378/vXr1Iisrq6IhWGOm16GLNDIN0D03kO1zo7nxxhu56qqruOqqq/jtb3/LpEmTWLBgAVOnTmXJkiV07tyZffv2ATBnzhxuuukm8vPz+fzzzxvPh1hUQUfoIk1MPXfPbbLtc6NZvnw5V1xxBQDf+973eO211wAYOHAgEyZM4JFHHqkI7gEDBvCrX/2Ke+65h23btvGlL30p7v00FB2hizQyDdA9N3Dtc2tqzpw5vPXWWyxatIi+ffuyatUqrrjiCnJzc1m0aBEXX3wxDz/8MMOGDUvI/uqKjtBFmpg66J4buPa5sZx33nnMmzcPgKKioor9fvjhh+Tm5jJ16lRSUlLYsWMHW7Zs4cwzz2TSpEmMGjWKtWvX1mqf9UlH6CJNzLEuuXfcAdu3Q9euoTA/ge65Fe1zb775Zu655x6Sk5NJT09nxowZDBo0iOXLl5OdnY2ZVbTPfe+992Ju71j73E6dOlFcXHzcusj2uUBc7XO3bt1Knz59cHdSUlIq+pZXJysri2bNQsetY8eO5aGHHuLqq6/mN7/5DSkpKTz22GMATJ48mU2bNuHuDB8+nOzsbO655x6efPJJWrRowRlnnMHtt98e1z4bktrnijQCap8r0ah9rojISSquQDezEWb2vpltNrMpVcwbY2ZuZlGfPUREpO5UG+hmlgTMAr4JZADjzewLr+I3s7bATcBbiS5S5GTQUKc/pXGqze9DPEfo/YHN7r7F3T8H5gGjosy7C7gHOBRlnYhUITk5mT179ijUBQiF+Z49e0hOTq7R/eJ5lUtnYEfEcgmQGznBzPoAXdx9kZlNjrUhMysACgC6du1ao0JFgiw1NZWSkhJKS0sbuhRpJJKTk0lNTa3RfU74ZYtm1gyYDkyobq67FwKFEHqVy4nuWyQoWrRoUaN3PIpEE88pl51Al4jl1PDYMW2BTGCpmW0Fvg4s1IVREZH6FU+grwC6m1k3M2sJjAMWHlvp7vvdvYO7p7t7OvAmMNLd9SJzEZF6VG2gu/tRYCKwBNgI/NHd15vZVDMbWdcFiohIfOI6h+7ui4HFlcbujDF3yImXJSIiNaV3ioqIBIQCXUQkIBToIiIBoUAXEQkIBbqISEAo0EVEAkKBLiISEAp0EZGAUKCLiASEAl1EJCAU6CIiAaFAFxEJCAW6iEhAKNBFRAJCgS4iEhAKdBGRgFCgi4gEhAJdRCQgFOgiIgGhQBcRCQgFuohIQCjQRUQCIq5AN7MRZva+mW02sylR1l9nZu+a2Woze83MMhJfqoiIVKXaQDezJGAW8E0gAxgfJbD/4O493b0XcC8wPeGViohIleI5Qu8PbHb3Le7+OTAPGBU5wd3/GbHYGvDElSgiIvFoHseczsCOiOUSILfyJDP7EXAL0BIYFm1DZlYAFAB07dq1prWKiEgVEnZR1N1nuftZwE+Bn8eYU+juOe6ek5KSkqhdi4gI8QX6TqBLxHJqeCyWecClJ1KUiIjUXDyBvgLobmbdzKwlMA5YGDnBzLpHLH4L2JS4EkVEJB7VnkN396NmNhFYAiQBv3X39WY2FVjp7guBiWZ2AXAE+AdwVV0WLSIiXxTPRVHcfTGwuNLYnRG3b0pwXSIiUkN6p6iISEAo0EVEAkKBLiISEAp0EZGAUKCLiASEAl1EJCAU6CIiAaFAFxEJCAW6iEhAKNBFRAJCgS4iEhAKdBGRgFCgi4gEhAJdRCQgFOgiIgGhQBcRCQgFuohIQCjQRUQCQoEuIhIQCnQRkYBQoIuIBIQCXUQkIOIKdDMbYWbvm9lmM5sSZf0tZrbBzNaa2Stmlpb4UkVEpCrVBrqZJQGzgG8CGcB4M8uoNO0dIMfds4BngHsTXaiIiFQtniP0/sBmd9/i7p8D84BRkRPcvdjdD4YX3wRSE1umiIhUJ55A7wzsiFguCY/Fcg3w52grzKzAzFaa2crS0tL4qxQRkWol9KKomX0XyAF+E229uxe6e46756SkpCRy1yIiJ73mcczZCXSJWE4Njx3HzC4A7gAGu/vhxJQnIiLxiucIfQXQ3cy6mVlLYBywMHKCmfUGHgZGuvsniS9TRESqU22gu/tRYCKwBNgI/NHd15vZVDMbGZ72G6AN8LSZrTazhTE2JyIidSSeUy64+2JgcaWxOyNuX5DgukREpIb0TlERkYBQoIuIBIQCXUQkIBToIiIBoUAXEQkIBbqISEAo0EVEAkKBLiISEAp0EZGAUKCLiASEAl1EJCAU6CIiAaFAFxEJCAW6iEhAKNBFRAJCgS4iEhAKdBGRgFCgi4gEhAJdRCQgFOgiIgGhQBcRCQgFuohIQMQV6GY2wszeN7PNZjYlyvrzzextMztqZpcnvkwREalOtYFuZknALOCbQAYw3swyKk3bDkwA/pDoAkVEJD7N45jTH9js7lsAzGweMArYcGyCu28NryuvgxpFRCQO8Zxy6QzsiFguCY/VmJkVmNlKM1tZWlpam02IiEgM9XpR1N0L3T3H3XNSUlLqc9ciIoEXT6DvBLpELKeGx0REpBGJJ9BXAN3NrJuZtQTGAQvrtiwREampagPd3Y8CE4ElwEbgj+6+3symmtlIADPrZ2YlwLeBh81sfV0WLSIiXxTPq1xw98XA4kpjd0bcXkHoVIyIiDQQvVNURCQgFOgiIgGhQBcRCQgFuohIQCjQRUQCQoEuIhIQCnQRkYBQoIuIBIQCXUQkIBToIiIBoUAXEQkIBbqISEAo0EVEAkKBLiISEAp0EZGAUKCLiASEAl1EJCAU6CIiAaFAFxEJCAW6iEhAKNBFRAJCgS4iEhBxBbqZjTCz981ss5lNibL+FDN7Krz+LTNLT3ShIiJStWoD3cySgFnAN4EMYLyZZVSadg3wD3f/KvAAcE+iCxURkarFc4TeH9js7lvc/XNgHjCq0pxRwO/Ct58BhpuZJa5MERGpTjyB3hnYEbFcEh6LOsfdjwL7gfaVN2RmBWa20sxWlpaW1q5iERGJql4virp7obvnuHtOSkpKfe5aRCTw4gn0nUCXiOXU8FjUOWbWHGgH7ElEgSIiEp94An0F0N3MuplZS2AcsLDSnIXAVeHblwN/cXdPXJkiIlKd5tVNcPejZjYRWAIkAb919/VmNhVY6e4LgUeBJ81sM7CXUOiLiEg9qjbQAdx9MbC40tidEbcPAd9ObGkiIlITeqeoiEhAKNBFRAJCgS4iEhAKdBGRgFCgi4gEhAJdRCQgFOgiIgGhQBcJKyqC9HRo1iz0vaiooSsSqZm43lgkEnRFRVBQAAcPhpa3bQstA+TnN1xdIjWhI3QR4I47/h3mxxw8GBoXaSoU6CLA9u01GxdpjBToIkDXrjUbF2mMFOgiwLRp0KrV8WOtWoXGRZoKBboIoQufhYWQlgZmoe+FhbogKk2LXuUiEpafrwCXpk1H6CIiAaFAFxEJCAW6iEhAKNBFRAJCgS4iEhDm7g2zY7NSYFuD7Dy2DsDuhi6iBppSvaq17jSleptSrdA4601z95RoKxos0BsjM1vp7jkNXUe8mlK9qrXuNKV6m1Kt0PTq1SkXEZGAUKCLiASEAv14hQ1dQA01pXpVa91pSvU2pVqhidWrc+giIgGhI3QRkYBQoIuIBMRJEehm9lsz+8TM1kWM3WVma81stZm9aGadqrj/l82sxMz+p7HXa2Zdw+s3mtkGM0tvxLXea2brw7XONDOry1pj1Rux7lYzczPrEOO+V5nZpvDXVY21VjPrZWbLwz/btWb2ncZaa8ScBn+MRayr7vegXh9jNeLugf8Czgf6AOsixr4ccXsSMKeK+z8I/AH4n8ZeL7AU+Eb4dhugVWOsFTgPeB1ICn8tB4Y0xM82PN4FWELozW4dotzvK8CW8PfTwrdPa6S1ng10D9/uBPwdOLUx1hoxr8EfY/HWW9+PsZp8nRRH6O7+KrC30tg/IxZbA1GvDptZX+B04MU6K7CS2tZrZhlAc3d/KXyfz9z9YOV5jaHW8Fgy0BI4BWgBfFxHZUbW9oV6wx4AfkKM3wPgIuAld9/r7v8AXgJG1E2VIbWt1d0/cPdN4dsfAZ8AUd9ZmCgn8HNtNI+xsCrrbYjHWE2c1B9wYWbTgCuB/cDQKOubAfcD3wUuqN/qvqi6egkdme0zs2eBbsDLwBR3L6u/KkOqq9Xdl5tZMaGjRyN0ZLaxfqsMMbNRwE53X1PFWZ/OwI6I5ZLwWL2Ks9bI+f0JPWl+WNe1Rdl3tbU2psdYnD/bRvMYi+akOEKPxd3vcPcuQBEwMcqUG4DF7l5Sv5VFF0e9zYE84DagH3AmMKHeCoxQXa1m9lXga0AqoWAcZmZ59VslmFkr4Hbgzvred03VtFYz6wg8CVzt7uV1WVuUfcdba6N4jNWg3kbzGIvmpA70CEXAmCjjA4CJZrYVuA+40szurs/CYohVbwmw2t23uPtRYAGh87TMNM4AAANZSURBVIQNKVato4E3w3+yfgb8mdDPu76dRehIa034/zkVeNvMzqg0byeh86vHpIbH6lO8tWJmXwYWAXe4+5v1WmVIvLU2lsdYvPU2xsdYhZP2lIuZdT92nhEYBbxXeY6750fMnwDkuPuU+qnwePHUC6wATjWzFHcvBYYBK+urxmPirHU78AMz+zWhUy6DgRn1VGIFd38X+I9jy+EHc467V+6wtwT4lZmdFl6+EPhZvRQZFm+tZtYSmA884e7P1GeNx8Rba2N5jNXg96BRPMZiOSmO0M1sLqFXUZwTfmnUNcDdZrbOzNYSenDeFJ6bY2b/24Dl1rre8Hm824BXzOxdQkH5SGOsFXiG0Hndd4E1wBp3f74ua62i3lhzI3+2e4G7CD2gVwBTw2ONrlZgLKFXcUyw0EtHV5tZr0Zaa4M4gd+Den+M1YTe+i8iEhAnxRG6iMjJQIEuIhIQCnQRkYBQoIuIBIQCXUQkIBTo0uSZ2Wfh7+lmdkWCt317peU3Erl9kURSoEuQpAM1CnQzq+7NdccFurufV8OaROqNAl2C5G4gL/xGmh+bWZKZ/cbMVlioL/gPAcxsiJktM7OFwIbw2AIzW2WhHuIF4bG7gS+Ft1cUHjv214CFt73OzN61cM/x8LaXmtkzZvaemRVZPF20RBLgpH3rvwTSFOA2d/8/AOFg3u/u/czsFOB1MzvWorUPkOnufwsvf9/d95rZl4AVZvYnd59iZhPdPdq7LC8DegHZQIfwfV4Nr+sN9AA+ItTzfSDwWuL/uSLH0xG6BNmFhJo9rQbeAtoD3cPr/hoR5gCTzGwN8CahJlzdqdogYK67l7n7x8D/I9R979i2S8IdDlcTOhUkUud0hC5BZsCN7r7kuEGzIcCBSssXAAPc/aCZLSX04Ru1dTjidhl6nEk90RG6BMmnQNuI5SXA9WbWAsDMzjaz1lHu1w74RzjMzwW+HrHuyLH7V7IM+E74PH0KoWZYf03Iv0KklnTkIEGyFigLnzp5nNDnVKYT6mttQClwaZT7vQBcZ2YbgfcJnXY5phBYa2ZvR7Z6JdSedgChTpEO/MTdd4WfEEQahLotiogEhE65iIgEhAJdRCQgFOgiIgGhQBcRCQgFuohIQCjQRUQCQoEuIhIQ/x9bdKDfjTRYkgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Here are 10 architectures:\n",
            "[0]\n",
            "[2 0]\n",
            "[2 0 0]\n",
            "[2 0 0 0]\n",
            "[2 1 0 0 1]\n",
            "[2 1 0 0 1 0]\n",
            "[2 1 0 0 0 0 1]\n",
            "[2 0 1 1 1 0 0 0]\n",
            "[2 0 0 0 0 0 1 0 0]\n",
            "[2 1 1 0 0 0 1 0 0 0]\n",
            "[2 0 0 0 1 1 1 0 0 0 0]\n",
            "[2 1 0 0 1 0 0 0 0 0 1 0]\n",
            "val_acc=0.5078125\n",
            "--------------------------------------------------------------------------------\n",
            "[1]\n",
            "[2 0]\n",
            "[2 0 0]\n",
            "[2 1 0 0]\n",
            "[2 0 0 0 0]\n",
            "[2 0 0 0 0 0]\n",
            "[2 0 0 1 1 0 0]\n",
            "[2 0 0 0 1 0 0 0]\n",
            "[2 0 0 0 0 0 1 1 0]\n",
            "[2 0 1 0 0 1 1 0 0 1]\n",
            "[2 0 0 1 0 1 1 0 0 1 1]\n",
            "[2 0 0 0 0 1 1 1 0 0 0 1]\n",
            "val_acc=0.4765625\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 0 0]\n",
            "[2 1 1 0]\n",
            "[0 0 0 0 0]\n",
            "[2 0 0 0 0 0]\n",
            "[2 0 0 0 0 0 1]\n",
            "[2 1 0 1 0 1 0 0]\n",
            "[2 1 0 0 0 1 0 0 0]\n",
            "[2 0 0 0 1 0 1 0 0 0]\n",
            "[2 1 0 0 1 0 1 0 0 0 0]\n",
            "[2 1 0 0 1 0 0 1 0 0 0 0]\n",
            "val_acc=0.46875\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 0 0]\n",
            "[0 0 0 1]\n",
            "[2 0 0 0 0]\n",
            "[2 0 1 0 0 0]\n",
            "[2 0 0 0 0 1 0]\n",
            "[2 0 0 0 0 0 1 0]\n",
            "[2 0 0 1 0 0 0 0 0]\n",
            "[2 0 0 0 0 0 0 0 1 0]\n",
            "[2 1 0 1 0 0 1 0 1 1 0]\n",
            "[2 0 0 1 1 0 0 0 0 0 0 0]\n",
            "val_acc=0.4921875\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 0 0]\n",
            "[2 0 1 0]\n",
            "[2 0 0 1 0]\n",
            "[2 0 1 1 1 1]\n",
            "[2 0 0 1 1 0 1]\n",
            "[2 0 0 0 0 0 1 0]\n",
            "[2 0 1 0 1 0 0 0 0]\n",
            "[2 0 0 0 0 0 0 0 0 0]\n",
            "[2 0 1 0 0 0 0 0 0 0 0]\n",
            "[2 0 0 0 0 0 0 0 0 0 1 1]\n",
            "val_acc=0.5\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 0 1]\n",
            "[1 1 1 0]\n",
            "[2 0 1 0 1]\n",
            "[2 0 1 1 0 0]\n",
            "[2 1 1 0 0 1 0]\n",
            "[1 1 0 0 0 0 0 1]\n",
            "[2 0 0 0 0 0 0 0 0]\n",
            "[2 0 0 0 0 0 1 1 0 0]\n",
            "[2 0 1 0 1 0 0 0 0 1 0]\n",
            "[2 0 0 0 1 1 1 0 1 0 0 0]\n",
            "val_acc=0.515625\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 1]\n",
            "[2 1 1]\n",
            "[2 0 0 1]\n",
            "[2 1 0 0 0]\n",
            "[2 0 1 1 1 1]\n",
            "[2 1 0 0 1 0 1]\n",
            "[2 0 1 1 0 0 1 0]\n",
            "[2 0 0 0 0 1 0 1 0]\n",
            "[2 1 0 0 0 0 0 0 0 0]\n",
            "[2 0 1 0 1 0 1 1 0 0 1]\n",
            "[2 0 0 0 0 1 1 1 0 0 0 1]\n",
            "val_acc=0.4921875\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 0 1]\n",
            "[2 0 0 1]\n",
            "[2 1 0 0 0]\n",
            "[2 1 0 0 1 0]\n",
            "[2 1 0 0 0 0 0]\n",
            "[2 0 0 0 0 0 1 0]\n",
            "[2 0 0 0 0 0 1 0 1]\n",
            "[2 0 0 0 1 0 1 0 0 0]\n",
            "[2 1 1 0 0 1 0 0 0 1 0]\n",
            "[2 0 0 0 0 0 0 0 0 0 1 0]\n",
            "val_acc=0.546875\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 1 0]\n",
            "[2 0 0 1]\n",
            "[2 1 0 0 0]\n",
            "[2 0 0 0 0 1]\n",
            "[2 1 1 1 0 1 0]\n",
            "[2 1 0 0 0 0 0 0]\n",
            "[2 0 0 0 1 0 0 0 0]\n",
            "[2 0 0 0 1 0 0 0 0 0]\n",
            "[2 0 0 0 1 0 0 0 0 0 0]\n",
            "[1 1 0 0 0 0 1 0 0 1 0 0]\n",
            "val_acc=0.46875\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 0 1]\n",
            "[2 0 0 0]\n",
            "[2 0 0 0 0]\n",
            "[2 1 0 0 0 1]\n",
            "[2 1 1 0 1 0 0]\n",
            "[0 0 1 1 1 1 0 1]\n",
            "[2 0 1 0 0 0 1 0 0]\n",
            "[2 0 0 0 0 0 0 1 0 0]\n",
            "[2 0 1 1 0 0 1 1 1 0 0]\n",
            "[2 0 0 1 0 0 1 0 0 0 0 1]\n",
            "val_acc=0.5234375\n",
            "--------------------------------------------------------------------------------\n",
            "Epoch 14: Eval\n",
            "valid_accuracy: 0.6028\n",
            "test_accuracy: 0.5818\n",
            "epoch=15\tch_step=0\tloss=0.938926\tlr=0.0253\t|g|=1.8469\tacc=0.6484\ttime=3.48it/s\n",
            "epoch=15\tch_step=50\tloss=1.007800\tlr=0.0253\t|g|=1.9749\tacc=0.6172\ttime=4.93it/s\n",
            "epoch=15\tch_step=100\tloss=1.352172\tlr=0.0253\t|g|=1.5343\tacc=0.5234\ttime=4.69it/s\n",
            "epoch=15\tch_step=150\tloss=1.117175\tlr=0.0253\t|g|=2.1569\tacc=0.5625\ttime=4.80it/s\n",
            "epoch=15\tch_step=200\tloss=1.160201\tlr=0.0253\t|g|=1.7362\tacc=0.6094\ttime=4.72it/s\n",
            "epoch=15\tch_step=250\tloss=1.063582\tlr=0.0253\t|g|=1.5045\tacc=0.5938\ttime=4.67it/s\n",
            "epoch=15\tch_step=300\tloss=1.287835\tlr=0.0253\t|g|=1.8237\tacc=0.5312\ttime=4.85it/s\n",
            "epoch=15\tch_step=350\tloss=1.201212\tlr=0.0253\t|g|=1.5610\tacc=0.5938\ttime=5.26it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 15: Training controller\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:145: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=1\tloss=0.537\tent=43.07\tlr=0.0010\t|g|=0.0300\tacc=0.6016\tbl=0.59\ttime=1.19it/s\n",
            "ctrl_step=3\tloss=-0.926\tent=43.46\tlr=0.0010\t|g|=0.0513\tacc=0.5703\tbl=0.60\ttime=1.24it/s\n",
            "ctrl_step=5\tloss=1.446\tent=43.38\tlr=0.0010\t|g|=0.0914\tacc=0.6328\tbl=0.60\ttime=1.23it/s\n",
            "ctrl_step=7\tloss=-4.758\tent=42.07\tlr=0.0010\t|g|=0.1426\tacc=0.4922\tbl=0.60\ttime=1.26it/s\n",
            "ctrl_step=9\tloss=0.270\tent=42.89\tlr=0.0010\t|g|=0.2113\tacc=0.6016\tbl=0.60\ttime=1.24it/s\n",
            "ctrl_step=11\tloss=-3.807\tent=42.27\tlr=0.0010\t|g|=0.1022\tacc=0.5156\tbl=0.60\ttime=1.25it/s\n",
            "ctrl_step=13\tloss=2.067\tent=42.37\tlr=0.0010\t|g|=0.0513\tacc=0.6484\tbl=0.60\ttime=1.21it/s\n",
            "ctrl_step=15\tloss=-2.389\tent=43.77\tlr=0.0010\t|g|=0.2051\tacc=0.5391\tbl=0.60\ttime=1.18it/s\n",
            "ctrl_step=17\tloss=-1.989\tent=42.45\tlr=0.0010\t|g|=0.0886\tacc=0.5547\tbl=0.60\ttime=1.21it/s\n",
            "ctrl_step=19\tloss=1.050\tent=41.17\tlr=0.0010\t|g|=0.2857\tacc=0.6328\tbl=0.60\ttime=1.22it/s\n",
            "ctrl_step=21\tloss=-0.526\tent=43.51\tlr=0.0010\t|g|=0.0691\tacc=0.5859\tbl=0.60\ttime=1.27it/s\n",
            "ctrl_step=23\tloss=3.189\tent=43.19\tlr=0.0010\t|g|=0.0321\tacc=0.6797\tbl=0.60\ttime=1.26it/s\n",
            "ctrl_step=25\tloss=-1.151\tent=42.57\tlr=0.0010\t|g|=0.0314\tacc=0.5781\tbl=0.60\ttime=1.23it/s\n",
            "ctrl_step=27\tloss=-1.589\tent=44.83\tlr=0.0010\t|g|=0.4724\tacc=0.5625\tbl=0.60\ttime=1.16it/s\n",
            "ctrl_step=29\tloss=-0.841\tent=41.79\tlr=0.0010\t|g|=0.1944\tacc=0.5781\tbl=0.60\ttime=1.27it/s\n",
            "ctrl_step=31\tloss=-2.197\tent=41.39\tlr=0.0010\t|g|=0.3797\tacc=0.5391\tbl=0.60\ttime=1.16it/s\n",
            "ctrl_step=33\tloss=1.029\tent=42.95\tlr=0.0010\t|g|=0.1122\tacc=0.6250\tbl=0.60\ttime=1.18it/s\n",
            "ctrl_step=35\tloss=2.443\tent=42.44\tlr=0.0010\t|g|=0.1565\tacc=0.6641\tbl=0.60\ttime=1.15it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Traceback (most recent call last):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=37\tloss=0.422\tent=42.49\tlr=0.0010\t|g|=0.0772\tacc=0.6094\tbl=0.60\ttime=1.14it/s\n",
            "ctrl_step=39\tloss=-1.539\tent=44.05\tlr=0.0010\t|g|=0.1170\tacc=0.5625\tbl=0.60\ttime=1.19it/s\n",
            "ctrl_step=41\tloss=-2.881\tent=42.89\tlr=0.0010\t|g|=0.2938\tacc=0.5078\tbl=0.60\ttime=1.20it/s\n",
            "ctrl_step=43\tloss=-0.758\tent=42.27\tlr=0.0010\t|g|=0.0737\tacc=0.5781\tbl=0.60\ttime=1.14it/s\n",
            "ctrl_step=45\tloss=-5.178\tent=44.48\tlr=0.0010\t|g|=0.0692\tacc=0.4844\tbl=0.60\ttime=1.19it/s\n",
            "ctrl_step=47\tloss=2.551\tent=43.56\tlr=0.0010\t|g|=0.1821\tacc=0.6641\tbl=0.61\ttime=1.19it/s\n",
            "ctrl_step=49\tloss=0.762\tent=43.39\tlr=0.0010\t|g|=0.0547\tacc=0.6172\tbl=0.60\ttime=1.13it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Here are 10 architectures:\n",
            "[2]\n",
            "[1 0]\n",
            "[2 0 1]\n",
            "[2 0 0 0]\n",
            "[2 0 0 0 1]\n",
            "[2 1 0 0 1 0]\n",
            "[2 0 0 1 1 1 0]\n",
            "[2 0 1 0 0 0 1 0]\n",
            "[2 0 0 0 0 0 0 1 0]\n",
            "[2 1 0 1 0 0 0 0 1 0]\n",
            "[2 1 0 0 0 0 0 0 0 1 0]\n",
            "[2 0 0 1 0 0 0 1 0 0 0 0]\n",
            "val_acc=0.5859375\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 1]\n",
            "[2 0 0]\n",
            "[2 0 0 0]\n",
            "[2 1 0 0 1]\n",
            "[2 0 1 0 1 0]\n",
            "[2 0 0 0 0 0 0]\n",
            "[2 0 0 0 0 0 0 0]\n",
            "[2 1 1 0 1 0 0 0 0]\n",
            "[2 0 0 0 0 0 0 1 0 0]\n",
            "[2 0 1 0 1 1 0 1 0 0 1]\n",
            "[2 0 0 0 0 1 0 0 0 0 0 0]\n",
            "val_acc=0.6875\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 0 0]\n",
            "[2 0 0 0]\n",
            "[2 1 0 0 0]\n",
            "[2 0 1 0 1 0]\n",
            "[2 0 0 0 1 0 0]\n",
            "[2 0 0 0 0 0 0 1]\n",
            "[2 0 0 0 0 0 1 0 0]\n",
            "[2 1 0 0 0 1 1 1 0 0]\n",
            "[2 0 1 0 0 0 1 0 0 0 0]\n",
            "[2 1 0 1 0 0 0 0 0 0 1 0]\n",
            "val_acc=0.625\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[1 0]\n",
            "[2 0 0]\n",
            "[2 0 0 0]\n",
            "[2 0 0 0 0]\n",
            "[2 1 0 0 1 0]\n",
            "[2 1 1 1 0 0 0]\n",
            "[2 1 0 0 0 0 0 0]\n",
            "[2 0 0 0 0 0 0 1 0]\n",
            "[0 0 0 1 0 0 0 0 1 0]\n",
            "[2 0 0 0 0 0 0 0 0 1 0]\n",
            "[2 0 0 0 0 1 1 0 1 0 1 0]\n",
            "val_acc=0.5546875\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 0 1]\n",
            "[2 0 0 0]\n",
            "[2 0 0 0 1]\n",
            "[2 0 0 0 1 0]\n",
            "[2 0 1 0 0 0 0]\n",
            "[1 0 1 0 0 1 1 1]\n",
            "[2 1 0 1 0 0 0 0 0]\n",
            "[2 0 0 0 0 0 0 0 0 0]\n",
            "[2 1 1 0 0 0 0 0 0 0 0]\n",
            "[2 0 0 0 0 0 0 0 1 1 1 1]\n",
            "val_acc=0.6328125\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[1 0]\n",
            "[2 0 1]\n",
            "[2 0 0 0]\n",
            "[2 0 0 0 0]\n",
            "[2 0 0 0 1 0]\n",
            "[2 1 0 0 0 0 0]\n",
            "[2 0 0 0 0 0 1 1]\n",
            "[2 0 0 0 0 0 0 0 0]\n",
            "[0 0 1 0 1 0 1 0 0 0]\n",
            "[2 0 0 1 0 1 1 0 0 0 0]\n",
            "[2 1 0 0 0 0 0 0 0 0 0 0]\n",
            "val_acc=0.5859375\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 1]\n",
            "[2 0 0]\n",
            "[2 1 1 0]\n",
            "[2 1 0 0 0]\n",
            "[2 0 0 0 0 0]\n",
            "[2 0 0 1 1 0 0]\n",
            "[2 0 0 0 0 0 0 0]\n",
            "[2 0 1 1 1 0 0 0 0]\n",
            "[2 0 0 0 0 0 0 0 1 1]\n",
            "[0 1 0 0 0 0 0 1 1 0 0]\n",
            "[2 0 1 0 1 0 0 0 0 0 0 0]\n",
            "val_acc=0.6796875\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 0 0]\n",
            "[2 0 0 0]\n",
            "[2 0 0 1 0]\n",
            "[2 0 1 1 0 0]\n",
            "[2 1 0 1 0 0 1]\n",
            "[2 0 0 0 1 0 0 0]\n",
            "[2 1 0 0 1 0 0 0 1]\n",
            "[2 1 0 0 0 0 0 1 0 0]\n",
            "[2 1 0 0 0 0 0 0 0 0 0]\n",
            "[2 1 0 0 0 1 0 0 1 0 0 0]\n",
            "val_acc=0.6171875\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 0 0]\n",
            "[2 0 1 0]\n",
            "[2 0 1 0 0]\n",
            "[2 0 0 0 0 1]\n",
            "[2 1 0 1 0 1 0]\n",
            "[2 0 0 0 0 0 0 0]\n",
            "[2 1 0 0 0 0 0 1 0]\n",
            "[2 0 0 0 0 1 0 0 1 1]\n",
            "[2 0 0 0 0 1 0 0 0 1 0]\n",
            "[2 0 0 1 0 0 0 0 0 0 0 1]\n",
            "val_acc=0.640625\n",
            "--------------------------------------------------------------------------------\n",
            "[0]\n",
            "[2 1]\n",
            "[2 0 0]\n",
            "[2 1 0 0]\n",
            "[2 0 0 1 0]\n",
            "[2 0 0 0 0 1]\n",
            "[2 0 0 0 0 0 1]\n",
            "[2 0 0 0 0 0 0 0]\n",
            "[2 0 0 0 0 0 1 0 0]\n",
            "[2 0 1 0 0 0 1 1 1 0]\n",
            "[2 0 0 0 1 0 0 1 1 0 1]\n",
            "[2 0 0 0 0 0 0 1 0 0 0 0]\n",
            "val_acc=0.6328125\n",
            "--------------------------------------------------------------------------------\n",
            "Epoch 15: Eval\n",
            "valid_accuracy: 0.6148\n",
            "test_accuracy: 0.5945\n",
            "epoch=16\tch_step=0\tloss=1.100860\tlr=0.0329\t|g|=1.6815\tacc=0.5391\ttime=3.40it/s\n",
            "epoch=16\tch_step=50\tloss=1.027972\tlr=0.0329\t|g|=1.7612\tacc=0.6641\ttime=4.66it/s\n",
            "epoch=16\tch_step=100\tloss=1.232602\tlr=0.0329\t|g|=1.7091\tacc=0.5391\ttime=4.83it/s\n",
            "epoch=16\tch_step=150\tloss=0.887940\tlr=0.0329\t|g|=0.9479\tacc=0.6719\ttime=4.65it/s\n",
            "epoch=16\tch_step=200\tloss=1.112848\tlr=0.0329\t|g|=1.4018\tacc=0.5938\ttime=4.50it/s\n",
            "epoch=16\tch_step=250\tloss=1.192335\tlr=0.0329\t|g|=1.8033\tacc=0.5938\ttime=4.96it/s\n",
            "epoch=16\tch_step=300\tloss=1.024436\tlr=0.0329\t|g|=1.4083\tacc=0.6406\ttime=4.57it/s\n",
            "epoch=16\tch_step=350\tloss=1.041732\tlr=0.0329\t|g|=1.5830\tacc=0.6562\ttime=5.12it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 16: Training controller\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:145: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=1\tloss=1.208\tent=43.25\tlr=0.0010\t|g|=0.3282\tacc=0.6328\tbl=0.61\ttime=1.18it/s\n",
            "ctrl_step=3\tloss=2.011\tent=43.95\tlr=0.0010\t|g|=0.1575\tacc=0.6562\tbl=0.61\ttime=1.25it/s\n",
            "ctrl_step=5\tloss=-1.305\tent=43.48\tlr=0.0010\t|g|=0.0592\tacc=0.5781\tbl=0.62\ttime=1.12it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Traceback (most recent call last):\n",
            "  File \"/usr/lib/python3.7/multiprocessing/queues.py\", line 242, in _feed\n",
            "    send_bytes(obj)\n",
            "  File \"/usr/lib/python3.7/multiprocessing/connection.py\", line 200, in send_bytes\n",
            "    self._send_bytes(m[offset:offset + size])\n",
            "  File \"/usr/lib/python3.7/multiprocessing/connection.py\", line 404, in _send_bytes\n",
            "    self._send(header + buf)\n",
            "  File \"/usr/lib/python3.7/multiprocessing/connection.py\", line 368, in _send\n",
            "    n = write(self._handle, buf)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=7\tloss=2.782\tent=43.13\tlr=0.0010\t|g|=0.2061\tacc=0.6875\tbl=0.61\ttime=1.16it/s\n",
            "ctrl_step=9\tloss=-9.198\tent=42.59\tlr=0.0010\t|g|=0.1835\tacc=0.4219\tbl=0.62\ttime=1.20it/s\n",
            "ctrl_step=11\tloss=1.114\tent=42.09\tlr=0.0010\t|g|=0.0877\tacc=0.6328\tbl=0.61\ttime=1.15it/s\n",
            "ctrl_step=13\tloss=-1.591\tent=42.59\tlr=0.0010\t|g|=0.0951\tacc=0.5781\tbl=0.61\ttime=1.19it/s\n",
            "ctrl_step=15\tloss=-0.076\tent=41.02\tlr=0.0010\t|g|=0.1113\tacc=0.6094\tbl=0.62\ttime=1.17it/s\n",
            "ctrl_step=17\tloss=-1.181\tent=40.86\tlr=0.0010\t|g|=0.4152\tacc=0.5703\tbl=0.61\ttime=1.19it/s\n",
            "ctrl_step=19\tloss=3.853\tent=41.47\tlr=0.0010\t|g|=0.0910\tacc=0.7266\tbl=0.62\ttime=1.22it/s\n",
            "ctrl_step=21\tloss=1.306\tent=42.55\tlr=0.0010\t|g|=0.0233\tacc=0.6406\tbl=0.61\ttime=1.22it/s\n",
            "ctrl_step=23\tloss=0.169\tent=43.12\tlr=0.0010\t|g|=0.3426\tacc=0.6172\tbl=0.62\ttime=1.11it/s\n",
            "ctrl_step=25\tloss=-3.542\tent=44.34\tlr=0.0010\t|g|=0.4345\tacc=0.5312\tbl=0.61\ttime=1.23it/s\n",
            "ctrl_step=27\tloss=-3.367\tent=42.43\tlr=0.0010\t|g|=0.0984\tacc=0.5312\tbl=0.61\ttime=1.17it/s\n",
            "ctrl_step=29\tloss=0.966\tent=41.07\tlr=0.0010\t|g|=0.3777\tacc=0.6328\tbl=0.62\ttime=1.17it/s\n",
            "ctrl_step=31\tloss=2.811\tent=43.20\tlr=0.0010\t|g|=0.2661\tacc=0.6797\tbl=0.62\ttime=1.16it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Traceback (most recent call last):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=33\tloss=0.691\tent=41.39\tlr=0.0010\t|g|=0.0782\tacc=0.6250\tbl=0.62\ttime=1.13it/s\n",
            "ctrl_step=35\tloss=6.516\tent=41.08\tlr=0.0010\t|g|=0.2691\tacc=0.7656\tbl=0.61\ttime=1.16it/s\n",
            "ctrl_step=37\tloss=1.095\tent=41.59\tlr=0.0010\t|g|=0.2246\tacc=0.6406\tbl=0.62\ttime=1.16it/s\n",
            "ctrl_step=39\tloss=-2.024\tent=43.08\tlr=0.0010\t|g|=0.0689\tacc=0.5703\tbl=0.62\ttime=1.14it/s\n",
            "ctrl_step=41\tloss=0.107\tent=42.28\tlr=0.0010\t|g|=0.0145\tacc=0.6172\tbl=0.62\ttime=1.19it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Traceback (most recent call last):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=43\tloss=-2.247\tent=43.17\tlr=0.0010\t|g|=0.1397\tacc=0.5703\tbl=0.62\ttime=1.13it/s\n",
            "ctrl_step=45\tloss=-0.579\tent=42.64\tlr=0.0010\t|g|=0.1637\tacc=0.6016\tbl=0.62\ttime=1.14it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Traceback (most recent call last):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=47\tloss=4.808\tent=42.91\tlr=0.0010\t|g|=0.5004\tacc=0.7344\tbl=0.62\ttime=1.11it/s\n",
            "ctrl_step=49\tloss=-0.008\tent=44.71\tlr=0.0010\t|g|=0.1782\tacc=0.6172\tbl=0.62\ttime=1.15it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Here are 10 architectures:\n",
            "[2]\n",
            "[2 1]\n",
            "[2 1 0]\n",
            "[2 0 1 1]\n",
            "[2 0 1 0 0]\n",
            "[2 0 0 0 0 0]\n",
            "[2 1 0 0 1 0 1]\n",
            "[2 1 0 0 0 1 1 0]\n",
            "[2 0 0 0 0 1 0 0 0]\n",
            "[2 0 0 0 0 0 1 0 0 1]\n",
            "[2 1 0 0 1 1 0 0 0 0 1]\n",
            "[2 1 0 1 0 0 0 1 1 1 1 0]\n",
            "val_acc=0.59375\n",
            "--------------------------------------------------------------------------------\n",
            "[0]\n",
            "[2 0]\n",
            "[2 0 0]\n",
            "[2 0 0 0]\n",
            "[2 0 0 0 1]\n",
            "[2 0 0 0 0 0]\n",
            "[2 0 0 0 0 1 0]\n",
            "[2 1 1 1 1 0 0 1]\n",
            "[2 1 0 0 1 1 1 0 1]\n",
            "[2 1 0 0 0 1 0 1 1 0]\n",
            "[2 0 0 0 0 1 1 0 0 1 0]\n",
            "[2 1 1 0 0 0 1 0 0 0 0 0]\n",
            "val_acc=0.5234375\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[0 0 0]\n",
            "[2 0 1 0]\n",
            "[2 1 0 1 0]\n",
            "[2 0 0 0 1 1]\n",
            "[2 0 1 0 1 0 1]\n",
            "[2 1 0 0 1 0 1 0]\n",
            "[2 0 1 1 0 0 1 0 1]\n",
            "[2 0 0 0 1 0 0 1 0 0]\n",
            "[2 1 0 0 1 1 1 1 1 0 0]\n",
            "[2 0 0 0 0 0 0 0 0 1 1 1]\n",
            "val_acc=0.6484375\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 0 0]\n",
            "[2 1 0 0]\n",
            "[2 1 1 1 1]\n",
            "[2 0 0 1 0 0]\n",
            "[2 0 0 0 0 0 0]\n",
            "[2 0 0 0 0 0 0 0]\n",
            "[1 1 1 0 1 1 1 1 0]\n",
            "[2 0 0 0 0 1 1 0 0 1]\n",
            "[2 1 0 0 1 1 1 0 0 1 0]\n",
            "[2 0 0 0 0 0 0 0 0 0 0 0]\n",
            "val_acc=0.640625\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[0 1]\n",
            "[2 0 0]\n",
            "[2 0 0 0]\n",
            "[2 0 0 0 1]\n",
            "[2 1 1 0 0 1]\n",
            "[2 0 1 1 0 0 0]\n",
            "[2 0 0 0 0 0 1 0]\n",
            "[0 0 0 0 0 0 1 0 0]\n",
            "[2 0 0 0 0 0 0 0 1 1]\n",
            "[2 0 1 0 0 0 0 0 0 1 0]\n",
            "[2 0 0 0 0 1 0 0 0 0 0 1]\n",
            "val_acc=0.578125\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 0 0]\n",
            "[2 0 0 0]\n",
            "[2 0 1 0 0]\n",
            "[2 0 1 0 1 0]\n",
            "[2 0 1 0 0 0 0]\n",
            "[2 0 0 0 0 0 1 1]\n",
            "[1 0 0 1 0 0 1 0 1]\n",
            "[2 0 0 0 1 0 1 1 0 0]\n",
            "[2 0 1 1 1 0 1 0 0 0 0]\n",
            "[2 0 0 0 0 0 0 1 1 0 0 1]\n",
            "val_acc=0.65625\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 1]\n",
            "[2 0 0]\n",
            "[2 0 1 1]\n",
            "[2 0 0 1 0]\n",
            "[2 0 0 1 0 1]\n",
            "[2 0 0 0 0 1 0]\n",
            "[2 0 0 0 0 0 0 0]\n",
            "[2 1 1 0 1 0 1 1 1]\n",
            "[2 0 0 0 0 0 0 0 0 0]\n",
            "[2 1 1 0 0 0 0 0 0 0 1]\n",
            "[2 0 0 0 0 0 0 0 0 0 1 0]\n",
            "val_acc=0.671875\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 1 1]\n",
            "[2 0 1 0]\n",
            "[2 1 1 0 0]\n",
            "[2 1 0 0 0 0]\n",
            "[2 0 0 0 0 1 1]\n",
            "[2 1 1 1 1 0 0 0]\n",
            "[2 0 1 1 0 1 1 0 1]\n",
            "[2 1 1 0 0 0 0 1 1 1]\n",
            "[2 1 0 0 0 0 0 0 1 0 0]\n",
            "[2 0 0 0 0 0 1 1 0 1 0 0]\n",
            "val_acc=0.6328125\n",
            "--------------------------------------------------------------------------------\n",
            "[0]\n",
            "[2 0]\n",
            "[2 0 0]\n",
            "[2 0 0 0]\n",
            "[2 1 0 1 0]\n",
            "[2 0 1 0 1 0]\n",
            "[2 0 1 0 0 1 0]\n",
            "[2 0 0 0 0 0 0 0]\n",
            "[2 1 0 0 0 0 1 1 0]\n",
            "[2 0 1 0 0 0 0 1 0 1]\n",
            "[2 0 0 0 0 0 0 0 0 0 0]\n",
            "[2 1 1 1 0 1 1 0 0 1 0 0]\n",
            "val_acc=0.5546875\n",
            "--------------------------------------------------------------------------------\n",
            "[0]\n",
            "[2 0]\n",
            "[2 0 0]\n",
            "[2 0 1 1]\n",
            "[2 0 1 1 0]\n",
            "[2 0 0 0 1 0]\n",
            "[0 1 0 0 0 0 1]\n",
            "[2 1 0 0 1 0 1 1]\n",
            "[2 0 1 1 0 0 0 0 1]\n",
            "[2 0 0 0 1 0 0 0 0 0]\n",
            "[2 1 0 0 0 0 0 0 0 0 0]\n",
            "[2 0 0 1 0 0 0 0 1 0 0 0]\n",
            "val_acc=0.5625\n",
            "--------------------------------------------------------------------------------\n",
            "Epoch 16: Eval\n",
            "valid_accuracy: 0.6124\n",
            "test_accuracy: 0.5865\n",
            "epoch=17\tch_step=0\tloss=1.340862\tlr=0.0398\t|g|=1.6939\tacc=0.4922\ttime=3.50it/s\n",
            "epoch=17\tch_step=50\tloss=1.043341\tlr=0.0398\t|g|=1.1159\tacc=0.6406\ttime=4.90it/s\n",
            "epoch=17\tch_step=100\tloss=1.055266\tlr=0.0398\t|g|=1.4108\tacc=0.6172\ttime=4.84it/s\n",
            "epoch=17\tch_step=150\tloss=1.073504\tlr=0.0398\t|g|=1.4320\tacc=0.5625\ttime=4.63it/s\n",
            "epoch=17\tch_step=200\tloss=1.505406\tlr=0.0398\t|g|=1.8845\tacc=0.4844\ttime=4.82it/s\n",
            "epoch=17\tch_step=250\tloss=0.961567\tlr=0.0398\t|g|=1.8227\tacc=0.6562\ttime=4.66it/s\n",
            "epoch=17\tch_step=300\tloss=1.033484\tlr=0.0398\t|g|=1.7919\tacc=0.6641\ttime=4.65it/s\n",
            "epoch=17\tch_step=350\tloss=1.137135\tlr=0.0398\t|g|=1.1733\tacc=0.6250\ttime=4.95it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 17: Training controller\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:145: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=1\tloss=-0.352\tent=44.41\tlr=0.0010\t|g|=0.0249\tacc=0.6094\tbl=0.62\ttime=1.13it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Traceback (most recent call last):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=3\tloss=-0.374\tent=45.46\tlr=0.0010\t|g|=0.3348\tacc=0.6094\tbl=0.62\ttime=1.19it/s\n",
            "ctrl_step=5\tloss=1.560\tent=44.93\tlr=0.0010\t|g|=0.0767\tacc=0.6562\tbl=0.63\ttime=1.13it/s\n",
            "ctrl_step=7\tloss=0.510\tent=44.52\tlr=0.0010\t|g|=0.2381\tacc=0.6406\tbl=0.63\ttime=1.12it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Traceback (most recent call last):\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/lib/python3.7/multiprocessing/queues.py\", line 242, in _feed\n",
            "    send_bytes(obj)\n",
            "Traceback (most recent call last):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=9\tloss=0.840\tent=43.66\tlr=0.0010\t|g|=0.5338\tacc=0.6484\tbl=0.63\ttime=0.74it/s\n",
            "ctrl_step=11\tloss=0.839\tent=42.49\tlr=0.0010\t|g|=0.0230\tacc=0.6484\tbl=0.63\ttime=1.12it/s\n",
            "ctrl_step=13\tloss=4.162\tent=43.06\tlr=0.0010\t|g|=0.0667\tacc=0.7188\tbl=0.63\ttime=1.07it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Traceback (most recent call last):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=15\tloss=1.269\tent=41.64\tlr=0.0010\t|g|=0.2413\tacc=0.6562\tbl=0.63\ttime=1.17it/s\n",
            "ctrl_step=17\tloss=-0.763\tent=42.54\tlr=0.0010\t|g|=0.2780\tacc=0.6016\tbl=0.63\ttime=1.11it/s\n",
            "ctrl_step=19\tloss=-1.616\tent=42.46\tlr=0.0010\t|g|=0.0949\tacc=0.5781\tbl=0.62\ttime=1.11it/s\n",
            "ctrl_step=21\tloss=1.691\tent=42.67\tlr=0.0010\t|g|=0.0672\tacc=0.6641\tbl=0.62\ttime=1.13it/s\n",
            "ctrl_step=23\tloss=-0.259\tent=40.36\tlr=0.0010\t|g|=0.4696\tacc=0.6172\tbl=0.63\ttime=1.19it/s\n",
            "ctrl_step=25\tloss=1.894\tent=42.03\tlr=0.0010\t|g|=0.0266\tacc=0.6719\tbl=0.63\ttime=1.16it/s\n",
            "ctrl_step=27\tloss=-2.911\tent=42.75\tlr=0.0010\t|g|=0.0968\tacc=0.5547\tbl=0.63\ttime=1.06it/s\n",
            "ctrl_step=29\tloss=0.857\tent=42.91\tlr=0.0010\t|g|=0.0290\tacc=0.6484\tbl=0.63\ttime=1.15it/s\n",
            "ctrl_step=31\tloss=3.594\tent=43.68\tlr=0.0010\t|g|=0.3486\tacc=0.7031\tbl=0.63\ttime=1.15it/s\n",
            "ctrl_step=33\tloss=-0.826\tent=44.57\tlr=0.0010\t|g|=0.1966\tacc=0.6016\tbl=0.62\ttime=1.11it/s\n",
            "ctrl_step=35\tloss=0.137\tent=42.78\tlr=0.0010\t|g|=0.1739\tacc=0.6250\tbl=0.63\ttime=1.11it/s\n",
            "ctrl_step=37\tloss=-1.619\tent=43.28\tlr=0.0010\t|g|=0.0434\tacc=0.5859\tbl=0.63\ttime=1.10it/s\n",
            "ctrl_step=39\tloss=3.175\tent=43.76\tlr=0.0010\t|g|=0.6416\tacc=0.6953\tbl=0.63\ttime=1.09it/s\n",
            "ctrl_step=41\tloss=-0.315\tent=44.52\tlr=0.0010\t|g|=0.1908\tacc=0.6172\tbl=0.63\ttime=1.10it/s\n",
            "ctrl_step=43\tloss=-3.649\tent=44.33\tlr=0.0010\t|g|=0.5108\tacc=0.5469\tbl=0.63\ttime=1.09it/s\n",
            "ctrl_step=45\tloss=-1.091\tent=43.24\tlr=0.0010\t|g|=0.0381\tacc=0.6016\tbl=0.63\ttime=1.14it/s\n",
            "ctrl_step=47\tloss=0.761\tent=44.07\tlr=0.0010\t|g|=0.0354\tacc=0.6484\tbl=0.63\ttime=1.09it/s\n",
            "ctrl_step=49\tloss=2.127\tent=43.83\tlr=0.0010\t|g|=0.5637\tacc=0.6797\tbl=0.63\ttime=1.09it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Here are 10 architectures:\n",
            "[2]\n",
            "[2 0]\n",
            "[2 1 0]\n",
            "[2 1 0 0]\n",
            "[2 0 1 0 1]\n",
            "[2 1 0 0 1 0]\n",
            "[2 0 1 1 0 0 0]\n",
            "[2 1 0 1 0 0 0 1]\n",
            "[2 1 0 0 0 0 0 0 1]\n",
            "[2 0 0 0 0 0 0 0 0 0]\n",
            "[2 0 0 0 1 0 0 1 0 0 0]\n",
            "[2 0 0 1 0 0 0 0 0 0 1 0]\n",
            "val_acc=0.5234375\n",
            "--------------------------------------------------------------------------------\n",
            "[0]\n",
            "[2 1]\n",
            "[2 1 0]\n",
            "[2 0 0 0]\n",
            "[2 1 0 0 0]\n",
            "[2 1 0 0 1 0]\n",
            "[2 0 1 0 1 1 0]\n",
            "[2 0 0 1 0 0 0 0]\n",
            "[2 0 0 0 0 0 1 0 1]\n",
            "[2 1 0 0 0 0 0 1 1 1]\n",
            "[2 0 0 1 0 0 1 1 0 0 0]\n",
            "[2 1 0 0 0 1 1 0 1 0 1 0]\n",
            "val_acc=0.453125\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 1 1]\n",
            "[2 0 0 0]\n",
            "[2 1 0 0 1]\n",
            "[2 0 1 0 0 1]\n",
            "[2 1 0 0 0 0 1]\n",
            "[1 0 0 0 0 0 0 0]\n",
            "[2 1 0 1 0 0 1 0 0]\n",
            "[0 1 1 0 1 0 1 0 1 0]\n",
            "[2 1 0 0 1 1 0 0 0 1 1]\n",
            "[2 0 1 0 1 1 0 0 0 0 0 0]\n",
            "val_acc=0.515625\n",
            "--------------------------------------------------------------------------------\n",
            "[0]\n",
            "[2 0]\n",
            "[2 0 0]\n",
            "[2 0 0 1]\n",
            "[2 0 0 0 0]\n",
            "[2 0 0 0 0 0]\n",
            "[2 0 1 0 0 0 1]\n",
            "[2 1 0 1 0 0 0 1]\n",
            "[2 1 0 0 0 0 0 0 0]\n",
            "[2 1 0 0 1 0 0 0 1 1]\n",
            "[2 0 0 0 1 0 0 0 0 1 0]\n",
            "[2 0 0 0 0 0 0 0 0 0 0 0]\n",
            "val_acc=0.484375\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[1 0 1]\n",
            "[2 0 0 0]\n",
            "[2 0 0 0 0]\n",
            "[2 0 0 0 0 1]\n",
            "[2 0 1 0 0 1 1]\n",
            "[2 0 0 0 1 1 0 0]\n",
            "[2 0 0 0 1 1 0 0 0]\n",
            "[2 0 0 0 1 0 0 0 1 0]\n",
            "[0 0 1 0 1 0 1 0 0 1 1]\n",
            "[2 0 0 0 0 0 1 0 1 0 0 1]\n",
            "val_acc=0.4765625\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 1]\n",
            "[2 0 0]\n",
            "[2 0 1 1]\n",
            "[2 0 0 0 0]\n",
            "[2 0 1 0 1 1]\n",
            "[2 1 1 1 0 0 1]\n",
            "[2 1 0 1 0 0 0 1]\n",
            "[2 1 0 0 1 1 0 0 1]\n",
            "[2 1 1 0 0 1 0 1 0 1]\n",
            "[2 0 1 0 1 0 0 1 0 0 1]\n",
            "[1 1 0 0 1 0 1 0 1 1 1 0]\n",
            "val_acc=0.5078125\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 1]\n",
            "[2 1 1]\n",
            "[2 0 1 0]\n",
            "[2 0 0 0 1]\n",
            "[2 0 0 0 1 0]\n",
            "[2 1 0 1 0 0 1]\n",
            "[2 0 1 0 0 0 1 1]\n",
            "[2 1 1 0 1 0 1 0 0]\n",
            "[2 0 1 0 0 0 0 1 0 0]\n",
            "[2 0 0 0 0 0 1 0 1 1 1]\n",
            "[1 1 1 1 0 0 1 1 0 0 1 0]\n",
            "val_acc=0.5390625\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 1]\n",
            "[0 0 0]\n",
            "[2 0 0 0]\n",
            "[2 0 0 0 1]\n",
            "[2 0 0 0 0 0]\n",
            "[2 0 1 0 1 0 0]\n",
            "[2 0 0 1 0 0 0 0]\n",
            "[2 1 1 1 1 1 1 0 0]\n",
            "[2 1 0 0 0 0 0 1 1 0]\n",
            "[2 0 0 0 1 0 0 0 0 0 0]\n",
            "[2 1 0 0 0 1 1 1 0 0 0 0]\n",
            "val_acc=0.4921875\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 1]\n",
            "[2 0 1]\n",
            "[2 1 1 0]\n",
            "[2 0 0 0 0]\n",
            "[2 0 1 1 1 1]\n",
            "[2 0 0 0 1 0 1]\n",
            "[2 0 0 0 1 1 1 0]\n",
            "[1 1 1 0 0 1 0 0 0]\n",
            "[2 1 1 1 0 0 0 0 1 0]\n",
            "[2 0 1 1 1 0 1 0 1 0 0]\n",
            "[2 0 1 0 0 1 0 0 0 0 0 0]\n",
            "val_acc=0.53125\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[0 0]\n",
            "[2 0 0]\n",
            "[2 0 1 1]\n",
            "[2 0 1 0 0]\n",
            "[1 0 0 1 1 1]\n",
            "[2 0 0 0 0 0 0]\n",
            "[2 0 0 1 0 0 0 1]\n",
            "[2 1 0 0 0 0 0 0 0]\n",
            "[2 0 0 0 1 0 1 0 1 0]\n",
            "[2 0 0 1 0 1 0 0 0 0 0]\n",
            "[1 0 0 1 1 1 0 0 1 0 1 0]\n",
            "val_acc=0.46875\n",
            "--------------------------------------------------------------------------------\n",
            "Epoch 17: Eval\n",
            "valid_accuracy: 0.6160\n",
            "test_accuracy: 0.5721\n",
            "epoch=18\tch_step=0\tloss=0.901467\tlr=0.0453\t|g|=0.9483\tacc=0.7031\ttime=3.41it/s\n",
            "epoch=18\tch_step=50\tloss=1.378331\tlr=0.0453\t|g|=2.1080\tacc=0.5078\ttime=4.64it/s\n",
            "epoch=18\tch_step=100\tloss=0.871249\tlr=0.0453\t|g|=1.1462\tacc=0.6875\ttime=4.52it/s\n",
            "epoch=18\tch_step=150\tloss=1.253098\tlr=0.0453\t|g|=1.4802\tacc=0.5625\ttime=4.66it/s\n",
            "epoch=18\tch_step=200\tloss=1.023153\tlr=0.0453\t|g|=1.0904\tacc=0.6172\ttime=4.61it/s\n",
            "epoch=18\tch_step=250\tloss=2.084192\tlr=0.0453\t|g|=3.1617\tacc=0.3047\ttime=4.79it/s\n",
            "epoch=18\tch_step=300\tloss=0.961010\tlr=0.0453\t|g|=0.9824\tacc=0.6406\ttime=4.54it/s\n",
            "epoch=18\tch_step=350\tloss=0.996144\tlr=0.0453\t|g|=1.1748\tacc=0.6484\ttime=5.42it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 18: Training controller\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:145: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "Traceback (most recent call last):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=1\tloss=1.227\tent=45.32\tlr=0.0010\t|g|=0.5186\tacc=0.6641\tbl=0.64\ttime=1.05it/s\n",
            "ctrl_step=3\tloss=1.940\tent=43.06\tlr=0.0010\t|g|=0.0187\tacc=0.6797\tbl=0.64\ttime=1.09it/s\n",
            "ctrl_step=5\tloss=-2.181\tent=43.74\tlr=0.0010\t|g|=0.2071\tacc=0.5938\tbl=0.64\ttime=1.08it/s\n",
            "ctrl_step=7\tloss=-7.534\tent=42.50\tlr=0.0010\t|g|=0.0576\tacc=0.4922\tbl=0.64\ttime=1.09it/s\n",
            "ctrl_step=9\tloss=2.988\tent=42.99\tlr=0.0010\t|g|=0.1354\tacc=0.7188\tbl=0.65\ttime=1.04it/s\n",
            "ctrl_step=11\tloss=1.506\tent=43.16\tlr=0.0010\t|g|=0.0349\tacc=0.6797\tbl=0.65\ttime=1.07it/s\n",
            "ctrl_step=13\tloss=-0.478\tent=42.02\tlr=0.0010\t|g|=0.5066\tacc=0.6328\tbl=0.65\ttime=1.13it/s\n",
            "ctrl_step=15\tloss=-3.751\tent=44.58\tlr=0.0010\t|g|=0.0805\tacc=0.5703\tbl=0.65\ttime=1.14it/s\n",
            "ctrl_step=17\tloss=0.187\tent=45.31\tlr=0.0010\t|g|=0.1373\tacc=0.6484\tbl=0.65\ttime=1.02it/s\n",
            "ctrl_step=19\tloss=-1.405\tent=43.17\tlr=0.0010\t|g|=0.0670\tacc=0.6172\tbl=0.65\ttime=1.08it/s\n",
            "ctrl_step=21\tloss=-1.435\tent=43.83\tlr=0.0010\t|g|=0.0171\tacc=0.6094\tbl=0.65\ttime=1.13it/s\n",
            "ctrl_step=23\tloss=0.188\tent=42.46\tlr=0.0010\t|g|=0.1916\tacc=0.6484\tbl=0.65\ttime=1.02it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Traceback (most recent call last):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=25\tloss=0.207\tent=41.20\tlr=0.0010\t|g|=0.6471\tacc=0.6328\tbl=0.63\ttime=1.07it/s\n",
            "ctrl_step=27\tloss=4.052\tent=43.92\tlr=0.0010\t|g|=0.0411\tacc=0.7266\tbl=0.64\ttime=1.07it/s\n",
            "ctrl_step=29\tloss=0.587\tent=43.86\tlr=0.0010\t|g|=0.1559\tacc=0.6484\tbl=0.64\ttime=1.06it/s\n",
            "ctrl_step=31\tloss=0.981\tent=43.83\tlr=0.0010\t|g|=0.0201\tacc=0.6641\tbl=0.64\ttime=1.06it/s\n",
            "ctrl_step=33\tloss=-0.175\tent=43.41\tlr=0.0010\t|g|=0.0193\tacc=0.6328\tbl=0.64\ttime=1.05it/s\n",
            "ctrl_step=35\tloss=-0.931\tent=43.18\tlr=0.0010\t|g|=0.2655\tacc=0.6172\tbl=0.64\ttime=1.12it/s\n",
            "ctrl_step=37\tloss=-1.025\tent=42.20\tlr=0.0010\t|g|=0.2978\tacc=0.6172\tbl=0.64\ttime=1.05it/s\n",
            "ctrl_step=39\tloss=1.911\tent=44.25\tlr=0.0010\t|g|=0.1354\tacc=0.6875\tbl=0.65\ttime=1.05it/s\n",
            "ctrl_step=41\tloss=3.190\tent=43.27\tlr=0.0010\t|g|=0.1879\tacc=0.7188\tbl=0.64\ttime=1.09it/s\n",
            "ctrl_step=43\tloss=2.402\tent=45.14\tlr=0.0010\t|g|=0.1107\tacc=0.6875\tbl=0.64\ttime=1.05it/s\n",
            "ctrl_step=45\tloss=-0.154\tent=44.21\tlr=0.0010\t|g|=0.3004\tacc=0.6406\tbl=0.65\ttime=1.09it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Traceback (most recent call last):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=47\tloss=0.676\tent=40.65\tlr=0.0010\t|g|=0.8255\tacc=0.6562\tbl=0.64\ttime=1.01it/s\n",
            "ctrl_step=49\tloss=4.699\tent=44.58\tlr=0.0010\t|g|=0.2530\tacc=0.7578\tbl=0.64\ttime=1.05it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Here are 10 architectures:\n",
            "[2]\n",
            "[2 0]\n",
            "[2 0 1]\n",
            "[2 1 0 1]\n",
            "[2 0 0 0 0]\n",
            "[2 0 0 0 1 0]\n",
            "[2 0 1 0 1 0 0]\n",
            "[2 0 1 0 0 0 0 1]\n",
            "[1 1 0 1 1 0 0 0 0]\n",
            "[2 1 0 0 0 1 0 0 0 0]\n",
            "[2 0 0 1 1 0 0 1 0 1 0]\n",
            "[2 0 0 0 1 0 1 0 1 0 0 0]\n",
            "val_acc=0.640625\n",
            "--------------------------------------------------------------------------------\n",
            "[1]\n",
            "[2 0]\n",
            "[2 0 0]\n",
            "[2 0 0 0]\n",
            "[2 1 0 0 1]\n",
            "[2 1 0 1 0 0]\n",
            "[2 1 0 1 0 0 0]\n",
            "[2 1 0 0 1 0 1 1]\n",
            "[2 1 1 1 0 0 0 0 0]\n",
            "[2 0 1 0 1 1 1 1 0 0]\n",
            "[0 0 0 1 0 0 0 1 0 0 1]\n",
            "[2 1 1 0 1 1 0 0 1 1 1 0]\n",
            "val_acc=0.421875\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[0 0]\n",
            "[2 1 1]\n",
            "[1 0 0 0]\n",
            "[2 0 0 0 0]\n",
            "[1 0 0 0 0 1]\n",
            "[2 0 0 0 1 0 0]\n",
            "[1 0 0 0 0 0 1 0]\n",
            "[2 0 0 0 0 0 0 0 1]\n",
            "[2 0 0 0 0 0 0 0 1 0]\n",
            "[2 0 0 1 0 0 1 0 0 1 0]\n",
            "[2 0 1 0 0 0 0 0 0 0 0 0]\n",
            "val_acc=0.4921875\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[1 0 1]\n",
            "[2 1 0 1]\n",
            "[2 0 0 1 0]\n",
            "[2 0 1 0 1 0]\n",
            "[2 0 0 0 0 1 0]\n",
            "[2 0 0 0 0 1 0 0]\n",
            "[2 1 0 0 0 1 1 1 0]\n",
            "[2 0 1 0 0 0 1 1 0 0]\n",
            "[2 0 0 0 0 0 0 0 0 1 1]\n",
            "[2 0 1 0 0 0 0 0 1 1 1 1]\n",
            "val_acc=0.6328125\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[0 1]\n",
            "[2 1 0]\n",
            "[2 1 1 0]\n",
            "[2 0 0 1 1]\n",
            "[2 1 0 1 0 1]\n",
            "[2 1 0 0 0 0 0]\n",
            "[1 0 0 0 0 1 0 0]\n",
            "[2 0 1 0 0 1 0 0 1]\n",
            "[2 0 0 0 0 0 0 1 0 0]\n",
            "[2 0 0 0 0 1 0 0 0 0 0]\n",
            "[2 0 1 0 0 1 0 0 0 0 0 0]\n",
            "val_acc=0.6171875\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[0 0 0]\n",
            "[2 0 1 0]\n",
            "[2 0 1 1 1]\n",
            "[2 1 0 0 0 0]\n",
            "[2 0 1 0 0 1 1]\n",
            "[2 1 0 0 0 0 0 0]\n",
            "[2 1 1 0 1 1 0 0 1]\n",
            "[2 0 0 1 0 0 0 1 0 0]\n",
            "[2 0 0 0 0 0 1 1 1 0 0]\n",
            "[2 1 1 1 1 1 0 0 0 0 0 0]\n",
            "val_acc=0.6171875\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 0 0]\n",
            "[2 0 0 0]\n",
            "[2 0 0 1 0]\n",
            "[2 0 0 1 0 1]\n",
            "[2 1 0 0 0 0 0]\n",
            "[2 1 0 0 0 0 1 0]\n",
            "[1 1 0 0 0 0 0 0 0]\n",
            "[2 0 0 0 1 0 1 0 0 1]\n",
            "[2 0 0 1 0 0 0 0 0 0 0]\n",
            "[2 1 0 0 0 0 0 1 0 0 0 1]\n",
            "val_acc=0.6484375\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 0 0]\n",
            "[2 0 0 0]\n",
            "[0 1 1 0 0]\n",
            "[2 0 1 0 0 1]\n",
            "[2 0 0 0 0 0 0]\n",
            "[2 0 0 0 1 0 0 0]\n",
            "[2 0 0 1 0 1 1 0 0]\n",
            "[1 0 1 0 0 1 0 0 1 0]\n",
            "[2 1 0 0 0 0 0 0 0 0 0]\n",
            "[2 0 0 0 0 0 0 0 0 0 0 0]\n",
            "val_acc=0.6015625\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[2 0 1]\n",
            "[2 1 0 0]\n",
            "[2 0 1 1 1]\n",
            "[2 0 1 0 1 0]\n",
            "[2 0 1 1 0 0 0]\n",
            "[1 0 0 0 0 0 1 0]\n",
            "[2 0 0 0 0 0 0 0 0]\n",
            "[2 0 1 0 0 1 1 0 0 0]\n",
            "[2 0 1 0 0 0 1 1 0 0 0]\n",
            "[2 0 0 0 0 0 0 0 0 0 0 0]\n",
            "val_acc=0.6484375\n",
            "--------------------------------------------------------------------------------\n",
            "[2]\n",
            "[2 0]\n",
            "[1 1 1]\n",
            "[2 1 0 0]\n",
            "[2 1 1 1 1]\n",
            "[2 0 0 1 1 0]\n",
            "[2 0 0 0 0 0 0]\n",
            "[2 0 0 0 1 0 0 1]\n",
            "[2 0 0 0 0 0 1 0 0]\n",
            "[2 0 1 0 0 1 1 0 0 0]\n",
            "[2 0 0 0 0 0 0 0 0 0 0]\n",
            "[2 0 0 0 1 0 0 0 0 0 0 0]\n",
            "val_acc=0.6171875\n",
            "--------------------------------------------------------------------------------\n",
            "Epoch 18: Eval\n",
            "valid_accuracy: 0.6681\n",
            "test_accuracy: 0.6241\n",
            "epoch=19\tch_step=0\tloss=0.979468\tlr=0.0488\t|g|=1.1948\tacc=0.6641\ttime=3.35it/s\n",
            "epoch=19\tch_step=50\tloss=1.520884\tlr=0.0488\t|g|=1.3773\tacc=0.4062\ttime=4.88it/s\n",
            "epoch=19\tch_step=100\tloss=1.182987\tlr=0.0488\t|g|=1.9052\tacc=0.5703\ttime=4.86it/s\n",
            "epoch=19\tch_step=150\tloss=1.053106\tlr=0.0488\t|g|=1.2988\tacc=0.6250\ttime=4.58it/s\n",
            "epoch=19\tch_step=200\tloss=1.016804\tlr=0.0488\t|g|=1.1360\tacc=0.6562\ttime=4.70it/s\n",
            "epoch=19\tch_step=250\tloss=0.946241\tlr=0.0488\t|g|=1.3668\tacc=0.6875\ttime=4.47it/s\n",
            "epoch=19\tch_step=300\tloss=1.030743\tlr=0.0488\t|g|=1.5625\tacc=0.6562\ttime=4.46it/s\n",
            "epoch=19\tch_step=350\tloss=1.300303\tlr=0.0488\t|g|=2.4724\tacc=0.5625\ttime=5.55it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AkquC/jMiPhsRPSOiMqvtlIj4XPK4fVc+ETPLX5qHkm5o22nVIk4Mm1nDCnlrS/BQ0rY9JwGzFqzm1pYrNq4giNpbWzYlEXgo6Y9at24dp556KuXl5Rx11FG1exR/+tOf6N27N71796ZPnz688847rFmzhmOOOYbevXvTo0cPnnrqqUZvryXxAHJmRXT5/17OgtcbHkp6TtUc3t+2/Yihm7du5sLfXsht8+ofSrr3v/bmhuM9lHRjXHPNNfTp04eHH36YmTNncu6557JgwQImTZrEzTffTP/+/dm0aRNt27Zl8uTJHHfccYwbN45t27axefPm3BtowbwnYNaC1U0AucrzUTOU9OTJkyktLWXYsGHbDQqXPZR0zTH1jRs3csYZZ9CjRw9Gjx7NokWLauvXHUr6+uuvp3fv3gwcOHC7oaS//vWvA4UbSro+Z599NnPmzNnhUNL1+fOf/1x7E5lBgwaxdu1a3n77bfr3788VV1zBz372MzZs2MCee+7JEUccwe2338748eN57rnn2HvvvXfqubQU3hMwK6IdfWMH6HJDF1Zs/OhQ0p3bd2b2yNk7vd00DyXdGGPHjuUrX/kKjz76KP3792fGjBkcc8wxPPnkk/z+979n5MiRXHHFFZx77rkF2V4xeE/ArAXbFbe2TPNQ0g350pe+VHt/gdmzZ1NSUsI+++zDK6+8Qs+ePfn2t7/NEUccwYsvvsiKFSvo2LEj3/zmN/nGN77B/Pnz895OS+QkYNaCjeg5gsn/NpnO7TsjROf2nZn8b5ObdJezNA8lXeMrX/kKZWVllJWVccYZZzB+/HjmzZtHeXk5Y8eOrT2ncMMNN9CjRw/Ky8tp3bo1J5xwArNnz6ZXr1706dOHe++9l8suu6zB7ewOcg4l3dw8lLR93HkoaWuqZh1K2szMPr6cBMzMUsxJwKwIWtphWNt9FPq94yRg1szatm3L2rVrnQis0SKCtWvX0rZt24Kt078TMGtmZWVlVFVVNeoSRrMabdu2paysrGDrcxIwa2atW7ema9euxQ7DDPDhIDOzVHMSMDNLMScBM7MUyysJSDpe0hJJSyWNrWd5Z0l/lLRQ0mxJZVnLtklakDymFzJ4MzNrmnzuMdwKuBkYAlQBcyVNj4js2/tMAu6KiDslDQKuA85Jlr0bEb0LHLeZmRVAPnsC/YClEbEsIrYA9wCn1KlzKDAzmZ5Vz3IzM2uB8kkCBwKrsuarkrJsfwe+mkyfBuwtaf9kvq2kSklzJJ1a3wYkjUrqVPraaTOz5lOoE8P/BQyQ9DdgALAa2JYs65yMbHc2cIOkz9ZtHBGTI6IiIipKS0sLFJKZmeWSz4/FVgMHZc2XJWW1IuI1kj0BSZ8CTo+IDcmy1cnfZZJmA32AV5ocuZmZNVk+ewJzgW6SukpqAwwHtrvKR1KJpJp1XQVMSco7SNqrpg7QH8g+oWxmZkWUMwlExAfAxcAM4AXgvohYJGmCpJOTagOBJZJeAjoCNfe+6w5USvo7mRPG19e5qsjMzIrIdxYzM/sY8J3FzMys0ZwEzMxSzEnAzCzFnATMzFLMScDMLMWcBMzMUsxJwMwsxZwEzMxSzEnAzCzFnATMzFLMScDMLMWcBMzMUsxJwMwsxZwEzMxSzEnAzCzFnATMzFIsryQg6XhJSyQtlTS2nuWdJf1R0kJJsyWVZS07T9LLyeO8QgZvZmZNkzMJSGoF3AycABwKnCXp0DrVJgF3RUQ5MAG4Lmm7H3ANcCTQD7hGUofChW9mZk2Rz55AP2BpRCyLiC3APcApdeocCsxMpmdlLT8OeDwi1kXEeuBx4Pimh21mZoWQTxI4EFiVNV+VlGX7O/DVZPo0YG9J++fZ1szMiqRQJ4b/Cxgg6W/AAGA1sC3fxpJGSaqUVFldXV2gkMzMLJd8ksBq4KCs+bKkrFZEvBYRX42IPsC4pGxDPm2TupMjoiIiKkpLSxv5FMzMbGflkwTmAt0kdZXUBhgOTM+uIKlEUs26rgKmJNMzgKGSOiQnhIcmZWZm1gLkTAIR8QFwMZkP7xeA+yJikaQJkk5Oqg0Elkh6CegITEzargOuJZNI5gITkjIzM2sBFBHFjmE7FRUVUVlZWewwzMx2K5LmRURFY9v5F8NmZinmJGBmlmJOAmZmKeYkYGaWYk4CZmYp5iRgZpZiTgJmZinmJGBmlmJOAmZmKeYkYGaWYk4CZmYp5iRgZpZiTgJmZinmJGBmlmJOAmZmKeYkYGaWYk4CZmYpllcSkHS8pCWSlkoaW8/yTpJmSfqbpIWSTkzKu0h6V9KC5HFLoZ+AmZntvD1zVZDUCrgZGAJUAXMlTY+IxVnVvkvm3sO/kHQo8CjQJVn2SkT0LmzYZmZWCPnsCfQDlkbEsojYAtwDnFKnTgD7JNPtgdcKF6KZme0q+SSBA4FVWfNVSVm28cDXJVWR2Qu4JGtZ1+Qw0Z8kfam+DUgaJalSUmV1dXX+0ZuZWZMU6sTwWcAdEVEGnAj8WtIewBqgU0T0Aa4A7pa0T93GETE5IioioqK0tLRAIZmZWS75JIHVwEFZ82VJWbYLgfsAIuIZoC1QEhHvR8TapHwe8ApwcFODNjOzwsgnCcwFuknqKqkNMByYXqfOSmAwgKTuZJJAtaTS5MQykj4DdAOWFSp4MzNrmpxXB0XEB5IuBmYArYApEbFI0gSgMiKmA2OA2ySNJnOSeGREhKRjgAmStgIfAv8REet22bMxM7NGUUQUO4btVFRURGVlZbHDMDPbrUiaFxEVjW3nXwybmaWYk4CZWYo5CZiZpZiTgJlZijkJmJmlmJOAmVmKOQmYmaWYk4CZWYo5CZiZpZiTgJlZijkJmJmlmJOAmVmKOQmYmaWYk4CZWYo5CZiZpZiTgJlZijkJmJmlWF5JQNLxkpZIWippbD3LO0maJelvkhZKOjFr2VVJuyWSjitk8GZm1jQ57zGc3Cj+ZmAIUAXMlTQ9IhZnVfsucF9E/ELSocCjQJdkejhwGPBp4AlJB0fEtkI/ETMza7x89gT6AUsjYllEbAHuAU6pUyeAfZLp9sBryfQpwD0R8X5EvAosTdZnZmYtQD5J4EBgVdZ8VVKWbTzwdUlVZPYCLmlEWySNklQpqbK6ujrP0M3MrKkKdWL4LOCOiCgDTgR+LSnvdUfE5IioiIiK0tLSAoVkZma55DwnAKwGDsqaL0vKsl0IHA8QEc9IaguU5NnWzMyKJJ9v63OBbpK6SmpD5kTv9Dp1VgKDASR1B9oC1Um94ZL2ktQV6AY8W6jgzcysaXLuCUTEB5IuBmYArYApEbFI0gSgMiKmA2OA2ySNJnOSeGREBLBI0n3AYuAD4D99ZZCZWcuhzGd1y1FRURGVlZXFDsPMbLciaV5EVDS2nX8xbGaWYk4CZmYp5iRgZpZiTgJmZinmJGBmlmJOAmZmKeYkYGaWYk4CZmYp5iRgZpZiTgJmZinmJGBmlmJOAmZmKeYkYGaWYk4CZmYp5iRgZpZiTgJmZinmJGBmlmJ5JQFJx0taImmppLH1LP+ppAXJ4yVJG7KWbctaVvfexGZmVkQ57zEsqRVwMzAEqALmSpoeEYtr6kTE6Kz6lwB9slbxbkT0LlzIZmZWKPnsCfQDlkbEsojYAtwDnLKD+mcB0woRnJmZ7Vr5JIEDgVVZ81VJ2UdI6gx0BWZmFbeVVClpjqRTG2g3KqlTWV1dnWfoZmbWVIU+MTwceCAitmWVdY6ICuBs4AZJn63bKCImR0RFRFSUlpYWOCQzM2tIPklgNXBQ1nxZUlaf4dQ5FBQRq5O/y4DZbH++wMzMiiifJDAX6Capq6Q2ZD7oP3KVj6RDgA7AM1llHSTtlUyXAP2BxXXbmplZceS8OigiPpB0MTADaAVMiYhFkiYAlRFRkxCGA/dERGQ17w7cKulDMgnn+uyriszMrLi0/Wd28VVUVERlZWWxwzAz261Impecf20U/2LYzCzFnATMzFLMScDMLMWcBMzMUsxJwMwsxZwEzMxSzEnAzCzFnATMzFLMScDMLMWcBMzMUsxJwMwsxZwEzMxSzEnAzCzFnATMzFLMScDMLMWcBMzMUsxJwMwsxfJKApKOl7RE0lJJY+tZ/lNJC5LHS5I2ZC07T9LLyeO8QgZvZmZNk/Mew5JaATcDQ4AqYK6k6dn3Co6I0Vn1LwH6JNP7AdcAFUAA85K26wv6LMzMbKfksyfQD1gaEcsiYgtwD3DKDuqfBUxLpo8DHo+IdckH/+PA8U0J2MzMCiefJHAgsCprviop+whJnYGuwMzGtJU0SlKlpMrq6up84jYzswIo9Inh4cADEbGtMY0iYnJEVERERWlpaYFDMjOzhuSTBFYDB2XNlyVl9RnOPw8FNbatmZk1s3ySwFygm6SuktqQ+aCfXreSpEOADsAzWcUzgKGSOkjqAAxNyszMrAXIeXVQRHwg6WIyH96tgCkRsUjSBKAyImoSwnDgnoiIrLbrJF1LJpEATIiIdYV9CmZmtrOU9ZndIlRUVERlZWWxwzAz261ImhcRFY1t518Mm5mlmJOAmVmKOQmYmaWYk4CZWYo5CZiZpZiTgJlZijkJmJmlmJOAmVmKOQmYmaWYk4CZWYo5CZiZpZiTgJlZijkJmJmlmJOAmVmKOQmYmaWYk4CZWYrllQQkHS9piaSlksY2UOdMSYslLZJ0d1b5NkkLksdHbktpZmbFk/P2kpJaATcDQ4AqYK6k6RGxOKtON+AqoH9ErJf0L1mreDciehc4bjMzK4B89gT6AUsjYllEbAHuAU6pU+ebwM0RsR4gIt4sbJhmZrYr5JMEDgRWZc1XJWXZDgYOlvQXSXMkHZ+1rK2kyqT81Po2IGlUUqeyurq6UU/AzMx2Xs7DQY1YTzdgIFAGPCmpZ0RsADpHxGpJnwFmSnouIl7JbhwRk4HJkLnRfIFiMjOzHPLZE1gNHJQ1X5aUZasCpkfE1oh4FXiJTFIgIlYnf5cBs4E+TYzZzMwKJJ8kMBfoJqmrpDbAcKDuVT4Pk9kLQFIJmcNDyyR1kLRXVnl/YDFmZtYi5DwcFBEfSLoYmAG0AqZExCJJE4DKiJieLBsqaTGwDfhWRKyV9AXgVkkfkkk412dfVWRmZsWliJZ1CL6ioiIqKyuLHYbZ9qZOhXHjYOVK6NQJJk6EESOKHZVZLUnzIqKise0KdWLY7ONr6lQYNQo2b87Mr1iRmQcnAtvtedgIs1zGjftnAqixeXOm3Gw35yRglsvKlY0rN9uNOAmY5dKpU+PKzXYjTgJmuUycCO3abV/Wrl2m3Gw35yRglsuIETB5MnTuDFLm7+TJPilsHwu+OsgsHyNG+EPfPpa8J2BmlmJOAmZmKeYkYGaWYk4CZmYp5iRgZpZiLW4AOUnVwIodVCkB3mqmcHaG42sax9c0jq9pduf4OkdEaWNX2OKSQC6SKndmpLzm4viaxvE1jeNrmjTG58NBZmYp5iRgZpZiu2MSmFzsAHJwfE3j+JrG8TVN6uLb7c4JmJlZ4eyOewJmZlYgTgJmZilW1CQgaYqkNyU9n1XWW9IcSQskVUrq10Db8yS9nDzOyyrvK+k5SUsl/UySmju+pM4zkhZJWihpWNayOyS9mrRfIKl3c8eX1NuWFcP0rPKukv6a9N+9kto0d3ySvpwV2wJJ70k6NVm2q/uvV/LaPSfpd5L2aaDt8ZKWJP00Nqt8V/dfzvgkHSRplqTFyXvwsqxl4yWtzuq/E5s7vqTe8qTOAkmVWeX7SXo8+b9+XFKH5o5P0ufrvP/elnR5sqyQ/Vfv65RvH6hQn4ERUbQHcAxwOPB8VtljwAnJ9InA7Hra7QcsS/52SKY7JMueBY4CBPyhZl3NHN/BQLdk+tPAGmDfZP4O4GvF7L9k2aYGyu8DhifTtwAXFSO+Oq/1OqBdM/XfXGBAMn0BcG097VoBrwCfAdoAfwcObab+yye+A4DDk+m9gZey4hsP/Fcx+y9Zthwoqaf8R8DYZHos8MNixFfntX6dzA+xCt1/9b5O+fQBBfwMLOqeQEQ8SeYffLtioCY7twdeq6fpccDjEbEuItYDjwPHSzoA2Cci5kSmN+4CTm3u+CLipYh4OZl+DXgTaPQv+XZVfA1JvjEMAh5Iiu6kCP1Xx/zwjywAAAXPSURBVNeAP0TE5hz1Gq2B+A4GnkymHwdOr6dpP2BpRCyLiC3APcApzdR/OeOLiDURMT+Zfgd4AThwZ+ModHw5nEKm36BI/VfHYOCViNjRKAY7ZQevUz59ULDPwJZ4TuBy4MeSVgGTgKvqqXMgsCprviopOzCZrlve3PHVSg53tCHzzbHGRGUOE/1U0l5Fiq9tcjhmTs2hFmB/YENEfJDMF73/gOHAtDplu7L/FpH5JwQ4AzionjoNvf+ao//yia+WpC5AH+CvWcUXJ/03pSmHW5oYXwCPSZonaVRWeceIWJNMvw50LFJ8Nep7/xW8/+q8Tvn0QcE+A1tiErgIGB0RBwGjgV8VOZ668o4vycq/Bs6PiA+T4quAQ4AjyOzKfbtI8XWOzM/PzwZukPTZAsfR1Phq+q8nMCOreFf33wXA/5E0j8wu+pYCr7+p8o5P0qeAB4HLI+LtpPgXwGeB3mQOU/6kSPF9MSIOB04A/lPSMXUrJN9kC30Ne2P6rw1wMnB/VnHB+6+B1wnYZX2wnZaYBM4DfpNM309m17uu1WyfwcuSstXJdN3y5o6P5ITT74FxETGnpjzZBYyIeB+4vaH2uzq+iFid/F0GzCbzLWQtsK+kmtuOFq3/EmcCD0XE1pqCXd1/EfFiRAyNiL5kvgG+Uk+1ht5/u7z/8owPSa3JfLBMjYjfZLV/IyK2JV9KbqM4/Zf9/nsTeCgrjjeS5F/zJeDNYsSXOAGYHxFvZLUvaP818Drl0wcF+wxsiUngNWBAMj0IeLmeOjOAoZI6JLtjQ4EZyS7U25KOSo7Pngv8trnjS75BPATcFREP1FlW8+KKzLG65+u2b4b4OtQcRpFUAvQHFiffOmaROQ4PmQ/sZu+/LGdRZ1d8V/efpH9J/u4BfJfMyd265gLdlLkSqA2ZQwbTm6P/8okv6ZtfAS9ExP/UWXZA1uxpFKH/JH1S0t4102T+f2vimE6m36BI/Zelwfdfokn9t4PXKZ8+KNxnYCHOcu/sg0wHrwG2kjl2dSHwRWAemSsu/gr0TepWAL/MansBsDR5nJ9VXkHmhXkF+L8kv4puzviArydtFmQ9eifLZgLPJTH+P+BTRYjvC0kMf0/+Xpi1zs+QubpgKZlv6nsV6fXtQuYbzB511rmr++8yMldpvARcX/P+IXOV16NZbU9M6rxCZm+vufovZ3xJHwewMOv9d2Ky7NdJ/y0k82FzQBHi+0zy+v+dzDH67P7bH/gjmS8HTwD7Fen1/SSZPbv2ddZZyP6r93VqqA/q+R8pyGegh40wM0uxlng4yMzMmomTgJlZijkJmJmlmJOAmVmKOQmYmaWYk4B9rEnalPztIunsAq/7O3Xmny7k+s2ag5OApUUXMkNk5C3rl78N2S4JRMQXGhmTWdE5CVhaXA98KRkDfrSkVpJ+LGluMhjYvwNIGijpKWXusbA4KXs4GehsUc1gZ5KuBz6RrG9qUlaz16Fk3c8n47oPy1r3bEkPSHpR0tTkV51mRZPrm47Zx8VYMuPAnwSQfJhvjIgjkiE0/iLpsaTu4UCPiHg1mb8gItZJ+gQwV9KDETFW0sURUd9Nbb5KZoCxXkBJ0qZm+OI+wGFkhs/4C5khO/5c+Kdrlh/vCVhaDQXOlbSAzPAV+wPdkmXPZiUAgEsl/R2YQ2bQrm7s2BeBaZEZaOwN4E9kRj2tWXdVZAYgW0DmMJVZ0XhPwNJKwCURMWO7Qmkg8I8688cCR0fEZkmzgbZN2O77WdPb8P+gFZn3BCwt3iEzfnyNGcBFyVC+SDo4GdGyrvbA+iQBHELmtn01tta0r+MpYFhy3qGUzG0Ony3IszArMH8LsbRYCGxLDuvcAdxI5lDM/OTkbDX134bvf4H/kPQCsITMIaEak4GFkuZHxIis8oeAo8mMkhnAlRHxepJEzFoUjyJqZpZiPhxkZpZiTgJmZinmJGBmlmJOAmZmKeYkYGaWYk4CZmYp5iRgZpZi/x9Iq+IT09RNdQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 19: Training controller\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:145: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ctrl_step=1\tloss=-2.294\tent=42.62\tlr=0.0010\t|g|=0.0376\tacc=0.6016\tbl=0.65\ttime=1.01it/s\n",
            "ctrl_step=3\tloss=2.232\tent=43.48\tlr=0.0010\t|g|=0.0770\tacc=0.6953\tbl=0.65\ttime=1.03it/s\n",
            "ctrl_step=5\tloss=4.337\tent=42.47\tlr=0.0010\t|g|=0.3133\tacc=0.7656\tbl=0.65\ttime=1.02it/s\n",
            "ctrl_step=7\tloss=0.915\tent=43.29\tlr=0.0010\t|g|=0.1182\tacc=0.6719\tbl=0.66\ttime=1.02it/s\n",
            "ctrl_step=9\tloss=0.913\tent=43.09\tlr=0.0010\t|g|=0.0369\tacc=0.6719\tbl=0.65\ttime=1.03it/s\n",
            "ctrl_step=11\tloss=0.459\tent=43.88\tlr=0.0010\t|g|=0.0593\tacc=0.6641\tbl=0.66\ttime=1.02it/s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Traceback (most recent call last):\n"
          ],
          "name": "stderr"
        }
      ]
    }
  ]
}