DenseNet-121.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DenseNet-121 MNIST Digits Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Network Architecture"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The network in this notebook is an implementation of the DenseNet-121 [1] architecture on the MNIST digits dataset (http://yann.lecun.com/exdb/mnist/) to train a handwritten digit classifier.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following figure illustrates the main concept of DenseNet: within each \"dense\" block, each layer is connected with each previous layer -- the feature maps are concatenated.\n",
    "\n",
    "\n",
    "![](images/densenet/densenet-fig-2.jpg)\n",
    "\n",
    "Note that this is somewhat related yet very different to ResNets. ResNets have skip connections approx. between every other layer (but don't connect all layers with each other). Also, ResNets skip connections work via addition\n",
    "\n",
    "$$\\mathbf{x}_{\\ell}=H_{\\ell}\\left(\\mathbf{X}_{\\ell-1}\\right)+\\mathbf{X}_{\\ell-1}$$,\n",
    "\n",
    "whereas $H_{\\ell}(\\cdot)$ can be a composite function  of operations such as Batch Normalization (BN), rectified linear units (ReLU), Pooling, or Convolution (Conv).\n",
    "\n",
    "In DenseNets, all the previous feature maps $\\mathbf{X}_{0}, \\dots, \\mathbf{X}_{\\ell}-1$ of a feature map $\\mathbf{X}_{\\ell}$ are concatenated:\n",
    "\n",
    "$$\\mathbf{x}_{\\ell}=H_{\\ell}\\left(\\left[\\mathbf{x}_{0}, \\mathbf{x}_{1}, \\ldots, \\mathbf{x}_{\\ell-1}\\right]\\right).$$\n",
    "\n",
    "Furthermore, in this particular notebook, we are considering the DenseNet-121, which is depicted below:\n",
    "\n",
    "\n",
    "\n",
    "![](images/densenet/densenet-tab-1-dnet121.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**References**\n",
    "    \n",
    "- [1] Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4700-4708), http://openaccess.thecvf.com/content_cvpr_2017/html/Huang_Densely_Connected_Convolutional_CVPR_2017_paper.html\n",
    "\n",
    "- [2] http://yann.lecun.com/exdb/mnist/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/Users/ZRC/miniconda3/envs/tryit/lib/python36.zip',\n",
       " '/Users/ZRC/miniconda3/envs/tryit/lib/python3.6',\n",
       " '/Users/ZRC/miniconda3/envs/tryit/lib/python3.6/lib-dynload',\n",
       " '',\n",
       " '/Users/ZRC/miniconda3/envs/tryit/lib/python3.6/site-packages',\n",
       " '/Users/ZRC/miniconda3/envs/tryit/lib/python3.6/site-packages/IPython/extensions',\n",
       " '/Users/ZRC/.ipython',\n",
       " '/Users/ZRC']"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sys\n",
    "sys.path.append(\"/Users/ZRC\")\n",
    "sys.path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import OrderedDict\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "\n",
    "from torch.utils.data import DataLoader\n",
    "from torch.utils.data import RandomSampler\n",
    "from torch.utils.data import Subset\n",
    "\n",
    "\n",
    "from torchvision import datasets\n",
    "from torchvision import transforms\n",
    "\n",
    "from torchsummary import summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from coke.visualization.image import show_batch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hyperparameters\n",
    "\n",
    "BATCH_SIZE = 64\n",
    "NUM_EPOCHS = 10\n",
    "LEARNING_RATE = 0.0001\n",
    "RANDOM_SEED = 7\n",
    "\n",
    "# Architecture\n",
    "NUM_CLASSES = 10\n",
    "GRAYSCALE = True\n",
    "\n",
    "# # other\n",
    "# torch.cuda.empty_cache()\n",
    "DEVICE = torch.device(\"cuda: 0\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_transforms = {\"train\": transforms.Compose([\n",
    "                            transforms.Resize((32,32)),\n",
    "                            transforms.ToTensor()]),\n",
    "                    \"test\": transforms.Compose([\n",
    "                            transforms.Resize((32,32)),\n",
    "                            transforms.ToTensor()])\n",
    "                   }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_indices = torch.arange(0, 59000)\n",
    "valid_indices = torch.arange(59000, 60000)\n",
    "\n",
    "\n",
    "\n",
    "train_and_valida_dataset = datasets.MNIST(root = \"data\",\n",
    "                                train = True,\n",
    "                                transform = data_transforms[\"train\"],\n",
    "                                download=True)\n",
    "\n",
    "test_dataset = datasets.MNIST(root = \"data\",\n",
    "                                train = False,\n",
    "                                transform = data_transforms[\"test\"],\n",
    "                                download=False)\n",
    "\n",
    "train_dataset = Subset(train_and_valida_dataset, train_indices)\n",
    "valid_dataset = Subset(train_and_valida_dataset, valid_indices)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "train_dataloader = DataLoader(dataset = train_dataset,\n",
    "                             batch_size=BATCH_SIZE,\n",
    "                             shuffle=True,\n",
    "                             num_workers=4)\n",
    "\n",
    "valid_dataloader = DataLoader(dataset = valid_dataset,\n",
    "                             batch_size=BATCH_SIZE,\n",
    "                             shuffle=False,\n",
    "                             num_workers=4)\n",
    "\n",
    "test_dataloader = DataLoader(dataset = test_dataset,\n",
    "                             batch_size=BATCH_SIZE,\n",
    "                             shuffle=False,\n",
    "                             num_workers=4)\n",
    "\n",
    "data_loader = {\"train\": train_dataloader, \n",
    "               \"val\": valid_dataloader,\n",
    "               \"test\": test_dataloader}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([64, 1, 32, 32]) torch.Size([64])\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "batch_samples,labels = next(iter(train_dataloader))\n",
    "print(batch_samples.size(),labels.size())\n",
    "show_batch(batch_samples.squeeze(), labels.numpy(), (4,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([64, 1, 32, 32]) torch.Size([64])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "batch_samples,labels = next(iter(valid_dataloader))\n",
    "print(batch_samples.shape,labels.shape)\n",
    "show_batch(batch_samples.squeeze(), labels.numpy(), (4,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([64, 1, 32, 32]) torch.Size([64])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "batch_samples,labels = next(iter(test_dataloader))\n",
    "print(batch_samples.shape,labels.shape)\n",
    "show_batch(batch_samples.squeeze(), labels.numpy(), (4,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](images/densenet/densenet-arch.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Composite function\n",
    "\n",
    "Each CONV block in the network representations in the paper (and in the blog) corresponds to an operation of —\n",
    "\n",
    "<center>BatchNorm→ReLU→Conv</center>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def composite(input_features,out_features, kernel_size, stride = 1, padding = 0):\n",
    "    \n",
    "    return torch.nn.Sequential(\n",
    "            torch.nn.BatchNorm2d(input_features),\n",
    "            torch.nn.ReLU(inplace=True),\n",
    "            torch.nn.Conv2d(input_features, \n",
    "                            out_features, \n",
    "                            kernel_size=kernel_size, \n",
    "                            stride=stride,\n",
    "                            padding = padding,\n",
    "                            bias=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dense layer\n",
    "Each dense-layer consists of 2 convolutional operations -\n",
    "- 1 X 1 CONV (conventional conv operation for extracting features)\n",
    "- 3 X 3 CONV (bringing down the feature depth/channel count)\n",
    "\n",
    "![denselayer.png](images/densenet/denselayer.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DenseLayer(torch.nn.Module):\n",
    "    def __init__(self, num_input_features, growth_rate = 32, bn_size = 2, drop_rate = 0):\n",
    "        super(DenseLayer, self).__init__()\n",
    "        \n",
    "        self.composite_1 = composite(input_features = num_input_features,\n",
    "                                     out_features = bn_size * growth_rate,\n",
    "                                     kernel_size = 1,\n",
    "                                     stride = 1,\n",
    "                                     padding = 0)\n",
    "        \n",
    "        \n",
    "        \n",
    "        self.composite_2 = composite(input_features = bn_size * growth_rate,\n",
    "                                     out_features = growth_rate,\n",
    "                                     kernel_size = 3,\n",
    "                                     stride = 1,\n",
    "                                     padding = 1)\n",
    "        \n",
    "        self.drop_rate = drop_rate\n",
    "            \n",
    "    def forward(self, *prev_features):\n",
    "        \n",
    "        ## concate in channels\n",
    "        concated_features = torch.cat(prev_features, 1)        \n",
    "        bottleneck_output = self.composite_1(concated_features)\n",
    "        new_features = self.composite_2(bottleneck_output)\n",
    "        \n",
    "        if self.drop_rate:\n",
    "            new_features = F.dropout(new_features, p=self.drop_rate, training= self.training)\n",
    "            \n",
    "        return new_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "       BatchNorm2d-1           [-1, 32, 32, 32]              64\n",
      "              ReLU-2           [-1, 32, 32, 32]               0\n",
      "            Conv2d-3           [-1, 64, 32, 32]           2,048\n",
      "       BatchNorm2d-4           [-1, 64, 32, 32]             128\n",
      "              ReLU-5           [-1, 64, 32, 32]               0\n",
      "            Conv2d-6           [-1, 32, 32, 32]          18,432\n",
      "================================================================\n",
      "Total params: 20,672\n",
      "Trainable params: 20,672\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 0.12\n",
      "Forward/backward pass size (MB): 2.25\n",
      "Params size (MB): 0.08\n",
      "Estimated Total Size (MB): 2.45\n",
      "----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "def test_DenseLayer():\n",
    "    model = DenseLayer(num_input_features = 32, growth_rate = 32, bn_size = 2).to(DEVICE)\n",
    "    summary(model, [(32,32,32)])\n",
    "test_DenseLayer()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dense block\n",
    "\n",
    "The concept of dense connections has been portrayed in dense blocks. A dense block comprises n dense layers. These dense layers are connected using a dense circuitry such that each dense layer receives feature maps from all preceding layers and passes it’s feature maps to all subsequent layers. The dimensions of the features (width, height) stay the same in a dense block.\n",
    "\n",
    "![](images/densenet/denseblock.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DenseBlock(torch.nn.Module):\n",
    "    def __init__(self, num_layers, num_input_features, bn_size, growth_rate, drop_rate):\n",
    "        super(DenseBlock, self).__init__()\n",
    "        \n",
    "        self.layers = torch.nn.ModuleList([\n",
    "            DenseLayer(\n",
    "            num_input_features + i * growth_rate,\n",
    "            growth_rate = growth_rate,\n",
    "            bn_size = bn_size,\n",
    "            drop_rate = drop_rate,\n",
    "            ) for i in range(num_layers)        \n",
    "        ])\n",
    "    \n",
    "    def forward(self, x):\n",
    "        x = [x]\n",
    "        for layer in self.layers:\n",
    "            x.append(layer(*x))\n",
    "        \n",
    "#         print(torch.cat(x,1).size()) # 64 + 6*32 = 256\n",
    "        return torch.cat(x,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "       BatchNorm2d-1             [-1, 64, 7, 7]             128\n",
      "              ReLU-2             [-1, 64, 7, 7]               0\n",
      "            Conv2d-3             [-1, 64, 7, 7]           4,096\n",
      "       BatchNorm2d-4             [-1, 64, 7, 7]             128\n",
      "              ReLU-5             [-1, 64, 7, 7]               0\n",
      "            Conv2d-6             [-1, 32, 7, 7]          18,432\n",
      "        DenseLayer-7             [-1, 32, 7, 7]               0\n",
      "       BatchNorm2d-8             [-1, 96, 7, 7]             192\n",
      "              ReLU-9             [-1, 96, 7, 7]               0\n",
      "           Conv2d-10             [-1, 64, 7, 7]           6,144\n",
      "      BatchNorm2d-11             [-1, 64, 7, 7]             128\n",
      "             ReLU-12             [-1, 64, 7, 7]               0\n",
      "           Conv2d-13             [-1, 32, 7, 7]          18,432\n",
      "       DenseLayer-14             [-1, 32, 7, 7]               0\n",
      "      BatchNorm2d-15            [-1, 128, 7, 7]             256\n",
      "             ReLU-16            [-1, 128, 7, 7]               0\n",
      "           Conv2d-17             [-1, 64, 7, 7]           8,192\n",
      "      BatchNorm2d-18             [-1, 64, 7, 7]             128\n",
      "             ReLU-19             [-1, 64, 7, 7]               0\n",
      "           Conv2d-20             [-1, 32, 7, 7]          18,432\n",
      "       DenseLayer-21             [-1, 32, 7, 7]               0\n",
      "      BatchNorm2d-22            [-1, 160, 7, 7]             320\n",
      "             ReLU-23            [-1, 160, 7, 7]               0\n",
      "           Conv2d-24             [-1, 64, 7, 7]          10,240\n",
      "      BatchNorm2d-25             [-1, 64, 7, 7]             128\n",
      "             ReLU-26             [-1, 64, 7, 7]               0\n",
      "           Conv2d-27             [-1, 32, 7, 7]          18,432\n",
      "       DenseLayer-28             [-1, 32, 7, 7]               0\n",
      "      BatchNorm2d-29            [-1, 192, 7, 7]             384\n",
      "             ReLU-30            [-1, 192, 7, 7]               0\n",
      "           Conv2d-31             [-1, 64, 7, 7]          12,288\n",
      "      BatchNorm2d-32             [-1, 64, 7, 7]             128\n",
      "             ReLU-33             [-1, 64, 7, 7]               0\n",
      "           Conv2d-34             [-1, 32, 7, 7]          18,432\n",
      "       DenseLayer-35             [-1, 32, 7, 7]               0\n",
      "      BatchNorm2d-36            [-1, 224, 7, 7]             448\n",
      "             ReLU-37            [-1, 224, 7, 7]               0\n",
      "           Conv2d-38             [-1, 64, 7, 7]          14,336\n",
      "      BatchNorm2d-39             [-1, 64, 7, 7]             128\n",
      "             ReLU-40             [-1, 64, 7, 7]               0\n",
      "           Conv2d-41             [-1, 32, 7, 7]          18,432\n",
      "       DenseLayer-42             [-1, 32, 7, 7]               0\n",
      "================================================================\n",
      "Total params: 168,384\n",
      "Trainable params: 168,384\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 0.01\n",
      "Forward/backward pass size (MB): 1.22\n",
      "Params size (MB): 0.64\n",
      "Estimated Total Size (MB): 1.87\n",
      "----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "def test_DenseBlock():\n",
    "    model = DenseBlock(num_layers = 6, num_input_features = 64, bn_size = 2, growth_rate = 32, drop_rate = 0)\n",
    "    model.to(DEVICE)\n",
    "    summary(model, (64,7,7))\n",
    "\n",
    "test_DenseBlock()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transition layer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At the end of each dense block, the number of feature-maps accumulates to a value of — input features + (number of dense layers x growth rate). So for 64 channel features entering a dense block of 6 dense-layers of growth rate 32, the number of channels accumulated at the end of the block will be — \n",
    "64 + (6 x 32) = 256. To bring down this channel count, a transition layer (or block) is added between two dense blocks. The transition layer consists of -\n",
    "\n",
    "- 1 X 1 CONV operation: The 1 X 1 CONV operation reduces the channel count to half.\n",
    "- 2 X 2 AVG POOL operation: The 2 X 2 AVG POOL layer is responsible for downsampling the features in terms of the width and height.\n",
    "\n",
    "![densenet-transition.png](images/densenet/densenet-transition.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Transition(torch.nn.Module):\n",
    "    \"\"\"\n",
    "    To bring down previous channel count\n",
    "    \"\"\"\n",
    "    def __init__(self, num_input_features, num_output_features):\n",
    "        super(Transition, self).__init__()\n",
    "        self.composite = composite(input_features = num_input_features,\n",
    "                                   out_features = num_output_features, \n",
    "                                   kernel_size = 1, \n",
    "                                   stride = 1, \n",
    "                                   padding = 0)\n",
    "        self.avg_pool = torch.nn.AvgPool2d(kernel_size=(2,2),\n",
    "                                          stride = (2,2))\n",
    "        \n",
    "    def forward(self, x):\n",
    "        return self.avg_pool(self.composite(x))    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "       BatchNorm2d-1          [-1, 256, 32, 32]             512\n",
      "              ReLU-2          [-1, 256, 32, 32]               0\n",
      "            Conv2d-3          [-1, 128, 32, 32]          32,768\n",
      "         AvgPool2d-4          [-1, 128, 16, 16]               0\n",
      "================================================================\n",
      "Total params: 33,280\n",
      "Trainable params: 33,280\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 1.00\n",
      "Forward/backward pass size (MB): 5.25\n",
      "Params size (MB): 0.13\n",
      "Estimated Total Size (MB): 6.38\n",
      "----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "def test_trainsition():\n",
    "    model = Transition(256,128)\n",
    "    model.to(DEVICE)\n",
    "    summary(model, (256,32,32))\n",
    "test_trainsition()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Full network\n",
    "As can be seen in the diagram below, the authors have chosen different number of dense layers for each of the three dense block.\n",
    "\n",
    "![densenet-fullnet.png](images/densenet/densenet-fullnet.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DenseNetZrc(torch.nn.Module):\n",
    "    \"\"\"\n",
    "    Args:\n",
    "        growth_rate (int) - how many filters to add each layer (`k` in paper)\n",
    "        block_config (list of 4 ints) - how many layers in each dense block\n",
    "        num_init_featuremaps (int) - the number of filters to learn in the first convolution layer\n",
    "        bn_size (int) - multiplicative factor for number of bottle neck layers\n",
    "          (i.e. bn_size * k features in the bottleneck layer)\n",
    "        drop_rate (float) - dropout rate after each dense layer\n",
    "        num_classes (int) - number of classification classes\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16),\n",
    "                 num_init_featuremaps=64, bn_size=4, drop_rate=0, num_classes=1000, grayscale=False):\n",
    "        super(DenseNetZrc, self).__init__()\n",
    "        \n",
    "        if grayscale:\n",
    "            in_channels = 1\n",
    "        else:\n",
    "            in_channels = 3\n",
    "            \n",
    "        self.features = torch.nn.Sequential(OrderedDict([\n",
    "            ( \"conv_0\", torch.nn.Conv2d(in_channels = in_channels,\n",
    "                                       out_channels = num_init_featuremaps,\n",
    "                                       kernel_size = 7,\n",
    "                                       stride = 2,\n",
    "                                       padding = 3, \n",
    "                                       bias = False) ),\n",
    "            ( 'norm0',torch.nn.BatchNorm2d(num_features = num_init_featuremaps) ),\n",
    "            ( 'relu0', torch.nn.ReLU(inplace = True) ),\n",
    "            ( 'pool0', torch.nn.MaxPool2d(kernel_size = 3, \n",
    "                                          stride = 2, \n",
    "                                          padding = 1) )\n",
    "        ]))  # [64, 7, 7] \n",
    "        \n",
    "        \n",
    "        num_features = num_init_featuremaps\n",
    "\n",
    "        \n",
    "        for index, num_layers in enumerate(block_config):\n",
    "            \n",
    "            dense_block = DenseBlock(num_layers = num_layers, \n",
    "                               num_input_features = num_features, \n",
    "                               bn_size = bn_size, \n",
    "                               growth_rate = growth_rate, \n",
    "                               drop_rate = drop_rate)\n",
    "            \n",
    "            self.features.add_module(\n",
    "                \"dense_block_{}\".format(index+1), dense_block)\n",
    "            num_features = num_features + num_layers * growth_rate\n",
    "            \n",
    "            if index != len(block_config) - 1:\n",
    "                transition = Transition(num_input_features = num_features,\n",
    "                                        num_output_features = num_features // 2)\n",
    "                self.features.add_module(\n",
    "                    \"transition_{}\".format(index+1), transition)\n",
    "                \n",
    "                num_features = num_features // 2\n",
    "        \n",
    "        \n",
    "        # Final Batch Norm\n",
    "        self.features.add_module(\"norm5\",torch.nn.BatchNorm2d(num_features))\n",
    "        self.classifier = torch.nn.Linear(num_features, num_classes)\n",
    "        \n",
    "        #############################################\n",
    "        # initialize the weight\n",
    "        #############################################\n",
    "        for name,module in self.named_modules():\n",
    "            if isinstance(module, torch.nn.Conv2d):\n",
    "                torch.nn.init.kaiming_normal_(module.weight)\n",
    "            elif isinstance(module, torch.nn.BatchNorm2d):\n",
    "                torch.nn.init.constant_(module.weight, 1)\n",
    "                torch.nn.init.constant_(module.bias, 0)\n",
    "            elif isinstance(module, torch.nn.Linear):\n",
    "                torch.nn.init.constant_(module.bias, 0)\n",
    "                \n",
    "    def forward(self, x):\n",
    "        features = self.features(x)\n",
    "        out = F.relu(features, inplace=True)\n",
    "        out = F.adaptive_avg_pool2d(out, (1, 1))\n",
    "        out = torch.flatten(out, 1)\n",
    "        logits = self.classifier(out)\n",
    "        probas = F.softmax(logits, dim=1)\n",
    "        \n",
    "        return logits, probas        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Conv2d-1           [-1, 64, 16, 16]           9,408\n",
      "       BatchNorm2d-2           [-1, 64, 16, 16]             128\n",
      "              ReLU-3           [-1, 64, 16, 16]               0\n",
      "         MaxPool2d-4             [-1, 64, 8, 8]               0\n",
      "       BatchNorm2d-5             [-1, 64, 8, 8]             128\n",
      "              ReLU-6             [-1, 64, 8, 8]               0\n",
      "            Conv2d-7            [-1, 128, 8, 8]           8,192\n",
      "       BatchNorm2d-8            [-1, 128, 8, 8]             256\n",
      "              ReLU-9            [-1, 128, 8, 8]               0\n",
      "           Conv2d-10             [-1, 32, 8, 8]          36,864\n",
      "       DenseLayer-11             [-1, 32, 8, 8]               0\n",
      "      BatchNorm2d-12             [-1, 96, 8, 8]             192\n",
      "             ReLU-13             [-1, 96, 8, 8]               0\n",
      "           Conv2d-14            [-1, 128, 8, 8]          12,288\n",
      "      BatchNorm2d-15            [-1, 128, 8, 8]             256\n",
      "             ReLU-16            [-1, 128, 8, 8]               0\n",
      "           Conv2d-17             [-1, 32, 8, 8]          36,864\n",
      "       DenseLayer-18             [-1, 32, 8, 8]               0\n",
      "      BatchNorm2d-19            [-1, 128, 8, 8]             256\n",
      "             ReLU-20            [-1, 128, 8, 8]               0\n",
      "           Conv2d-21            [-1, 128, 8, 8]          16,384\n",
      "      BatchNorm2d-22            [-1, 128, 8, 8]             256\n",
      "             ReLU-23            [-1, 128, 8, 8]               0\n",
      "           Conv2d-24             [-1, 32, 8, 8]          36,864\n",
      "       DenseLayer-25             [-1, 32, 8, 8]               0\n",
      "      BatchNorm2d-26            [-1, 160, 8, 8]             320\n",
      "             ReLU-27            [-1, 160, 8, 8]               0\n",
      "           Conv2d-28            [-1, 128, 8, 8]          20,480\n",
      "      BatchNorm2d-29            [-1, 128, 8, 8]             256\n",
      "             ReLU-30            [-1, 128, 8, 8]               0\n",
      "           Conv2d-31             [-1, 32, 8, 8]          36,864\n",
      "       DenseLayer-32             [-1, 32, 8, 8]               0\n",
      "      BatchNorm2d-33            [-1, 192, 8, 8]             384\n",
      "             ReLU-34            [-1, 192, 8, 8]               0\n",
      "           Conv2d-35            [-1, 128, 8, 8]          24,576\n",
      "      BatchNorm2d-36            [-1, 128, 8, 8]             256\n",
      "             ReLU-37            [-1, 128, 8, 8]               0\n",
      "           Conv2d-38             [-1, 32, 8, 8]          36,864\n",
      "       DenseLayer-39             [-1, 32, 8, 8]               0\n",
      "      BatchNorm2d-40            [-1, 224, 8, 8]             448\n",
      "             ReLU-41            [-1, 224, 8, 8]               0\n",
      "           Conv2d-42            [-1, 128, 8, 8]          28,672\n",
      "      BatchNorm2d-43            [-1, 128, 8, 8]             256\n",
      "             ReLU-44            [-1, 128, 8, 8]               0\n",
      "           Conv2d-45             [-1, 32, 8, 8]          36,864\n",
      "       DenseLayer-46             [-1, 32, 8, 8]               0\n",
      "       DenseBlock-47            [-1, 256, 8, 8]               0\n",
      "      BatchNorm2d-48            [-1, 256, 8, 8]             512\n",
      "             ReLU-49            [-1, 256, 8, 8]               0\n",
      "           Conv2d-50            [-1, 128, 8, 8]          32,768\n",
      "        AvgPool2d-51            [-1, 128, 4, 4]               0\n",
      "       Transition-52            [-1, 128, 4, 4]               0\n",
      "      BatchNorm2d-53            [-1, 128, 4, 4]             256\n",
      "             ReLU-54            [-1, 128, 4, 4]               0\n",
      "           Conv2d-55            [-1, 128, 4, 4]          16,384\n",
      "      BatchNorm2d-56            [-1, 128, 4, 4]             256\n",
      "             ReLU-57            [-1, 128, 4, 4]               0\n",
      "           Conv2d-58             [-1, 32, 4, 4]          36,864\n",
      "       DenseLayer-59             [-1, 32, 4, 4]               0\n",
      "      BatchNorm2d-60            [-1, 160, 4, 4]             320\n",
      "             ReLU-61            [-1, 160, 4, 4]               0\n",
      "           Conv2d-62            [-1, 128, 4, 4]          20,480\n",
      "      BatchNorm2d-63            [-1, 128, 4, 4]             256\n",
      "             ReLU-64            [-1, 128, 4, 4]               0\n",
      "           Conv2d-65             [-1, 32, 4, 4]          36,864\n",
      "       DenseLayer-66             [-1, 32, 4, 4]               0\n",
      "      BatchNorm2d-67            [-1, 192, 4, 4]             384\n",
      "             ReLU-68            [-1, 192, 4, 4]               0\n",
      "           Conv2d-69            [-1, 128, 4, 4]          24,576\n",
      "      BatchNorm2d-70            [-1, 128, 4, 4]             256\n",
      "             ReLU-71            [-1, 128, 4, 4]               0\n",
      "           Conv2d-72             [-1, 32, 4, 4]          36,864\n",
      "       DenseLayer-73             [-1, 32, 4, 4]               0\n",
      "      BatchNorm2d-74            [-1, 224, 4, 4]             448\n",
      "             ReLU-75            [-1, 224, 4, 4]               0\n",
      "           Conv2d-76            [-1, 128, 4, 4]          28,672\n",
      "      BatchNorm2d-77            [-1, 128, 4, 4]             256\n",
      "             ReLU-78            [-1, 128, 4, 4]               0\n",
      "           Conv2d-79             [-1, 32, 4, 4]          36,864\n",
      "       DenseLayer-80             [-1, 32, 4, 4]               0\n",
      "      BatchNorm2d-81            [-1, 256, 4, 4]             512\n",
      "             ReLU-82            [-1, 256, 4, 4]               0\n",
      "           Conv2d-83            [-1, 128, 4, 4]          32,768\n",
      "      BatchNorm2d-84            [-1, 128, 4, 4]             256\n",
      "             ReLU-85            [-1, 128, 4, 4]               0\n",
      "           Conv2d-86             [-1, 32, 4, 4]          36,864\n",
      "       DenseLayer-87             [-1, 32, 4, 4]               0\n",
      "      BatchNorm2d-88            [-1, 288, 4, 4]             576\n",
      "             ReLU-89            [-1, 288, 4, 4]               0\n",
      "           Conv2d-90            [-1, 128, 4, 4]          36,864\n",
      "      BatchNorm2d-91            [-1, 128, 4, 4]             256\n",
      "             ReLU-92            [-1, 128, 4, 4]               0\n",
      "           Conv2d-93             [-1, 32, 4, 4]          36,864\n",
      "       DenseLayer-94             [-1, 32, 4, 4]               0\n",
      "      BatchNorm2d-95            [-1, 320, 4, 4]             640\n",
      "             ReLU-96            [-1, 320, 4, 4]               0\n",
      "           Conv2d-97            [-1, 128, 4, 4]          40,960\n",
      "      BatchNorm2d-98            [-1, 128, 4, 4]             256\n",
      "             ReLU-99            [-1, 128, 4, 4]               0\n",
      "          Conv2d-100             [-1, 32, 4, 4]          36,864\n",
      "      DenseLayer-101             [-1, 32, 4, 4]               0\n",
      "     BatchNorm2d-102            [-1, 352, 4, 4]             704\n",
      "            ReLU-103            [-1, 352, 4, 4]               0\n",
      "          Conv2d-104            [-1, 128, 4, 4]          45,056\n",
      "     BatchNorm2d-105            [-1, 128, 4, 4]             256\n",
      "            ReLU-106            [-1, 128, 4, 4]               0\n",
      "          Conv2d-107             [-1, 32, 4, 4]          36,864\n",
      "      DenseLayer-108             [-1, 32, 4, 4]               0\n",
      "     BatchNorm2d-109            [-1, 384, 4, 4]             768\n",
      "            ReLU-110            [-1, 384, 4, 4]               0\n",
      "          Conv2d-111            [-1, 128, 4, 4]          49,152\n",
      "     BatchNorm2d-112            [-1, 128, 4, 4]             256\n",
      "            ReLU-113            [-1, 128, 4, 4]               0\n",
      "          Conv2d-114             [-1, 32, 4, 4]          36,864\n",
      "      DenseLayer-115             [-1, 32, 4, 4]               0\n",
      "     BatchNorm2d-116            [-1, 416, 4, 4]             832\n",
      "            ReLU-117            [-1, 416, 4, 4]               0\n",
      "          Conv2d-118            [-1, 128, 4, 4]          53,248\n",
      "     BatchNorm2d-119            [-1, 128, 4, 4]             256\n",
      "            ReLU-120            [-1, 128, 4, 4]               0\n",
      "          Conv2d-121             [-1, 32, 4, 4]          36,864\n",
      "      DenseLayer-122             [-1, 32, 4, 4]               0\n",
      "     BatchNorm2d-123            [-1, 448, 4, 4]             896\n",
      "            ReLU-124            [-1, 448, 4, 4]               0\n",
      "          Conv2d-125            [-1, 128, 4, 4]          57,344\n",
      "     BatchNorm2d-126            [-1, 128, 4, 4]             256\n",
      "            ReLU-127            [-1, 128, 4, 4]               0\n",
      "          Conv2d-128             [-1, 32, 4, 4]          36,864\n",
      "      DenseLayer-129             [-1, 32, 4, 4]               0\n",
      "     BatchNorm2d-130            [-1, 480, 4, 4]             960\n",
      "            ReLU-131            [-1, 480, 4, 4]               0\n",
      "          Conv2d-132            [-1, 128, 4, 4]          61,440\n",
      "     BatchNorm2d-133            [-1, 128, 4, 4]             256\n",
      "            ReLU-134            [-1, 128, 4, 4]               0\n",
      "          Conv2d-135             [-1, 32, 4, 4]          36,864\n",
      "      DenseLayer-136             [-1, 32, 4, 4]               0\n",
      "      DenseBlock-137            [-1, 512, 4, 4]               0\n",
      "     BatchNorm2d-138            [-1, 512, 4, 4]           1,024\n",
      "            ReLU-139            [-1, 512, 4, 4]               0\n",
      "          Conv2d-140            [-1, 256, 4, 4]         131,072\n",
      "       AvgPool2d-141            [-1, 256, 2, 2]               0\n",
      "      Transition-142            [-1, 256, 2, 2]               0\n",
      "     BatchNorm2d-143            [-1, 256, 2, 2]             512\n",
      "            ReLU-144            [-1, 256, 2, 2]               0\n",
      "          Conv2d-145            [-1, 128, 2, 2]          32,768\n",
      "     BatchNorm2d-146            [-1, 128, 2, 2]             256\n",
      "            ReLU-147            [-1, 128, 2, 2]               0\n",
      "          Conv2d-148             [-1, 32, 2, 2]          36,864\n",
      "      DenseLayer-149             [-1, 32, 2, 2]               0\n",
      "     BatchNorm2d-150            [-1, 288, 2, 2]             576\n",
      "            ReLU-151            [-1, 288, 2, 2]               0\n",
      "          Conv2d-152            [-1, 128, 2, 2]          36,864\n",
      "     BatchNorm2d-153            [-1, 128, 2, 2]             256\n",
      "            ReLU-154            [-1, 128, 2, 2]               0\n",
      "          Conv2d-155             [-1, 32, 2, 2]          36,864\n",
      "      DenseLayer-156             [-1, 32, 2, 2]               0\n",
      "     BatchNorm2d-157            [-1, 320, 2, 2]             640\n",
      "            ReLU-158            [-1, 320, 2, 2]               0\n",
      "          Conv2d-159            [-1, 128, 2, 2]          40,960\n",
      "     BatchNorm2d-160            [-1, 128, 2, 2]             256\n",
      "            ReLU-161            [-1, 128, 2, 2]               0\n",
      "          Conv2d-162             [-1, 32, 2, 2]          36,864\n",
      "      DenseLayer-163             [-1, 32, 2, 2]               0\n",
      "     BatchNorm2d-164            [-1, 352, 2, 2]             704\n",
      "            ReLU-165            [-1, 352, 2, 2]               0\n",
      "          Conv2d-166            [-1, 128, 2, 2]          45,056\n",
      "     BatchNorm2d-167            [-1, 128, 2, 2]             256\n",
      "            ReLU-168            [-1, 128, 2, 2]               0\n",
      "          Conv2d-169             [-1, 32, 2, 2]          36,864\n",
      "      DenseLayer-170             [-1, 32, 2, 2]               0\n",
      "     BatchNorm2d-171            [-1, 384, 2, 2]             768\n",
      "            ReLU-172            [-1, 384, 2, 2]               0\n",
      "          Conv2d-173            [-1, 128, 2, 2]          49,152\n",
      "     BatchNorm2d-174            [-1, 128, 2, 2]             256\n",
      "            ReLU-175            [-1, 128, 2, 2]               0\n",
      "          Conv2d-176             [-1, 32, 2, 2]          36,864\n",
      "      DenseLayer-177             [-1, 32, 2, 2]               0\n",
      "     BatchNorm2d-178            [-1, 416, 2, 2]             832\n",
      "            ReLU-179            [-1, 416, 2, 2]               0\n",
      "          Conv2d-180            [-1, 128, 2, 2]          53,248\n",
      "     BatchNorm2d-181            [-1, 128, 2, 2]             256\n",
      "            ReLU-182            [-1, 128, 2, 2]               0\n",
      "          Conv2d-183             [-1, 32, 2, 2]          36,864\n",
      "      DenseLayer-184             [-1, 32, 2, 2]               0\n",
      "     BatchNorm2d-185            [-1, 448, 2, 2]             896\n",
      "            ReLU-186            [-1, 448, 2, 2]               0\n",
      "          Conv2d-187            [-1, 128, 2, 2]          57,344\n",
      "     BatchNorm2d-188            [-1, 128, 2, 2]             256\n",
      "            ReLU-189            [-1, 128, 2, 2]               0\n",
      "          Conv2d-190             [-1, 32, 2, 2]          36,864\n",
      "      DenseLayer-191             [-1, 32, 2, 2]               0\n",
      "     BatchNorm2d-192            [-1, 480, 2, 2]             960\n",
      "            ReLU-193            [-1, 480, 2, 2]               0\n",
      "          Conv2d-194            [-1, 128, 2, 2]          61,440\n",
      "     BatchNorm2d-195            [-1, 128, 2, 2]             256\n",
      "            ReLU-196            [-1, 128, 2, 2]               0\n",
      "          Conv2d-197             [-1, 32, 2, 2]          36,864\n",
      "      DenseLayer-198             [-1, 32, 2, 2]               0\n",
      "     BatchNorm2d-199            [-1, 512, 2, 2]           1,024\n",
      "            ReLU-200            [-1, 512, 2, 2]               0\n",
      "          Conv2d-201            [-1, 128, 2, 2]          65,536\n",
      "     BatchNorm2d-202            [-1, 128, 2, 2]             256\n",
      "            ReLU-203            [-1, 128, 2, 2]               0\n",
      "          Conv2d-204             [-1, 32, 2, 2]          36,864\n",
      "      DenseLayer-205             [-1, 32, 2, 2]               0\n",
      "     BatchNorm2d-206            [-1, 544, 2, 2]           1,088\n",
      "            ReLU-207            [-1, 544, 2, 2]               0\n",
      "          Conv2d-208            [-1, 128, 2, 2]          69,632\n",
      "     BatchNorm2d-209            [-1, 128, 2, 2]             256\n",
      "            ReLU-210            [-1, 128, 2, 2]               0\n",
      "          Conv2d-211             [-1, 32, 2, 2]          36,864\n",
      "      DenseLayer-212             [-1, 32, 2, 2]               0\n",
      "     BatchNorm2d-213            [-1, 576, 2, 2]           1,152\n",
      "            ReLU-214            [-1, 576, 2, 2]               0\n",
      "          Conv2d-215            [-1, 128, 2, 2]          73,728\n",
      "     BatchNorm2d-216            [-1, 128, 2, 2]             256\n",
      "            ReLU-217            [-1, 128, 2, 2]               0\n",
      "          Conv2d-218             [-1, 32, 2, 2]          36,864\n",
      "      DenseLayer-219             [-1, 32, 2, 2]               0\n",
      "     BatchNorm2d-220            [-1, 608, 2, 2]           1,216\n",
      "            ReLU-221            [-1, 608, 2, 2]               0\n",
      "          Conv2d-222            [-1, 128, 2, 2]          77,824\n",
      "     BatchNorm2d-223            [-1, 128, 2, 2]             256\n",
      "            ReLU-224            [-1, 128, 2, 2]               0\n",
      "          Conv2d-225             [-1, 32, 2, 2]          36,864\n",
      "      DenseLayer-226             [-1, 32, 2, 2]               0\n",
      "     BatchNorm2d-227            [-1, 640, 2, 2]           1,280\n",
      "            ReLU-228            [-1, 640, 2, 2]               0\n",
      "          Conv2d-229            [-1, 128, 2, 2]          81,920\n",
      "     BatchNorm2d-230            [-1, 128, 2, 2]             256\n",
      "            ReLU-231            [-1, 128, 2, 2]               0\n",
      "          Conv2d-232             [-1, 32, 2, 2]          36,864\n",
      "      DenseLayer-233             [-1, 32, 2, 2]               0\n",
      "     BatchNorm2d-234            [-1, 672, 2, 2]           1,344\n",
      "            ReLU-235            [-1, 672, 2, 2]               0\n",
      "          Conv2d-236            [-1, 128, 2, 2]          86,016\n",
      "     BatchNorm2d-237            [-1, 128, 2, 2]             256\n",
      "            ReLU-238            [-1, 128, 2, 2]               0\n",
      "          Conv2d-239             [-1, 32, 2, 2]          36,864\n",
      "      DenseLayer-240             [-1, 32, 2, 2]               0\n",
      "     BatchNorm2d-241            [-1, 704, 2, 2]           1,408\n",
      "            ReLU-242            [-1, 704, 2, 2]               0\n",
      "          Conv2d-243            [-1, 128, 2, 2]          90,112\n",
      "     BatchNorm2d-244            [-1, 128, 2, 2]             256\n",
      "            ReLU-245            [-1, 128, 2, 2]               0\n",
      "          Conv2d-246             [-1, 32, 2, 2]          36,864\n",
      "      DenseLayer-247             [-1, 32, 2, 2]               0\n",
      "     BatchNorm2d-248            [-1, 736, 2, 2]           1,472\n",
      "            ReLU-249            [-1, 736, 2, 2]               0\n",
      "          Conv2d-250            [-1, 128, 2, 2]          94,208\n",
      "     BatchNorm2d-251            [-1, 128, 2, 2]             256\n",
      "            ReLU-252            [-1, 128, 2, 2]               0\n",
      "          Conv2d-253             [-1, 32, 2, 2]          36,864\n",
      "      DenseLayer-254             [-1, 32, 2, 2]               0\n",
      "     BatchNorm2d-255            [-1, 768, 2, 2]           1,536\n",
      "            ReLU-256            [-1, 768, 2, 2]               0\n",
      "          Conv2d-257            [-1, 128, 2, 2]          98,304\n",
      "     BatchNorm2d-258            [-1, 128, 2, 2]             256\n",
      "            ReLU-259            [-1, 128, 2, 2]               0\n",
      "          Conv2d-260             [-1, 32, 2, 2]          36,864\n",
      "      DenseLayer-261             [-1, 32, 2, 2]               0\n",
      "     BatchNorm2d-262            [-1, 800, 2, 2]           1,600\n",
      "            ReLU-263            [-1, 800, 2, 2]               0\n",
      "          Conv2d-264            [-1, 128, 2, 2]         102,400\n",
      "     BatchNorm2d-265            [-1, 128, 2, 2]             256\n",
      "            ReLU-266            [-1, 128, 2, 2]               0\n",
      "          Conv2d-267             [-1, 32, 2, 2]          36,864\n",
      "      DenseLayer-268             [-1, 32, 2, 2]               0\n",
      "     BatchNorm2d-269            [-1, 832, 2, 2]           1,664\n",
      "            ReLU-270            [-1, 832, 2, 2]               0\n",
      "          Conv2d-271            [-1, 128, 2, 2]         106,496\n",
      "     BatchNorm2d-272            [-1, 128, 2, 2]             256\n",
      "            ReLU-273            [-1, 128, 2, 2]               0\n",
      "          Conv2d-274             [-1, 32, 2, 2]          36,864\n",
      "      DenseLayer-275             [-1, 32, 2, 2]               0\n",
      "     BatchNorm2d-276            [-1, 864, 2, 2]           1,728\n",
      "            ReLU-277            [-1, 864, 2, 2]               0\n",
      "          Conv2d-278            [-1, 128, 2, 2]         110,592\n",
      "     BatchNorm2d-279            [-1, 128, 2, 2]             256\n",
      "            ReLU-280            [-1, 128, 2, 2]               0\n",
      "          Conv2d-281             [-1, 32, 2, 2]          36,864\n",
      "      DenseLayer-282             [-1, 32, 2, 2]               0\n",
      "     BatchNorm2d-283            [-1, 896, 2, 2]           1,792\n",
      "            ReLU-284            [-1, 896, 2, 2]               0\n",
      "          Conv2d-285            [-1, 128, 2, 2]         114,688\n",
      "     BatchNorm2d-286            [-1, 128, 2, 2]             256\n",
      "            ReLU-287            [-1, 128, 2, 2]               0\n",
      "          Conv2d-288             [-1, 32, 2, 2]          36,864\n",
      "      DenseLayer-289             [-1, 32, 2, 2]               0\n",
      "     BatchNorm2d-290            [-1, 928, 2, 2]           1,856\n",
      "            ReLU-291            [-1, 928, 2, 2]               0\n",
      "          Conv2d-292            [-1, 128, 2, 2]         118,784\n",
      "     BatchNorm2d-293            [-1, 128, 2, 2]             256\n",
      "            ReLU-294            [-1, 128, 2, 2]               0\n",
      "          Conv2d-295             [-1, 32, 2, 2]          36,864\n",
      "      DenseLayer-296             [-1, 32, 2, 2]               0\n",
      "     BatchNorm2d-297            [-1, 960, 2, 2]           1,920\n",
      "            ReLU-298            [-1, 960, 2, 2]               0\n",
      "          Conv2d-299            [-1, 128, 2, 2]         122,880\n",
      "     BatchNorm2d-300            [-1, 128, 2, 2]             256\n",
      "            ReLU-301            [-1, 128, 2, 2]               0\n",
      "          Conv2d-302             [-1, 32, 2, 2]          36,864\n",
      "      DenseLayer-303             [-1, 32, 2, 2]               0\n",
      "     BatchNorm2d-304            [-1, 992, 2, 2]           1,984\n",
      "            ReLU-305            [-1, 992, 2, 2]               0\n",
      "          Conv2d-306            [-1, 128, 2, 2]         126,976\n",
      "     BatchNorm2d-307            [-1, 128, 2, 2]             256\n",
      "            ReLU-308            [-1, 128, 2, 2]               0\n",
      "          Conv2d-309             [-1, 32, 2, 2]          36,864\n",
      "      DenseLayer-310             [-1, 32, 2, 2]               0\n",
      "      DenseBlock-311           [-1, 1024, 2, 2]               0\n",
      "     BatchNorm2d-312           [-1, 1024, 2, 2]           2,048\n",
      "            ReLU-313           [-1, 1024, 2, 2]               0\n",
      "          Conv2d-314            [-1, 512, 2, 2]         524,288\n",
      "       AvgPool2d-315            [-1, 512, 1, 1]               0\n",
      "      Transition-316            [-1, 512, 1, 1]               0\n",
      "     BatchNorm2d-317            [-1, 512, 1, 1]           1,024\n",
      "            ReLU-318            [-1, 512, 1, 1]               0\n",
      "          Conv2d-319            [-1, 128, 1, 1]          65,536\n",
      "     BatchNorm2d-320            [-1, 128, 1, 1]             256\n",
      "            ReLU-321            [-1, 128, 1, 1]               0\n",
      "          Conv2d-322             [-1, 32, 1, 1]          36,864\n",
      "      DenseLayer-323             [-1, 32, 1, 1]               0\n",
      "     BatchNorm2d-324            [-1, 544, 1, 1]           1,088\n",
      "            ReLU-325            [-1, 544, 1, 1]               0\n",
      "          Conv2d-326            [-1, 128, 1, 1]          69,632\n",
      "     BatchNorm2d-327            [-1, 128, 1, 1]             256\n",
      "            ReLU-328            [-1, 128, 1, 1]               0\n",
      "          Conv2d-329             [-1, 32, 1, 1]          36,864\n",
      "      DenseLayer-330             [-1, 32, 1, 1]               0\n",
      "     BatchNorm2d-331            [-1, 576, 1, 1]           1,152\n",
      "            ReLU-332            [-1, 576, 1, 1]               0\n",
      "          Conv2d-333            [-1, 128, 1, 1]          73,728\n",
      "     BatchNorm2d-334            [-1, 128, 1, 1]             256\n",
      "            ReLU-335            [-1, 128, 1, 1]               0\n",
      "          Conv2d-336             [-1, 32, 1, 1]          36,864\n",
      "      DenseLayer-337             [-1, 32, 1, 1]               0\n",
      "     BatchNorm2d-338            [-1, 608, 1, 1]           1,216\n",
      "            ReLU-339            [-1, 608, 1, 1]               0\n",
      "          Conv2d-340            [-1, 128, 1, 1]          77,824\n",
      "     BatchNorm2d-341            [-1, 128, 1, 1]             256\n",
      "            ReLU-342            [-1, 128, 1, 1]               0\n",
      "          Conv2d-343             [-1, 32, 1, 1]          36,864\n",
      "      DenseLayer-344             [-1, 32, 1, 1]               0\n",
      "     BatchNorm2d-345            [-1, 640, 1, 1]           1,280\n",
      "            ReLU-346            [-1, 640, 1, 1]               0\n",
      "          Conv2d-347            [-1, 128, 1, 1]          81,920\n",
      "     BatchNorm2d-348            [-1, 128, 1, 1]             256\n",
      "            ReLU-349            [-1, 128, 1, 1]               0\n",
      "          Conv2d-350             [-1, 32, 1, 1]          36,864\n",
      "      DenseLayer-351             [-1, 32, 1, 1]               0\n",
      "     BatchNorm2d-352            [-1, 672, 1, 1]           1,344\n",
      "            ReLU-353            [-1, 672, 1, 1]               0\n",
      "          Conv2d-354            [-1, 128, 1, 1]          86,016\n",
      "     BatchNorm2d-355            [-1, 128, 1, 1]             256\n",
      "            ReLU-356            [-1, 128, 1, 1]               0\n",
      "          Conv2d-357             [-1, 32, 1, 1]          36,864\n",
      "      DenseLayer-358             [-1, 32, 1, 1]               0\n",
      "     BatchNorm2d-359            [-1, 704, 1, 1]           1,408\n",
      "            ReLU-360            [-1, 704, 1, 1]               0\n",
      "          Conv2d-361            [-1, 128, 1, 1]          90,112\n",
      "     BatchNorm2d-362            [-1, 128, 1, 1]             256\n",
      "            ReLU-363            [-1, 128, 1, 1]               0\n",
      "          Conv2d-364             [-1, 32, 1, 1]          36,864\n",
      "      DenseLayer-365             [-1, 32, 1, 1]               0\n",
      "     BatchNorm2d-366            [-1, 736, 1, 1]           1,472\n",
      "            ReLU-367            [-1, 736, 1, 1]               0\n",
      "          Conv2d-368            [-1, 128, 1, 1]          94,208\n",
      "     BatchNorm2d-369            [-1, 128, 1, 1]             256\n",
      "            ReLU-370            [-1, 128, 1, 1]               0\n",
      "          Conv2d-371             [-1, 32, 1, 1]          36,864\n",
      "      DenseLayer-372             [-1, 32, 1, 1]               0\n",
      "     BatchNorm2d-373            [-1, 768, 1, 1]           1,536\n",
      "            ReLU-374            [-1, 768, 1, 1]               0\n",
      "          Conv2d-375            [-1, 128, 1, 1]          98,304\n",
      "     BatchNorm2d-376            [-1, 128, 1, 1]             256\n",
      "            ReLU-377            [-1, 128, 1, 1]               0\n",
      "          Conv2d-378             [-1, 32, 1, 1]          36,864\n",
      "      DenseLayer-379             [-1, 32, 1, 1]               0\n",
      "     BatchNorm2d-380            [-1, 800, 1, 1]           1,600\n",
      "            ReLU-381            [-1, 800, 1, 1]               0\n",
      "          Conv2d-382            [-1, 128, 1, 1]         102,400\n",
      "     BatchNorm2d-383            [-1, 128, 1, 1]             256\n",
      "            ReLU-384            [-1, 128, 1, 1]               0\n",
      "          Conv2d-385             [-1, 32, 1, 1]          36,864\n",
      "      DenseLayer-386             [-1, 32, 1, 1]               0\n",
      "     BatchNorm2d-387            [-1, 832, 1, 1]           1,664\n",
      "            ReLU-388            [-1, 832, 1, 1]               0\n",
      "          Conv2d-389            [-1, 128, 1, 1]         106,496\n",
      "     BatchNorm2d-390            [-1, 128, 1, 1]             256\n",
      "            ReLU-391            [-1, 128, 1, 1]               0\n",
      "          Conv2d-392             [-1, 32, 1, 1]          36,864\n",
      "      DenseLayer-393             [-1, 32, 1, 1]               0\n",
      "     BatchNorm2d-394            [-1, 864, 1, 1]           1,728\n",
      "            ReLU-395            [-1, 864, 1, 1]               0\n",
      "          Conv2d-396            [-1, 128, 1, 1]         110,592\n",
      "     BatchNorm2d-397            [-1, 128, 1, 1]             256\n",
      "            ReLU-398            [-1, 128, 1, 1]               0\n",
      "          Conv2d-399             [-1, 32, 1, 1]          36,864\n",
      "      DenseLayer-400             [-1, 32, 1, 1]               0\n",
      "     BatchNorm2d-401            [-1, 896, 1, 1]           1,792\n",
      "            ReLU-402            [-1, 896, 1, 1]               0\n",
      "          Conv2d-403            [-1, 128, 1, 1]         114,688\n",
      "     BatchNorm2d-404            [-1, 128, 1, 1]             256\n",
      "            ReLU-405            [-1, 128, 1, 1]               0\n",
      "          Conv2d-406             [-1, 32, 1, 1]          36,864\n",
      "      DenseLayer-407             [-1, 32, 1, 1]               0\n",
      "     BatchNorm2d-408            [-1, 928, 1, 1]           1,856\n",
      "            ReLU-409            [-1, 928, 1, 1]               0\n",
      "          Conv2d-410            [-1, 128, 1, 1]         118,784\n",
      "     BatchNorm2d-411            [-1, 128, 1, 1]             256\n",
      "            ReLU-412            [-1, 128, 1, 1]               0\n",
      "          Conv2d-413             [-1, 32, 1, 1]          36,864\n",
      "      DenseLayer-414             [-1, 32, 1, 1]               0\n",
      "     BatchNorm2d-415            [-1, 960, 1, 1]           1,920\n",
      "            ReLU-416            [-1, 960, 1, 1]               0\n",
      "          Conv2d-417            [-1, 128, 1, 1]         122,880\n",
      "     BatchNorm2d-418            [-1, 128, 1, 1]             256\n",
      "            ReLU-419            [-1, 128, 1, 1]               0\n",
      "          Conv2d-420             [-1, 32, 1, 1]          36,864\n",
      "      DenseLayer-421             [-1, 32, 1, 1]               0\n",
      "     BatchNorm2d-422            [-1, 992, 1, 1]           1,984\n",
      "            ReLU-423            [-1, 992, 1, 1]               0\n",
      "          Conv2d-424            [-1, 128, 1, 1]         126,976\n",
      "     BatchNorm2d-425            [-1, 128, 1, 1]             256\n",
      "            ReLU-426            [-1, 128, 1, 1]               0\n",
      "          Conv2d-427             [-1, 32, 1, 1]          36,864\n",
      "      DenseLayer-428             [-1, 32, 1, 1]               0\n",
      "      DenseBlock-429           [-1, 1024, 1, 1]               0\n",
      "     BatchNorm2d-430           [-1, 1024, 1, 1]           2,048\n",
      "          Linear-431                   [-1, 10]          10,250\n",
      "================================================================\n",
      "Total params: 6,964,106\n",
      "Trainable params: 6,964,106\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 0.01\n",
      "Forward/backward pass size (MB): 6.43\n",
      "Params size (MB): 26.57\n",
      "Estimated Total Size (MB): 33.00\n",
      "----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "def test_DenseNetZrc():\n",
    "    model = DenseNetZrc(num_classes=10)\n",
    "    model = model.to(DEVICE)\n",
    "    summary(model, (3,32,32))\n",
    "\n",
    "test_DenseNetZrc()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = DenseNetZrc(num_classes=NUM_CLASSES, grayscale=GRAYSCALE)\n",
    "model.to(DEVICE)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr = LEARNING_RATE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_accuracy(model, data_loader, device):\n",
    "    model.eval()\n",
    "    correct_pred, num_examples = 0, 0\n",
    "    for i, (features, targets) in enumerate(data_loader):\n",
    "            \n",
    "        features = features.to(device)\n",
    "        targets = targets.to(device)\n",
    "\n",
    "        logits, probas = model(features)\n",
    "        _, predicted_labels = torch.max(probas, 1)\n",
    "        num_examples += targets.size(0)\n",
    "        correct_pred += (predicted_labels == targets).sum()\n",
    "    return correct_pred.float()/num_examples * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(model, data_loader, optimizer, num_epochs,batch_size, device,metric_func, random_seed = 7):\n",
    "    # Manual seed for deterministic data loader\n",
    "    torch.manual_seed(random_seed)\n",
    "    \n",
    "    loss_list = []\n",
    "    train_acc_list, valid_acc_list = [], []\n",
    "    \n",
    "    for epoch in range(num_epochs):\n",
    "        # set training mode\n",
    "        model.train() \n",
    "        for batch_idx, (features, targets) in enumerate(data_loader[\"train\"]):\n",
    "            features = features.to(device)\n",
    "            targets = targets.to(device)\n",
    "\n",
    "\n",
    "            ## forward pass\n",
    "            logits, probas = model(features)\n",
    "            loss = F.cross_entropy(logits,targets)\n",
    "\n",
    "            # backward pass\n",
    "            # clear the gradients of all tensors being optimized\n",
    "            optimizer.zero_grad()\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "\n",
    "            ### Login\n",
    "            loss_list.append(loss.item())\n",
    "            if not batch_idx % 50:\n",
    "                print ('Epoch: {0:03d}/{1:03d} | Batch {2:03d}/{3:03d} | Loss: {4:.2f}'.format(\n",
    "                    epoch+1, num_epochs, batch_idx, \n",
    "                         len(train_dataset)//batch_size, loss))\n",
    "\n",
    "        with torch.set_grad_enabled(False):\n",
    "            train_acc = metric_func(model, data_loader[\"train\"], device)\n",
    "            valid_acc = metric_func(model, data_loader[\"val\"], device)\n",
    "            \n",
    "            print('Epoch: {0:03d}/{1:03d} training accuracy: {2:.2f}'.format(\n",
    "                  epoch+1, num_epochs, train_acc))\n",
    "            \n",
    "            print('Epoch: {0:03d}/{1:03d} validation accuracy: {2:.2f}'.format(\n",
    "                  epoch+1, num_epochs, valid_acc))\n",
    "            \n",
    "            train_acc_list.append(train_acc)\n",
    "            valid_acc_list.append(valid_acc)\n",
    "            \n",
    "    return loss_list, train_acc_list, valid_acc_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 001/010 | Batch 000/921 | Loss: 2.33\n",
      "Epoch: 001/010 | Batch 050/921 | Loss: 1.00\n",
      "Epoch: 001/010 | Batch 100/921 | Loss: 0.35\n",
      "Epoch: 001/010 | Batch 150/921 | Loss: 0.25\n",
      "Epoch: 001/010 | Batch 200/921 | Loss: 0.11\n",
      "Epoch: 001/010 | Batch 250/921 | Loss: 0.16\n",
      "Epoch: 001/010 | Batch 300/921 | Loss: 0.09\n",
      "Epoch: 001/010 | Batch 350/921 | Loss: 0.10\n",
      "Epoch: 001/010 | Batch 400/921 | Loss: 0.13\n",
      "Epoch: 001/010 | Batch 450/921 | Loss: 0.10\n",
      "Epoch: 001/010 | Batch 500/921 | Loss: 0.17\n",
      "Epoch: 001/010 | Batch 550/921 | Loss: 0.08\n",
      "Epoch: 001/010 | Batch 600/921 | Loss: 0.10\n",
      "Epoch: 001/010 | Batch 650/921 | Loss: 0.11\n",
      "Epoch: 001/010 | Batch 700/921 | Loss: 0.08\n",
      "Epoch: 001/010 | Batch 750/921 | Loss: 0.22\n",
      "Epoch: 001/010 | Batch 800/921 | Loss: 0.04\n",
      "Epoch: 001/010 | Batch 850/921 | Loss: 0.13\n",
      "Epoch: 001/010 | Batch 900/921 | Loss: 0.08\n",
      "Epoch: 001/010 training accuracy: 98.69\n",
      "Epoch: 001/010 validation accuracy: 98.30\n",
      "Epoch: 002/010 | Batch 000/921 | Loss: 0.03\n",
      "Epoch: 002/010 | Batch 050/921 | Loss: 0.09\n",
      "Epoch: 002/010 | Batch 100/921 | Loss: 0.03\n",
      "Epoch: 002/010 | Batch 150/921 | Loss: 0.03\n",
      "Epoch: 002/010 | Batch 200/921 | Loss: 0.02\n",
      "Epoch: 002/010 | Batch 250/921 | Loss: 0.14\n",
      "Epoch: 002/010 | Batch 300/921 | Loss: 0.05\n",
      "Epoch: 002/010 | Batch 350/921 | Loss: 0.05\n",
      "Epoch: 002/010 | Batch 400/921 | Loss: 0.06\n",
      "Epoch: 002/010 | Batch 450/921 | Loss: 0.05\n",
      "Epoch: 002/010 | Batch 500/921 | Loss: 0.02\n",
      "Epoch: 002/010 | Batch 550/921 | Loss: 0.05\n",
      "Epoch: 002/010 | Batch 600/921 | Loss: 0.04\n",
      "Epoch: 002/010 | Batch 650/921 | Loss: 0.11\n",
      "Epoch: 002/010 | Batch 700/921 | Loss: 0.01\n",
      "Epoch: 002/010 | Batch 750/921 | Loss: 0.11\n",
      "Epoch: 002/010 | Batch 800/921 | Loss: 0.07\n",
      "Epoch: 002/010 | Batch 850/921 | Loss: 0.03\n",
      "Epoch: 002/010 | Batch 900/921 | Loss: 0.03\n",
      "Epoch: 002/010 training accuracy: 99.48\n",
      "Epoch: 002/010 validation accuracy: 98.20\n",
      "Epoch: 003/010 | Batch 000/921 | Loss: 0.01\n",
      "Epoch: 003/010 | Batch 050/921 | Loss: 0.08\n",
      "Epoch: 003/010 | Batch 100/921 | Loss: 0.05\n",
      "Epoch: 003/010 | Batch 150/921 | Loss: 0.04\n",
      "Epoch: 003/010 | Batch 200/921 | Loss: 0.01\n",
      "Epoch: 003/010 | Batch 250/921 | Loss: 0.02\n",
      "Epoch: 003/010 | Batch 300/921 | Loss: 0.01\n",
      "Epoch: 003/010 | Batch 350/921 | Loss: 0.00\n",
      "Epoch: 003/010 | Batch 400/921 | Loss: 0.01\n",
      "Epoch: 003/010 | Batch 450/921 | Loss: 0.03\n",
      "Epoch: 003/010 | Batch 500/921 | Loss: 0.08\n",
      "Epoch: 003/010 | Batch 550/921 | Loss: 0.02\n",
      "Epoch: 003/010 | Batch 600/921 | Loss: 0.04\n",
      "Epoch: 003/010 | Batch 650/921 | Loss: 0.05\n",
      "Epoch: 003/010 | Batch 700/921 | Loss: 0.00\n",
      "Epoch: 003/010 | Batch 750/921 | Loss: 0.02\n",
      "Epoch: 003/010 | Batch 800/921 | Loss: 0.01\n",
      "Epoch: 003/010 | Batch 850/921 | Loss: 0.01\n",
      "Epoch: 003/010 | Batch 900/921 | Loss: 0.01\n",
      "Epoch: 003/010 training accuracy: 99.62\n",
      "Epoch: 003/010 validation accuracy: 98.20\n",
      "Epoch: 004/010 | Batch 000/921 | Loss: 0.02\n",
      "Epoch: 004/010 | Batch 050/921 | Loss: 0.01\n",
      "Epoch: 004/010 | Batch 100/921 | Loss: 0.02\n",
      "Epoch: 004/010 | Batch 150/921 | Loss: 0.00\n",
      "Epoch: 004/010 | Batch 200/921 | Loss: 0.03\n",
      "Epoch: 004/010 | Batch 250/921 | Loss: 0.01\n",
      "Epoch: 004/010 | Batch 300/921 | Loss: 0.01\n",
      "Epoch: 004/010 | Batch 350/921 | Loss: 0.02\n",
      "Epoch: 004/010 | Batch 400/921 | Loss: 0.02\n",
      "Epoch: 004/010 | Batch 450/921 | Loss: 0.01\n",
      "Epoch: 004/010 | Batch 500/921 | Loss: 0.00\n",
      "Epoch: 004/010 | Batch 550/921 | Loss: 0.01\n",
      "Epoch: 004/010 | Batch 600/921 | Loss: 0.05\n",
      "Epoch: 004/010 | Batch 650/921 | Loss: 0.12\n",
      "Epoch: 004/010 | Batch 700/921 | Loss: 0.02\n",
      "Epoch: 004/010 | Batch 750/921 | Loss: 0.08\n",
      "Epoch: 004/010 | Batch 800/921 | Loss: 0.03\n",
      "Epoch: 004/010 | Batch 850/921 | Loss: 0.01\n",
      "Epoch: 004/010 | Batch 900/921 | Loss: 0.04\n",
      "Epoch: 004/010 training accuracy: 99.58\n",
      "Epoch: 004/010 validation accuracy: 98.20\n",
      "Epoch: 005/010 | Batch 000/921 | Loss: 0.00\n",
      "Epoch: 005/010 | Batch 050/921 | Loss: 0.04\n",
      "Epoch: 005/010 | Batch 100/921 | Loss: 0.00\n",
      "Epoch: 005/010 | Batch 150/921 | Loss: 0.00\n",
      "Epoch: 005/010 | Batch 200/921 | Loss: 0.01\n",
      "Epoch: 005/010 | Batch 250/921 | Loss: 0.01\n",
      "Epoch: 005/010 | Batch 300/921 | Loss: 0.00\n",
      "Epoch: 005/010 | Batch 350/921 | Loss: 0.02\n",
      "Epoch: 005/010 | Batch 400/921 | Loss: 0.05\n",
      "Epoch: 005/010 | Batch 450/921 | Loss: 0.03\n",
      "Epoch: 005/010 | Batch 500/921 | Loss: 0.06\n",
      "Epoch: 005/010 | Batch 550/921 | Loss: 0.02\n",
      "Epoch: 005/010 | Batch 600/921 | Loss: 0.02\n",
      "Epoch: 005/010 | Batch 650/921 | Loss: 0.03\n",
      "Epoch: 005/010 | Batch 700/921 | Loss: 0.03\n",
      "Epoch: 005/010 | Batch 750/921 | Loss: 0.08\n",
      "Epoch: 005/010 | Batch 800/921 | Loss: 0.00\n",
      "Epoch: 005/010 | Batch 850/921 | Loss: 0.00\n",
      "Epoch: 005/010 | Batch 900/921 | Loss: 0.03\n",
      "Epoch: 005/010 training accuracy: 99.71\n",
      "Epoch: 005/010 validation accuracy: 98.70\n",
      "Epoch: 006/010 | Batch 000/921 | Loss: 0.01\n",
      "Epoch: 006/010 | Batch 050/921 | Loss: 0.07\n",
      "Epoch: 006/010 | Batch 100/921 | Loss: 0.02\n",
      "Epoch: 006/010 | Batch 150/921 | Loss: 0.00\n",
      "Epoch: 006/010 | Batch 200/921 | Loss: 0.06\n",
      "Epoch: 006/010 | Batch 250/921 | Loss: 0.01\n",
      "Epoch: 006/010 | Batch 300/921 | Loss: 0.01\n",
      "Epoch: 006/010 | Batch 350/921 | Loss: 0.01\n",
      "Epoch: 006/010 | Batch 400/921 | Loss: 0.01\n",
      "Epoch: 006/010 | Batch 450/921 | Loss: 0.00\n",
      "Epoch: 006/010 | Batch 500/921 | Loss: 0.00\n",
      "Epoch: 006/010 | Batch 550/921 | Loss: 0.03\n",
      "Epoch: 006/010 | Batch 600/921 | Loss: 0.00\n",
      "Epoch: 006/010 | Batch 650/921 | Loss: 0.01\n",
      "Epoch: 006/010 | Batch 700/921 | Loss: 0.00\n",
      "Epoch: 006/010 | Batch 750/921 | Loss: 0.01\n",
      "Epoch: 006/010 | Batch 800/921 | Loss: 0.01\n",
      "Epoch: 006/010 | Batch 850/921 | Loss: 0.00\n",
      "Epoch: 006/010 | Batch 900/921 | Loss: 0.03\n",
      "Epoch: 006/010 training accuracy: 99.70\n",
      "Epoch: 006/010 validation accuracy: 98.80\n",
      "Epoch: 007/010 | Batch 000/921 | Loss: 0.00\n",
      "Epoch: 007/010 | Batch 050/921 | Loss: 0.02\n",
      "Epoch: 007/010 | Batch 100/921 | Loss: 0.04\n",
      "Epoch: 007/010 | Batch 150/921 | Loss: 0.01\n",
      "Epoch: 007/010 | Batch 200/921 | Loss: 0.03\n",
      "Epoch: 007/010 | Batch 250/921 | Loss: 0.00\n",
      "Epoch: 007/010 | Batch 300/921 | Loss: 0.00\n",
      "Epoch: 007/010 | Batch 350/921 | Loss: 0.01\n",
      "Epoch: 007/010 | Batch 400/921 | Loss: 0.00\n",
      "Epoch: 007/010 | Batch 450/921 | Loss: 0.02\n",
      "Epoch: 007/010 | Batch 500/921 | Loss: 0.00\n",
      "Epoch: 007/010 | Batch 550/921 | Loss: 0.00\n",
      "Epoch: 007/010 | Batch 600/921 | Loss: 0.00\n",
      "Epoch: 007/010 | Batch 650/921 | Loss: 0.00\n",
      "Epoch: 007/010 | Batch 700/921 | Loss: 0.00\n",
      "Epoch: 007/010 | Batch 750/921 | Loss: 0.12\n",
      "Epoch: 007/010 | Batch 800/921 | Loss: 0.00\n",
      "Epoch: 007/010 | Batch 850/921 | Loss: 0.04\n",
      "Epoch: 007/010 | Batch 900/921 | Loss: 0.06\n",
      "Epoch: 007/010 training accuracy: 99.73\n",
      "Epoch: 007/010 validation accuracy: 98.70\n",
      "Epoch: 008/010 | Batch 000/921 | Loss: 0.01\n",
      "Epoch: 008/010 | Batch 050/921 | Loss: 0.01\n",
      "Epoch: 008/010 | Batch 100/921 | Loss: 0.00\n",
      "Epoch: 008/010 | Batch 150/921 | Loss: 0.01\n",
      "Epoch: 008/010 | Batch 200/921 | Loss: 0.01\n",
      "Epoch: 008/010 | Batch 250/921 | Loss: 0.00\n",
      "Epoch: 008/010 | Batch 300/921 | Loss: 0.00\n",
      "Epoch: 008/010 | Batch 350/921 | Loss: 0.04\n",
      "Epoch: 008/010 | Batch 400/921 | Loss: 0.00\n",
      "Epoch: 008/010 | Batch 450/921 | Loss: 0.01\n",
      "Epoch: 008/010 | Batch 500/921 | Loss: 0.01\n",
      "Epoch: 008/010 | Batch 550/921 | Loss: 0.03\n",
      "Epoch: 008/010 | Batch 600/921 | Loss: 0.00\n",
      "Epoch: 008/010 | Batch 650/921 | Loss: 0.00\n",
      "Epoch: 008/010 | Batch 700/921 | Loss: 0.02\n",
      "Epoch: 008/010 | Batch 750/921 | Loss: 0.00\n",
      "Epoch: 008/010 | Batch 800/921 | Loss: 0.00\n",
      "Epoch: 008/010 | Batch 850/921 | Loss: 0.02\n",
      "Epoch: 008/010 | Batch 900/921 | Loss: 0.06\n",
      "Epoch: 008/010 training accuracy: 99.80\n",
      "Epoch: 008/010 validation accuracy: 98.20\n",
      "Epoch: 009/010 | Batch 000/921 | Loss: 0.00\n",
      "Epoch: 009/010 | Batch 050/921 | Loss: 0.00\n",
      "Epoch: 009/010 | Batch 100/921 | Loss: 0.00\n",
      "Epoch: 009/010 | Batch 150/921 | Loss: 0.00\n",
      "Epoch: 009/010 | Batch 200/921 | Loss: 0.00\n",
      "Epoch: 009/010 | Batch 250/921 | Loss: 0.03\n",
      "Epoch: 009/010 | Batch 300/921 | Loss: 0.03\n",
      "Epoch: 009/010 | Batch 350/921 | Loss: 0.01\n",
      "Epoch: 009/010 | Batch 400/921 | Loss: 0.01\n",
      "Epoch: 009/010 | Batch 450/921 | Loss: 0.01\n",
      "Epoch: 009/010 | Batch 500/921 | Loss: 0.01\n",
      "Epoch: 009/010 | Batch 550/921 | Loss: 0.00\n",
      "Epoch: 009/010 | Batch 600/921 | Loss: 0.00\n",
      "Epoch: 009/010 | Batch 650/921 | Loss: 0.00\n",
      "Epoch: 009/010 | Batch 700/921 | Loss: 0.01\n",
      "Epoch: 009/010 | Batch 750/921 | Loss: 0.08\n",
      "Epoch: 009/010 | Batch 800/921 | Loss: 0.01\n",
      "Epoch: 009/010 | Batch 850/921 | Loss: 0.08\n",
      "Epoch: 009/010 | Batch 900/921 | Loss: 0.01\n",
      "Epoch: 009/010 training accuracy: 99.81\n",
      "Epoch: 009/010 validation accuracy: 98.10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 010/010 | Batch 000/921 | Loss: 0.06\n",
      "Epoch: 010/010 | Batch 050/921 | Loss: 0.04\n",
      "Epoch: 010/010 | Batch 100/921 | Loss: 0.00\n",
      "Epoch: 010/010 | Batch 150/921 | Loss: 0.00\n",
      "Epoch: 010/010 | Batch 200/921 | Loss: 0.00\n",
      "Epoch: 010/010 | Batch 250/921 | Loss: 0.00\n",
      "Epoch: 010/010 | Batch 300/921 | Loss: 0.00\n",
      "Epoch: 010/010 | Batch 350/921 | Loss: 0.03\n",
      "Epoch: 010/010 | Batch 400/921 | Loss: 0.00\n",
      "Epoch: 010/010 | Batch 450/921 | Loss: 0.04\n",
      "Epoch: 010/010 | Batch 500/921 | Loss: 0.01\n",
      "Epoch: 010/010 | Batch 550/921 | Loss: 0.00\n",
      "Epoch: 010/010 | Batch 600/921 | Loss: 0.02\n",
      "Epoch: 010/010 | Batch 650/921 | Loss: 0.04\n",
      "Epoch: 010/010 | Batch 700/921 | Loss: 0.00\n",
      "Epoch: 010/010 | Batch 750/921 | Loss: 0.00\n",
      "Epoch: 010/010 | Batch 800/921 | Loss: 0.01\n",
      "Epoch: 010/010 | Batch 850/921 | Loss: 0.00\n",
      "Epoch: 010/010 | Batch 900/921 | Loss: 0.01\n",
      "Epoch: 010/010 training accuracy: 99.88\n",
      "Epoch: 010/010 validation accuracy: 98.90\n"
     ]
    }
   ],
   "source": [
    "loss_list, train_acc_list, valid_acc_list = train_model(model, \n",
    "            data_loader, \n",
    "            optimizer, \n",
    "            NUM_EPOCHS, \n",
    "            device = DEVICE, \n",
    "            batch_size = BATCH_SIZE,\n",
    "            metric_func = compute_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": [
    "plt.plot(loss_list, label='Minibatch cost')\n",
    "plt.plot(np.convolve(loss_list, \n",
    "                     np.ones(200,)/200, mode='valid'), \n",
    "         label='Running average')\n",
    "\n",
    "plt.ylabel('Cross Entropy')\n",
    "plt.xlabel('Iteration')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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": [
    "plt.plot(np.arange(1, NUM_EPOCHS+1), train_acc_list, label='Training')\n",
    "plt.plot(np.arange(1, NUM_EPOCHS+1), valid_acc_list, label='Validation')\n",
    "\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation ACC: 98.90%\n",
      "Test ACC: 99.10%\n"
     ]
    }
   ],
   "source": [
    "with torch.set_grad_enabled(False):\n",
    "    test_acc = compute_accuracy(model=model,\n",
    "                           data_loader=data_loader[\"test\"],\n",
    "                           device=DEVICE)\n",
    "    \n",
    "    valid_acc = compute_accuracy(model=model,\n",
    "                            data_loader=data_loader[\"val\"],\n",
    "                            device=DEVICE)\n",
    "    \n",
    "\n",
    "print(f'Validation ACC: {valid_acc:.2f}%')\n",
    "print(f'Test ACC: {test_acc:.2f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reference\n",
    "\n",
    "- https://towardsdatascience.com/understanding-and-visualizing-densenets-7f688092391a\n",
    "- https://github.com/rasbt/deeplearning-models"
   ]
  },
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   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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