Keras 支持masking Flatten Keras实现支持masking的Flatten层代码
蕉叉熵 人气:0不知道为什么,我总是需要实现某种骚操作,而这种骚操作往往是Keras不支持的。例如,我有一个padding过的矩阵,那么它一定是带masking的,然后我想要把它Flatten,再输入到Dense层。然而Keras的Flatten层不支持masking。
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.]
补充知识:pytorch中的reshape()、view()、transpose()和flatten()
1、torch.reshape()
reshape()可以由torch.reshape(),也可由torch.Tensor.reshape()调用
其作用是在不改变tensor元素数目的情况下改变tensor的shape
import torch import numpy as np a = np.arange(24) b = a.reshape(4,3,2) print(np.shape(a)) print(b,np.shape(b)) '''结果 (24,) [[[ 0 1] [ 2 3] [ 4 5]] [[ 6 7] [ 8 9] [10 11]] [[12 13] [14 15] [16 17]] [[18 19] [20 21] [22 23]]] (4, 3, 2) '''
2、view()
view()只可以由torch.Tensor.view()来调用
view()和reshape()在效果上是一样的,区别是view()只能操作contiguous的tensor,且view后的tensor和原tensor共享存储,reshape()对于是否contiuous的tensor都可以操作。
3、transpose()
torch.transpose(input, dim0, dim1) -> Tensor
将输入数据input的第dim0维和dim1维进行交换
#官方例子 >>> x = torch.randn(2, 3) >>> x tensor([[ 0.9068, 1.8803, -0.5021], [-0.6576, 0.6334, -0.8961]]) >>> torch.transpose(x, 0, 1) tensor([[ 0.9068, -0.6576], [ 1.8803, 0.6334], [-0.5021, -0.8961]])
4、flatten()
torch.flatten()的输入是tensor
torch.flatten(input, start_dim=0, end_dim=-1) → Tensor
其作用是将输入tensor的第start_dim维到end_dim维之间的数据“拉平”成一维tensor,
#官方例子 >>> t = torch.tensor([[[1, 2], [3, 4]], [[5, 6], [7, 8]]]) >>> torch.flatten(t) tensor([1, 2, 3, 4, 5, 6, 7, 8]) >>> torch.flatten(t, start_dim=1) tensor([[1, 2, 3, 4], [5, 6, 7, 8]])
torch.nn.Flatten()可以理解为一种网络结构,类似Conv2d、Linear。一般放在卷积层和全连接层之间,将卷积层输出“拉平”成一维,
>>> m = torch.nn.Sequential( torch.nn.Conv2d(1, 32, 5, 1, 1), torch.nn.Flatten(), torch.nn.Linear(160,10)) >>> m Sequential( (0): Conv2d(1, 32, kernel_size=(5, 5), stride=(1, 1), padding=(1, 1)) (1): Flatten() (2): Linear(in_features=160, out_features=10, bias=True) )
以上这篇Keras实现支持masking的Flatten层代码就是小编分享给大家的全部内容了,希望能给大家一个参考,也希望大家多多支持。
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