convnets.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(keras)\n",
    "library(ggplot2)\n",
    "library(tidyverse)\n",
    "library(rsample)\n",
    "library(tictoc)\n",
    "library(grid)\n",
    "library(gridExtra)\n",
    "library(ggplotify)\n",
    "\n",
    "source('library.r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist <- dataset_mnist()\n",
    "c(c(X_train, y_train), c(X_test, y_test)) %<-% mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train <- array_reshape(X_train, c(60000, 28 * 28)) / 255\n",
    "X_test <- array_reshape(X_test, c(10000, 28 * 28)) / 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train <- to_categorical(y_train)\n",
    "y_test <- to_categorical(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MNIST with dense network and dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "________________________________________________________________________________\n",
      "Layer (type)                        Output Shape                    Param #     \n",
      "================================================================================\n",
      "dense_13 (Dense)                    (None, 512)                     401920      \n",
      "________________________________________________________________________________\n",
      "dropout_9 (Dropout)                 (None, 512)                     0           \n",
      "________________________________________________________________________________\n",
      "dense_14 (Dense)                    (None, 512)                     262656      \n",
      "________________________________________________________________________________\n",
      "dropout_10 (Dropout)                (None, 512)                     0           \n",
      "________________________________________________________________________________\n",
      "dense_15 (Dense)                    (None, 10)                      5130        \n",
      "================================================================================\n",
      "Total params: 669,706\n",
      "Trainable params: 669,706\n",
      "Non-trainable params: 0\n",
      "________________________________________________________________________________\n",
      "Epoch 1/100: 47/46 - loss: 1.515465 - validation loss: 0.9105526\n",
      "Epoch 2/100: 47/46 - loss: 0.8834897 - validation loss: 0.6845308\n",
      "Epoch 3/100: 47/46 - loss: 0.6609287 - validation loss: 0.5116785\n",
      "Epoch 4/100: 47/46 - loss: 0.5259099 - validation loss: 0.4687868\n",
      "Epoch 5/100: 47/46 - loss: 0.4445052 - validation loss: 0.391512\n",
      "Epoch 6/100: 47/46 - loss: 0.3903629 - validation loss: 0.3235155\n",
      "Epoch 7/100: 47/46 - loss: 0.3512299 - validation loss: 0.342657\n",
      "Epoch 8/100: 47/46 - loss: 0.3223273 - validation loss: 0.2789263\n",
      "Epoch 9/100: 47/46 - loss: 0.2971421 - validation loss: 0.2571915\n",
      "Epoch 10/100: 47/46 - loss: 0.2840478 - validation loss: 0.2440253\n",
      "Epoch 11/100: 47/46 - loss: 0.2711287 - validation loss: 0.228529\n",
      "Epoch 12/100: 47/46 - loss: 0.2611858 - validation loss: 0.2282617\n",
      "Epoch 13/100: 47/46 - loss: 0.2482961 - validation loss: 0.2589799\n",
      "Epoch 14/100: 47/46 - loss: 0.2426664 - validation loss: 0.2251094\n",
      "Epoch 15/100: 47/46 - loss: 0.2418732 - validation loss: 0.2023069\n",
      "Epoch 16/100: 47/46 - loss: 0.229695 - validation loss: 0.2300272\n",
      "Epoch 17/100: 47/46 - loss: 0.22685 - validation loss: 0.1994303\n",
      "Epoch 18/100: 47/46 - loss: 0.2202462 - validation loss: 0.209022\n",
      "Epoch 19/100: 47/46 - loss: 0.2242569 - validation loss: 0.2040206\n",
      "Epoch 20/100: 47/46 - loss: 0.2140468 - validation loss: 0.1931458\n",
      "Epoch 21/100: 47/46 - loss: 0.2122193 - validation loss: 0.1817891\n",
      "Epoch 22/100: 47/46 - loss: 0.2067249 - validation loss: 0.1841434\n",
      "Epoch 23/100: 47/46 - loss: 0.2029953 - validation loss: 0.1991114\n",
      "Epoch 24/100: 47/46 - loss: 0.2063489 - validation loss: 0.1816215\n",
      "Epoch 25/100: 47/46 - loss: 0.1997122 - validation loss: 0.194922\n",
      "Epoch 26/100: 47/46 - loss: 0.2024612 - validation loss: 0.1934607\n",
      "Epoch 27/100: 47/46 - loss: 0.195265 - validation loss: 0.2036283\n",
      "Epoch 28/100: 47/46 - loss: 0.1989763 - validation loss: 0.1872897\n",
      "Epoch 29/100: 47/46 - loss: 0.1881852 - validation loss: 0.1951422\n",
      "Epoch 30/100: 47/46 - loss: 0.1951986 - validation loss: 0.2044429\n",
      "Epoch 31/100: 47/46 - loss: 0.1888678 - validation loss: 0.1826639\n",
      "Epoch 32/100: 47/46 - loss: 0.1900178 - validation loss: 0.1640964\n",
      "Epoch 33/100: 47/46 - loss: 0.1882507 - validation loss: 0.1704812\n",
      "Epoch 34/100: 47/46 - loss: 0.1872673 - validation loss: 0.1905321\n",
      "Epoch 35/100: 47/46 - loss: 0.1885778 - validation loss: 0.1722508\n",
      "Epoch 36/100: 47/46 - loss: 0.1817156 - validation loss: 0.1983821\n",
      "Epoch 37/100: 47/46 - loss: 0.1867999 - validation loss: 0.1709802\n",
      "Epoch 38/100: 47/46 - loss: 0.1813304 - validation loss: 0.1647944\n",
      "Epoch 39/100: 47/46 - loss: 0.1810313 - validation loss: 0.1739649\n",
      "Epoch 40/100: 47/46 - loss: 0.1866964 - validation loss: 0.1807797\n",
      "Epoch 41/100: 47/46 - loss: 0.1754491 - validation loss: 0.162345\n",
      "Epoch 42/100: 47/46 - loss: 0.1817902 - validation loss: 0.1630245\n",
      "Epoch 43/100: 47/46 - loss: 0.1774922 - validation loss: 0.1621516\n",
      "Epoch 44/100: 47/46 - loss: 0.1773452 - validation loss: 0.2333975\n",
      "Epoch 45/100: 47/46 - loss: 0.1769144 - validation loss: 0.1896753\n",
      "Epoch 46/100: 47/46 - loss: 0.181295 - validation loss: 0.202266\n",
      "Epoch 47/100: 47/46 - loss: 0.1796292 - validation loss: 0.1631205\n",
      "Epoch 48/100: 47/46 - loss: 0.1784225 - validation loss: 0.1653368\n",
      "Epoch 49/100: 47/46 - loss: 0.174652 - validation loss: 0.163811\n",
      "Epoch 50/100: 47/46 - loss: 0.1686176 - validation loss: 0.1944564\n",
      "Epoch 51/100: 47/46 - loss: 0.1777186 - validation loss: 0.199095\n",
      "Epoch 52/100: 47/46 - loss: 0.1706033 - validation loss: 0.2765382\n",
      "Epoch 53/100: 47/46 - loss: 0.1725197 - validation loss: 0.164683\n",
      "Epoch 54/100: 47/46 - loss: 0.1729718 - validation loss: 0.1915752\n",
      "Epoch 55/100: 47/46 - loss: 0.167432 - validation loss: 0.1555423\n",
      "Epoch 56/100: 47/46 - loss: 0.1709016 - validation loss: 0.1541306\n",
      "Epoch 57/100: 47/46 - loss: 0.1744873 - validation loss: 0.1784604\n",
      "Epoch 58/100: 47/46 - loss: 0.1688596 - validation loss: 0.1987839\n",
      "Epoch 59/100: 47/46 - loss: 0.1788769 - validation loss: 0.1674405\n",
      "Epoch 60/100: 47/46 - loss: 0.1675999 - validation loss: 0.1640229\n",
      "Epoch 61/100: 47/46 - loss: 0.1736823 - validation loss: 0.1602812\n",
      "Epoch 62/100: 47/46 - loss: 0.1672815 - validation loss: 0.1821885\n",
      "Epoch 63/100: 47/46 - loss: 0.1685768 - validation loss: 0.1547407\n",
      "Epoch 64/100: 47/46 - loss: 0.1661178 - validation loss: 0.1698567\n",
      "Epoch 65/100: 47/46 - loss: 0.1679302 - validation loss: 0.1653852\n",
      "Epoch 66/100: 47/46 - loss: 0.1659222 - validation loss: 0.1627668\n",
      "Epoch 67/100: 47/46 - loss: 0.1669859 - validation loss: 0.1661051\n",
      "Epoch 68/100: 47/46 - loss: 0.1640107 - validation loss: 0.1549095\n",
      "Epoch 69/100: 47/46 - loss: 0.1653097 - validation loss: 0.1590473\n",
      "Epoch 70/100: 47/46 - loss: 0.1692104 - validation loss: 0.152689\n",
      "Epoch 71/100: 47/46 - loss: 0.1620795 - validation loss: 0.1810493\n",
      "Epoch 72/100: 47/46 - loss: 0.1642142 - validation loss: 0.178436\n",
      "Epoch 73/100: 47/46 - loss: 0.1659265 - validation loss: 0.1628919\n",
      "Epoch 74/100: 47/46 - loss: 0.1667059 - validation loss: 0.2271387\n",
      "Epoch 75/100: 47/46 - loss: 0.1678264 - validation loss: 0.1839782\n",
      "Epoch 76/100: 47/46 - loss: 0.1637462 - validation loss: 0.160926\n",
      "Epoch 77/100: 47/46 - loss: 0.1719832 - validation loss: 0.1524681\n",
      "Epoch 78/100: 47/46 - loss: 0.1588693 - validation loss: 0.1570916\n",
      "Epoch 79/100: 47/46 - loss: 0.1626724 - validation loss: 0.1623244\n",
      "Epoch 80/100: 47/46 - loss: 0.1640733 - validation loss: 0.1584462\n",
      "Epoch 81/100: 47/46 - loss: 0.1674485 - validation loss: 0.157311\n",
      "Epoch 82/100: 47/46 - loss: 0.1655351 - validation loss: 0.174872\n",
      "Epoch 83/100: 47/46 - loss: 0.1570931 - validation loss: 0.1588311\n",
      "Epoch 84/100: 47/46 - loss: 0.1642104 - validation loss: 0.1579123\n",
      "Epoch 85/100: 47/46 - loss: 0.1613003 - validation loss: 0.1832592\n",
      "Epoch 86/100: 47/46 - loss: 0.1612116 - validation loss: 0.1585269\n",
      "Epoch 87/100: 47/46 - loss: 0.1585304 - validation loss: 0.1715171\n",
      "Epoch 88/100: 47/46 - loss: 0.1675255 - validation loss: 0.1626451\n",
      "Epoch 89/100: 47/46 - loss: 0.1642049 - validation loss: 0.1616436\n",
      "Epoch 90/100: 47/46 - loss: 0.1604173 - validation loss: 0.1589026\n",
      "Epoch 91/100: 47/46 - loss: 0.1592876 - validation loss: 0.1567782\n",
      "Epoch 92/100: 47/46 - loss: 0.1599812 - validation loss: 0.2606195\n",
      "Epoch 93/100: 47/46 - loss: 0.1631912 - validation loss: 0.1539055\n",
      "Epoch 94/100: 47/46 - loss: 0.1599159 - validation loss: 0.1541985\n",
      "Epoch 95/100: 47/46 - loss: 0.158451 - validation loss: 0.1811948\n",
      "Epoch 96/100: 47/46 - loss: 0.1601435 - validation loss: 0.1499263\n",
      "Epoch 97/100: 47/46 - loss: 0.1573548 - validation loss: 0.1489423\n",
      "Epoch 98/100: 47/46 - loss: 0.1618426 - validation loss: 0.15512\n",
      "Epoch 99/100: 47/46 - loss: 0.1563539 - validation loss: 0.1538178\n",
      "Epoch 100/100: 47/46 - loss: 0.1626692 - validation loss: 0.1552049\n"
     ]
    }
   ],
   "source": [
    "num_epochs <- 100\n",
    "batch_size <- 1024\n",
    "\n",
    "model <- keras_model_sequential() %>%\n",
    "    layer_dense(units = 512, activation = \"relu\", input_shape = c(28 * 28),\n",
    "                kernel_regularizer = regularizer_l2(0.001)) %>%\n",
    "    layer_dropout(rate = 0.5) %>%\n",
    "    layer_dense(units = 512, activation = \"relu\", kernel_regularizer = regularizer_l2(0.001)) %>%\n",
    "    layer_dropout(rate = 0.5) %>%\n",
    "    layer_dense(units = 10, activation = \"softmax\")\n",
    "\n",
    "model %>% compile(\n",
    "    optimizer = optimizer_rmsprop(),\n",
    "    loss = \"categorical_crossentropy\",\n",
    "    metrics = \"accuracy\"\n",
    ")\n",
    "cat(summary(model))\n",
    "\n",
    "history <- model %>% fit(\n",
    "    X_train, y_train,\n",
    "    epochs = num_epochs,\n",
    "    batch_size = batch_size,\n",
    "    validation_split = 0.2,\n",
    "    callbacks = c(Progress$new(as.integer(0.8 * 60000 / batch_size), num_epochs, 10))\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtAAAAHgCAIAAAADp837AAAACXBIWXMAABJ0AAASdAHeZh94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\n",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$loss</dt>\n",
       "\t\t<dd>0.147275869035721</dd>\n",
       "\t<dt>$acc</dt>\n",
       "\t\t<dd>0.9807</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$loss] 0.147275869035721\n",
       "\\item[\\$acc] 0.9807\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$loss\n",
       ":   0.147275869035721\n",
       "$acc\n",
       ":   0.9807\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "$loss\n",
       "[1] 0.1472759\n",
       "\n",
       "$acc\n",
       "[1] 0.9807\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model %>% evaluate(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MNIST with convnet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist <- dataset_mnist()\n",
    "c(c(X_train, y_train), c(X_test, y_test)) %<-% mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train <- array_reshape(X_train, c(60000, 28, 28, 1)) / 255\n",
    "X_test <- array_reshape(X_test, c(10000, 28, 28, 1)) / 255\n",
    "\n",
    "y_train <- to_categorical(y_train)\n",
    "y_test <- to_categorical(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "________________________________________________________________________________\n",
      "Layer (type)                        Output Shape                    Param #     \n",
      "================================================================================\n",
      "conv2d_10 (Conv2D)                  (None, 26, 26, 32)              320         \n",
      "________________________________________________________________________________\n",
      "max_pooling2d_7 (MaxPooling2D)      (None, 13, 13, 32)              0           \n",
      "________________________________________________________________________________\n",
      "conv2d_11 (Conv2D)                  (None, 11, 11, 64)              18496       \n",
      "________________________________________________________________________________\n",
      "max_pooling2d_8 (MaxPooling2D)      (None, 5, 5, 64)                0           \n",
      "________________________________________________________________________________\n",
      "conv2d_12 (Conv2D)                  (None, 3, 3, 64)                36928       \n",
      "________________________________________________________________________________\n",
      "flatten_4 (Flatten)                 (None, 576)                     0           \n",
      "________________________________________________________________________________\n",
      "dense_22 (Dense)                    (None, 64)                      36928       \n",
      "________________________________________________________________________________\n",
      "dense_23 (Dense)                    (None, 10)                      650         \n",
      "================================================================================\n",
      "Total params: 93,322\n",
      "Trainable params: 93,322\n",
      "Non-trainable params: 0\n",
      "________________________________________________________________________________\n",
      "Epoch 1/25: 188/187 - loss: 0.4117338 - validation loss: 0.2139344\n",
      "Epoch 2/25: 188/187 - loss: 0.09304311 - validation loss: 0.06530856\n",
      "Epoch 3/25: 188/187 - loss: 0.05694641 - validation loss: 0.07936666\n",
      "Epoch 4/25: 188/187 - loss: 0.04240315 - validation loss: 0.0499877\n",
      "Epoch 5/25: 188/187 - loss: 0.03260391 - validation loss: 0.03862358\n",
      "Epoch 6/25: 188/187 - loss: 0.02717182 - validation loss: 0.04982443\n",
      "Epoch 7/25: 188/187 - loss: 0.02064589 - validation loss: 0.03922748\n",
      "Epoch 8/25: 188/187 - loss: 0.01794996 - validation loss: 0.05504347\n",
      "Epoch 9/25: 188/187 - loss: 0.01457514 - validation loss: 0.04228169\n",
      "Epoch 10/25: 188/187 - loss: 0.01167286 - validation loss: 0.04027869\n",
      "Epoch 11/25: 188/187 - loss: 0.01002686 - validation loss: 0.0382393\n",
      "Epoch 12/25: 188/187 - loss: 0.00795583 - validation loss: 0.0409738\n",
      "Epoch 13/25: 188/187 - loss: 0.007581436 - validation loss: 0.04240921\n",
      "Epoch 14/25: 188/187 - loss: 0.005914865 - validation loss: 0.05955631\n",
      "Epoch 15/25: 188/187 - loss: 0.005668505 - validation loss: 0.04364512\n",
      "Epoch 16/25: 188/187 - loss: 0.004824996 - validation loss: 0.05622955\n",
      "Epoch 17/25: 188/187 - loss: 0.004294826 - validation loss: 0.04285772\n",
      "Epoch 18/25: 188/187 - loss: 0.003703461 - validation loss: 0.05497206\n",
      "Epoch 19/25: 188/187 - loss: 0.003701148 - validation loss: 0.04076453\n",
      "Epoch 20/25: 188/187 - loss: 0.002776536 - validation loss: 0.04824364\n",
      "Epoch 21/25: 188/187 - loss: 0.002614949 - validation loss: 0.05422551\n",
      "Epoch 22/25: 188/187 - loss: 0.002242503 - validation loss: 0.04876575\n",
      "Epoch 23/25: 188/187 - loss: 0.003033108 - validation loss: 0.05540102\n",
      "Epoch 24/25: 188/187 - loss: 0.002135614 - validation loss: 0.06333506\n",
      "Epoch 25/25: 188/187 - loss: 0.00180591 - validation loss: 0.0629218\n"
     ]
    }
   ],
   "source": [
    "num_epochs <- 25\n",
    "batch_size <- 256\n",
    "\n",
    "model <- keras_model_sequential() %>%\n",
    "    layer_conv_2d(filters = 32, kernel_size = c(3, 3), activation = \"relu\", input_shape = c(28, 28, 1)) %>%\n",
    "    layer_max_pooling_2d(pool_size = c(2, 2)) %>%\n",
    "    layer_conv_2d(filters = 64, kernel_size = c(3, 3), activation = \"relu\") %>%\n",
    "    layer_max_pooling_2d(pool_size = c(2, 2)) %>%\n",
    "    layer_conv_2d(filters = 64, kernel_size = c(3, 3), activation = \"relu\") %>%\n",
    "    layer_flatten() %>%\n",
    "    layer_dense(units = 64, activation = \"relu\", input_shape = c(28 * 28)) %>%\n",
    "    layer_dense(units = 10, activation = \"softmax\")\n",
    "\n",
    "model %>% compile(\n",
    "    optimizer = optimizer_rmsprop(),\n",
    "    loss = \"categorical_crossentropy\",\n",
    "    metrics = \"accuracy\"\n",
    ")\n",
    "cat(summary(model))\n",
    "\n",
    "history <- model %>% fit(\n",
    "    X_train, y_train,\n",
    "    epochs = num_epochs,\n",
    "    batch_size = batch_size,\n",
    "    validation_split = 0.2,\n",
    "    callbacks = c(Progress$new(as.integer(0.8 * 60000 / batch_size), num_epochs, 10))\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtAAAAHgCAIAAAADp837AAAACXBIWXMAABJ0AAASdAHeZh94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\nQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$loss</dt>\n",
       "\t\t<dd>0.0524719997837778</dd>\n",
       "\t<dt>$acc</dt>\n",
       "\t\t<dd>0.9916</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$loss] 0.0524719997837778\n",
       "\\item[\\$acc] 0.9916\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$loss\n",
       ":   0.0524719997837778\n",
       "$acc\n",
       ":   0.9916\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "$loss\n",
       "[1] 0.052472\n",
       "\n",
       "$acc\n",
       "[1] 0.9916\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model %>% evaluate(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA8AAAAHgCAIAAADlh5PTAAAACXBIWXMAABJ0AAASdAHeZh94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\nAABAgkZZlivvBQAAAECS9D8B1Qb+JU/j0fAAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title “Activations for layer conv2d_10_input”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "create_activation_model <- function(model) {\n",
    "    is_conv_or_max_pool_layer <- function(layer) {\n",
    "        name <- layer$name\n",
    "        grepl('conv', name) || grepl('max_pooling', name)\n",
    "    }\n",
    "\n",
    "    layers <- Filter(is_conv_or_max_pool_layer, model$layers)\n",
    "    layer_outputs <- lapply(layers, function(l) l$output)\n",
    "    keras_model(inputs = model$input, outputs = layer_outputs)\n",
    "}\n",
    "                            \n",
    "plot_activations <- function(activation_model, image) {\n",
    "    plot_activation <- function(activation, num_cols = 8) {\n",
    "        num_feature_maps <- dim(activation)[[4]]\n",
    "        num_rows <- ceiling(num_feature_maps / num_cols)\n",
    "\n",
    "        op <- par(mfrow = c(num_rows, num_cols), mai = rep_len(0.01, 4), oma = c(0, 0, 0, 0))\n",
    "        options(repr.plot.width = 8, repr.plot.height = num_rows)\n",
    "\n",
    "        for (i in 1:dim(activation)[[4]]) {\n",
    "            row <- (i %/% num_cols)\n",
    "            col <- i %% num_cols - 1\n",
    "\n",
    "            image(t(apply(activation[1,,, i], 2, rev)), axes = FALSE, asp = 1, col = topo.colors(12))    \n",
    "        }\n",
    "\n",
    "        par(op)\n",
    "    }\n",
    "    \n",
    "    # Format image\n",
    "    \n",
    "    input_shape <- append(c(1), unlist(activation_model$layers[[1]]$input_shape))\n",
    "    image_reshaped <- array_reshape(image, input_shape)\n",
    "    \n",
    "    # Plot input image\n",
    "    \n",
    "#     plot(as.raster(image_reshaped[1,,,1]))\n",
    "#     title(\"Input image\", cex.main = 0.75)\n",
    "    \n",
    "    # Plot activations\n",
    "    \n",
    "    activations <- activation_model %>% predict(image_reshaped)\n",
    "    plot_activation(activations[[1]])\n",
    "    title(paste(\"Activations for layer\", activation_model$layers[[1]]$name), cex.main = 0.75, line = -1)\n",
    "}\n",
    "                            \n",
    "activation_model <- create_activation_model(model)\n",
    "img_reshaped <- activation_model %>% plot_activations(X_train[1,,,])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "List of 5\n",
      " $ : num [1, 1:26, 1:26, 1:32] 0 0 0 0 0 0 0 0 0 0 ...\n",
      " $ : num [1, 1:13, 1:13, 1:32] 0 0 0 0 0 0 0 0 0 0 ...\n",
      " $ : num [1, 1:11, 1:11, 1:64] 0 0.00911 0.00622 0 0 ...\n",
      " $ : num [1, 1:5, 1:5, 1:64] 0.25439 0.00622 0 0 0.19554 ...\n",
      " $ : num [1, 1:3, 1:3, 1:64] 3.552 0 0 0.195 0 ...\n"
     ]
    }
   ],
   "source": [
    "activations <- activation_model %>% predict(img_reshaped)\n",
    "glimpse(activations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA8AAAAHgCAMAAABdO/S2AAACglBMVEUAAAABAQECAgIDAwME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\nitOU2AAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "par(fig = c(0, 0.1, 0, 0.1), mai = rep_len(0.01, 4))\n",
    "plot(as.raster(X_train[1,,,1]))\n",
    "\n",
    "par(fig = c(0.1, 0.2, 0, 0.1), new = TRUE, mai = rep_len(0.01, 4))\n",
    "plot(as.raster(X_train[1,,,1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtAAAAHgCAMAAAC7G6qeAAACnVBMVEUAAAABAQECAgIDAwME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\n+T8w+yq/mlqZ9QAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid.arrange(grobs = list(as.ggplot(~image(X_train[1,,,1])), as.ggplot(~image(X_train[2,,,1]))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " num [1, 1:26, 1:26, 1:32] 0 0 0 0 0 0 0 0 0 0 ...\n"
     ]
    }
   ],
   "source": [
    "glimpse(activations[[1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "options(repr.plot.width = 8, repr.plot.height = 4)\n",
    "\n",
    "activation <- activations[[1]]\n",
    "grobs <- list()\n",
    "for (i in 1:dim(activation)[4]) {\n",
    "    grobs[[i]] <- as.ggplot(~image(t(apply(activation[1,,, i], 2, rev)), axes = FALSE, asp = 1, col = topo.colors(12)))\n",
    "\n",
    "    \n",
    "#     if (i > 8)\n",
    "#         grobs[[i]] <- grobs[[i]] + theme(plot.margin = unit(c(-1, -1, -1, -1), \"pt\"))\n",
    "#     else\n",
    "        grobs[[i]] <- grobs[[i]] + theme(plot.margin = unit(c(-25, -25, -15, -25), \"pt\"))\n",
    "}\n",
    "\n",
    "#\n",
    "\n",
    "grid.arrange(grobs = grobs, nrow = 4, ncol = 8, top = \"number bois\", padding = unit(5, \"cm\"))\n",
    "# grobs <- lapply(grobs, \"+\", theme(plot.margin = unit(c(0, 0, 0, 0), \"cm\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [],
   "source": [
    "?grid.arrange"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cats and dogs with convnets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_datagen <- image_data_generator(rescale = 1 / 255)\n",
    "val_datagen <- image_data_generator(rescale = 1 / 255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_generator <- flow_images_from_directory(\n",
    "    '../cats-and-dogs/train/',\n",
    "    train_datagen,\n",
    "    target_size = c(100, 100),\n",
    "    batch_size = 20,\n",
    "    class_mode = \"binary\"\n",
    ")\n",
    "\n",
    "val_generator <- flow_images_from_directory(\n",
    "    '../cats-and-dogs/val/',\n",
    "    val_datagen,\n",
    "    target_size = c(100, 100),\n",
    "    batch_size = 20,\n",
    "    class_mode = \"binary\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " num [1:20, 1:100, 1:100, 1:3] 0.412 0.925 0.251 0.231 0.843 ...\n"
     ]
    }
   ],
   "source": [
    "glimpse(generator_next(train_generator)[[1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "________________________________________________________________________________\n",
      "Layer (type)                        Output Shape                    Param #     \n",
      "================================================================================\n",
      "conv2d_13 (Conv2D)                  (None, 98, 98, 32)              896         \n",
      "________________________________________________________________________________\n",
      "max_pooling2d_9 (MaxPooling2D)      (None, 49, 49, 32)              0           \n",
      "________________________________________________________________________________\n",
      "conv2d_14 (Conv2D)                  (None, 47, 47, 64)              18496       \n",
      "________________________________________________________________________________\n",
      "max_pooling2d_10 (MaxPooling2D)     (None, 23, 23, 64)              0           \n",
      "________________________________________________________________________________\n",
      "conv2d_15 (Conv2D)                  (None, 21, 21, 128)             73856       \n",
      "________________________________________________________________________________\n",
      "max_pooling2d_11 (MaxPooling2D)     (None, 10, 10, 128)             0           \n",
      "________________________________________________________________________________\n",
      "conv2d_16 (Conv2D)                  (None, 8, 8, 128)               147584      \n",
      "________________________________________________________________________________\n",
      "flatten_5 (Flatten)                 (None, 8192)                    0           \n",
      "________________________________________________________________________________\n",
      "dropout_11 (Dropout)                (None, 8192)                    0           \n",
      "________________________________________________________________________________\n",
      "dense_24 (Dense)                    (None, 64)                      524352      \n",
      "________________________________________________________________________________\n",
      "dense_25 (Dense)                    (None, 1)                       65          \n",
      "================================================================================\n",
      "Total params: 765,249\n",
      "Trainable params: 765,249\n",
      "Non-trainable params: 0\n",
      "________________________________________________________________________________\n",
      "Epoch 1/25: 100/100 - loss: 0.6936527 - validation loss: 0.6882147\n",
      "Epoch 2/25: 100/100 - loss: 0.6818025 - validation loss: 0.6805246\n",
      "Epoch 3/25: 100/100 - loss: 0.6615236 - validation loss: 0.6993784\n",
      "Epoch 4/25: 100/100 - loss: 0.6458764 - validation loss: 0.6535041\n",
      "Epoch 5/25: 100/100 - loss: 0.6342349 - validation loss: 0.6553883\n",
      "Epoch 6/25: 100/100 - loss: 0.6196363 - validation loss: 0.6652934\n",
      "Epoch 7/25: 100/100 - loss: 0.6049585 - validation loss: 0.6289089\n",
      "Epoch 8/25: 100/100 - loss: 0.5899962 - validation loss: 0.6114473\n",
      "Epoch 9/25: 100/100 - loss: 0.564783 - validation loss: 0.5942044\n",
      "Epoch 10/25: 100/100 - loss: 0.5447407 - validation loss: 0.6018879\n",
      "Epoch 11/25: 100/100 - loss: 0.5158655 - validation loss: 0.5907611\n",
      "Epoch 12/25: 100/100 - loss: 0.5031084 - validation loss: 0.5513071\n",
      "Epoch 13/25: 100/100 - loss: 0.4892975 - validation loss: 0.5503792\n",
      "Epoch 14/25: 100/100 - loss: 0.4711316 - validation loss: 0.5574117\n",
      "Epoch 15/25: 100/100 - loss: 0.4548591 - validation loss: 0.5640458\n",
      "Epoch 16/25: 100/100 - loss: 0.4393131 - validation loss: 0.5794702\n",
      "Epoch 17/25: 100/100 - loss: 0.4217797 - validation loss: 0.537135\n",
      "Epoch 18/25: 100/100 - loss: 0.4068739 - validation loss: 0.5369406\n",
      "Epoch 19/25: 100/100 - loss: 0.4046608 - validation loss: 0.5655032\n",
      "Epoch 20/25: 100/100 - loss: 0.3817598 - validation loss: 0.5657968\n",
      "Epoch 21/25: 100/100 - loss: 0.373687 - validation loss: 0.5469343\n",
      "Epoch 22/25: 100/100 - loss: 0.360225 - validation loss: 0.57048\n",
      "Epoch 23/25: 100/100 - loss: 0.3464824 - validation loss: 0.5877577\n",
      "Epoch 24/25: 100/100 - loss: 0.3366468 - validation loss: 0.5501976\n",
      "Epoch 25/25: 100/100 - loss: 0.3204166 - validation loss: 0.5447457\n"
     ]
    }
   ],
   "source": [
    "num_epochs <- 25\n",
    "batch_size <- 256\n",
    "\n",
    "model <- keras_model_sequential() %>%\n",
    "    layer_conv_2d(filters = 32, kernel_size = c(3, 3), activation = \"relu\", input_shape = c(100, 100, 3)) %>%\n",
    "    layer_max_pooling_2d(pool_size = c(2, 2)) %>%\n",
    "    layer_conv_2d(filters = 64, kernel_size = c(3, 3), activation = \"relu\") %>%\n",
    "    layer_max_pooling_2d(pool_size = c(2, 2)) %>%\n",
    "    layer_conv_2d(filters = 128, kernel_size = c(3, 3), activation = \"relu\") %>%\n",
    "    layer_max_pooling_2d(pool_size = c(2, 2)) %>%\n",
    "    layer_conv_2d(filters = 128, kernel_size = c(3, 3), activation = \"relu\") %>%\n",
    "    layer_flatten() %>%\n",
    "    layer_dropout(rate = 0.5) %>%\n",
    "    layer_dense(units = 64, activation = \"relu\") %>%\n",
    "    layer_dense(units = 1, activation = \"sigmoid\")\n",
    "\n",
    "model %>% compile(\n",
    "    optimizer = optimizer_rmsprop(lr = 0.0001),\n",
    "    loss = \"binary_crossentropy\",\n",
    "    metrics = c(\"acc\")\n",
    ")\n",
    "cat(summary(model))\n",
    "\n",
    "history <- model %>% fit_generator(\n",
    "    train_generator,\n",
    "    epochs = num_epochs,\n",
    "    steps_per_epoch = 100,\n",
    "    validation_data = val_generator,\n",
    "    validation_steps = 50,\n",
    "    callbacks = c(Progress$new(100, num_epochs, 10))\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtAAAAHgCAIAAAADp837AAAACXBIWXMAABJ0AAASdAHeZh94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\nAAAAAIDN/T/eNL+S7C/hbQAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$loss</dt>\n",
       "\t\t<dd>0.591296145915985</dd>\n",
       "\t<dt>$acc</dt>\n",
       "\t\t<dd>0.714000000953674</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$loss] 0.591296145915985\n",
       "\\item[\\$acc] 0.714000000953674\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$loss\n",
       ":   0.591296145915985\n",
       "$acc\n",
       ":   0.714000000953674\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "$loss\n",
       "[1] 0.5912961\n",
       "\n",
       "$acc\n",
       "[1] 0.714\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_generator <- flow_images_from_directory(\n",
    "    '../cats-and-dogs/test/',\n",
    "    val_datagen,\n",
    "    target_size = c(100, 100),\n",
    "    batch_size = 20,\n",
    "    class_mode = \"binary\"\n",
    ")\n",
    "\n",
    "model %>% evaluate_generator(test_generator, steps = 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data augmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_datagen_augmented <- image_data_generator(\n",
    "    rescale = 1 / 255,\n",
    "    rotation_range = 40,\n",
    "    width_shift_range = 0.2,\n",
    "    height_shift_range = 0.2,\n",
    "    shear_range = 0.2,\n",
    "    zoom_range = 0.2,\n",
    "    horizontal_flip = TRUE\n",
    ")\n",
    "\n",
    "train_generator_augmented <- flow_images_from_directory(\n",
    "    '../cats-and-dogs/train/',\n",
    "    train_datagen_augmented,\n",
    "    target_size = c(100, 100),\n",
    "    batch_size = 20,\n",
    "    class_mode = \"binary\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "________________________________________________________________________________\n",
      "Layer (type)                        Output Shape                    Param #     \n",
      "================================================================================\n",
      "conv2d_17 (Conv2D)                  (None, 98, 98, 32)              896         \n",
      "________________________________________________________________________________\n",
      "max_pooling2d_12 (MaxPooling2D)     (None, 49, 49, 32)              0           \n",
      "________________________________________________________________________________\n",
      "conv2d_18 (Conv2D)                  (None, 47, 47, 64)              18496       \n",
      "________________________________________________________________________________\n",
      "max_pooling2d_13 (MaxPooling2D)     (None, 23, 23, 64)              0           \n",
      "________________________________________________________________________________\n",
      "conv2d_19 (Conv2D)                  (None, 21, 21, 128)             73856       \n",
      "________________________________________________________________________________\n",
      "max_pooling2d_14 (MaxPooling2D)     (None, 10, 10, 128)             0           \n",
      "________________________________________________________________________________\n",
      "conv2d_20 (Conv2D)                  (None, 8, 8, 128)               147584      \n",
      "________________________________________________________________________________\n",
      "flatten_6 (Flatten)                 (None, 8192)                    0           \n",
      "________________________________________________________________________________\n",
      "dropout_12 (Dropout)                (None, 8192)                    0           \n",
      "________________________________________________________________________________\n",
      "dense_26 (Dense)                    (None, 64)                      524352      \n",
      "________________________________________________________________________________\n",
      "dense_27 (Dense)                    (None, 1)                       65          \n",
      "================================================================================\n",
      "Total params: 765,249\n",
      "Trainable params: 765,249\n",
      "Non-trainable params: 0\n",
      "________________________________________________________________________________\n",
      "Epoch 1/25: 100/100 - loss: 0.6934043 - validation loss: 0.6890331\n",
      "Epoch 2/25: 100/100 - loss: 0.6858838 - validation loss: 0.68344\n",
      "Epoch 3/25: 100/100 - loss: 0.6749051 - validation loss: 0.6656349\n",
      "Epoch 4/25: 100/100 - loss: 0.6728706 - validation loss: 0.6625474\n",
      "Epoch 5/25: 100/100 - loss: 0.6713284 - validation loss: 0.6921573\n",
      "Epoch 6/25: 100/100 - loss: 0.6648202 - validation loss: 0.6655031\n",
      "Epoch 7/25: 100/100 - loss: 0.6592622 - validation loss: 0.7167628\n",
      "Epoch 8/25: 100/100 - loss: 0.6599379 - validation loss: 0.6752478\n",
      "Epoch 9/25: 100/100 - loss: 0.652253 - validation loss: 0.6411192\n",
      "Epoch 10/25: 100/100 - loss: 0.6507901 - validation loss: 0.6487101\n",
      "Epoch 11/25: 100/100 - loss: 0.6412191 - validation loss: 0.6379161\n",
      "Epoch 12/25: 100/100 - loss: 0.6367223 - validation loss: 0.6218786\n",
      "Epoch 13/25: 100/100 - loss: 0.6332267 - validation loss: 0.621968\n",
      "Epoch 14/25: 100/100 - loss: 0.6282101 - validation loss: 0.6019352\n",
      "Epoch 15/25: 100/100 - loss: 0.6277532 - validation loss: 0.6004945\n",
      "Epoch 16/25: 100/100 - loss: 0.6280206 - validation loss: 0.6290601\n",
      "Epoch 17/25: 100/100 - loss: 0.616505 - validation loss: 0.6532344\n",
      "Epoch 18/25: 100/100 - loss: 0.6093038 - validation loss: 0.5976878\n",
      "Epoch 19/25: 100/100 - loss: 0.6059054 - validation loss: 0.5778601\n",
      "Epoch 20/25: 100/100 - loss: 0.596552 - validation loss: 0.6130717\n",
      "Epoch 21/25: 100/100 - loss: 0.6034721 - validation loss: 0.5735595\n",
      "Epoch 22/25: 100/100 - loss: 0.593855 - validation loss: 0.5832574\n",
      "Epoch 23/25: 100/100 - loss: 0.5960776 - validation loss: 0.5745022\n",
      "Epoch 24/25: 100/100 - loss: 0.5882148 - validation loss: 0.5935836\n",
      "Epoch 25/25: 100/100 - loss: 0.5861124 - validation loss: 0.5481646\n"
     ]
    }
   ],
   "source": [
    "num_epochs <- 25\n",
    "batch_size <- 128\n",
    "\n",
    "model <- keras_model_sequential() %>%\n",
    "    layer_conv_2d(filters = 32, kernel_size = c(3, 3), activation = \"relu\", input_shape = c(100, 100, 3)) %>%\n",
    "    layer_max_pooling_2d(pool_size = c(2, 2)) %>%\n",
    "    layer_conv_2d(filters = 64, kernel_size = c(3, 3), activation = \"relu\") %>%\n",
    "    layer_max_pooling_2d(pool_size = c(2, 2)) %>%\n",
    "    layer_conv_2d(filters = 128, kernel_size = c(3, 3), activation = \"relu\") %>%\n",
    "    layer_max_pooling_2d(pool_size = c(2, 2)) %>%\n",
    "    layer_conv_2d(filters = 128, kernel_size = c(3, 3), activation = \"relu\") %>%\n",
    "    layer_flatten() %>%\n",
    "    layer_dropout(rate = 0.5) %>%\n",
    "    layer_dense(units = 64, activation = \"relu\") %>%\n",
    "    layer_dense(units = 1, activation = \"sigmoid\")\n",
    "\n",
    "model %>% compile(\n",
    "    optimizer = optimizer_rmsprop(lr = 0.0001),\n",
    "    loss = \"binary_crossentropy\",\n",
    "    metrics = c(\"acc\")\n",
    ")\n",
    "cat(summary(model))\n",
    "\n",
    "history <- model %>% fit_generator(\n",
    "    train_generator_augmented,\n",
    "    epochs = num_epochs,\n",
    "    steps_per_epoch = 100,\n",
    "    validation_data = val_generator,\n",
    "    validation_steps = 50,\n",
    "    callbacks = c(Progress$new(100, num_epochs, 10))\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtAAAAHgCAIAAAADp837AAAACXBIWXMAABJ0AAASdAHeZh94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\nAQcAAAAA7O7/+7ACCcL3++cAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$loss</dt>\n",
       "\t\t<dd>0.546601613163948</dd>\n",
       "\t<dt>$acc</dt>\n",
       "\t\t<dd>0.708000001907349</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$loss] 0.546601613163948\n",
       "\\item[\\$acc] 0.708000001907349\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$loss\n",
       ":   0.546601613163948\n",
       "$acc\n",
       ":   0.708000001907349\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "$loss\n",
       "[1] 0.5466016\n",
       "\n",
       "$acc\n",
       "[1] 0.708\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_generator <- flow_images_from_directory(\n",
    "    '../cats-and-dogs/test/',\n",
    "    val_datagen,\n",
    "    target_size = c(100, 100),\n",
    "    batch_size = 20,\n",
    "    class_mode = \"binary\"\n",
    ")\n",
    "\n",
    "model %>% evaluate_generator(test_generator, steps = 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "img <- image_load('../cats-and-dogs/test/cat/image-0010.jpg', target_size = c(100, 100))\n",
    "img <- image_to_array(img)\n",
    "img <- array_reshape(img, c(1, 100, 100, 3))\n",
    "img <- img / 255\n",
    "plot(as.raster(img[1,,,]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "layer_outputs <- lapply(model$layers[1:7], function(l) l$output)\n",
    "activation_model <- keras_model(inputs = model$input, outputs = layer_outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "activations <- activation_model %>% predict(img)\n",
    "glimpse(activations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "image(t(apply(activations[[3]][1,,, 4], 2, rev)), axes = FALSE, asp = 1, col = topo.colors(12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "activation <- activations[[1]]\n",
    "dim(activation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "plot_activation <- function(activation, num_cols = 8) {\n",
    "    num_feature_maps <- dim(activation)[[4]]\n",
    "    num_rows <- ceiling(num_feature_maps / num_cols)\n",
    "    \n",
    "    op <- par(mfrow = c(num_rows, num_cols), mai = rep_len(0.01, 4))\n",
    "    options(repr.plot.width = 8, repr.plot.height = num_rows)\n",
    "    \n",
    "    for (i in 1:dim(activation)[[4]]) {\n",
    "        row <- (i %/% num_cols)\n",
    "        col <- i %% num_cols - 1\n",
    "\n",
    "        image(t(apply(activation[1,,, i], 2, rev)), axes = FALSE, asp = 1, col = topo.colors(12))    \n",
    "    }\n",
    "\n",
    "    par(op)\n",
    "}\n",
    "\n",
    "# plot_activation(activations[[1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "plot_activations <- function(model, input) {\n",
    "    activations <- model %>% predict(input)\n",
    "    num_layers <- length(activations)\n",
    "    \n",
    "    for (i in 1:num_layers) {\n",
    "        plot_activation(activations[[i]])\n",
    "        title(main = model$layers[[i]]$name, line = 1)\n",
    "    }\n",
    "}\n",
    "\n",
    "plot_activations(activation_model, img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pre-trained networks (VGGNet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "conv_base <- application_vgg16(\n",
    "    weights = \"imagenet\",\n",
    "    include_top = FALSE,\n",
    "    input_shape = c(100, 100, 3)\n",
    ")\n",
    "summary(conv_base)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "freeze_weights(conv_base)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_epochs <- 10\\\n",
    "\n",
    "model <- keras_model_sequential() %>%\n",
    "    conv_base %>%\n",
    "    layer_flatten() %>%\n",
    "    layer_dense(units = 256, activation = \"relu\") %>%\n",
    "    layer_dense(units = 1, activation = \"sigmoid\")\n",
    "\n",
    "model %>% compile(\n",
    "  loss = \"binary_crossentropy\",\n",
    "  optimizer = optimizer_rmsprop(lr = 1e-5),\n",
    "  metrics = c(\"accuracy\")\n",
    ")\n",
    "\n",
    "cat(summary(model))\n",
    "\n",
    "history <- model %>% fit_generator(\n",
    "    train_generator,\n",
    "    epochs = num_epochs,\n",
    "    steps_per_epoch = 100,\n",
    "    validation_data = val_generator,\n",
    "    validation_steps = 50,\n",
    "    callbacks = c(Progress$new(100, num_epochs, 10))\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_generator <- flow_images_from_directory(\n",
    "    '../cats-and-dogs/test/',\n",
    "    val_datagen,\n",
    "    target_size = c(100, 100),\n",
    "    batch_size = 20,\n",
    "    class_mode = \"binary\"\n",
    ")\n",
    "\n",
    "model %>% evaluate_generator(test_generator, steps = 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}