keras 高级技巧--------重写Layer

keras 高级技巧--------重写Layer

huml126 2019-03-21 21:03:17 609 收藏 1
分类专栏： DL
版权
keras 高级技巧
概述
在使用keras快速建模时，会遇到现有的库无法实现，需要自己做一些定制，有两种方式：

不涉及参数weights的优化训练，可以使用keras.layers.Lambda()
使用类支出keras.layers.Layer
build(input_shape): this is where you will define your weights. This method must set self.built = True at the end, which can be done by calling super([Layer], self).build().
call(x): this is where the layer’s logic lives. Unless you want your layer to support masking, you only have to care about the first argument passed to call: the input tensor.
compute_output_shape(input_shape): in case your layer modifies the shape of its input, you should specify here the shape transformation logic. This allows Keras to do automatic shape inference.
环境依赖
Python 3.6
TensorFlow 1.12
keras 2.2.4
例子
使用Lambda
from keras.models import Sequential
from keras.backend import concatenate
from keras.layers import Input, Lambda
from keras.layers import Dense
import tensorflow as tf
import keras.backend as K

 

model = Sequential()
model.add(Dense(32, input_dim=32))
# now: model.output_shape == (None, 32)
# note: `None` is the batch dimension

model.add(RepeatVector(3))
# now: model.output_shape == (None, 3, 32)
model.add(Lambda(lambda x: x ** 2))
def antirectifier(x):
gamma=tf.Variable(10.0, trainable=True)
x -= K.mean(x, axis=1, keepdims=True)
x = K.l2_normalize(x, axis=1)
pos = K.relu(x)
neg = K.relu(-x)
return tf.multiply(gamma,K.concatenate([pos, neg], axis=1))

def antirectifier_output_shape(input_shape):
shape = list(input_shape)
assert len(shape) == 2 # only valid for 2D tensors
shape[-1] *= 2
return tuple(shape)

model.add(Lambda(antirectifier,
output_shape=antirectifier_output_shape))
model.summary()
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输出：

使用自定义layer
from keras import backend as K
from keras.layers import Layer
from keras.models import Sequential
from keras.layers import Input
from keras.layers import Dense
import tensorflow as tf
import keras.backend as K

class MyLayer(Layer):

def __init__(self, output_dim, **kwargs):
self.output_dim = output_dim
super(MyLayer, self).__init__(**kwargs)

def build(self, input_shape):
# Create a trainable weight variable for this layer.
self.kernel = self.add_weight(name='kernel',
shape=(input_shape[1], self.output_dim),
initializer='uniform',
trainable=True)
super(MyLayer, self).build(input_shape) # Be sure to call this at the end

def call(self, x):
return K.dot(x, self.kernel)

def compute_output_shape(self, input_shape):
return (input_shape[0], self.output_dim)

model = Sequential()
model.add(Dense(32, input_dim=32))
model.add(MyLayer(100))
model.summary()
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输出：

参考
[1]: Keras Lambda
[2]: Writing your own Keras layers
[3]: 使用Keras编写自定义网络层（layer）
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版权声明：本文为CSDN博主「huml126」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。
原文链接：https://blog.csdn.net/huml126/java/article/details/88725917