resnet18 tensorflow

以下是使用TensorFlow 2.0实现resnet18的示例代码：

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

def conv_bn_relu(inputs, filters, kernel_size, strides):
    x = Conv2D(filters=filters, kernel_size=kernel_size, strides=strides, padding='same')(inputs)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    return x

def identity_block(inputs, filters):
    x = conv_bn_relu(inputs, filters, kernel_size=3, strides=1)
    x = conv_bn_relu(x, filters, kernel_size=3, strides=1)
    x = Add()([x, inputs])
    x = Activation('relu')(x)
    return x

def conv_block(inputs, filters, strides):
    shortcut = inputs
    x = conv_bn_relu(inputs, filters, kernel_size=3, strides=strides)
    x = conv_bn_relu(x, filters, kernel_size=3, strides=1)
    shortcut = Conv2D(filters=filters, kernel_size=1, strides=strides)(shortcut)
    shortcut = BatchNormalization()(shortcut)
    x = Add()([x, shortcut])
    x = Activation('relu')(x)
    return x

def resnet18():
    inputs = Input(shape=(224, 224, 3))
    x = ZeroPadding2D(padding=(3, 3))(inputs)
    x = Conv2D(filters=64, kernel_size=7, strides=2)(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = MaxPooling2D(pool_size=3, strides=2, padding='same')(x)

    x = conv_block(x, filters=64, strides=1)
    x = identity_block(x, filters=64)
    x = identity_block(x, filters=64)

    x = conv_block(x, filters=128, strides=2)
    x = identity_block(x, filters=128)
    x = identity_block(x, filters=128)

    x = conv_block(x, filters=256, strides=2)
    x = identity_block(x, filters=256)
    x = identity_block(x, filters=256)

    x = conv_block(x, filters=512, strides=2)
    x = identity_block(x, filters=512)
    x = identity_block(x, filters=512)

    x = Flatten()(x)
    x = Dense(units=1000, activation='softmax')(x)

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

这里定义了一些辅助函数，如conv_bn_relu、identity_block和conv_block，用于构建ResNet18的基本块。然后，定义了resnet18函数，该函数返回一个ResNet18模型。在这个函数中，我们首先定义了输入层，然后进行了一些预处理，接着构建了ResNet18的各个块，并在最后添加了一个全连接层，输出1000个类别的概率分布。