RNN Overview
References:
- Long Short Term Memory, Sepp Hochreiter & Jurgen Schmidhuber, Neural Computation 9(8): 1735-1780, 1997.
""" Recurrent Neural Network. A Recurrent Neural Network (LSTM) implementation example using TensorFlow library. This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/) Links: [Long Short Term Memory](http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf) [MNIST Dataset](http://yann.lecun.com/exdb/mnist/). Author: Aymeric Damien Project: https://github.com/aymericdamien/TensorFlow-Examples/ """ from __future__ import print_function import tensorflow as tf from tensorflow.contrib import rnn # Import MNIST data from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) ''' To classify images using a recurrent neural network, we consider every image row as a sequence of pixels. Because MNIST image shape is 28*28px, we will then handle 28 sequences of 28 steps for every sample. ''' # Training Parameters learning_rate = 0.001 training_steps = 10000 batch_size = 128 display_step = 200 # Network Parameters num_input = 28 # MNIST data input (img shape: 28*28) timesteps = 28 # timesteps num_hidden = 128 # hidden layer num of features num_classes = 10 # MNIST total classes (0-9 digits) # tf Graph input X = tf.placeholder("float", [None, timesteps, num_input]) Y = tf.placeholder("float", [None, num_classes]) # Define weights weights = { 'out': tf.Variable(tf.random_normal([num_hidden, num_classes])) } biases = { 'out': tf.Variable(tf.random_normal([num_classes])) } def RNN(x, weights, biases): # Prepare data shape to match `rnn` function requirements # Current data input shape: (batch_size, timesteps, n_input) # Required shape: 'timesteps' tensors list of shape (batch_size, n_input) # Unstack to get a list of 'timesteps' tensors of shape (batch_size, n_input) x = tf.unstack(x, timesteps, 1) # Define a lstm cell with tensorflow lstm_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0) # Get lstm cell output outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32) # Linear activation, using rnn inner loop last output return tf.matmul(outputs[-1], weights['out']) + biases['out'] logits = RNN(X, weights, biases) prediction = tf.nn.softmax(logits) # Define loss and optimizer loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits( logits=logits, labels=Y)) optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate) train_op = optimizer.minimize(loss_op) # Evaluate model (with test logits, for dropout to be disabled) correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1)) accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) # Initialize the variables (i.e. assign their default value) init = tf.global_variables_initializer() # Start training with tf.Session() as sess: # Run the initializer sess.run(init) for step in range(1, training_steps+1): batch_x, batch_y = mnist.train.next_batch(batch_size) # Reshape data to get 28 seq of 28 elements batch_x = batch_x.reshape((batch_size, timesteps, num_input)) # Run optimization op (backprop) sess.run(train_op, feed_dict={X: batch_x, Y: batch_y}) if step % display_step == 0 or step == 1: # Calculate batch loss and accuracy loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x, Y: batch_y}) print("Step " + str(step) + ", Minibatch Loss= " + \ "{:.4f}".format(loss) + ", Training Accuracy= " + \ "{:.3f}".format(acc)) print("Optimization Finished!") # Calculate accuracy for 128 mnist test images test_len = 128 test_data = mnist.test.images[:test_len].reshape((-1, timesteps, num_input)) test_label = mnist.test.labels[:test_len] print("Testing Accuracy:", \ sess.run(accuracy, feed_dict={X: test_data, Y: test_label}))