def __init__(self, rng, srng, dropout_rate, input, d_in, d_out,
W=None, b=None, activation=T.tanh):
"""
Apart from the `srng` and `dropout_rate`, the parameters are
identical to those of `HiddenLayer`.
"""
super(DropoutHiddenLayer, self).__init__(
rng=rng, input=input, d_in=d_in, d_out=d_out, W=W, b=b,
activation=activation
)
self.output = theano_utils.apply_dropout(srng, self.output, p=dropout_rate)
def __init__(self, rng, srng, dropout_rate, input, input_shape,
filter_shape, pool_shape=(2, 2), activation=T.tanh, W=None,
b=None):
"""
Apart from the `srng` and `dropout_rate`, the parameters are
identical to those of `ConvMaxPoolLayer`.
"""
super(DropoutConvMaxPoolLayer, self).__init__(
rng, input, input_shape, filter_shape, pool_shape, activation, W, b
)
self.output = theano_utils.apply_dropout(srng, self.output, p=dropout_rate)
def __init__(self,
rng,
srng,
dropout_rate,
input,
input_shape,
filter_shape,
pool_shape=(2, 2),
activation=T.tanh,
W=None,
b=None):
"""
Apart from the `srng` and `dropout_rate`, the parameters are
identical to those of `ConvMaxPoolLayer`.
"""
super(DropoutConvMaxPoolLayer,
self).__init__(rng, input, input_shape, filter_shape, pool_shape,
activation, W, b)
self.output = theano_utils.apply_dropout(srng,
self.output,
p=dropout_rate)