RecognitionModelTraining.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Recogntion Model Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a base for this notebook, [Yassine Ghouzam's tutorial](https://www.kaggle.com/yassineghouzam/introduction-to-cnn-keras-0-997-top-6) was taken  \n",
    "**NOTE:** make sure you train it with GPU or otherwise it will take a while. If you don't have proper GPU locally, use free [Kaggle Kernel](https://www.kaggle.com/kernels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1 Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "f67b9393-8ea1-4e23-b856-2ce149cfe421",
    "_execution_state": "idle",
    "_uuid": "72334cb006d02a4bcfc2a2fe622524eba824c6f8"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow 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",
    "from matplotlib.pyplot import figure\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "# Set the random seed\n",
    "random_seed = 2\n",
    "num_classes = 52\n",
    "epochs = 10 # Turn epochs to 30 to get 0.9967 accuracy\n",
    "batch_size = 86\n",
    "\n",
    "np.random.seed(random_seed)\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import itertools\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",
    "\n",
    "\n",
    "sns.set(style='white', context='notebook', palette='deep')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "6d2fb3e6-ab71-4974-b5a2-4af1ebdb99f4",
    "_execution_state": "idle",
    "_uuid": "86061d98eccaa02efe0dab0fa3884e71fcf4c310"
   },
   "source": [
    "# 2. Data preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "5e51d00e-62fd-4141-bf73-50ac4f2da7d0",
    "_execution_state": "idle",
    "_uuid": "84bbd5ab8d7895bd430d5ecfe2f7ddf77baa7b74"
   },
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"./data/emnist/processed_balanced_train.csv\")\n",
    "test = pd.read_csv(\"./data/emnist/processed_balanced_test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "86570a36-5c20-460a-9dfd-2070548532a7",
    "_execution_state": "idle",
    "_uuid": "1213b979d5ed3e0d13824d17d694c79d2ece92fa"
   },
   "outputs": [],
   "source": [
    "Y_train = train[\"label\"]\n",
    "Y_test = test[\"label\"]\n",
    "\n",
    "# Drop 'label' column\n",
    "X_train = train.drop(labels = [\"label\"], axis = 1) \n",
    "X_test = test.drop(labels = [\"label\"], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_cell_guid": "4b7f3e78-44dc-4561-b1f0-9429ee024cf4",
    "_execution_state": "idle",
    "_uuid": "cabefd1478d5c1bdfe57fd6a34395340916a854c"
   },
   "outputs": [],
   "source": [
    "# Reshape image in 3 dimensions (height = 28px, width = 28px , canal = 1)\n",
    "X_train = X_train.values.reshape(-1,28,28,1)\n",
    "X_test = X_test.values.reshape(-1,28,28,1)\n",
    "\n",
    "# Encode labels to one hot vectors (ex : 2 -> [0,0,1,0,0,0,0,0,...,0,0])\n",
    "Y_train = to_categorical(Y_train, num_classes = num_classes)\n",
    "Y_test = to_categorical(Y_test, num_classes = num_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_cell_guid": "dcd25ebb-d845-4d32-9867-082e352b1396",
    "_execution_state": "idle",
    "_uuid": "b779ac76d8317647db92d5a88b4098d212d72884"
   },
   "outputs": [],
   "source": [
    "# Split the train and the validation set for the fitting\n",
    "X_train, X_val, Y_train, Y_val = train_test_split(X_train, Y_train, test_size = 0.1, random_state=random_seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_cell_guid": "5f76131b-4ba0-45f1-a98c-bd4e7d561793",
    "_execution_state": "idle",
    "_uuid": "e0dae8943d3d35f075dba3d7ba31bde1d4bf2ff4",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Some examples\n",
    "g = plt.imshow(X_train[10][:,:,0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "d5265777-aeb3-449d-b171-d88cad74c0a4",
    "_uuid": "5fa18b37a9acd9e098bac1d12264b0dd4310fdd3"
   },
   "source": [
    "# 3. CNN\n",
    "## 3.1 Define the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_cell_guid": "1e0f3f88-2ad7-459e-8e02-aecc5f3511ae",
    "_execution_state": "idle",
    "_uuid": "f7991ef6871a26f9fa57acdcd460a69bab53e804"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "WARNING:tensorflow:From /opt/conda/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n"
     ]
    }
   ],
   "source": [
    "# Set the CNN model \n",
    "# my CNN architechture is In -> [[Conv2D->relu]*2 -> MaxPool2D -> Dropout]*2 -> Flatten -> Dense -> Dropout -> Out\n",
    "\n",
    "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(num_classes, activation = \"softmax\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "0e26b6a3-ef9d-4ae5-9d79-d5e4b073251a",
    "_uuid": "7065512cf892ba49f0c06e239e3a20ff13667ef2"
   },
   "source": [
    "## 3.2 Set the optimizer and annealer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_cell_guid": "a4c55409-6a65-400a-b5e8-a1dc535429c0",
    "_execution_state": "idle",
    "_uuid": "420c704367b397b8255fefe9d882b35ac8929b95"
   },
   "outputs": [],
   "source": [
    "# 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\"])\n",
    "\n",
    "# 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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "241a0f4f-13f5-4b13-be1e-4e3e4a714c06",
    "_uuid": "f24df64b223e0177c94025b6767ab19b722c5386"
   },
   "source": [
    "## 3.3 Data augmentation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_cell_guid": "b342befe-1a6f-44bf-8dab-28033a729122",
    "_execution_state": "idle",
    "_uuid": "21d6192c87d92d497c797656474bccd9cefc5647"
   },
   "outputs": [],
   "source": [
    "# With data augmentation to prevent overfitting (accuracy 0.99286)\n",
    "\n",
    "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=5,  # 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\n",
    "\n",
    "\n",
    "datagen.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_cell_guid": "b453af8d-9736-43e3-b486-7a1cd7dd8909",
    "_execution_state": "idle",
    "_uuid": "cf36b3d029f95b553be02d612e097a9769ee8252"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Epoch 1/10\n",
      " - 29s - loss: 1.0785 - acc: 0.6797 - val_loss: 0.3820 - val_acc: 0.8692\n",
      "Epoch 2/10\n",
      " - 26s - loss: 0.5454 - acc: 0.8217 - val_loss: 0.3597 - val_acc: 0.8736\n",
      "Epoch 3/10\n",
      " - 26s - loss: 0.4883 - acc: 0.8383 - val_loss: 0.3418 - val_acc: 0.8804\n",
      "Epoch 4/10\n",
      " - 26s - loss: 0.4729 - acc: 0.8458 - val_loss: 0.3371 - val_acc: 0.8839\n",
      "Epoch 5/10\n",
      " - 25s - loss: 0.4723 - acc: 0.8485 - val_loss: 0.3375 - val_acc: 0.8839\n",
      "Epoch 6/10\n",
      " - 25s - loss: 0.4723 - acc: 0.8483 - val_loss: 0.3682 - val_acc: 0.8772\n",
      "Epoch 7/10\n",
      " - 27s - loss: 0.4791 - acc: 0.8488 - val_loss: 0.3494 - val_acc: 0.8797\n",
      "\n",
      "Epoch 00007: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
      "Epoch 8/10\n",
      " - 25s - loss: 0.4396 - acc: 0.8575 - val_loss: 0.3333 - val_acc: 0.8878\n",
      "Epoch 9/10\n",
      " - 25s - loss: 0.4388 - acc: 0.8588 - val_loss: 0.3428 - val_acc: 0.8852\n",
      "Epoch 10/10\n",
      " - 27s - loss: 0.4422 - acc: 0.8582 - val_loss: 0.3469 - val_acc: 0.8829\n"
     ]
    }
   ],
   "source": [
    "# Fit the model\n",
    "history = model.fit_generator(datagen.flow(X_train,Y_train, batch_size=batch_size),\n",
    "                              epochs = epochs, validation_data = (X_val,Y_val),\n",
    "                              verbose = 2, steps_per_epoch=X_train.shape[0] // batch_size\n",
    "                              , callbacks=[learning_rate_reduction])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "e758621d-b27b-40ff-a93f-bebd2e0e5243",
    "_uuid": "0a1834f2a9f2db15dcaba4a84004b9627d714469"
   },
   "source": [
    "# 4. Evaluate the model\n",
    "## 4.1 Training and validation curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_cell_guid": "eb4b1b73-cbd4-40e8-9790-066fcef4c4c0",
    "_execution_state": "idle",
    "_uuid": "3a831860dd5bb65c8ead1ddcf4ae18ae20dd7f3e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the loss and accuracy curves for training and validation \n",
    "fig, ax = plt.subplots(2,1)\n",
    "ax[0].plot(history.history['loss'], color='b', label=\"Training loss\")\n",
    "ax[0].plot(history.history['val_loss'], color='r', label=\"validation loss\",axes =ax[0])\n",
    "legend = ax[0].legend(loc='best', shadow=True)\n",
    "\n",
    "ax[1].plot(history.history['acc'], color='b', label=\"Training accuracy\")\n",
    "ax[1].plot(history.history['val_acc'], color='r',label=\"Validation accuracy\")\n",
    "legend = ax[1].legend(loc='best', shadow=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "de1c65bd-4a88-4351-9f4b-562e72e7e0fd",
    "_uuid": "63698d7d51381b33892ce164b0f21930abb3e937"
   },
   "source": [
    "## 4.2 Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_cell_guid": "11361e73-8250-4bf5-a353-b0f8ea83e659",
    "_execution_state": "idle",
    "_uuid": "16e161179bf1b51ba66c39b2cead883f1db3a9c7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Look at confusion matrix \n",
    "\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, cm[i, j],\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "\n",
    "# Predict the values from the validation dataset\n",
    "Y_pred = model.predict(X_val)\n",
    "# Convert predictions classes to one hot vectors \n",
    "Y_pred_classes = np.argmax(Y_pred,axis = 1) \n",
    "# Convert validation observations to one hot vectors\n",
    "Y_true = np.argmax(Y_val,axis = 1) \n",
    "# compute the confusion matrix\n",
    "confusion_mtx = confusion_matrix(Y_true, Y_pred_classes) \n",
    "# plot the confusion matrix\n",
    "\n",
    "figure(figsize=(14, 14))\n",
    "plot_confusion_matrix(confusion_mtx, classes = range(num_classes)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.3 Most common error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_cell_guid": "7b0f31b8-c18b-4529-b0d8-eb4c31e30bbf",
    "_execution_state": "idle",
    "_uuid": "e7a3d6449b499a29db224e42e950f21ca1ec4e36"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1008x1008 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display some error results \n",
    "\n",
    "# Errors are difference between predicted labels and true labels\n",
    "errors = (Y_pred_classes - Y_true != 0)\n",
    "\n",
    "Y_pred_classes_errors = Y_pred_classes[errors]\n",
    "Y_pred_errors = Y_pred[errors]\n",
    "Y_true_errors = Y_true[errors]\n",
    "X_val_errors = X_val[errors]\n",
    "\n",
    "def display_errors(errors_index,img_errors,pred_errors, obs_errors):\n",
    "    \"\"\" This function shows 6 images with their predicted and real labels\"\"\"\n",
    "    n = 0\n",
    "    nrows = 2\n",
    "    ncols = 3\n",
    "    fig, ax = plt.subplots(nrows + 1,ncols,sharex=True,sharey=True)\n",
    "    for row in range(nrows):\n",
    "        for col in range(ncols):\n",
    "            error = errors_index[n]\n",
    "            ax[row*2,col].imshow((img_errors[error]).reshape((28,28)))\n",
    "            ax[row*2,col].set_title(\"Predicted label :{}\\nTrue label :{}\".format(pred_errors[error],obs_errors[error]))\n",
    "            n += 1\n",
    "            \n",
    "figure(figsize=(14, 14))\n",
    "\n",
    "# Probabilities of the wrong predicted numbers\n",
    "Y_pred_errors_prob = np.max(Y_pred_errors,axis = 1)\n",
    "\n",
    "# Predicted probabilities of the true values in the error set\n",
    "true_prob_errors = np.diagonal(np.take(Y_pred_errors, Y_true_errors, axis=1))\n",
    "\n",
    "# Difference between the probability of the predicted label and the true label\n",
    "delta_pred_true_errors = Y_pred_errors_prob - true_prob_errors\n",
    "\n",
    "# Sorted list of the delta prob errors\n",
    "sorted_dela_errors = np.argsort(delta_pred_true_errors)\n",
    "\n",
    "# Top 6 errors \n",
    "most_important_errors = sorted_dela_errors[-6:]\n",
    "\n",
    "# Show the top 6 errors\n",
    "display_errors(most_important_errors, X_val_errors, Y_pred_classes_errors, Y_true_errors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accurasy on train set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_cell_guid": "05ff3b9f-c3bb-4cec-a8c2-2c128e8f15b3",
    "_execution_state": "idle",
    "_uuid": "7f17e7bf0a54a01a52fef2d554780f6bc6580dc6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overall accuracy: 88.58653846153847 %\n"
     ]
    }
   ],
   "source": [
    "# predict results\n",
    "results = model.predict(X_test)\n",
    "\n",
    "# select the indix with the maximum probability\n",
    "results = np.argmax(results,axis = 1)\n",
    "\n",
    "Y_test = np.argmax(Y_test,axis = 1) \n",
    "conf_mat = confusion_matrix(results, Y_test)\n",
    "acc = np.sum(conf_mat.diagonal()) / np.sum(conf_mat)\n",
    "print('Overall accuracy: {} %'.format(acc*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_cell_guid": "b5f1f39f-13b8-439a-8913-0f120e3d47a9",
    "_execution_state": "idle",
    "_uuid": "369dfaab09240f3f12bcff91953ffd315ab84985"
   },
   "outputs": [],
   "source": [
    "model.save('alphanum_model_binary_ex_88.h5')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
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