TensorFlow keras卷积神经网络 添加L2正则化

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model = keras.models.Sequential([         #卷积层1         keras.layers.Conv2D(32,kernel_size=5,strides=1,padding="same",data_format="channels_last",activation=tf.nn.relu,kernel_regularizer=keras.regularizers.l2(0.01)),         #池化层1         keras.layers.MaxPool2D(pool_size=2,strides=2,padding="same"),         #卷积层2         keras.layers.Conv2D(64,kernel_size=5,strides=1,padding="same",data_format="channels_last",activation=tf.nn.relu),         #池化层2         keras.layers.MaxPool2D(pool_size=2,strides=2,padding="same"),         #数据整理         keras.layers.Flatten(),         #1024个，全连接层         keras.layers.Dense(1024,activation=tf.nn.relu),         #100个，全连接层         keras.layers.Dense(100,activation=tf.nn.softmax)     ])

　　

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import os os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'   from tensorflow.python.keras.datasets import cifar100 from tensorflow.python import keras import tensorflow as tf     class CNNMnist(object):       model = keras.models.Sequential([         #卷积层1         keras.layers.Conv2D(32,kernel_size=5,strides=1,padding="same",data_format="channels_last",activation=tf.nn.relu,kernel_regularizer=keras.regularizers.l2(0.01)),         #池化层1         keras.layers.MaxPool2D(pool_size=2,strides=2,padding="same"),         #卷积层2         keras.layers.Conv2D(64,kernel_size=5,strides=1,padding="same",data_format="channels_last",activation=tf.nn.relu),         #池化层2         keras.layers.MaxPool2D(pool_size=2,strides=2,padding="same"),         #数据整理         keras.layers.Flatten(),         #1024个，全连接层         keras.layers.Dense(1024,activation=tf.nn.relu),         #100个，全连接层         keras.layers.Dense(100,activation=tf.nn.softmax)     ])       def __init__(self):         (self.x_train,self.y_train),(self.x_test,self.y_test) = cifar100.load_data()           self.x_train = self.x_train/255.0         self.x_test = self.x_test/255.0         def compile(self):         CNNMnist.model.compile(optimizer=keras.optimizers.Adam(),loss=keras.losses.sparse_categorical_crossentropy,metrics=["accuracy"])       def fit(self):         CNNMnist.model.fit(self.x_train,self.y_train,epochs=1,batch_size=32)       def evaluate(self):         test_loss,test_acc = CNNMnist.model.evaluate(self.x_test,self.y_test)         print(test_loss,test_acc)   if __name__ == '__main__':     cnn = CNNMnist()     print(CNNMnist.model.summary())     cnn.compile()     cnn.fit()