music_gan_v2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "mnist-gan-v2.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "bN5dqV7DRf47",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 534
        },
        "outputId": "af1934a7-521f-42e4-8ffa-6c4e84c716f7"
      },
      "source": [
        "%load_ext autoreload\n",
        "%matplotlib inline\n",
        "%autoreload 2\n",
        "%load utils\n",
        "\n",
        "!pip install TensorboardX\n",
        "from utils import Logger\n",
        "\n",
        "\n",
        "from IPython import display\n",
        "\n",
        "\n",
        "from torch.utils.data import DataLoader\n",
        "import torchvision\n",
        "import torch\n",
        "from torch.optim import Adam\n",
        "from torch.autograd import Variable\n",
        "\n",
        "import torch.nn as nn\n",
        "from torchvision import transforms, datasets\n",
        "import pandas as pd\n",
        "from skimage import io, transform\n",
        "import tensorflow as tf\n",
        "import logging\n",
        "from tensorflow import layers\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "import os\n",
        "import zipfile"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mfind_user_code\u001b[0;34m(self, target, raw, py_only, skip_encoding_cookie, search_ns)\u001b[0m\n\u001b[1;32m   3184\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m                                              \u001b[0;31m# User namespace\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3185\u001b[0;31m             \u001b[0mcodeobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muser_ns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3186\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<string>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'utils' is not defined",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-4-825a62407894>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'matplotlib inline'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'autoreload 2'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'load utils'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msystem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'pip install TensorboardX'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mmagic\u001b[0;34m(self, arg_s)\u001b[0m\n\u001b[1;32m   2158\u001b[0m         \u001b[0mmagic_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmagic_arg_s\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marg_s\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpartition\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m' '\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2159\u001b[0m         \u001b[0mmagic_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmagic_name\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprefilter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mESC_MAGIC\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2160\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmagic_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmagic_arg_s\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2162\u001b[0m     \u001b[0;31m#-------------------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_line_magic\u001b[0;34m(self, magic_name, line)\u001b[0m\n\u001b[1;32m   2079\u001b[0m                 \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'local_ns'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstack_depth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf_locals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2080\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2081\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2082\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2083\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m</usr/local/lib/python3.6/dist-packages/decorator.py:decorator-gen-46>\u001b[0m in \u001b[0;36mload\u001b[0;34m(self, arg_s)\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/IPython/core/magic.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m    186\u001b[0m     \u001b[0;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    187\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 188\u001b[0;31m         \u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    189\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    190\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/IPython/core/magics/code.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(self, arg_s)\u001b[0m\n\u001b[1;32m    348\u001b[0m         \u001b[0msearch_ns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'n'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mopts\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    349\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 350\u001b[0;31m         \u001b[0mcontents\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshell\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfind_user_code\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msearch_ns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msearch_ns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    351\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    352\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;34m's'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mopts\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mfind_user_code\u001b[0;34m(self, target, raw, py_only, skip_encoding_cookie, search_ns)\u001b[0m\n\u001b[1;32m   3186\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3187\u001b[0m             raise ValueError((\"'%s' was not found in history, as a file, url, \"\n\u001b[0;32m-> 3188\u001b[0;31m                                 \"nor in the user namespace.\") % target)\n\u001b[0m\u001b[1;32m   3189\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3190\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcodeobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstring_types\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: 'utils' was not found in history, as a file, url, nor in the user namespace."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XnQt05Y9Uxqc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "batch_size = 64"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DhB7LGWwR0Ez",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 63
        },
        "outputId": "43b45e3a-754c-4fca-f0f3-880b1a202f87"
      },
      "source": [
        "!unzip blues.zip \n",
        "\n",
        "\n"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "unzip:  cannot find or open blues.zip, blues.zip.zip or blues.zip.ZIP.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qvRLyK4iSDGi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 181
        },
        "outputId": "b758b56c-9bbc-4dc3-ca01-54465d9a1f19"
      },
      "source": [
        "DATA_FOLDER = 'blues'\n",
        "dataset = datasets.ImageFolder(DATA_FOLDER,transform=transforms.ToTensor())   # Set dataset"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-7-ce2e2b089005>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mDATA_FOLDER\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'blues'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdatasets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mImageFolder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDATA_FOLDER\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtransforms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mToTensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m   \u001b[0;31m# Set dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'datasets' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FGYUJyqRS5Mt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_loader = torch.utils.data.DataLoader(dataset=dataset, batch_size=5, shuffle=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jEVQLnt9SGY7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "img = dataset[0][0]\n",
        "img = img.numpy()\n",
        "image = np.squeeze(img[0])\n",
        "img[0]\n",
        "\n",
        "\n",
        "plt.imshow(img[0], cmap = 'gray')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I4dV7QVaSa4d",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "class Discriminator(nn.Module):\n",
        "  \n",
        "  def __init__(self, input_size, hidden_dim, output_size):\n",
        "    super(Discriminator, self).__init__()\n",
        "    \n",
        "    # Layers\n",
        "    self.conv1 = nn.Linear(input_size, hidden_dim*4)    # Input Layer\n",
        "    self.conv2 = nn.Linear(hidden_dim*4, hidden_dim*2)\n",
        "    self.conv3 = nn.Linear(hidden_dim*2, hidden_dim)\n",
        "    self.conv4 = nn.Linear(hidden_dim, output_size)  # Final Layer\n",
        "    \n",
        "    # Dropout layer\n",
        "    self.dropout = nn.Dropout(0.3)\n",
        "    \n",
        "  def forward(self, x):\n",
        "    # Flatten image\n",
        "    x = x.view(-1, 558*544)\n",
        "      \n",
        "    x = F.leaky_relu(self.conv1(x), 0.2)   # (input, negative_slope)\n",
        "    x = self.dropout(x)\n",
        "    x = F.leaky_relu(self.conv2(x), 0.2)\n",
        "    x = self.dropout(x)\n",
        "    x = F.leaky_relu(self.conv3(x), 0.2)\n",
        "    x = self.dropout(x)\n",
        "     \n",
        "    # Final Layer\n",
        "    out = self.conv4(x)\n",
        "    return out"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Nmzpe2ISeqs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Generator(nn.Module):\n",
        "  \n",
        "  def __init__(self, input_size, hidden_dim, output_size):\n",
        "    super(Generator, self).__init__()\n",
        "    \n",
        "    # Layers\n",
        "    self.conv1 = nn.Linear(input_size, hidden_dim)    # Input Layer\n",
        "    self.conv2 = nn.Linear(hidden_dim, hidden_dim*2)\n",
        "    self.conv3 = nn.Linear(hidden_dim*2, hidden_dim*4)\n",
        "    self.conv4 = nn.Linear(hidden_dim*4, output_size)  # Final Layer\n",
        "    \n",
        "    # Dropout layer\n",
        "    self.dropout = nn.Dropout(0.3)\n",
        "  \n",
        "  def forward(self, x):\n",
        "    x = F.leaky_relu(self.conv1(x), 0.2)   # (input, negative_slope)\n",
        "    x = self.dropout(x)\n",
        "    x = F.leaky_relu(self.conv2(x), 0.2)\n",
        "    x = self.dropout(x)\n",
        "    x = F.leaky_relu(self.conv3(x), 0.2)\n",
        "    x = self.dropout(x)\n",
        "     \n",
        "    # Final Layer\n",
        "    output = F.tanh(self.conv4(x))\n",
        "      \n",
        "    return output"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "umg9XkDOShO4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Discriminator Hyperarameters\n",
        "\n",
        "# Size of input image\n",
        "input_size = 303552\n",
        "# Size of discriminator output\n",
        "d_output_size = 1\n",
        "# Size of last hidden layer in discriminator (layer 3)\n",
        "d_hidden_size = 32\n",
        "\n",
        "\n",
        "# Generator Hyperparamters\n",
        "\n",
        "# Size of latent vector to give to Generator (reffered as noise)\n",
        "z_size = 100\n",
        "# Size of discriminator output (generated image)\n",
        "g_output_size = 303552\n",
        "# Size of first hidden layer\n",
        "g_hidden_size = 32"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "34ynVkWmSpKF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "D = Discriminator(input_size, d_hidden_size, d_output_size)\n",
        "G = Generator(z_size, g_hidden_size, g_output_size)\n",
        "\n",
        "print(D)\n",
        "print()\n",
        "print(G)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZZF0sxt1SrLB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Calculating losses\n",
        "def real_loss(D_out, smooth=False):\n",
        "    batch_size = D_out.size(0)\n",
        "    # label smoothing\n",
        "    if smooth:\n",
        "        # smooth, real labels = 0.9\n",
        "        labels = torch.ones(batch_size)*0.9\n",
        "    else:\n",
        "        labels = torch.ones(batch_size) # real labels = 1\n",
        "        \n",
        "    # numerically stable loss\n",
        "    criterion = nn.BCEWithLogitsLoss()\n",
        "    # calculate loss\n",
        "    loss = criterion(D_out.squeeze(), labels)\n",
        "    return loss\n",
        "\n",
        "def fake_loss(D_out):\n",
        "    batch_size = D_out.size(0)\n",
        "    labels = torch.zeros(batch_size) # fake labels = 0\n",
        "    criterion = nn.BCEWithLogitsLoss()\n",
        "    # calculate loss\n",
        "    loss = criterion(D_out.squeeze(), labels)\n",
        "    return loss"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LN7I9QTzSs6-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "import torch.optim as optim\n",
        "\n",
        "# Learning Rate\n",
        "lr = 0.002\n",
        "\n",
        "# Optimizers\n",
        "d_optimizer = optim.Adam(D.parameters(), lr)\n",
        "g_optimizer = optim.Adam(G.parameters(), lr)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iz2YNgvuSui1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pickle as pkl\n",
        "\n",
        "# training hyperparams\n",
        "num_epochs = 100\n",
        "\n",
        "# keep track of loss and generated, \"fake\" samples\n",
        "samples = []\n",
        "losses = []\n",
        "\n",
        "print_every = 200\n",
        "\n",
        "# Get some fixed data for sampling. These are images that are held\n",
        "# constant throughout training, and allow us to inspect the model's performance\n",
        "sample_size=5\n",
        "fixed_z = np.random.uniform(-1, 1, size=(sample_size, z_size))\n",
        "fixed_z = torch.from_numpy(fixed_z).float()\n",
        "\n",
        "# train the network\n",
        "D.train()\n",
        "G.train()\n",
        "for epoch in range(num_epochs):\n",
        "    \n",
        "    for batch_i, (real_images, _) in enumerate(train_loader):\n",
        "                \n",
        "        real_images\n",
        "        batch_size = real_images.size(0)\n",
        "        print(batch_size)\n",
        "        ## Important rescaling step ## \n",
        "        real_images = real_images*2 - 5  # rescale input images from [0,1) to [-1, 1)\n",
        "        print(batch_size)\n",
        "        \n",
        "        if(batch_size == 1):\n",
        "          batch_size=5\n",
        "        #  TRAIN THE DISCRIMINATOR\n",
        "        d_optimizer.zero_grad()\n",
        "        \n",
        "        # 1. Train with real images\n",
        "\n",
        "        # Compute the discriminator losses on real images \n",
        "        # smooth the real labels\n",
        "        D_real = D(real_images)\n",
        "        d_real_loss = real_loss(D_real, smooth=True)\n",
        "        \n",
        "        # 2. Train with fake images\n",
        "        \n",
        "        # Generate fake images\n",
        "        z = np.random.uniform(-1, 1, size=(batch_size, z_size))\n",
        "        z = torch.from_numpy(z).float()\n",
        "        fake_images = G(z)\n",
        "        \n",
        "        # Compute the discriminator losses on fake images        \n",
        "        D_fake = D(fake_images)\n",
        "        d_fake_loss = fake_loss(D_fake)\n",
        "        \n",
        "        # add up loss and perform backprop\n",
        "        d_loss = d_real_loss + d_fake_loss\n",
        "        d_loss.backward()\n",
        "        d_optimizer.step()\n",
        "        \n",
        "        \n",
        "\n",
        "        #  TRAIN THE GENERATOR\n",
        "        g_optimizer.zero_grad()\n",
        "        \n",
        "        # 1. Train with fake images and flipped labels\n",
        "        \n",
        "        # Generate fake images\n",
        "        z = np.random.uniform(-1, 1, size=(batch_size, z_size))\n",
        "        z = torch.from_numpy(z).float()\n",
        "        fake_images = G(z)\n",
        "        \n",
        "        # Compute the discriminator losses on fake images \n",
        "        # using flipped labels!\n",
        "        D_fake = D(fake_images)\n",
        "        g_loss = real_loss(D_fake) # use real loss to flip labels\n",
        "        \n",
        "        # perform backprop\n",
        "        g_loss.backward()\n",
        "        g_optimizer.step()\n",
        "\n",
        "        # Print some loss stats\n",
        "        if batch_i % print_every == 0:\n",
        "            # print discriminator and generator loss\n",
        "            print('Epoch [{:5d}/{:5d}] | d_loss: {:6.4f} | g_loss: {:6.4f}'.format(\n",
        "                    epoch+1, num_epochs, d_loss.item(), g_loss.item()))\n",
        "\n",
        "    \n",
        "    # AFTER EACH EPOCH\n",
        "    # append discriminator loss and generator loss\n",
        "    losses.append((d_loss.item(), g_loss.item()))\n",
        "    \n",
        "    # generate and save sample, fake images\n",
        "    G.eval() # eval mode for generating samples\n",
        "    samples_z = G(fixed_z)\n",
        "    samples.append(samples_z)\n",
        "    G.train() # back to train mode"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "479tRWdcSxnZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Save training generator samples\n",
        "with open('train_samples.pkl', 'wb') as f:\n",
        "  pkl.dump(samples, f)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GL1eWP4cbiD-",
        "colab_type": "code",
        "colab": {}
      },
      "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": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wCVjhPs-bjM6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Helper function for viewing a list of passed in sample images\n",
        "def view_samples(epoch, samples):\n",
        "    fig, axes = plt.subplots(figsize=(7,7), nrows=4, ncols=4, sharey=True, sharex=True)\n",
        "    for ax, img in zip(axes.flatten(), samples[epoch]):\n",
        "        img = img.detach()\n",
        "        ax.xaxis.set_visible(False)\n",
        "        ax.yaxis.set_visible(False)\n",
        "        im = ax.imshow(img, cmap='grey')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dbxxae-GbmjL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Load samples from generator, taken while training\n",
        "with open('train_samples.pkl', 'rb') as f:\n",
        "    samples = pkl.load(f)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u6cGgT2qbjKT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "rows = 10 # split epochs into 10, so 100/10 = every 10 epochs\n",
        "cols = 6\n",
        "fig, axes = plt.subplots(figsize=(7,12), nrows=rows, ncols=cols, sharex=True, sharey=True)\n",
        "\n",
        "for sample, ax_row in zip(samples[::int(len(samples)/rows)], axes):\n",
        "    for img, ax in zip(sample[::int(len(sample)/cols)], ax_row):\n",
        "        img = img.detach()\n",
        "        ax.imshow(img, cmap='Greys_r')\n",
        "        ax.xaxis.set_visible(False)\n",
        "        ax.yaxis.set_visible(False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RQ-vFfWGbjHm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}