# -*- coding: utf-8 -*-
import sys
import numpy
from HiddenLayer import HiddenLayer
from LogisticRegression import LogisticRegression
from utils import *
class MLP(object):
def __init__(self, input, label, n_in, n_hidden, n_out, rng=None):
self.x = input
self.y = label
if rng is None:
rng = numpy.random.RandomState(1234)
# construct hidden_layer
self.hidden_layer = HiddenLayer(input=self.x,
n_in=n_in,
n_out=n_hidden,
rng=rng,
activation=tanh)
# construct log_layer
self.log_layer = LogisticRegression(input=self.hidden_layer.output,
label=self.y,
n_in=n_hidden,
n_out=n_out)
def train(self):
# forward hidden_layer
layer_input = self.hidden_layer.forward()
# forward & backward log_layer
# self.log_layer.forward(input=layer_input)
self.log_layer.train(input=layer_input)
# backward hidden_layer
self.hidden_layer.backward(prev_layer=self.log_layer)
# backward log_layer
# self.log_layer.backward()
def predict(self, x):
x = self.hidden_layer.output(input=x)
return self.log_layer.predict(x)
def test_mlp(n_epochs=5000):
x = numpy.array([[0, 0],
[0, 1],
[1, 0],
[1, 1]])
y = numpy.array([[0, 1],
[1, 0],
[1, 0],
[0, 1]])
rng = numpy.random.RandomState(123)
# construct MLP
classifier = MLP(input=x, label=y, n_in=2, n_hidden=3, n_out=2, rng=rng)
# train
for epoch in xrange(n_epochs):
classifier.train()
# test
print classifier.predict(x)
if __name__ == "__main__":
test_mlp()