Berthine_Auto-Encoder exercise.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Berthine Nyunga_Autoencoder_Tutorial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "from torch import nn\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision import transforms\n",
    "from torchvision.datasets import FashionMNIST\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert vector to image\n",
    "def to_img(x):\n",
    "    x = 0.5 * (x + 1)\n",
    "    x = x.view(x.size(0), 28, 28)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "bs = 1000 #batch_size\n",
    "\n",
    "transform = transforms.Compose([transforms.ToTensor()])\n",
    "\n",
    "traindataset = FashionMNIST('./data', download=True, train=True, transform=transform)\n",
    "testdataset = FashionMNIST('./data', download=True, train=False, transform=transform)\n",
    "\n",
    "# Loaders\n",
    "trainloader = torch.utils.data.DataLoader(traindataset, batch_size=bs, shuffle=True, num_workers=4)\n",
    "testloader = torch.utils.data.DataLoader(testdataset, batch_size=bs, shuffle=False, num_workers=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Displaying routine\n",
    "def display_images(in_, out, n=1):\n",
    "    for N in range(n):\n",
    "        if in_ is not None:\n",
    "            in_pic = to_img(in_.cpu().data)\n",
    "            plt.figure(figsize=(18, 6))\n",
    "            for i in range(4):\n",
    "                plt.subplot(1,4,i+1)\n",
    "                plt.imshow(in_pic[i+4*N])\n",
    "                plt.axis('off')\n",
    "        out_pic = to_img(out.cpu().data)\n",
    "        plt.figure(figsize=(18, 6))\n",
    "        for i in range(4):\n",
    "            plt.subplot(1,4,i+1)\n",
    "            plt.imshow(out_pic[i+4*N])\n",
    "            plt.axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class AutoEncoder(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(AutoEncoder, self).__init__()\n",
    "        self.encoder = nn.Sequential(\n",
    "        nn.Linear(28*28, 100),\n",
    "        nn.ReLU())\n",
    "        \n",
    "        self.decoder = nn.Sequential(\n",
    "        nn.Linear(100, 28*28),\n",
    "        nn.ReLU())\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.encoder(x)\n",
    "        x = self.decoder(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_ae = AutoEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configure the optimiszer and criterion\n",
    "learning_rate = 1e-3\n",
    "\n",
    "optimizer = torch.optim.Adam(model_ae.parameters(), lr=learning_rate)\n",
    "criterion = nn.MSELoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch [1/10], loss:0.0431\n",
      "epoch [2/10], loss:0.0314\n",
      "epoch [3/10], loss:0.0264\n",
      "epoch [4/10], loss:0.0236\n",
      "epoch [5/10], loss:0.0212\n",
      "epoch [6/10], loss:0.0199\n",
      "epoch [7/10], loss:0.0182\n",
      "epoch [8/10], loss:0.0178\n",
      "epoch [9/10], loss:0.0172\n",
      "epoch [10/10], loss:0.0173\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_epochs = 10\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    for data in trainloader:\n",
    "        img, _ = data\n",
    "        img.requires_grad_()\n",
    "        img = img.view(img.size(0), -1)\n",
    "        output = model_ae(img)  \n",
    "        loss = criterion(output, img.data)\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "    # ===================log========================\n",
    "    print(f'epoch [{epoch + 1}/{num_epochs}], loss:{loss.item():.4f}')\n",
    "    display_images(None, output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.autograd import Variable\n",
    "plt.style.use('default')\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_images, labels = next(iter(testloader))\n",
    "\n",
    "test_images = Variable(test_images.view(test_images.size()[0], -1))\n",
    "test_output = model_ae(test_images)\n",
    "  \n",
    "inp = test_images.view(-1, 28, 28)\n",
    "out = test_output.view(-1, 28, 28)\n",
    "\n",
    "fig = plt.figure()\n",
    "plot = fig.add_subplot(1, 2, 1)\n",
    "plot.set_title('Original Image')\n",
    "imgplot = plt.imshow(inp[0].cpu(), cmap='gray')\n",
    "\n",
    "plot = fig.add_subplot(1, 2, 2)\n",
    "plot.set_title('Generated Image')\n",
    "imgplot = plt.imshow(out[0].cpu().detach(), cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercises\n",
    "\n",
    "Your exercise should you choose to accept, is to \n",
    "\n",
    "1. change the model class to use Convolution Layers and re-run with the same training code in cells below.\n",
    "2. Edit the code above for denoising autoencoder? (bonus point if your code it)\n",
    "3. Briefly describe the performance of using the fully connected auto-encoder to the CNN_Autoencoder\n",
    "\n",
    "Deadline: Today: 2:pm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CNN_AutoEncoder(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(CNN_AutoEncoder, self).__init__()\n",
    "        self.encoder = nn.Sequential(nn.Conv2d(1, 16, kernel_size=3 ),\n",
    "                                    nn.LeakyReLU(),\n",
    "                                    \n",
    "                                    nn.Conv2d(16, 8, kernel_size=3),\n",
    "                                    nn.LeakyReLU())\n",
    "       \n",
    "        self.decoder = nn.Sequential(nn.ConvTranspose2d(8, 16,  kernel_size = 3),\n",
    "                                     nn.LeakyReLU(),\n",
    "                                     nn.ConvTranspose2d(16, 1, kernel_size= 3),\n",
    "                                     nn.LeakyReLU())\n",
    "                                     \n",
    "    \n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.encoder(x)\n",
    "        x = self.decoder(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_1 = CNN_AutoEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CNN_AutoEncoder(\n",
       "  (encoder): Sequential(\n",
       "    (0): Conv2d(1, 16, kernel_size=(3, 3), stride=(1, 1))\n",
       "    (1): LeakyReLU(negative_slope=0.01)\n",
       "    (2): Conv2d(16, 8, kernel_size=(3, 3), stride=(1, 1))\n",
       "    (3): LeakyReLU(negative_slope=0.01)\n",
       "  )\n",
       "  (decoder): Sequential(\n",
       "    (0): ConvTranspose2d(8, 16, kernel_size=(3, 3), stride=(1, 1))\n",
       "    (1): LeakyReLU(negative_slope=0.01)\n",
       "    (2): ConvTranspose2d(16, 1, kernel_size=(3, 3), stride=(1, 1))\n",
       "    (3): LeakyReLU(negative_slope=0.01)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configure the optimiszer and criterion\n",
    "learning_rate = 1e-3\n",
    "\n",
    "optimizer = torch.optim.Adam(model_1.parameters(), lr=learning_rate)\n",
    "criterion = nn.MSELoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch [1/10], loss:0.0123\n",
      "epoch [2/10], loss:0.0055\n",
      "epoch [3/10], loss:0.0028\n",
      "epoch [4/10], loss:0.0020\n",
      "epoch [5/10], loss:0.0018\n",
      "epoch [6/10], loss:0.0015\n",
      "epoch [7/10], loss:0.0014\n",
      "epoch [8/10], loss:0.0013\n",
      "epoch [9/10], loss:0.0012\n",
      "epoch [10/10], loss:0.0012\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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2Hotr8xnBV2x+08tiPjhR65rPv2ls7szXzsoj+b4zDZXXnXHlflnpop/28n9Qq9Pubj71+/Uct5T/vYEcz2o/NYVDP93H1hvk/6C3mQ9+4XjzwU+GlZ1zVqh9Ty+4urnPvZRS1sbNffTT/nlx7f7xyZh/Ye3GmO8e5PXbe6uN2ep0GNcemyzF/K5enkP2LjQ/M0spZfxQfmZPw43j9s9fFtdee2At5m3kF30AAABoEYM+AAAAtIhBHwAAAFrEoA8AAAAtYtAHAACAFjHoAwAAQIsY9AEAAKBF+mf6AM5WnX7zR9PbuyeuHd+37+k+nCdscuBgzK/4z7kr/q6t+W970dfcFfPp1twMfc+bmrvGr/6duLSs79kW8+6h5l7QUko5ft32mHcmuW/44Iuby5Iv+YtaYfDp655/Vql8jov/45MxXwrXdSmlXPbm5t7bUkrpVbrs89pc/NydY9ullDKe9Zq3Xfnc5t33oJOv+6XeKOYHNpqv7csHR+Pa3nq+9rqvezDmH+nn+8LK+BMxT395ZxAK1nnCHn5Nvm6veXt+5h559aUx70zyOTTNt4XYB1/re69cOmWSH9mlX6mFHi089c72+rHn+0ZtfXfzSR7Qk1C55ZRJ5Tut3TN7lc+9vx7Wex04Jxy/Op/An/vU5TFfONZ8Y1j74Y249vC0+XleSinD7jjm3crFtz5rvgBG4V3iy/vOF9e+zeb3/FJKeevOu2P+H2Jayq9+7jWN2dLuPAcceEWeI56TXyHPSX7RBwAAgBYx6AMAAECLGPQBAACgRQz6AAAA0CIGfQAAAGgRgz4AAAC0iEEfAAAAWiSX07ZYb1vuUlx91XWN2eKB3NPY610R88k998X8dJoefCzmu//64pjfd/15Mb/yigMx33e8efuPvTx3bw7emLf9PZfmvuv/+tZvifnqBZWO9d+6pzGbVDp3OTvMxrl7dtDJ/bHHJkuN2VIvl0LXuuZPToYxr/XmTivdt3Fteepd26WUMpnz34wH3ebP5oOnnhvXji6oFGZX1M6Jmu5ic9H5dH19rm0/m3SXlxuz8Uq+v06PHI35xvbLYj7r5u3POvn62NzenC8cq2y7culMhnnfnXGlyz7ddiqXfXdU2fZ8l85cps014KWUUiZL+Y+rfe61v326kLdfuV1zDlh+JJ8ku1/3SMwf/sxFjdm333h7XHtoshLz2vvEYic/F9Mzu9uZxrXLlW2vTvO7zMFJnqEOvuVrYr74keZs5dZH49r1ta0xbyO/6AMAAECLGPQBAACgRQz6AAAA0CIGfQAAAGgRgz4AAAC0iEEfAAAAWqS19XqzV94U8xMXNVcilVLKto/va8zG+3N9w9o3vjTmoxdeEPOVd+eauHlMV3OtxbZ3/FXMH19+Zczvu2Uj5rNtzZUgu993b1xb/ijX2XzgPdfH/JFX59P98p/9ZMxzmQltcHizueKrlFJ2DJqvn+VKvV7N5jSfn1t6+doah3q9Wl3OvP/i2yt5+/1KFdCxzebKm1qN0MLKfJ/7vFToPTG93bmadf2FlzdmgxP53l97rtWq1Gp5rYYunaKz3nw1bzXThcp/ME9zZqU1dlZ5g6xcuqVyy5tP5di7m5X6vEGl1rCy/fi95NslZ4mNXflL3rV4KuaPhO/5uUv749qDk1wDN6j0N9Yqb0fhfWFauSlNOpW8ctO5Y3NnzI++INf3Xf6e5u/l6p25hvv20XNi3kZ+0QcAAIAWMegDAABAixj0AQAAoEUM+gAAANAiBn0AAABoEYM+AAAAtIhBHwAAAFrkdLaYnlanvuvmmC8/nLuNh0dyT+NdP3lFY3bza/K2v2P3H8T8X/37vxvzxa99ccx7f/rpmM+l29ytWUopJy/Ly/e+K59Sh3/oZGP2sg/n/svf/dQrYz78SC4Uvuq/5O1PppXSX1pvdZzPoV7oo6/1vafe2lJKmczmKbwupRuKo4eVzt1pZd/dSml0bfsblcLscfhsVitF4bu3N99TnhadyvcyqxRq82WV7uW1CwaNWW9jvmtj2758fh67Jl+bs17ef38thJXO9Npf1lvP51el8rp0JmF97dStHVxlfe3YepuV7QfhVlxKKaU7rvwHlWOfDCsHUFnf22z+D6aD+c5nzg5HNpZjPjjW/D1fM3w0rv3c+iUxX+zke1pNel/J01EpvdpNrWJU8v322qv3x7z/N83X1o7Balx7cm++9nbG9NzkF30AAABoEYM+AAAAtIhBHwAAAFrEoA8AAAAtYtAHAACAFjHoAwAAQIsY9AEAAKBFcrnxnDrD5iLS2cbGXNu++Z9+KubfuP2OmP/SA98Q8/GXLmrM7vvl58a1P3fF9TG/6PZUulvK237792L+b7/ruxuz6Wc+F9dWVbrkL/pYbthcvvORmI8X9zZmh/7x1ri2/1hzz3Ippez6fO72nNz9pZjP05fdO//8vO9Dh/K2OStsTnO/63K3ufi5Wyl2Xh3lPvhepat+Uvl32WG3+drsVrZdM+/6fjd/Nt1QSj2aVTp3d+RrK9+RnoBw3fMkjHJperp8xsvzfQe1zvXZoLL9yvpp6FyfzVmZPu2fuc712uc27/pZr/K5z/G1d6b5cws14l/e9Zxvx53wxU/yo4CzxMpD+Rzaf8W2mG9e3HySLXbyu3TtubfczTPUaN4TOKi9iwwqF9eJyVLMbzn/7ph/9OHlxuyHdn0srr1t+RUxbyO/6AMAAECLGPQBAACgRQz6AAAA0CIGfQAAAGgRgz4AAAC0iEEfAAAAWsSgDwAAAC0yV9Fi//JLYz6+/4F5Nh99+DdzF+IX/vCCmHeOPB7zazcfbcwe/cmb49rxS07EfPCHqzH/0Q/9YMy7b2r+95mrPxOXzq2/mvsxxw/n1urtH15vzP7khS+Ka8/7fC7V3fJg87ZLKaX7/OtivnbZ1phvbG/uNR0t5b7V834zd31zdljs5W7b0yl1yT8Rqdu2XykC73bm23evUpg9rZSJ97vN95WN6SCuXTqD3xlP3OTosZhvv/NoY3b4O/M5UN33YqWLvtK5vrk9Lx9ta75+ehuVfVcuvVrfe7WrPvycU1tbVTv26vafetd9pWa8+rl1xzmvSZ9rKaVMFpqzuT93nhHHnpdPkpV+/iI7jzWfpIudvO31ynNvSy+/704qz9zeHO8b09rJX7E+y3/b9UsPx/yjs6sbs5998Na4dvFQ5X7cQn7RBwAAgBYx6AMAAECLGPQBAACgRQz6AAAA0CIGfQAAAGgRgz4AAAC0iEEfAAAAWqQ/z+L7vu+SmPc2m/MtD+X+ya371mJ+1Rvvivkdt1wc84t3DWOe/MNL3hvzd/74N8d8ui8f+/X//GTMD3/dFY3ZqTfcHNf2NnN35oPfnbs9v/T634r5d937+pi/+aKPNmZ7+++Lax8c5zLjPznx/JjfdvcLYr65lot3u4Pmvu7F25fjWs4N5w1XYz4J/bHL3c24ttZVX8t7Jd8z0/ra2knl33yr+6508g4rpdWLveb8xGQxrr12+dGY31t2xJyzQ+eh/Y3ZeN/1cW1369acV557Cxedinl5NG+/f6q5m3kyrFz3o9zrPF3I6zvTSl92qNuu9blXqrhL5ZZVV9l/NzySZ3N20c/bZd/Jrwulv9r84Yy2PPu6vM9JlZ9CVx/aEvNh+JoHlROwV8mntYtzDr3K83xS5tv35iyPnnv7h5/ytr95950xv/2Ca57yts9VftEHAACAFjHoAwAAQIsY9AEAAKBFDPoAAADQIgZ9AAAAaBGDPgAAALSIQR8AAABaJJcZVuz5aO6cXntOc1f9sSt7ce36rpWY3/PZq2J+1XMfiXmtozL1Ov/Pg7mP/aHXLcT8gn+0J+bP39XcJ1xKKSeOHGjMjq7lzunRX++M+eIX8ynxyvf/WMyXHs992b/0+HMas85dD8S1s83cU15z2cYdc63vDJvP5+7Vl8e1lcpdzhJXLD0W82nojx108rl/NuvVCq0r0ufyRGwbNJd9H97Mz4Lv2fHJmL+/vPwpHRPPrMnRY43ZVW/7eFw7ftULY35yT36ubR5ajnl3V6Uwvtucd0a1ayNvu1tdX9l6WD5tfqR9Oa+9IXbysXcm+dh7G3nzk/CaWOuxn+VXzKra9qf5Na8MToXQT2znhspj8Yab9sX83j++sjH70mh3XNutzCjdyrVXua3Mte9J5eLqVXa+cBrfld75yMtiPt557r6nPVVuNwAAANAiBn0AAABoEYM+AAAAtIhBHwAAAFrEoA8AAAAtYtAHAACAFpmrXq/7sdtjvjJrrljIhUl1F9T+g06udZmFYyullM0d28POcy3GVYtHYj69bUvMH7w350uH7mvOKn93qfzdp1vvogubw1074trZYu4Cmg3z6dxdH8W8DPL68bbm6sLuYyfytjkn7FvfFfNt/eYauCuGh+LaxW4+/wbd+UoYB7U+qDn2Pc+2n4ilbnN15uHNfF943/FcrdZP95xSynj/ozHn6dFLz9RSSmdL83NvtrYW177hNz4Q8zdvz3W71/6nfxDz/qn8XO1Mm/PxSuVd4/zKtTWs5Jv595rOenMVVrfSNNUZ57974eh89Xmzyk9NtQaxpFvZd7URtJJPBzkfLzdvYHNbZd+cHSrnwA9d/Bcx/2ebzfV6B8f5JFgOz8RSShnN2x8ZTGsXZkWtnq9mnr/ttuv+W8xveChXhHeXc9XqdDXXyp+N/KIPAAAALWLQBwAAgBYx6AMAAECLGPQBAACgRQz6AAAA0CIGfQAAAGgRgz4AAAC0SC4OrznDnezRnMc2OXqsOUzZ02Cuxuqz+TsppUyPHW/MOstLeXFYW0q9Fne2mXvMZ5u5t7S/ZaUxi+cLZ43OcBjzLZXi524odp7Mamdg1i1n77VbO7ZBpZD7dHb+LnbzdX30VZfFfMu7Hn06D4cG02sujfnDt2xtzPqV6uLfe2B7zPdc9f6YD67Lz5bJJP8mMn6w+dmwtD+v3fn5nK/vyq9px6/OndVbLmt+NvW7ee3mOO979US+n84283XfOzbHfWHe+22+ZZXKbaVMFnI+aj6dy/DIfMfOM2NlXz7/f+ad3x/zKz94uDHr/f187Z2cLsa81lU/neX7Sq/z1CeN2rYrl07plnzsHz55/ZM8ov/rJb/+UzG/5PZ84U9XKw+bc5Bf9AEAAKBFDPoAAADQIgZ9AAAAaBGDPgAAALSIQR8AAABaxKAPAAAALWLQBwAAgBbJJZHwNIsdlWd5f+XkaHMfMeeG7tWXx3zn4PaYHxktP+V9Dyq9tbVe3G4nd9lPwr/b9iq9tfPqlXxs8+jOue0Tl+au7i1zbZ0n6sQVzV3zpZSy9737G7PJPffFtZ1fz68yv3jrm2K+cHE+R0b50Es37H60LZ+/R3bkTvV+5bG47d78e03/szsasx1f2ohrh3c9mnc+qXRxd/LfNjn0WF4ftz3f71Sz0eZc62s6g4XGrLf34rg2N33zTNnzi3+Z/4PK+T3rDxqz1y7fE9e++8RNMa+d/bX3jdGs+Z5XexeZlPx3145tW2895u+49yUxv7h8rjG79F9WvrNnIb/oAwAAQIsY9AEAAKBFDPoAAADQIgZ9AAAAaBGDPgAAALSIQR8AAABaxKAPAAAALZLLZwFaZLxjKeaT2VP/t89p5d9Nl7u5t3la2Xe3k/u4e6W5+3ZSObZhpbl5sTuKea1Xt9bpm/62aWXbte9sc3v+3HhmbHn3p2I+mVY62YPZOJ+/y+/5RM6f8p6fgErXdpmdveenPvenbjZqvt+Pv3T/M3cgnD6VazedA7edvCGu3dU7FfPV6TDm3U7z+0AppZRZrzHqlco9qfI8r9kM+y6llPW7t8+1ff5fftEHAACAFjHoAwAAQIsY9AEAAKBFDPoAAADQIgZ9AAAAaBGDPgAAALSIQR8AAABapH+mDwDgmXLq4tw9e9HC0coWdjQmB0a5+7XWJX94M7d51/rku6H7trb2VMmfy2p/Iea9kjt7V6d5/ea0+VG0Ph7EtRuz/BibXZf7iHmGTOfrXj5nVbq2OUd18j3V907y9tu/NuY/cdNHYv6p45fFfMdgLeYb4Zk7neXfgIfdccxr7xtX7jgY88WDlWtrDt2VlZhPT7XvfcEv+gAAANAiBn0AAABoEYM+AC4XWAsAAAK7SURBVAAAtIhBHwAAAFrEoA8AAAAtYtAHAACAFjHoAwAAQIvkAuI5dYbN3cyzjY3TuWvgLJV6TE93h+nGjvxvmwdH22L++ZMXNmZXrxyKa3cPTsR8x0LuvV3qbsZ8NOs1ZrV/0V3qjWJeO/ZeyZ3Rh8e5u3atTBuzxX4+tuXK57K5Oog5wJM2y/c8nh5tnSOm09wVP+zm594li0djfunw8ZhPwlvBdJbfGNK7RimlLHfz97Kjtxrz7fdPYj6XyWnc9lnKL/oAAADQIgZ9AAAAaBGDPgAAALSIQR8AAABaxKAPAAAALWLQBwAAgBY5rfV653L1BXB6nO4KvWRtd660uXXbZ2L+DVvvaMwWO7m2ZUd3HPNPLzwW80ml8mbQad7++ixXzC1Ujn1rN1f/DSrre6E+r5R8fLevXRbX7uqfjHl/+Oyr0wFog7bOEbNJfhd59dI9Ma9V3C1W6vm6qdK2snZ9mt8nausfHJ0X84Wj+V1pHtP19dO27bOVX/QBAACgRQz6AAAA0CIGfQAAAGgRgz4AAAC0iEEfAAAAWsSgDwAAAC1i0AcAAIAW6Z/OjXcGC43ZbLR5OncNnKXO5H1h74dOxPz7Rm+N+fruWWM23pm7X4c75+tv7eTa3dLrNffidru5x37Qy13zg7DtUkoZTfK/GY8mufN3PG7O11ebz5dSSpmt5sfY1rtz3t26NebTE/mc4enhfQH4aufyfaF/0YWN2fC+xbj2Z6+8NebnDU/FfNDJz+xpaX6h6Hfy+8CJcT72/avbYn73wxfE/KrV/C7Fk+MXfQAAAGgRgz4AAAC0iEEfAAAAWsSgDwAAAC1i0AcAAIAWMegDAABAixj0AQAAoEU6s1lzLzQAAABwbvGLPgAAALSIQR8AAABaxKAPAAAALWLQBwAAgBYx6AMAAECLGPQBAACgRf4XSMCJIAersPwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1296x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_epochs = 10\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    for data in trainloader:\n",
    "        img, _ = data\n",
    "        img.requires_grad_()\n",
    "        output = model_1(img)  \n",
    "        loss = criterion(output, img.data)\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "    # ===================log========================\n",
    "    print(f'epoch [{epoch + 1}/{num_epochs}], loss:{loss.item():.4f}')\n",
    "    display_images(None, output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.autograd import Variable\n",
    "plt.style.use('default')\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_images, labels = next(iter(testloader))\n",
    "\n",
    "test_images = Variable(test_images.view(test_images.size()[0], -1))\n",
    "test_output = model_ae(test_images)\n",
    "  \n",
    "inp = test_images.view(-1, 28, 28)\n",
    "out = test_output.view(-1, 28, 28)\n",
    "\n",
    "fig = plt.figure()\n",
    "plot = fig.add_subplot(1, 2, 1)\n",
    "plot.set_title('Original Image')\n",
    "imgplot = plt.imshow(inp[2].cpu(), cmap='gray')\n",
    "\n",
    "plot = fig.add_subplot(1, 2, 2)\n",
    "plot.set_title('Generated Image')\n",
    "imgplot = plt.imshow(out[2].cpu().detach(), cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 3\n",
    "### Briefly describe the performance of using the fully connected auto-encoder to the CNN_Autoencoder\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The performance of CNN_Autoencoder is that the error is very small (loss) compared to the fully connected and  also the images generated are clear than the one generated using the fully connected. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 2\n",
    "### Edit the code above for denoising autoencoder? (bonus point if your code it)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "bs = 20 #batch_size\n",
    "\n",
    "transform = transforms.Compose([transforms.ToTensor()])\n",
    "\n",
    "traindataset = FashionMNIST('./data', download=True, train=True, transform=transform)\n",
    "testdataset = FashionMNIST('./data', download=True, train=False, transform=transform)\n",
    "\n",
    "# Loaders\n",
    "train_loader = torch.utils.data.DataLoader(traindataset, batch_size=bs, shuffle=True, num_workers=0)\n",
    "test_loader = torch.utils.data.DataLoader(testdataset, batch_size=bs, shuffle=False, num_workers=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fce71b38150>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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du+8PVhbrr+4J1UWbWKN6rHhvG8Fu1zLnVnZTb7fFlrGOOO6Sp0N1Q9fHxnvsnncW1uz925+Gxmp6YH1tSQ8Ox37Ku45ZVlgz+tym0FgTKTyCZrbUzO43syfNbIOZfa61fb6Z3WNmz7T+n9fWTABgCiK/hkYlfd7dT5L0fkmfNbOTJH1B0n3ufryk+1qfA8C0Kgwxd9/i7o+2Ph6StFHSEkkXSrq5VXazpIsO1iQBYCKTOglhZssknSLpN5IG3H1L60uvSBoodWYAEBAOMTObLekHkq50951jv+buLskn+L6VZrbGzNbUFXsfPQCICoWYmdW0L8BucffbW5u3mtmi1tcXSRoc73vdfZW7r3D3FTWV98azACDF/jppkm6StNHdrxvzpTslXdb6+DJJPyp/egBwYJE+sTMlfUrSE2a2rrXti5KulXSrmX1G0vOSLjk4UwSAiRWGmLv/ShOvzXluudMBgMmZkR37Iwv6CmtOrMU6sgeq20J1C6uzQ3Wjm14I1UV89tAXQ3UbRoq78fss9jeaaCd7J3RibtGrCXqsXtpt/tPSO0N1V+qDobp3PFF8NUFvpTs01oLqrlDdQDV2rEYXzikuei401IS4dhJAaoQYgNQIMQCpEWIAUiPEAKRGiAFIjRADkBohBiA1QgxAajOyY3+0t1pYs9tja55f8YdPhOp+ePzdobqISm9vaWNJ0rbmIYU1vZXYMkfdKrcrvmLjrsD0NlWPdcVHu+fL1B3sxK9VYz9v6/YW37/LZ5X789H9RvRdHYp9feuHQ3U3Ln0wVDe0rHhf+x8ODTUhnokBSI0QA5AaIQYgNUIMQGqEGIDUCDEAqRFiAFIjxACkNiObXXt+/NvCmk/9+MzgaFtDVR/V8uB4xeqnvzNY+etQVdOLf9d0B5d2jjaUVsd/G9GDqhbch8jcovs57MWN1VK8SXjYy3tI2Wknh+pq964trPno4ujPd2x56ujjpV9tdrIG8EwMQGqEGIDUCDEAqRFiAFIjxACkRogBSI0QA5AaIQYgNUIMQGozsmM/wrqCU7dYTvtobJlieXG3+MihsbltGX0zVLezubCwpie4zPLOZk+orq5YJ3ufjRTWDHstNFYl2BXfbbGloiNeb8aWip5fjR2reuAKgKfrO0Nj7Tpqdqiut7hhP86CS4QHHgfThWdiAFIjxACkRogBSI0QA5AaIQYgNUIMQGqEGIDUCDEAqRFiAFJL27Hvo6Oljlfp6wvVNXcVr0H+4sdi3cyLumId2Sf7YGHNYdVYh33dh0N1sd55aVbgioi6x0arB7vAI9cmRH87VxXrno+qBjreF1ZjP2svnRe7P064vbim0t8fGqs5NBSqm0kKj7WZLTWz+83sSTPbYGafa22/2sw2m9m61r8LDv50AeDtIs/ERiV93t0fNbN+SWvN7J7W177m7l85eNMDgAMrDDF33yJpS+vjITPbKGnJwZ4YAERM6sS+mS2TdIqk37Q2XWFmj5vZajObV/LcAKBQOMTMbLakH0i60t13SrpB0rGSlmvfM7WvTvB9K81sjZmtqWtvCVMGgP8TCjEzq2lfgN3i7rdLkrtvdfeGuzcl3Sjp9PG+191XufsKd19R06yy5g0AkmJ/nTRJN0na6O7Xjdm+aEzZxZLWlz89ADiwyF8nz5T0KUlPmNm61rYvSrrUzJZLckmbJF1+UGYIAAcQ+evkrySN18F3V/nTAYDJSduxX7YyrwCY82RsTfkrTj0jVPfUzuI19t8195XQWLtGY+clm+P+3vpjFRV3lZ/QtzU01qLajlBdT6W4Zz+6Dv/6PUeE6h7efnSobsMfFhfWVGfF5rb4/vKuCvQ9e0oba6bh2kkAqRFiAFIjxACkRogBSI0QA5AaIQYgNUIMQGqEGIDUzINLApdhjs33M+zcabs9AH867vXb1rr7iv2380wMQGqEGIDUCDEAqRFiAFIjxACkRogBSI0QA5AaIQYgNUIMQGrT2rFvZq9Ken6/zYdJem3aJlG+7POX8u9D9vlL+fdhOuZ/lLsv2H/jtIbYeMxszXiXEmSRff5S/n3IPn8p/z50cv68nASQGiEGILWZEGKrOj2BNmWfv5R/H7LPX8q/Dx2bf8fPiQFAO2bCMzEAmLKOhZiZnW9mT5nZ783sC52aRzvMbJOZPWFm68xsTafnE2Fmq81s0MzWj9k238zuMbNnWv/P6+QcD2SC+V9tZptbx2GdmV3QyTkeiJktNbP7zexJM9tgZp9rbc90DCbah44ch468nDSzqqSnJZ0n6SVJj0i61N2fnPbJtMHMNkla4e5p+nvM7GxJb0r6d3f/s9a2f5a03d2vbf1Cmefu/9DJeU5kgvlfLelNd/9KJ+cWYWaLJC1y90fNrF/SWkkXSfq08hyDifbhEnXgOHTqmdjpkn7v7s+5+4ik70m6sENz+X/F3R+QtH2/zRdKurn18c3a9wM5I00w/zTcfYu7P9r6eEjSRklLlOsYTLQPHdGpEFsi6cUxn7+kDt4JbXBJPzOztWa2stOTacOAu29pffyKpIFOTmaKrjCzx1svN2fsS7GxzGyZpFMk/UZJj8F++yB14DhwYr89Z7n7qZI+JumzrZc6qfm+8wvZ/mR9g6RjJS2XtEXSVzs7nWJmNlvSDyRd6e47x34tyzEYZx86chw6FWKbJS0d8/kRrW2puPvm1v+Dku7QvpfJGW1tned463zHYIfnMynuvtXdG+7elHSjZvhxMLOa9j34b3H321ubUx2D8fahU8ehUyH2iKTjzexoM+uW9ElJd3ZoLlNiZn2tk5oysz5JH5G0/sDfNWPdKemy1seXSfpRB+cyaW89+Fsu1gw+DmZmkm6StNHdrxvzpTTHYKJ96NRx6Fiza+vPr1+XVJW02t2v6chEpsjMjtG+Z1+S1CXpOxn2wcy+K+kc7Vt1YKukL0n6oaRbJR2pfauMXOLuM/Lk+QTzP0f7XsK4pE2SLh9zfmlGMbOzJP1S0hOSmq3NX9S+c0pZjsFE+3CpOnAc6NgHkBon9gGkRogBSI0QA5AaIQYgNUIMQGqEGIDUCDEAqRFiAFL7H1gcCBHuvAxbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "    \n",
    "# obtain one batch of training images\n",
    "dataiter = iter(train_loader)\n",
    "images, labels = dataiter.next()\n",
    "images = images.numpy()\n",
    "\n",
    "# get one image from the batch\n",
    "img = np.squeeze(images[0])\n",
    "\n",
    "fig = plt.figure(figsize = (5,5)) \n",
    "ax = fig.add_subplot(111)\n",
    "ax.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "# define the NN architecture\n",
    "class ConvDenoiser(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(ConvDenoiser, self).__init__()\n",
    "        # encoder layers #\n",
    "        self.conv1 = nn.Conv2d(1, 32, 3, padding=1)  \n",
    "        self.conv2 = nn.Conv2d(32, 16, 3, padding=1)\n",
    "        self.conv3 = nn.Conv2d(16, 8, 3, padding=1)\n",
    "        self.pool = nn.MaxPool2d(2, 2)\n",
    "        \n",
    "        ## decoder layers ##\n",
    "        # transpose layer to increase the spatial dimension of the image output\n",
    "        self.t_conv1 = nn.ConvTranspose2d(8, 8, 3, stride=2)  \n",
    "        self.t_conv2 = nn.ConvTranspose2d(8, 16, 2, stride=2)\n",
    "        self.t_conv3 = nn.ConvTranspose2d(16, 32, 2, stride=2)\n",
    "        self.conv_out = nn.Conv2d(32, 1, 3, padding=1)\n",
    "\n",
    "\n",
    "    def forward(self, x):\n",
    "        ## encode ##\n",
    "        # add hidden layers with relu activation function\n",
    "        # and maxpooling after\n",
    "        x = F.relu(self.conv1(x))\n",
    "        x = self.pool(x)\n",
    "        x = F.relu(self.conv2(x))\n",
    "        x = self.pool(x)\n",
    "        x = F.relu(self.conv3(x))\n",
    "        x = self.pool(x)  \n",
    "        \n",
    "        ## decode ##\n",
    "        # add transpose conv layers, with relu activation function\n",
    "        x = F.relu(self.t_conv1(x))\n",
    "        x = F.relu(self.t_conv2(x))\n",
    "        x = F.relu(self.t_conv3(x))\n",
    "        x = F.sigmoid(self.conv_out(x))\n",
    "                \n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ConvDenoiser(\n",
      "  (conv1): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (conv2): Conv2d(32, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (conv3): Conv2d(16, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (t_conv1): ConvTranspose2d(8, 8, kernel_size=(3, 3), stride=(2, 2))\n",
      "  (t_conv2): ConvTranspose2d(8, 16, kernel_size=(2, 2), stride=(2, 2))\n",
      "  (t_conv3): ConvTranspose2d(16, 32, kernel_size=(2, 2), stride=(2, 2))\n",
      "  (conv_out): Conv2d(32, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "model = ConvDenoiser()\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "## loss function and optimizer\n",
    "\n",
    "criterion = nn.MSELoss()\n",
    "\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr = 0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 \tTraining Loss: 0.743701\n",
      "Epoch: 2 \tTraining Loss: 0.527604\n",
      "Epoch: 3 \tTraining Loss: 0.495628\n",
      "Epoch: 4 \tTraining Loss: 0.477163\n",
      "Epoch: 5 \tTraining Loss: 0.465035\n",
      "Epoch: 6 \tTraining Loss: 0.457739\n",
      "Epoch: 7 \tTraining Loss: 0.452504\n",
      "Epoch: 8 \tTraining Loss: 0.448330\n",
      "Epoch: 9 \tTraining Loss: 0.444817\n",
      "Epoch: 10 \tTraining Loss: 0.443059\n"
     ]
    }
   ],
   "source": [
    "## Training\n",
    "\n",
    "# number of epochs \n",
    "n_epochs = 10\n",
    "\n",
    "# for adding noise to images\n",
    "noise_factor=0.5\n",
    "for epoch in range( 1,n_epochs+1):\n",
    "    # monitor training loss\n",
    "    train_loss = 0.0\n",
    "    for data in train_loader:\n",
    "        images, _ = data\n",
    "        \n",
    "        ## add random noise to the input images\n",
    "        noisy_imgs = images + noise_factor * torch.randn(*images.shape)\n",
    "        # Clip the images to be between 0 and 1\n",
    "        noisy_imgs = np.clip(noisy_imgs, 0., 1.)\n",
    "        optimizer.zero_grad()\n",
    "        outputs = model(noisy_imgs)\n",
    "        # the \"target\" is still the original, not-noisy images\n",
    "        loss = criterion(outputs, images)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        # update running training loss\n",
    "        train_loss += loss.item()*images.size(0)\n",
    "            \n",
    "    # print avg training statistics \n",
    "    train_loss = train_loss/len(train_loader)\n",
    "    print('Epoch: {} \\tTraining Loss: {:.6f}'.format(epoch,train_loss))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x288 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# obtain one batch of test images\n",
    "dataiter = iter(test_loader)\n",
    "images, labels = dataiter.next()\n",
    "\n",
    "# add noise to the test images\n",
    "noisy_imgs = images + noise_factor * torch.randn(*images.shape)\n",
    "noisy_imgs = np.clip(noisy_imgs, 0., 1.)\n",
    "\n",
    "# get sample outputs\n",
    "output = model(noisy_imgs)\n",
    "# prep images for display\n",
    "noisy_imgs = noisy_imgs.numpy()\n",
    "\n",
    "# output is resized into a batch of iages\n",
    "output = output.view(bs, 1, 28, 28)\n",
    "# use detach when it's an output that requires_grad\n",
    "output = output.detach().numpy()\n",
    "\n",
    "# plot the first ten input images and then reconstructed images\n",
    "fig, axes = plt.subplots(nrows=2, ncols=10, sharex=True, sharey=True, figsize=(25,4))\n",
    "\n",
    "# input images on top row, reconstructions on bottom\n",
    "for noisy_imgs, row in zip([noisy_imgs, output], axes):\n",
    "    for img, ax in zip(noisy_imgs, row):\n",
    "        ax.imshow(np.squeeze(img), cmap='gray')\n",
    "        ax.get_xaxis().set_visible(False)\n",
    "        ax.get_yaxis().set_visible(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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