Lab12/Lab 12-0 eunseoChoi.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# lab-12-0-rnn-basics-keras-eager"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.5.0\n"
     ]
    }
   ],
   "source": [
    "# setup\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras import Sequential, Model\n",
    "\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# One hot encoding for each char in 'hello'\n",
    "h = [1, 0, 0, 0]\n",
    "e = [0, 1, 0, 0]\n",
    "l = [0, 0, 1, 0]\n",
    "o = [0, 0, 0, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## One cell: 4 (input_dim) in 2 (hidden_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_data: [[[1. 0. 0. 0.]]], shape: (1, 1, 4)\n",
      "outputs: [[[-0.75035316 -0.75911593]]], shape: (1, 1, 2)\n",
      "states: [[-0.75035316 -0.75911593]], shape: (1, 2)\n"
     ]
    }
   ],
   "source": [
    "# One cell RNN input_dim (4) -> output_dim (2)\n",
    "x_data = np.array([[h]], dtype=np.float32)\n",
    "\n",
    "hidden_size = 2\n",
    "cell = layers.SimpleRNNCell(units=hidden_size) # creating SimpleRNNCell\n",
    "rnn = layers.RNN(cell, return_sequences=True, return_state=True) # analogous to tf.nn.dynamic_rnn\n",
    "outputs, states = rnn(x_data)\n",
    "\n",
    "print('x_data: {}, shape: {}'.format(x_data, x_data.shape))\n",
    "print('outputs: {}, shape: {}'.format(outputs, outputs.shape))\n",
    "print('states: {}, shape: {}'.format(states, states.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_data: [[[1. 0. 0. 0.]]], shape: (1, 1, 4)\n",
      "outputs: [[[-0.06409216  0.33891198]]], shape: (1, 1, 2)\n",
      "states: [[-0.06409216  0.33891198]], shape: (1, 2)\n"
     ]
    }
   ],
   "source": [
    "# equivalent to above case\n",
    "rnn = layers.SimpleRNN(units=hidden_size, return_sequences=True,\n",
    "                       return_state=True) # layers.SimpleRNNCell + layers.RNN\n",
    "\n",
    "outputs, states = rnn(x_data)\n",
    "\n",
    "print('x_data: {}, shape: {}'.format(x_data, x_data.shape))\n",
    "print('outputs: {}, shape: {}'.format(outputs, outputs.shape))\n",
    "print('states: {}, shape: {}'.format(states, states.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unfolding to n sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_data: [[[1. 0. 0. 0.]\n",
      "  [0. 1. 0. 0.]\n",
      "  [0. 0. 1. 0.]\n",
      "  [0. 0. 1. 0.]\n",
      "  [0. 0. 0. 1.]]], shape: (1, 5, 4) \n",
      "\n",
      "outputs: [[[ 0.36450136  0.5311629 ]\n",
      "  [-0.58015275  0.14804101]\n",
      "  [-0.6679625  -0.57617736]\n",
      "  [-0.6802451  -0.8822647 ]\n",
      "  [-0.3548778  -0.9426156 ]]], shape: (1, 5, 2) \n",
      "\n",
      "states: [[-0.3548778 -0.9426156]], shape: (1, 2)\n"
     ]
    }
   ],
   "source": [
    "# One cell RNN input_dim (4) -> output_dim (2). sequence: 5\n",
    "x_data = np.array([[h, e, l, l, o]], dtype=np.float32)\n",
    "\n",
    "hidden_size = 2\n",
    "rnn = layers.SimpleRNN(units=2, return_sequences=True, return_state=True)    \n",
    "outputs, states = rnn(x_data)\n",
    "\n",
    "print('x_data: {}, shape: {} \\n'.format(x_data, x_data.shape))\n",
    "print('outputs: {}, shape: {} \\n'.format(outputs, outputs.shape))\n",
    "print('states: {}, shape: {}'.format(states, states.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batching input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_data: [[[1. 0. 0. 0.]\n",
      "  [0. 1. 0. 0.]\n",
      "  [0. 0. 1. 0.]\n",
      "  [0. 0. 1. 0.]\n",
      "  [0. 0. 0. 1.]]\n",
      "\n",
      " [[0. 1. 0. 0.]\n",
      "  [0. 0. 0. 1.]\n",
      "  [0. 0. 1. 0.]\n",
      "  [0. 0. 1. 0.]\n",
      "  [0. 0. 1. 0.]]\n",
      "\n",
      " [[0. 0. 1. 0.]\n",
      "  [0. 0. 1. 0.]\n",
      "  [0. 1. 0. 0.]\n",
      "  [0. 1. 0. 0.]\n",
      "  [0. 0. 1. 0.]]], shape: (3, 5, 4) \n",
      "\n",
      "outputs: [[[-0.25771114 -0.7358563 ]\n",
      "  [-0.8264204  -0.5415024 ]\n",
      "  [-0.481155    0.04128402]\n",
      "  [ 0.1432467   0.1481679 ]\n",
      "  [ 0.6602772   0.10124869]]\n",
      "\n",
      " [[-0.4190176  -0.3212638 ]\n",
      "  [ 0.07257547  0.22877866]\n",
      "  [ 0.56681335 -0.1547552 ]\n",
      "  [ 0.5814704  -0.65340734]\n",
      "  [ 0.2830945  -0.8046877 ]]\n",
      "\n",
      " [[ 0.397449   -0.24186242]\n",
      "  [ 0.45368224 -0.6087327 ]\n",
      "  [-0.5536164  -0.78995585]\n",
      "  [-0.88701844 -0.39155006]\n",
      "  [-0.42034143  0.18182525]]], shape: (3, 5, 2) \n",
      "\n",
      "states: [[ 0.6602772   0.10124869]\n",
      " [ 0.2830945  -0.8046877 ]\n",
      " [-0.42034143  0.18182525]], shape: (3, 2)\n"
     ]
    }
   ],
   "source": [
    "# One cell RNN input_dim (4) -> output_dim (2). sequence: 5, batch 3\n",
    "# 3 batches 'hello', 'eolll', 'lleel'\n",
    "x_data = np.array([[h, e, l, l, o],\n",
    "                   [e, o, l, l, l],\n",
    "                   [l, l, e, e, l]], dtype=np.float32)\n",
    "\n",
    "hidden_size = 2\n",
    "rnn = layers.SimpleRNN(units=2, return_sequences=True, return_state=True)    \n",
    "outputs, states = rnn(x_data)\n",
    "\n",
    "print('x_data: {}, shape: {} \\n'.format(x_data, x_data.shape))\n",
    "print('outputs: {}, shape: {} \\n'.format(outputs, outputs.shape))\n",
    "print('states: {}, shape: {}'.format(states, states.shape))"
   ]
  }
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