Question

根据BasicRNNCell的文件：

__call__(
    inputs,
    state,
    scope=None)

Args:
inputs: 2-D tensor with shape [batch_size x input_size].

input_size似乎在不同的运行中有所不同？据我所知，RN input_size确定内部权重矩阵W_x的形状为(input_size, hidden_state_size)，它应该是一致的。如果我交替使用input_size=3和input_size=4运行此单元格会怎样？

Answer 1

inputs是二维张量：[batch_size x input_size]。

您是对的，input_size必须与RNN小区的num_units对应。但batch_size可能会有所不同，只需与调用的另一个参数state相对应。

试试这段代码：

import tensorflow as tf
from tensorflow.contrib.rnn import BasicRNNCell

dim = 10
x = tf.placeholder(tf.float32, shape=[None, dim])
y = tf.placeholder(tf.float32, shape=[4, dim])
z = tf.placeholder(tf.float32, shape=[None, dim + 1])
print('x, y, z:', x.shape, y.shape, z.shape)

cell = BasicRNNCell(dim)
state1 = cell.zero_state(batch_size=4, dtype=tf.float32)
state2 = cell.zero_state(batch_size=8, dtype=tf.float32)

out1, out2 = cell(x, state1)
print(out1.shape, out2.shape)

out1, out2 = cell(x, state2)
print(out1.shape, out2.shape)

out1, out2 = cell(y, state1)
print(out1.shape, out2.shape)

这是输出：

x, y, z: (?, 10) (4, 10) (?, 11)
(4, 10) (4, 10)
(8, 10) (8, 10)
(4, 10) (4, 10)

此单元格接受两个状态x y state1，并且不接受z任何状态。以下两个调用都会导致错误：

out1, out2 = cell(y, state2)     # ERROR: dimensions mismatch
print(out1.shape, out2.shape)

out1, out2 = cell(z, state1)     # ERROR: dimensions mismatch
print(out1.shape, out2.shape)

张量流中BasicRNNCell中的input_size是什么？

1 个答案: