notebooks/14-2.pytorch.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "TensorFlow 2.3 on Python 3.6 (CUDA 10.1)",
      "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": "3.6.9"
    },
    "colab": {
      "name": "14-2.pytorch.ipynb",
      "provenance": []
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eiDEYKpY6xvP"
      },
      "source": [
        "# 파이토치 *헬로, 월드!*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8jvIdBsq6xvT"
      },
      "source": [
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/rickiepark/dl-illustrated/blob/master/notebooks/14-2.pytorch.ipynb)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EUp6cauV6xvT"
      },
      "source": [
        "import torch"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WZidIGxY6xvT"
      },
      "source": [
        "x = torch.zeros(28, 28, 1, dtype=torch.uint8)\n",
        "y = torch.randn(28, 28, 1, dtype=torch.float32)"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "iR508tZj6xvU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e18d2e78-7614-4c96-86ea-1196db9a65ed"
      },
      "source": [
        "print(x.shape)\n",
        "print(x.dtype)\n",
        "print(y.shape)\n",
        "print(y.dtype)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([28, 28, 1])\n",
            "torch.uint8\n",
            "torch.Size([28, 28, 1])\n",
            "torch.float32\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a9qtv42j6xvU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "63a1d119-f5c1-4747-bf4e-405f1c2310b3"
      },
      "source": [
        "x = torch.zeros(3, 3, dtype=torch.float32, requires_grad=True)\n",
        "\n",
        "y = x - 4\n",
        "z = y**3 * 6\n",
        "out = z.mean()\n",
        "\n",
        "out.backward()\n",
        "\n",
        "print(x.grad)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[32., 32., 32.],\n",
            "        [32., 32., 32.],\n",
            "        [32., 32., 32.]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wPIdVoTU6xvV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b6edd4a2-a7e8-486b-af0e-52a1dc96a3d2"
      },
      "source": [
        "# 입력과 출력으로 랜덤한 텐서를 정의합니다.\n",
        "x = torch.randn(32, 784, requires_grad=True)\n",
        "y = torch.randint(low=0, high=10, size=(32,))\n",
        "\n",
        "# Sequential 클래스를 사용해 모델을 정의합니다.\n",
        "model = torch.nn.Sequential(\n",
        "    torch.nn.Linear(784, 100),\n",
        "    torch.nn.Sigmoid(),\n",
        "    torch.nn.Linear(100, 10),\n",
        "    torch.nn.LogSoftmax(dim=1)\n",
        ")\n",
        "\n",
        "# 옵티마이저와 손실 함수를 정의합니다.\n",
        "optimizer = torch.optim.Adam(model.parameters())\n",
        "loss_fn = torch.nn.NLLLoss()\n",
        "\n",
        "for step in range(1000):\n",
        "    # 정방향 계산으로 예측을 만듭니다.\n",
        "    y_hat = model(x)\n",
        "    # 손실을 계산합니다.\n",
        "    loss = loss_fn(y_hat, y)\n",
        "    # 역전파를 수행하기 전에 그레이디언트를 0으로 만듭니다.\n",
        "    optimizer.zero_grad()\n",
        "    # 손실에 대한 그레이디언트를 계산합니다.\n",
        "    loss.backward()\n",
        "    # 결과를 출력합니다.\n",
        "    print('Step: {:4d} - loss: {:0.4f}'.format(step+1, loss.item()))\n",
        "    # 모델 파라미터를 업데이트합니다.\n",
        "    optimizer.step()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Step:    1 - loss: 2.3298\n",
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}