def __init__(self, args, incoming, num_filters, filter_size, stride=(1, 1),
pad=0, untie_biases=False,
W=init.GlorotUniform(), b=init.Constant(0.),
nonlinearity=nonlinearities.rectify,
convolution=T.nnet.conv2d, **kwargs):
super(Conv2DLayerWithReg, self).__init__(incoming, **kwargs)
if nonlinearity is None:
self.nonlinearity = nonlinearities.identity
else:
self.nonlinearity = nonlinearity
self.num_filters = num_filters
self.filter_size = _as_tuple(filter_size, 2)
self.stride = _as_tuple(stride, 2)
self.untie_biases = untie_biases
self.convolution = convolution
if pad == 'valid':
self.pad = (0, 0)
elif pad in ('full', 'same'):
self.pad = pad
else:
self.pad = _as_tuple(pad, 2, int)
self.W = self.add_param(W, self.get_W_shape(), name="W")
if b is None:
self.b = None
else:
if self.untie_biases:
biases_shape = (num_filters, self.output_shape[2], self.
output_shape[3])
else:
biases_shape = (num_filters,)
self.b = self.add_param(b, biases_shape, name="b",
regularizable=False)
if args.regL1 is True:
self.L1 = self.add_param(init.Constant(args.regInit['L1']),
self.get_W_shape() , name="L1")
if args.regL2 is True:
self.L2 = self.add_param(init.Constant(args.regInit['L2']),
self.get_W_shape() , name="L2")
python类identity()的实例源码
def __init__(self, incoming, num_filters, filter_size, stride=(1, 1),
pad=0, untie_biases=False,
W=init.GlorotUniform(), b=init.Constant(0.),
nonlinearity=nonlinearities.rectify,
convolution=T.nnet.conv2d, **kwargs):
super(Conv2DXLayer, self).__init__(incoming, **kwargs)
if nonlinearity is None:
self.nonlinearity = nonlinearities.identity
else:
self.nonlinearity = nonlinearity
self.num_filters = num_filters
self.filter_size = as_tuple(filter_size, 2)
self.stride = as_tuple(stride, 2)
self.untie_biases = untie_biases
self.convolution = convolution
if pad == 'same':
if any(s % 2 == 0 for s in self.filter_size):
raise NotImplementedError(
'`same` padding requires odd filter size.')
if pad == 'strictsamex':
if not (stride == 1 or stride == (1, 1)):
raise NotImplementedError(
'`strictsamex` padding requires stride=(1, 1) or 1')
if pad == 'valid':
self.pad = (0, 0)
elif pad in ('full', 'same', 'strictsamex'):
self.pad = pad
else:
self.pad = as_tuple(pad, 2, int)
self.W = self.add_param(W, self.get_W_shape(), name="W")
if b is None:
self.b = None
else:
if self.untie_biases:
biases_shape = (num_filters, self.output_shape[2], self.
output_shape[3])
else:
biases_shape = (num_filters,)
self.b = self.add_param(b, biases_shape, name="b",
regularizable=False)
