写一个resnet模型

ResNet是一个深度卷积神经网络，特点是残差学习，能够有效地解决深度卷积神经网络中的梯度消失问题。以下是一个简单的ResNet模型实现：

import tensorflow as tf

def conv_bn_relu(x, filters, kernel_size, strides=1):
    x = tf.keras.layers.Conv2D(filters=filters, kernel_size=kernel_size, strides=strides, padding='same', use_bias=False)(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.ReLU()(x)
    return x

def res_block(x, filters, strides=1):
    shortcut = x
    x = conv_bn_relu(x, filters, 3, strides)
    x = conv_bn_relu(x, filters, 3)
    if strides != 1 or shortcut.shape[3] != filters:
        shortcut = tf.keras.layers.Conv2D(filters=filters, kernel_size=1, strides=strides, use_bias=False)(shortcut)
        shortcut = tf.keras.layers.BatchNormalization()(shortcut)
    x = tf.keras.layers.Add()([x, shortcut])
    x = tf.keras.layers.ReLU()(x)
    return x

def resnet(input_shape, num_classes):
    inputs = tf.keras.Input(shape=input_shape)
    x = conv_bn_relu(inputs, 64, 7, 2)
    x = tf.keras.layers.MaxPooling2D(pool_size=3, strides=2, padding='same')(x)
    for filters in [64, 128, 256, 512]:
        x = res_block(x, filters)
        for _ in range(1, 3):
            x = res_block(x, filters, strides=1)
    x = tf.keras.layers.GlobalAveragePooling2D()(x)
    outputs = tf.keras.layers.Dense(units=num_classes, activation='softmax')(x)
    model = tf.keras.Model(inputs=inputs, outputs=outputs)
    return model

这个ResNet模型包含4个阶段，每个阶段包含若干个残差块。在每个阶段的开始处使用步幅为2的卷积和池化层进行下采样，以减小特征图的尺寸。最后使用全局平均池化层将特征图压缩为向量，然后使用一个全连接层将其映射到类别概率