上一篇中构建了简单的RNN网络，这里介绍如何利用TensorFlow（r1.1）构建LSTM网络。与RNN相比，LSTM增加了memory单元，用于动态记忆和忘记网络状态。
建模部分
与RNN不同之处在于，LSTM有两个隐含状态，所以需要在state部分需要修改为：
cell_state = tf.placeholder(tf.float32, [batch_size, state_size])
hidden_state = tf.placeholder(tf.float32, [batch_size, state_size])
init_state = tf.contrib.rnn.LSTMStateTuple(cell_state, hidden_state)
cell单元的声明也修改为LSTM：
cell = tf.contrib.rnn.BasicLSTMCell(state_size, state_is_tuple=True)
logits_series 的计算也需要同时考虑两个隐含状态：
W1 = tf.Variable(np.random.rand(state_size, num_classes),dtype=tf.float32)
b1 = tf.Variable(np.zeros((1,num_classes)), 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)
logits_series = []
for state in states_series:
logits_series.append(tf.matmul(state[0], W1) + tf.matmul(state[1], W2) + b2)
全部代码
from __future__ import print_function, division
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
num_epochs = 100
total_series_length = 50000
truncated_backprop_length = 15
state_size = 4
num_classes = 2
echo_step = 3
batch_size = 5
num_batches = total_series_length//batch_size//truncated_backprop_length
def generateData():
x = np.array(np.random.choice(2, total_series_length, p=[0.5, 0.5]))
y = np.roll(x, echo_step)
y[0:echo_step] = 0
x = x.reshape((batch_size, -1)) # The first index changing slowest, subseries as rows
y = y.reshape((batch_size, -1))
return (x, y)
batchX_placeholder = tf.placeholder(tf.float32, [batch_size, truncated_backprop_length])
batchY_placeholder = tf.placeholder(tf.int32, [batch_size, truncated_backprop_length])
# Unpack columns
inputs_series = tf.split(batchX_placeholder, truncated_backprop_length, axis=1)
labels_series = tf.unstack(batchY_placeholder, axis=1)
# Forward passes
_current_cell_state = np.zeros((batch_size, state_size))
_current_hidden_state = np.zeros((batch_size, state_size))
cell_state = tf.placeholder(tf.float32, [batch_size, state_size])
hidden_state = tf.placeholder(tf.float32, [batch_size, state_size])
init_state = tf.contrib.rnn.LSTMStateTuple(cell_state, hidden_state)
cell = tf.contrib.rnn.BasicLSTMCell(state_size, state_is_tuple=True)
current_state = init_state
states_series = []
for current_input in inputs_series:
with tf.variable_scope('rnn') as vs:
try:
output, current_state = cell(current_input, current_state)
except:
vs.reuse_variables()
output, current_state = cell(current_input, current_state)
states_series.append(current_state)
W1 = tf.Variable(np.random.rand(state_size, num_classes),dtype=tf.float32)
b1 = tf.Variable(np.zeros((1,num_classes)), 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)
logits_series = []
for state in states_series:
logits_series.append(tf.matmul(state[0], W1) + tf.matmul(state[1], W2) + b2)
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(0.3).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] < 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, :] * 0.5, width=1, color="red")
plt.bar(left_offset, single_output_series * 0.3, 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_cell_state = np.zeros((batch_size, state_size))
_current_hidden_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,
cell_state: _current_cell_state,
hidden_state: _current_hidden_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()
参考文献：
网友评论