tensorflow：tf.split 被赋予奇怪的参数

Question

这是代码(from here):

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
from tensorflow.python.ops import rnn, rnn_cell
mnist = input_data.read_data_sets("/tmp/data/", one_hot = True)

hm_epochs = 3
n_classes = 10
batch_size = 128
chunk_size = 28
n_chunks = 28
rnn_size = 128


x = tf.placeholder('float', [None, n_chunks,chunk_size])
y = tf.placeholder('float')
def recurrent_neural_network(x):
    layer = {'weights':tf.Variable(tf.random_normal([rnn_size,n_classes])),
             'biases':tf.Variable(tf.random_normal([n_classes]))}

    x = tf.transpose(x, [1,0,2])
    x = tf.reshape(x, [-1, chunk_size])
    x = tf.split(0, n_chunks, x)

    lstm_cell = rnn_cell.BasicLSTMCell(rnn_size,state_is_tuple=True)
    outputs, states = rnn.rnn(lstm_cell, x, dtype=tf.float32)

    output = tf.matmul(outputs[-1],layer['weights']) + layer['biases']

    return output
def train_neural_network(x):
    prediction = recurrent_neural_network(x)
    cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(prediction,y) )
    optimizer = tf.train.AdamOptimizer().minimize(cost)


    with tf.Session() as sess:
        sess.run(tf.initialize_all_variables())

        for epoch in range(hm_epochs):
            epoch_loss = 0
            for _ in range(int(mnist.train.num_examples/batch_size)):
                epoch_x, epoch_y = mnist.train.next_batch(batch_size)
                epoch_x = epoch_x.reshape((batch_size,n_chunks,chunk_size))

                _, c = sess.run([optimizer, cost], feed_dict={x: epoch_x, y: epoch_y})
                epoch_loss += c

            print('Epoch', epoch, 'completed out of',hm_epochs,'loss:',epoch_loss)

        correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))

        accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
        print('Accuracy:',accuracy.eval({x:mnist.test.images.reshape((-1, n_chunks, chunk_size)), y:mnist.test.labels}))

train_neural_network(x)

我在理解 x = tf.split(0, n_chunks, x) 时遇到问题，更具体地说是第三个参数（x-输入）。按 documenation 这应该是轴……但那不可能，对吧？ x 不是一维的吗？如果这是微不足道的，我深表歉意，我是初学者，无法理解。也许这只是形式，但如果是我不明白它是如何工作的...

Answer 1

By documenation this should be axis...but that can't be, right?

从tensorflow 1.0开始，tf.split的第一个参数不是轴，但我假设代码是使用第一个参数确实是轴的旧版本编写的。

Isn't x one dimensional?

x 不是一维的。在调用 tf.split 之前，x 使用以下语句从 3 维重塑为 2 维：

x = tf.reshape(x, [-1, chunk_size])

该语句将 x 重塑为具有二维的张量：第二维的大小为 chunk_size 并且推断出第一维的大小（即 -1 在这里表示）。

tensorflow：tf.split 被赋予奇怪的参数

tensorflow: tf.split is given weird parameters

neural-network

deep-learning

tensorflow

recurrent-neural-network

rnn