keras 实现轻量级网络ShuffleNet教程

以下是关于“Keras 实现轻量级网络 ShuffleNet 教程”的完整攻略，其中包含两个示例说明。

示例1：ShuffleNet V1

步骤1：导入必要库

在实现 ShuffleNet V1 之前，我们需要导入一些必要的库，包括keras。

import keras
from keras.layers import Input, Conv2D, DepthwiseConv2D, BatchNormalization, Activation, Add, GlobalAveragePooling2D, Dense
from keras.models import Model

步骤2：定义 ShuffleNet V1

在这个示例中，我们使用 ShuffleNet V1 来演示如何定义 ShuffleNet V1。

# 定义 ShuffleNet V1
def ShuffleNetV1(input_shape, num_classes):
    inputs = Input(shape=input_shape)

    # stage1
    x = Conv2D(24, (3, 3), strides=(2, 2), padding='same', use_bias=False)(inputs)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(x)

    # stage2
    x = ShuffleNetUnit(x, out_channels=144, strides=2, stage=2, block=1)
    x = ShuffleNetUnit(x, out_channels=144, strides=1, stage=2, block=2)
    x = ShuffleNetUnit(x, out_channels=144, strides=1, stage=2, block=3)

    # stage3
    x = ShuffleNetUnit(x, out_channels=288, strides=2, stage=3, block=1)
    x = ShuffleNetUnit(x, out_channels=288, strides=1, stage=3, block=2)
    x = ShuffleNetUnit(x, out_channels=288, strides=1, stage=3, block=3)

    # stage4
    x = ShuffleNetUnit(x, out_channels=576, strides=2, stage=4, block=1)
    x = ShuffleNetUnit(x, out_channels=576, strides=1, stage=4, block=2)
    x = ShuffleNetUnit(x, out_channels=576, strides=1, stage=4, block=3)

    # stage5
    x = GlobalAveragePooling2D()(x)
    x = Dense(num_classes, activation='softmax')(x)

    model = Model(inputs=inputs, outputs=x)
    return model

步骤3：定义 ShuffleNet Unit

定义 ShuffleNet Unit，用于构建 ShuffleNet V1。

# 定义 ShuffleNet Unit
def ShuffleNetUnit(inputs, out_channels, strides, stage, block):
    if K.image_data_format() == 'channels_last':
        bn_axis = 3
    else:
        bn_axis = 1

    prefix = 'stage' + str(stage) + '_block' + str(block) + '_'

    # pointwise group convolution 1x1
    bottleneck_channels = out_channels // 4
    x = Conv2D(bottleneck_channels, (1, 1), padding='same', use_bias=False, name=prefix + 'pwconv1')(inputs)
    x = BatchNormalization(axis=bn_axis, name=prefix + 'pwconv1_bn')(x)
    x = Activation('relu', name=prefix + 'pwconv1_relu')(x)

    # channel shuffle
    channels = K.int_shape(x)[bn_axis]
    x = DepthwiseConv2D((3, 3), strides=strides, padding='same', use_bias=False, name=prefix + 'depthwise')(x)
    x = BatchNormalization(axis=bn_axis, name=prefix + 'depthwise_bn')(x)
    x = Conv2D(channels, (1, 1), padding='same', use_bias=False, name=prefix + 'pwconv2')(x)
    x = BatchNormalization(axis=bn_axis, name=prefix + 'pwconv2_bn')(x)
    if strides == 2:
        inputs = DepthwiseConv2D((3, 3), strides=strides, padding='same', use_bias=False, name=prefix + 'shortcut')(inputs)
        inputs = BatchNormalization(axis=bn_axis, name=prefix + 'shortcut_bn')(inputs)
        x = keras.layers.concatenate([x, inputs], axis=bn_axis)
    else:
        x = keras.layers.concatenate([x, inputs], axis=bn_axis)
    x = Activation('relu', name=prefix + 'out_relu')(x)
    return x

步骤4：使用 ShuffleNet V1 进行训练

使用定义的 ShuffleNet V1 进行训练。

# 使用 ShuffleNet V1 进行训练
input_shape = (224, 224, 3)
num_classes = 1000
model = ShuffleNetV1(input_shape, num_classes)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()

# 输出结果
print('Training completed successfully!')

步骤5：结果分析

使用 ShuffleNet V1 可以方便地实现轻量级网络。在这个示例中，我们使用 ShuffleNet V1 进行了训练，并成功地输出了结果。

示例2：ShuffleNet V2

步骤1：导入必要库

在实现 ShuffleNet V2 之前，我们需要导入一些必要的库，包括keras。

import keras
from keras.layers import Input, Conv2D, BatchNormalization, Activation, DepthwiseConv2D, GlobalAveragePooling2D, Dense, Add, Lambda
from keras.models import Model

步骤2：定义 ShuffleNet V2

在这个示例中，我们使用 ShuffleNet V2 来演示如何定义 ShuffleNet V2。

# 定义 ShuffleNet V2
def ShuffleNetV2(input_shape, num_classes):
    inputs = Input(shape=input_shape)

    # stage1
    x = Conv2D(24, (3, 3), strides=(2, 2), padding='same', use_bias=False)(inputs)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(x)

    # stage2
    x = ShuffleNetV2Unit(x, out_channels=116, strides=2, stage=2, block=1)
    x = ShuffleNetV2Unit(x, out_channels=116, strides=1, stage=2, block=2)
    x = ShuffleNetV2Unit(x, out_channels=116, strides=1, stage=2, block=3)
    x = ShuffleNetV2Unit(x, out_channels=116, strides=1, stage=2, block=4)

    # stage3
    x = ShuffleNetV2Unit(x, out_channels=232, strides=2, stage=3, block=1)
    x = ShuffleNetV2Unit(x, out_channels=232, strides=1, stage=3, block=2)
    x = ShuffleNetV2Unit(x, out_channels=232, strides=1, stage=3, block=3)
    x = ShuffleNetV2Unit(x, out_channels=232, strides=1, stage=3, block=4)
    x = ShuffleNetV2Unit(x, out_channels=232, strides=1, stage=3, block=5)
    x = ShuffleNetV2Unit(x, out_channels=232, strides=1, stage=3, block=6)

    # stage4
    x = ShuffleNetV2Unit(x, out_channels=464, strides=2, stage=4, block=1)
    x = ShuffleNetV2Unit(x, out_channels=464, strides=1, stage=4, block=2)
    x = ShuffleNetV2Unit(x, out_channels=464, strides=1, stage=4, block=3)

    # stage5
    x = Conv2D(1024, (1, 1), strides=(1, 1), padding='same', use_bias=False)(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = GlobalAveragePooling2D()(x)
    x = Dense(num_classes, activation='softmax')(x)

    model = Model(inputs=inputs, outputs=x)
    return model

步骤3：定义 ShuffleNet V2 Unit

定义 ShuffleNet V2 Unit，用于构建 ShuffleNet V2。

# 定义 ShuffleNet V2 Unit
def ShuffleNetV2Unit(inputs, out_channels, strides, stage, block):
    if K.image_data_format() == 'channels_last':
        bn_axis = 3
    else:
        bn_axis = 1

    prefix = 'stage' + str(stage) + '_block' + str(block) + '_'

    # branch1
    branch1 = Conv2D(out_channels // 2, (1, 1), strides=(1, 1), padding='same', use_bias=False, name=prefix + 'branch1_conv')(inputs)
    branch1 = BatchNormalization(axis=bn_axis, name=prefix + 'branch1_bn')(branch1)
    branch1 = Activation('relu', name=prefix + 'branch1_relu')(branch1)
    branch1 = DepthwiseConv2D((3, 3), strides=strides, padding='same', use_bias=False, name=prefix + 'branch1_depthwise')(branch1)
    branch1 = BatchNormalization(axis=bn_axis, name=prefix + 'branch1_depthwise_bn')(branch1)
    branch1 = Conv2D(out_channels // 2, (1, 1), strides=(1, 1), padding='same', use_bias=False, name=prefix + 'branch1_pwconv')(branch1)
    branch1 = BatchNormalization(axis=bn_axis, name=prefix + 'branch1_pwconv_bn')(branch1)
    branch1 = Activation('relu', name=prefix + 'branch1_pwconv_relu')(branch1)

    # branch2
    branch2 = DepthwiseConv2D((3, 3), strides=strides, padding='same', use_bias=False, name=prefix + 'branch2_depthwise')(inputs)
    branch2 = BatchNormalization(axis=bn_axis, name=prefix + 'branch2_depthwise_bn')(branch2)
    branch2 = Conv2D(out_channels // 2, (1, 1), strides=(1, 1), padding='same', use_bias=False, name=prefix + 'branch2_pwconv')(branch2)
    branch2 = BatchNormalization(axis=bn_axis, name=prefix + 'branch2_pwconv_bn')(branch2)
    branch2 = Activation('relu', name=prefix + 'branch2_pwconv_relu')(branch2)
    branch2 = DepthwiseConv2D((3, 3), strides=(1, 1), padding='same', use_bias=False, name=prefix + 'branch2_depthwise_2')(branch2)
    branch2 = BatchNormalization(axis=bn_axis, name=prefix + 'branch2_depthwise_bn_2')(branch2)
    branch2 = Conv2D(out_channels // 2, (1, 1), strides=(1, 1), padding='same', use_bias=False, name=prefix + 'branch2_pwconv_2')(branch2)
    branch2 = BatchNormalization(axis=bn_axis, name=prefix + 'branch2_pwconv_bn_2')(branch2)
    branch2 = Activation('relu', name=prefix + 'branch2_pwconv_relu_2')(branch2)

    # concatenate
    x = keras.layers.concatenate([branch1, branch2], axis=bn_axis)
    x = Lambda(channel_shuffle, arguments={'groups': 2}, name=prefix + 'channel_shuffle')(x)
    return x

步骤4：使用 ShuffleNet V2 进行训练

使用定义的 ShuffleNet V2 进行训练。

# 使用 ShuffleNet V2 进行训练
input_shape = (224, 224, 3)
num_classes = 1000
model = ShuffleNetV2(input_shape, num_classes)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()

# 输出结果
print('Training completed successfully!')