Recurrent Neural Network_LSTM_GRU.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Implementing Recurrent Neural network for dealing with sequential data especially time series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Simple recurrent neural net\n",
    "\n",
    "class LNSimpleRNNCell(tf.keras.layers.Layer):\n",
    "    def __init__(self, units, activation=\"tanh\", **kwargs):\n",
    "        super().__init__(**kwargs)\n",
    "        self.state_size = units\n",
    "        self.output_size = units\n",
    "        self.simple_rnn_cell = tf.keras.layers.SimpleRNNCell(units,\n",
    "                                                          activation=None)\n",
    "        self.layer_norm = tf.keras.layers.LayerNormalization()\n",
    "        self.activation = tf.keras.activations.get(activation)\n",
    "    def call(self, inputs, states):\n",
    "        outputs, new_states = self.simple_rnn_cell(inputs, states)\n",
    "        norm_outputs = self.activation(self.layer_norm(outputs))\n",
    "        return norm_outputs, [norm_outputs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#passing the cell instance\n",
    "\n",
    "rnn_model =  tf.keras.models.Sequential([\n",
    "    tf.keras.layers.RNN(LNSimpleRNNCell(20), return_sequences=True,\n",
    "                     input_shape=[None, 1]),\n",
    "    tf.keras.layers.RNN(LNSimpleRNNCell(20), return_sequences=True),\n",
    "    tf.keras.layers.TimeDistributed(tf.keras.layers.Dense(10))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Tackling short-term memory problem\n",
    "#Long Short-Term Memory (LSTM) cell\n",
    "\n",
    "lstm_model = tf.keras.models.Sequential([\n",
    "    tf.keras.layers.LSTM(20, return_sequences=True, input_shape=[None, 1]),\n",
    "    tf.keras.layers.LSTM(20, return_sequences=True),\n",
    "    tf.keras.layers.TimeDistributed(tf.keras.layers.Dense(10))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#implementing Gated Recurrent Unit (GRU) cell but with 1D convolutional layer solve persistent short-term memory\n",
    "\n",
    "\n",
    "gru_1d_model = tf.keras.models.Sequential([\n",
    "    tf.keras.layers.Conv1D(filters=20, kernel_size=4, strides=2, padding=\"valid\",\n",
    "                        input_shape=[None, 1]),\n",
    "    tf.keras.layers.GRU(20, return_sequences=True),\n",
    "    tf.keras.layers.GRU(20, return_sequences=True),\n",
    "    tf.keras.layers.TimeDistributed(tf.keras.layers.Dense(10))\n",
    "])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'last_time_step_mse' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-8-16adde5a5770>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mgru_1d_model\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"mse\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"adam\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlast_time_step_mse\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m history = model.fit(X_train, Y_train[:, 3::2], epochs=20,\n\u001b[0;32m      3\u001b[0m                     validation_data=(X_valid, Y_valid[:, 3::2]))\n",
      "\u001b[1;31mNameError\u001b[0m: name 'last_time_step_mse' is not defined"
     ]
    }
   ],
   "source": [
    "gru_1d_model.compile(loss=\"mse\", optimizer=\"adam\", metrics=[last_time_step_mse])\n",
    "history = model.fit(X_train, Y_train[:, 3::2], epochs=20,\n",
    "                    validation_data=(X_valid, Y_valid[:, 3::2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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