Xavier (Glorot) initialization sets initial weights to maintain roughly the same variance of activations and gradients across layers. This prevents the vanishing or exploding gradient problem in networks using sigmoid or tanh activations.
Given a raw weight matrix W with values in [0, 1], fan_in (number of input units), and fan_out (number of output units), scale the weights to Xavier uniform initialization.
Return a matrix of four-decimal floats with the same shape as W.
Input: W = [[0.5, 0.5], [0.5, 0.5]], fan_in = 2, fan_out = 2
Output: [[0.0, 0.0], [0.0, 0.0]]
Explanation: A raw midpoint of 0.5 maps to the center of the symmetric Xavier range.
Input: W = [[0, 1], [1, 0]], fan_in = 2, fan_out = 2
Output: [[-1.2247, 1.2247], [1.2247, -1.2247]]
Compute the symmetric limit from fan_in plus fan_out.
Map each raw value v with v times two limits minus one limit.
Accepts: array
Accepts: number
Accepts: number
Xavier (Glorot) initialization sets initial weights to maintain roughly the same variance of activations and gradients across layers. This prevents the vanishing or exploding gradient problem in networks using sigmoid or tanh activations.
Given a raw weight matrix W with values in [0, 1], fan_in (number of input units), and fan_out (number of output units), scale the weights to Xavier uniform initialization.
Return a matrix of four-decimal floats with the same shape as W.
Input: W = [[0.5, 0.5], [0.5, 0.5]], fan_in = 2, fan_out = 2
Output: [[0.0, 0.0], [0.0, 0.0]]
Explanation: A raw midpoint of 0.5 maps to the center of the symmetric Xavier range.
Input: W = [[0, 1], [1, 0]], fan_in = 2, fan_out = 2
Output: [[-1.2247, 1.2247], [1.2247, -1.2247]]
Compute the symmetric limit from fan_in plus fan_out.
Map each raw value v with v times two limits minus one limit.
Accepts: array
Accepts: number
Accepts: number