exercises/1_5_CNN_Layers/5_2. Visualize Your Net.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CNN for Classification\n",
    "---\n",
    "In this notebook, we define **and train** an CNN to classify images from the [Fashion-MNIST database](https://github.com/zalandoresearch/fashion-mnist)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the [data](http://pytorch.org/docs/master/torchvision/datasets.html)\n",
    "\n",
    "In this cell, we load in both **training and test** datasets from the FashionMNIST class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data, number of images:  60000\n",
      "Test data, number of images:  10000\n"
     ]
    }
   ],
   "source": [
    "# our basic libraries\n",
    "import torch\n",
    "import torchvision\n",
    "\n",
    "# data loading and transforming\n",
    "from torchvision.datasets import FashionMNIST\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision import transforms\n",
    "\n",
    "from collections import OrderedDict\n",
    "\n",
    "# The output of torchvision datasets are PILImage images of range [0, 1]. \n",
    "# We transform them to Tensors for input into a CNN\n",
    "\n",
    "## Define a transform to read the data in as a tensor\n",
    "data_transform = transforms.ToTensor()\n",
    "\n",
    "# choose the training and test datasets\n",
    "train_data = FashionMNIST(root='./data', train=True,\n",
    "                                   download=True, transform=data_transform)\n",
    "\n",
    "test_data = FashionMNIST(root='./data', train=False,\n",
    "                                  download=True, transform=data_transform)\n",
    "\n",
    "\n",
    "# Print out some stats about the training and test data\n",
    "print('Train data, number of images: ', len(train_data))\n",
    "print('Test data, number of images: ', len(test_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prepare data loaders, set the batch_size\n",
    "## you can try changing the batch_size to be larger or smaller\n",
    "## when you get to training your network, see how batch_size affects the loss\n",
    "batch_size = 20\n",
    "\n",
    "train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True)\n",
    "test_loader = DataLoader(test_data, batch_size=batch_size, shuffle=True)\n",
    "\n",
    "# specify the image classes\n",
    "classes = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', \n",
    "           'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize some training data\n",
    "\n",
    "This cell iterates over the training dataset, loading a random batch of image/label data, using `dataiter.next()`. It then plots the batch of images and labels in a `2 x batch_size/2` grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10caadd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\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",
    "# plot the images in the batch, along with the corresponding labels\n",
    "fig = plt.figure(figsize=(25, 4))\n",
    "for idx in np.arange(batch_size):\n",
    "    ax = fig.add_subplot(2, batch_size/2, idx+1, xticks=[], yticks=[])\n",
    "    ax.imshow(np.squeeze(images[idx]), cmap='gray')\n",
    "    ax.set_title(classes[labels[idx]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the network architecture\n",
    "\n",
    "The various layers that make up any neural network are documented, [here](http://pytorch.org/docs/master/nn.html). For a convolutional neural network, we'll use a simple series of layers:\n",
    "* Convolutional layers\n",
    "* Maxpooling layers\n",
    "* Fully-connected (linear) layers\n",
    "\n",
    "You are also encouraged to look at adding [dropout layers](http://pytorch.org/docs/stable/nn.html#dropout) to avoid overfitting this data.\n",
    "\n",
    "---\n",
    "\n",
    "### Define the Net\n",
    "\n",
    "Define the layers of your **best, saved model from the classification exercise** in the function `__init__` and define the feedforward behavior of that Net in the function `forward`. Defining the architecture here, will allow you to instantiate and load your best Net."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sequential(\n",
      "  (conv1): Conv2d(1, 10, kernel_size=(3, 3), stride=(1, 1))\n",
      "  (relu1): ReLU()\n",
      "  (bachnorm1): BatchNorm2d(10, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "class Net(nn.Module):\n",
    "\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        self.layer1 = nn.Sequential(\n",
    "            OrderedDict([\n",
    "            ('conv1', nn.Conv2d(1, 10, kernel_size=3)),\n",
    "            ('relu1',nn.ReLU()),\n",
    "            ('bachnorm1', nn.BatchNorm2d(10))]))\n",
    "        \n",
    "        self.layer2 = nn.Sequential(OrderedDict([\n",
    "            ('maxp1', nn.MaxPool2d(2,2))]))\n",
    "        \n",
    "        self.layer3 = nn.Sequential(OrderedDict([\n",
    "            ('conv2', nn.Conv2d(10, 20, kernel_size=3)),\n",
    "            ('relu2', nn.ReLU())]))\n",
    "        \n",
    "        self.layer4 = nn.Sequential(OrderedDict([\n",
    "            ('fc1', nn.Linear(11*11*20,200 )),\n",
    "            ('relu3', nn.ReLU()),\n",
    "            ('dropout4',  nn.Dropout(0.4))]))\n",
    "        \n",
    "        self.layer5 = nn.Sequential(OrderedDict([\n",
    "            ('fc2', nn.Linear(200, 100)),\n",
    "            ('relu4', nn.ReLU()),\n",
    "            ('dropout5',  nn.Dropout(0.4))]))\n",
    "        \n",
    "        self.layer6 = nn.Sequential(OrderedDict([\n",
    "            ('fc3', nn.Linear(100, 10)),\n",
    "            ('relu5', nn.ReLU())]))\n",
    "        \n",
    "        \n",
    "    ## define the feedforward behavior\n",
    "    def forward(self, x):\n",
    "        out = self.layer1(x)\n",
    "        out = self.layer2(out)\n",
    "        out = self.layer3(out)\n",
    "        out = out.view(out.size(0), -1)\n",
    "        out = self.layer4(out)\n",
    "        out = self.layer5(out)\n",
    "        out = self.layer6(out)\n",
    "        # final output\n",
    "        return F.log_softmax(out, dim=1)\n",
    "\n",
    "# instantiate and print your Net\n",
    "net = Net()\n",
    "print(net.layer1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load a Trained, Saved Model\n",
    "\n",
    "To instantiate a trained model, you'll first instantiate a new `Net()` and then initialize it with a saved dictionary of parameters. This notebook needs to know the network architecture, as defined above, and once it knows what the \"Net\" class looks like, we can instantiate a model and load in an already trained network.\n",
    "\n",
    "You should have a trained net in `saved_models/`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Net(\n",
      "  (layer1): Sequential(\n",
      "    (conv1): Conv2d(1, 10, kernel_size=(3, 3), stride=(1, 1))\n",
      "    (relu1): ReLU()\n",
      "    (bachnorm1): BatchNorm2d(10, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  )\n",
      "  (layer2): Sequential(\n",
      "    (maxp1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (layer3): Sequential(\n",
      "    (conv2): Conv2d(10, 20, kernel_size=(3, 3), stride=(1, 1))\n",
      "    (relu2): ReLU()\n",
      "  )\n",
      "  (layer4): Sequential(\n",
      "    (fc1): Linear(in_features=2420, out_features=200, bias=True)\n",
      "    (relu3): ReLU()\n",
      "    (dropout4): Dropout(p=0.4)\n",
      "  )\n",
      "  (layer5): Sequential(\n",
      "    (fc2): Linear(in_features=200, out_features=100, bias=True)\n",
      "    (relu4): ReLU()\n",
      "    (dropout5): Dropout(p=0.4)\n",
      "  )\n",
      "  (layer6): Sequential(\n",
      "    (fc3): Linear(in_features=100, out_features=10, bias=True)\n",
      "    (relu5): ReLU()\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "# instantiate your Net\n",
    "net = Net()\n",
    "\n",
    "# load the net parameters by name, uncomment the line below to load your model\n",
    "net.load_state_dict(torch.load('saved_models/model_91.pt'))\n",
    "\n",
    "print(net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Visualization\n",
    "\n",
    "To see what your network has learned, make a plot of the learned image filter weights and the activation maps (for a given image) at each convolutional layer.\n",
    "\n",
    "### Visualize the learned filter weights and activation maps of the convolutional layers in your trained Net\n",
    "\n",
    "Choose a sample input image and apply the filters in every convolutional layer to that image to see the activation map."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First convolutional layer\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fd5a240>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# As a reminder, here is how we got the weights in the first conv layer (conv1), before\n",
    "weights = net.layer1.conv1.weight.data\n",
    "w = weights.numpy()\n",
    "\n",
    "# for 10 filters\n",
    "fig=plt.figure(figsize=(20, 8))\n",
    "columns = 5\n",
    "rows = 2\n",
    "for i in range(0, columns*rows):\n",
    "    fig.add_subplot(rows, columns, i+1)\n",
    "    plt.imshow(w[i][0], cmap='gray')\n",
    "    \n",
    "print('First convolutional layer')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Second convolutional layer\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ff17c50>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "weights = net.layer3.conv2.weight.data\n",
    "w = weights.numpy()\n",
    "\n",
    "# for 20 filters\n",
    "fig=plt.figure(figsize=(20, 16))\n",
    "columns = 5\n",
    "rows = 4\n",
    "for i in range(0, columns*rows):\n",
    "    fig.add_subplot(rows, columns, i+1)\n",
    "    plt.imshow(w[i][0], cmap='gray')\n",
    "    \n",
    "print('Second convolutional layer')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Activation Maps\n",
    "\n",
    "Next, you'll see how to use OpenCV's `filter2D` function to apply these filters to a sample test image and produce a series of **activation maps** as a result. We'll do this for the first and second convolutional layers and these activation maps whould really give you a sense for what features each filter learns to extract."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e77ee48>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e831f60>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# obtain one batch of testing images\n",
    "dataiter = iter(test_loader)\n",
    "images, labels = dataiter.next()\n",
    "images = images.numpy()\n",
    "\n",
    "# select an image by index\n",
    "idx = 7\n",
    "img = np.squeeze(images[idx])\n",
    "\n",
    "# Use OpenCV's filter2D function \n",
    "# apply a specific set of filter weights (like the one's displayed above) to the test image\n",
    "\n",
    "import cv2\n",
    "plt.imshow(img, cmap='gray')\n",
    "\n",
    "weights = net.layer3.conv2.weight.data\n",
    "w = weights.numpy()\n",
    "\n",
    "# second conv layer\n",
    "# for 20 filters\n",
    "fig=plt.figure(figsize=(30, 20))\n",
    "columns = 5*2\n",
    "rows = 4\n",
    "for i in range(0, columns*rows):\n",
    "    fig.add_subplot(rows, columns, i+1)\n",
    "    if ((i%2)==0):\n",
    "        plt.imshow(w[int(i/2)][0], cmap='gray')\n",
    "    else:\n",
    "        c = cv2.filter2D(img, -1, w[int((i-1)/2)][0])\n",
    "        plt.imshow(c, cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question: Choose a filter from one of your trained convolutional layers; looking at these activations, what purpose do you think it plays? What kind of feature do you think it detects?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer**: In the second convolutional layer (conv2), the 10 the filter; row 2 column 5, appers to detect edges with an 45 degrees tilt. It seems to detect the opening of the t-shrit, and the slope from the shoulder to the openeings, which have an angle of about 45 degress."
   ]
  }
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