Modified_Autoencoder_Code_for_Basic_Gans.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Modified Autoencoder Code for Basic Gans.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/preritt/Udacity-AIND/blob/master/Modified_Autoencoder_Code_for_Basic_Gans.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "plA-DhxVXFkh",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.examples.tutorials.mnist import input_data\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.gridspec as gridspec\n",
        "import os"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "8jTQ5711XNPW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def xavier_init(size):\n",
        "    in_dim = size[0]\n",
        "    xavier_stddev = 1. / tf.sqrt(in_dim / 2.)\n",
        "    return tf.random_normal(shape=size, stddev=xavier_stddev)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "mZUeneVyXPBO",
        "colab_type": "code",
        "outputId": "c0386ffe-cf05-48a8-c757-5893d9d40d7e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        }
      },
      "cell_type": "code",
      "source": [
        "X = tf.placeholder(tf.float32, shape=[None, 784])\n",
        "\n",
        "D_W1 = tf.Variable(xavier_init([784, 128]))\n",
        "D_b1 = tf.Variable(tf.zeros(shape=[128]))\n",
        "\n",
        "D_W2 = tf.Variable(xavier_init([128, 1]))\n",
        "D_b2 = tf.Variable(tf.zeros(shape=[1]))\n",
        "\n",
        "theta_D = [D_W1, D_W2, D_b1, D_b2]\n"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "VCl2bIqxXRZZ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "Z = tf.placeholder(tf.float32, shape=[None, 784])\n",
        "\n",
        "G_W1 = tf.Variable(xavier_init([784, 128]))\n",
        "G_b1 = tf.Variable(tf.zeros(shape=[128]))\n",
        "\n",
        "G_W2 = tf.Variable(xavier_init([128, 128]))\n",
        "G_b2 = tf.Variable(tf.zeros(shape=[128]))\n",
        "\n",
        "G_W3 = tf.Variable(xavier_init([128, 128]))\n",
        "G_b3 = tf.Variable(tf.zeros(shape=[128]))\n",
        "\n",
        "G_W4 = tf.Variable(xavier_init([128, 784]))\n",
        "G_b4 = tf.Variable(tf.zeros(shape=[784]))\n",
        "\n",
        "theta_G = [G_W1, G_W2, G_b1, G_b2, G_W3, G_b3,G_W4, G_b4]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ugfqigZzXUWw",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def sample_Z(m, n):\n",
        "    return np.random.uniform(-1., 1., size=[m, n])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "H1fb334hXWPj",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def generator(z):\n",
        "    G_h1 = tf.nn.relu(tf.matmul(z, G_W1) + G_b1)\n",
        "    G_h2 = tf.nn.relu(tf.matmul(G_h1, G_W2) + G_b2) # encoded value\n",
        "    G_h3 = tf.nn.relu(tf.matmul(G_h2, G_W3) + G_b3)\n",
        "    logits = tf.matmul(G_h3, G_W4) + G_b4\n",
        "    G_prob = tf.nn.sigmoid(logits)\n",
        "\n",
        "    return G_prob, logits"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "BnqZkgx2XX1b",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def discriminator(x):\n",
        "    D_h1 = tf.nn.relu(tf.matmul(x, D_W1) + D_b1)\n",
        "    D_logit = tf.matmul(D_h1, D_W2) + D_b2\n",
        "    D_prob = tf.nn.sigmoid(D_logit)\n",
        "\n",
        "    return D_prob, D_logit"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "LUS5x_QjXZiP",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def plot(samples):\n",
        "    fig = plt.figure(figsize=(4, 4))\n",
        "    gs = gridspec.GridSpec(4, 4)\n",
        "    gs.update(wspace=0.05, hspace=0.05)\n",
        "\n",
        "    for i, sample in enumerate(samples):\n",
        "        ax = plt.subplot(gs[i])\n",
        "        plt.axis('off')\n",
        "        ax.set_xticklabels([])\n",
        "        ax.set_yticklabels([])\n",
        "        ax.set_aspect('equal')\n",
        "        plt.imshow(sample.reshape(28, 28), cmap='Greys_r')\n",
        "\n",
        "    return fig"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "YuoljUAjXbar",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "G_fake_decoded_prob, G_fake_decoded_logits = generator(X)\n",
        "D_real, D_logit_real = discriminator(X)\n",
        "D_fake, D_logit_fake = discriminator(G_fake_decoded_prob)\n",
        "\n",
        "# D_loss = -tf.reduce_mean(tf.log(D_real) + tf.log(1. - D_fake))\n",
        "# G_loss = -tf.reduce_mean(tf.log(D_fake))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "JeV4RyWZXdfH",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Alternative losses:\n",
        "# -------------------\n",
        "D_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_logit_real, labels=tf.ones_like(D_logit_real)))\n",
        "D_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_logit_fake, labels=tf.zeros_like(D_logit_fake)))\n",
        "D_loss = D_loss_real + D_loss_fake\n",
        "G_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_logit_fake, labels=tf.ones_like(D_logit_fake)))\n",
        "\n",
        "recon_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=G_fake_decoded_logits, labels=X))\n",
        "G_loss = G_loss + 20*recon_loss\n",
        "\n",
        "D_solver = tf.train.AdamOptimizer().minimize(D_loss, var_list=theta_D)\n",
        "G_solver = tf.train.AdamOptimizer().minimize(G_loss, var_list=theta_G)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "EPPTsbbyXf62",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "outputId": "6f973c94-0c4d-489d-a903-57ccab2b48ff"
      },
      "cell_type": "code",
      "source": [
        "mb_size = 128\n",
        "Z_dim = 784\n",
        "\n",
        "mnist = input_data.read_data_sets('../../MNIST_data', one_hot=True)\n",
        "\n",
        "sess = tf.Session()\n",
        "sess.run(tf.global_variables_initializer())\n",
        "\n",
        "if not os.path.exists('out/'):\n",
        "    os.makedirs('out/')"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Extracting ../../MNIST_data/train-images-idx3-ubyte.gz\n",
            "Extracting ../../MNIST_data/train-labels-idx1-ubyte.gz\n",
            "Extracting ../../MNIST_data/t10k-images-idx3-ubyte.gz\n",
            "Extracting ../../MNIST_data/t10k-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "JUEHL_CRooMb",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "i=0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Q5qF8gPqooKh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 563
        },
        "outputId": "ecbd3770-32eb-4fad-b584-6dec0d516395"
      },
      "cell_type": "code",
      "source": [
        "for it in range(50000):\n",
        "    X_mb, _ = mnist.train.next_batch(mb_size)\n",
        "#     z_mb = np.random.randn(mb_size, z_dim)\n",
        "\n",
        "#     _, recon_loss_curr = sess.run([G_solver, recon_loss], feed_dict={X: X_mb})\n",
        "    _, D_loss_curr, recon_loss_curr = sess.run([D_solver, D_loss, recon_loss], feed_dict={X: X_mb})\n",
        "    _, G_loss_curr = sess.run([G_solver, G_loss], feed_dict={X: X_mb})\n",
        "\n",
        "    if it % 1000 == 0:\n",
        "        print('Iter: {}; D_loss: {:.4}; G_loss: {:.4}; Recon_loss: {:.4}'\n",
        "              .format(it, D_loss_curr, G_loss_curr, recon_loss_curr))\n",
        "\n",
        "        samples = sess.run(G_fake_decoded_prob, feed_dict={X: np.reshape(X_mb[1], newshape=(1,-1))})\n",
        "\n",
        "        fig = plot(samples)\n",
        "        plt.savefig('out/{}.png'.format(str(i).zfill(3)), bbox_inches='tight')\n",
        "        i += 1\n",
        "        plt.close(fig)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Iter: 0; D_loss: 2.175; G_loss: 15.86; Recon_loss: 0.7141\n",
            "Iter: 1000; D_loss: 1.365; G_loss: 2.735; Recon_loss: 0.09787\n",
            "Iter: 2000; D_loss: 1.344; G_loss: 2.332; Recon_loss: 0.08106\n",
            "Iter: 3000; D_loss: 1.388; G_loss: 2.31; Recon_loss: 0.08006\n",
            "Iter: 4000; D_loss: 1.365; G_loss: 2.228; Recon_loss: 0.0749\n",
            "Iter: 5000; D_loss: 1.372; G_loss: 2.218; Recon_loss: 0.07473\n",
            "Iter: 6000; D_loss: 1.371; G_loss: 2.169; Recon_loss: 0.073\n",
            "Iter: 7000; D_loss: 1.363; G_loss: 2.15; Recon_loss: 0.07273\n",
            "Iter: 8000; D_loss: 1.394; G_loss: 2.177; Recon_loss: 0.07312\n",
            "Iter: 9000; D_loss: 1.39; G_loss: 2.193; Recon_loss: 0.0734\n",
            "Iter: 10000; D_loss: 1.361; G_loss: 2.101; Recon_loss: 0.07009\n",
            "Iter: 11000; D_loss: 1.363; G_loss: 2.203; Recon_loss: 0.07477\n",
            "Iter: 12000; D_loss: 1.359; G_loss: 2.209; Recon_loss: 0.07394\n",
            "Iter: 13000; D_loss: 1.373; G_loss: 2.173; Recon_loss: 0.07221\n",
            "Iter: 14000; D_loss: 1.364; G_loss: 2.15; Recon_loss: 0.07178\n",
            "Iter: 15000; D_loss: 1.358; G_loss: 2.147; Recon_loss: 0.07191\n",
            "Iter: 16000; D_loss: 1.365; G_loss: 2.115; Recon_loss: 0.06946\n",
            "Iter: 17000; D_loss: 1.363; G_loss: 2.129; Recon_loss: 0.06956\n",
            "Iter: 18000; D_loss: 1.371; G_loss: 2.12; Recon_loss: 0.07178\n",
            "Iter: 19000; D_loss: 1.376; G_loss: 2.135; Recon_loss: 0.07033\n",
            "Iter: 20000; D_loss: 1.368; G_loss: 2.148; Recon_loss: 0.0711\n",
            "Iter: 21000; D_loss: 1.378; G_loss: 2.12; Recon_loss: 0.07164\n",
            "Iter: 22000; D_loss: 1.366; G_loss: 2.089; Recon_loss: 0.06922\n",
            "Iter: 23000; D_loss: 1.354; G_loss: 2.123; Recon_loss: 0.07038\n",
            "Iter: 24000; D_loss: 1.375; G_loss: 2.212; Recon_loss: 0.07402\n",
            "Iter: 25000; D_loss: 1.368; G_loss: 2.231; Recon_loss: 0.07554\n",
            "Iter: 26000; D_loss: 1.368; G_loss: 2.161; Recon_loss: 0.07177\n",
            "Iter: 27000; D_loss: 1.365; G_loss: 2.154; Recon_loss: 0.0708\n",
            "Iter: 28000; D_loss: 1.375; G_loss: 2.192; Recon_loss: 0.07278\n",
            "Iter: 29000; D_loss: 1.36; G_loss: 2.197; Recon_loss: 0.0733\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "CpR6gGheooHJ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "uiKvJzSFXkEZ",
        "colab_type": "code",
        "outputId": "6e4e8575-0559-407a-c789-cea016c28e73",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3654
        }
      },
      "cell_type": "code",
      "source": [
        "i = 0\n",
        "\n",
        "DlossesTrack = []\n",
        "GlossesTrack = []\n",
        "losses = []\n",
        "for it in range(50000):\n",
        "    if it % 1000 == 0:\n",
        "        samples = sess.run(G_sample, feed_dict={X: sample_Z(16, Z_dim)})\n",
        "\n",
        "        fig = plot(samples)\n",
        "        plt.savefig('out/{}.png'.format(str(i).zfill(3)), bbox_inches='tight')\n",
        "        i += 1\n",
        "        plt.close(fig)\n",
        "\n",
        "    X_mb, _ = mnist.train.next_batch(mb_size)\n",
        "\n",
        "    _, D_loss_curr = sess.run([D_solver, D_loss], feed_dict={X: X_mb, Z: sample_Z(mb_size, Z_dim)})\n",
        "    _, G_loss_curr = sess.run([G_solver, G_loss], feed_dict={Z: sample_Z(mb_size, Z_dim)})\n",
        "#     print(D_loss)\n",
        "#     losses.append((D_loss_curr, G_loss_curr))\n",
        "#     DlossesTrack.append(D_loss_curr)\n",
        "#     GlossesTrack.append(G_loss)\n",
        "    if it % 1000 == 0:\n",
        "        losses.append((D_loss_curr, G_loss_curr))\n",
        "        print('Iter: {}'.format(it))\n",
        "        print('D loss: {:.4}'. format(D_loss_curr))\n",
        "        print('G_loss: {:.4}'.format(G_loss_curr))\n",
        "        print()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Iter: 0\n",
            "D loss: 0.6405\n",
            "G_loss: 2.151\n",
            "\n",
            "Iter: 1000\n",
            "D loss: 0.5933\n",
            "G_loss: 2.45\n",
            "\n",
            "Iter: 2000\n",
            "D loss: 0.7804\n",
            "G_loss: 2.524\n",
            "\n",
            "Iter: 3000\n",
            "D loss: 0.7889\n",
            "G_loss: 2.288\n",
            "\n",
            "Iter: 4000\n",
            "D loss: 0.607\n",
            "G_loss: 2.346\n",
            "\n",
            "Iter: 5000\n",
            "D loss: 0.7482\n",
            "G_loss: 2.442\n",
            "\n",
            "Iter: 6000\n",
            "D loss: 0.5621\n",
            "G_loss: 2.403\n",
            "\n",
            "Iter: 7000\n",
            "D loss: 0.7113\n",
            "G_loss: 2.499\n",
            "\n",
            "Iter: 8000\n",
            "D loss: 0.652\n",
            "G_loss: 2.107\n",
            "\n",
            "Iter: 9000\n",
            "D loss: 0.5281\n",
            "G_loss: 2.172\n",
            "\n",
            "Iter: 10000\n",
            "D loss: 0.5741\n",
            "G_loss: 2.146\n",
            "\n",
            "Iter: 11000\n",
            "D loss: 0.545\n",
            "G_loss: 2.338\n",
            "\n",
            "Iter: 12000\n",
            "D loss: 0.5209\n",
            "G_loss: 2.419\n",
            "\n",
            "Iter: 13000\n",
            "D loss: 0.6842\n",
            "G_loss: 2.366\n",
            "\n",
            "Iter: 14000\n",
            "D loss: 0.57\n",
            "G_loss: 2.397\n",
            "\n",
            "Iter: 15000\n",
            "D loss: 0.5508\n",
            "G_loss: 2.192\n",
            "\n",
            "Iter: 16000\n",
            "D loss: 0.6118\n",
            "G_loss: 2.362\n",
            "\n",
            "Iter: 17000\n",
            "D loss: 0.5899\n",
            "G_loss: 1.984\n",
            "\n",
            "Iter: 18000\n",
            "D loss: 0.5487\n",
            "G_loss: 2.096\n",
            "\n",
            "Iter: 19000\n",
            "D loss: 0.5302\n",
            "G_loss: 2.218\n",
            "\n",
            "Iter: 20000\n",
            "D loss: 0.5349\n",
            "G_loss: 2.215\n",
            "\n",
            "Iter: 21000\n",
            "D loss: 0.5996\n",
            "G_loss: 2.315\n",
            "\n",
            "Iter: 22000\n",
            "D loss: 0.5132\n",
            "G_loss: 2.336\n",
            "\n",
            "Iter: 23000\n",
            "D loss: 0.625\n",
            "G_loss: 2.738\n",
            "\n",
            "Iter: 24000\n",
            "D loss: 0.5924\n",
            "G_loss: 2.59\n",
            "\n",
            "Iter: 25000\n",
            "D loss: 0.6524\n",
            "G_loss: 2.579\n",
            "\n",
            "Iter: 26000\n",
            "D loss: 0.6615\n",
            "G_loss: 1.917\n",
            "\n",
            "Iter: 27000\n",
            "D loss: 0.6566\n",
            "G_loss: 2.047\n",
            "\n",
            "Iter: 28000\n",
            "D loss: 0.5297\n",
            "G_loss: 2.31\n",
            "\n",
            "Iter: 29000\n",
            "D loss: 0.5549\n",
            "G_loss: 2.818\n",
            "\n",
            "Iter: 30000\n",
            "D loss: 0.5735\n",
            "G_loss: 2.359\n",
            "\n",
            "Iter: 31000\n",
            "D loss: 0.6085\n",
            "G_loss: 2.173\n",
            "\n",
            "Iter: 32000\n",
            "D loss: 0.5623\n",
            "G_loss: 2.622\n",
            "\n",
            "Iter: 33000\n",
            "D loss: 0.488\n",
            "G_loss: 2.444\n",
            "\n",
            "Iter: 34000\n",
            "D loss: 0.5741\n",
            "G_loss: 2.587\n",
            "\n",
            "Iter: 35000\n",
            "D loss: 0.5997\n",
            "G_loss: 1.968\n",
            "\n",
            "Iter: 36000\n",
            "D loss: 0.7049\n",
            "G_loss: 2.291\n",
            "\n",
            "Iter: 37000\n",
            "D loss: 0.6331\n",
            "G_loss: 2.432\n",
            "\n",
            "Iter: 38000\n",
            "D loss: 0.504\n",
            "G_loss: 2.529\n",
            "\n",
            "Iter: 39000\n",
            "D loss: 0.6063\n",
            "G_loss: 2.465\n",
            "\n",
            "Iter: 40000\n",
            "D loss: 0.588\n",
            "G_loss: 2.602\n",
            "\n",
            "Iter: 41000\n",
            "D loss: 0.601\n",
            "G_loss: 2.478\n",
            "\n",
            "Iter: 42000\n",
            "D loss: 0.5242\n",
            "G_loss: 2.503\n",
            "\n",
            "Iter: 43000\n",
            "D loss: 0.5385\n",
            "G_loss: 2.969\n",
            "\n",
            "Iter: 44000\n",
            "D loss: 0.5773\n",
            "G_loss: 2.425\n",
            "\n",
            "Iter: 45000\n",
            "D loss: 0.6076\n",
            "G_loss: 2.897\n",
            "\n",
            "Iter: 46000\n",
            "D loss: 0.5535\n",
            "G_loss: 2.692\n",
            "\n",
            "Iter: 47000\n",
            "D loss: 0.4846\n",
            "G_loss: 2.585\n",
            "\n",
            "Iter: 48000\n",
            "D loss: 0.5776\n",
            "G_loss: 2.667\n",
            "\n",
            "Iter: 49000\n",
            "D loss: 0.5445\n",
            "G_loss: 2.629\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "QrUheXDAedG4",
        "colab_type": "code",
        "outputId": "4362f696-3728-48b1-d6c4-ef83ed022bdb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        }
      },
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "losses = np.array(losses)\n",
        "plt.plot(losses.T[0], label='Discriminator')\n",
        "plt.plot(losses.T[1], label='Generator')\n",
        "plt.title(\"Training Losses\")\n",
        "plt.legend()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fbeb4b05470>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fBPi+TKm8JQhfkDUDgK1lO/3VxD5NesIttm3bSk7OEMaPn+B5bMWKG3G5XFRWVvD73z+M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\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "RuJl3FHvc7Le",
        "colab_type": "code",
        "outputId": "6c658164-5ce6-495d-ec95-1c0bd1995d7e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        }
      },
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "losses = np.array(losses)\n",
        "plt.plot(losses.T[0], label='Discriminator')\n",
        "plt.plot(losses.T[1], label='Generator')\n",
        "plt.title(\"Training Losses\")\n",
        "plt.legend()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fbeb852c048>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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0v/baJn8Tary1yEnOlo8Vl1SjITFY3+LykIZ4rKQKrRKU1rPSauP3WVxcLX8X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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}