def __init__(self, input=None, label=None,\

n_ins=2, hidden_layer_sizes=[3, 3], n_outs=2,\

rng=None):

self.x = input

self.y = label

self.sigmoid_layers = []

self.rbm_layers = []

self.n_layers = len(hidden_layer_sizes) # = len(self.rbm_layers)

if rng is None:

rng = numpy.random.RandomState(1234)

assert self.n_layers > 0

# construct multi-layer

for i in xrange(self.n_layers):

# layer_size

if i == 0:

input_size = n_ins

else:

input_size = hidden_layer_sizes[i - 1]

# layer_input

if i == 0:

layer_input = self.x

else:

layer_input = self.sigmoid_layers[-1].sample_h_given_v()

# construct sigmoid_layer

sigmoid_layer = HiddenLayer(input=layer_input,

n_in=input_size,

n_out=hidden_layer_sizes[i],

rng=rng,

activation=sigmoid)

self.sigmoid_layers.append(sigmoid_layer)

# construct rbm_layer

if i == 0:

rbm_layer = CRBM(input=layer_input, # continuous-valued inputs

n_visible=input_size,

n_hidden=hidden_layer_sizes[i],

W=sigmoid_layer.W, # W, b are shared

hbias=sigmoid_layer.b)

else:

rbm_layer = RBM(input=layer_input,

n_visible=input_size,

n_hidden=hidden_layer_sizes[i],

W=sigmoid_layer.W, # W, b are shared

hbias=sigmoid_layer.b)

self.rbm_layers.append(rbm_layer)

# layer for output using Logistic Regression

self.log_layer = LogisticRegression(input=self.sigmoid_layers[-1].sample_h_given_v(),

label=self.y,

n_in=hidden_layer_sizes[-1],

n_out=n_outs)

# finetune cost: the negative log likelihood of the logistic regression layer

finetune_lr=0.1, finetune_epochs=200):

x = numpy.array([[0.4, 0.5, 0.5, 0., 0., 0.],

[0.5, 0.3, 0.5, 0., 0., 0.],

[0.4, 0.5, 0.5, 0., 0., 0.],

[0., 0., 0.5, 0.3, 0.5, 0.],

[0., 0., 0.5, 0.4, 0.5, 0.],

[0., 0., 0.5, 0.5, 0.5, 0.]])

y = numpy.array([[1, 0],

[1, 0],

[1, 0],

[0, 1],

[0, 1],

[0, 1]])

rng = numpy.random.RandomState(123)

# construct DBN

dbn = CDBN(input=x, label=y, n_ins=6, hidden_layer_sizes=[5, 5], n_outs=2, rng=rng)

# pre-training (TrainUnsupervisedDBN)

dbn.pretrain(lr=pretrain_lr, k=1, epochs=pretraining_epochs)

# fine-tuning (DBNSupervisedFineTuning)

dbn.finetune(lr=finetune_lr, epochs=finetune_epochs)

# test

x = numpy.array([[0.5, 0.5, 0., 0., 0., 0.],

[0., 0., 0., 0.5, 0.5, 0.],

[0.5, 0.5, 0.5, 0.5, 0.5, 0.]])