如何在 keras 中具有并行卷积层? [英] How to have parallel convolutional layers in keras?

问题描述

我对神经网络和 keras 有点陌生.我有一些大小为 6*7 的图像，过滤器的大小为 15.我想有几个过滤器并在每个过滤器上分别训练一个卷积层，然后将它们组合起来.我在这里看过一个例子:

I am a little new to neural networks and keras. I have some images with size 6*7 and the size of the filter is 15. I want to have several filters and train a convolutional layer separately on each and then combine them. I have looked at one example here:

model = Sequential()
model.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1],
                    border_mode='valid',
                    input_shape=input_shape))
model.add(Activation('relu'))
model.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1]))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=pool_size))
model.add(Dropout(0.25))
model.add(Flatten(input_shape=input_shape))
model.add(Dense(128))
model.add(Activation('relu'))
model.add(Dense(128))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_classes))
model.add(Activation('tanh'))

此模型适用于一个过滤器.谁能给我一些关于如何修改模型以使用并行卷积层的提示.

This model works with one filter. Can anybody give me some hints on how to modify the model to work with parallel convolutional layers.

谢谢

推荐答案

我的方法是创建其他模型，定义所有并行卷积和拉动操作，并将所有并行结果张量连接到单个输出张量.现在您可以像层一样在序列模型中添加这个并行模型图.这是我的解决方案，希望它能解决您的问题.

My approach is to create other model that defines all parallel convolution and pulling operations and concat all parallel result tensors to single output tensor. Now you can add this parallel model graph in your sequential model just like layer. Here is my solution, hope it solves your problem.

# variable initialization 
from keras import Input, Model, Sequential
from keras.layers import Conv2D, MaxPooling2D, Concatenate, Activation, Dropout, Flatten, Dense

nb_filters =100
kernel_size= {}
kernel_size[0]= [3,3]
kernel_size[1]= [4,4]
kernel_size[2]= [5,5]
input_shape=(32, 32, 3)
pool_size = (2,2)
nb_classes =2
no_parallel_filters = 3

# create seperate model graph for parallel processing with different filter sizes
# apply 'same' padding so that ll produce o/p tensor of same size for concatination
# cancat all paralle output

inp = Input(shape=input_shape)
convs = []
for k_no in range(len(kernel_size)):
    conv = Conv2D(nb_filters, kernel_size[k_no][0], kernel_size[k_no][1],
                    border_mode='same',
                         activation='relu',
                    input_shape=input_shape)(inp)
    pool = MaxPooling2D(pool_size=pool_size)(conv)
    convs.append(pool)

if len(kernel_size) > 1:
    out = Concatenate()(convs)
else:
    out = convs[0]

conv_model = Model(input=inp, output=out)

# add created model grapg in sequential model

model = Sequential()
model.add(conv_model)        # add model just like layer
model.add(Conv2D(nb_filters, kernel_size[1][0], kernel_size[1][0]))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=pool_size))
model.add(Dropout(0.25))
model.add(Flatten(input_shape=input_shape))
model.add(Dense(128))
model.add(Activation('relu'))
model.add(Dense(128))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_classes))
model.add(Activation('tanh'))

有关更多信息，请参阅类似问题:组合输出多个模型合并为一个模型

For more information refer similar question: Combining the outputs of multiple models into one model

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