Digit Recognizer - CNN LDS LR.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "a98050d29f40adccc3360218a9b0679b493060fe"
   },
   "source": [
    "## Loading in and checking the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using plaidml.keras.backend backend.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "%matplotlib inline\n",
    "import os\n",
    "\n",
    "np.random.seed(2)\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import itertools\n",
    "\n",
    "os.environ[\"KERAS_BACKEND\"] = \"plaidml.keras.backend\"\n",
    "\n",
    "from keras.utils.np_utils import to_categorical # convert to one-hot-encoding\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D\n",
    "from keras.optimizers import RMSprop\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.callbacks import ReduceLROnPlateau\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = np.load('./datasets/aug_data_obj.npy')\n",
    "train_label = np.load('./datasets/aug_labels_obj.npy')\n",
    "test_data = pd.read_csv('./datasets/test.csv')\n",
    "train_data = pd.DataFrame(train_data)\n",
    "train_label = pd.DataFrame(train_label)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "31836e63382dad6d5a5f24cec4df24f105bde875"
   },
   "source": [
    "## Splitting the data and its label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x11906c7f0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "train_label.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_uuid": "0fc59a6cabf8f5953a4e06314bbd8e30c993590d"
   },
   "outputs": [],
   "source": [
    "train_data = train_data / 255.0\n",
    "test_data = test_data / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = train_data.values.reshape(-1,28,28,1)\n",
    "test_data = test_data.values.reshape(-1,28,28,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 40 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import cm\n",
    "\n",
    "plt.figure(figsize=(15,6))\n",
    "for i in range(40):  \n",
    "    plt.subplot(4, 10, i+1)\n",
    "    plt.imshow(train_data[-i][:,:,0], cmap=cm.binary)\n",
    "    plt.title(\"predict=%d\" % train_label.values[-i],y=0.9)\n",
    "    plt.axis('off')\n",
    "plt.subplots_adjust(wspace=0.3, hspace=-0.1)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_label = to_categorical(train_label, num_classes=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "s_train_data, s_val_data, s_train_label, s_val_label = train_test_split(train_data, train_label, test_size=0.002, stratify=train_label)\n",
    "train_data=None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "de7c10e92e0ea4fd95b47fce47ac854fa8ba6821"
   },
   "source": [
    "## Checking the image quality of a random value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_uuid": "1765ab2eadd35dfdc33a9ceb38de2fd42cc093d8"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 40 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import cm\n",
    "\n",
    "plt.figure(figsize=(15,6))\n",
    "for i in range(40):  \n",
    "    plt.subplot(4, 10, i+1)\n",
    "    plt.imshow(s_train_data[i][:,:,0], cmap=cm.binary)\n",
    "    plt.title(\"predict=%d\" % np.argmax(s_train_label[i]),y=0.9)\n",
    "    plt.axis('off')\n",
    "plt.subplots_adjust(wspace=0.3, hspace=-0.1)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "44d0b9bd6afb17922e40be8405d150f38d8134a9"
   },
   "source": [
    "# Attempting to solve using a CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_uuid": "02ccf14f347aa6d81e6239ba9864e9534f6886e8"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:plaidml:Opening device \"metal_amd_radeon_vega_frontier_edition.0\"\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same', \n",
    "                 activation ='relu', input_shape = (28,28,1)))\n",
    "model.add(Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same', \n",
    "                 activation ='relu'))\n",
    "model.add(MaxPool2D(pool_size=(2,2)))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "\n",
    "model.add(Conv2D(filters = 64, kernel_size = (3,3),padding = 'Same', \n",
    "                 activation ='relu'))\n",
    "model.add(Conv2D(filters = 64, kernel_size = (3,3),padding = 'Same', \n",
    "                 activation ='relu'))\n",
    "model.add(MaxPool2D(pool_size=(2,2), strides=(2,2)))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(256, activation = \"relu\"))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(10, activation = \"softmax\"))\n",
    "\n",
    "# Define the optimizer\n",
    "optimizer = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0)\n",
    "\n",
    "# Compile the model\n",
    "model.compile(optimizer = optimizer , loss = \"categorical_crossentropy\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_uuid": "387e93667da8c4e60c31fbbd9fa5a2c6b92d703b"
   },
   "outputs": [],
   "source": [
    "# Set a learning rate annealer\n",
    "learning_rate_reduction = ReduceLROnPlateau(monitor='val_acc', \n",
    "                                            patience=3, \n",
    "                                            verbose=1, \n",
    "                                            factor=0.5, \n",
    "                                            min_lr=0.00001)\n",
    "\n",
    "epochs = 140\n",
    "batch_size = 7680 # Warning size choosen based on a GPU with 16GB of memory and utalizes ~95%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "datagen = ImageDataGenerator(\n",
    "        featurewise_center=False,  # set input mean to 0 over the dataset\n",
    "        samplewise_center=False,  # set each sample mean to 0\n",
    "        featurewise_std_normalization=False,  # divide inputs by std of the dataset\n",
    "        samplewise_std_normalization=False,  # divide each input by its std\n",
    "        zca_whitening=False,  # apply ZCA whitening\n",
    "        rotation_range=10,  # randomly rotate images in the range (degrees, 0 to 180)\n",
    "        zoom_range = 0.1, # Randomly zoom image \n",
    "        width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)\n",
    "        height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)\n",
    "        horizontal_flip=False,  # randomly flip images\n",
    "        vertical_flip=False)  # randomly flip images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "datagen.fit(s_train_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing results with given data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/140\n",
      " - 462s - loss: 1.0387 - acc: 0.6620 - val_loss: 0.1905 - val_acc: 0.9438\n",
      "Epoch 2/140\n",
      " - 455s - loss: 0.2694 - acc: 0.9182 - val_loss: 0.0900 - val_acc: 0.9705\n",
      "Epoch 3/140\n",
      " - 455s - loss: 0.1478 - acc: 0.9557 - val_loss: 0.0526 - val_acc: 0.9810\n",
      "Epoch 4/140\n",
      " - 455s - loss: 0.1017 - acc: 0.9695 - val_loss: 0.0223 - val_acc: 0.9924\n",
      "Epoch 5/140\n",
      " - 456s - loss: 0.0794 - acc: 0.9764 - val_loss: 0.0288 - val_acc: 0.9910\n",
      "Epoch 6/140\n",
      " - 455s - loss: 0.0662 - acc: 0.9804 - val_loss: 0.0208 - val_acc: 0.9933\n",
      "Epoch 7/140\n",
      " - 455s - loss: 0.0568 - acc: 0.9831 - val_loss: 0.0164 - val_acc: 0.9948\n",
      "Epoch 8/140\n",
      " - 456s - loss: 0.0500 - acc: 0.9850 - val_loss: 0.0150 - val_acc: 0.9948\n",
      "Epoch 9/140\n",
      " - 455s - loss: 0.0452 - acc: 0.9864 - val_loss: 0.0169 - val_acc: 0.9938\n",
      "Epoch 10/140\n",
      " - 456s - loss: 0.0420 - acc: 0.9873 - val_loss: 0.0150 - val_acc: 0.9957\n",
      "Epoch 11/140\n",
      " - 455s - loss: 0.0388 - acc: 0.9885 - val_loss: 0.0217 - val_acc: 0.9914\n",
      "Epoch 12/140\n",
      " - 455s - loss: 0.0360 - acc: 0.9891 - val_loss: 0.0119 - val_acc: 0.9967\n",
      "Epoch 13/140\n",
      " - 455s - loss: 0.0340 - acc: 0.9899 - val_loss: 0.0137 - val_acc: 0.9962\n",
      "Epoch 14/140\n",
      " - 455s - loss: 0.0326 - acc: 0.9901 - val_loss: 0.0110 - val_acc: 0.9971\n",
      "Epoch 15/140\n",
      " - 455s - loss: 0.0308 - acc: 0.9907 - val_loss: 0.0116 - val_acc: 0.9967\n",
      "Epoch 16/140\n",
      " - 455s - loss: 0.0290 - acc: 0.9914 - val_loss: 0.0078 - val_acc: 0.9981\n",
      "Epoch 17/140\n",
      " - 457s - loss: 0.0279 - acc: 0.9916 - val_loss: 0.0084 - val_acc: 0.9981\n",
      "Epoch 18/140\n",
      " - 454s - loss: 0.0268 - acc: 0.9919 - val_loss: 0.0101 - val_acc: 0.9976\n",
      "Epoch 19/140\n",
      " - 455s - loss: 0.0260 - acc: 0.9920 - val_loss: 0.0106 - val_acc: 0.9967\n",
      "\n",
      "Epoch 00019: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
      "Epoch 20/140\n",
      " - 455s - loss: 0.0222 - acc: 0.9932 - val_loss: 0.0075 - val_acc: 0.9986\n",
      "Epoch 21/140\n",
      " - 455s - loss: 0.0207 - acc: 0.9938 - val_loss: 0.0061 - val_acc: 0.9981\n",
      "Epoch 22/140\n",
      " - 455s - loss: 0.0202 - acc: 0.9938 - val_loss: 0.0061 - val_acc: 0.9986\n",
      "Epoch 23/140\n",
      " - 455s - loss: 0.0204 - acc: 0.9939 - val_loss: 0.0063 - val_acc: 0.9986\n",
      "\n",
      "Epoch 00023: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
      "Epoch 24/140\n",
      " - 455s - loss: 0.0186 - acc: 0.9944 - val_loss: 0.0055 - val_acc: 0.9986\n",
      "Epoch 25/140\n",
      " - 457s - loss: 0.0183 - acc: 0.9944 - val_loss: 0.0059 - val_acc: 0.9990\n",
      "Epoch 26/140\n",
      " - 453s - loss: 0.0179 - acc: 0.9945 - val_loss: 0.0065 - val_acc: 0.9971\n",
      "Epoch 27/140\n",
      " - 455s - loss: 0.0178 - acc: 0.9946 - val_loss: 0.0059 - val_acc: 0.9981\n",
      "Epoch 28/140\n",
      " - 455s - loss: 0.0176 - acc: 0.9946 - val_loss: 0.0052 - val_acc: 0.9986\n",
      "\n",
      "Epoch 00028: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814.\n",
      "Epoch 29/140\n",
      " - 455s - loss: 0.0170 - acc: 0.9949 - val_loss: 0.0058 - val_acc: 0.9990\n",
      "Epoch 30/140\n",
      " - 455s - loss: 0.0168 - acc: 0.9949 - val_loss: 0.0050 - val_acc: 0.9990\n",
      "Epoch 31/140\n",
      " - 455s - loss: 0.0164 - acc: 0.9950 - val_loss: 0.0047 - val_acc: 0.9986\n",
      "\n",
      "Epoch 00031: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05.\n",
      "Epoch 32/140\n",
      " - 455s - loss: 0.0165 - acc: 0.9950 - val_loss: 0.0055 - val_acc: 0.9986\n",
      "Epoch 33/140\n",
      " - 455s - loss: 0.0163 - acc: 0.9950 - val_loss: 0.0052 - val_acc: 0.9990\n",
      "Epoch 34/140\n",
      " - 455s - loss: 0.0162 - acc: 0.9951 - val_loss: 0.0048 - val_acc: 0.9990\n",
      "\n",
      "Epoch 00034: ReduceLROnPlateau reducing learning rate to 3.125000148429535e-05.\n",
      "Epoch 35/140\n",
      " - 457s - loss: 0.0160 - acc: 0.9951 - val_loss: 0.0049 - val_acc: 0.9990\n",
      "Epoch 36/140\n",
      " - 455s - loss: 0.0161 - acc: 0.9950 - val_loss: 0.0051 - val_acc: 0.9990\n",
      "Epoch 37/140\n",
      " - 454s - loss: 0.0161 - acc: 0.9951 - val_loss: 0.0049 - val_acc: 0.9986\n",
      "\n",
      "Epoch 00037: ReduceLROnPlateau reducing learning rate to 1.5625000742147677e-05.\n",
      "Epoch 38/140\n",
      " - 455s - loss: 0.0161 - acc: 0.9951 - val_loss: 0.0050 - val_acc: 0.9986\n",
      "Epoch 39/140\n",
      " - 455s - loss: 0.0160 - acc: 0.9952 - val_loss: 0.0050 - val_acc: 0.9986\n",
      "Epoch 40/140\n",
      " - 457s - loss: 0.0159 - acc: 0.9951 - val_loss: 0.0050 - val_acc: 0.9990\n",
      "\n",
      "Epoch 00040: ReduceLROnPlateau reducing learning rate to 1e-05.\n",
      "Epoch 41/140\n",
      " - 453s - loss: 0.0160 - acc: 0.9952 - val_loss: 0.0051 - val_acc: 0.9990\n",
      "Epoch 42/140\n",
      " - 455s - loss: 0.0160 - acc: 0.9951 - val_loss: 0.0052 - val_acc: 0.9986\n",
      "Epoch 43/140\n",
      " - 457s - loss: 0.0158 - acc: 0.9952 - val_loss: 0.0052 - val_acc: 0.9986\n",
      "Epoch 44/140\n",
      " - 455s - loss: 0.0158 - acc: 0.9953 - val_loss: 0.0051 - val_acc: 0.9990\n",
      "Epoch 45/140\n",
      " - 454s - loss: 0.0160 - acc: 0.9951 - val_loss: 0.0052 - val_acc: 0.9986\n",
      "Epoch 46/140\n",
      " - 457s - loss: 0.0160 - acc: 0.9951 - val_loss: 0.0050 - val_acc: 0.9990\n",
      "Epoch 47/140\n",
      " - 455s - loss: 0.0158 - acc: 0.9951 - val_loss: 0.0050 - val_acc: 0.9990\n",
      "Epoch 48/140\n",
      " - 454s - loss: 0.0160 - acc: 0.9952 - val_loss: 0.0052 - val_acc: 0.9986\n",
      "Epoch 49/140\n",
      " - 457s - loss: 0.0158 - acc: 0.9952 - val_loss: 0.0051 - val_acc: 0.9990\n",
      "Epoch 50/140\n",
      " - 454s - loss: 0.0160 - acc: 0.9952 - val_loss: 0.0050 - val_acc: 0.9990\n",
      "Epoch 51/140\n",
      " - 455s - loss: 0.0160 - acc: 0.9951 - val_loss: 0.0050 - val_acc: 0.9986\n",
      "Epoch 52/140\n",
      " - 455s - loss: 0.0160 - acc: 0.9952 - val_loss: 0.0052 - val_acc: 0.9990\n",
      "Epoch 53/140\n",
      " - 457s - loss: 0.0157 - acc: 0.9952 - val_loss: 0.0049 - val_acc: 0.9990\n",
      "Epoch 54/140\n",
      " - 453s - loss: 0.0157 - acc: 0.9952 - val_loss: 0.0051 - val_acc: 0.9986\n",
      "Epoch 55/140\n",
      " - 457s - loss: 0.0154 - acc: 0.9953 - val_loss: 0.0051 - val_acc: 0.9990\n",
      "Epoch 56/140\n",
      " - 455s - loss: 0.0159 - acc: 0.9952 - val_loss: 0.0051 - val_acc: 0.9990\n",
      "Epoch 57/140\n",
      " - 455s - loss: 0.0154 - acc: 0.9953 - val_loss: 0.0051 - val_acc: 0.9986\n",
      "Epoch 58/140\n",
      " - 454s - loss: 0.0161 - acc: 0.9953 - val_loss: 0.0052 - val_acc: 0.9986\n",
      "Epoch 59/140\n",
      " - 455s - loss: 0.0160 - acc: 0.9952 - val_loss: 0.0051 - val_acc: 0.9990\n",
      "Epoch 60/140\n",
      " - 457s - loss: 0.0159 - acc: 0.9952 - val_loss: 0.0050 - val_acc: 0.9986\n",
      "Epoch 61/140\n",
      " - 455s - loss: 0.0158 - acc: 0.9952 - val_loss: 0.0051 - val_acc: 0.9986\n",
      "Epoch 62/140\n",
      " - 453s - loss: 0.0159 - acc: 0.9952 - val_loss: 0.0051 - val_acc: 0.9986\n",
      "Epoch 63/140\n",
      " - 457s - loss: 0.0158 - acc: 0.9952 - val_loss: 0.0049 - val_acc: 0.9990\n",
      "Epoch 64/140\n",
      " - 454s - loss: 0.0158 - acc: 0.9952 - val_loss: 0.0048 - val_acc: 0.9986\n",
      "Epoch 65/140\n",
      " - 455s - loss: 0.0156 - acc: 0.9953 - val_loss: 0.0050 - val_acc: 0.9990\n",
      "Epoch 66/140\n",
      " - 457s - loss: 0.0158 - acc: 0.9952 - val_loss: 0.0051 - val_acc: 0.9990\n",
      "Epoch 67/140\n",
      " - 454s - loss: 0.0157 - acc: 0.9952 - val_loss: 0.0051 - val_acc: 0.9990\n",
      "Epoch 68/140\n",
      " - 455s - loss: 0.0156 - acc: 0.9953 - val_loss: 0.0050 - val_acc: 0.9986\n",
      "Epoch 69/140\n",
      " - 457s - loss: 0.0157 - acc: 0.9952 - val_loss: 0.0051 - val_acc: 0.9990\n",
      "Epoch 70/140\n",
      " - 453s - loss: 0.0152 - acc: 0.9953 - val_loss: 0.0049 - val_acc: 0.9990\n",
      "Epoch 71/140\n",
      " - 457s - loss: 0.0158 - acc: 0.9953 - val_loss: 0.0050 - val_acc: 0.9990\n",
      "Epoch 72/140\n",
      " - 455s - loss: 0.0153 - acc: 0.9954 - val_loss: 0.0050 - val_acc: 0.9990\n",
      "Epoch 73/140\n",
      " - 455s - loss: 0.0157 - acc: 0.9952 - val_loss: 0.0050 - val_acc: 0.9990\n",
      "Epoch 74/140\n",
      " - 455s - loss: 0.0155 - acc: 0.9954 - val_loss: 0.0050 - val_acc: 0.9990\n",
      "Epoch 75/140\n",
      " - 455s - loss: 0.0155 - acc: 0.9952 - val_loss: 0.0051 - val_acc: 0.9986\n",
      "Epoch 76/140\n",
      " - 453s - loss: 0.0154 - acc: 0.9953 - val_loss: 0.0051 - val_acc: 0.9990\n",
      "Epoch 77/140\n",
      " - 455s - loss: 0.0153 - acc: 0.9953 - val_loss: 0.0051 - val_acc: 0.9990\n",
      "Epoch 78/140\n",
      " - 457s - loss: 0.0153 - acc: 0.9953 - val_loss: 0.0049 - val_acc: 0.9990\n",
      "Epoch 79/140\n",
      " - 453s - loss: 0.0154 - acc: 0.9953 - val_loss: 0.0052 - val_acc: 0.9986\n",
      "Epoch 80/140\n",
      " - 457s - loss: 0.0157 - acc: 0.9953 - val_loss: 0.0050 - val_acc: 0.9986\n",
      "Epoch 81/140\n",
      " - 454s - loss: 0.0158 - acc: 0.9953 - val_loss: 0.0049 - val_acc: 0.9990\n",
      "Epoch 82/140\n",
      " - 457s - loss: 0.0154 - acc: 0.9953 - val_loss: 0.0050 - val_acc: 0.9990\n",
      "Epoch 83/140\n",
      " - 454s - loss: 0.0155 - acc: 0.9953 - val_loss: 0.0049 - val_acc: 0.9990\n",
      "Epoch 84/140\n",
      " - 457s - loss: 0.0157 - acc: 0.9953 - val_loss: 0.0050 - val_acc: 0.9990\n",
      "Epoch 85/140\n",
      " - 454s - loss: 0.0156 - acc: 0.9953 - val_loss: 0.0048 - val_acc: 0.9990\n",
      "Epoch 86/140\n",
      " - 457s - loss: 0.0154 - acc: 0.9952 - val_loss: 0.0050 - val_acc: 0.9990\n",
      "Epoch 87/140\n",
      " - 455s - loss: 0.0157 - acc: 0.9953 - val_loss: 0.0049 - val_acc: 0.9990\n",
      "Epoch 88/140\n",
      " - 455s - loss: 0.0152 - acc: 0.9953 - val_loss: 0.0050 - val_acc: 0.9986\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 89/140\n",
      " - 454s - loss: 0.0158 - acc: 0.9952 - val_loss: 0.0049 - val_acc: 0.9990\n",
      "Epoch 90/140\n",
      " - 457s - loss: 0.0155 - acc: 0.9953 - val_loss: 0.0048 - val_acc: 0.9990\n",
      "Epoch 91/140\n",
      " - 456s - loss: 0.0153 - acc: 0.9954 - val_loss: 0.0048 - val_acc: 0.9990\n",
      "Epoch 92/140\n",
      " - 455s - loss: 0.0151 - acc: 0.9954 - val_loss: 0.0048 - val_acc: 0.9990\n",
      "Epoch 93/140\n",
      " - 455s - loss: 0.0153 - acc: 0.9952 - val_loss: 0.0048 - val_acc: 0.9990\n",
      "Epoch 94/140\n",
      " - 454s - loss: 0.0154 - acc: 0.9953 - val_loss: 0.0049 - val_acc: 0.9990\n",
      "Epoch 95/140\n",
      " - 455s - loss: 0.0153 - acc: 0.9954 - val_loss: 0.0046 - val_acc: 0.9990\n",
      "Epoch 96/140\n",
      " - 457s - loss: 0.0153 - acc: 0.9954 - val_loss: 0.0048 - val_acc: 0.9990\n",
      "Epoch 97/140\n",
      " - 454s - loss: 0.0157 - acc: 0.9953 - val_loss: 0.0047 - val_acc: 0.9990\n",
      "Epoch 98/140\n",
      " - 455s - loss: 0.0152 - acc: 0.9954 - val_loss: 0.0046 - val_acc: 0.9990\n",
      "Epoch 99/140\n",
      " - 457s - loss: 0.0159 - acc: 0.9953 - val_loss: 0.0047 - val_acc: 0.9990\n",
      "Epoch 100/140\n",
      " - 455s - loss: 0.0155 - acc: 0.9953 - val_loss: 0.0046 - val_acc: 0.9990\n",
      "Epoch 101/140\n",
      " - 455s - loss: 0.0154 - acc: 0.9954 - val_loss: 0.0047 - val_acc: 0.9990\n",
      "Epoch 102/140\n",
      " - 453s - loss: 0.0152 - acc: 0.9954 - val_loss: 0.0047 - val_acc: 0.9990\n",
      "Epoch 103/140\n",
      " - 456s - loss: 0.0152 - acc: 0.9953 - val_loss: 0.0049 - val_acc: 0.9990\n",
      "Epoch 104/140\n",
      " - 457s - loss: 0.0154 - acc: 0.9953 - val_loss: 0.0048 - val_acc: 0.9990\n",
      "Epoch 105/140\n",
      " - 455s - loss: 0.0153 - acc: 0.9953 - val_loss: 0.0048 - val_acc: 0.9990\n",
      "Epoch 106/140\n",
      " - 455s - loss: 0.0150 - acc: 0.9955 - val_loss: 0.0048 - val_acc: 0.9990\n",
      "Epoch 107/140\n",
      " - 455s - loss: 0.0156 - acc: 0.9953 - val_loss: 0.0047 - val_acc: 0.9990\n",
      "Epoch 108/140\n",
      " - 455s - loss: 0.0153 - acc: 0.9953 - val_loss: 0.0046 - val_acc: 0.9990\n",
      "Epoch 109/140\n",
      " - 455s - loss: 0.0150 - acc: 0.9954 - val_loss: 0.0047 - val_acc: 0.9990\n",
      "Epoch 110/140\n",
      " - 453s - loss: 0.0153 - acc: 0.9954 - val_loss: 0.0049 - val_acc: 0.9990\n",
      "Epoch 111/140\n",
      " - 455s - loss: 0.0150 - acc: 0.9955 - val_loss: 0.0047 - val_acc: 0.9990\n",
      "Epoch 112/140\n",
      " - 457s - loss: 0.0153 - acc: 0.9954 - val_loss: 0.0048 - val_acc: 0.9990\n",
      "Epoch 113/140\n",
      " - 455s - loss: 0.0153 - acc: 0.9954 - val_loss: 0.0048 - val_acc: 0.9990\n",
      "Epoch 114/140\n",
      " - 453s - loss: 0.0154 - acc: 0.9953 - val_loss: 0.0048 - val_acc: 0.9990\n",
      "Epoch 115/140\n",
      " - 457s - loss: 0.0154 - acc: 0.9953 - val_loss: 0.0048 - val_acc: 0.9990\n",
      "Epoch 116/140\n",
      " - 455s - loss: 0.0154 - acc: 0.9953 - val_loss: 0.0048 - val_acc: 0.9990\n",
      "Epoch 117/140\n",
      " - 455s - loss: 0.0155 - acc: 0.9953 - val_loss: 0.0047 - val_acc: 0.9990\n",
      "Epoch 118/140\n",
      " - 455s - loss: 0.0152 - acc: 0.9954 - val_loss: 0.0048 - val_acc: 0.9990\n",
      "Epoch 119/140\n",
      " - 456s - loss: 0.0152 - acc: 0.9953 - val_loss: 0.0048 - val_acc: 0.9990\n",
      "Epoch 120/140\n",
      " - 455s - loss: 0.0155 - acc: 0.9953 - val_loss: 0.0048 - val_acc: 0.9990\n",
      "Epoch 121/140\n",
      " - 455s - loss: 0.0153 - acc: 0.9953 - val_loss: 0.0049 - val_acc: 0.9990\n",
      "Epoch 122/140\n",
      " - 453s - loss: 0.0151 - acc: 0.9954 - val_loss: 0.0047 - val_acc: 0.9990\n",
      "Epoch 123/140\n",
      " - 457s - loss: 0.0153 - acc: 0.9954 - val_loss: 0.0048 - val_acc: 0.9990\n",
      "Epoch 124/140\n",
      " - 455s - loss: 0.0154 - acc: 0.9954 - val_loss: 0.0047 - val_acc: 0.9990\n",
      "Epoch 125/140\n",
      " - 454s - loss: 0.0151 - acc: 0.9953 - val_loss: 0.0049 - val_acc: 0.9990\n",
      "Epoch 126/140\n",
      " - 457s - loss: 0.0150 - acc: 0.9954 - val_loss: 0.0047 - val_acc: 0.9990\n",
      "Epoch 127/140\n",
      " - 455s - loss: 0.0153 - acc: 0.9953 - val_loss: 0.0049 - val_acc: 0.9990\n",
      "Epoch 128/140\n",
      " - 455s - loss: 0.0153 - acc: 0.9954 - val_loss: 0.0048 - val_acc: 0.9990\n",
      "Epoch 129/140\n",
      " - 455s - loss: 0.0151 - acc: 0.9954 - val_loss: 0.0050 - val_acc: 0.9990\n",
      "Epoch 130/140\n",
      " - 455s - loss: 0.0151 - acc: 0.9954 - val_loss: 0.0049 - val_acc: 0.9990\n",
      "Epoch 131/140\n",
      " - 455s - loss: 0.0153 - acc: 0.9953 - val_loss: 0.0047 - val_acc: 0.9990\n",
      "Epoch 132/140\n",
      " - 455s - loss: 0.0152 - acc: 0.9954 - val_loss: 0.0046 - val_acc: 0.9990\n",
      "Epoch 133/140\n",
      " - 455s - loss: 0.0151 - acc: 0.9954 - val_loss: 0.0047 - val_acc: 0.9990\n",
      "Epoch 134/140\n",
      " - 455s - loss: 0.0151 - acc: 0.9954 - val_loss: 0.0049 - val_acc: 0.9990\n",
      "Epoch 135/140\n",
      " - 455s - loss: 0.0152 - acc: 0.9954 - val_loss: 0.0047 - val_acc: 0.9990\n",
      "Epoch 136/140\n",
      " - 455s - loss: 0.0153 - acc: 0.9953 - val_loss: 0.0047 - val_acc: 0.9990\n",
      "Epoch 137/140\n",
      " - 455s - loss: 0.0154 - acc: 0.9953 - val_loss: 0.0047 - val_acc: 0.9990\n",
      "Epoch 138/140\n",
      " - 455s - loss: 0.0151 - acc: 0.9954 - val_loss: 0.0046 - val_acc: 0.9990\n",
      "Epoch 139/140\n",
      " - 455s - loss: 0.0151 - acc: 0.9955 - val_loss: 0.0046 - val_acc: 0.9990\n",
      "Epoch 140/140\n",
      " - 455s - loss: 0.0152 - acc: 0.9954 - val_loss: 0.0045 - val_acc: 0.9990\n"
     ]
    }
   ],
   "source": [
    "import scipy.ndimage\n",
    "history = model.fit_generator(datagen.flow(s_train_data,s_train_label, batch_size=batch_size),\n",
    "                              epochs = epochs, validation_data = (s_val_data,s_val_label),\n",
    "                              verbose = 2, steps_per_epoch=s_train_data.shape[0] // batch_size\n",
    "                              , callbacks=[learning_rate_reduction])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building our results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = model.predict(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = np.argmax(results,axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 40 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Preview predictions\n",
    "plt.figure(figsize=(15,6))\n",
    "for i in range(40):  \n",
    "    plt.subplot(4, 10, i+1)\n",
    "    plt.imshow(test_data[i][:,:,0])\n",
    "    plt.title(\"predict=%d\" % results[i],y=0.9)\n",
    "    plt.axis('off')\n",
    "plt.subplots_adjust(wspace=0.3, hspace=-0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exporting our results for Kaggle\n",
    "df = pd.DataFrame(results)\n",
    "df.index.name='ImageId'\n",
    "df.index+=1\n",
    "df.columns=['Label']\n",
    "df.to_csv('./datasets/results-cnn-lds.csv', header=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "scored 99.585% on Kaggle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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