What is variance scaling in TF learn explain

This recipe explains what is variance scaling in TF learn

Recipe Objective

This recipe explains what is variance scaling in TFLayer.

Explore the BERT Variants - ALBERT vs DistilBERT

Variance Scaling

Its syntax is: tflearn.initializations.variance_scaling (factor=2.0, mode='FAN_IN', uniform=False, seed=None, dtype=tf.float32)
where its arguments are factor which is a multiplicative factor, mode which is String. 'FAN_IN', 'FAN_OUT', 'FAN_AVG', uniform which tells whether to use uniform or normal distributed random initialization, dtype which is the tensor data type and seed which is used to create a random seed for the distribution.

if mode='FAN_OUT':
    x = fan_in
elif mode='FAN_AVG':
    x = (fan_in + fan_out)/2.0
elif mode='FAN_IN':
    x = fan_out
y=sqrt(factor / x)
truncated_normal(shape, 0.0, stddev=y)

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