create_mnist_dataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "6f48b484",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch, torchvision\n",
    "import torchvision.datasets as datasets\n",
    "from torchvision import transforms\n",
    "from torch.utils.data import DataLoader\n",
    "import numpy as np\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torchvision import datasets, transforms\n",
    "from torch.autograd import Variable\n",
    "from torchvision.utils import save_image\n",
    "\n",
    "# Device configuration\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bf8d159",
   "metadata": {},
   "source": [
    "# Generating the dataset\n",
    "This part of the notebook is encoded in `generate_dataset.py` and served as a exploration notebook. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "23712536",
   "metadata": {},
   "outputs": [],
   "source": [
    "transform = transforms.Compose([transforms.ToTensor(),\n",
    "                                transforms.Normalize((0.1307,), (0.3081,))])\n",
    "\n",
    "mnist_trainset = datasets.MNIST(root='./data', train=True, download=False, transform=transform)\n",
    "mnist_testset = datasets.MNIST(root='./data', train=False, download=False, transform=transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e0006ca3",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_loader = DataLoader(mnist_trainset, batch_size=len(mnist_trainset))\n",
    "test_loader = DataLoader(mnist_testset, batch_size=len(mnist_testset))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "018c8a57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 1, 28, 28)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(iter(test_loader))[0].numpy().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "2034f1d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset_array = next(iter(train_loader))[1].numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "e7f5cdad",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = next(iter(train_loader))[0].numpy()\n",
    "y_train = next(iter(train_loader))[1].numpy()\n",
    "x_test = next(iter(test_loader))[0].numpy()\n",
    "y_test = next(iter(test_loader))[1].numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b64bf4de",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save(\"x_train\", x_train)\n",
    "np.save(\"y_train\", y_train)\n",
    "np.save(\"x_test\", x_test)\n",
    "np.save(\"y_test\", y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "61801df7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000,)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset_array.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12b15aa0",
   "metadata": {},
   "source": [
    "# Get the generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "4981ca55",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Generator(nn.Module):\n",
    "    def __init__(self, g_input_dim, g_output_dim):\n",
    "        super(Generator, self).__init__()       \n",
    "        self.fc1 = nn.Linear(g_input_dim, 256)\n",
    "        self.fc2 = nn.Linear(self.fc1.out_features, self.fc1.out_features*2)\n",
    "        self.fc3 = nn.Linear(self.fc2.out_features, self.fc2.out_features*2)\n",
    "        self.fc4 = nn.Linear(self.fc3.out_features, g_output_dim)\n",
    "    \n",
    "    # forward method\n",
    "    def forward(self, x): \n",
    "        x = F.leaky_relu(self.fc1(x), 0.2)\n",
    "        x = F.leaky_relu(self.fc2(x), 0.2)\n",
    "        x = F.leaky_relu(self.fc3(x), 0.2)\n",
    "        return torch.tanh(self.fc4(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "936b083d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# build network\n",
    "z_dim = 100\n",
    "mnist_dim = mnist_trainset.train_data.size(1) * mnist_trainset.train_data.size(2)\n",
    "\n",
    "G = Generator(g_input_dim = z_dim, g_output_dim = mnist_dim).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "1ecf451c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Generator(\n",
       "  (fc1): Linear(in_features=100, out_features=256, bias=True)\n",
       "  (fc2): Linear(in_features=256, out_features=512, bias=True)\n",
       "  (fc3): Linear(in_features=512, out_features=1024, bias=True)\n",
       "  (fc4): Linear(in_features=1024, out_features=784, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "809ff340",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_real = next(iter(train_loader))[0].numpy()[:1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "5b08bc19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x_real)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "8dbeeb87",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+Q9JTkjZoKFizO9TbQg0dom+QtL72d36n37tCX21537hcFkiCE3RAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kMT/A65XcTMQuIbWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.imshow(x_real[0][0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "5338b525",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Generator(\n",
       "  (fc1): Linear(in_features=100, out_features=256, bias=True)\n",
       "  (fc2): Linear(in_features=256, out_features=512, bias=True)\n",
       "  (fc3): Linear(in_features=512, out_features=1024, bias=True)\n",
       "  (fc4): Linear(in_features=1024, out_features=784, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PATH = \"latest_model\"\n",
    "G = Generator(g_input_dim = 100, g_output_dim = 784).to(device)\n",
    "G.load_state_dict(torch.load(PATH))\n",
    "G.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "021b7dad",
   "metadata": {},
   "outputs": [],
   "source": [
    "bs = 1\n",
    "\n",
    "x_fake = []\n",
    "\n",
    "for i in range(1000):\n",
    "    # Can we make the batch size bigger?\n",
    "    z = Variable(torch.randn(bs, z_dim).to(device))\n",
    "    x_f = G(z).detach().numpy().reshape(1,28,28)\n",
    "    x_fake.append(x_f)\n",
    "    \n",
    "x_fake = np.array(x_fake)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "13d94d77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 1, 28, 28)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_fake.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "e3fab183",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 1, 28, 28)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_real.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "49d2e9ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = np.stack((x_fake,x_real), axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "1655f38f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 2, 28, 28)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "cb6b9f1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.imshow(x_train[50][0][0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "72c55556",
   "metadata": {},
   "outputs": [],
   "source": [
    "# np.save(\"x_real\", x_real)\n",
    "np.save(\"x_fake\", x_fake)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "d9789392",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 784)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_fake[0].reshape(1,784).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "482b7c13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 28, 28)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_fake[1].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2354912",
   "metadata": {},
   "source": [
    "# Creating Inference Dataset\n",
    "The following is an exploratory notebook used in the file ``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "08d729b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "ad8d9934",
   "metadata": {},
   "outputs": [],
   "source": [
    "json_str = json.dumps(x_fake.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "a090b315",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(json_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "9b5857e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"inference.json\", \"w\") as f:\n",
    "    f.write(json_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e356c44d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}