tf.contrib.layers.variance_scaling_initializer

Returns an initializer that generates tensors without scaling variance.

tf.contrib.layers.variance_scaling_initializer(
    factor=2.0, mode='FAN_IN', uniform=False, seed=None, dtype=tf.dtypes.float32
)

When initializing a deep network, it is in principle advantageous to keep the scale of the input variance constant, so it does not explode or diminish by reaching the final layer. This initializer use the following formula:

if mode='FAN_IN': # Count only number of input connections.
  n = fan_in
elif mode='FAN_OUT': # Count only number of output connections.
  n = fan_out
elif mode='FAN_AVG': # Average number of inputs and output connections.
  n = (fan_in + fan_out)/2.0

  truncated_normal(shape, 0.0, stddev=sqrt(factor / n))

• To get Delving Deep into Rectifiers (also know as the "MSRA initialization"), use (Default):
  factor=2.0 mode='FAN_IN' uniform=False
• To get Convolutional Architecture for Fast Feature Embedding, use:
  factor=1.0 mode='FAN_IN' uniform=True
• To get Understanding the difficulty of training deep feedforward neural networks, use:
  factor=1.0 mode='FAN_AVG' uniform=True.
• To get xavier_initializer use either:
  factor=1.0 mode='FAN_AVG' uniform=True, or
  factor=1.0 mode='FAN_AVG' uniform=False.

Args
factor	Float. A multiplicative factor.
mode	String. 'FAN_IN', 'FAN_OUT', 'FAN_AVG'.
uniform	Whether to use uniform or normal distributed random initialization.
seed	A Python integer. Used to create random seeds. See tf.compat.v1.set_random_seed for behavior.
dtype	The data type. Only floating point types are supported.

Returns
An initializer that generates tensors with unit variance.

Raises
ValueError	if dtype is not a floating point type.
TypeError	if mode is not in ['FAN_IN', 'FAN_OUT', 'FAN_AVG'].