python实现reset残差网络案例

以下是一个简单的 Python 代码示例，用于实现 ResNet：

import tensorflow as tf
from tensorflow.keras.layers import Conv2D, BatchNormalization, Activation, Add


def residual_block(input_data, filters, kernel_size=3, strides=1, activation='relu'):
    x = Conv2D(filters=filters, kernel_size=kernel_size, strides=strides, padding='same')(input_data)
    x = BatchNormalization()(x)
    x = Activation(activation)(x)
    x = Conv2D(filters=filters, kernel_size=kernel_size, strides=1, padding='same')(x)
    x = BatchNormalization()(x)
    x = Add()([x, input_data])
    x = Activation(activation)(x)
    return x


def resnet(input_shape, num_classes):
    input_data = tf.keras.Input(shape=input_shape)
    x = Conv2D(filters=64, kernel_size=7, strides=2, padding='same')(input_data)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = tf.keras.layers.MaxPooling2D(pool_size=3, strides=2, padding='same')(x)

    x = residual_block(x, filters=64, strides=1)
    x = residual_block(x, filters=64, strides=1)
    x = residual_block(x, filters=64, strides=1)

    x = residual_block(x, filters=128, strides=2)
    x = residual_block(x, filters=128, strides=1)
    x = residual_block(x, filters=128, strides=1)
    x = residual_block(x, filters=128, strides=1)

    x = residual_block(x, filters=256, strides=2)
    x = residual_block(x, filters=256, strides=1)
    x = residual_block(x, filters=256, strides=1)
    x = residual_block(x, filters=256, strides=1)
    x = residual_block(x, filters=256, strides=1)
    x = residual_block(x, filters=256, strides=1)

    x = residual_block(x, filters=512, strides=2)
    x = residual_block(x, filters=512, strides=1)
    x = residual_block(x, filters=512, strides=1)

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

    model = tf.keras.Model(inputs=input_data, outputs=output)
    return model


# 使用 ResNet 模型
model = resnet(input_shape=(224, 224, 3), num_classes=10)
model.summary()

在这个示例中，我们定义了一个 `residual_block` 函数来实现 ResNet 的残差块。然后，我们定义了一个 `resnet` 函数来创建整个 ResNet 模型。最后，我们可以使用 `resnet` 函数创建一个 ResNet 模型并将其用于我们的数据集。