13a. Sequence Basics [experimental].ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Representing Data for Training Sequential Networks in Keras\n",
    "When we use sequential networks, the most common form of data is text. In order to represent a sentence, we will need to make a few decisions. \n",
    "- Will our network fundamentally represent characters? Words? What are the building blocks for our structure?\n",
    "- What is the maximum sequence length? \n",
    "- What is my maximum vocabulary size?\n",
    "- What style of sequence network should I use?\n",
    "- Can I incorporate an Embedding? What size?\n",
    "- What is my problem type? Many to one? Many to many? Sequence-to-Sequence?\n",
    "\n",
    "Let's start with a simple example from the IMdB dataset and see what the representation in Keras should look like for a sequential convolutional neural network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['KMP_DUPLICATE_LIB_OK']='True'\n",
    "\n",
    "import numpy as np\n",
    "from tensorflow.keras.datasets import imdb\n",
    "from tensorflow.keras.preprocessing import sequence\n",
    "\n",
    "\n",
    "# load the dataset but only keep the top n words, zero the rest\n",
    "top_words = 1000\n",
    "(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=top_words)\n",
    "\n",
    "# truncate and pad input sequences to be the same length\n",
    "max_review_length = 500\n",
    "X_train = sequence.pad_sequences(X_train, maxlen=max_review_length)\n",
    "X_test = sequence.pad_sequences(X_test, maxlen=max_review_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'> (25000, 500)\n",
      "<class 'numpy.ndarray'> (500,)\n",
      "Vocabulary size: 999\n",
      "(25000,) 0 1\n"
     ]
    }
   ],
   "source": [
    "print(type(X_train), X_train.shape)\n",
    "print(type(X_train[0]), X_train[0].shape)\n",
    "print('Vocabulary size:', np.max(X_train))\n",
    "print(y_train.shape, np.min(y_train), np.max(y_train))\n",
    "NUM_CLASSES = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The IMDB ratings data contains: a number of different reviews and the sentiment of the review as positive or negative. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metal device set to: Apple M2\n",
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)           [(None, 500)]        0           []                               \n",
      "                                                                                                  \n",
      " embedding (Embedding)          (None, 500, 50)      50000       ['input_1[0][0]']                \n",
      "                                                                                                  \n",
      " conv1d (Conv1D)                (None, 496, 64)      16064       ['embedding[0][0]']              \n",
      "                                                                                                  \n",
      " max_pooling1d (MaxPooling1D)   (None, 99, 64)       0           ['conv1d[0][0]']                 \n",
      "                                                                                                  \n",
      " dropout (Dropout)              (None, 99, 64)       0           ['max_pooling1d[0][0]']          \n",
      "                                                                                                  \n",
      " conv1d_1 (Conv1D)              (None, 95, 64)       20544       ['dropout[0][0]']                \n",
      "                                                                                                  \n",
      " max_pooling1d_1 (MaxPooling1D)  (None, 19, 64)      0           ['conv1d_1[0][0]']               \n",
      "                                                                                                  \n",
      " dropout_1 (Dropout)            (None, 19, 64)       0           ['max_pooling1d_1[0][0]']        \n",
      "                                                                                                  \n",
      " conv1d_2 (Conv1D)              (None, 15, 64)       20544       ['dropout_1[0][0]']              \n",
      "                                                                                                  \n",
      " dropout_2 (Dropout)            (None, 15, 64)       0           ['conv1d_2[0][0]']               \n",
      "                                                                                                  \n",
      " global_average_pooling1d (Glob  (None, 64)          0           ['dropout_2[0][0]']              \n",
      " alAveragePooling1D)                                                                              \n",
      "                                                                                                  \n",
      " subtract (Subtract)            (None, 15, 64)       0           ['dropout_2[0][0]',              \n",
      "                                                                  'global_average_pooling1d[0][0]'\n",
      "                                                                 ]                                \n",
      "                                                                                                  \n",
      " tf.math.pow (TFOpLambda)       (None, 15, 64)       0           ['subtract[0][0]']               \n",
      "                                                                                                  \n",
      " global_average_pooling1d_1 (Gl  (None, 64)          0           ['tf.math.pow[0][0]']            \n",
      " obalAveragePooling1D)                                                                            \n",
      "                                                                                                  \n",
      " concat_1 (Concatenate)         (None, 128)          0           ['global_average_pooling1d[0][0]'\n",
      "                                                                 , 'global_average_pooling1d_1[0][\n",
      "                                                                 0]']                             \n",
      "                                                                                                  \n",
      " dense (Dense)                  (None, 64)           8256        ['concat_1[0][0]']               \n",
      "                                                                                                  \n",
      " dropout_3 (Dropout)            (None, 64)           0           ['dense[0][0]']                  \n",
      "                                                                                                  \n",
      " dense_1 (Dense)                (None, 1)            65          ['dropout_3[0][0]']              \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 115,473\n",
      "Trainable params: 115,473\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n",
      "None\n"
     ]
    },
    {
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H1CpOAAAAAElFTkSuQmCC",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Conv1D, MaxPooling1D, GlobalAveragePooling1D\n",
    "from tensorflow.keras.layers import Flatten, Dense, Dropout\n",
    "from tensorflow.keras.layers import Embedding, Input, Concatenate\n",
    "from tensorflow.keras.layers import Subtract\n",
    "from tensorflow.keras.utils import plot_model\n",
    "import tensorflow as tf\n",
    "\n",
    "EMBED_SIZE = 50\n",
    "# the input is a list of integers, 500 long\n",
    "sequence_input = Input(shape=(X_train.shape[1], ))\n",
    "\n",
    "# this will reduce the input dimension from VOCAB_SIZE to 50 for each word\n",
    "# the lenght will be the maximum number of words in a document, so 500\n",
    "embedded_sequences = Embedding(top_words, # input dimension (max int of OHE)\n",
    "                EMBED_SIZE, # output dimension size\n",
    "                input_length=max_review_length)(sequence_input) # number of words in each sequence\n",
    "\n",
    "# starting sequence size is 500 (words) by 50 (embedded features)\n",
    "x = Conv1D(64, 5, activation='relu',\n",
    "           kernel_initializer='he_uniform')(embedded_sequences)\n",
    "\n",
    "# after conv, length becomes: 500-4=496 \n",
    "# so overall size is 496 by 64\n",
    "\n",
    "# now pool across time\n",
    "x = MaxPooling1D(5)(x)# after max pool, 496/5 -> 99 by 64\n",
    "x = Dropout(0.2)(x)\n",
    "\n",
    "# extract additional features\n",
    "x = Conv1D(64, 5, activation='relu',\n",
    "           kernel_initializer='he_uniform')(x)\n",
    "\n",
    "# new size is 95 after the conovlutions\n",
    "x = MaxPooling1D(5)(x) # after max pool, size is 95/5 = 19 by 64\n",
    "x = Dropout(0.2)(x)\n",
    "\n",
    "# more features through CNN processing!\n",
    "x = Conv1D(64, 5, activation='relu',\n",
    "           kernel_initializer='he_uniform')(x)\n",
    "x = Dropout(0.2)(x)\n",
    "\n",
    "# after convolution, size becomes 15 elements long\n",
    "# Take the mean of these elements across features, result is 64 elements\n",
    "x_mean = GlobalAveragePooling1D()(x) # this is the size to globally flatten \n",
    "\n",
    "# Take the variance of these elements across features, result is 64 elements\n",
    "x_tmp = Subtract()([x,x_mean])\n",
    "x_std = GlobalAveragePooling1D()(x_tmp**2)\n",
    "\n",
    "x = Concatenate(name='concat_1')([x_mean,x_std])\n",
    "\n",
    "x = Dense(64, activation='relu',\n",
    "          kernel_initializer='he_uniform')(x)\n",
    "\n",
    "x = Dropout(0.2)(x)\n",
    "\n",
    "preds = Dense(NUM_CLASSES, activation='sigmoid',\n",
    "              kernel_initializer='glorot_uniform')(x)\n",
    "\n",
    "model = Model(sequence_input, preds)\n",
    "\n",
    "print(model.summary())\n",
    "\n",
    "# you will need to install pydot properly on your machine to get this running\n",
    "plot_model(\n",
    "    model, to_file='model.png', show_shapes=True, show_layer_names=True,\n",
    "    rankdir='LR', expand_nested=False, dpi=96\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.\n",
      "WARNING:absl:There is a known slowdown when using v2.11+ Keras optimizers on M1/M2 Macs. Falling back to the legacy Keras optimizer, i.e., `tf.keras.optimizers.legacy.Adam`.\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.optimizers.legacy import Adam\n",
    "from tensorflow.keras.optimizers.schedules import ExponentialDecay\n",
    "\n",
    "\n",
    "lr_schedule = ExponentialDecay(\n",
    "    initial_learning_rate=0.001,\n",
    "    decay_steps=10000,\n",
    "    decay_rate=0.95,\n",
    "    staircase=True) \n",
    "# clipnorm=1.0, \n",
    "\n",
    "opt = Adam(epsilon=0.0001, learning_rate=lr_schedule)\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer=opt,\n",
    "              metrics=['acc'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-10-01 09:45:01.674029: W tensorflow/tsl/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "391/391 [==============================] - 43s 107ms/step - loss: 0.6284 - acc: 0.6006 - val_loss: 0.4158 - val_acc: 0.8095\n",
      "Epoch 2/5\n",
      "391/391 [==============================] - 35s 90ms/step - loss: 0.3603 - acc: 0.8430 - val_loss: 0.3361 - val_acc: 0.8554\n",
      "Epoch 3/5\n",
      "391/391 [==============================] - 34s 87ms/step - loss: 0.3090 - acc: 0.8709 - val_loss: 0.3191 - val_acc: 0.8659\n",
      "Epoch 4/5\n",
      "391/391 [==============================] - 34s 86ms/step - loss: 0.2820 - acc: 0.8833 - val_loss: 0.3035 - val_acc: 0.8699\n",
      "Epoch 5/5\n",
      "391/391 [==============================] - 34s 86ms/step - loss: 0.2487 - acc: 0.8994 - val_loss: 0.2983 - val_acc: 0.8722\n",
      "CPU times: user 26.9 s, sys: 11.7 s, total: 38.5 s\n",
      "Wall time: 2min 59s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "\n",
    "history = []\n",
    "tmp = model.fit(X_train, y_train, epochs=5, \n",
    "                batch_size=64, \n",
    "                validation_data=(X_test, y_test))\n",
    "history.append( tmp )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "391/391 [==============================] - 34s 86ms/step - loss: 0.2097 - acc: 0.9182 - val_loss: 0.3156 - val_acc: 0.8699\n",
      "Epoch 2/5\n",
      "391/391 [==============================] - 31s 79ms/step - loss: 0.1805 - acc: 0.9294 - val_loss: 0.3204 - val_acc: 0.8731\n",
      "Epoch 3/5\n",
      "391/391 [==============================] - 11s 27ms/step - loss: 0.1442 - acc: 0.9441 - val_loss: 0.3442 - val_acc: 0.8689\n",
      "Epoch 4/5\n",
      "391/391 [==============================] - 11s 28ms/step - loss: 0.1152 - acc: 0.9570 - val_loss: 0.4279 - val_acc: 0.8562\n",
      "Epoch 5/5\n",
      "391/391 [==============================] - 11s 29ms/step - loss: 0.0969 - acc: 0.9628 - val_loss: 0.4362 - val_acc: 0.8572\n"
     ]
    }
   ],
   "source": [
    "# run this as many times as we want! Adds 5 epochs each time\n",
    "tmp = model.fit(X_train, y_train, epochs=5, \n",
    "                batch_size=64, \n",
    "                validation_data=(X_test, y_test))\n",
    "history.append( tmp )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import metrics as mt\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# combine all the history from training together\n",
    "combined = dict()\n",
    "for key in ['acc','val_acc','loss','val_loss']:\n",
    "    combined[key] = np.hstack([x.history[key] for x in history])\n",
    "    \n",
    "# summarize history for accuracy\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.subplot(121)\n",
    "plt.plot(combined['acc'])\n",
    "plt.plot(combined['val_acc'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "\n",
    "# summarize history for loss\n",
    "plt.subplot(122)\n",
    "plt.plot(combined['loss'])\n",
    "plt.plot(combined['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[back to slides]**\n",
    "\n",
    "# Transformers\n",
    "TODO: explain more on transformers. Covered in lecture, but no description ready yet here. **This is a major reason that this is still classified as experimental.** \n",
    "\n",
    "Example manipulated from the following: https://keras.io/examples/nlp/text_classification_with_transformer/\n",
    "\n",
    "Also, the reference for using multi heads is here:\n",
    "https://keras.io/api/layers/attention_layers/multi_head_attention/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import MultiHeadAttention, LayerNormalization\n",
    "from tensorflow.keras import Sequential\n",
    "from tensorflow.keras.layers import Layer\n",
    "\n",
    "# The transformer architecture \n",
    "class TransformerBlock(Layer): # inherit from Keras Layer\n",
    "    def __init__(self, embed_dim, num_heads, ff_dim, rate=0.2):\n",
    "        super().__init__()\n",
    "        # setup the model heads and feedforward network\n",
    "        self.att = MultiHeadAttention(num_heads=num_heads, \n",
    "                                      key_dim=embed_dim)\n",
    "        \n",
    "        # make a two layer network that processes the attention\n",
    "        self.ffn = Sequential()\n",
    "        self.ffn.add( Dense(ff_dim, activation='relu') )\n",
    "        self.ffn.add( Dense(embed_dim) )\n",
    "        \n",
    "        self.layernorm1 = LayerNormalization(epsilon=1e-6)\n",
    "        self.layernorm2 = LayerNormalization(epsilon=1e-6)\n",
    "        self.dropout1 = Dropout(rate)\n",
    "        self.dropout2 = Dropout(rate)\n",
    "\n",
    "    def call(self, inputs, training):\n",
    "        # apply the layers as needed (similar to PyTorch)\n",
    "        \n",
    "        # get the attention output from multi heads\n",
    "        # Using same inpout here is self-attention\n",
    "        # call inputs are (query, value, key) \n",
    "        # if only two inputs given, value and key are assumed the same\n",
    "        attn_output = self.att(inputs, inputs)\n",
    "        \n",
    "        # create residual output, with attention\n",
    "        out1 = self.layernorm1(inputs + attn_output)\n",
    "        \n",
    "        # apply dropout if training\n",
    "        out1 = self.dropout1(out1, training=training)\n",
    "        \n",
    "        # place through feed forward after layer norm\n",
    "        ffn_output = self.ffn(out1)\n",
    "        out2 = self.layernorm2(out1 + ffn_output)\n",
    "        \n",
    "        # apply dropout if training\n",
    "        out2 = self.dropout2(out2, training=training)\n",
    "        #return the residual from Dense layer\n",
    "        return out2\n",
    "    \n",
    "class TokenAndPositionEmbedding(Layer):\n",
    "    def __init__(self, maxlen, vocab_size, embed_dim):\n",
    "        super().__init__()\n",
    "        # create two embeddings \n",
    "        # one for processing the tokens (words)\n",
    "        self.token_emb = Embedding(input_dim=vocab_size, \n",
    "                                   output_dim=embed_dim)\n",
    "        # another embedding for processing the position\n",
    "        self.pos_emb = Embedding(input_dim=maxlen, \n",
    "                                 output_dim=embed_dim)\n",
    "\n",
    "    def call(self, x):\n",
    "        # create a static position measure (input)\n",
    "        maxlen = tf.shape(x)[-1]\n",
    "        positions = tf.range(start=0, limit=maxlen, delta=1)\n",
    "        # positions now goes from 0 to 500 (for IMdB) by 1\n",
    "        positions = self.pos_emb(positions)# embed these positions\n",
    "        x = self.token_emb(x) # embed the tokens\n",
    "        return x + positions # add embeddngs to get final embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_2 (InputLayer)        [(None, 500)]             0         \n",
      "                                                                 \n",
      " token_and_position_embeddin  (None, 500, 32)          48000     \n",
      " g (TokenAndPositionEmbeddin                                     \n",
      " g)                                                              \n",
      "                                                                 \n",
      " transformer_block (Transfor  (None, 500, 32)          10656     \n",
      " merBlock)                                                       \n",
      "                                                                 \n",
      " global_average_pooling1d_2   (None, 32)               0         \n",
      " (GlobalAveragePooling1D)                                        \n",
      "                                                                 \n",
      " dropout_6 (Dropout)         (None, 32)                0         \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 20)                660       \n",
      "                                                                 \n",
      " dropout_7 (Dropout)         (None, 20)                0         \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 1)                 21        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 59,337\n",
      "Trainable params: 59,337\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "embed_dim = 32  # Embedding size for each token\n",
    "num_heads = 2  # Number of attention heads\n",
    "ff_dim = 32  # Hidden layer size in feed forward network inside transformer\n",
    "\n",
    "inputs = Input(shape=(X_train.shape[1],))\n",
    "x = TokenAndPositionEmbedding(X_train.shape[1], top_words, embed_dim)(inputs)\n",
    "x = TransformerBlock(embed_dim, num_heads, ff_dim)(x)\n",
    "\n",
    "x = GlobalAveragePooling1D()(x)\n",
    "x = Dropout(0.2)(x)\n",
    "x = Dense(20, activation='relu')(x)\n",
    "x = Dropout(0.2)(x)\n",
    "outputs = Dense(NUM_CLASSES, activation='sigmoid',\n",
    "              kernel_initializer='glorot_uniform')(x)\n",
    "\n",
    "model_xformer = Model(inputs=inputs, outputs=outputs)\n",
    "print(model_xformer.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2\n",
      "391/391 [==============================] - 175s 445ms/step - loss: 0.5993 - accuracy: 0.6333 - val_loss: 0.3731 - val_accuracy: 0.8354\n",
      "Epoch 2/2\n",
      "391/391 [==============================] - 171s 437ms/step - loss: 0.3323 - accuracy: 0.8611 - val_loss: 0.3026 - val_accuracy: 0.8712\n"
     ]
    }
   ],
   "source": [
    "model_xformer.compile(optimizer='adam', \n",
    "                      loss='binary_crossentropy', \n",
    "                      metrics=['accuracy'])\n",
    "\n",
    "history = model_xformer.fit(\n",
    "    X_train, y_train, batch_size=64, epochs=2, \n",
    "    validation_data=(X_test, y_test)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "782/782 [==============================] - 5s 6ms/step\n",
      "782/782 [==============================] - 43s 54ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "yhat_cnn = model.predict(X_test)\n",
    "yhat_xformer = model_xformer.predict(X_test)\n",
    "\n",
    "acc = [mt.accuracy_score(y_test,np.round(yhat_cnn)),\n",
    "       mt.accuracy_score(y_test,np.round(yhat_xformer)),\n",
    "      ]\n",
    "\n",
    "plt.bar([1,2],acc)\n",
    "plt.xticks([1,2],['CNN','XFORMER'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_____\n",
    "\n",
    "# Sequences with Pre-processing in Keras\n",
    "\n",
    "## Moving to 20 Newsgroups\n",
    "So we should be able to convert a new dataset into the same format as above. Let's do this from scratch, converting the 20 news groups data. \n",
    "- http://qwone.com/~jason/20Newsgroups/\n",
    "\n",
    "We looked at this a while back when we created the tf-idf and bag-of-words models. This time, we are not going to get rid of the sequence of words for classification. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert 20 newsgroups into keras sequence format\n",
    "from sklearn.datasets import fetch_20newsgroups\n",
    "bunch = fetch_20newsgroups(subset='train', shuffle=True, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------Random Document---------\n",
      "================================\n",
      "Document Label:  rec.sport.baseball\n",
      "================================\n",
      "From: spork@camelot.bradley.edu (Richard Izzo)\n",
      "Subject: Re: Royals\n",
      "Nntp-Posting-Host: camelot.bradley.edu\n",
      "Organization: Bradley University\n",
      "Distribution: na\n",
      "Lines: 20\n",
      "\n",
      "In <randall.734911319@moose> randall@informix.com (Randall Rhea) writes:\n",
      "\n",
      ">The Royals are darkness.  They are the void of our time.\n",
      ">When they play, shame descends upon the land like a cold front\n",
      ">from Canada.   They are a humiliation to all who have lived and\n",
      ">all who shall ever live.   They are utterly and completely\n",
      ">doomed.\n",
      ">Other than that, I guess they're OK.\n",
      "\n",
      "\tOh, lighten up.  What depresses me is that they might actually \n",
      "finish last, which I believe hasn't happened since their second season in \n",
      "1970.  Never mind that Gubizca is 0-2 with a 16.50 ERA, Gardner at 7.50,\n",
      "our main recent acquisitions (Lind, McReynolds, Jose) are averaging .210,\n",
      "David Cone is 0-2 (about how he was doing in KC before joining the Mets \n",
      "several years ago), our hitting sucks, and our pitching has collapsed, \n",
      "and we've won one game at home; they've won more games in their first ten\n",
      "games than last year, and Brian McRae is actually batting over the \n",
      "Mendoza line!\n",
      "\n",
      "rich.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# display a random document and label\n",
    "idx = round(np.random.rand()*len(bunch.data))\n",
    "print('--------Random Document---------')\n",
    "print('================================')\n",
    "print('Document Label: ',bunch.target_names[bunch.target[idx]])\n",
    "print('================================')\n",
    "print(\"\\n\".join(bunch.data[idx].split(\"\\n\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 134142 unique tokens. Distilled to 134142 top words.\n",
      "Shape of data tensor: (11314, 1000)\n",
      "Shape of label tensor: (11314, 20)\n",
      "134142\n",
      "CPU times: user 1.76 s, sys: 39.3 ms, total: 1.8 s\n",
      "Wall time: 1.8 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras.preprocessing.text import Tokenizer\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "\n",
    "NUM_TOP_WORDS = None # use entire vocabulary!\n",
    "MAX_ART_LEN = 1000 # maximum and minimum number of words\n",
    "\n",
    "#tokenize the text\n",
    "tokenizer = Tokenizer(num_words=NUM_TOP_WORDS)\n",
    "tokenizer.fit_on_texts(bunch.data)\n",
    "# save as sequences with integers replacing words\n",
    "sequences = tokenizer.texts_to_sequences(bunch.data)\n",
    "\n",
    "word_index = tokenizer.word_index\n",
    "NUM_TOP_WORDS = len(word_index) if NUM_TOP_WORDS==None else NUM_TOP_WORDS\n",
    "top_words = min((len(word_index),NUM_TOP_WORDS))\n",
    "print('Found %s unique tokens. Distilled to %d top words.' % (len(word_index),top_words))\n",
    "\n",
    "X = pad_sequences(sequences, maxlen=MAX_ART_LEN)\n",
    "\n",
    "y_ohe = keras.utils.to_categorical(bunch.target)\n",
    "print('Shape of data tensor:', X.shape)\n",
    "print('Shape of label tensor:', y_ohe.shape)\n",
    "print(np.max(X))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So that's it! The representation is now:\n",
    "- each word is converted to an integer \n",
    "- each article is a series of integers that represent the correct ordering of words\n",
    "- the target is one hot encoded\n",
    "\n",
    "___"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train Shape: (9051, 1000) Label Shape: (9051, 20)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics as mt\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# Split it into train / test subsets\n",
    "X_train, X_test, y_train_ohe, y_test_ohe = train_test_split(X, y_ohe, test_size=0.2,\n",
    "                                                            stratify=bunch.target, \n",
    "                                                            random_state=42)\n",
    "NUM_CLASSES = 20\n",
    "\n",
    "# print some stats of the data\n",
    "print(\"X_train Shape:\",X_train.shape, \"Label Shape:\", y_train_ohe.shape)\n",
    "uniq_classes = np.sum(y_train_ohe,axis=0)\n",
    "plt.bar(list(range(20)),uniq_classes)\n",
    "plt.xticks(list(range(20)), bunch.target_names, rotation='vertical')\n",
    "plt.ylabel(\"Number of Instances in Each Class\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the embedding\n",
    "But this is going to be a more involved process. Maybe we can speed up the training by loading up a pre-trained embedding of the words?! Recall that the GloVe embedding uses pre-trained word embeddings in order to group contextually similar words together. \n",
    "\n",
    "A number of versions of the GloVe embedding matrix are pre-trained using various document. Let's use a version trained from 6B different documents from wikipedia (this is actually one of the smaller datasets trained upon--so the vocabulary might be a bit limited).\n",
    "\n",
    "Let's use the GloVe word embedding in keras. We will follow the example at:\n",
    "- https://blog.keras.io/using-pre-trained-word-embeddings-in-a-keras-model.html\n",
    "\n",
    "You can download the nearly 1GB pretrained embeddings here:\n",
    "- http://nlp.stanford.edu/data/glove.6B.zip\n",
    "\n",
    "Let's take a quick look at the format of the file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m.\u001b[m\u001b[m                   glove.6B.100d.txt   glove.6B.300d.txt   glove.840B.300d.txt\r\n",
      "\u001b[34m..\u001b[m\u001b[m                  glove.6B.200d.txt   glove.6B.50d.txt\r\n"
     ]
    }
   ],
   "source": [
    "!ls -a \"large_data/glove/\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the 0.418 0.24968 -0.41242 0.1217 0.34527 -0.044457 -0.49688 -0.17862 -0.00066023 -0.6566 0.27843 -0.14767 -0.55677 0.14658 -0.0095095 0.011658 0.10204 -0.12792 -0.8443 -0.12181 -0.016801 -0.33279 -0.1552 -0.23131 -0.19181 -1.8823 -0.76746 0.099051 -0.42125 -0.19526 4.0071 -0.18594 -0.52287 -0.31681 0.00059213 0.0074449 0.17778 -0.15897 0.012041 -0.054223 -0.29871 -0.15749 -0.34758 -0.045637 -0.44251 0.18785 0.0027849 -0.18411 -0.11514 -0.78581\r\n",
      ", 0.013441 0.23682 -0.16899 0.40951 0.63812 0.47709 -0.42852 -0.55641 -0.364 -0.23938 0.13001 -0.063734 -0.39575 -0.48162 0.23291 0.090201 -0.13324 0.078639 -0.41634 -0.15428 0.10068 0.48891 0.31226 -0.1252 -0.037512 -1.5179 0.12612 -0.02442 -0.042961 -0.28351 3.5416 -0.11956 -0.014533 -0.1499 0.21864 -0.33412 -0.13872 0.31806 0.70358 0.44858 -0.080262 0.63003 0.32111 -0.46765 0.22786 0.36034 -0.37818 -0.56657 0.044691 0.30392\r\n",
      ". 0.15164 0.30177 -0.16763 0.17684 0.31719 0.33973 -0.43478 -0.31086 -0.44999 -0.29486 0.16608 0.11963 -0.41328 -0.42353 0.59868 0.28825 -0.11547 -0.041848 -0.67989 -0.25063 0.18472 0.086876 0.46582 0.015035 0.043474 -1.4671 -0.30384 -0.023441 0.30589 -0.21785 3.746 0.0042284 -0.18436 -0.46209 0.098329 -0.11907 0.23919 0.1161 0.41705 0.056763 -6.3681e-05 0.068987 0.087939 -0.10285 -0.13931 0.22314 -0.080803 -0.35652 0.016413 0.10216\r\n",
      "of 0.70853 0.57088 -0.4716 0.18048 0.54449 0.72603 0.18157 -0.52393 0.10381 -0.17566 0.078852 -0.36216 -0.11829 -0.83336 0.11917 -0.16605 0.061555 -0.012719 -0.56623 0.013616 0.22851 -0.14396 -0.067549 -0.38157 -0.23698 -1.7037 -0.86692 -0.26704 -0.2589 0.1767 3.8676 -0.1613 -0.13273 -0.68881 0.18444 0.0052464 -0.33874 -0.078956 0.24185 0.36576 -0.34727 0.28483 0.075693 -0.062178 -0.38988 0.22902 -0.21617 -0.22562 -0.093918 -0.80375\r\n",
      "to 0.68047 -0.039263 0.30186 -0.17792 0.42962 0.032246 -0.41376 0.13228 -0.29847 -0.085253 0.17118 0.22419 -0.10046 -0.43653 0.33418 0.67846 0.057204 -0.34448 -0.42785 -0.43275 0.55963 0.10032 0.18677 -0.26854 0.037334 -2.0932 0.22171 -0.39868 0.20912 -0.55725 3.8826 0.47466 -0.95658 -0.37788 0.20869 -0.32752 0.12751 0.088359 0.16351 -0.21634 -0.094375 0.018324 0.21048 -0.03088 -0.19722 0.082279 -0.09434 -0.073297 -0.064699 -0.26044\r\n",
      "and 0.26818 0.14346 -0.27877 0.016257 0.11384 0.69923 -0.51332 -0.47368 -0.33075 -0.13834 0.2702 0.30938 -0.45012 -0.4127 -0.09932 0.038085 0.029749 0.10076 -0.25058 -0.51818 0.34558 0.44922 0.48791 -0.080866 -0.10121 -1.3777 -0.10866 -0.23201 0.012839 -0.46508 3.8463 0.31362 0.13643 -0.52244 0.3302 0.33707 -0.35601 0.32431 0.12041 0.3512 -0.069043 0.36885 0.25168 -0.24517 0.25381 0.1367 -0.31178 -0.6321 -0.25028 -0.38097\r\n",
      "in 0.33042 0.24995 -0.60874 0.10923 0.036372 0.151 -0.55083 -0.074239 -0.092307 -0.32821 0.09598 -0.82269 -0.36717 -0.67009 0.42909 0.016496 -0.23573 0.12864 -1.0953 0.43334 0.57067 -0.1036 0.20422 0.078308 -0.42795 -1.7984 -0.27865 0.11954 -0.12689 0.031744 3.8631 -0.17786 -0.082434 -0.62698 0.26497 -0.057185 -0.073521 0.46103 0.30862 0.12498 -0.48609 -0.0080272 0.031184 -0.36576 -0.42699 0.42164 -0.11666 -0.50703 -0.027273 -0.53285\r\n",
      "a 0.21705 0.46515 -0.46757 0.10082 1.0135 0.74845 -0.53104 -0.26256 0.16812 0.13182 -0.24909 -0.44185 -0.21739 0.51004 0.13448 -0.43141 -0.03123 0.20674 -0.78138 -0.20148 -0.097401 0.16088 -0.61836 -0.18504 -0.12461 -2.2526 -0.22321 0.5043 0.32257 0.15313 3.9636 -0.71365 -0.67012 0.28388 0.21738 0.14433 0.25926 0.23434 0.4274 -0.44451 0.13813 0.36973 -0.64289 0.024142 -0.039315 -0.26037 0.12017 -0.043782 0.41013 0.1796\r\n",
      "\" 0.25769 0.45629 -0.76974 -0.37679 0.59272 -0.063527 0.20545 -0.57385 -0.29009 -0.13662 0.32728 1.4719 -0.73681 -0.12036 0.71354 -0.46098 0.65248 0.48887 -0.51558 0.039951 -0.34307 -0.014087 0.86488 0.3546 0.7999 -1.4995 -1.8153 0.41128 0.23921 -0.43139 3.6623 -0.79834 -0.54538 0.16943 -0.82017 -0.3461 0.69495 -1.2256 -0.17992 -0.057474 0.030498 -0.39543 -0.38515 -1.0002 0.087599 -0.31009 -0.34677 -0.31438 0.75004 0.97065\r\n",
      "'s 0.23727 0.40478 -0.20547 0.58805 0.65533 0.32867 -0.81964 -0.23236 0.27428 0.24265 0.054992 0.16296 -1.2555 -0.086437 0.44536 0.096561 -0.16519 0.058378 -0.38598 0.086977 0.0033869 0.55095 -0.77697 -0.62096 0.092948 -2.5685 -0.67739 0.10151 -0.48643 -0.057805 3.1859 -0.017554 -0.16138 0.055486 -0.25885 -0.33938 -0.19928 0.26049 0.10478 -0.55934 -0.12342 0.65961 -0.51802 -0.82995 -0.082739 0.28155 -0.423 -0.27378 -0.007901 -0.030231\r\n"
     ]
    }
   ],
   "source": [
    "!head \"large_data/glove/glove.6B.50d.txt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 400000 word vectors.\n",
      "Embedding Shape: (134143, 100) \n",
      " Total words found: 56914 \n",
      " Percentage: 42.427856839343086\n",
      "CPU times: user 3.37 s, sys: 195 ms, total: 3.56 s\n",
      "Wall time: 3.62 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "EMBED_SIZE = 100\n",
    "# the embed size should match the file you load glove from\n",
    "embeddings_index = {}\n",
    "f = open('large_data/glove/glove.6B.100d.txt')\n",
    "# save key/array pairs of the embeddings\n",
    "#  the key of the dictionary is the word, the array is the embedding\n",
    "for line in f:\n",
    "    values = line.split()\n",
    "    word = values[0]\n",
    "    coefs = np.asarray(values[1:], dtype='float32')\n",
    "    embeddings_index[word] = coefs\n",
    "f.close()\n",
    "\n",
    "print('Found %s word vectors.' % len(embeddings_index))\n",
    "\n",
    "# now fill in the matrix, using the ordering from the\n",
    "#  keras word tokenizer from before\n",
    "found_words = 0\n",
    "embedding_matrix = np.zeros((len(word_index) + 1, EMBED_SIZE))\n",
    "for word, i in word_index.items():\n",
    "    embedding_vector = embeddings_index.get(word)\n",
    "    if embedding_vector is not None:\n",
    "        # words not found in embedding index will be ALL-ZEROS\n",
    "        embedding_matrix[i] = embedding_vector\n",
    "        found_words = found_words+1\n",
    "\n",
    "print(\"Embedding Shape:\",embedding_matrix.shape, \"\\n\",\n",
    "      \"Total words found:\",found_words, \"\\n\",\n",
    "      \"Percentage:\",100*found_words/embedding_matrix.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import Embedding\n",
    "\n",
    "# save this embedding now\n",
    "embedding_layer = Embedding(len(word_index) + 1,\n",
    "                            EMBED_SIZE,\n",
    "                            weights=[embedding_matrix],# here is the embedding getting saved\n",
    "                            input_length=MAX_ART_LEN,\n",
    "                            trainable=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "# Start with Convolutional (no std vectors)\n",
    "Since all the sequences are now the same length, it is possible to construct a convolutional network on top of the embedded sequence outputs. Once embedded, there really is not too much difference in representation about the data and an image (though the data clearly has a different intuition). We will follow these steps:\n",
    "- use an embedding layer to translate to a dense representation of the entire sequence\n",
    "- train 1D filters to convolve with the output of the embedded sequence\n",
    " - note: 1D filters doesn't mean they are only one dimension, it means the filters are looped through in a single dimension. The size of the filter is [`kernel_size x embed_size`]. It loops through the entire embedding, over a number of words at a time.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_3 (InputLayer)        [(None, 1000)]            0         \n",
      "                                                                 \n",
      " embedding_3 (Embedding)     (None, 1000, 100)         13414300  \n",
      "                                                                 \n",
      " conv1d_3 (Conv1D)           (None, 996, 128)          64128     \n",
      "                                                                 \n",
      " max_pooling1d_2 (MaxPooling  (None, 199, 128)         0         \n",
      " 1D)                                                             \n",
      "                                                                 \n",
      " dropout_8 (Dropout)         (None, 199, 128)          0         \n",
      "                                                                 \n",
      " conv1d_4 (Conv1D)           (None, 195, 128)          82048     \n",
      "                                                                 \n",
      " max_pooling1d_3 (MaxPooling  (None, 39, 128)          0         \n",
      " 1D)                                                             \n",
      "                                                                 \n",
      " dropout_9 (Dropout)         (None, 39, 128)           0         \n",
      "                                                                 \n",
      " conv1d_5 (Conv1D)           (None, 35, 128)           82048     \n",
      "                                                                 \n",
      " max_pooling1d_4 (MaxPooling  (None, 1, 128)           0         \n",
      " 1D)                                                             \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 128)               0         \n",
      "                                                                 \n",
      " dropout_10 (Dropout)        (None, 128)               0         \n",
      "                                                                 \n",
      " dense_6 (Dense)             (None, 128)               16512     \n",
      "                                                                 \n",
      " dense_7 (Dense)             (None, 20)                2580      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 13,661,616\n",
      "Trainable params: 247,316\n",
      "Non-trainable params: 13,414,300\n",
      "_________________________________________________________________\n",
      "None\n",
      "Epoch 1/2\n",
      "71/71 [==============================] - 6s 76ms/step - loss: 3.0151 - acc: 0.0965 - val_loss: 2.5257 - val_acc: 0.2024\n",
      "Epoch 2/2\n",
      "71/71 [==============================] - 4s 62ms/step - loss: 2.3562 - acc: 0.2143 - val_loss: 2.0714 - val_acc: 0.3186\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Conv1D, MaxPooling1D, Flatten, Dense\n",
    "from tensorflow.keras.layers import Dense, Input, Dropout\n",
    "from tensorflow.keras.layers import Embedding\n",
    "\n",
    "EMBED_SIZE = 100  # same size as loaded from GLOVE\n",
    "sequence_input = Input(shape=(MAX_ART_LEN,), dtype='int32')\n",
    "# starting size: 1000\n",
    "embedded_sequences = embedding_layer(sequence_input) # from previous embedding\n",
    "x = Conv1D(128, 5, activation='relu',\n",
    "           kernel_initializer='he_uniform')(embedded_sequences)\n",
    "\n",
    "# after conv, size becomes: 1000-4=996\n",
    "x = MaxPooling1D(5)(x)# after max pool, 996/5 = 199\n",
    "x = Dropout(0.2)(x)\n",
    "x = Conv1D(128, 5, activation='relu',\n",
    "           kernel_initializer='he_uniform')(x)\n",
    "\n",
    "# new size is 195\n",
    "x = MaxPooling1D(5)(x) # after max pool, size is 195/5 = 39\n",
    "x = Dropout(0.2)(x)\n",
    "x = Conv1D(128, 5, activation='relu',\n",
    "           kernel_initializer='he_uniform')(x)\n",
    "\n",
    "# after convolution, size becomes 35 elements long\n",
    "x = MaxPooling1D(35)(x) # this is the size to globally flatten \n",
    "# flattened vector max pools across each of the 35 elements\n",
    "# so vectors is now 128 dimensions (same as num output filters)\n",
    "x = Flatten()(x)\n",
    "x = Dropout(0.2)(x)\n",
    "x = Dense(128, activation='relu',\n",
    "          kernel_initializer='he_uniform')(x)\n",
    "\n",
    "preds = Dense(NUM_CLASSES, activation='softmax',\n",
    "              kernel_initializer='glorot_uniform')(x)\n",
    "\n",
    "model = Model(sequence_input, preds)\n",
    "\n",
    "# if representing as OHE, use categorical_crossentropy\n",
    "# if representing the class as an integer, use sparse_categorical_crossentropy\n",
    "model.compile(loss='categorical_crossentropy', \n",
    "              optimizer='rmsprop',\n",
    "              metrics=['acc'])\n",
    "\n",
    "print(model.summary())\n",
    "\n",
    "histories = []\n",
    "tmp = model.fit(X_train, y_train_ohe, validation_data=(X_test, y_test_ohe),\n",
    "          epochs=2, batch_size=128)\n",
    "histories.append(tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "71/71 [==============================] - 4s 61ms/step - loss: 1.9181 - acc: 0.3299 - val_loss: 1.7370 - val_acc: 0.3814\n",
      "Epoch 2/20\n",
      "71/71 [==============================] - 4s 61ms/step - loss: 1.6153 - acc: 0.4268 - val_loss: 1.4513 - val_acc: 0.5068\n",
      "Epoch 3/20\n",
      "71/71 [==============================] - 4s 60ms/step - loss: 1.3840 - acc: 0.5145 - val_loss: 1.3691 - val_acc: 0.5197\n",
      "Epoch 4/20\n",
      "71/71 [==============================] - 4s 62ms/step - loss: 1.2186 - acc: 0.5657 - val_loss: 1.1029 - val_acc: 0.6394\n",
      "Epoch 5/20\n",
      "71/71 [==============================] - 4s 60ms/step - loss: 1.0360 - acc: 0.6358 - val_loss: 1.0587 - val_acc: 0.6372\n",
      "Epoch 6/20\n",
      "71/71 [==============================] - 4s 60ms/step - loss: 0.9271 - acc: 0.6757 - val_loss: 0.8865 - val_acc: 0.7004\n",
      "Epoch 7/20\n",
      "71/71 [==============================] - 4s 60ms/step - loss: 0.8293 - acc: 0.7090 - val_loss: 0.8306 - val_acc: 0.7123\n",
      "Epoch 8/20\n",
      "71/71 [==============================] - 4s 60ms/step - loss: 0.7293 - acc: 0.7485 - val_loss: 0.8013 - val_acc: 0.7296\n",
      "Epoch 9/20\n",
      "71/71 [==============================] - 4s 61ms/step - loss: 0.6524 - acc: 0.7772 - val_loss: 0.7462 - val_acc: 0.7530\n",
      "Epoch 10/20\n",
      "71/71 [==============================] - 4s 62ms/step - loss: 0.5828 - acc: 0.7987 - val_loss: 0.7504 - val_acc: 0.7539\n",
      "Epoch 11/20\n",
      "71/71 [==============================] - 4s 60ms/step - loss: 0.5090 - acc: 0.8247 - val_loss: 0.7852 - val_acc: 0.7437\n",
      "Epoch 12/20\n",
      "71/71 [==============================] - 4s 61ms/step - loss: 0.4589 - acc: 0.8399 - val_loss: 0.7448 - val_acc: 0.7711\n",
      "Epoch 13/20\n",
      "71/71 [==============================] - 4s 61ms/step - loss: 0.4231 - acc: 0.8579 - val_loss: 0.6651 - val_acc: 0.8016\n",
      "Epoch 14/20\n",
      "71/71 [==============================] - 4s 62ms/step - loss: 0.3757 - acc: 0.8696 - val_loss: 0.6555 - val_acc: 0.7932\n",
      "Epoch 15/20\n",
      "71/71 [==============================] - 4s 62ms/step - loss: 0.3424 - acc: 0.8835 - val_loss: 0.6345 - val_acc: 0.8034\n",
      "Epoch 16/20\n",
      "71/71 [==============================] - 4s 62ms/step - loss: 0.3145 - acc: 0.8945 - val_loss: 0.6535 - val_acc: 0.7923\n",
      "Epoch 17/20\n",
      "71/71 [==============================] - 4s 62ms/step - loss: 0.2785 - acc: 0.9052 - val_loss: 0.7184 - val_acc: 0.7941\n",
      "Epoch 18/20\n",
      "71/71 [==============================] - 5s 64ms/step - loss: 0.2578 - acc: 0.9124 - val_loss: 0.5833 - val_acc: 0.8290\n",
      "Epoch 19/20\n",
      "71/71 [==============================] - 5s 66ms/step - loss: 0.2223 - acc: 0.9277 - val_loss: 0.8916 - val_acc: 0.7852\n",
      "Epoch 20/20\n",
      "71/71 [==============================] - 5s 68ms/step - loss: 0.2209 - acc: 0.9235 - val_loss: 0.5995 - val_acc: 0.8259\n"
     ]
    }
   ],
   "source": [
    "tmp = model.fit(X_train, y_train_ohe, \n",
    "                validation_data=(X_test, y_test_ohe),\n",
    "                epochs=20, batch_size=128)\n",
    "histories.append(tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "71/71 [==============================] - 5s 69ms/step - loss: 0.1817 - acc: 0.9398 - val_loss: 0.6614 - val_acc: 0.8118\n",
      "Epoch 2/20\n",
      "71/71 [==============================] - 5s 77ms/step - loss: 0.1739 - acc: 0.9433 - val_loss: 0.6478 - val_acc: 0.8228\n",
      "Epoch 3/20\n",
      "71/71 [==============================] - 6s 85ms/step - loss: 0.1708 - acc: 0.9456 - val_loss: 0.7077 - val_acc: 0.8224\n",
      "Epoch 4/20\n",
      "71/71 [==============================] - 6s 88ms/step - loss: 0.1506 - acc: 0.9496 - val_loss: 0.8419 - val_acc: 0.7914\n",
      "Epoch 5/20\n",
      "71/71 [==============================] - 6s 88ms/step - loss: 0.1506 - acc: 0.9506 - val_loss: 0.7773 - val_acc: 0.8109\n",
      "Epoch 6/20\n",
      "71/71 [==============================] - 7s 96ms/step - loss: 0.1383 - acc: 0.9536 - val_loss: 0.6780 - val_acc: 0.8246\n",
      "Epoch 7/20\n",
      "71/71 [==============================] - 7s 93ms/step - loss: 0.1179 - acc: 0.9600 - val_loss: 0.6324 - val_acc: 0.8405\n",
      "Epoch 8/20\n",
      "71/71 [==============================] - 7s 98ms/step - loss: 0.1149 - acc: 0.9635 - val_loss: 0.6450 - val_acc: 0.8493\n",
      "Epoch 9/20\n",
      "71/71 [==============================] - 7s 92ms/step - loss: 0.1157 - acc: 0.9622 - val_loss: 0.7992 - val_acc: 0.8153\n",
      "Epoch 10/20\n",
      "71/71 [==============================] - 6s 84ms/step - loss: 0.1051 - acc: 0.9663 - val_loss: 0.8976 - val_acc: 0.8016\n",
      "Epoch 11/20\n",
      "71/71 [==============================] - 6s 82ms/step - loss: 0.1096 - acc: 0.9676 - val_loss: 0.7998 - val_acc: 0.8193\n",
      "Epoch 12/20\n",
      "71/71 [==============================] - 6s 82ms/step - loss: 0.0959 - acc: 0.9706 - val_loss: 0.7103 - val_acc: 0.8343\n",
      "Epoch 13/20\n",
      "71/71 [==============================] - 6s 84ms/step - loss: 0.0925 - acc: 0.9705 - val_loss: 0.7217 - val_acc: 0.8436\n",
      "Epoch 14/20\n",
      "71/71 [==============================] - 6s 89ms/step - loss: 0.0988 - acc: 0.9708 - val_loss: 0.6901 - val_acc: 0.8343\n",
      "Epoch 15/20\n",
      "71/71 [==============================] - 7s 92ms/step - loss: 0.0839 - acc: 0.9724 - val_loss: 0.8417 - val_acc: 0.8250\n",
      "Epoch 16/20\n",
      "71/71 [==============================] - 6s 91ms/step - loss: 0.0996 - acc: 0.9684 - val_loss: 0.7401 - val_acc: 0.8365\n",
      "Epoch 17/20\n",
      "71/71 [==============================] - 6s 86ms/step - loss: 0.0773 - acc: 0.9740 - val_loss: 0.7394 - val_acc: 0.8396\n",
      "Epoch 18/20\n",
      "71/71 [==============================] - 6s 80ms/step - loss: 0.0841 - acc: 0.9753 - val_loss: 0.7210 - val_acc: 0.8387\n",
      "Epoch 19/20\n",
      "71/71 [==============================] - 6s 80ms/step - loss: 0.0819 - acc: 0.9735 - val_loss: 0.6860 - val_acc: 0.8458\n",
      "Epoch 20/20\n",
      "71/71 [==============================] - 6s 80ms/step - loss: 0.0720 - acc: 0.9776 - val_loss: 0.7714 - val_acc: 0.8387\n"
     ]
    }
   ],
   "source": [
    "tmp = model.fit(X_train, y_train_ohe, \n",
    "                validation_data=(X_test, y_test_ohe),\n",
    "               epochs=20, batch_size=128)\n",
    "histories.append(tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import metrics as mt\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# combine all the history from training together\n",
    "combined = dict()\n",
    "for key in ['acc','val_acc','loss','val_loss']:\n",
    "    combined[key] = np.hstack([x.history[key] for x in histories])\n",
    "    \n",
    "# summarize history for accuracy\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.subplot(121)\n",
    "plt.plot(combined['acc'])\n",
    "plt.plot(combined['val_acc'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "\n",
    "# summarize history for loss\n",
    "plt.subplot(122)\n",
    "plt.plot(combined['loss'])\n",
    "plt.plot(combined['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay. So we seem to be maxing out the performance on the validation set. The results are pretty good--I would expect these to be about a top 10 submission on a class-based Kaggle competition. Nice!\n",
    "- https://inclass.kaggle.com/c/cs5740-20-newsgroups-classification/leaderboard"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convolutional with Variance Pooling\n",
    "Add varinace pooling results on 20 news groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_5\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_6 (InputLayer)           [(None, 1000)]       0           []                               \n",
      "                                                                                                  \n",
      " embedding_3 (Embedding)        (None, 1000, 100)    13414300    ['input_6[0][0]']                \n",
      "                                                                                                  \n",
      " conv1d_6 (Conv1D)              (None, 996, 128)     64128       ['embedding_3[1][0]']            \n",
      "                                                                                                  \n",
      " max_pooling1d_5 (MaxPooling1D)  (None, 199, 128)    0           ['conv1d_6[0][0]']               \n",
      "                                                                                                  \n",
      " dropout_21 (Dropout)           (None, 199, 128)     0           ['max_pooling1d_5[0][0]']        \n",
      "                                                                                                  \n",
      " conv1d_7 (Conv1D)              (None, 195, 128)     82048       ['dropout_21[0][0]']             \n",
      "                                                                                                  \n",
      " max_pooling1d_6 (MaxPooling1D)  (None, 39, 128)     0           ['conv1d_7[0][0]']               \n",
      "                                                                                                  \n",
      " dropout_22 (Dropout)           (None, 39, 128)      0           ['max_pooling1d_6[0][0]']        \n",
      "                                                                                                  \n",
      " conv1d_8 (Conv1D)              (None, 35, 128)      82048       ['dropout_22[0][0]']             \n",
      "                                                                                                  \n",
      " global_average_pooling1d_5 (Gl  (None, 128)         0           ['conv1d_8[0][0]']               \n",
      " obalAveragePooling1D)                                                                            \n",
      "                                                                                                  \n",
      " subtract_1 (Subtract)          (None, 35, 128)      0           ['conv1d_8[0][0]',               \n",
      "                                                                  'global_average_pooling1d_5[0][0\n",
      "                                                                 ]']                              \n",
      "                                                                                                  \n",
      " tf.math.pow_1 (TFOpLambda)     (None, 35, 128)      0           ['subtract_1[0][0]']             \n",
      "                                                                                                  \n",
      " global_average_pooling1d_6 (Gl  (None, 128)         0           ['tf.math.pow_1[0][0]']          \n",
      " obalAveragePooling1D)                                                                            \n",
      "                                                                                                  \n",
      " concat_1 (Concatenate)         (None, 256)          0           ['global_average_pooling1d_5[0][0\n",
      "                                                                 ]',                              \n",
      "                                                                  'global_average_pooling1d_6[0][0\n",
      "                                                                 ]']                              \n",
      "                                                                                                  \n",
      " dense_18 (Dense)               (None, 128)          32896       ['concat_1[0][0]']               \n",
      "                                                                                                  \n",
      " dropout_23 (Dropout)           (None, 128)          0           ['dense_18[0][0]']               \n",
      "                                                                                                  \n",
      " dense_19 (Dense)               (None, 20)           2580        ['dropout_23[0][0]']             \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 13,678,000\n",
      "Trainable params: 263,700\n",
      "Non-trainable params: 13,414,300\n",
      "__________________________________________________________________________________________________\n",
      "None\n",
      "Epoch 1/2\n",
      "71/71 [==============================] - 8s 77ms/step - loss: 2.9024 - acc: 0.0924 - val_loss: 2.6272 - val_acc: 0.1745\n",
      "Epoch 2/2\n",
      "71/71 [==============================] - 5s 64ms/step - loss: 2.4048 - acc: 0.2158 - val_loss: 2.0190 - val_acc: 0.3429\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Conv1D, MaxPooling1D, Flatten, Dense\n",
    "from tensorflow.keras.layers import Dense, Input, Dropout\n",
    "from tensorflow.keras.layers import Embedding\n",
    "\n",
    "EMBED_SIZE = 100  # same size as loaded from GLOVE\n",
    "sequence_input = Input(shape=(MAX_ART_LEN,), dtype='int32')\n",
    "# starting size: 1000\n",
    "embedded_sequences = embedding_layer(sequence_input) # from previous embedding\n",
    "x = Conv1D(128, 5, activation='relu',\n",
    "           kernel_initializer='he_uniform')(embedded_sequences)\n",
    "\n",
    "# after conv, size becomes: 1000-4=996\n",
    "x = MaxPooling1D(5)(x)# after max pool, 996/5 = 199\n",
    "x = Dropout(0.2)(x)\n",
    "x = Conv1D(128, 5, activation='relu',\n",
    "           kernel_initializer='he_uniform')(x)\n",
    "\n",
    "# new size is 195\n",
    "x = MaxPooling1D(5)(x) # after max pool, size is 195/5 = 39\n",
    "x = Dropout(0.2)(x)\n",
    "x = Conv1D(128, 5, activation='relu',\n",
    "           kernel_initializer='he_uniform')(x)\n",
    "\n",
    "# after convolution, size becomes 15 elements long\n",
    "# Take the mean of these elements across features, result is 128 elements\n",
    "x_mean = GlobalAveragePooling1D()(x) # this is the size to globally flatten \n",
    "\n",
    "# Take the variance of these elements across features, result is 128 elements\n",
    "x_tmp = Subtract()([x,x_mean])\n",
    "x_std = GlobalAveragePooling1D()(x_tmp**2)\n",
    "\n",
    "x = Concatenate(name='concat_1')([x_mean,x_std])\n",
    "\n",
    "\n",
    "x = Dense(128, activation='relu',\n",
    "          kernel_initializer='he_uniform')(x)\n",
    "\n",
    "x = Dropout(0.2)(x)\n",
    "\n",
    "preds = Dense(NUM_CLASSES, activation='softmax',\n",
    "              kernel_initializer='glorot_uniform')(x)\n",
    "\n",
    "model_xvec = Model(sequence_input, preds)\n",
    "\n",
    "# if representing as OHE, use categorical_crossentropy\n",
    "# if representing the class as an integer, use sparse_categorical_crossentropy\n",
    "model_xvec.compile(loss='categorical_crossentropy', \n",
    "              optimizer='rmsprop',\n",
    "              metrics=['acc'])\n",
    "\n",
    "print(model_xvec.summary())\n",
    "\n",
    "histories = []\n",
    "tmp = model_xvec.fit(X_train, y_train_ohe, validation_data=(X_test, y_test_ohe),\n",
    "          epochs=2, batch_size=128)\n",
    "histories.append(tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "71/71 [==============================] - 4s 62ms/step - loss: 0.2354 - acc: 0.9240 - val_loss: 0.6575 - val_acc: 0.8140\n",
      "Epoch 2/20\n",
      "71/71 [==============================] - 4s 61ms/step - loss: 0.2065 - acc: 0.9334 - val_loss: 1.0458 - val_acc: 0.7583\n",
      "Epoch 3/20\n",
      "71/71 [==============================] - 4s 61ms/step - loss: 0.2053 - acc: 0.9359 - val_loss: 1.2354 - val_acc: 0.7287\n",
      "Epoch 4/20\n",
      "71/71 [==============================] - 4s 61ms/step - loss: 0.1971 - acc: 0.9360 - val_loss: 0.7341 - val_acc: 0.8162\n",
      "Epoch 5/20\n",
      "71/71 [==============================] - 4s 61ms/step - loss: 0.1769 - acc: 0.9441 - val_loss: 0.7792 - val_acc: 0.8157\n",
      "Epoch 6/20\n",
      "71/71 [==============================] - 4s 61ms/step - loss: 0.1757 - acc: 0.9490 - val_loss: 0.6404 - val_acc: 0.8378\n",
      "Epoch 7/20\n",
      "71/71 [==============================] - 4s 61ms/step - loss: 0.1520 - acc: 0.9535 - val_loss: 0.6350 - val_acc: 0.8418\n",
      "Epoch 8/20\n",
      "71/71 [==============================] - 4s 61ms/step - loss: 0.1462 - acc: 0.9550 - val_loss: 0.7498 - val_acc: 0.8210\n",
      "Epoch 9/20\n",
      "71/71 [==============================] - 4s 61ms/step - loss: 0.1621 - acc: 0.9544 - val_loss: 0.6584 - val_acc: 0.8285\n",
      "Epoch 10/20\n",
      "71/71 [==============================] - 4s 61ms/step - loss: 0.1444 - acc: 0.9612 - val_loss: 0.7110 - val_acc: 0.8272\n",
      "Epoch 11/20\n",
      "71/71 [==============================] - 4s 61ms/step - loss: 0.1232 - acc: 0.9631 - val_loss: 0.7162 - val_acc: 0.8414\n",
      "Epoch 12/20\n",
      "71/71 [==============================] - 4s 61ms/step - loss: 0.1226 - acc: 0.9650 - val_loss: 0.6842 - val_acc: 0.8365\n",
      "Epoch 13/20\n",
      "71/71 [==============================] - 4s 61ms/step - loss: 0.1169 - acc: 0.9650 - val_loss: 0.8030 - val_acc: 0.8290\n",
      "Epoch 14/20\n",
      "71/71 [==============================] - 4s 61ms/step - loss: 0.1101 - acc: 0.9692 - val_loss: 0.8661 - val_acc: 0.8330\n",
      "Epoch 15/20\n",
      "71/71 [==============================] - 4s 62ms/step - loss: 0.1035 - acc: 0.9694 - val_loss: 0.8669 - val_acc: 0.8312\n",
      "Epoch 16/20\n",
      "71/71 [==============================] - 4s 61ms/step - loss: 0.1038 - acc: 0.9696 - val_loss: 0.7684 - val_acc: 0.8392\n",
      "Epoch 17/20\n",
      "71/71 [==============================] - 4s 61ms/step - loss: 0.0910 - acc: 0.9719 - val_loss: 0.8358 - val_acc: 0.8171\n",
      "Epoch 18/20\n",
      "71/71 [==============================] - 4s 61ms/step - loss: 0.0896 - acc: 0.9750 - val_loss: 0.7689 - val_acc: 0.8449\n",
      "Epoch 19/20\n",
      "71/71 [==============================] - 4s 62ms/step - loss: 0.0909 - acc: 0.9717 - val_loss: 0.8362 - val_acc: 0.8427\n",
      "Epoch 20/20\n",
      "71/71 [==============================] - 4s 61ms/step - loss: 0.0855 - acc: 0.9760 - val_loss: 0.8335 - val_acc: 0.8325\n"
     ]
    }
   ],
   "source": [
    "tmp = model_xvec.fit(X_train, y_train_ohe, \n",
    "                validation_data=(X_test, y_test_ohe),\n",
    "               epochs=20, batch_size=128)\n",
    "histories.append(tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import metrics as mt\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# combine all the history from training together\n",
    "combined = dict()\n",
    "for key in ['acc','val_acc','loss','val_loss']:\n",
    "    combined[key] = np.hstack([x.history[key] for x in histories])\n",
    "    \n",
    "# summarize history for accuracy\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.subplot(121)\n",
    "plt.plot(combined['acc'])\n",
    "plt.plot(combined['val_acc'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "\n",
    "# summarize history for loss\n",
    "plt.subplot(122)\n",
    "plt.plot(combined['loss'])\n",
    "plt.plot(combined['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transformer, 20 News Groups\n",
    "Now lets run the same experiment with a transformer architecture. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# first, let's replace the original embedding in the xformer\n",
    "# with our custom GloVe embedding\n",
    "\n",
    "class GloveTokenAndPositionEmbedding(Layer):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        # create two embeddings \n",
    "        # one for processing the tokens, pretrained (words)\n",
    "        self.token_emb = Embedding(len(word_index) + 1,\n",
    "                            EMBED_SIZE,\n",
    "                            weights=[embedding_matrix],# here is the embedding getting saved\n",
    "                            input_length=MAX_ART_LEN,\n",
    "                            trainable=False)\n",
    "        \n",
    "        # another embedding for processing the position\n",
    "        self.pos_emb = Embedding(MAX_ART_LEN, \n",
    "                                 EMBED_SIZE, \n",
    "                                 input_length=MAX_ART_LEN,\n",
    "                                 trainable=True\n",
    "                                )\n",
    "\n",
    "    def call(self, x):\n",
    "        # create a static position measure (input)\n",
    "        maxlen = tf.shape(x)[-1]\n",
    "        positions = tf.range(start=0, limit=maxlen, delta=1)\n",
    "        # positions now goes from 0 to 500 (for IMdB) by 1\n",
    "        positions = self.pos_emb(positions)# embed these positions\n",
    "        x = self.token_emb(x) # embed the tokens\n",
    "        return x + positions # add embeddngs to get final embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_3\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_4 (InputLayer)        [(None, 1000)]            0         \n",
      "                                                                 \n",
      " glove_token_and_position_em  (None, 1000, 100)        13514300  \n",
      " bedding (GloveTokenAndPosit                                     \n",
      " ionEmbedding)                                                   \n",
      "                                                                 \n",
      " transformer_block_1 (Transf  (None, 1000, 100)        168232    \n",
      " ormerBlock)                                                     \n",
      "                                                                 \n",
      " global_average_pooling1d_3   (None, 100)              0         \n",
      " (GlobalAveragePooling1D)                                        \n",
      "                                                                 \n",
      " dropout_13 (Dropout)        (None, 100)               0         \n",
      "                                                                 \n",
      " dense_10 (Dense)            (None, 64)                6464      \n",
      "                                                                 \n",
      " dropout_14 (Dropout)        (None, 64)                0         \n",
      "                                                                 \n",
      " dense_11 (Dense)            (None, 20)                1300      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 13,690,296\n",
      "Trainable params: 275,996\n",
      "Non-trainable params: 13,414,300\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "num_heads = 4  # Number of attention heads\n",
    "ff_dim = 32  # Hidden layer size in feed forward network inside transformer\n",
    "\n",
    "inputs = Input(shape=(X_train.shape[1],))\n",
    "x = GloveTokenAndPositionEmbedding()(inputs)\n",
    "x = TransformerBlock(EMBED_SIZE, num_heads, ff_dim)(x)\n",
    "\n",
    "x = GlobalAveragePooling1D()(x)\n",
    "x = Dropout(0.2)(x)\n",
    "x = Dense(64, activation='relu')(x)\n",
    "x = Dropout(0.2)(x)\n",
    "preds = Dense(NUM_CLASSES, activation='softmax',\n",
    "              kernel_initializer='glorot_uniform')(x)\n",
    "\n",
    "model_xformer20 = Model(inputs=inputs, outputs=preds)\n",
    "print(model_xformer20.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/6\n",
      "566/566 [==============================] - 465s 818ms/step - loss: 2.0154 - acc: 0.3530 - val_loss: 1.1482 - val_acc: 0.6372\n",
      "Epoch 2/6\n",
      "566/566 [==============================] - 479s 845ms/step - loss: 1.1036 - acc: 0.6446 - val_loss: 0.9828 - val_acc: 0.6814\n",
      "Epoch 3/6\n",
      "566/566 [==============================] - 486s 858ms/step - loss: 0.9112 - acc: 0.7143 - val_loss: 0.8833 - val_acc: 0.7274\n",
      "Epoch 4/6\n",
      "566/566 [==============================] - 533s 941ms/step - loss: 0.7791 - acc: 0.7484 - val_loss: 0.8442 - val_acc: 0.7477\n",
      "Epoch 5/6\n",
      "566/566 [==============================] - 538s 950ms/step - loss: 0.6896 - acc: 0.7830 - val_loss: 0.8326 - val_acc: 0.7419\n",
      "Epoch 6/6\n",
      "566/566 [==============================] - 536s 945ms/step - loss: 0.6181 - acc: 0.8017 - val_loss: 0.8152 - val_acc: 0.7583\n"
     ]
    }
   ],
   "source": [
    "model_xformer20.compile(loss='categorical_crossentropy', \n",
    "              optimizer='rmsprop',\n",
    "              metrics=['acc'])\n",
    "histories = []\n",
    "tmp = model_xformer20.fit(X_train, y_train_ohe, \n",
    "                validation_data=(X_test, y_test_ohe),\n",
    "               epochs=6, batch_size=16)\n",
    "histories.append(tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/8\n",
      "566/566 [==============================] - 549s 970ms/step - loss: 0.5533 - acc: 0.8216 - val_loss: 0.8129 - val_acc: 0.7605\n",
      "Epoch 2/8\n",
      "566/566 [==============================] - 533s 940ms/step - loss: 0.5089 - acc: 0.8369 - val_loss: 0.9298 - val_acc: 0.7556\n",
      "Epoch 3/8\n",
      "566/566 [==============================] - 487s 860ms/step - loss: 0.4615 - acc: 0.8535 - val_loss: 0.9082 - val_acc: 0.7415\n",
      "Epoch 4/8\n",
      "566/566 [==============================] - 446s 788ms/step - loss: 0.4248 - acc: 0.8662 - val_loss: 0.9459 - val_acc: 0.7490\n",
      "Epoch 5/8\n",
      "566/566 [==============================] - 439s 776ms/step - loss: 0.3960 - acc: 0.8732 - val_loss: 0.8913 - val_acc: 0.7631\n",
      "Epoch 6/8\n",
      "566/566 [==============================] - 478s 844ms/step - loss: 0.3524 - acc: 0.8879 - val_loss: 0.9898 - val_acc: 0.7530\n",
      "Epoch 7/8\n",
      "566/566 [==============================] - 482s 851ms/step - loss: 0.3175 - acc: 0.8967 - val_loss: 1.1232 - val_acc: 0.7490\n",
      "Epoch 8/8\n",
      "566/566 [==============================] - 478s 843ms/step - loss: 0.3006 - acc: 0.9060 - val_loss: 1.1511 - val_acc: 0.7415\n",
      "CPU times: user 3h 47min 15s, sys: 1h 8min 58s, total: 4h 56min 13s\n",
      "Wall time: 1h 4min 52s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "\n",
    "tmp = model_xformer20.fit(X_train, y_train_ohe, \n",
    "                validation_data=(X_test, y_test_ohe),\n",
    "               epochs=8, batch_size=16)\n",
    "histories.append(tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import metrics as mt\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# combine all the history from training together\n",
    "combined = dict()\n",
    "for key in ['acc','val_acc','loss','val_loss']:\n",
    "    combined[key] = np.hstack([x.history[key] for x in histories])\n",
    "    \n",
    "# summarize history for accuracy\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.subplot(121)\n",
    "plt.plot(combined['acc'])\n",
    "plt.plot(combined['val_acc'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "\n",
    "# summarize history for loss\n",
    "plt.subplot(122)\n",
    "plt.plot(combined['loss'])\n",
    "plt.plot(combined['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transformer More Layers!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_4\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_5 (InputLayer)        [(None, 1000)]            0         \n",
      "                                                                 \n",
      " glove_token_and_position_em  (None, 1000, 100)        13514300  \n",
      " bedding_1 (GloveTokenAndPos                                     \n",
      " itionEmbedding)                                                 \n",
      "                                                                 \n",
      " transformer_block_2 (Transf  (None, 1000, 100)        168232    \n",
      " ormerBlock)                                                     \n",
      "                                                                 \n",
      " transformer_block_3 (Transf  (None, 1000, 100)        168232    \n",
      " ormerBlock)                                                     \n",
      "                                                                 \n",
      " global_average_pooling1d_4   (None, 100)              0         \n",
      " (GlobalAveragePooling1D)                                        \n",
      "                                                                 \n",
      " dropout_19 (Dropout)        (None, 100)               0         \n",
      "                                                                 \n",
      " dense_16 (Dense)            (None, 64)                6464      \n",
      "                                                                 \n",
      " dropout_20 (Dropout)        (None, 64)                0         \n",
      "                                                                 \n",
      " dense_17 (Dense)            (None, 20)                1300      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 13,858,528\n",
      "Trainable params: 444,228\n",
      "Non-trainable params: 13,414,300\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "num_heads = 4  # Number of attention heads\n",
    "ff_dim = 32  # Hidden layer size in feed forward network inside transformer\n",
    "\n",
    "inputs = Input(shape=(X_train.shape[1],))\n",
    "x = GloveTokenAndPositionEmbedding()(inputs)\n",
    "x = TransformerBlock(EMBED_SIZE, num_heads, ff_dim)(x)\n",
    "x = TransformerBlock(EMBED_SIZE, num_heads, ff_dim)(x)\n",
    "\n",
    "x = GlobalAveragePooling1D()(x)\n",
    "x = Dropout(0.2)(x)\n",
    "x = Dense(64, activation='relu')(x)\n",
    "x = Dropout(0.2)(x)\n",
    "preds = Dense(NUM_CLASSES, activation='softmax',\n",
    "              kernel_initializer='glorot_uniform')(x)\n",
    "\n",
    "model_xformer20 = Model(inputs=inputs, outputs=preds)\n",
    "print(model_xformer20.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/6\n",
      "566/566 [==============================] - 1049s 2s/step - loss: 2.1491 - acc: 0.3058 - val_loss: 1.5519 - val_acc: 0.4936\n",
      "Epoch 2/6\n",
      "566/566 [==============================] - 1087s 2s/step - loss: 1.2112 - acc: 0.6114 - val_loss: 1.0972 - val_acc: 0.6513\n",
      "Epoch 3/6\n",
      "566/566 [==============================] - 1092s 2s/step - loss: 0.9603 - acc: 0.6912 - val_loss: 0.9444 - val_acc: 0.7070\n",
      "Epoch 4/6\n",
      "566/566 [==============================] - 1131s 2s/step - loss: 0.8337 - acc: 0.7303 - val_loss: 0.8634 - val_acc: 0.7384\n",
      "Epoch 5/6\n",
      "566/566 [==============================] - 1111s 2s/step - loss: 0.7556 - acc: 0.7573 - val_loss: 0.9181 - val_acc: 0.7225\n",
      "Epoch 6/6\n",
      "566/566 [==============================] - 1090s 2s/step - loss: 0.6961 - acc: 0.7763 - val_loss: 0.9286 - val_acc: 0.7260\n"
     ]
    }
   ],
   "source": [
    "model_xformer20.compile(loss='categorical_crossentropy', \n",
    "              optimizer='rmsprop',\n",
    "              metrics=['acc'])\n",
    "histories = []\n",
    "tmp = model_xformer20.fit(X_train, y_train_ohe, \n",
    "                validation_data=(X_test, y_test_ohe),\n",
    "               epochs=6, batch_size=16)\n",
    "histories.append(tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/8\n",
      "566/566 [==============================] - 1136s 2s/step - loss: 0.6445 - acc: 0.7990 - val_loss: 0.8380 - val_acc: 0.7468\n",
      "Epoch 2/8\n",
      "566/566 [==============================] - 1126s 2s/step - loss: 0.6117 - acc: 0.8074 - val_loss: 0.8691 - val_acc: 0.7543\n",
      "Epoch 3/8\n",
      "566/566 [==============================] - 1139s 2s/step - loss: 0.5486 - acc: 0.8259 - val_loss: 0.9242 - val_acc: 0.7486\n",
      "Epoch 4/8\n",
      "566/566 [==============================] - 1136s 2s/step - loss: 0.5133 - acc: 0.8374 - val_loss: 0.8256 - val_acc: 0.7614\n",
      "Epoch 5/8\n",
      "566/566 [==============================] - 1134s 2s/step - loss: 0.4875 - acc: 0.8457 - val_loss: 0.8903 - val_acc: 0.7720\n",
      "Epoch 6/8\n",
      "566/566 [==============================] - 7629s 13s/step - loss: 0.4594 - acc: 0.8573 - val_loss: 0.8951 - val_acc: 0.7623\n",
      "Epoch 7/8\n",
      "566/566 [==============================] - 3525s 6s/step - loss: 0.4246 - acc: 0.8664 - val_loss: 0.8843 - val_acc: 0.7720\n",
      "Epoch 8/8\n",
      "566/566 [==============================] - 1041s 2s/step - loss: 0.3957 - acc: 0.8803 - val_loss: 0.9746 - val_acc: 0.7574\n",
      "CPU times: user 7h 12min 37s, sys: 2h 59min 57s, total: 10h 12min 34s\n",
      "Wall time: 4h 57min 45s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "\n",
    "tmp = model_xformer20.fit(X_train, y_train_ohe, \n",
    "                validation_data=(X_test, y_test_ohe),\n",
    "               epochs=8, batch_size=16)\n",
    "histories.append(tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import metrics as mt\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# combine all the history from training together\n",
    "combined = dict()\n",
    "for key in ['acc','val_acc','loss','val_loss']:\n",
    "    combined[key] = np.hstack([x.history[key] for x in histories])\n",
    "    \n",
    "# summarize history for accuracy\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.subplot(121)\n",
    "plt.plot(combined['acc'])\n",
    "plt.plot(combined['val_acc'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "\n",
    "# summarize history for loss\n",
    "plt.subplot(122)\n",
    "plt.plot(combined['loss'])\n",
    "plt.plot(combined['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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