def __init__(self, num_hidden, num_visible, data=None, binary_visible=True, hidden_activation_function = Sigmoid(), dropout=0.0):
'''Initialize a restricted boltzmann machine on GPU
num_visible: The number of visible units. data.shape[0]
num_hidden: The number of hidden units.
binary: True if the visible units are binary, False if the visible units
are normally distributed.
'''
self.num_hidden = num_hidden
self.num_visible = num_visible
self.hidden_unittype = hidden_activation_function
self.visible_unittype = self.hidden_unittype if binary_visible else Gaussian()
if(data is not None):
self.data = data#isinstance(data, gp.garray) and data or gp.garray(data.astype(np.float32))
self.num_visible = data.shape[1]
self.weights = 0.1 * gp.randn(self.num_visible,self.num_hidden)
self.hidden_bias = -4. * gp.ones(self.num_hidden)
self.visible_bias = gp.zeros(self.num_visible)
self.grad_weights = gp.zeros(self.weights.shape)
self.grad_visible = gp.zeros(self.visible_bias.shape)
self.grad_hidden = gp.zeros(self.hidden_bias.shape)
self.dropout = dropout
if(dropout is None or dropout==0):
self.cd1 = CD1
else:
self.cd1=CD1_dropout
def sampleStates(self, acts):
if self.krizNoise:
return self.activate(acts + gp.randn(*acts.shape))
tiny = 1e-30
stddev = gp.sqrt(acts.sigmoid() + tiny)
return self.activate( acts + stddev*gp.randn(*acts.shape) )
