valid_size > 0.1 and per-sample weights bug?

[kevin-bensema]

It appears that per-sample weights end up mismatched with their samples when valid_size > 0.1. I've copied-in a minimal broken example. The weights array and the desired outputs are identical, but at the beginning of each batch, they are only guaranteed to match if valid_size == 0.0 - if valid_size > 0.1, they typically do not match.

I'll submit a PR when I've got a fix.

#!/usr/bin/env python3
import sknn.mlp
import numpy as np

def callback(event, **variables):
  if event == 'on_batch_start':
    print('yb', np.transpose(variables['yb']))
    print('wb', variables['wb'])
    print('-------------------------')


if __name__ == '__main__':
  samples = 20
  X = np.random.random_sample((20, 4))
  y = np.arange(0, 20, dtype = np.float32)
  y /= 20
  w = y.copy()

  layers = [sknn.mlp.Layer('Rectifier', units = 10),
            sknn.mlp.Layer('Linear', units = 1)]
  
  print('#######################################')
  print('  With valid_size = 0.0 ')
  print('#######################################')
  net = sknn.mlp.Regressor(layers = layers, n_iter = 1, valid_size = 0.0,
                           callback = callback, batch_size = 4)
  net.fit(X, y, w)
  print('#######################################')
  print('  With valid_size = 0.1')
  print('#######################################')
  net = sknn.mlp.Regressor(layers = layers, n_iter = 1, valid_size = 0.1,
                           callback = callback, batch_size = 4)
  net.fit(X, y, w)