From 5952d7bcb305ebb51e6ca2aa143c0ddf42cfc5ca Mon Sep 17 00:00:00 2001 From: jzhang533 Date: Fri, 28 Aug 2020 13:11:45 +0800 Subject: [PATCH] dynamic graph example added (#876) --- .../dynamic_graph/dynamic_graph.ipynb | 258 ++++++++++++++++++ 1 file changed, 258 insertions(+) create mode 100644 paddle2.0_docs/dynamic_graph/dynamic_graph.ipynb diff --git a/paddle2.0_docs/dynamic_graph/dynamic_graph.ipynb b/paddle2.0_docs/dynamic_graph/dynamic_graph.ipynb new file mode 100644 index 00000000..fec70c5c --- /dev/null +++ b/paddle2.0_docs/dynamic_graph/dynamic_graph.ipynb @@ -0,0 +1,258 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 动态图\n", + "\n", + "从飞桨开源框架2.0beta版本开始，飞桨默认为用户开启了动态图模式。在这种模式下，每次执行一个运算，可以立即得到结果（而不是事先定义好网络结构，然后再执行）。\n", + "\n", + "在动态图模式下，您可以更加方便的组织代码，更容易的调试程序，本示例教程将向你介绍飞桨的动态图的使用。\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 设置环境\n", + "\n", + "我们将使用飞桨2.0beta版本，并确认已经开启了动态图模式。" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0.0\n", + "edf5f3173a25ae2230e9619ab5426317b4bd7cde\n" + ] + } + ], + "source": [ + "import paddle\n", + "import paddle.nn.functional as F\n", + "import numpy as np\n", + "\n", + "paddle.disable_static()\n", + "print(paddle.__version__)\n", + "print(paddle.__git_commit__)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 基本用法\n", + "\n", + "在动态图模式下，您可以直接运行一个飞桨提供的API，它会立刻返回结果到python。不再需要首先创建一个计算图，然后再给定数据去运行。" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-1.4396907 -0.59741247]\n", + " [ 1.4717171 -0.06998838]\n", + " [-1.2790705 0.4278928 ]\n", + " [ 1.1862146 -1.895377 ]]\n", + "[1. 2.]\n", + "[[-0.4396907 1.4025875 ]\n", + " [ 2.4717171 1.9300116 ]\n", + " [-0.2790705 2.4278927 ]\n", + " [ 2.1862144 0.10462296]]\n", + "[-2.6345158 1.3317404 -0.4232849 -2.6045394]\n" + ] + } + ], + "source": [ + "a = paddle.randn([4, 2])\n", + "b = paddle.arange(1, 3, dtype='float32')\n", + "\n", + "print(a.numpy())\n", + "print(b.numpy())\n", + "\n", + "c = a + b\n", + "print(c.numpy())\n", + "\n", + "d = paddle.matmul(a, b)\n", + "print(d.numpy())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 使用python的控制流\n", + "\n", + "动态图模式下，您可以使用python的条件判断和循环，这类控制语句来执行神经网络的计算。（不再需要`cond`, `loop`这类OP)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 +> [5 6 7]\n", + "1 -> [-3 -3 -3]\n", + "2 -> [-3 -1 3]\n", + "3 +> [ 5 13 33]\n", + "4 -> [-3 11 75]\n", + "5 +> [ 5 37 249]\n", + "6 -> [ -3 59 723]\n", + "7 -> [ -3 123 2181]\n", + "8 -> [ -3 251 6555]\n", + "9 -> [ -3 507 19677]\n" + ] + } + ], + "source": [ + "a = paddle.to_tensor(np.array([1, 2, 3]))\n", + "b = paddle.to_tensor(np.array([4, 5, 6]))\n", + "\n", + "for i in range(10):\n", + " r = paddle.rand([1,])\n", + " if r > 0.5:\n", + " c = paddle.pow(a, i) + b\n", + " print(\"{} +> {}\".format(i, c.numpy()))\n", + " else:\n", + " c = paddle.pow(a, i) - b\n", + " print(\"{} -> {}\".format(i, c.numpy()))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 构建更加灵活的网络\n", + "\n", + "- 使用动态图可以用来创建更加灵活的网络，比如根据控制流选择不同的分支网络，和方便的构建权重共享的网络。接下来我们来看一个具体的例子，在这个例子中，第二个线性变换只有0.5的可能性会运行。\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "class MyModel(paddle.nn.Layer):\n", + " def __init__(self, input_size, hidden_size):\n", + " super(MyModel, self).__init__()\n", + " self.linear1 = paddle.nn.Linear(input_size, hidden_size)\n", + " self.linear2 = paddle.nn.Linear(hidden_size, hidden_size)\n", + " self.linear3 = paddle.nn.Linear(hidden_size, 1)\n", + "\n", + " def forward(self, inputs):\n", + " x = self.linear1(inputs)\n", + " x = F.relu(x)\n", + "\n", + " if paddle.rand([1,]) > 0.5: \n", + " x = self.linear2(x)\n", + " x = F.relu(x)\n", + "\n", + " x = self.linear3(x)\n", + " \n", + " return x " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 [87.28088]\n", + "200 [57.775795]\n", + "400 [42.70884]\n", + "600 [45.509155]\n", + "800 [29.966158]\n", + "1000 [11.885025]\n", + "1200 [16.888378]\n", + "1400 [3.5780585]\n", + "1600 [5.3149533]\n", + "1800 [4.501356]\n", + "2000 [3.022315]\n", + "2200 [1.7214009]\n", + "2400 [0.3694626]\n", + "2600 [0.31249344]\n", + "2800 [0.1450614]\n" + ] + } + ], + "source": [ + "total_data, batch_size, input_size, hidden_size = 1000, 64, 128, 256\n", + "\n", + "x_data = np.random.randn(total_data, input_size).astype(np.float32)\n", + "y_data = np.random.randn(total_data, 1).astype(np.float32)\n", + "\n", + "model = MyModel(input_size, hidden_size)\n", + "\n", + "loss_fn = paddle.nn.MSELoss(reduction='sum')\n", + "optimizer = paddle.optimizer.SGD(learning_rate=0.0001, \n", + " parameter_list=model.parameters())\n", + "\n", + "for t in range(200 * (total_data // batch_size)):\n", + " idx = np.random.choice(total_data, batch_size, replace=False)\n", + " x = paddle.to_tensor(x_data[idx,:])\n", + " y = paddle.to_tensor(y_data[idx,:])\n", + " y_pred = model(x)\n", + "\n", + " loss = loss_fn(y_pred, y)\n", + " if t % 200 == 0:\n", + " print(t, loss.numpy())\n", + "\n", + " loss.backward()\n", + " optimizer.minimize(loss)\n", + " model.clear_gradients()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The end\n", + "\n", + "可以看到使用动态图带来了更灵活易用的方式来组网和训练。" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}