tensorflow搭建ResNet50V2

以下为使用TensorFlow搭建ResNet50V2的步骤：

1. 导入必要的库

import tensorflow as tf
from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, BatchNormalization, Activation, Dense, Flatten, Add, ZeroPadding2D, AveragePooling2D
from tensorflow.keras.models import Model
from tensorflow.keras import backend as K

2. 定义卷积块函数

def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2)):
    """
    定义ResNet中的卷积块
    """
    filters1, filters2, filters3 = filters
    if K.image_data_format() == 'channels_last':
        bn_axis = 3
    else:
        bn_axis = 1

    # 定义卷积块名称
    conv_name_base = 'res' + str(stage) + block + '_branch'
    bn_name_base = 'bn' + str(stage) + block + '_branch'

    # 第一层卷积
    x = Conv2D(filters1, (1, 1), strides=strides, name=conv_name_base + '2a')(input_tensor)
    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
    x = Activation('relu')(x)

    # 第二层卷积
    x = Conv2D(filters2, kernel_size, padding='same', name=conv_name_base + '2b')(x)
    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
    x = Activation('relu')(x)

    # 第三层卷积
    x = Conv2D(filters3, (1, 1), name=conv_name_base + '2c')(x)
    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)

    # shortcut
    shortcut = Conv2D(filters3, (1, 1), strides=strides, name=conv_name_base + '1')(input_tensor)
    shortcut = BatchNormalization(axis=bn_axis, name=bn_name_base + '1')(shortcut)

    x = Add()([x, shortcut])
    x = Activation('relu')(x)
    return x

3. 定义恒等块函数

def identity_block(input_tensor, kernel_size, filters, stage, block):
    """
    定义ResNet中的恒等块
    """
    filters1, filters2, filters3 = filters
    if K.image_data_format() == 'channels_last':
        bn_axis = 3
    else:
        bn_axis = 1

    # 定义恒等块名称
    conv_name_base = 'res' + str(stage) + block + '_branch'
    bn_name_base = 'bn' + str(stage) + block + '_branch'

    # 第一层卷积
    x = Conv2D(filters1, (1, 1), name=conv_name_base + '2a')(input_tensor)
    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
    x = Activation('relu')(x)

    # 第二层卷积
    x = Conv2D(filters2, kernel_size, padding='same', name=conv_name_base + '2b')(x)
    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
    x = Activation('relu')(x)

    # 第三层卷积
    x = Conv2D(filters3, (1, 1), name=conv_name_base + '2c')(x)
    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)

    x = Add()([x, input_tensor])
    x = Activation('relu')(x)
    return x

4. 定义ResNet50V2模型

def ResNet50V2(input_shape=(224, 224, 3), classes=1000):
    """
    定义ResNet50V2模型
    """
    if K.image_data_format() == 'channels_last':
        bn_axis = 3
    else:
        bn_axis = 1

    # 输入层
    img_input = Input(shape=input_shape)

    # 第一层卷积
    x = ZeroPadding2D(padding=(3, 3))(img_input)
    x = Conv2D(64, (7, 7), strides=(2, 2), name='conv1')(x)
    x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x)
    x = Activation('relu')(x)
    x = MaxPooling2D((3, 3), strides=(2, 2))(x)

    # 卷积块
    x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))
    x = identity_block(x, 3, [64, 64, 256], stage=2, block='b')
    x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')

    # 卷积块
    x = conv_block(x, 3, [128, 128, 512], stage=3, block='a')
    x = identity_block(x, 3, [128, 128, 512], stage=3, block='b')
    x = identity_block(x, 3, [128, 128, 512], stage=3, block='c')
    x = identity_block(x, 3, [128, 128, 512], stage=3, block='d')

    # 卷积块
    x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a')
    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b')
    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c')
    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d')
    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e')
    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f')

    # 卷积块
    x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a')
    x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b')
    x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c')

    # 平均池化层
    x = AveragePooling2D((7, 7), name='avg_pool')(x)

    # 全连接层
    x = Flatten()(x)
    x = Dense(classes, activation='softmax', name='fc1000')(x)

    # 构建模型
    model = Model(img_input, x, name='resnet50v2')

    return model

5. 编译模型并训练

model = ResNet50V2(input_shape=(224, 224, 3), classes=1000)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, epochs=10, batch_size=32)

以上就是使用TensorFlow搭建ResNet50V2的步骤。