ResNet网络模型实现MNIST手写数字识别的代码示例

以下是使用ResNet网络模型实现MNIST手写数字识别的代码示例：

import tensorflow as tf
from tensorflow.keras.datasets import mnist
from tensorflow.keras.layers import Input, Conv2D, BatchNormalization, ReLU, Add, GlobalAveragePooling2D, Dense
from tensorflow.keras.models import Model

# 加载MNIST数据集
(x_train, y_train), (x_test, y_test) = mnist.load_data()

# 数据预处理：将像素值缩放到0到1之间，并增加一维通道数
x_train = x_train.astype('float32') / 255.0
x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)
x_test = x_test.astype('float32') / 255.0
x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)

# 定义ResNet网络模型
def residual_block(inputs, filters, strides=1):
    shortcut = inputs
    # 第一个卷积层
    x = Conv2D(filters, kernel_size=3, strides=strides, padding='same')(inputs)
    x = BatchNormalization()(x)
    x = ReLU()(x)
    # 第二个卷积层
    x = Conv2D(filters, kernel_size=3, strides=1, padding='same')(x)
    x = BatchNormalization()(x)
    # 如果输入和输出的维度不同，则使用1x1卷积调整维度
    if shortcut.shape[-1] != filters:
        shortcut = Conv2D(filters, kernel_size=1, strides=strides, padding='same')(shortcut)
        shortcut = BatchNormalization()(shortcut)
    # 将残差块的输出与输入相加，构成下一层的输入
    x = Add()([x, shortcut])
    x = ReLU()(x)
    return x

def ResNet(input_shape=(28, 28, 1), num_classes=10):
    inputs = Input(shape=input_shape)
    # 第一层卷积
    x = Conv2D(64, kernel_size=3, strides=1, padding='same')(inputs)
    x = BatchNormalization()(x)
    x = ReLU()(x)
    # 残差块组1
    x = residual_block(x, filters=64, strides=1)
    x = residual_block(x, filters=64, strides=1)
    x = residual_block(x, filters=64, strides=1)
    # 残差块组2
    x = residual_block(x, filters=128, strides=2)
    x = residual_block(x, filters=128, strides=1)
    x = residual_block(x, filters=128, strides=1)
    # 残差块组3
    x = residual_block(x, filters=256, strides=2)
    x = residual_block(x, filters=256, strides=1)
    x = residual_block(x, filters=256, strides=1)
    # 残差块组4
    x = residual_block(x, filters=512, strides=2)
    x = residual_block(x, filters=512, strides=1)
    x = residual_block(x, filters=512, strides=1)
    # 全局平均池化
    x = GlobalAveragePooling2D()(x)
    # 全连接层
    x = Dense(num_classes, activation='softmax')(x)
    model = Model(inputs=inputs, outputs=x)
    return model

# 创建ResNet模型
model = ResNet(num_classes=10)
# 编译模型
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
# 训练模型
model.fit(x_train, y_train, batch_size=128, epochs=10, validation_data=(x_test, y_test))