What distinguishes the utilization of weight decay in an optimizer from weight decay applied to neural network layers? Let's examine this through an example:
%%%% ipt is our input matrix.
weight_decay = 1e-4
x = LSTM(12, kernel_regularizer = l2(weight_decay), bias_regularizer = l2(weight_decay))(ipt)
out = Dense(1, activation = 'relu', kernel_regularizer = l2(weight_decay), bias_regularizer = l2(weight_decay))(x)
dl_model = Model(ipt, out)
dl_model.compile(optimizer = tf.keras.optimizers.Adam(learning_rate = 0.001, decay = 1e-5), loss = 'mse')
The initial utilization of weight decay involves applying L2 regularizer (both bias and kernel), while the second one is within the context of the Adam optimizer (decay parameter). What are the primary distinctions between these two approaches?
L2 regularization on weights directly influences the model's architecture by penalizing the magnitude of weights in each layer to prevent overfitting.
The Adam optimizer's decay parameter controls the learning rate scheduling and does not directly regularize the model's weights. It is used to fine-tune the optimization process.
Both techniques can be preferred to train models, but they serve different purposes in the training process. Weight regularization like L1 and L2, is a form of explicit constraint on the model's weights, while learning rate scheduling (Adam's decay) is a method for controlling the optimization process during training.
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