CS146_Winter2021_PS3.ipynb

{
  "nbformat": 4,
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  "metadata": {
    "colab": {
      "name": "Copy of CS146-Winter2021-PS3.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/alyssamw/cs35l/blob/master/CS146_Winter2021_PS3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZQ57FP3Zdq19"
      },
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import torch\n",
        "from torch.utils.data import TensorDataset, DataLoader\n",
        "from PIL import Image"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PyqBmQZE7Uqw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a879055a-d591-4092-a329-25de874ea397"
      },
      "source": [
        "# To add your own Drive Run this cell.\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q4RwWjtN2sf4"
      },
      "source": [
        "######################################################################\n",
        "# OneLayerNetwork\n",
        "######################################################################\n",
        "\n",
        "class OneLayerNetwork(torch.nn.Module):\n",
        "    def __init__(self):\n",
        "        super(OneLayerNetwork, self).__init__()\n",
        "\n",
        "        ### ========== TODO : START ========== ###\n",
        "        ### part d: implement OneLayerNetwork with torch.nn.Linear\n",
        "        self.layer = torch.nn.Linear(784, 3)\n",
        "        ### ========== TODO : END ========== ###\n",
        "\n",
        "    def forward(self, x):\n",
        "        # x.shape = (n_batch, n_features)\n",
        "\n",
        "        ### ========== TODO : START ========== ###\n",
        "        ### part d: implement the foward function\n",
        "        loss = torch.nn.CrossEntropyLoss()\n",
        "        input = x\n",
        "        outputs = self.layer(x)\n",
        "        #outputs = loss(input,target)\n",
        "\n",
        "        ### ========== TODO : END ========== ###\n",
        "        return outputs"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JRbW6WPv25tE"
      },
      "source": [
        "######################################################################\n",
        "# TwoLayerNetwork\n",
        "######################################################################\n",
        "\n",
        "class TwoLayerNetwork(torch.nn.Module):\n",
        "    def __init__(self):\n",
        "        super(TwoLayerNetwork, self).__init__()\n",
        "        ### ========== TODO : START ========== ###\n",
        "        ### part g: implement TwoLayerNetwork with torch.nn.Linear\n",
        "        self.layer1 = torch.nn.Linear(784, 400)\n",
        "        self.layer2 = torch.nn.Linear(400, 3)\n",
        "        ### ========== TODO : END ========== ###\n",
        "\n",
        "    def forward(self, x):\n",
        "        # x.shape = (n_batch, n_features)\n",
        "\n",
        "        ### ========== TODO : START ========== ###\n",
        "        ### part g: implement the foward function\n",
        "        input = x\n",
        "        out1 = self.layer1(x)\n",
        "        outputs = self.layer2(out1)\n",
        "        ### ========== TODO : END ========== ###\n",
        "        return outputs"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I06zHwgTeSs3"
      },
      "source": [
        "# load data from csv\n",
        "# X.shape = (n_examples, n_features), y.shape = (n_examples, )\n",
        "def load_data(filename):\n",
        "    data = np.loadtxt(filename)\n",
        "    y = data[:, 0].astype(int)\n",
        "    X = data[:, 1:].astype(np.float32) / 255\n",
        "    return X, y"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JvP1DJfteUOn"
      },
      "source": [
        "# plot one example\n",
        "# x.shape = (features, )\n",
        "def plot_img(x):\n",
        "    x = x.reshape(28, 28)\n",
        "    img = Image.fromarray(x*255)\n",
        "    plt.figure()\n",
        "    plt.imshow(img)\n",
        "    return"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RqaCgw9Z4Eef"
      },
      "source": [
        "def evaluate_loss(model, criterion, dataloader):\n",
        "    model.eval()\n",
        "    total_loss = 0.0\n",
        "    for batch_X, batch_y in dataloader:\n",
        "        outputs = model(batch_X)\n",
        "        loss = criterion(outputs, batch_y)\n",
        "        total_loss += loss.item()\n",
        "        \n",
        "    return total_loss / len(dataloader)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uEnwn4sC4F3B"
      },
      "source": [
        "def evaluate_acc(model, dataloader):\n",
        "    model.eval()\n",
        "    total_acc = 0.0\n",
        "    for batch_X, batch_y in dataloader:\n",
        "        outputs = model(batch_X)\n",
        "        predictions = torch.argmax(outputs, dim=1)\n",
        "        total_acc += (predictions==batch_y).sum()\n",
        "        \n",
        "    return total_acc / len(dataloader.dataset)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6OpXc_t7Jc5_"
      },
      "source": [
        "def train(model, criterion, optimizer, train_loader, valid_loader):\n",
        "    train_loss_list = []\n",
        "    valid_loss_list = []\n",
        "    train_acc_list = []\n",
        "    valid_acc_list = []\n",
        "    for epoch in range(1, 31):\n",
        "        model.train()\n",
        "        for batch_X, batch_y in train_loader:\n",
        "            ### ========== TODO : START ========== ###\n",
        "            ### part f: implement the training process\n",
        "            y_pred = model.forward(batch_X)\n",
        "            loss = criterion(y_pred, batch_y)\n",
        "\n",
        "            optimizer.zero_grad()\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "            ### ========== TODO : END ========== ###\n",
        "            \n",
        "        train_loss = evaluate_loss(model, criterion, train_loader)\n",
        "        valid_loss = evaluate_loss(model, criterion, valid_loader)\n",
        "        train_acc = evaluate_acc(model, train_loader)\n",
        "        valid_acc = evaluate_acc(model, valid_loader)\n",
        "        train_loss_list.append(train_loss)\n",
        "        valid_loss_list.append(valid_loss)\n",
        "        train_acc_list.append(train_acc)\n",
        "        valid_acc_list.append(valid_acc)\n",
        "\n",
        "        print(f\"| epoch {epoch:2d} | train loss {train_loss:.6f} | train acc {train_acc:.6f} | valid loss {valid_loss:.6f} | valid acc {valid_acc:.6f} |\")\n",
        "\n",
        "    return train_loss_list, valid_loss_list, train_acc_list, valid_acc_list"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KMmurb4-d39G",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "cellView": "code",
        "outputId": "0f1536b0-75c9-4017-fcf6-c3bafdd9e4eb"
      },
      "source": [
        "#@title\n",
        "######################################################################\n",
        "# main\n",
        "######################################################################\n",
        "\n",
        "def main():\n",
        "\n",
        "    # fix random seed\n",
        "    np.random.seed(0)\n",
        "    torch.manual_seed(0)\n",
        "\n",
        "    # load data with correct file path\n",
        "\n",
        "    ### ========== TODO : START ========== ###\n",
        "    data_directory_path =  \"/content/drive/My Drive/cs146\"\n",
        "    ### ========== TODO : END ========== ###\n",
        "\n",
        "    # X.shape = (n_examples, n_features)\n",
        "    # y.shape = (n_examples, )\n",
        "    X_train, y_train = load_data(os.path.join(data_directory_path, \"ps3_train.csv\"))\n",
        "    X_valid, y_valid = load_data(os.path.join(data_directory_path, \"ps3_valid.csv\"))\n",
        "    X_test, y_test = load_data(os.path.join(data_directory_path, \"ps3_test.csv\"))\n",
        "\n",
        "    ### ========== TODO : START ========== ###\n",
        "    ### part a: print out three training images with different labels\n",
        "    \n",
        "    for i in range(0,3):\n",
        "      plot_img(X_train[i])\n",
        "    \n",
        "    ### ========== TODO : END ========== ###\n",
        "\n",
        "    print(\"Data preparation...\")\n",
        "\n",
        "    ### ========== TODO : START ========== ###\n",
        "    ### part b: convert numpy arrays to tensors\n",
        "    X_train = torch.tensor(X_train)\n",
        "    X_valid = torch.tensor(X_valid)\n",
        "    X_test = torch.tensor(X_test)\n",
        "\n",
        "    y_train = torch.tensor(y_train)\n",
        "    y_valid = torch.tensor(y_valid)\n",
        "    y_test = torch.tensor(y_test)\n",
        "    \n",
        "    ### ========== TODO : END ========== ###\n",
        "\n",
        "    ### ========== TODO : START ========== ###\n",
        "    ### part c: prepare dataloaders for training, validation, and testing\n",
        "    ###         we expect to get a batch of pairs (x_n, y_n) from the dataloader\n",
        "    train_loader = DataLoader(TensorDataset(X_train, y_train), 10)\n",
        "    valid_loader = DataLoader(TensorDataset(X_valid, y_valid), 10)\n",
        "    test_loader = DataLoader(TensorDataset(X_test, y_test), 10)\n",
        "    \n",
        "    ### ========== TODO : END ========== ###\n",
        "\n",
        "    ### ========== TODO : START ========== ###\n",
        "    ### part e: prepare OneLayerNetwork, criterion, and optimizer\n",
        "    model_one = OneLayerNetwork()\n",
        "    criterion = torch.nn.CrossEntropyLoss()\n",
        "    optimizer = torch.optim.SGD(model_one.parameters(), lr=0.0005)\n",
        "    \n",
        "    ### ========== TODO : END ========== ###\n",
        "\n",
        "    print(\"Start training OneLayerNetwork...\")\n",
        "    results_one = train(model_one, criterion, optimizer, train_loader, valid_loader)\n",
        "    print(\"Done!\")\n",
        "\n",
        "    ### ========== TODO : START ========== ###\n",
        "    ### part h: prepare TwoLayerNetwork, criterion, and optimizer\n",
        "    model_two = TwoLayerNetwork()\n",
        "    criterion = torch.nn.CrossEntropyLoss()\n",
        "    optimizer = torch.optim.SGD(model_two.parameters(), lr=0.0005)\n",
        "    \n",
        "    ### ========== TODO : END ========== ###\n",
        "\n",
        "    print(\"Start training TwoLayerNetwork...\")\n",
        "    results_two = train(model_two, criterion, optimizer, train_loader, valid_loader)\n",
        "    print(\"Done!\")\n",
        "\n",
        "    one_train_loss, one_valid_loss, one_train_acc, one_valid_acc = results_one\n",
        "    two_train_loss, two_valid_loss, two_train_acc, two_valid_acc = results_two\n",
        "\n",
        "    ### ========== TODO : START ========== ###\n",
        "    ### part i: generate a plot to comare one_train_loss, one_valid_loss, two_train_loss, two_valid_loss\n",
        "    plt.figure()\n",
        "    plt.plot(range(0, len(one_train_loss)), one_train_loss, label=\"One Layer Train\")\n",
        "    plt.plot(range(0, len(one_valid_loss)), one_valid_loss, label=\"One Layer Valid\")\n",
        "    plt.plot(range(0, len(two_train_loss)), two_train_loss, label=\"Two Layer Train\")\n",
        "    plt.plot(range(0, len(two_valid_loss)), two_valid_loss, label=\"Two Layer Valid\")\n",
        "    plt.title(\"Loss against Epochs for One and Two Layer Networks\")\n",
        "    plt.xlabel(\"Num Epochs\")\n",
        "    plt.ylabel(\"Loss\")\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "    ### ========== TODO : END ========== ###\n",
        "\n",
        "    ### ========== TODO : START ========== ###\n",
        "    ### part j: generate a plot to comare one_train_acc, one_valid_acc, two_train_acc, two_valid_acc\n",
        "    plt.figure()\n",
        "    plt.plot(range(0, len(one_train_acc)), one_train_acc, label=\"One Layer Train\")\n",
        "    plt.plot(range(0, len(one_valid_acc)), one_valid_acc, label=\"One Layer Valid\")\n",
        "    plt.plot(range(0, len(two_train_acc)), two_train_acc, label=\"Two Layer Train\")\n",
        "    plt.plot(range(0, len(two_valid_acc)), two_valid_acc, label=\"Two Layer Valid\")\n",
        "    plt.title(\"Accuracy against Epochs for One and Two Layer Networks\")\n",
        "    plt.xlabel(\"Num Epochs\")\n",
        "    plt.ylabel(\"Accuracy\")\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "    ### ========== TODO : END ========== ##\n",
        "\n",
        "    ### ========== TODO : START ========== ###\n",
        "    ### part k: calculate the test accuracy\n",
        "    one_test_acc = evaluate_acc(model_one, test_loader)\n",
        "    two_test_acc = evaluate_acc(model_two, test_loader)\n",
        "    print(f\"One Layer Test: {one_test_acc:.6f}, Two Layer Test: {two_test_acc:.6f}\")\n",
        "    ### ========== TODO : END ========== ###\n",
        "\n",
        "    ### ========== TODO : START ========== ###\n",
        "    ### part l: replace the SGD optimizer with the Adam optimizer and do the experiments again\n",
        "    model_one = OneLayerNetwork()\n",
        "    model_two = TwoLayerNetwork()\n",
        "    optimizer = torch.optim.Adam(model_one.parameters(), lr=0.0005)\n",
        "    print(\"Start training OneLayerNetwork with Adam...\")\n",
        "    results_one = train(model_one, criterion, optimizer, train_loader, valid_loader)\n",
        "    print(\"Done!\")\n",
        "\n",
        "    optimizer = torch.optim.Adam(model_two.parameters(), lr=0.0005)\n",
        "    print(\"Start training TwoLayerNetwork with Adam...\")\n",
        "    results_two = train(model_two, criterion, optimizer, train_loader, valid_loader)\n",
        "    print(\"Done!\")\n",
        "\n",
        "    one_train_loss, one_valid_loss, one_train_acc, one_valid_acc = results_one\n",
        "    two_train_loss, two_valid_loss, two_train_acc, two_valid_acc = results_two\n",
        "\n",
        "    ### part i: generate a plot to comare one_train_loss, one_valid_loss, two_train_loss, two_valid_loss\n",
        "    plt.figure()\n",
        "    plt.plot(range(0, len(one_train_loss)), one_train_loss, label=\"One Layer Train\")\n",
        "    plt.plot(range(0, len(one_valid_loss)), one_valid_loss, label=\"One Layer Valid\")\n",
        "    plt.plot(range(0, len(two_train_loss)), two_train_loss, label=\"Two Layer Train\")\n",
        "    plt.plot(range(0, len(two_valid_loss)), two_valid_loss, label=\"Two Layer Valid\")\n",
        "    plt.title(\"Loss against Epochs for One and Two Layer Networks with Adam\")\n",
        "    plt.xlabel(\"Num Epochs\")\n",
        "    plt.ylabel(\"Loss\")\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "    \n",
        "    ### part j: generate a plot to comare one_train_acc, one_valid_acc, two_train_acc, two_valid_acc\n",
        "    plt.figure()\n",
        "    plt.plot(range(0, len(one_train_acc)), one_train_acc, label=\"One Layer Train\")\n",
        "    plt.plot(range(0, len(one_valid_acc)), one_valid_acc, label=\"One Layer Valid\")\n",
        "    plt.plot(range(0, len(two_train_acc)), two_train_acc, label=\"Two Layer Train\")\n",
        "    plt.plot(range(0, len(two_valid_acc)), two_valid_acc, label=\"Two Layer Valid\")\n",
        "    plt.title(\"Accuracy against Epochs for One and Two Layer Networks with Adam\")\n",
        "    plt.xlabel(\"Num Epochs\")\n",
        "    plt.ylabel(\"Accuracy\")\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "    ### part k: calculate the test accuracy\n",
        "    one_test_acc = evaluate_acc(model_one, test_loader)\n",
        "    two_test_acc = evaluate_acc(model_two, test_loader)\n",
        "    print(f\"(Adam) One Layer Test: {one_test_acc:.6f}, Two Layer Test: {two_test_acc:.6f}\")\n",
        "\n",
        "    ### ========== TODO : END ========== ###\n",
        "\n",
        "\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    main()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Data preparation...\n",
            "Start training OneLayerNetwork...\n",
            "| epoch  1 | train loss 1.075398 | train acc 0.453333 | valid loss 1.084938 | valid acc 0.453333 |\n",
            "| epoch  2 | train loss 1.021364 | train acc 0.566667 | valid loss 1.031102 | valid acc 0.553333 |\n",
            "| epoch  3 | train loss 0.972648 | train acc 0.630000 | valid loss 0.982742 | valid acc 0.593333 |\n",
            "| epoch  4 | train loss 0.928398 | train acc 0.710000 | valid loss 0.938953 | valid acc 0.640000 |\n",
            "| epoch  5 | train loss 0.887963 | train acc 0.783333 | valid loss 0.899045 | valid acc 0.700000 |\n",
            "| epoch  6 | train loss 0.850839 | train acc 0.826667 | valid loss 0.862485 | valid acc 0.753333 |\n",
            "| epoch  7 | train loss 0.816627 | train acc 0.850000 | valid loss 0.828852 | valid acc 0.793333 |\n",
            "| epoch  8 | train loss 0.785000 | train acc 0.886667 | valid loss 0.797807 | valid acc 0.846667 |\n",
            "| epoch  9 | train loss 0.755688 | train acc 0.900000 | valid loss 0.769067 | valid acc 0.866667 |\n",
            "| epoch 10 | train loss 0.728461 | train acc 0.903333 | valid loss 0.742397 | valid acc 0.873333 |\n",
            "| epoch 11 | train loss 0.703122 | train acc 0.913333 | valid loss 0.717596 | valid acc 0.880000 |\n",
            "| epoch 12 | train loss 0.679499 | train acc 0.920000 | valid loss 0.694488 | valid acc 0.886667 |\n",
            "| epoch 13 | train loss 0.657439 | train acc 0.933333 | valid loss 0.672921 | valid acc 0.886667 |\n",
            "| epoch 14 | train loss 0.636807 | train acc 0.943333 | valid loss 0.652760 | valid acc 0.886667 |\n",
            "| epoch 15 | train loss 0.617482 | train acc 0.943333 | valid loss 0.633883 | valid acc 0.886667 |\n",
            "| epoch 16 | train loss 0.599356 | train acc 0.943333 | valid loss 0.616184 | valid acc 0.886667 |\n",
            "| epoch 17 | train loss 0.582330 | train acc 0.943333 | valid loss 0.599565 | valid acc 0.893333 |\n",
            "| epoch 18 | train loss 0.566316 | train acc 0.943333 | valid loss 0.583938 | valid acc 0.900000 |\n",
            "| epoch 19 | train loss 0.551234 | train acc 0.943333 | valid loss 0.569225 | valid acc 0.906667 |\n",
            "| epoch 20 | train loss 0.537010 | train acc 0.943333 | valid loss 0.555355 | valid acc 0.906667 |\n",
            "| epoch 21 | train loss 0.523580 | train acc 0.943333 | valid loss 0.542262 | valid acc 0.906667 |\n",
            "| epoch 22 | train loss 0.510882 | train acc 0.943333 | valid loss 0.529888 | valid acc 0.906667 |\n",
            "| epoch 23 | train loss 0.498862 | train acc 0.950000 | valid loss 0.518179 | valid acc 0.906667 |\n",
            "| epoch 24 | train loss 0.487470 | train acc 0.950000 | valid loss 0.507086 | valid acc 0.906667 |\n",
            "| epoch 25 | train loss 0.476660 | train acc 0.950000 | valid loss 0.496564 | valid acc 0.906667 |\n",
            "| epoch 26 | train loss 0.466391 | train acc 0.953333 | valid loss 0.486573 | valid acc 0.926667 |\n",
            "| epoch 27 | train loss 0.456625 | train acc 0.953333 | valid loss 0.477076 | valid acc 0.926667 |\n",
            "| epoch 28 | train loss 0.447328 | train acc 0.953333 | valid loss 0.468038 | valid acc 0.926667 |\n",
            "| epoch 29 | train loss 0.438467 | train acc 0.956667 | valid loss 0.459429 | valid acc 0.933333 |\n",
            "| epoch 30 | train loss 0.430013 | train acc 0.956667 | valid loss 0.451220 | valid acc 0.940000 |\n",
            "Done!\n",
            "Start training TwoLayerNetwork...\n",
            "| epoch  1 | train loss 1.075845 | train acc 0.390000 | valid loss 1.077749 | valid acc 0.420000 |\n",
            "| epoch  2 | train loss 1.047239 | train acc 0.596667 | valid loss 1.048995 | valid acc 0.600000 |\n",
            "| epoch  3 | train loss 1.020207 | train acc 0.716667 | valid loss 1.021886 | valid acc 0.720000 |\n",
            "| epoch  4 | train loss 0.994547 | train acc 0.773333 | valid loss 0.996207 | valid acc 0.793333 |\n",
            "| epoch  5 | train loss 0.970093 | train acc 0.823333 | valid loss 0.971786 | valid acc 0.813333 |\n",
            "| epoch  6 | train loss 0.946710 | train acc 0.860000 | valid loss 0.948480 | valid acc 0.853333 |\n",
            "| epoch  7 | train loss 0.924287 | train acc 0.876667 | valid loss 0.926171 | valid acc 0.873333 |\n",
            "| epoch  8 | train loss 0.902732 | train acc 0.890000 | valid loss 0.904762 | valid acc 0.893333 |\n",
            "| epoch  9 | train loss 0.881967 | train acc 0.896667 | valid loss 0.884171 | valid acc 0.913333 |\n",
            "| epoch 10 | train loss 0.861928 | train acc 0.900000 | valid loss 0.864329 | valid acc 0.920000 |\n",
            "| epoch 11 | train loss 0.842560 | train acc 0.910000 | valid loss 0.845179 | valid acc 0.920000 |\n",
            "| epoch 12 | train loss 0.823817 | train acc 0.920000 | valid loss 0.826671 | valid acc 0.920000 |\n",
            "| epoch 13 | train loss 0.805658 | train acc 0.923333 | valid loss 0.808764 | valid acc 0.920000 |\n",
            "| epoch 14 | train loss 0.788051 | train acc 0.930000 | valid loss 0.791421 | valid acc 0.926667 |\n",
            "| epoch 15 | train loss 0.770965 | train acc 0.933333 | valid loss 0.774611 | valid acc 0.926667 |\n",
            "| epoch 16 | train loss 0.754375 | train acc 0.940000 | valid loss 0.758306 | valid acc 0.926667 |\n",
            "| epoch 17 | train loss 0.738260 | train acc 0.946667 | valid loss 0.742483 | valid acc 0.926667 |\n",
            "| epoch 18 | train loss 0.722598 | train acc 0.946667 | valid loss 0.727121 | valid acc 0.933333 |\n",
            "| epoch 19 | train loss 0.707374 | train acc 0.950000 | valid loss 0.712202 | valid acc 0.926667 |\n",
            "| epoch 20 | train loss 0.692571 | train acc 0.953333 | valid loss 0.697708 | valid acc 0.926667 |\n",
            "| epoch 21 | train loss 0.678176 | train acc 0.953333 | valid loss 0.683625 | valid acc 0.926667 |\n",
            "| epoch 22 | train loss 0.664175 | train acc 0.953333 | valid loss 0.669938 | valid acc 0.933333 |\n",
            "| epoch 23 | train loss 0.650557 | train acc 0.956667 | valid loss 0.656636 | valid acc 0.933333 |\n",
            "| epoch 24 | train loss 0.637311 | train acc 0.956667 | valid loss 0.643706 | valid acc 0.933333 |\n",
            "| epoch 25 | train loss 0.624427 | train acc 0.960000 | valid loss 0.631139 | valid acc 0.933333 |\n",
            "| epoch 26 | train loss 0.611894 | train acc 0.963333 | valid loss 0.618923 | valid acc 0.940000 |\n",
            "| epoch 27 | train loss 0.599705 | train acc 0.963333 | valid loss 0.607049 | valid acc 0.940000 |\n",
            "| epoch 28 | train loss 0.587850 | train acc 0.963333 | valid loss 0.595508 | valid acc 0.933333 |\n",
            "| epoch 29 | train loss 0.576321 | train acc 0.963333 | valid loss 0.584291 | valid acc 0.933333 |\n",
            "| epoch 30 | train loss 0.565109 | train acc 0.960000 | valid loss 0.573390 | valid acc 0.933333 |\n",
            "Done!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAALwklEQVR4nO3dX4hc5R3G8eeJmpuYi6TSZdFYrSi4BBpLkEpDtYhivTAKIuaipFS6IioKvajohUIpSKmWXgkrirFYRdBgEKnaEExzI9lIajamaiqRZFmzCbnwD2L+/XoxJ7LqzpnNnHPmjPl9P7DMzPvOzPlxyJP3/JlzXkeEAJz5FrVdAIDBIOxAEoQdSIKwA0kQdiCJswe5MNsc+gcaFhGer73SyG77Btvv295r+4Eq3wWgWe73PLvtsyR9IOk6SQckbZe0LiLeK/kMIzvQsCZG9isl7Y2IjyLiqKQXJK2t8H0AGlQl7OdL2j/n9YGi7Rtsj9uetD1ZYVkAKmr8AF1ETEiakNiMB9pUZWSflrRizusLijYAQ6hK2LdLutT2xbYXS7pd0qZ6ygJQt7434yPiuO17JL0u6SxJT0fE7toqA1Crvk+99bUw9tmBxjXyoxoA3x+EHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kARhB5Ig7EAShB1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJEHYgCcIOJNH3lM0YHkuXLu3at2PHjtLPfvnll6X99957b2n/1q1bS/sxPCqF3fY+SZ9JOiHpeESsrqMoAPWrY2T/ZUQcruF7ADSIfXYgiaphD0lv2N5he3y+N9getz1pe7LisgBUUHUzfk1ETNv+oaQ3bf83Ir5xxCYiJiRNSJLtqLg8AH2qNLJHxHTxOCtpo6Qr6ygKQP36DrvtJbaXnnou6XpJU3UVBqBeVTbjRyRttH3qe/4REf+spSqclq+++qpr3/79+0s/e/XVV5f2P/TQQ6X9nGf//ug77BHxkaSf1FgLgAZx6g1IgrADSRB2IAnCDiRB2IEkuMT1DHD06NGufYcPV7tG6cILLyztX7x4cWl/WW0YLEZ2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUiC8+woddlll5X2X3XVVaX9b731Vp3loAJGdiAJwg4kQdiBJAg7kARhB5Ig7EAShB1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUiC69nPcNu2bSvtv/XWW0v7iym5u7rrrrtK+7mefXj0HNltP2171vbUnLbltt+0/WHxuKzZMgFUtZDN+Gck3fCttgckbY6ISyVtLl4DGGI9wx4RWyUd+VbzWkkbiucbJN1cc10AatbvPvtIRMwUzz+RNNLtjbbHJY33uRwANal8gC4iwnaU9E9ImpCksvcBaFa/p94O2h6VpOJxtr6SADSh37BvkrS+eL5e0iv1lAOgKT03420/L+kaSefZPiDpYUmPSnrR9h2SPpZ0W5NFon+7du0q7Y9gzyqLnmGPiHVduq6tuRYADeLnskAShB1IgrADSRB2IAnCDiTBJa5nuGPHjpX2nzhxorT/7LPL/4mMjY2V9i9ZsqRr3xdffFH6WdSLkR1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkvAgL3HkTjXDp9clsL3Oo/f69zM6Otq179ChQ6WfRX8iYt77fzOyA0kQdiAJwg4kQdiBJAg7kARhB5Ig7EAShB1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBLcNz45e95Ln7+2aFH5eHDy5Mk6y0GDeo7stp+2PWt7ak7bI7anbe8s/m5stkwAVS1kM/4ZSTfM0/7XiFhV/L1Wb1kA6tYz7BGxVdKRAdQCoEFVDtDdY/vdYjN/Wbc32R63PWl7ssKyAFTUb9ifkHSJpFWSZiQ91u2NETEREasjYnWfywJQg77CHhEHI+JERJyU9KSkK+stC0Dd+gq77bn3B75F0lS39wIYDj3Ps9t+XtI1ks6zfUDSw5Kusb1KUkjaJ+nOBmtEg3rd973XefRBzjuAanqGPSLWzdP8VAO1AGgQP5cFkiDsQBKEHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiAJbiWd3NRU+a0IxsbGBlQJmsbIDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJcJ49uZUrV7ZdAgaEkR1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkuA8e3K2S/sXLSofD3pN6Yzh0XNkt73C9hbb79nebfu+on257Tdtf1g8Lmu+XAD9Wshm/HFJv4+IMUk/k3S37TFJD0jaHBGXStpcvAYwpHqGPSJmIuKd4vlnkvZIOl/SWkkbirdtkHRzU0UCqO609tltXyTpCklvSxqJiJmi6xNJI10+My5pvP8SAdRhwUfjbZ8r6SVJ90fEp3P7IiIkxXyfi4iJiFgdEasrVQqgkgWF3fY56gT9uYh4uWg+aHu06B+VNNtMiQDq0HMz3p1zM09J2hMRj8/p2iRpvaRHi8dXGqkQjdq4cWNp/+WXX17a39mow/fBQvbZfy7p15J22d5ZtD2oTshftH2HpI8l3dZMiQDq0DPsEbFNUrdfXlxbbzkAmsLPZYEkCDuQBGEHkiDsQBKEHUiCS1yTm5mZ6f0mnBEY2YEkCDuQBGEHkiDsQBKEHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiAJD/K+37a5yfiQWb58eWn/li1bSvunp6dL+2+66aaufcePHy/9LPoTEfPeDZqRHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeS6Hme3fYKSc9KGpEUkiYi4m+2H5H0O0mHirc+GBGv9fguzrMDDet2nn0hYR+VNBoR79heKmmHpJvVmY/984j4y0KLIOxA87qFfSHzs89Imimef2Z7j6Tz6y0PQNNOa5/d9kWSrpD0dtF0j+13bT9te1mXz4zbnrQ9WalSAJUs+Lfxts+V9JakP0XEy7ZHJB1WZz/+j+ps6v+2x3ewGQ80rO99dkmyfY6kVyW9HhGPz9N/kaRXI2Jlj+8h7EDD+r4QxrYlPSVpz9ygFwfuTrlF0lTVIgE0ZyFH49dI+rekXZJOFs0PSlonaZU6m/H7JN1ZHMwr+y5GdqBhlTbj60LYgeZxPTuQHGEHkiDsQBKEHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiCJnjecrNlhSR/PeX1e0TaMhrW2Ya1LorZ+1Vnbj7p1DPR69u8s3J6MiNWtFVBiWGsb1rokauvXoGpjMx5IgrADSbQd9omWl19mWGsb1rokauvXQGprdZ8dwOC0PbIDGBDCDiTRStht32D7fdt7bT/QRg3d2N5ne5ftnW3PT1fMoTdre2pO23Lbb9r+sHicd469lmp7xPZ0se522r6xpdpW2N5i+z3bu23fV7S3uu5K6hrIehv4PrvtsyR9IOk6SQckbZe0LiLeG2ghXdjeJ2l1RLT+Awzbv5D0uaRnT02tZfvPko5ExKPFf5TLIuIPQ1LbIzrNabwbqq3bNOO/UYvrrs7pz/vRxsh+paS9EfFRRByV9IKktS3UMfQiYqukI99qXitpQ/F8gzr/WAauS21DISJmIuKd4vlnkk5NM97quiupayDaCPv5kvbPeX1AwzXfe0h6w/YO2+NtFzOPkTnTbH0iaaTNYubRcxrvQfrWNONDs+76mf68Kg7QfdeaiPippF9JurvYXB1K0dkHG6Zzp09IukSdOQBnJD3WZjHFNOMvSbo/Ij6d29fmupunroGstzbCPi1pxZzXFxRtQyEipovHWUkb1dntGCYHT82gWzzOtlzP1yLiYESciIiTkp5Ui+uumGb8JUnPRcTLRXPr626+uga13toI+3ZJl9q+2PZiSbdL2tRCHd9he0lx4ES2l0i6XsM3FfUmSeuL5+slvdJiLd8wLNN4d5tmXC2vu9anP4+Igf9JulGdI/L/k/RQGzV0qevHkv5T/O1uuzZJz6uzWXdMnWMbd0j6gaTNkj6U9C9Jy4eotr+rM7X3u+oEa7Sl2taos4n+rqSdxd+Nba+7kroGst74uSyQBAfogCQIO5AEYQeSIOxAEoQdSIKwA0kQdiCJ/wPJALtkhiQasgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Jd6j/ivx+SU9U0UONvv5c0v9kP+9W3Zukteo/rTup/msbCyX9iaStknok/ZekMS3U27+rf2rvt9UfrHEV9Xaj+k/R35a0J/u5o+rXLtFXU143bpcFguACHRAEYQeCIOxAEIQdCIKwA0EQdiAIwg4E8f8JkKiCMl/M8gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vd6ruTdILqp3WfaHatY2fSJokaZuk9yX9t6TzOqi3/1Jtae+3VQvW1Ip6u0G1U/S3Je3KvuZWve8SfbVlv/FyWSAILtABQRB2IAjCDgRB2IEgCDsQBGEHgiDsQBD/Dzj/kqsTaxxYAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "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": "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": [
            "One Layer Test: 0.960000, Two Layer Test: 0.933333\n",
            "Start training OneLayerNetwork with Adam...\n",
            "| epoch  1 | train loss 0.652176 | train acc 0.930000 | valid loss 0.643495 | valid acc 0.900000 |\n",
            "| epoch  2 | train loss 0.448845 | train acc 0.960000 | valid loss 0.450156 | valid acc 0.953333 |\n",
            "| epoch  3 | train loss 0.340191 | train acc 0.970000 | valid loss 0.347157 | valid acc 0.953333 |\n",
            "| epoch  4 | train loss 0.275654 | train acc 0.976667 | valid loss 0.286565 | valid acc 0.953333 |\n",
            "| epoch  5 | train loss 0.233069 | train acc 0.976667 | valid loss 0.247042 | valid acc 0.953333 |\n",
            "| epoch  6 | train loss 0.202674 | train acc 0.976667 | valid loss 0.219164 | valid acc 0.960000 |\n",
            "| epoch  7 | train loss 0.179708 | train acc 0.976667 | valid loss 0.198359 | valid acc 0.960000 |\n",
            "| epoch  8 | train loss 0.161607 | train acc 0.980000 | valid loss 0.182174 | valid acc 0.960000 |\n",
            "| epoch  9 | train loss 0.146879 | train acc 0.980000 | valid loss 0.169180 | valid acc 0.966667 |\n",
            "| epoch 10 | train loss 0.134596 | train acc 0.980000 | valid loss 0.158487 | valid acc 0.973333 |\n",
            "| epoch 11 | train loss 0.124151 | train acc 0.983333 | valid loss 0.149514 | valid acc 0.973333 |\n",
            "| epoch 12 | train loss 0.115131 | train acc 0.986667 | valid loss 0.141863 | valid acc 0.980000 |\n",
            "| epoch 13 | train loss 0.107242 | train acc 0.986667 | valid loss 0.135252 | valid acc 0.980000 |\n",
            "| epoch 14 | train loss 0.100269 | train acc 0.986667 | valid loss 0.129476 | valid acc 0.980000 |\n",
            "| epoch 15 | train loss 0.094054 | train acc 0.990000 | valid loss 0.124380 | valid acc 0.980000 |\n",
            "| epoch 16 | train loss 0.088473 | train acc 0.990000 | valid loss 0.119847 | valid acc 0.980000 |\n",
            "| epoch 17 | train loss 0.083429 | train acc 0.990000 | valid loss 0.115785 | valid acc 0.980000 |\n",
            "| epoch 18 | train loss 0.078848 | train acc 0.990000 | valid loss 0.112121 | valid acc 0.980000 |\n",
            "| epoch 19 | train loss 0.074666 | train acc 0.990000 | valid loss 0.108797 | valid acc 0.980000 |\n",
            "| epoch 20 | train loss 0.070834 | train acc 0.990000 | valid loss 0.105767 | valid acc 0.980000 |\n",
            "| epoch 21 | train loss 0.067309 | train acc 0.990000 | valid loss 0.102990 | valid acc 0.980000 |\n",
            "| epoch 22 | train loss 0.064057 | train acc 0.990000 | valid loss 0.100436 | valid acc 0.980000 |\n",
            "| epoch 23 | train loss 0.061046 | train acc 0.993333 | valid loss 0.098077 | valid acc 0.980000 |\n",
            "| epoch 24 | train loss 0.058253 | train acc 0.993333 | valid loss 0.095891 | valid acc 0.980000 |\n",
            "| epoch 25 | train loss 0.055655 | train acc 0.993333 | valid loss 0.093858 | valid acc 0.980000 |\n",
            "| epoch 26 | train loss 0.053232 | train acc 0.996667 | valid loss 0.091962 | valid acc 0.980000 |\n",
            "| epoch 27 | train loss 0.050969 | train acc 0.996667 | valid loss 0.090190 | valid acc 0.980000 |\n",
            "| epoch 28 | train loss 0.048851 | train acc 1.000000 | valid loss 0.088529 | valid acc 0.980000 |\n",
            "| epoch 29 | train loss 0.046865 | train acc 1.000000 | valid loss 0.086968 | valid acc 0.980000 |\n",
            "| epoch 30 | train loss 0.045000 | train acc 1.000000 | valid loss 0.085498 | valid acc 0.980000 |\n",
            "Done!\n",
            "Start training TwoLayerNetwork with Adam...\n",
            "| epoch  1 | train loss 0.124231 | train acc 0.976667 | valid loss 0.147870 | valid acc 0.960000 |\n",
            "| epoch  2 | train loss 0.058078 | train acc 0.986667 | valid loss 0.097314 | valid acc 0.966667 |\n",
            "| epoch  3 | train loss 0.030513 | train acc 1.000000 | valid loss 0.080880 | valid acc 0.973333 |\n",
            "| epoch  4 | train loss 0.019278 | train acc 1.000000 | valid loss 0.074285 | valid acc 0.980000 |\n",
            "| epoch  5 | train loss 0.012529 | train acc 1.000000 | valid loss 0.067273 | valid acc 0.973333 |\n",
            "| epoch  6 | train loss 0.009076 | train acc 1.000000 | valid loss 0.063921 | valid acc 0.980000 |\n",
            "| epoch  7 | train loss 0.006857 | train acc 1.000000 | valid loss 0.061711 | valid acc 0.980000 |\n",
            "| epoch  8 | train loss 0.005369 | train acc 1.000000 | valid loss 0.060061 | valid acc 0.980000 |\n",
            "| epoch  9 | train loss 0.004322 | train acc 1.000000 | valid loss 0.058838 | valid acc 0.980000 |\n",
            "| epoch 10 | train loss 0.003556 | train acc 1.000000 | valid loss 0.057897 | valid acc 0.980000 |\n",
            "| epoch 11 | train loss 0.002980 | train acc 1.000000 | valid loss 0.057148 | valid acc 0.980000 |\n",
            "| epoch 12 | train loss 0.002534 | train acc 1.000000 | valid loss 0.056540 | valid acc 0.980000 |\n",
            "| epoch 13 | train loss 0.002183 | train acc 1.000000 | valid loss 0.056036 | valid acc 0.980000 |\n",
            "| epoch 14 | train loss 0.001900 | train acc 1.000000 | valid loss 0.055613 | valid acc 0.980000 |\n",
            "| epoch 15 | train loss 0.001670 | train acc 1.000000 | valid loss 0.055253 | valid acc 0.980000 |\n",
            "| epoch 16 | train loss 0.001479 | train acc 1.000000 | valid loss 0.054943 | valid acc 0.980000 |\n",
            "| epoch 17 | train loss 0.001320 | train acc 1.000000 | valid loss 0.054674 | valid acc 0.980000 |\n",
            "| epoch 18 | train loss 0.001185 | train acc 1.000000 | valid loss 0.054439 | valid acc 0.980000 |\n",
            "| epoch 19 | train loss 0.001070 | train acc 1.000000 | valid loss 0.054232 | valid acc 0.980000 |\n",
            "| epoch 20 | train loss 0.000971 | train acc 1.000000 | valid loss 0.054048 | valid acc 0.980000 |\n",
            "| epoch 21 | train loss 0.000885 | train acc 1.000000 | valid loss 0.053885 | valid acc 0.980000 |\n",
            "| epoch 22 | train loss 0.000810 | train acc 1.000000 | valid loss 0.053738 | valid acc 0.980000 |\n",
            "| epoch 23 | train loss 0.000744 | train acc 1.000000 | valid loss 0.053607 | valid acc 0.980000 |\n",
            "| epoch 24 | train loss 0.000686 | train acc 1.000000 | valid loss 0.053488 | valid acc 0.980000 |\n",
            "| epoch 25 | train loss 0.000634 | train acc 1.000000 | valid loss 0.053380 | valid acc 0.980000 |\n",
            "| epoch 26 | train loss 0.000588 | train acc 1.000000 | valid loss 0.053283 | valid acc 0.980000 |\n",
            "| epoch 27 | train loss 0.000547 | train acc 1.000000 | valid loss 0.053194 | valid acc 0.980000 |\n",
            "| epoch 28 | train loss 0.000510 | train acc 1.000000 | valid loss 0.053113 | valid acc 0.980000 |\n",
            "| epoch 29 | train loss 0.000476 | train acc 1.000000 | valid loss 0.053039 | valid acc 0.980000 |\n",
            "| epoch 30 | train loss 0.000446 | train acc 1.000000 | valid loss 0.052971 | valid acc 0.980000 |\n",
            "Done!\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": "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": [
            "(Adam) One Layer Test: 0.973333, Two Layer Test: 0.973333\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}