kernel_regularizer=regularizers.l2(0.0001)), Dense(output_dim=hidden4_num_units, input_dim=hidden3_num_units, activation='relu', kernel_regularizer=regularizers.l2(0.0001)), Dense(output_dim=hidden5_num_units, input_dim=hidden4_num_units, activation='relu', kernel_regularizer=regularizers.l2(...
kernel_initializer='glorot_uniform', kernel_regularizer=regularizers.l2(0.0), input_shape=(X_train.shape[1],) ) ) model.add(Dense(2,activation=act_func, kernel_initializer='glorot_uniform')) model.add(Dense(10,activation=act_func, kernel_initializer='glorot_uniform')) model.add(Dense(X_tr...
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...
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=regularizers.l2(RegularizationFactor),# 运用L2正则化 # activation=ActivationMethod ), 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 layers.BatchNormalization(),# 引入Batch Normalizing,加快网络收敛与增强网络稳固性 ...
LSTM(256, kernel_regularizer=keras.regularizers.l1_l2(0.001, 0.01))#加入l1和l2 # 添加一些其他正则化 Dense(256, kernel_regularizer=keras.regularizers.l1(0.01),bias_regularizer=keras.regularizers.l1(0.01),activity_regularizer=keras.regularizers.l1(0.01)) ...
kernel_regularizer=regularizers.l2(0.0), input_shape=(X_train.shape[1],) ) ) model.add(Dense(2,activation=act_func, kernel_initializer='glorot_uniform')) model.add(Dense(10,activation=act_func, kernel_initializer='glorot_uniform'))
kernel_regularizer=l2(1e-4))(residual) residual = BatchNormalization()(residual) residual = Activation('relu')(residual) residual = Conv2D(out_channels, 3, padding='same', kernel_initializer='he_normal', kernel_regularizer=l2(1e-4))(residual) ...
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, ...
kernel_regularizer=regularizers.l2(0.0), input_shape=(X_train.shape[1],) ) ) model.add(Dense(2,activation=act_func, kernel_initializer='glorot_uniform')) model.add(Dense(10,activation=act_func, kernel_initializer='glorot_uniform'))