我正要在 TensorFlow 中学习 LSTM，并尝试实现一个示例，该示例（幸运的是）试图预测一些由简单数学函数生成的时间序列/数字序列。
但是我使用了一种不同的方式来构建训练数据，其动机是使用 LSTM 对视频表示进行无监督学习：
LSTM 未来预测模型
选项 5：
input data label
1,2,3,4 5,6,7,8
2,3,4,5 6,7,8,9
3,4,5,6 7,8,9,10
...
除了这篇论文，我（尝试）从给定的 TensorFlow RNN 示例中获取灵感。我当前的完整解决方案如下所示：
import math
import random
import numpy as np
import tensorflow as tf
LSTM_SIZE = 64
LSTM_LAYERS = 2
BATCH_SIZE = 16
NUM_T_STEPS = 4
MAX_STEPS = 1000
LAMBDA_REG = 5e-4
def ground_truth_func(i, j, t):
return i * math.pow(t, 2) + j
def get_batch(batch_size):
seq = np.zeros([batch_size, NUM_T_STEPS, 1], dtype=np.float32)
tgt = np.zeros([batch_size, NUM_T_STEPS], dtype=np.float32)
for b in xrange(batch_size):
i = float(random.randint(-25, 25))
j = float(random.randint(-100, 100))
for t in xrange(NUM_T_STEPS):
value = ground_truth_func(i, j, t)
seq[b, t, 0] = value
for t in xrange(NUM_T_STEPS):
tgt[b, t] = ground_truth_func(i, j, t + NUM_T_STEPS)
return seq, tgt
# Placeholder for the inputs in a given iteration
sequence = tf.placeholder(tf.float32, [BATCH_SIZE, NUM_T_STEPS, 1])
target = tf.placeholder(tf.float32, [BATCH_SIZE, NUM_T_STEPS])
fc1_weight = tf.get_variable('w1', [LSTM_SIZE, 1], initializer=tf.random_normal_initializer(mean=0.0, stddev=1.0))
fc1_bias = tf.get_variable('b1', [1], initializer=tf.constant_initializer(0.1))
# ENCODER
with tf.variable_scope('ENC_LSTM'):
lstm = tf.nn.rnn_cell.LSTMCell(LSTM_SIZE)
multi_lstm = tf.nn.rnn_cell.MultiRNNCell([lstm] * LSTM_LAYERS)
initial_state = multi_lstm.zero_state(BATCH_SIZE, tf.float32)
state = initial_state
for t_step in xrange(NUM_T_STEPS):
if t_step > 0:
tf.get_variable_scope().reuse_variables()
# state value is updated after processing each batch of sequences
output, state = multi_lstm(sequence[:, t_step, :], state)
learned_representation = state
# DECODER
with tf.variable_scope('DEC_LSTM'):
lstm = tf.nn.rnn_cell.LSTMCell(LSTM_SIZE)
multi_lstm = tf.nn.rnn_cell.MultiRNNCell([lstm] * LSTM_LAYERS)
state = learned_representation
logits_stacked = None
loss = 0.0
for t_step in xrange(NUM_T_STEPS):
if t_step > 0:
tf.get_variable_scope().reuse_variables()
# state value is updated after processing each batch of sequences
output, state = multi_lstm(sequence[:, t_step, :], state)
# output can be used to make next number prediction
logits = tf.matmul(output, fc1_weight) + fc1_bias
if logits_stacked is None:
logits_stacked = logits
else:
logits_stacked = tf.concat(1, [logits_stacked, logits])
loss += tf.reduce_sum(tf.square(logits - target[:, t_step])) / BATCH_SIZE
reg_loss = loss + LAMBDA_REG * (tf.nn.l2_loss(fc1_weight) + tf.nn.l2_loss(fc1_bias))
train = tf.train.AdamOptimizer().minimize(reg_loss)
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
total_loss = 0.0
for step in xrange(MAX_STEPS):
seq_batch, target_batch = get_batch(BATCH_SIZE)
feed = {sequence: seq_batch, target: target_batch}
_, current_loss = sess.run([train, reg_loss], feed)
if step % 10 == 0:
print("@{}: {}".format(step, current_loss))
total_loss += current_loss
print('Total loss:', total_loss)
print('### SIMPLE EVAL: ###')
seq_batch, target_batch = get_batch(BATCH_SIZE)
feed = {sequence: seq_batch, target: target_batch}
prediction = sess.run([logits_stacked], feed)
for b in xrange(BATCH_SIZE):
print("{} -> {})".format(str(seq_batch[b, :, 0]), target_batch[b, :]))
print(" `-> Prediction: {}".format(prediction[0][b]))
此示例输出如下所示：
### SIMPLE EVAL: ###
# [input seq] -> [target prediction]
# `-> Prediction: [model prediction]
[ 33. 53. 113. 213.] -> [ 353. 533. 753. 1013.])
`-> Prediction: [ 19.74548721 28.3149128 33.11489105 35.06603241]
[ -17. -32. -77. -152.] -> [-257. -392. -557. -752.])
`-> Prediction: [-16.38951683 -24.3657589 -29.49801064 -31.58583832]
[ -7. -4. 5. 20.] -> [ 41. 68. 101. 140.])
`-> Prediction: [ 14.14126873 22.74848557 31.29668617 36.73633194]
...
该模型是一个LSTM 自动编码器，每层有 2 层。
不幸的是，正如您在结果中看到的那样，该模型没有正确学习序列。我可能只是在某个地方犯了一个严重的错误，或者 1000-10000 个训练步骤对于 LSTM 来说只是少数。正如我所说，我也刚刚开始正确理解/使用 LSTM。但希望这能给你一些关于实施的灵感。
