Implementing an l2 loss into a tensorflow Sequential regression model - python-3.x

Question

I created a keras- tensorflow model, much influenced by
this guide
which looks like
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)
It is a regression model and instead of the loss = 'mse' I would like to use
tf keras mse loss together with an L2 regularization term. The question is
How can I add a predefined regularizer function (I think, it is this one ) into the model.compile statement.
How can I write a completely custom loss function and add it to model.compile.

Answer 1

You can add regularization as either a layer parameter or as a layer.
Use it as a layer parameter looks like below
model.add(layers.Dense(8,
kernel_regularizer=regularizers.l2(0.01),
activity_regularizer=regularizers.l1(0.01)))
Sample code with first dense layer regularized and a custom loss function
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)