如何将tensorflow中的dropout应用于多维张量?

请参阅的文档 tf.nn.dropout :

默认情况下,每个元素都是独立保留或删除的。如果 指定了noise\u形状,它必须可以广播到x的形状, 只有具有noise\u shape[i]==shape(x)[i]的尺寸才会 独立决策

所以很简单:

import tensorflow as tf
import numpy as np

data = np.arange(300).reshape((1, 1, 300))
data = np.tile(data, (2, 20, 1))

data_op = tf.convert_to_tensor(data.astype(np.float32))
data_op = tf.nn.dropout(data_op, 0.5, noise_shape=[2, 1, 300])

with tf.Session() as sess:
    data = sess.run(data_op)

for b in range(2):
    for c in range(20):
        assert np.allclose(data[0, 0, :], data[0, c, :])
        assert np.allclose(data[1, 0, :], data[1, c, :])

print((data[0, 0, :] - data[1, 0, :]).sum())
# output something != 0 with high probability#