def param_init_fflayer(options, params, prefix='ff', nin=None, nout=None, ortho=True):
"""
Affine transformation + point-wise nonlinearity
"""
if nin == None:
nin = options['dim_proj']
if nout == None:
nout = options['dim_proj']
params[_p(prefix,'W')] = xavier_weight(nin, nout)
params[_p(prefix,'b')] = numpy.zeros((nout,)).astype('float32')
return params
def param_init_fclayer(options, params, prefix='fc', nin=None, nout=None, ortho=True):
"""
Affine transformation + point-wise nonlinearity
"""
if nin == None:
nin = options['dim_proj']
if nout == None:
nout = options['nCategories']
params[prefix+'_w'] = theano.shared(value=xavier_weight(nin, nout), borrow=True)
params[prefix+'_b'] = theano.shared(value=np.zeros((nout,)).astype('float32'), borrow=True)
return params
def init_weights(self):
xavier_weight(self.linear1.weight)
self.linear1.bias.data.fill_(0)
xavier_weight(self.linear2.weight)
self.linear1.bias.data.fill_(0)
xavier_weight(self.linear3.weight)
self.linear1.bias.data.fill_(0)
def param_init_fflayer(options,
params,
prefix='ff',
nin=None,
nout=None,
ortho=True):
"""
Affine transformation + point-wise nonlinearity
"""
if nin == None:
nin = options['dim_proj']
if nout == None:
nout = options['dim_proj']
params[_p(prefix, 'W')] = xavier_weight(nin, nout)
params[_p(prefix, 'b')] = numpy.zeros((nout, )).astype('float32')
return params
def init_weights(self):
xavier_weight(self.linear.weight)
# init.xavier_normal(self.linear.weight)
self.linear.bias.data.fill_(0)
def init_weights(self):
xavier_weight(self.decoderini.weight)
self.decoderini.bias.data.fill_(0)
