Keras实现支持masking的Flatten层代码示例

本篇文章小编给大家分享一下Keras实现支持masking的Flatten层代码示例，代码介绍的很详细，小编觉得挺不错的，现在分享给大家供大家参考，有需要的小伙伴们可以来看看。

Keras原本Flatten的实现

class Flatten(Layer):
 def __init__(self, **kwargs):
  super(Flatten, self).__init__(**kwargs)
  self.input_spec = InputSpec(min_ndim=3)

 def compute_output_shape(self, input_shape):
  if not all(input_shape[1:]):
   raise ValueError('The shape of the input to "Flatten" '
        'is not fully defined '
        '(got ' + str(input_shape[1:]) + '. '
        'Make sure to pass a complete "input_shape" '
        'or "batch_input_shape" argument to the first '
        'layer in your model.')
  return (input_shape[0], np.prod(input_shape[1:]))

 def call(self, inputs):
  return K.batch_flatten(inputs)

自定义支持masking的实现

事实上，Keras层的mask有时候是需要参与运算的，比如Dense之类的，有时候则只是做某种变换然后传递给后面的层。Flatten属于后者，因为mask总是与input有相同的shape，所以我们要做的就是在compute_mask函数里对mask也做flatten。

from keras import backend as K
from keras.engine.topology import Layer
import tensorflow as tf
import numpy as np

class MyFlatten(Layer):
 def __init__(self, **kwargs):
  self.supports_masking = True
  super(MyFlatten, self).__init__(**kwargs)

 def compute_mask(self, inputs, mask=None):
  if mask==None:
   return mask
  return K.batch_flatten(mask)

 def call(self, inputs, mask=None):
  return K.batch_flatten(inputs)

 def compute_output_shape(self, input_shape):
  return (input_shape[0], np.prod(input_shape[1:]))

正确性检验

from keras.layers import *
from keras.models import Model
from MyFlatten import MyFlatten
from MySumLayer import MySumLayer
from keras.initializers import ones

data = [[1,0,0,0],
  [1,2,0,0],
  [1,2,3,0],
  [1,2,3,4]]

A = Input(shape=[4]) # None * 4
emb = Embedding(5, 3, mask_zero=True, embeddings_initializer=ones())(A) # None * 4 * 3
fla = MyFlatten()(emb) # None * 12
out = MySumLayer(axis=1)(fla) # None * 1

model = Model(inputs=[A], outputs=[out])
print model.predict(data)

输出：

[ 3. 6. 9. 12.]