resnet50的代码

以下是ResNet50的Keras实现代码：

from tensorflow.keras import layers
from tensorflow.keras.models import Model

def conv_block(inputs, filters, kernel_size, strides=(2, 2), padding='same'):
    x = layers.Conv2D(filters, kernel_size, strides=strides, padding=padding)(inputs)
    x = layers.BatchNormalization()(x)
    x = layers.Activation('relu')(x)
    return x

def identity_block(inputs, filters, kernel_size, padding='same'):
    x = layers.Conv2D(filters, kernel_size, padding=padding)(inputs)
    x = layers.BatchNormalization()(x)
    x = layers.Activation('relu')(x)
    x = layers.Conv2D(filters, kernel_size, padding=padding)(x)
    x = layers.BatchNormalization()(x)
    x = layers.add([x, inputs])
    x = layers.Activation('relu')(x)
    return x

def ResNet50(input_shape=(224, 224, 3), classes=1000):
    inputs = layers.Input(shape=input_shape)
    x = conv_block(inputs, filters=64, kernel_size=(7, 7), strides=(2, 2))
    x = layers.MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(x)

    x = identity_block(x, filters=64, kernel_size=(3, 3))
    x = identity_block(x, filters=64, kernel_size=(3, 3))
    x = identity_block(x, filters=64, kernel_size=(3, 3))

    x = conv_block(x, filters=128, kernel_size=(3, 3))
    x = identity_block(x, filters=128, kernel_size=(3, 3))
    x = identity_block(x, filters=128, kernel_size=(3, 3))
    x = identity_block(x, filters=128, kernel_size=(3, 3))

    x = conv_block(x, filters=256, kernel_size=(3, 3))
    x = identity_block(x, filters=256, kernel_size=(3, 3))
    x = identity_block(x, filters=256, kernel_size=(3, 3))
    x = identity_block(x, filters=256, kernel_size=(3, 3))
    x = identity_block(x, filters=256, kernel_size=(3, 3))
    x = identity_block(x, filters=256, kernel_size=(3, 3))

    x = conv_block(x, filters=512, kernel_size=(3, 3))
    x = identity_block(x, filters=512, kernel_size=(3, 3))
    x = identity_block(x, filters=512, kernel_size=(3, 3))

    x = layers.GlobalAveragePooling2D()(x)
    x = layers.Dense(classes, activation='softmax')(x)

    model = Model(inputs, x, name='resnet50')
    return model

此代码实现了ResNet50的主体结构，包括卷积块和恒等块，并使用了Batch Normalization和ReLU激活函数等技术来提高模型性能。