tensorflow python残差网络模型代码

以下是一个简单的 TensorFlow Python 残差网络模型代码示例：

import tensorflow as tf

def residual_block(inputs, filters, kernel_size):
    x = tf.keras.layers.Conv2D(filters, kernel_size, padding='same')(inputs)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.Activation('relu')(x)
    x = tf.keras.layers.Conv2D(filters, kernel_size, padding='same')(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.Add()([x, inputs])
    x = tf.keras.layers.Activation('relu')(x)
    return x

inputs = tf.keras.layers.Input(shape=(224, 224, 3))
x = tf.keras.layers.Conv2D(64, 7, strides=2, padding='same')(inputs)
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.Activation('relu')(x)
x = tf.keras.layers.MaxPooling2D(pool_size=3, strides=2, padding='same')(x)

filters = 64
for i in range(3):
    x = residual_block(x, filters, 3)
filters *= 2
for i in range(4):
    x = residual_block(x, filters, 3)
filters *= 2
for i in range(6):
    x = residual_block(x, filters, 3)
filters *= 2
for i in range(3):
    x = residual_block(x, filters, 3)

x = tf.keras.layers.GlobalAveragePooling2D()(x)
outputs = tf.keras.layers.Dense(1000, activation='softmax')(x)

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

这是一个基于 ResNet 架构的残差网络模型，用于图像分类任务。