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)

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