TensorFlow循环神经网络系列实践教程

《How to build a Recurrent Neural Network in TensorFlow》by Erik Hallström

#coding: utf-8#Env:#tensorflow.__version__: 1.1.0#Linux#python2.7from __future__ import divisionimport numpy as npimport tensorflow as tfimport matplotlib.pyplot as pltnum_epochs = 100total_series_length = 50000truncated_backprop_length = 15state_size = 4num_classes = 2echo_step = 3batch_size = 5num_batches = total_series_length//batch_size//truncated_backprop_lengthdef generateData():    x = np.array(np.random.choice(2, total_series_length, p=[.5, .5]))    y = np.roll(x, echo_step)    y[0:echo_step] = 0    x = x.reshape(batch_size, -1)    y = y.reshape(batch_size, -1)    return x, ybatchX_placeholder = tf.placeholder(tf.float32, [batch_size, truncated_backprop_length])batchY_placeholder = tf.placeholder(tf.int32, [batch_size, truncated_backprop_length])init_state = tf.placeholder(tf.float32, [batch_size, state_size])W = tf.Variable(np.random.rand(state_size+1, state_size), dtype=tf.float32)b = tf.Variable(np.zeros((1, state_size)), dtype=tf.float32)W2 = tf.Variable(np.random.rand(state_size, num_classes), dtype=tf.float32)b2 = tf.Variable(np.zeros((1, num_classes)), dtype=tf.float32)inputs_series = tf.unstack(batchX_placeholder, axis=1)labels_series = tf.unstack(batchY_placeholder, axis=1)current_state = init_statestates_series = []for current_input in inputs_series:    current_input = tf.reshape(current_input, [batch_size, 1])    input_and_state_concatenated = tf.concat([current_input, current_state], 1)    next_state = tf.tanh(tf.matmul(input_and_state_concatenated, W) + b)    states_series.append(next_state)    current_state = next_statelogits_series = [tf.matmul(state, W2) + b2 for state in states_series]predictions_series = [tf.nn.softmax(logits) for logits in logits_series]losses = [tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=labels)           for logits, labels in zip(logits_series, labels_series)]total_loss = tf.reduce_mean(losses)train_step = tf.train.AdagradOptimizer(.3).minimize(total_loss)def plot(loss_list, predictions_series, batchX, batchY):    plt.subplot(2,3,1)    plt.cla()    plt.plot(loss_list)    for batch_series_idx in range(5):        one_hot_output_series = np.array(predictions_series)[:, batch_series_idx, :]        single_output_series = np.array([(1 if out[0] < .5 else 0)for out in one_hot_output_series])        plt.subplot(2, 3, batch_series_idx+2)        plt.cla()        plt.axis([0, truncated_backprop_length, 0, 2])        left_offset = range(truncated_backprop_length)        plt.bar(left_offset, batchX[batch_series_idx, :], width=1, color='blue')        plt.bar(left_offset, batchY[batch_series_idx, :]*.5, width=1, color='red')        plt.bar(left_offset, single_output_series * .3, width=1, color='green')        plt.draw()        plt.pause(0.0001)with tf.Session() as sess:    sess.run(tf.global_variables_initializer())    plt.ion()    plt.figure()    plt.show()    loss_list = []    for epoch_idx in range(num_epochs):        x, y = generateData()        _current_state = np.zeros((batch_size, state_size))        print 'New data, epoch', epoch_idx        for batch_idx in range(num_batches):            start_idx = batch_idx * truncated_backprop_length            end_idx = start_idx + truncated_backprop_length            batchX = x[: ,start_idx:end_idx]            batchY = y[: ,start_idx:end_idx]            _total_loss, _train_step, _current_state, _predictions_series = sess.run(            [total_loss, train_step, current_state, predictions_series],            feed_dict = {batchX_placeholder: batchX,                         batchY_placeholder: batchY,                         init_state: _current_state})            loss_list.append(_total_loss)            if batch_idx % 100 == 0:                print 'Step', batch_idx, 'Loss', _total_loss                plot(loss_list, _predictions_series, batchX, batchY)plt.ioff()plt.show()