resnet50的写法

ResNet50是一种深度卷积神经网络，由50个卷积层组成，是ResNet系列的一部分，由微软研究院的Kaiming He等人于2016年提出。

以下是ResNet50的Keras实现：

from keras.layers import Input, Conv2D, MaxPooling2D, ZeroPadding2D, Flatten, Dense, Activation, add
from keras.models import Model

def identity_block(input_tensor, kernel_size, filters, stage, block):
    """identity block"""
    F1, F2, F3 = filters
    conv_name_base = 'res' + str(stage) + block + '_branch'
    bn_name_base = 'bn' + str(stage) + block + '_branch'

    x = Conv2D(filters=F1, kernel_size=(1, 1), strides=(1, 1), padding='valid', name=conv_name_base + '2a')(input_tensor)
    x = Activation('relu')(x)
    x = Conv2D(filters=F2, kernel_size=kernel_size, strides=(1, 1), padding='same', name=conv_name_base + '2b')(x)
    x = Activation('relu')(x)
    x = Conv2D(filters=F3, kernel_size=(1, 1), strides=(1, 1), padding='valid', name=conv_name_base + '2c')(x)
    x = add([x, input_tensor])
    x = Activation('relu')(x)
    return x


def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2)):
    """convolution block"""
    F1, F2, F3 = filters
    conv_name_base = 'res' + str(stage) + block + '_branch'
    bn_name_base = 'bn' + str(stage) + block + '_branch'

    x = Conv2D(filters=F1, kernel_size=(1, 1), strides=strides, padding='valid', name=conv_name_base + '2a')(input_tensor)
    x = Activation('relu')(x)
    x = Conv2D(filters=F2, kernel_size=kernel_size, strides=(1, 1), padding='same', name=conv_name_base + '2b')(x)
    x = Activation('relu')(x)
    x = Conv2D(filters=F3, kernel_size=(1, 1), strides=(1, 1), padding='valid', name=conv_name_base + '2c')(x)
    shortcut = Conv2D(filters=F3, kernel_size=(1, 1), strides=strides, padding='valid', name=conv_name_base + '1')(input_tensor)
    x = add([x, shortcut])
    x = Activation('relu')(x)
    return x


def ResNet50(input_shape=(224, 224, 3), classes=1000):
    """ResNet50"""
    X_input = Input(input_shape)
    X = ZeroPadding2D((3, 3))(X_input)

    # stage 1
    X = Conv2D(64, (7, 7), strides=(2, 2), name='conv1')(X)
    X = Activation('relu')(X)
    X = MaxPooling2D((3, 3), strides=(2, 2))(X)

    # stage 2
    X = conv_block(X, kernel_size=3, filters=[64, 64, 256], stage=2, block='a', strides=(1, 1))
    X = identity_block(X, kernel_size=3, filters=[64, 64, 256], stage=2, block='b')
    X = identity_block(X, kernel_size=3, filters=[64, 64, 256], stage=2, block='c')

    # stage 3
    X = conv_block(X, kernel_size=3, filters=[128, 128, 512], stage=3, block='a')
    X = identity_block(X, kernel_size=3, filters=[128, 128, 512], stage=3, block='b')
    X = identity_block(X, kernel_size=3, filters=[128, 128, 512], stage=3, block='c')
    X = identity_block(X, kernel_size=3, filters=[128, 128, 512], stage=3, block='d')

    # stage 4
    X = conv_block(X, kernel_size=3, filters=[256, 256, 1024], stage=4, block='a')
    X = identity_block(X, kernel_size=3, filters=[256, 256, 1024], stage=4, block='b')
    X = identity_block(X, kernel_size=3, filters=[256, 256, 1024], stage=4, block='c')
    X = identity_block(X, kernel_size=3, filters=[256, 256, 1024], stage=4, block='d')
    X = identity_block(X, kernel_size=3, filters=[256, 256, 1024], stage=4, block='e')
    X = identity_block(X, kernel_size=3, filters=[256, 256, 1024], stage=4, block='f')

    # stage 5
    X = conv_block(X, kernel_size=3, filters=[512, 512, 2048], stage=5, block='a')
    X = identity_block(X, kernel_size=3, filters=[512, 512, 2048], stage=5, block='b')
    X = identity_block(X, kernel_size=3, filters=[512, 512, 2048], stage=5, block='c')

    # output layer
    X = AveragePooling2D((7, 7), name='avg_pool')(X)
    X = Flatten()(X)
    X = Dense(classes, activation='softmax', name='fc' + str(classes))(X)

    # create model
    model = Model(inputs=X_input, outputs=X, name='ResNet50')
    return model

以上是ResNet50的Keras实现，其中identity_block和conv_block分别实现了ResNet50中的残差块和卷积块，ResNet50函数则将这些块组合起来构成了整个网络。在实现过程中，我们采用了Keras中的函数式API，使得代码更加简洁易懂。