在张量流顺序回归模型中实现 l2 损失

Question

我创建了一个 keras-tensorflow 模型，受以下因素影响很大 this guide 看起来像

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import time 
import numpy as np
import sys
from keras import losses


model = keras.Sequential()
model.add(layers.Dense(nodes,activation = tf.keras.activations.relu, input_shape=[len(data_initial.keys())]))
model.add(layers.Dense(64,activation = tf.keras.activations.relu))
model.add(layers.Dropout(0.1, noise_shape=None))
model.add(layers.Dense(1))

model.compile(loss='mse',    # <-------- Here we define the loss function 
              optimizer=tf.keras.optimizers.Adam(lr= 0.01,
                                                beta_1 = 0.01,
                                                beta_2 = 0.001,
                                                epsilon= 0.03),
                                                metrics=['mae', 'mse'])
model.fit(train_data,train_labels,epochs = 200)

这是一个回归模型，而不是 loss = 'mse' 我想使用 tf keras mse loss 和一个L2 正则化项。问题是

• 如何在 model.compile 语句中添加预定义的正则化函数（我认为是 this one ）。

• 如何编写完全自定义的损失函数并将其添加到 model.compile。

Answer 1

可以将正则化添加为图层参数或作为图层。

将其用作图层参数如下下面

model.add(layers.Dense(8, 
          kernel_regularizer=regularizers.l2(0.01),
          activity_regularizer=regularizers.l1(0.01)))

使用第一密集层的样本代码正规化和自定义丢失功能 h3>

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import time 
import numpy as np
import sys
from keras import losses
from keras import regularizers
import keras.backend as K


model = keras.Sequential()
model.add(layers.Dense(8,activation = tf.keras.activations.relu, input_shape=(8,), 
                       kernel_regularizer=regularizers.l2(0.01), 
                       activity_regularizer=regularizers.l1(0.01)))

model.add(layers.Dense(4,activation = tf.keras.activations.relu))
model.add(layers.Dropout(0.1, noise_shape=None))
model.add(layers.Dense(1))


def custom_loss(y_true, y_pred):
    return K.mean(y_true - y_pred)**2

model.compile(loss=custom_loss,
              optimizer=tf.keras.optimizers.Adam(lr= 0.01,
                                                beta_1 = 0.01,
                                                beta_2 = 0.001,
                                                epsilon= 0.03),
                                                metrics=['mae', 'mse'])

model.fit(np.random.randn(10,8),np.random.randn(10,1),epochs = 1)