def reshape(x, shape, name=None):
"""
Reinterpret input samples as having different tensor dimensions
One dimension may be specified as 0 and will be inferred
The output tensor has the same shape as 'shape'.
The backward pass propagates the received gradient for the output-shape to the input shape.
Examples:
>>> C.eval(C.reshape([[0,1],[2,3],[4,5]], (2,3)))
[array([[[ 0., 4., 3.],
[ 2., 1., 5.]]])]
Args:
x: tensor to be reshaped
shape: a tuple defining the resulting shape
name: the name of the node in the network
Returns:
:class:`cntk.graph.ComputationNode`
"""
from cntk.ops.cntk1 import NewReshape
if not np.isscalar(shape):
# cntk uses column major, thus we reverse the shape
shape = tuple(reversed(shape))
op = NewReshape(x, shape, 0, 0, name = name)
wrap_numpy_arrays(op)
op.rank = get_rank(shape)
return op
def reshape(x, shape, name=None):
"""
Reinterpret input samples as having different tensor dimensions
One dimension may be specified as 0 and will be inferred
The output tensor has the same shape as 'shape'.
The backward pass propagates the received gradient for the output-shape to the input shape.
Examples:
>>> C.eval(C.reshape([[0,1],[2,3],[4,5]], (2,3)))
[array([[[ 0., 4., 3.],
[ 2., 1., 5.]]])]
Args:
x: tensor to be reshaped
shape (tuple): a tuple defining the resulting shape
name (str): the name of the node in the network
Returns:
:class:`cntk.graph.ComputationNode`
"""
from cntk.ops.cntk1 import NewReshape
if not np.isscalar(shape):
# cntk uses column major, thus we reverse the shape
shape = tuple(reversed(shape))
op = NewReshape(x, shape, 0, 0, name=name)
wrap_numpy_arrays(op)
op.rank = get_rank(shape)
return op
def reshape(x, shape, name=None):
"""
Reinterpret input samples as having different tensor dimensions
One dimension may be specified as 0 and will be inferred
The output tensor has the same shape as 'shape'.
The backward pass propagates the received gradient for the output-shape to the input shape.
Examples:
>>> C.eval(C.reshape([[0,1],[2,3],[4,5]], (2,3)))
[array([[[ 0., 4., 3.],
[ 2., 1., 5.]]])]
Args:
x: tensor to be reshaped
shape: a tuple defining the resulting shape
name: the name of the node in the network
Returns:
:class:`cntk.graph.ComputationNode`
"""
from cntk.ops.cntk1 import NewReshape
return NewReshape(x, shape, 0, 0, name=name)