Question

我得到了以下（伪）代码from doc：

words_in_dataset = tf.placeholder(tf.float32, [time_steps, batch_size, num_features])
...
for current_batch_of_words in words_in_dataset:

但是我们如何迭代占位符呢？

Answer 1

除非你eager execution基本上对动态进行评估，否则你不能这样做。在你的情况下，这只是伪代码，虽然是一个误导性的代码，只是暗示了算法如何超过给定单词数据集中的批次。

Answer 2

占位符仅适用于会话图模式。它们在Eager模式下不可用，没有意义。占位符用于将张量提供给图形，因为您没有构建图形，首先不需要占位符。

就占位符的迭代和伪代码而言，伪代码只显示我们将实现的算法。

要迭代占位符，您将执行以下操作：

import tensorflow as tf
X = tf.placeholder(dtype=tf.float32, shape=[5])
with tf.Session() as sess:
    for i in range(5):
        print(sess.run(X[i], feed_dict={X : [1,2,3,4,5]}))

然后输出将是：

1.0

2.0

3.0

4.0

5.0

切片规则与numpy数组相同。

Answer 3

由于必须在time steps中拆分每个批次，您可以执行以下操作：

for current_batch_of_words in tf.unstack(words_in_dataset,axis=0):

示例代码，

# Set LSTM params

time_steps = 3
num_features = 5
batch_size = 2

#Input placeholder
words_in_dataset = tf.placeholder(tf.float32, [time_steps, batch_size, num_features])

lstm= tf.contrib.rnn.BasicLSTMCell(num_units=10)

# Initial state of the LSTM memory.
hidden_state, current_state = basic_cell.zero_state(batch_size, dtype=tf.float32)
state = hidden_state, current_state

# Create a loop of N LSTM cells, N = time_steps.
outputs = []
for current_batch_of_words in tf.unstack(words_in_dataset,axis=0):

    # The value of state is updated after processing each batch of words.
    output, state= lstm(current_batch_of_words, state)
    outputs.append(output)

什么是Tensorflow中占位符的迭代？

3 个答案: