def feedback(self, outputs):
assert self.output_dim == 0
eye = tensor.eye(self.num_outputs)
check_theano_variable(outputs, None, "int")
output_shape = [outputs.shape[i]
for i in range(outputs.ndim)] + [self.feedback_dim]
return eye[outputs.flatten()].reshape(output_shape)
def test_check_theano_variable():
check_theano_variable(None, 3, 'float')
check_theano_variable([[1, 2]], 2, 'int')
assert_raises(ValueError, check_theano_variable,
tensor.vector(), 2, 'float')
assert_raises(ValueError, check_theano_variable,
tensor.vector(), 1, 'int')
def feedback(self, outputs):
assert self.output_dim == 0
eye = tensor.eye(self.num_outputs)
check_theano_variable(outputs, None, "int")
output_shape = [outputs.shape[i]
for i in range(outputs.ndim)] + [self.feedback_dim]
return eye[outputs.flatten()].reshape(output_shape)
def apply(self, indices):
"""Perform lookup.
Parameters
----------
indices : :class:`~tensor.TensorVariable`
The indices of interest. The dtype must be integer.
Returns
-------
output : :class:`~tensor.TensorVariable`
Representations for the indices of the query. Has :math:`k+1`
dimensions, where :math:`k` is the number of dimensions of the
`indices` parameter. The last dimension stands for the
representation element.
"""
check_theano_variable(indices, None, "int")
output_shape = [indices.shape[i]
for i in range(indices.ndim)] + [self.dim]
return self.W[indices.flatten()].reshape(output_shape) + self.b
def apply(self, indices):
"""Perform lookup.
Parameters
----------
indices : :class:`~tensor.TensorVariable`
The indices of interest. The dtype must be integer.
Returns
-------
output : :class:`~tensor.TensorVariable`
Representations for the indices of the query. Has :math:`k+1`
dimensions, where :math:`k` is the number of dimensions of the
`indices` parameter. The last dimension stands for the
representation element.
"""
check_theano_variable(indices, None, "int")
output_shape = [indices.shape[i]
for i in range(indices.ndim)] + [self.dim]
return self.W[indices.flatten()].reshape(output_shape) + self.b
def apply(self, indices):
check_theano_variable(indices, None, ("int", "uint"))
output_shape = [indices.shape[i]
for i in range(indices.ndim)] + [self.dim]
return self.W[indices.flatten()].reshape(output_shape)
def apply(self, indices):
check_theano_variable(indices, None, ("int", "uint"))
output_shape = [indices.shape[i]
for i in range(indices.ndim)] + [self.dim]
return self.W[indices.flatten()].reshape(output_shape)
def test_check_theano_variable():
check_theano_variable(None, 3, 'float')
check_theano_variable([[1, 2]], 2, 'int')
assert_raises(ValueError, check_theano_variable, tensor.vector(), 2,
'float')
assert_raises(ValueError, check_theano_variable, tensor.vector(), 1, 'int')
