def __init__(self,
input_dim,
hidden_dim,
init='glorot_uniform',
activation='linear',
weights=None):
nvis = input_dim
nhid = hidden_dim
W_shape = nhid, nvis
lim = np.sqrt(6. / (2 * nvis + 1))
W_init = np.random.uniform(-lim, lim, W_shape)
W = theano.shared(W_init)
hbias = theano.shared(np.zeros((nhid, 1)), broadcastable=[False, True])
self.init = initializations.get(init)
self.activation = activations.get(activation)
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.output_dim = input_dim
self.input = T.matrix()
#maybe need to replace the initialization function
self.W = self.init((self.input_dim, self.hidden_dim))
self.b = shared_zeros((self.hidden_dim))
#self.b_tilde = shared_zeros((self.input_dim))
self.params = [self.W, self.b]
if weights is not None:
self.set_weights(weights)
def __init__(self, input_dim, hidden_dim, init='glorot_uniform', activation='linear', weights=None):
nvis = input_dim
nhid = hidden_dim
W_shape = nhid,nvis
lim=np.sqrt(6./(2*nvis+1))
W_init=np.random.uniform(-lim,lim,W_shape)
W=theano.shared(W_init)
hbias=theano.shared(np.zeros((nhid,1)),broadcastable=[False,True])
self.init = initializations.get(init)
self.activation = activations.get(activation)
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.output_dim = input_dim
self.input = T.matrix()
#maybe need to replace the initialization function
self.W = self.init((self.input_dim, self.hidden_dim))
self.b = shared_zeros((self.hidden_dim))
#self.b_tilde = shared_zeros((self.input_dim))
self.params = [self.W, self.b]
if weights is not None:
self.set_weights(weights)
def __init__(self,
activation='tanh',
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
**kwargs):
super(MyLayer_one, self).__init__(**kwargs)
self.activation = activations.get(activation)
self.kernel_initializer = kernel_initializer
self.bias_initializer = bias_initializer
def __init__(self,
units,
activation='tanh',
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
**kwargs):
super(binary_indicator_layer, self).__init__(**kwargs)
self.activation = activations.get(activation)
self.kernel_initializer = kernel_initializer
self.bias_initializer = bias_initializer
self.units = units
def __init__(self,
units,
classes,
activation='tanh',
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
**kwargs):
super(target_representation_layer, self).__init__(**kwargs)
self.activation = activations.get(activation)
self.kernel_initializer = kernel_initializer
self.bias_initializer = bias_initializer
self.units = units
self.classes = classes
def __init__(self,
input_dim,
proj_dim=128,
init='uniform',
activation='sigmoid',
weights=None):
super(NodeContextProduct, self).__init__()
self.input_dim = input_dim
self.proj_dim = proj_dim
self.init = initializations.get(init)
self.activation = activations.get(activation)
self.input = T.imatrix()
# two different embeddings for pivot word and its context
# because p(w|c) != p(c|w)
self.W_w = self.init((input_dim, proj_dim))
self.W_c = self.init((input_dim, proj_dim))
self.params = [self.W_w, self.W_c]
if weights is not None:
self.set_weights(weights)
def __init__(self, activation0='tanh', activation1='softmax', kernel_initializer='glorot_uniform', bias_initializer='zeros', **kwargs):
self.activation0 = activations.get(activation0)
self.activation1 = activations.get(activation1)
self.kernel_initializer = kernel_initializer
self.bias_initializer = bias_initializer
super(SelfAttentionScore, self).__init__(**kwargs)