def __init__(self, rng, input, n_in, n_out, W=None, b=None):
self.input = input
W, b = utils.init_W_b(W, b, rng, n_in, n_out)
self.W = W
self.b = b
lin_output = T.dot(input, self.W) + self.b
self.output = T.maximum(lin_output, 0)
# parameters of the model
self.params = [self.W, self.b]
def __init__(self, rng, input, n_in, n_out, W=None, b=None, p=0.5, is_train=0):
self.input = input
W, b = utils.init_W_b(W, b, rng, n_in, n_out)
self.W = W
self.b = b
lin_output = T.dot(input, self.W) + self.b
output = T.maximum(lin_output, 0)
# multiply output and drop -> in an approximation the scaling effects cancel out
train_output = utils.drop(numpy.cast[theano.config.floatX](1.0 / p) * output)
# is_train is a pseudo boolean theano variable for switching between training and prediction
self.output = T.switch(T.neq(is_train, 0), train_output, output)
# parameters of the model
self.params = [self.W, self.b]
