machine-learning - tensorflow 中 BasicRNNCell 的 input_size 是多少？

Question

根据BasicRNNCell的文档:

__call__(
    inputs,
    state,
    scope=None)

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

似乎input_size在不同的运行中可能不同？据我对RNN的了解，input_size决定了形状(input_size,hidden_​​state_size)的内部权重矩阵W_x，并且它应该是一致的。如果我交替使用 input_size=3 和 input_size=4 运行此单元会怎样？

Answer 1

inputs 是一个二维张量:[batch_size x input_size]。

你是对的，input_size必须对应于RNN单元的num_units。但是 batch_size 可以变化，并且只需与调用的另一个参数 state 相对应。

试试这个代码:

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)

此单元格接受具有两种状态的 x，以及具有 state1 的 y，并且不接受具有任何状态的 z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)