kernel_regularizer=regularizers.l1(0.0001)), Dense(output_dim=hidden2_num_units, input_dim=hidden1_num_units, activation='relu', kernel_regularizer=regularizers.l1(0.0001)), Dense(output_dim=hidden3_num_units, input_dim=hidden2_num_units, acti...
Dense(256, kernel_regularizer=keras.regularizers.l1(0.01),bias_regularizer=keras.regularizers.l1(0.01), activity_regularizer=keras.regularizers.l1(0.01)) 1. 2. 上面也提过,def l1()、def l2() 、def l1_l2(),用来在定义层时调用(keras.regularizers.l1,keras.regularizers.l2,keras.regularizers.l1...
scales = Dense(out_channels, activation=None, kernel_initializer='he_normal', kernel_regularizer=l2(1e-4))(abs_mean) scales = BatchNormalization()(scales) scales = Activation('relu')(scales) scales = Dense(out_channels, activation='sigmoid', kernel_regularizer=l2(1e-4))(scales) scales =...
58 kernel_regularizer=regularizers.l2(RegularizationFactor), # 运用L2正则化 59 # activation=ActivationMethod 60 ), 61 layers.LeakyReLU(), # 引入LeakyReLU这一改良的ReLU激活函数,从而加快模型收敛,减少过拟合 62 layers.BatchNormalization(), # 引入Batch Normalizing,加快网络收敛与增强网络稳固性 63 layers...
kernel_regularizer=regularizers.l2(RegularizationFactor), # activation=ActivationMethod ), layers.LeakyReLU(), layers.BatchNormalization(), layers.Dropout(DropoutValue[1]), layers.Dense(HiddenLayer[2], kernel_regularizer=regularizers.l2(RegularizationFactor), # activation=ActivationMethod ), layers.LeakyR...
kernel_regularizer=l2(1e-4) ) ) # 编译模型 model.compile( optimizer='Adam',# 优化其 loss='categorical_crossentropy',# 损失函数 metrics=['accuracy']# 算法衡量指标 ) # 模型训练 model.fit( x=x_train, y=y_train, batch_size=batch_size, ...
# 向模型添加L2权重正则化fromkerasimportregularizersfromkerasimportlayers,models model=models.Sequential()model.add(layers.Dense(16,kernel_regularizer=regularizers.l2(0.001),activation="relu",input_shape=(10000,)))model.add(layers.Dense(16,kernel_regularizer=regularizers.l2(0.001),activation="relu"))...
其中 activation 以字符串给出,可以使用的激活函数有 relu、softmox、sigmod、tanh等;其中 kernel_regularizer 填入函数名,我们可以使用 l1 正则化和 l2 正则化,分别使用函数 tf.eras.regularizers.l1()、tf.keras.regularizers.l2()。 3.卷积层:tf.keras.layers.Conv2D(filers=卷积核个数,kernel_size=卷积核...
kernel_regularizer=l2(1e-4))(residual) # Calculate global means residual_abs = Lambda(abs_backend)(residual) abs_mean = GlobalAveragePooling2D()(residual_abs) # Calculate scaling coefficients scales = Dense(out_channels, activation=None, kernel_initializer='he_normal', ...
kernel_initializer:权重初始化方法,为预定义初始化方法名的字符串,或用于初始化权重的函数。请参考前面的“网络层对象”部分的介绍。 bias_initializer:偏置初始化方法,为预定义初始化方法名的字符串,或用于初始化偏置的函数。请参考前面的“网络层对象”部分的介绍。 kernel_regularizer:施加在权重上的正则项,请参考...