我正在创建一个自定义层,其权重需要在激活前按元素乘以。当输出和输入形状相同时,我可以让它工作。当我将一阶数组作为输入,将二阶数组作为输出时,就会出现问题。 tensorflow.multiply 支持广播,但是当我尝试在 Layer.call(x, self.kernel) 中使用它时 将 x 乘以 self.kernel 变量它提示说它们是不同的形状说:
ValueError: Dimensions must be equal, but are 4 and 3 for 'my_layer_1/Mul' (op: 'Mul') with input shapes: [?,4], [4,3].
这是我的代码:
from keras import backend as K
from keras.engine.topology import Layer
import tensorflow as tf
from keras.models import Sequential
import numpy as np
class MyLayer(Layer):
def __init__(self, output_dims, **kwargs):
self.output_dims = output_dims
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=self.output_dims,
initializer='ones',
trainable=True)
super(MyLayer, self).build(input_shape) # Be sure to call this somewhere!
def call(self, x):
#multiply wont work here?
return K.tf.multiply(x, self.kernel)
def compute_output_shape(self, input_shape):
return (self.output_dims)
mInput = np.array([[1,2,3,4]])
inShape = (4,)
net = Sequential()
outShape = (4,3)
l1 = MyLayer(outShape, input_shape= inShape)
net.add(l1)
net.compile(loss='mean_absolute_error', optimizer='adam', metrics=['accuracy'])
p = net.predict(x=mInput, batch_size=1)
print(p)
编辑: 给定输入形状 (4,) 和输出形状 (4,3),权重矩阵应与输出形状相同并用 1 初始化。所以在上面的代码中输入是[1,2,3,4],权重矩阵应该是[[1,1,1,1],[1,1,1,1],[1,1,1 ,1]] 并且输出应该看起来像 [[1,2,3,4],[1,2,3,4],[1,2,3,4]]
最佳答案
相乘前,需要重复元素来增加形状。
您可以为此使用 K.repeat_elements
。 (将 keras.backend 导入为 K
)
class MyLayer(Layer):
#there are some difficulties for different types of shapes
#let's use a 'repeat_count' instead, increasing only one dimension
def __init__(self, repeat_count,**kwargs):
self.repeat_count = repeat_count
super(MyLayer, self).__init__(**kwargs)
def build(self, input_shape):
#first, let's get the output_shape
output_shape = self.compute_output_shape(input_shape)
weight_shape = (1,) + output_shape[1:] #replace the batch size by 1
self.kernel = self.add_weight(name='kernel',
shape=weight_shape,
initializer='ones',
trainable=True)
super(MyLayer, self).build(input_shape) # Be sure to call this somewhere!
#here, we need to repeat the elements before multiplying
def call(self, x):
if self.repeat_count > 1:
#we add the extra dimension:
x = K.expand_dims(x, axis=1)
#we replicate the elements
x = K.repeat_elements(x, rep=self.repeat_count, axis=1)
#multiply
return x * self.kernel
#make sure we comput the ouptut shape according to what we did in "call"
def compute_output_shape(self, input_shape):
if self.repeat_count > 1:
return (input_shape[0],self.repeat_count) + input_shape[1:]
else:
return input_shape
关于python - 在keras自定义层中进行广播的逐元素乘法,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/47289116/