LinearGAN/LinearGAN Implementation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ce24bf71-729e-40b6-8070-67d464f26c32",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import torch.nn as nn\n",
    "from torch.utils.tensorboard import SummaryWriter\n",
    "from LinearGAN import Discriminator, Generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7a27f3a7-e984-47b3-94c1-de93b2a462cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 1])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = torch.randn(1, 784)\n",
    "model = Discriminator(784)\n",
    "\n",
    "out = model(x)\n",
    "out.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7bd2ff38-f856-49e2-8b1c-ba000dd1b6b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 784])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = torch.randn(1, 64)\n",
    "model = Generator(64, 784)\n",
    "\n",
    "out = model(x)\n",
    "out.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "94920c91-992d-4ce2-b7c6-92c30e8dd413",
   "metadata": {},
   "outputs": [],
   "source": [
    "device = 'cpu'\n",
    "lr = 3e-4\n",
    "z_dim = 64\n",
    "image_dim = 28 * 28 * 1 \n",
    "batch_size = 32\n",
    "num_epochs = 500"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "57287954-9576-4bce-b989-a570cbe0ba39",
   "metadata": {},
   "outputs": [],
   "source": [
    "disc = Discriminator(image_dim).to(device)\n",
    "gen = Generator(z_dim, image_dim).to(device)\n",
    "fixed_noise = torch.randn((batch_size, 64)).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e8bf68ae-ea0f-432a-8ac4-a10b630b81c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "transforms = torchvision.transforms.Compose([\n",
    "    torchvision.transforms.ToTensor(), \n",
    "    torchvision.transforms.Normalize((0.5), (0.5)), \n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7eaafce5-a9ec-4952-a442-dff9994e6b27",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
      "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to Datasets/MNIST/raw/train-images-idx3-ubyte.gz\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "136a2ca995c54636b1f0abd23945292b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/9912422 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting Datasets/MNIST/raw/train-images-idx3-ubyte.gz to Datasets/MNIST/raw\n",
      "\n",
      "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
      "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to Datasets/MNIST/raw/train-labels-idx1-ubyte.gz\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "553ed9adaf0f4856a5521bba2d1814c3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting Datasets/MNIST/raw/train-labels-idx1-ubyte.gz to Datasets/MNIST/raw\n",
      "\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to Datasets/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a4f84222149f41ec9c5e71afc1ef9571",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1648877 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting Datasets/MNIST/raw/t10k-images-idx3-ubyte.gz to Datasets/MNIST/raw\n",
      "\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to Datasets/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b60fb7c45f3b428aa5b458929f5bf5a2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4542 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting Datasets/MNIST/raw/t10k-labels-idx1-ubyte.gz to Datasets/MNIST/raw\n",
      "\n"
     ]
    }
   ],
   "source": [
    "dataset = torchvision.datasets.MNIST(root=\"Datasets/\", transform=transforms, download=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4f2792d5-d17c-416e-8797-c3078ae428a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x15229b310>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.imshow(dataset[0][0].permute(1, 2, 0), cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ad9d6b47-38d9-4f53-b4b9-d219832330fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4d91ffc0-9d35-4874-9e9d-2d488773bc52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x152601880>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(next(iter(loader))[0][0].permute(1, 2, 0), cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fcd18191-63fd-45d7-9689-6e2f7386e1ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "opt_disc = torch.optim.Adam(disc.parameters(), lr=lr)\n",
    "opt_gen = torch.optim.Adam(gen.parameters(), lr=lr)\n",
    "criterion = nn.BCELoss()\n",
    "writer_fake = SummaryWriter(f\"Runs/LinearGAN/Fake\")\n",
    "writer_real = SummaryWriter(f\"Runs/LinearGAN/Real\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e49de106-3232-4d70-a538-2b9605fadaaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "gen.train()\n",
    "disc.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b32d81d5-c477-43d5-83bb-a77cd3787407",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████| 1875/1875 [00:21<00:00, 89.26it/s]\n",
      "  0%|▏                                      | 7/1875 [00:00<00:28, 65.30it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [337/500] \n",
      "Discriminator Loss: 0.4807, Generator Loss: 1.3186\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [338/500] \n",
      "Discriminator Loss: 0.5340, Generator Loss: 1.2875\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [339/500] \n",
      "Discriminator Loss: 0.6116, Generator Loss: 1.1289\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
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      "Epoch [340/500] \n",
      "Discriminator Loss: 0.6016, Generator Loss: 1.3589\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "Epoch [341/500] \n",
      "Discriminator Loss: 0.5199, Generator Loss: 1.2424\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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    },
    {
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      "Epoch [342/500] \n",
      "Discriminator Loss: 0.5314, Generator Loss: 1.2690\n"
     ]
    },
    {
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     "output_type": "stream",
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    },
    {
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      "Epoch [343/500] \n",
      "Discriminator Loss: 0.5915, Generator Loss: 1.0341\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "Epoch [344/500] \n",
      "Discriminator Loss: 0.5973, Generator Loss: 1.1339\n"
     ]
    },
    {
     "name": "stderr",
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      "Epoch [345/500] \n",
      "Discriminator Loss: 0.4409, Generator Loss: 1.3687\n"
     ]
    },
    {
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    },
    {
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      "Epoch [346/500] \n",
      "Discriminator Loss: 0.5120, Generator Loss: 1.2327\n"
     ]
    },
    {
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      "Epoch [347/500] \n",
      "Discriminator Loss: 0.5597, Generator Loss: 1.2282\n"
     ]
    },
    {
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      "Epoch [348/500] \n",
      "Discriminator Loss: 0.5111, Generator Loss: 1.3811\n"
     ]
    },
    {
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    },
    {
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      "Epoch [349/500] \n",
      "Discriminator Loss: 0.5305, Generator Loss: 1.2188\n"
     ]
    },
    {
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    },
    {
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      "Epoch [350/500] \n",
      "Discriminator Loss: 0.5267, Generator Loss: 0.9946\n"
     ]
    },
    {
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      "Epoch [351/500] \n",
      "Discriminator Loss: 0.5339, Generator Loss: 1.2959\n"
     ]
    },
    {
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      "Discriminator Loss: 0.4684, Generator Loss: 1.4905\n"
     ]
    },
    {
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      "Epoch [353/500] \n",
      "Discriminator Loss: 0.4818, Generator Loss: 1.2617\n"
     ]
    },
    {
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      "Epoch [354/500] \n",
      "Discriminator Loss: 0.5990, Generator Loss: 1.3266\n"
     ]
    },
    {
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    },
    {
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      "Epoch [355/500] \n",
      "Discriminator Loss: 0.6396, Generator Loss: 1.2603\n"
     ]
    },
    {
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    {
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      "Epoch [356/500] \n",
      "Discriminator Loss: 0.5454, Generator Loss: 1.2592\n"
     ]
    },
    {
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      "Epoch [480/500] \n",
      "Discriminator Loss: 0.4940, Generator Loss: 1.2531\n"
     ]
    },
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      "Epoch [481/500] \n",
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     ]
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      "Epoch [482/500] \n",
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     ]
    },
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      "Epoch [483/500] \n",
      "Discriminator Loss: 0.5323, Generator Loss: 1.4272\n"
     ]
    },
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      "Epoch [484/500] \n",
      "Discriminator Loss: 0.5377, Generator Loss: 1.1915\n"
     ]
    },
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      "Epoch [485/500] \n",
      "Discriminator Loss: 0.6514, Generator Loss: 1.1482\n"
     ]
    },
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      "Epoch [486/500] \n",
      "Discriminator Loss: 0.6067, Generator Loss: 1.1112\n"
     ]
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      "Discriminator Loss: 0.5401, Generator Loss: 1.2058\n"
     ]
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      "Epoch [488/500] \n",
      "Discriminator Loss: 0.5861, Generator Loss: 1.2540\n"
     ]
    },
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      "Epoch [489/500] \n",
      "Discriminator Loss: 0.5583, Generator Loss: 1.6974\n"
     ]
    },
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      "Epoch [490/500] \n",
      "Discriminator Loss: 0.5277, Generator Loss: 1.5668\n"
     ]
    },
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      "Epoch [491/500] \n",
      "Discriminator Loss: 0.6548, Generator Loss: 1.2485\n"
     ]
    },
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      "Epoch [492/500] \n",
      "Discriminator Loss: 0.5808, Generator Loss: 1.0830\n"
     ]
    },
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      "Epoch [493/500] \n",
      "Discriminator Loss: 0.5378, Generator Loss: 0.9930\n"
     ]
    },
    {
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      "Epoch [494/500] \n",
      "Discriminator Loss: 0.5704, Generator Loss: 1.2387\n"
     ]
    },
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      "Epoch [495/500] \n",
      "Discriminator Loss: 0.4562, Generator Loss: 1.5111\n"
     ]
    },
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      "Epoch [496/500] \n",
      "Discriminator Loss: 0.5466, Generator Loss: 1.5535\n"
     ]
    },
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      "Epoch [497/500] \n",
      "Discriminator Loss: 0.4297, Generator Loss: 1.5467\n"
     ]
    },
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      "Epoch [498/500] \n",
      "Discriminator Loss: 0.4958, Generator Loss: 1.1453\n"
     ]
    },
    {
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     "text": [
      "Epoch [499/500] \n",
      "Discriminator Loss: 0.6281, Generator Loss: 1.2259\n"
     ]
    },
    {
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     "output_type": "stream",
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     "output_type": "stream",
     "text": [
      "Epoch [500/500] \n",
      "Discriminator Loss: 0.5457, Generator Loss: 1.0770\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "import tqdm\n",
    "\n",
    "step = 0\n",
    " \n",
    "for epoch in range(num_epochs):\n",
    "    for batch_idx, (real, _) in enumerate(tqdm.tqdm(loader)):\n",
    "        \n",
    "        real = real.view(-1, 784).to(device)\n",
    "        noise = torch.randn((batch_size, z_dim)).to(device)\n",
    "        fake = gen(noise).to(device)\n",
    "        \n",
    "        disc_real = disc(real).view(-1)\n",
    "        lossD_real = criterion(disc_real, torch.ones_like(disc_real))\n",
    "    \n",
    "        disc_fake = disc(fake).view(-1)\n",
    "        lossD_fake = criterion(disc_fake, torch.zeros_like(disc_fake))\n",
    "        \n",
    "        lossD = (lossD_fake + lossD_real) / 2.0\n",
    "        \n",
    "        disc.zero_grad()\n",
    "        lossD.backward(retain_graph=True)\n",
    "        opt_disc.step()\n",
    "        \n",
    "        \n",
    "        output = disc(fake).view(-1).to(device)\n",
    "        lossG = criterion(output, torch.ones_like(output))\n",
    "        gen.zero_grad()\n",
    "        lossG.backward()\n",
    "        opt_gen.step()\n",
    "        \n",
    "        if batch_idx == 0:\n",
    "            print(\n",
    "                 f\"Epoch [{epoch+1}/{num_epochs}] \\nDiscriminator Loss: {lossD:.4f}, Generator Loss: {lossG:.4f}\"\n",
    "            )\n",
    "                \n",
    "            with torch.no_grad():\n",
    "                fake = gen(fixed_noise).reshape(-1, 1, 28, 28)\n",
    "                data = real.reshape(-1, 1, 28, 28)\n",
    "                img_fake_grid = torchvision.utils.make_grid(fake, normalize=True)\n",
    "                img_real_grid = torchvision.utils.make_grid(data, normalize=True)\n",
    "             \n",
    "                writer_real.add_image('LinearGAN MNIST Discriminator', img_real_grid, global_step=step)\n",
    "\n",
    "                writer_fake.add_image('LinearGAN MNIST Generator', img_fake_grid, global_step=step)\n",
    "    \n",
    "                step+=1 \n",
    "                  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9dcd4d96-b14a-4e6b-9bcb-989c5b7e8bc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Generator(\n",
       "  (gen): Sequential(\n",
       "    (0): Linear(in_features=64, out_features=256, bias=True)\n",
       "    (1): ReLU(inplace=True)\n",
       "    (2): Linear(in_features=256, out_features=784, bias=True)\n",
       "    (3): Tanh()\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ngen = gen.to(torch.device('mps'))\n",
    "ngen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9bb97fc7-8847-4b21-90ca-2cb956e0c078",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import torch.nn.functional as F\n",
    "\n",
    "x = torch.randn(4, 64).to(torch.device('mps'))\n",
    "out = ngen(x)\n",
    "out = out.view(-1, 1, 28, 28)\n",
    "image_unflat = F.relu(out.detach().cpu().view(-1, 1, 28, 28))\n",
    "image_grid = torchvision.utils.make_grid(image_unflat[:4], nrow=2)\n",
    "plt.imshow(image_grid.permute(1, 2, 0).squeeze())\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "data-science",
   "language": "python",
   "name": "data-science"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}