{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": [
    "from keras.layers import Input, Embedding, LSTM, Dense\n",
    "from keras.models import Model\n",
    "\n",
    "# Headline input: meant to receive sequences of 100 integers, between 1 and 10000.\n",
    "# Note that we can name any layer by passing it a \"name\" argument.\n",
    "main_input = Input(shape=(100,), dtype='int32', name='main_input')\n",
    "\n",
    "# This embedding layer will encode the input sequence\n",
    "# into a sequence of dense 512-dimensional vectors.\n",
    "x = Embedding(output_dim=512, input_dim=10000, input_length=100)(main_input)\n",
    "\n",
    "# A LSTM will transform the vector sequence into a single vector,\n",
    "# containing information about the entire sequence\n",
    "lstm_out = LSTM(32)(x)\n",
    "auxiliary_output = Dense(1, activation='sigmoid', name='aux_output')(lstm_out)\n",
    "auxiliary_input = Input(shape=(5,), name='aux_input')\n",
    "\n",
    "# We stack a deep densely-connected network on top\n",
    "x = Dense(64, activation='relu')(x)\n",
    "x = Dense(64, activation='relu')(x)\n",
    "x = Dense(64, activation='relu')(x)\n",
    "\n",
    "# And finally we add the main logistic regression layer\n",
    "main_output = Dense(1, activation='sigmoid', name='main_output')(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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