RNNtest.py

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data


# data input
mnist = input_data.read_data_sets('mnist_data', one_hot=True)

# hyperparameters
lr = 0.001
training_iters = 100000
batch_size = 128
display_step = 10

n_inputs = 28  # MNIST data input (img shape: 28*28)
n_steps = 28   # time steps
n_hidden_unis = 128  # neurons in hidden layer
n_classes = 10 # MNIST classes (0-9 digits)

# tf Grapg input
x = tf.placeholder(tf.float32, [None, n_steps, n_inputs])
y = tf.placeholder(tf.float32, [None, n_classes])

# define weights
weights = {
    #(28, 128)
    'in': tf.Variable(tf.random_normal([n_inputs, n_hidden_unis])),
    #(128, 10)
    'out': tf.Variable(tf.random_normal([n_hidden_unis, n_classes]))
}
biases = {
    #(128,)
    'in': tf.Variable(tf.constant(0.1, shape=[n_hidden_unis,])),
    #(10,)
    'out': tf.Variable(tf.constant(0.1, shape=[n_classes,]))
}

def RNN(X, weights, biases):
    # hidden layer for input to cell
    #######################################
    #X(128 batch, 28 steps, 28 inputs)
    # ==>(128*28,28 inputs)
    X = tf.reshape(X, [-1, n_inputs])
    # X_in ==> (128batch*28steps, 128 hidden_unis)
    X_in = tf.matmul(X, weights['in'] )+ biases['in']
    # ==> (128, 28, 128)
    X_in = tf.reshape(X_in, [-1, n_steps, n_hidden_unis ])

    # cell
    ########################################
    lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(n_hidden_unis, forget_bias= 1.0, state_is_tuple= True)
    # lstm cell os divided into two parts(c_state, m_state)
    _init_state = lstm_cell.zero_state(batch_size, tf.float32)

    outputs, states = tf.nn.dynamic_rnn(lstm_cell, X_in, initial_state=_init_state, time_major=False)

    # hidden layer for output as the final results
    ##############################################
    results = tf.matmul(states[1], weights['out']) + biases['out']
    # outputs from (128 batch_size, 28 steps, 128 hidden_unis) ==> (28, 128, 128)
    #outputs = tf.unpack(tf.transpose(outputs, [1,0,2]))
    #results = tf.matmul(outputs[-1], weights['out']) + biases['out']
    return results


pred = RNN(x, weights, biases)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))
train_op = tf.train.AdamOptimizer(lr).minimize(cost)

correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))


init = tf.initialize_all_variables()
with tf.Session() as sess:
    sess.run(init)
    step = 0
    while step*batch_size<training_iters:
        batch_xs, batch_ys = mnist.train.next_batch(batch_size)
        batch_xs = batch_xs.reshape([batch_size, n_steps, n_inputs])
        sess.run([train_op], feed_dict={x: batch_xs, y: batch_ys})
        if step%20 == 0:
            print(sess.run(accuracy, feed_dict={x: batch_xs, y: batch_ys}))
        step += 1