如何为Convolution2D设置权重?

问题描述

我想设置Convolution2D图层的权重:

conv = Convolution2D(conv_out_size, window_size, embedding_size,
                     border_mode='same',
                     activation='relu',
                     weights=weights,
                     name='conv_{:d}'.format(i))(in_x)

但是我不确定这里的预期.我已经尝试了几件事，但是大多数时候我都得到了

but I am not sure what's expected here. I've tried several thing but most of the time I get

ValueError: You called `set_weights(weights)` on layer "conv_0" with a  weight list of length 1, but the layer was expecting 2 weights. 

不确定这到底意味着什么.

Not sure what this exactly means.

推荐答案

您应该通过set_weights方法将一个numpy数组传递给卷积层.

You should pass a numpy array to your convolutional layer through the set_weights method.

请记住，卷积层的权重不仅是每个单独过滤器的权重，而且是 bias .因此，如果要设置权重，则需要添加一个额外的尺寸.

Remember that the weights of a convolutional layer are not only the weights of each individual filter, but also the bias. So if you want to set your weights you need to add an extra dimension.

例如，如果您要设置一个1x3x3过滤器，除中心元素外所有权重均为零，则应将其设置为:

For example, if you want to set a 1x3x3 filter with all weights zero except for the central element, you should make it:

w = np.asarray([ 
    [[[
    [0,0,0],
    [0,2,0],
    [0,0,0]
    ]]]
    ])

然后设置它.

对于代码，您可以运行:

For a code you could run:

#!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import print_function
import numpy as np
np.random.seed(1234)
from keras.layers import Input
from keras.layers.convolutional import Convolution2D
from keras.models import Model
print("Building Model...")
inp = Input(shape=(1,None,None))
output   = Convolution2D(1, 3, 3, border_mode='same', init='normal',bias=False)(inp)
model_network = Model(input=inp, output=output)
print("Weights before change:")
print (model_network.layers[1].get_weights())
w = np.asarray([ 
    [[[
    [0,0,0],
    [0,2,0],
    [0,0,0]
    ]]]
    ])
input_mat = np.asarray([ 
    [[
    [1.,2.,3.],
    [4.,5.,6.],
    [7.,8.,9.]
    ]]
    ])
model_network.layers[1].set_weights(w)
print("Weights after change:")
print(model_network.layers[1].get_weights())
print("Input:")
print(input_mat)
print("Output:")
print(model_network.predict(input_mat))

尝试更改卷积填充器中的中心元素(示例中为2).

Try changing the central element in the convolutional fillter (2 in the example).

代码的作用:

首先建立一个模型.

inp = Input(shape=(1,None,None))
output   = Convolution2D(1, 3, 3, border_mode='same', init='normal',bias=False)(inp)
model_network = Model(input=inp, output=output)

打印原始权重(以正态分布初始化，init ='normal')

Print your original weights (initialized with normal distribution, init='normal' )

print (model_network.layers[1].get_weights())

创建所需的重量张量w和一些输入input_mat

Create your desired weight tensor w and some input input_mat

w = np.asarray([ 
    [[[
    [0,0,0],
    [0,2,0],
    [0,0,0]
    ]]]
    ])
input_mat = np.asarray([ 
    [[
    [1.,2.,3.],
    [4.,5.,6.],
    [7.,8.,9.]
    ]]
    ])

设置体重并打印出来

model_network.layers[1].set_weights(w)
print("Weights after change:")
print(model_network.layers[1].get_weights())

最后，使用它来生成带有预测的输出(预测会自动编译您的模型)

Finally, use it to generate output with predict (predict automatically compiles your model)

print(model_network.predict(input_mat))

示例输出:

Using Theano backend.
Building Model...
Weights before change:
[array([[[[ 0.02357176, -0.05954878,  0.07163535],
         [-0.01563259, -0.03602944,  0.04435815],
         [ 0.04297942, -0.03182618,  0.00078482]]]], dtype=float32)]
Weights after change:
[array([[[[ 0.,  0.,  0.],
         [ 0.,  2.,  0.],
         [ 0.,  0.,  0.]]]], dtype=float32)]
Input:
[[[[ 1.  2.  3.]
   [ 4.  5.  6.]
   [ 7.  8.  9.]]]]
Output:
[[[[  2.   4.   6.]
   [  8.  10.  12.]
   [ 14.  16.  18.]]]]

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