EfficientNet网络代码

以下是使用 Keras 实现的 EfficientNetB0 网络的代码示例：

from keras.models import Model
from keras.layers import Input, Conv2D, BatchNormalization, Activation, Add, Dropout, GlobalAveragePooling2D, Dense
from keras.utils import plot_model
from keras import backend as K

# 定义 Swish 激活函数
def swish(x):
    return K.sigmoid(x) * x

# 定义 Depthwise Convolution 模块
def depthwise_conv_block(inputs, pointwise_conv_filters, alpha=1.0, depth_multiplier=1, strides=(1, 1)):
    pointwise_conv_filters = int(pointwise_conv_filters * alpha)

    x = inputs
    # Depthwise Convolution
    x = DepthwiseConv2D((3, 3), padding='same', depth_multiplier=depth_multiplier, strides=strides)(x)
    x = BatchNormalization()(x)
    x = Activation(swish)(x)

    # Pointwise Convolution
    x = Conv2D(pointwise_conv_filters, (1, 1), padding='same', strides=(1, 1))(x)
    x = BatchNormalization()(x)
    x = Activation(swish)(x)

    return x

# 定义 Squeeze-and-Excitation 模块
def squeeze_excite_block(inputs, filters, ratio=16):
    # Squeeze
    se = GlobalAveragePooling2D()(inputs)
    se = Dense(filters // ratio, activation='relu')(se)
    # Excite
    se = Dense(filters, activation='sigmoid')(se)
    x = inputs
    x = Multiply()([x, se])
    return x

# 定义 EfficientNetB0 网络
def EfficientNetB0(input_shape, classes):
    inputs = Input(shape=input_shape)

    # Stem
    x = Conv2D(32, (3, 3), strides=(2, 2), padding='same')(inputs)
    x = BatchNormalization()(x)
    x = Activation(swish)(x)

    # MBConv1
    x = depthwise_conv_block(x, 16, alpha=1.0, depth_multiplier=1, strides=(1, 1))

    # MBConv6
    x = depthwise_conv_block(x, 24, alpha=1.0, depth_multiplier=1, strides=(2, 2))
    x = squeeze_excite_block(x, 24)
    x = depthwise_conv_block(x, 24, alpha=1.0, depth_multiplier=1, strides=(1, 1))
    x = squeeze_excite_block(x, 24)
    x = depthwise_conv_block(x, 24, alpha=1.0, depth_multiplier=1, strides=(1, 1))
    x = squeeze_excite_block(x, 24)

    # MBConv6
    x = depthwise_conv_block(x, 40, alpha=1.0, depth_multiplier=1, strides=(2, 2))
    x = squeeze_excite_block(x, 40)
    x = depthwise_conv_block(x, 40, alpha=1.0, depth_multiplier=1, strides=(1, 1))
    x = squeeze_excite_block(x, 40)
    x = depthwise_conv_block(x, 40, alpha=1.0, depth_multiplier=1, strides=(1, 1))
    x = squeeze_excite_block(x, 40)

    # MBConv6
    x = depthwise_conv_block(x, 80, alpha=1.0, depth_multiplier=1, strides=(2, 2))
    x = squeeze_excite_block(x, 80)
    x = depthwise_conv_block(x, 80, alpha=1.0, depth_multiplier=1, strides=(1, 1))
    x = squeeze_excite_block(x, 80)
    x = depthwise_conv_block(x, 80, alpha=1.0, depth_multiplier=1, strides=(1, 1))
    x = squeeze_excite_block(x, 80)
    x = depthwise_conv_block(x, 80, alpha=1.0, depth_multiplier=1, strides=(1, 1))
    x = squeeze_excite_block(x, 80)

    # MBConv6
    x = depthwise_conv_block(x, 112, alpha=1.0, depth_multiplier=1, strides=(1, 1))
    x = squeeze_excite_block(x, 112)
    x = depthwise_conv_block(x, 112, alpha=1.0, depth_multiplier=1, strides=(1, 1))
    x = squeeze_excite_block(x, 112)
    x = depthwise_conv_block(x, 112, alpha=1.0, depth_multiplier=1, strides=(1, 1))
    x = squeeze_excite_block(x, 112)
    x = depthwise_conv_block(x, 112, alpha=1.0, depth_multiplier=1, strides=(1, 1))
    x = squeeze_excite_block(x, 112)

    # MBConv6
    x = depthwise_conv_block(x, 192, alpha=1.0, depth_multiplier=1, strides=(2, 2))
    x = squeeze_excite_block(x, 192)
    x = depthwise_conv_block(x, 192, alpha=1.0, depth_multiplier=1, strides=(1, 1))
    x = squeeze_excite_block(x, 192)
    x = depthwise_conv_block(x, 192, alpha=1.0, depth_multiplier=1, strides=(1, 1))
    x = squeeze_excite_block(x, 192)
    x = depthwise_conv_block(x, 192, alpha=1.0, depth_multiplier=1, strides=(1, 1))
    x = squeeze_excite_block(x, 192)

    # MBConv6
    x = depthwise_conv_block(x, 320, alpha=1.0, depth_multiplier=1, strides=(1, 1))
    x = squeeze_excite_block(x, 320)

    # Top
    x = Conv2D(1280, (1, 1), padding='same')(x)
    x = BatchNormalization()(x)
    x = Activation(swish)(x)

    # Average Pooling
    x = GlobalAveragePooling2D()(x)

    # Dropout
    x = Dropout(0.2)(x)

    # Output
    outputs = Dense(classes, activation='softmax')(x)

    # 创建模型
    model = Model(inputs, outputs)

    return model

注意，这个代码示例中只实现了 EfficientNetB0 网络的部分层，如果要实现完整的 EfficientNet 网络，需要根据具体的网络结构进行修改。