# Import MINST data import input_data mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) import tensorflow as tf from tensorflow.models.rnn import rnn, rnn_cell import numpy as np ''' To classify images using a reccurent 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. from aymericdamien code. ''' # Parameters learning_rate = 0.001 training_iters = 100000 batch_size = 128 display_step = 10 # Network Parameters n_input = 28 # MNIST data input (img shape: 28*28) n_steps = 28 # timesteps n_hidden = 128 # hidden layer num of features n_classes = 10 # MNIST total classes (0-9 digits) # tf Graph input x = tf.placeholder("float", [None, n_steps, n_input]) # Tensorflow LSTM cell requires 2x n_hidden length (state & cell) # WHY? bc to make computation faster tf does a single multiplication. So we concatenate the c,h parameters. istate = tf.placeholder("float", [None, 2*n_hidden]) y = tf.placeholder("float", [None, n_classes]) # Define weights weights = { 'hidden': tf.Variable(tf.random_normal([n_input, n_hidden])), # Hidden layer weights 'out': tf.Variable(tf.random_normal([n_hidden, n_classes])) } biases = { 'hidden': tf.Variable(tf.random_normal([n_hidden])), 'out': tf.Variable(tf.random_normal([n_classes])) } def RNN(_X, _istate, _weights, _biases): """ args: _X: input, 28x28 in this case. _istate: initial state. """ # input shape: (batch_size, n_steps, n_input) _X = tf.transpose(_X, [1, 0, 2]) # permute n_steps and batch_size # _X = tf.Print(_X, [tf.shape(_X)], message="transposed: ") # Reshape to prepare input to hidden activation _X = tf.reshape(_X, [-1, n_input]) # (n_steps*batch_size, n_input) # Linear activation this is very optional. # _X = tf.matmul(_X, _weights['hidden']) + _biases['hidden'] # Define a lstm cell with tensorflow lstm_cell = rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0) # Split data because rnn cell needs a list of inputs for the RNN inner loop _X = tf.split(0, n_steps, _X) # n_steps * (batch_size, n_hidden) # Get lstm cell output outputs, states = rnn.rnn(lstm_cell, _X, initial_state=_istate) # Linear activation # Get inner loop last output return tf.matmul(outputs[-1], _weights['out']) + _biases['out'] pred = RNN(x, istate, weights, biases) # Define loss and optimizer cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y)) # Softmax loss optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # Adam Optimizer # Evaluate model correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1)) accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) # Initializing the variables init = tf.initialize_all_variables() # Launch the graph with tf.Session() as sess: sess.run(init) step = 1 # Keep training until reach max iterations while step * batch_size < training_iters: batch_xs, batch_ys = mnist.train.next_batch(batch_size) # Reshape data to get 28 seq of 28 elements batch_xs = batch_xs.reshape((batch_size, n_steps, n_input)) # Fit training using batch data sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys, istate: np.zeros((batch_size, 2*n_hidden))}) if step % display_step == 0: # Calculate batch accuracy acc = sess.run(accuracy, feed_dict={x: batch_xs, y: batch_ys, istate: np.zeros((batch_size, 2*n_hidden))}) # Calculate batch loss loss = sess.run(cost, feed_dict={x: batch_xs, y: batch_ys, istate: np.zeros((batch_size, 2*n_hidden))}) print "Iter " + str(step*batch_size) + ", Minibatch Loss= " + "{:.6f}".format(loss) + \ ", Training Accuracy= " + "{:.5f}".format(acc) step += 1 print "Optimization Finished!" # Calculate accuracy for 256 mnist test images test_len = 256 test_data = mnist.test.images[:test_len].reshape((-1, n_steps, n_input)) test_label = mnist.test.labels[:test_len] print "Testing Accuracy:", sess.run(accuracy, feed_dict={x: test_data, y: test_label, istate: np.zeros((test_len, 2*n_hidden))})