def init_params(self):
options = self.options
insize = options.get('insize')
outsize = options.get('outsize')
init = options.get('init')
W = options.get('W')
if W is None:
W = init((insize, outsize))
W = theano.shared(
value=W,
name='W',
borrow=True
)
b = options.get('b')
if b is None:
b = np.zeros(outsize, dtype=theano.config.floatX)
b = theano.shared(
value=b,
name='b',
borrow=True
)
return [W, b]
def init_params(self):
options = self.options
in_dim = options.get('insize')
out_dim = options.get('outsize')
init = options.get('init')
W_softmax = options.get('W_softmax')
if W_softmax is None:
W_softmax = init((in_dim, out_dim))
W_softmax = theano.shared(
value=W_softmax,
name='W_softmax',
borrow=True
)
b_softmax = options.get('b_softmax')
if b_softmax is None:
b_softmax = np.zeros(out_dim, dtype=theano.config.floatX)
b_softmax = theano.shared(
value=b_softmax,
name='b_softmax',
borrow=True
)
return [W_softmax, b_softmax]
def init_params(self):
insize = self.options.get("insize")
outsize = self.options.get("outsize")
W = self.options.get("W")
b = self.options.get("b")
W_h = self.options.get("W_h")
init = self.options.get("init")
if W is None:
W = init((insize, outsize))
if b is None:
b = np.zeros(shape=(outsize,), dtype=theano.config.floatX)
if W_h is None:
W_h = init((outsize, outsize))
W = theano.shared(value=W, borrow=True, name="W")
b = theano.shared(value=b, borrow=True, name="b")
W_h = theano.shared(value=W_h, borrow=True, name="W_h")
return [W, b, W_h]
def init_params(self):
insize = self.options.get('insize')
outsize = self.options.get('outsize')
W = self.options.get('W')
b = self.options.get('b')
W_h = self.options.get('W_h')
init = self.options.get('init')
if W is None:
W = init((insize, outsize))
if b is None:
b = np.zeros(shape=(outsize,), dtype=theano.config.floatX)
if W_h is None:
W_h = init((outsize, outsize))
W = theano.shared(value=W, borrow=True, name='W')
b = theano.shared(value=b, borrow=True, name='b')
W_h = theano.shared(value=W_h, borrow=True, name='W_h')
return [W, b, W_h]
def init_params(self):
options = self.options
insize = options.get("insize")
outsize = options.get("outsize")
init = options.get("init")
W = options.get("W")
if W is None:
W = init((insize, outsize))
W = theano.shared(value=W, name="W", borrow=True)
b = options.get("b")
if b is None:
b = np.zeros(outsize, dtype=theano.config.floatX)
b = theano.shared(value=b, name="b", borrow=True)
return [W, b]
def make_shared_matrix(p, ins, outs):
if p is None:
p = init((ins, outs))
return theano.shared(value=p, borrow=True)
def make_shared_matrix(p, ins, outs):
if p is None:
p = init((ins, outs))
return theano.shared(value=p, borrow=True)
