基于TensorFlow2.0的resnet152模型代码

以下是基于TensorFlow2.0的resnet152模型代码：

import tensorflow as tf

def conv3x3_block(inputs, filters, strides=1, name=""):
    x = tf.keras.layers.Conv2D(filters, kernel_size=3, strides=strides, padding="same", use_bias=False, name=name+"_conv")(inputs)
    x = tf.keras.layers.BatchNormalization(name=name+"_bn")(x)
    x = tf.keras.layers.Activation("relu", name=name+"_relu")(x)
    return x

def conv1x1_block(inputs, filters, name=""):
    x = tf.keras.layers.Conv2D(filters, kernel_size=1, padding="same", use_bias=False, name=name+"_conv")(inputs)
    x = tf.keras.layers.BatchNormalization(name=name+"_bn")(x)
    x = tf.keras.layers.Activation("relu", name=name+"_relu")(x)
    return x

def resnet_identity_block(inputs, filters, name=""):
    x = conv3x3_block(inputs, filters, name=name+"_conv1")
    x = conv3x3_block(x, filters, name=name+"_conv2")
    x = tf.keras.layers.Add(name=name+"_add")([inputs, x])
    x = tf.keras.layers.Activation("relu", name=name+"_relu")(x)
    return x

def resnet_bottleneck_block(inputs, filters, strides=1, name=""):
    x = conv1x1_block(inputs, filters//4, name=name+"_conv1")
    x = conv3x3_block(x, filters//4, strides=strides, name=name+"_conv2")
    x = conv1x1_block(x, filters, name=name+"_conv3")
    shortcut = conv1x1_block(inputs, filters, name=name+"_shortcut")
    x = tf.keras.layers.Add(name=name+"_add")([shortcut, x])
    x = tf.keras.layers.Activation("relu", name=name+"_relu")(x)
    return x

def resnet_block(inputs, filters, blocks, strides=1, block_func=resnet_bottleneck_block, name=""):
    x = block_func(inputs, filters, strides=strides, name=name+"_block1")
    for i in range(2, blocks+1):
        x = block_func(x, filters, name=name+"_block"+str(i))
    return x

def ResNet152(input_shape=(224, 224, 3), num_classes=1000):
    inputs = tf.keras.layers.Input(shape=input_shape, name="input")
    x = tf.keras.layers.ZeroPadding2D(padding=((3, 3), (3, 3)), name="padding")(inputs)
    x = tf.keras.layers.Conv2D(64, kernel_size=7, strides=2, padding="valid", use_bias=False, name="conv1")(x)
    x = tf.keras.layers.BatchNormalization(name="bn1")(x)
    x = tf.keras.layers.Activation("relu", name="relu1")(x)
    x = tf.keras.layers.ZeroPadding2D(padding=((1, 1), (1, 1)), name="padding1")(x)
    x = tf.keras.layers.MaxPooling2D(pool_size=3, strides=2, padding="valid", name="pool1")(x)

    x = resnet_block(x, 256, 3, strides=1, block_func=resnet_identity_block, name="res2")
    x = resnet_block(x, 512, 8, strides=2, block_func=resnet_bottleneck_block, name="res3")
    x = resnet_block(x, 1024, 36, strides=2, block_func=resnet_bottleneck_block, name="res4")
    x = resnet_block(x, 2048, 3, strides=2, block_func=resnet_bottleneck_block, name="res5")

    x = tf.keras.layers.GlobalAveragePooling2D(name="pool5")(x)
    x = tf.keras.layers.Dense(num_classes, name="fc1000")(x)
    x = tf.keras.layers.Activation("softmax", name="softmax")(x)

    model = tf.keras.Model(inputs, x, name="resnet152")
    return model

该代码实现了ResNet152模型，包括标准的残差块、瓶颈块和整个网络结构。可以通过调用`ResNet152()`函数来创建ResNet152模型。默认输入形状为`(224, 224, 3)`，输出类别数为1000。