1D ConvNet on Sentiment Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"../Pics/MLSb-T.png\" width=\"160\">\n",
    "<br><br>\n",
    "<center><u><H1>1D ConvNet on Sentiment Analysis</H1></u></center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from keras.backend.tensorflow_backend import set_session\n",
    "config = tf.ConfigProto()\n",
    "config.gpu_options.allow_growth = True\n",
    "config.log_device_placement = True\n",
    "sess = tf.Session(config=config)\n",
    "set_session(sess)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.models import Sequential, Model\n",
    "from keras.layers.convolutional import Conv1D, MaxPooling1D\n",
    "from keras.layers import Dense, Embedding, LSTM, CuDNNLSTM, Dropout, Flatten, Input\n",
    "from keras.datasets import imdb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_words = 20000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=num_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25000 train_data\n",
      "25000 test_data\n"
     ]
    }
   ],
   "source": [
    "print(len(X_train), 'train_data')\n",
    "print(len(X_test), 'test_data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_len = 256\n",
    "embedding_size = 50\n",
    "batch_size = 128"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sequences per time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 14, 22, 16, 43, 530, 973, 1622, 1385, 65, 458, 4468, 66, 3941, 4, 173, 36, 256, 5, 25, 100, 43, 838, 112, 50, 670, 2, 9, 35, 480, 284, 5, 150, 4, 172, 112, 167, 2, 336, 385, 39, 4, 172, 4536, 1111, 17, 546, 38, 13, 447, 4, 192, 50, 16, 6, 147, 2025, 19, 14, 22, 4, 1920, 4613, 469, 4, 22, 71, 87, 12, 16, 43, 530, 38, 76, 15, 13, 1247, 4, 22, 17, 515, 17, 12, 16, 626, 18, 19193, 5, 62, 386, 12, 8, 316, 8, 106, 5, 4, 2223, 5244, 16, 480, 66, 3785, 33, 4, 130, 12, 16, 38, 619, 5, 25, 124, 51, 36, 135, 48, 25, 1415, 33, 6, 22, 12, 215, 28, 77, 52, 5, 14, 407, 16, 82, 10311, 8, 4, 107, 117, 5952, 15, 256, 4, 2, 7, 3766, 5, 723, 36, 71, 43, 530, 476, 26, 400, 317, 46, 7, 4, 12118, 1029, 13, 104, 88, 4, 381, 15, 297, 98, 32, 2071, 56, 26, 141, 6, 194, 7486, 18, 4, 226, 22, 21, 134, 476, 26, 480, 5, 144, 30, 5535, 18, 51, 36, 28, 224, 92, 25, 104, 4, 226, 65, 16, 38, 1334, 88, 12, 16, 283, 5, 16, 4472, 113, 103, 32, 15, 16, 5345, 19, 178, 32]\n"
     ]
    }
   ],
   "source": [
    "print(X_train[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "189"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "pad  =  'post' #'pre'\n",
    "#Convert our lists to equal length sequences\n",
    "X_train_pad = pad_sequences(X_train, maxlen=max_len, padding=pad, truncating=pad)\n",
    "X_test_pad = pad_sequences(X_test, maxlen=max_len, padding=pad, truncating=pad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25000, 256)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_pad.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "           5,    25,   100,    43,   838,   112,    50,   670,     2,\n",
       "           9,    35,   480,   284,     5,   150,     4,   172,   112,\n",
       "         167,     2,   336,   385,    39,     4,   172,  4536,  1111,\n",
       "          17,   546,    38,    13,   447,     4,   192,    50,    16,\n",
       "           6,   147,  2025,    19,    14,    22,     4,  1920,  4613,\n",
       "         469,     4,    22,    71,    87,    12,    16,    43,   530,\n",
       "          38,    76,    15,    13,  1247,     4,    22,    17,   515,\n",
       "          17,    12,    16,   626,    18, 19193,     5,    62,   386,\n",
       "          12,     8,   316,     8,   106,     5,     4,  2223,  5244,\n",
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       "          25,  1415,    33,     6,    22,    12,   215,    28,    77,\n",
       "          52,     5,    14,   407,    16,    82, 10311,     8,     4,\n",
       "         107,   117,  5952,    15,   256,     4,     2,     7,  3766,\n",
       "           5,   723,    36,    71,    43,   530,   476,    26,   400,\n",
       "         317,    46,     7,     4, 12118,  1029,    13,   104,    88,\n",
       "           4,   381,    15,   297,    98,    32,  2071,    56,    26,\n",
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       "         134,   476,    26,   480,     5,   144,    30,  5535,    18,\n",
       "          51,    36,    28,   224,    92,    25,   104,     4,   226,\n",
       "          65,    16,    38,  1334,    88,    12,    16,   283,     5,\n",
       "          16,  4472,   113,   103,    32,    15,    16,  5345,    19,\n",
       "         178,    32,     0,     0,     0,     0,     0,     0,     0,\n",
       "           0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
       "           0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
       "           0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
       "           0,     0,     0,     0])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_pad[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating the model (Model 0):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model0 = Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model0.add(Embedding(input_dim=num_words,\n",
    "                    output_dim=embedding_size,\n",
    "                    input_length=max_len,\n",
    "                    name='layer_embedding'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model0.add(Conv1D(filters=128, kernel_size=3, padding='same', activation='relu'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model0.add(MaxPooling1D(pool_size=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model0.add(Conv1D(filters=128, kernel_size=3, padding='same', activation='relu'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model0.add(MaxPooling1D(pool_size=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model0.add(Conv1D(filters=128, kernel_size=3, padding='same', activation='relu'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model0.add(MaxPooling1D(pool_size=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model0.add(Dropout(0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model0.add(Flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model0.add(Dense(250, activation='relu'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model0.add(Dense(1, activation='sigmoid', name='classification'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compiling the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model0.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model0.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "model0.fit(X_train_pad, y_train, validation_split=0.05, epochs=10, batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "eval_ = model0.evaluate(X_test_pad, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Loss: {0:.5}\".format(eval_[0]))\n",
    "print(\"Accuracy: {0:.2%}\".format(eval_[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model 1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model1 = Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model1.add(Embedding(input_dim=num_words,\n",
    "                    output_dim=embedding_size,\n",
    "                    input_length=max_len,\n",
    "                    name='layer_embedding'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model1.add(Conv1D(filters=128, kernel_size=5, padding='same', activation='relu'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model1.add(MaxPooling1D(pool_size=3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model1.add(CuDNNLSTM(128))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model1.add(Dropout(0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model1.add(Dense(1, activation='sigmoid', name='classification'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compiling the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model1.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model1.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "model1.fit(X_train_pad, y_train, validation_split=0.05, epochs=10, batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "eval_ = model1.evaluate(X_test_pad, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Loss: {0:.5}\".format(eval_[0]))\n",
    "print(\"Accuracy: {0:.2%}\".format(eval_[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conv1D model with multiple kernel sizes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import concatenate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "conv = []\n",
    "filter_sizes = [3,4,5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding_layer = Embedding(input_dim=num_words,\n",
    "                    output_dim=embedding_size,\n",
    "                    input_length=max_len,\n",
    "                    name='layer_embedding')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "seq_input = Input(shape=(max_len,), dtype='int32')\n",
    "embed_seq = embedding_layer(seq_input)\n",
    "\n",
    "for f in filter_sizes:\n",
    "    _conv = Conv1D(filters=128,kernel_size=f,activation='relu')(embed_seq)\n",
    "    _pool = MaxPooling1D(5)(_conv)\n",
    "    conv.append(_pool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "_concat = concatenate(conv, axis=1)\n",
    "_conv1= Conv1D(128, 5, activation='relu')(_concat)\n",
    "_pool1 = MaxPooling1D(5)(_conv1)\n",
    "_pool1 = Dropout(0.5)(_pool1)\n",
    "_conv2 = Conv1D(128, 5, activation='relu')(_pool1)\n",
    "_pool2 = MaxPooling1D(5)(_conv2)\n",
    "_flat = Flatten()(_pool2)\n",
    "_flat = Dropout(0.5)(_flat)\n",
    "_dense = Dense(128, activation='relu')(_flat)\n",
    "preds = Dense(1, activation='sigmoid')(_dense)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Model(seq_input, preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_3 (InputLayer)            (None, 256)          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding_3 (Embedding)         (None, 256, 50)      1000000     input_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_12 (Conv1D)              (None, 254, 128)     19328       embedding_3[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_13 (Conv1D)              (None, 253, 128)     25728       embedding_3[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_14 (Conv1D)              (None, 252, 128)     32128       embedding_3[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_12 (MaxPooling1D) (None, 50, 128)      0           conv1d_12[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_13 (MaxPooling1D) (None, 50, 128)      0           conv1d_13[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_14 (MaxPooling1D) (None, 50, 128)      0           conv1d_14[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_3 (Concatenate)     (None, 150, 128)     0           max_pooling1d_12[0][0]           \n",
      "                                                                 max_pooling1d_13[0][0]           \n",
      "                                                                 max_pooling1d_14[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_15 (Conv1D)              (None, 146, 128)     82048       concatenate_3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_15 (MaxPooling1D) (None, 29, 128)      0           conv1d_15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dropout_5 (Dropout)             (None, 29, 128)      0           max_pooling1d_15[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_16 (Conv1D)              (None, 25, 128)      82048       dropout_5[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_16 (MaxPooling1D) (None, 5, 128)       0           conv1d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "flatten_3 (Flatten)             (None, 640)          0           max_pooling1d_16[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "dropout_6 (Dropout)             (None, 640)          0           flatten_3[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dense_5 (Dense)                 (None, 128)          82048       dropout_6[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dense_6 (Dense)                 (None, 1)            129         dense_5[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 1,323,457\n",
      "Trainable params: 1,323,457\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 23750 samples, validate on 1250 samples\n",
      "Epoch 1/10\n",
      "23750/23750 [==============================] - 28s 1ms/step - loss: 0.5837 - acc: 0.6356 - val_loss: 0.3801 - val_acc: 0.8480\n",
      "Epoch 2/10\n",
      "23750/23750 [==============================] - 4s 174us/step - loss: 0.2785 - acc: 0.8848 - val_loss: 0.3236 - val_acc: 0.8728\n",
      "Epoch 3/10\n",
      "23750/23750 [==============================] - 4s 174us/step - loss: 0.1923 - acc: 0.9280 - val_loss: 0.2943 - val_acc: 0.8736\n",
      "Epoch 4/10\n",
      "23750/23750 [==============================] - 4s 174us/step - loss: 0.1386 - acc: 0.9489 - val_loss: 0.2997 - val_acc: 0.8736\n",
      "Epoch 5/10\n",
      "23750/23750 [==============================] - 4s 174us/step - loss: 0.0922 - acc: 0.9681 - val_loss: 0.3669 - val_acc: 0.8720\n",
      "Epoch 6/10\n",
      "23750/23750 [==============================] - 4s 173us/step - loss: 0.0525 - acc: 0.9829 - val_loss: 0.5083 - val_acc: 0.8568\n",
      "Epoch 7/10\n",
      "23750/23750 [==============================] - 4s 174us/step - loss: 0.0279 - acc: 0.9906 - val_loss: 1.2122 - val_acc: 0.8232\n",
      "Epoch 8/10\n",
      "23750/23750 [==============================] - 4s 173us/step - loss: 0.0178 - acc: 0.9943 - val_loss: 0.8527 - val_acc: 0.8496\n",
      "Epoch 9/10\n",
      "23750/23750 [==============================] - 4s 173us/step - loss: 0.0106 - acc: 0.9969 - val_loss: 0.8245 - val_acc: 0.8720\n",
      "Epoch 10/10\n",
      "23750/23750 [==============================] - 4s 173us/step - loss: 0.0080 - acc: 0.9981 - val_loss: 0.8223 - val_acc: 0.8704\n",
      "Wall time: 1min 5s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x263698396d8>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "model.fit(X_train_pad, y_train, validation_split=0.05, epochs=10, batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25000/25000 [==============================] - 3s 108us/step\n",
      "Wall time: 2.71 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "eval_ = model.evaluate(X_test_pad, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 0.87396\n",
      "Accuracy: 85.02%\n"
     ]
    }
   ],
   "source": [
    "print(\"Loss: {0:.5}\".format(eval_[0]))\n",
    "print(\"Accuracy: {0:.2%}\".format(eval_[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reference:\n",
    "\n",
    "https://keras.io/layers/convolutional/"
   ]
  }
 ],
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