def __init__(self, input, input_units, output_units, activation):
self.input = input
self.activation = activation
self.output_units = output_units
scale = 1.0 / np.sqrt(input_units)
initW = np.asarray(get_rng().uniform(low=-scale, high=scale, size=(input_units, output_units)), dtype=floatX)
initb = np.asarray(get_rng().uniform(low=-scale, high=scale, size=(output_units,)), dtype=floatX)
self.W = shared(initW)
self.b = shared(initb)
def __init__(self, input, filter_shape, input_shape, activation, subsample=(1, 1)):
self.input = input
self.activation = activation
self.subsample = subsample
self.input_shape = input_shape
self.filter_shape = filter_shape
scale = 1.0 / np.sqrt(np.prod(input_shape[1:]))
initW = np.asarray(get_rng().uniform(low=-scale, high=scale, size=filter_shape), dtype=floatX)
initb = np.asarray(get_rng().uniform(low=-scale, high=scale, size=(filter_shape[0],)), dtype=floatX)
self.W = shared(initW)
self.b = shared(initb)
