resnet18代码复现tensorflow

以下是使用TensorFlow复现ResNet18的代码示例：

import tensorflow as tf

def conv_block(inputs, filters, kernel_size, strides):
    x = tf.keras.layers.Conv2D(filters=filters, kernel_size=kernel_size, strides=strides, padding='same')(inputs)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.ReLU()(x)
    return x

def identity_block(inputs, filters):
    x = conv_block(inputs, filters=filters, kernel_size=3, strides=1)
    x = tf.keras.layers.Conv2D(filters=filters, kernel_size=3, strides=1, padding='same')(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.add([x, inputs])
    x = tf.keras.layers.ReLU()(x)
    return x

def resnet18(input_shape=(224, 224, 3), num_classes=1000):
    inputs = tf.keras.layers.Input(shape=input_shape)
    x = conv_block(inputs, filters=64, kernel_size=7, strides=2)
    x = tf.keras.layers.MaxPooling2D(pool_size=3, strides=2, padding='same')(x)

    for i in range(2):
        x = identity_block(x, filters=64)

    x = conv_block(x, filters=128, kernel_size=3, strides=2)
    for i in range(2):
        x = identity_block(x, filters=128)

    x = conv_block(x, filters=256, kernel_size=3, strides=2)
    for i in range(2):
        x = identity_block(x, filters=256)

    x = conv_block(x, filters=512, kernel_size=3, strides=2)
    for i in range(2):
        x = identity_block(x, filters=512)

    x = tf.keras.layers.GlobalAveragePooling2D()(x)
    x = tf.keras.layers.Dense(units=num_classes, activation='softmax')(x)

    model = tf.keras.models.Model(inputs=inputs, outputs=x)
    return model