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python - 具有未知 batch_size 的 Keras repeat_elements

问题描述

(?,61,80)我有一个函数，我需要用 2D 张量的大小来做 Keras batch_dot 的大小(40,61)。维度?用于自定义层中的批量大小。在使用 Kerasrepeat_elements时，我们需要指定批量大小以使其成为(batch_size, 40,61). 但是，repeat_elements不适用于?批量大小。

代码是

M1 = K.expand_dims(M,axis=0)
BatchM = K.repeat_elements(x=M1,rep=batch_size,axis=0)
out1 = K.batch_dot(BatchM,Ash1,axes=[2,1])

这M是 size 的二维张量(40,61)。BatchM应该给出(batch_size,40,61)并且Ash1是大小(?,61,80)。

编辑1：

A= Input(shape=(61,80))
M= K.variable(np.random.rand(40,61))
n=1

import tensorflow as tf
M1 = K.expand_dims(M,axis=0)
BatchM = K.repeat_elements(x=M1,rep=tf.shape(A)[0],axis=0)
out1 = K.batch_dot(BatchM,Ash1,axes=[2,1])

此返回错误显示：

Traceback (most recent call last)

 File "<ipython-input-7-edc5ef31181b>", line 3, in <module>
    BatchM = K.repeat_elements(x=M1,rep=tf.shape(A)[0],axis=0)

  File "/home/hanumant/.conda/envs/kerasenv/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py", line 2092, in repeat_elements
    x_rep = [s for s in splits for _ in range(rep)]

  File "/home/hanumant/.conda/envs/kerasenv/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py", line 2092, in <listcomp>
    x_rep = [s for s in splits for _ in range(rep)]

TypeError: 'Tensor' object cannot be interpreted as an integer

标签： pythontensorflowkerasbackend

解决方案

事实上，你不需要repeat_elements用未知的batch_size。您可以将K.dot()andK.permute_dimensions直接用于相同的目的。

def customer_dot(a,b):
    a = K.permute_dimensions(a, (0, 2, 1))  # x = (?,80,61)
    b = K.permute_dimensions(b, (1, 0))  # kernel = (61,40)
    ab_dot = K.permute_dimensions(K.dot(a, b), (0, 2, 1)) # ab_dot = (?,40,80)
    return ab_dot

A = Input(shape=(61,80))
M = K.variable(np.random.rand(40,61))

result = customer_dot(A,M)
print(result.shape)

# print
(?, 40, 80)

并且你可以通过下面的例子看到结果和你的代码操作的结果是一样的。

# print
A = K.constant(np.random.rand(3,2,4))
M = K.constant(np.random.rand(5,2))

M1 = K.expand_dims(M,axis=0)
BatchM = K.repeat_elements(x=M1,rep=K.int_shape(A)[0],axis=0)
out1 = K.batch_dot(BatchM,A,axes=[2,1])
print(K.eval(out1))
result = customer_dot(A,M)
print(K.eval(result))

[[[0.07588554 0.19896106 0.4122516  0.16694324]
  [0.02837059 0.07994501 0.15250334 0.05631477]
  [0.02922964 0.03180532 0.17185953 0.11346529]
  [0.24399586 0.64474815 1.3240533  0.53126353]
  [0.06582426 0.0952256  0.38014278 0.22963922]]

 [[0.05856805 0.31629622 0.37190455 0.15167782]
  [0.02006819 0.12145159 0.1384899  0.0497717 ]
  [0.03729554 0.09602766 0.14768752 0.11432388]
  [0.18666261 1.0198846  1.1952925  0.481425  ]
  [0.07623056 0.2298356  0.33025196 0.22802524]]

 [[0.29545793 0.27023914 0.14775626 0.22487558]
  [0.10839225 0.10083499 0.05140937 0.07595014]
  [0.13047284 0.10567644 0.08779343 0.15208915]
  [0.9481214  0.868726   0.47162086 0.7157058 ]
  [0.28504598 0.23714545 0.18145116 0.30803293]]]
[[[0.07588554 0.19896106 0.4122516  0.16694324]
  [0.02837059 0.07994501 0.15250334 0.05631477]
  [0.02922964 0.03180532 0.17185953 0.11346529]
  [0.24399586 0.64474815 1.3240533  0.53126353]
  [0.06582426 0.0952256  0.38014278 0.22963922]]

 [[0.05856805 0.31629622 0.37190455 0.15167782]
  [0.02006819 0.12145159 0.1384899  0.0497717 ]
  [0.03729554 0.09602766 0.14768752 0.11432388]
  [0.18666261 1.0198846  1.1952925  0.481425  ]
  [0.07623056 0.2298356  0.33025196 0.22802524]]

 [[0.29545793 0.27023914 0.14775626 0.22487558]
  [0.10839225 0.10083499 0.05140937 0.07595014]
  [0.13047284 0.10567644 0.08779343 0.15208915]
  [0.9481214  0.868726   0.47162086 0.7157058 ]
  [0.28504598 0.23714545 0.18145116 0.30803293]]]

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