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README update
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‎README.rst‎

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********************
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`TensorFlow World`_
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`TensorFlow Course`_
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********************
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.. image:: https://travis-ci.org/astorfi/TensorFlow-World.svg?branch=master
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:target: https://travis-ci.org/astorfi/TensorFlow-World
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+----+-----------------------------------+-----------------------------------------------------------------------------------------------+----------------------------------------------+
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| 10 | *Autoencoders* | `Undercomplete Autoencoder <udercompleteautoencodercode_>`_ | `Documentation <Documentationauto_>`_ |
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+----+-----------------------------------+-----------------------------------------------------------------------------------------------+----------------------------------------------+
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| 10 | *RNN* | `RNN`_ / `IPython <RNNIpython_>`_ | |
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+----+-----------------------------------+-----------------------------------------------------------------------------------------------+----------------------------------------------+
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.. ~~~~~~~~~~~~
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.. **Welcome**
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.. | 3 | `Linear SVM`_ | |
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.. +---+---------------------------------------------+----------------------------------------+
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.. | 4 | `MultiClass Kernel SVM`_ | |
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.. +---+---------------------------------------------+----------------------------------------+
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.. ~~~~~~~~~~~~~~~~~~~
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.. **Neural Networks**
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.. _udercompleteautoencodercode: codes/3-neural_networks/undercomplete-autoencoder
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.. _Documentationauto: docs/tutorials/3-neural_network/autoencoder
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.. _RNN: codes/codes/3-neural_networks/recurrent-neural-networks/code/rnn.py
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.. _RNNIpython: codes/codes/3-neural_networks/recurrent-neural-networks/code/rnn.py
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.. +---+---------------------------------------------+----------------------------------------+
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.. | # | Source Code | |
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.. +===+=============================================+========================================+

‎codes/3-neural_networks/recurrent-neural-networks/code/rnn.ipynb‎

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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"name": "Untitled0.ipynb",
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"version": "0.3.2",
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"provenance": [],
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"collapsed_sections": []
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"accelerator": "GPU"
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},
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"cells": [
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{
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"metadata": {
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"id": "UBpbr4JZKYTz",
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"colab_type": "text"
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},
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"cell_type": "markdown",
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"source": [
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"# MNIST Digit Classification Using Recurrent Neural Networks"
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]
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},
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{
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"metadata": {
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"id": "CxeZAiQkLMNR",
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"colab_type": "code",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 704
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},
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"outputId": "77ad5785-4e41-40d0-896e-ed9e6873b4ae"
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},
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"cell_type": "code",
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"source": [
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"import tensorflow as tf\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import argparse\n",
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"\n",
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"######################\n",
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"# Optimization Flags #\n",
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"######################\n",
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"\n",
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"learning_rate = 0.001 # initial learning rate\n",
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"seed = 111\n",
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"\n",
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"##################\n",
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"# Training Flags #\n",
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"##################\n",
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"batch_size = 128 # Batch size for training\n",
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"num_epoch = 10 # Number of training iterations\n",
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"\n",
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"###############\n",
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"# Model Flags #\n",
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"###############\n",
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"hidden_size = 128 # Number of neurons for RNN hodden layer\n",
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"\n",
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"# Reset the graph set the random numbers to be the same using \"seed\"\n",
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"tf.reset_default_graph()\n",
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"tf.set_random_seed(seed)\n",
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"np.random.seed(seed)\n",
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"\n",
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"# Divide 28x28 images to rows of data to feed to RNN as sequantial information\n",
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"step_size = 28\n",
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"input_size = 28\n",
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"output_size = 10\n",
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"\n",
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"# Input tensors\n",
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"X = tf.placeholder(tf.float32, [None, step_size, input_size])\n",
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"y = tf.placeholder(tf.int32, [None])\n",
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"\n",
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"# Rnn\n",
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"cell = tf.nn.rnn_cell.BasicRNNCell(num_units=hidden_size)\n",
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"output, state = tf.nn.dynamic_rnn(cell, X, dtype=tf.float32)\n",
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"\n",
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"# Forward pass and loss calcualtion\n",
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"logits = tf.layers.dense(state, output_size)\n",
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"cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits)\n",
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"loss = tf.reduce_mean(cross_entropy)\n",
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"\n",
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"# optimizer\n",
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"optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss)\n",
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"\n",
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"# Prediction\n",
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"prediction = tf.nn.in_top_k(logits, y, 1)\n",
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"accuracy = tf.reduce_mean(tf.cast(prediction, tf.float32))\n",
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"\n",
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"# input data\n",
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"from tensorflow.examples.tutorials.mnist import input_data\n",
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"mnist = input_data.read_data_sets(\"MNIST_data/\")\n",
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"\n",
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"# Process MNIST\n",
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"X_test = mnist.test.images # X_test shape: [num_test, 28*28]\n",
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"X_test = X_test.reshape([-1, step_size, input_size])\n",
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"y_test = mnist.test.labels\n",
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"\n",
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"# initialize the variables\n",
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"init = tf.global_variables_initializer()\n",
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"\n",
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"# Empty list for tracking\n",
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"loss_train_list = []\n",
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"acc_train_list = []\n",
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"\n",
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"# train the model\n",
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"with tf.Session() as sess:\n",
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" sess.run(init)\n",
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" n_batches = mnist.train.num_examples // batch_size\n",
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" for epoch in range(num_epoch):\n",
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" for batch in range(n_batches):\n",
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" X_train, y_train = mnist.train.next_batch(batch_size)\n",
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" X_train = X_train.reshape([-1, step_size, input_size])\n",
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" sess.run(optimizer, feed_dict={X: X_train, y: y_train})\n",
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" loss_train, acc_train = sess.run(\n",
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" [loss, accuracy], feed_dict={X: X_train, y: y_train})\n",
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" loss_train_list.append(loss_train)\n",
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" acc_train_list.append(acc_train)\n",
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" print('Epoch: {}, Train Loss: {:.3f}, Train Acc: {:.3f}'.format(\n",
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" epoch + 1, loss_train, acc_train))\n",
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" loss_test, acc_test = sess.run(\n",
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" [loss, accuracy], feed_dict={X: X_test, y: y_test})\n",
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" print('Test Loss: {:.3f}, Test Acc: {:.3f}'.format(loss_test, acc_test))\n"
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],
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"execution_count": 2,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"WARNING:tensorflow:From <ipython-input-2-dc6c3a05d58e>:56: read_data_sets (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
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"Instructions for updating:\n",
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"Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n",
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"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:260: maybe_download (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
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"Instructions for updating:\n",
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"Please write your own downloading logic.\n",
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"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/learn/python/learn/datasets/base.py:252: _internal_retry.<locals>.wrap.<locals>.wrapped_fn (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
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"Instructions for updating:\n",
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"Please use urllib or similar directly.\n",
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"Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.\n",
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"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:262: extract_images (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
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"Instructions for updating:\n",
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"Please use tf.data to implement this functionality.\n",
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"Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
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"Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.\n",
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"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:267: extract_labels (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
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"Instructions for updating:\n",
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"Please use tf.data to implement this functionality.\n",
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"Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
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"Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.\n",
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"Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
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"Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.\n",
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"Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n",
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"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:290: DataSet.__init__ (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
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"Instructions for updating:\n",
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"Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n",
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"Epoch: 1, Train Loss: 0.279, Train Acc: 0.898\n",
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"Epoch: 2, Train Loss: 0.124, Train Acc: 0.969\n",
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"Epoch: 3, Train Loss: 0.145, Train Acc: 0.977\n",
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"Epoch: 4, Train Loss: 0.231, Train Acc: 0.914\n",
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"Epoch: 5, Train Loss: 0.088, Train Acc: 0.961\n",
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"Epoch: 6, Train Loss: 0.104, Train Acc: 0.961\n",
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"Epoch: 7, Train Loss: 0.174, Train Acc: 0.961\n",
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"Epoch: 8, Train Loss: 0.099, Train Acc: 0.961\n",
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"Epoch: 9, Train Loss: 0.075, Train Acc: 0.961\n",
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"Epoch: 10, Train Loss: 0.081, Train Acc: 0.969\n",
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"Test Loss: 0.124, Test Acc: 0.965\n"
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],
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"name": "stdout"
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}
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]
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},
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{
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"metadata": {
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"id": "nkPppIILLN5Z",
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"colab_type": "code",
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"colab": {}
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},
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"cell_type": "code",
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"source": [
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""
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],
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"execution_count": 0,
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"outputs": []
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}
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]
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}

‎codes/3-neural_networks/recurrent-neural-networks/code/rnn.py‎

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'''
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MNIST using Recurrent Neural Network to predict handwritten digits
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In this tutorial, I am going to demonstrate how to use recurrent neural
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network to predict the famous handwritten digits "MNIST".
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The MNIST dataset consists:
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mnist.train: 55000 training images
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mnist.validation: 5000 validation images
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mnist.test: 10000 test images
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Each image is 28 pixels (rows) by 28 pixels (cols).
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'''
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import tensorflow as tf
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import matplotlib.pyplot as plt
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# Optimization Flags #
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######################
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parser.add_argument('--learning_rate', default=0.001, type=float, help='initial learning rate')
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parser.add_argument('--seed', default=111, type=int, help='seed')
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tf.app.flags.DEFINE_float('learning_rate', default=0.001, help='initial learning rate')
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tf.app.flags.DEFINE_integer('seed', default=111, help='seed')
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# Training Flags #
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##################
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parser.add_argument('--batch_size', default=128, type=int, help='Batch size for training')
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parser.add_argument('--num_epoch', default=10, type=int, help='Number of training iterations')
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parser.add_argument('--batch_per_log', default=10, type=int, help='Print the log at what number of batches?')
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tf.app.flags.DEFINE_integer('batch_size', default=128, help='Batch size for training')
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tf.app.flags.DEFINE_integer('num_epoch', default=10, help='Number of training iterations')
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tf.app.flags.DEFINE_integer('batch_per_log', default=10, help='Print the log at what number of batches?')
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# Model Flags #
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parser.add_argument('--hidden_size', default=128, type=int, help='Number of neurons for RNN hodden layer')
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tf.app.flags.DEFINE_integer('hidden_size', default=128, help='Number of neurons for RNN hodden layer')
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# Add all arguments to parser
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args = parser.parse_args()
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# Store all elemnts in FLAG structure!
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args = tf.app.flags.FLAGS
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# Reset the graph set the random numbers to be the same using "seed"

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