Details

Type: Bug

Status: Closed

Priority: Major

Resolution: Won't Fix

Affects Version/s: 0.7

Fix Version/s: 0.10.0

Component/s: Classification

Labels:None
Description
The initialisation to compute the gradient descent weight updates for the output units should be wrong:
In the comment: "dy / dw is just w since y = x' * w + b."
This is wrong. dy/dw is x (ignoring the indices). The same initialisation is done in the code.
Check by using neural network terminology:
The gradient machine is a specialized version of a multi layer perceptron (MLP).
In a MLP the gradient for computing the "weight change" for the output units is:
dE / dw_ij = dE / dz_i * dz_i / d_ij with z_i = sum_j (w_ij * a_j)
here: i index of the output layer; j index of the hidden layer
(d stands for the partial derivatives)
here: z_i = a_i (no squashing in the output layer)
with the special loss (cost function) is E = 1  a_g + a_b = 1  z_g + z_b
with
g index of output unit with target value: +1 (positive class)
b: random output unit with target value: 0
=>
dE / dw_gj = dE/dz_g * dz_g/dw_gj = 1 * a_j (a_j: activity of the hidden unit
j)
dE / dw_bj = dE/dz_b * dz_b/dw_bj = +1 * a_j (a_j: activity of the hidden unit
j)
That's the same if the comment would be correct:
dy /dw = x (x is here the activation of the hidden unit) * (1) for weights to
the output unit with target value +1.

In neural network implementations it's common to compute the gradient
numerically for a test of the implementation. This can be done by:
dE/dw_ij = (E(w_ij + epsilon) E(w_ij  epsilon) ) / (2* (epsilon))
Issue Links
 is superceded by

MAHOUT1265 Add Multilayer Perceptron
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The new MLP implementation renders this Jira Obsolete.