def get_hidden_values(self, input):
""" Computes the values of the hidden layer.
Arguments:
input: the data to feed through the input layer to the
hidden layer.
Returns:
The encoded (lower dimensional) feature vector for a given
asin in the form of a 1 hot encoding vector.
"""
if type(input) == T.TensorVariable:
sparse_input = basic.csc_from_dense(input)
else:
sparse_input = input
lin_output = basic.dot(sparse_input, self.W)
if self.activation is None and self.b is None:
return lin_output
elif self.activation is None:
return lin_output + self.b
elif self.b is None:
return self.activation(lin_output)
else:
return self.activation(lin_output + self.b)
def output(self, seq_output=True):
x = self.layer_below.output(seq_output=seq_output)
# return self.activation(T.dot(X, self.W) + self.b)
# dropout = Dropout()
# if self.sparse_inputs:
# x = dropout.sparse_dropout(x, self.drop_value, self.num_features_nonzero) # Not written completely
# else:
# x = dropout.dropout_layer(x, self.drop_value)#, train)
# convolve
# theano.shared(value=np.zeros(shape,dtype=theano.config.floatX))
# output = zero0s((self.inputD,self.size))
supports = list()
# for i in range(len(self.adjacency)):
if not self.featureless:
if self.sparse_inputs:
pre_sup = sp.dot(x, self.W)
else:
# print(x.shape)
# print(self.W.shape)
# exit()
pre_sup = T.dot(x, self.W)
else:
pre_sup = self.W[i]
support = T.dot(self.adjacency, pre_sup) #5.5 5.10x10.5
supports.append(support)
output = support
if self.bias:
output += self.b
return self.activation(output)
def get_hidden_values(self, input):
#if type(input) != basic.SparseVariable:
if type(input) == T.TensorVariable:
sparse_input = basic.csc_from_dense(input)
else:
sparse_input = input
#x_is_sparse_variable = basic._is_sparse_variable(input)
#w_is_sparse_variable = basic._is_sparse_variable(self.W)
#if not x_is_sparse_variable and not w_is_sparse_variable:
# fn = T.dot
#else:
# fn = basic.dot
lin_output = basic.dot(sparse_input, self.W)
if self.activation is None and self.b is None:
return lin_output
elif self.activation is None:
return lin_output + self.b
elif self.b is None:
return self.activation(lin_output)
else:
return self.activation(lin_output + self.b)
def SpDotTransferFunction(x, W, b):
if b !=None:
return ST.dot(x, W) + b
else:
return ST.dot(x,W)
if _is_sparse_variable(x):
raise TypeError(x)
if _is_sparse_variable(y):
raise TypeError(y)
if not _is_sparse(p):
raise TypeError(p)
rval = p.__class__(p.multiply(numpy.dot(x, y.T)))
out[0] = rval
def grad(self, (x, y, p), (gz,)):
rval = [
dot(gz, y),
dot(gz.T, x),
None
]
return rval
sampling_dot = SamplingDot()
class SamplingDotCsr(gof.Op):
"""
Optimized SamplingDot when the pattern P is a CSR matrix.
If we have the input of mixed dtype, we insert cast elemwise in the graph
to be able to call blas function as they don't allow mixed dtype.
def SpDotTransferFunction(x, W, b):
if b != None:
return ST.dot(x, W) + b
else:
return ST.dot(x, W)
if _is_sparse_variable(x):
raise TypeError(x)
if _is_sparse_variable(y):
raise TypeError(y)
if not _is_sparse(p):
raise TypeError(p)
rval = p.__class__(p.multiply(numpy.dot(x, y.T)))
out[0] = rval
def grad(self, (x, y, p), (gz,)):
rval = [
dot(p * gz, y),
dot(p.T * gz.T, x),
None
]
return rval
sampling_dot = SamplingDot()
class SamplingDotCsr(gof.Op):
"""
Optimized SamplingDot when the pattern P is a CSR matrix.
If we have the input of mixed dtype, we insert cast elemwise in the graph
to be able to call blas function as they don't allow mixed dtype.
def perform(self, node, (x, y, p), (out, )):
if _is_sparse_variable(x):
raise TypeError(x)
if _is_sparse_variable(y):
raise TypeError(y)
if not _is_sparse(p):
raise TypeError(p)
rval = p.__class__(p.multiply(numpy.dot(x, y.T)))
out[0] = rval
def grad(self, (x, y, p), (gz, )):
rval = [dot(p * gz, y), dot(p.T * gz.T, x), None]
return rval
sampling_dot = SamplingDot()
class SamplingDotCsr(gof.Op):
"""
Optimized SamplingDot when the pattern P is a CSR matrix.
If we have the input of mixed dtype, we insert cast elemwise in the graph
to be able to call blas function as they don't allow mixed dtype.
"""
