怎么训练resnet20模型并保存权重

训练resnet20模型并保存权重的步骤如下：

1. 导入相关库和数据集

import tensorflow as tf
from tensorflow.keras.datasets import cifar10

(x_train, y_train), (x_test, y_test) = cifar10.load_data()
x_train = x_train.astype('float32') / 255.0
x_test = x_test.astype('float32') / 255.0

2. 定义ResNet20模型

def resnet20():
    input_shape = (32, 32, 3)
    num_classes = 10
    inputs = tf.keras.layers.Input(shape=input_shape)
    x = tf.keras.layers.Conv2D(16, (3, 3), padding='same', activation='relu')(inputs)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.Conv2D(16, (3, 3), padding='same', activation='relu')(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.Conv2D(16, (3, 3), padding='same', activation='relu')(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.add([x, inputs])
    x = tf.keras.layers.Conv2D(32, (3, 3), padding='same', activation='relu')(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.Conv2D(32, (3, 3), padding='same', activation='relu')(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.Conv2D(32, (3, 3), padding='same', activation='relu')(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.add([x, inputs])
    x = tf.keras.layers.Conv2D(64, (3, 3), padding='same', activation='relu')(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.Conv2D(64, (3, 3), padding='same', activation='relu')(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.Conv2D(64, (3, 3), padding='same', activation='relu')(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.add([x, inputs])
    x = tf.keras.layers.GlobalAvgPool2D()(x)
    outputs = tf.keras.layers.Dense(num_classes, activation='softmax')(x)
    model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
    return model

model = resnet20()

3. 编译模型

model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

4. 训练模型

history = model.fit(x_train, y_train, epochs=10, validation_data=(x_test, y_test))

5. 保存模型权重

model.save_weights('resnet20_weights.h5')

这样就可以训练resnet20模型并保存权重了。