如何在keras中使用并行卷积层？

Question

我对神经网络和keras有点新。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分别训练卷积层，然后将它们组合起来。我在这里看了一个例子：

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。任何人都可以给我一些关于如何修改模型以使用并行卷积层的提示。

由于

Answer 1

以下是在keras版本2中设计并行卷积和子采样层网络的示例。我希望这可以解决您的问题。

rows, cols = 100, 15
def create_convnet(img_path='network_image.png'):
    input_shape = Input(shape=(rows, cols, 1))

    tower_1 = Conv2D(20, (100, 5), padding='same', activation='relu')(input_shape)
    tower_1 = MaxPooling2D((1, 11), strides=(1, 1), padding='same')(tower_1)

    tower_2 = Conv2D(20, (100, 7), padding='same', activation='relu')(input_shape)
    tower_2 = MaxPooling2D((1, 9), strides=(1, 1), padding='same')(tower_2)

    tower_3 = Conv2D(20, (100, 10), padding='same', activation='relu')(input_shape)
    tower_3 = MaxPooling2D((1, 6), strides=(1, 1), padding='same')(tower_3)

    merged = keras.layers.concatenate([tower_1, tower_2, tower_3], axis=1)
    merged = Flatten()(merged)

    out = Dense(200, activation='relu')(merged)
    out = Dense(num_classes, activation='softmax')(out)

    model = Model(input_shape, out)
    plot_model(model, to_file=img_path)
    return model

此网络的图像将如下所示

Answer 2

我的方法是创建定义所有并行卷积和拉动操作的其他模型，并将所有并行结果张量连接到单输出​​张量。现在，您可以像顺序层一样在顺序模型中添加此并行模型图。这是我的解决方案，希望它能解决您的问题。

# 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：Combining the outputs of multiple models into one model