利用resnet训练mnist数据集

ResNet是一种深度卷积神经网络，可以有效地解决深层神经网络中的梯度消失和梯度爆炸问题。在MNIST数据集上使用ResNet进行训练可以得到很好的准确率。下面是一个简单的代码示例：

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

# 加载MNIST数据集并进行预处理
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_train = x_train.reshape(x_train.shape + (1,))
x_test = x_test.reshape(x_test.shape + (1,))
y_train = tf.keras.utils.to_categorical(y_train, 10)
y_test = tf.keras.utils.to_categorical(y_test, 10)

# 构建ResNet模型
def resnet_block(inputs, num_filters=16, kernel_size=3, strides=1, activation='relu'):
    x = Conv2D(num_filters, kernel_size=kernel_size, strides=strides, padding='same')(inputs)
    x = BatchNormalization()(x)
    x = Activation(activation)(x)
    x = Conv2D(num_filters, kernel_size=kernel_size, strides=1, padding='same')(x)
    x = BatchNormalization()(x)
    if strides > 1:
        inputs = Conv2D(num_filters, kernel_size=1, strides=strides, padding='same')(inputs)
        inputs = BatchNormalization()(inputs)
    x = Add()([x, inputs])
    x = Activation(activation)(x)
    return x

inputs = Input(shape=(28, 28, 1))
x = Conv2D(16, 3, activation='relu')(inputs)
x = resnet_block(x, num_filters=16, kernel_size=3, strides=1)
x = resnet_block(x, num_filters=16, kernel_size=3, strides=1)
x = resnet_block(x, num_filters=32, kernel_size=3, strides=2)
x = resnet_block(x, num_filters=32, kernel_size=3, strides=1)
x = resnet_block(x, num_filters=64, kernel_size=3, strides=2)
x = resnet_block(x, num_filters=64, kernel_size=3, strides=1)
x = Flatten()(x)
outputs = Dense(10, activation='softmax')(x)

model = Model(inputs=inputs, outputs=outputs)

# 编译并训练模型
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(x_train, y_train, batch_size=128, epochs=10, validation_data=(x_test, y_test))

在这个示例中，我们使用了ResNet的基本块来构建一个6层深的卷积神经网络。我们还使用了批量归一化和残差连接来提高模型的性能。最后，我们对模型进行了编译和训练，使用交叉熵损失和Adam优化器。在训练期间，我们还使用了验证集来监控模型的性能。