SqueezeNet的python代码

下面是使用Keras实现SqueezeNet的一个简单示例代码：

from keras.layers import Input, Conv2D, MaxPooling2D, concatenate, Dropout, Flatten, Dense
from keras.models import Model


def fire_module(x, squeeze=16, expand=64):
    # Squeeze layer
    s = Conv2D(squeeze, (1, 1), activation='relu')(x)
    # Expand layer
    e1 = Conv2D(expand, (1, 1), activation='relu')(s)
    e3 = Conv2D(expand, (3, 3), padding='same', activation='relu')(s)
    # Concatenate expand layers
    output = concatenate([e1, e3], axis=3)
    return output


def SqueezeNet(input_shape=(224, 224, 3), classes=1000):
    input = Input(shape=input_shape)
    
    x = Conv2D(64, (3, 3), strides=(2, 2), padding='valid', activation='relu')(input)
    x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(x)
    
    x = fire_module(x, squeeze=16, expand=64)
    x = fire_module(x, squeeze=16, expand=64)
    x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(x)
    
    x = fire_module(x, squeeze=32, expand=128)
    x = fire_module(x, squeeze=32, expand=128)
    x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(x)
    
    x = fire_module(x, squeeze=48, expand=192)
    x = fire_module(x, squeeze=48, expand=192)
    x = fire_module(x, squeeze=64, expand=256)
    x = fire_module(x, squeeze=64, expand=256)
    
    x = Dropout(0.5)(x)
    x = Conv2D(classes, (1, 1), padding='valid')(x)
    x = Flatten()(x)
    output = Dense(classes, activation='softmax')(x)
    
    model = Model(input, output, name='SqueezeNet')
    return model

这里定义了`fire_module`函数，该函数实现了SqueezeNet中的“Fire模块”。然后，通过堆叠多个“Fire模块”和池化层来构建整个SqueezeNet模型。最后，添加一个Dropout层和全连接层来输出分类结果。