mihaelacr

# -*- coding: utf-8 -*-
"""
This module creates an optimization of hyper-parameters of a DBN using hyperopt library
Check out the library here: https://github.com/hyperopt/hyperopt).
example run:
python hyperopt_exampleMNIST.py --trainSize=10000 --path=...

REFERENCES
[1] - Bergstra, James, Dan Yamins, and David D. Cox. "Hyperopt: A python library for optimizing the hyperparameters of machine learning algorithms." (2013).

mihaelacr

def MNISTmain():
  # TODO: max_col_norm
  h0 = maxout.Maxout(layer_name='h0', num_units=1200, num_pieces=2, W_lr_scale=1.0, irange=0.005, b_lr_scale=1.0)
  h1 = maxout.Maxout(layer_name='h1', num_units=1200, num_pieces=2, W_lr_scale=1.0, irange=0.005, b_lr_scale=1.0)
  # h2 = maxout.Maxout(layer_name='h2, num_units=1200, num_pieces=2, W_lr_scale=1.0, irange=0.005, b_lr_scale=1.0)
  outlayer = mlp.Softmax(layer_name='y', n_classes=10, irange=0)

  # layers = [h0, h1, h2, outlayer]
  layers = [h0, h1, outlayer]

mihaelacr

# The aim of this file is to check
# the theory that softmax is caused by the same input

import numpy as np

a = np.array([[-0., -0., -0., -1., -0., -0., -0., -0., -0., -0.],
       [-0., -0., -0., -1., -0., -0., -0., -0., -0., -0.],
       [-0., -0., -0., -0., -0., -0., -0., -0., -1., -0.],
       [-0., -1., -0., -0., -0., -0., -0., -0., -0., -0.],
       [-0., -0., -0., -1., -0., -0., -0., -0., -0., -0.],

mihaelacr

import numpy as np

import restrictedBoltzmannMachine as rbm
import theano
from theano import tensor as T

theanoFloat  = theano.config.floatX

"""In all the above topLayer does not mean the top most layer, but rather the
layer above the current one."""