def __init__(self,
in_size,
out_size,
bias=0,
nobias=False,
initialW=None,
initial_bias=None,
normalized=False):
super(SequenceLinear, self).__init__(W=(in_size, out_size))
if initialW is None:
initialW = weight_init.uniform((in_size, out_size))
self.W.data[...] = initialW
if normalized:
self.add_param('gamma', (out_size, ))
self.add_param('beta', (out_size, ))
self.gamma.data[...] = numpy.ones((out_size, ),
dtype=numpy.float32)
self.beta.data[...] = numpy.zeros((out_size, ),
dtype=numpy.float32)
nobias = True
if nobias:
self.b = None
else:
self.add_param('b', out_size)
if initial_bias is None:
initial_bias = bias
self.b.data[...] = initial_bias
self.add_persistent('normalized', normalized)
def __init__(self,
in_size,
out_size,
bias=0,
nobias=False,
W_x=None,
W_h=None,
initial_bias=None,
reverse=False,
normalized=False,
stateful=False):
super(SequenceLSTM, self).__init__(W_x=(in_size, 4 * out_size),
W_h=(out_size, 4 * out_size))
if W_x is None:
W_x = weight_init.uniform((in_size, 4 * out_size),
low=-0.04,
high=0.04)
if W_h is None:
W_h = weight_init.uniform((out_size, 4 * out_size),
low=-0.04,
high=0.04)
self.W_x.data[...] = W_x
self.W_h.data[...] = W_h
if normalized:
self.add_param('gamma', (4 * out_size, ))
self.add_param('beta', (4 * out_size, ))
self.gamma.data[...] = numpy.ones((4 * out_size, ),
dtype=numpy.float32)
self.beta.data[...] = numpy.zeros((4 * out_size, ),
dtype=numpy.float32)
nobias = True
if nobias:
self.b = None
else:
self.add_param('b', 4 * out_size)
if initial_bias is None:
initial_bias = bias
self.b.data[...] = initial_bias
self.add_persistent('reverse', reverse)
self.add_persistent('normalized', normalized)
self.add_persistent('stateful', stateful)
self.reset_states()
def __init__(self, in_size, out_size, bias=0, nobias=False,
W_x=None, W_h=None, initial_bias=None, reverse=False,
normalized=False, stateful=False):
super(SequenceLSTM, self).__init__(W_x=(in_size, 4 * out_size),
W_h=(out_size, 4 * out_size))
if W_x is None:
W_x = weight_init.uniform((in_size, 4 * out_size), low=-0.04,
high=0.04)
if W_h is None:
W_h = weight_init.uniform((out_size, 4 * out_size), low=-0.04,
high=0.04)
self.W_x.data[...] = W_x
self.W_h.data[...] = W_h
if normalized:
self.add_param('gamma', (4 * out_size,))
self.add_param('beta', (4 * out_size,))
self.gamma.data[...] = numpy.ones((4 * out_size,),
dtype=numpy.float32)
self.beta.data[...] = numpy.zeros((4 * out_size,),
dtype=numpy.float32)
nobias = True
if nobias:
self.b = None
else:
self.add_param('b', 4 * out_size)
if initial_bias is None:
initial_bias = bias
self.b.data[...] = initial_bias
self.add_persistent('reverse', reverse)
self.add_persistent('normalized', normalized)
self.add_persistent('stateful', stateful)
self.reset_states()
def __init__(self, in_size, out_size, bias=0, nobias=False,
initialW=None, initial_bias=None, normalized=False):
super(SequenceLinear, self).__init__(W=(in_size, out_size))
if initialW is None:
initialW = weight_init.uniform((in_size, out_size))
self.W.data[...] = initialW
if normalized:
self.add_param('gamma', (out_size,))
self.add_param('beta', (out_size,))
self.gamma.data[...] = numpy.ones((out_size,), dtype=numpy.float32)
self.beta.data[...] = numpy.zeros((out_size,), dtype=numpy.float32)
nobias = True
if nobias:
self.b = None
else:
self.add_param('b', out_size)
if initial_bias is None:
initial_bias = bias
self.b.data[...] = initial_bias
self.add_persistent('normalized', normalized)
