Keras Error: Argument weight_decay Must Be a Float. Received: weight_decay=None

When working with Keras, you may encounter the following error:

Argument `weight_decay` must be a float. Received: weight_decay=None

This error occurs when you try to use a weight decay regularizer with a value of None. Weight decay is a technique used to prevent overfitting by penalizing large weights in the model. It is typically applied to the weights of convolutional and fully connected layers.

Understanding the Error

In Keras, weight decay is implemented as a regularization loss function. The weight decay loss is added to the total loss function of the model, and it encourages the model to have smaller weights. This helps to prevent overfitting by reducing the reliance on individual features and promoting more generalizable solutions.

The weight_decay argument in Keras regularizers expects a float value that specifies the weight decay rate. This rate determines how strongly the weight decay loss is applied. A higher weight decay rate results in stronger regularization.

However, in the error message, you have set weight_decay to None. This means that you are not specifying a weight decay rate, which is causing the error.

Solution: Specify a Weight Decay Rate

To resolve the error, you need to specify a valid weight decay rate as a float value. You can do this when creating the regularizer:

from keras import regularizers

# Create a weight decay regularizer with a rate of 0.01
weight_decay_regularizer = regularizers.l2(0.01)

You can then apply this regularizer to your model's layers:

model = tf.keras.Sequential([
    tf.keras.layers.Dense(10, kernel_regularizer=weight_decay_regularizer)
])

By specifying a weight decay rate, you enable the regularization effect and help prevent overfitting in your model.

Handling Default Weight Decay

In some cases, you may want to use the default weight decay rate provided by Keras. For convolutional and fully connected layers, the default weight decay rate is 0.0001.

To use the default weight decay rate, simply omit the weight_decay argument when creating the regularizer:

weight_decay_regularizer = regularizers.l2()

Conclusion

The error "Argument weight_decay must be a float. Received: weight_decay=None" occurs when you try to use a weight decay regularizer without specifying a valid weight decay rate. To resolve this error, you need to specify a weight decay rate as a float value. You can either explicitly set the rate or use the default rate provided by Keras. By using weight decay regularization, you can help prevent overfitting and improve the generalization performance of your Keras models. Keras models.