10.Use of Filter Size, Padding and Stride.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Applying filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_20 (Conv2D)           (None, 6, 6, 1)           10        \n",
      "=================================================================\n",
      "Total params: 10\n",
      "Trainable params: 10\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "(1, 6, 6, 1)\n",
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   ],
   "source": [
    "from numpy import asarray\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D\n",
    "\n",
    "# Initialising the input(8x8)\n",
    "data = [[0, 0, 0, 1, 1, 0, 0, 0],\n",
    "[0, 0, 0, 1, 1, 0, 0, 0],\n",
    "[0, 0, 0, 1, 1, 0, 0, 0],\n",
    "[0, 0, 0, 1, 1, 0, 0, 0],\n",
    "[0, 0, 0, 1, 1, 0, 0, 0],\n",
    "[0, 0, 0, 1, 1, 0, 0, 0],\n",
    "[0, 0, 0, 1, 1, 0, 0, 0],\n",
    "[0, 0, 0, 1, 1, 0, 0, 0]]\n",
    "\n",
    "# converting to array\n",
    "data = asarray(data)\n",
    "# reshaping the input by adding the sample and the channel\n",
    "data = data.reshape(1,8,8,1)\n",
    "\n",
    "# Create a model\n",
    "model = Sequential()\n",
    "\n",
    "# adding one 3x3 filter to the input\n",
    "model.add(Conv2D(1, (3,3), input_shape=(8,8,1)))\n",
    "\n",
    "# customizing the filter to a vertical line detector\n",
    "detector = [[[[0]],[[1]],[[0]]],\n",
    "[[[0]],[[1]],[[0]]],\n",
    "[[[0]],[[1]],[[0]]]]\n",
    "\n",
    "# added detector and bias\n",
    "weights = [asarray(detector), asarray([0.0])]\n",
    "\n",
    "# set the weight to the model\n",
    "model.set_weights(weights)\n",
    "print(model.summary())\n",
    "\n",
    "# apply filter to the input data\n",
    "yhat = model.predict(data)\n",
    "print(yhat.shape)\n",
    "# print the feature map to confirm if the line detector works\n",
    "# take each rows\n",
    "for r in range(yhat.shape[1]):\n",
    "    # print columns\n",
    "    for c in range(yhat.shape[2]):\n",
    "        print([yhat[0,r,c,0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_28 (Conv2D)           (None, 6, 6, 1)           10        \n",
      "_________________________________________________________________\n",
      "conv2d_29 (Conv2D)           (None, 4, 4, 1)           10        \n",
      "=================================================================\n",
      "Total params: 20\n",
      "Trainable params: 20\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# stacked convolutional layers\n",
    "from keras.layers import Conv1D\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Conv2D(1, (3,3), input_shape=(8,8,1)))\n",
    "model.add(Conv2D(1, (3,3)))\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add padding to the input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_31 (Conv2D)           (None, 6, 6, 1)           10        \n",
      "=================================================================\n",
      "Total params: 10\n",
      "Trainable params: 10\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# padding same denotes that paddin will be added such that the shape of the feature map will be same as that of the input\n",
    "# padding valid means no padding will be added\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Conv2D(1, (3,3), padding='valid', input_shape=(8,8,1)))\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## stacked convolutional layers with padding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_33 (Conv2D)           (None, 8, 8, 1)           10        \n",
      "_________________________________________________________________\n",
      "conv2d_34 (Conv2D)           (None, 8, 8, 1)           10        \n",
      "_________________________________________________________________\n",
      "conv2d_35 (Conv2D)           (None, 8, 8, 1)           10        \n",
      "=================================================================\n",
      "Total params: 30\n",
      "Trainable params: 30\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# even with the addition of three layers of layers with padding, the shape of the ouput remains the same\n",
    "model = Sequential()\n",
    "model.add(Conv2D(1,(3,3), padding='same',input_shape=(8,8,1)))\n",
    "model.add(Conv2D(1,(3,3), padding='same'))\n",
    "model.add(Conv2D(1, (3,3), padding='same'))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Applying Strides"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.0]\n",
      "[3.0]\n",
      "[0.0]\n",
      "[0.0]\n",
      "[3.0]\n",
      "[0.0]\n",
      "[0.0]\n",
      "[3.0]\n",
      "[0.0]\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_40 (Conv2D)           (None, 3, 3, 1)           10        \n",
      "=================================================================\n",
      "Total params: 10\n",
      "Trainable params: 10\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Here we are downsasmpling the input with strides\n",
    "\n",
    "data = [[0, 0, 0, 1, 1, 0, 0, 0],\n",
    "[0, 0, 0, 1, 1, 0, 0, 0],\n",
    "[0, 0, 0, 1, 1, 0, 0, 0],\n",
    "[0, 0, 0, 1, 1, 0, 0, 0],\n",
    "[0, 0, 0, 1, 1, 0, 0, 0],\n",
    "[0, 0, 0, 1, 1, 0, 0, 0],\n",
    "[0, 0, 0, 1, 1, 0, 0, 0],\n",
    "[0, 0, 0, 1, 1, 0, 0, 0]]\n",
    "\n",
    "data = asarray(data)\n",
    "data = data.reshape(1,8,8,1)\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Conv2D(1, (3,3), strides=(2,2), input_shape=(8,8,1)))\n",
    "\n",
    "# vertical line detector\n",
    "detector = [[[[0]],[[1]],[[0]]],\n",
    "[[[0]],[[1]],[[0]]],\n",
    "[[[0]],[[1]],[[0]]]]\n",
    "\n",
    "weights = [asarray(detector), asarray([0.0])]\n",
    "\n",
    "model.set_weights(weights)\n",
    "\n",
    "yhat = model.predict(data)\n",
    "\n",
    "for r in range(yhat.shape[1]):\n",
    "    for c in range(yhat.shape[2]):\n",
    "        print([yhat[0,r,c,0]])\n",
    "print(model.summary())"
   ]
  }
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