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_regularizer=regularizers.l1(0.0001)), Dense(output_dim=hidden3_num_units, input_dim=hidden2_num_units, activation='relu', kernel_regularizer=regularizers.l1(0.0001)), Dense(output_dim=hidden4_num_units, input_dim=hidden3_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...
padding='same', kernel_initializer='he_normal', 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...
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...
# 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"))...
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 ...
kernel_regularizer=regularizers.l1_l2(l1=0.01,l2=0.01),#L1及L2正则项 use_bias=True))# 隐藏层 model.add(Dropout(0.1))# dropout法 model.add(Dense(1,use_bias=True))# 输出层 模型编译 设定学习目标为(最小化)回归预测损失mse,优化算法为adam ...
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=self._weight_init, kernel_regularizer=l2(self._weight_decay), use_bias=self._use_bias)(net) else: shortcut = net return add([convs, shortcut]) return f # "Stacking Residual Units on the same stage" def _layer(self, block, n_input_plane, n_output_plane, count, ...