def delay(x, initial_state=None, time_step=1, name=''):
'''
This function combines :func:`~cntk.ops.sequence.past_value` and :func:`~cntk.ops.sequence.future_value` into a single function.
This is useful when the time_step is computed and can be positive, negative, or 0.
Args:
x: the tensor (or its name) from which the past value is obtained
initial_state: tensor or scalar representing the initial value to be used when the input tensor is shifted in time.
time_step (int): the number of time steps to look into the past, where negative values mean to look into the future, and 0 means a no-op (default 1).
name (str, optional): the name of the Function instance in the network
'''
from ...ops import alias, element_select, element_divide, placeholder, exp
if time_step > 0:
return past_value(x,
time_step=time_step,
initial_state=initial_state,
name=name)
elif time_step < 0:
return future_value(x,
time_step=-time_step,
initial_state=initial_state,
name=name)
else:
if name:
return alias(x, name)
else:
return x
def delay(x, initial_state=None, time_step=1, name=''):
'''
This function combines :func:`~cntk.ops.sequence.past_value` and :func:`~cntk.ops.sequence.future_value` into a single function.
This is useful when the time_step is computed and can be positive, negative, or 0.
Args:
x: the tensor (or its name) from which the past value is obtained
initial_state: tensor or scalar representing the initial value to be used when the input tensor is shifted in time.
time_step (int): the number of time steps to look into the past, where negative values mean to look into the future, and 0 means a no-op (default 1).
name (str, optional): the name of the Function instance in the network
'''
from ...ops import alias
if time_step > 0:
return past_value (x, time_step= time_step, initial_state=initial_state, name=name)
elif time_step < 0:
return future_value(x, time_step=-time_step, initial_state=initial_state, name=name)
else:
if name:
return alias(x, name)
else:
return x
def past_value(x, initial_state=None, time_step=1, name=''):
'''
This function returns the past value w.r.t. ``x``. It is most often used when
creating RNNs. The resulting tensor has the same shape as the input but is
the previous logical sample. The ``time_step`` parameter is the number of steps
to look into the past and is 1 by default. If there is no past value (i.e.
the current sample is the first one in the tensor) then the ``initial_state``
value is returned.
The initial state can be a constant (scalar or tensor), a learnable tensor
or input data (which has a batch dimension, as needed for sequence-to-sequence models).
Example:
>>> # create example input: one sequence with 4 tensors of shape (3, 2)
>>> from cntk.layers.typing import Tensor, Sequence
>>> x = C.sequence.input((3,2))
>>> x0 = np.reshape(np.arange(24,dtype=np.float32),(1,4,3,2))
>>> x0
array([[[[ 0., 1.],
[ 2., 3.],
[ 4., 5.]],
<BLANKLINE>
[[ 6., 7.],
[ 8., 9.],
[ 10., 11.]],
<BLANKLINE>
[[ 12., 13.],
[ 14., 15.],
[ 16., 17.]],
<BLANKLINE>
[[ 18., 19.],
[ 20., 21.],
[ 22., 23.]]]], dtype=float32)
>>> # this demonstrates how past_value shifts the sequence by one, padding with initial_state
>>> y = C.sequence.past_value(x) # initial_state is 0 by default
>>> y.eval({x:x0})
[array([[[ 0., 0.],
[ 0., 0.],
[ 0., 0.]],
<BLANKLINE>
[[ 0., 1.],
[ 2., 3.],
[ 4., 5.]],
<BLANKLINE>
[[ 6., 7.],
[ 8., 9.],
[ 10., 11.]],
<BLANKLINE>
[[ 12., 13.],
[ 14., 15.],
[ 16., 17.]]], dtype=float32)]
>>> # here, we pass a the initial_state as input data (e.g. sequence-to-sequence)
>>> s = C.input((3,2)) # not a sequence, e.g. a final encoder hidden state
>>> s0 = np.reshape(np.arange(6,dtype=np.float32)/2,(1,3,2))
>>> s0
array([[[ 0. , 0.5],
[ 1. , 1.5],
[ 2. , 2.5]]], dtype=float32)
>>> y = C.sequence.past_value(x, initial_state=s)
>>> y.eval({x:x0, s:s0}) # same as the previous example except for the first time step
[array([[[ 0. , 0.5],
[ 1. , 1.5],
[ 2. , 2.5]],
<BLANKLINE>
[[ 0. , 1. ],
[ 2. , 3. ],
[ 4. , 5. ]],
<BLANKLINE>
[[ 6. , 7. ],
[ 8. , 9. ],
[ 10. , 11. ]],
<BLANKLINE>
[[ 12. , 13. ],
[ 14. , 15. ],
[ 16. , 17. ]]], dtype=float32)]
Args:
x: the tensor (or its name) from which the past value is obtained
initial_state: tensor or scalar representing the initial value to be used when the input tensor is shifted in time.
time_step (int): the number of time steps to look into the past (default 1)
name (str, optional): the name of the Function instance in the network
Returns:
:class:`~cntk.ops.functions.Function`
'''
from cntk.internal import sanitize_dtype_cntk
from cntk.cntk_py import Constant, past_value
if initial_state is None:
initial_state = Constant.scalar(sanitize_dtype_cntk(np.float32), 0.0)
else:
initial_state = sanitize_input(initial_state)
x = sanitize_input(x)
return past_value(x, initial_state, time_step, name)
def past_value(x, initial_state=None, time_step=1, name=''):
'''
This function returns the past value w.r.t. ``x``. It is most often used when
creating RNNs. The resulting tensor has the same shape as the input but is
the previous logical sample. The ``time_step`` parameter is the number of steps
to look into the past and is 1 by default. If there is no past value (i.e.
the current sample is the first one in the tensor) then the ``initial_state``
value is returned.
The initial state can be a constant (scalar or tensor), a learnable tensor
or input data (which has a batch dimension, as needed for sequence-to-sequence models).
Example:
>>> # create example input: one sequence with 4 tensors of shape (3, 2)
>>> from cntk.layers.typing import Tensor, Sequence
>>> x = C.sequence.input_variable((3,2))
>>> x0 = np.reshape(np.arange(24,dtype=np.float32),(1,4,3,2))
>>> x0
array([[[[ 0., 1.],
[ 2., 3.],
[ 4., 5.]],
<BLANKLINE>
[[ 6., 7.],
[ 8., 9.],
[ 10., 11.]],
<BLANKLINE>
[[ 12., 13.],
[ 14., 15.],
[ 16., 17.]],
<BLANKLINE>
[[ 18., 19.],
[ 20., 21.],
[ 22., 23.]]]], dtype=float32)
>>> # this demonstrates how past_value shifts the sequence by one, padding with initial_state
>>> y = C.sequence.past_value(x) # initial_state is 0 by default
>>> y.eval({x:x0})
[array([[[ 0., 0.],
[ 0., 0.],
[ 0., 0.]],
<BLANKLINE>
[[ 0., 1.],
[ 2., 3.],
[ 4., 5.]],
<BLANKLINE>
[[ 6., 7.],
[ 8., 9.],
[ 10., 11.]],
<BLANKLINE>
[[ 12., 13.],
[ 14., 15.],
[ 16., 17.]]], dtype=float32)]
>>> # here, we pass a the initial_state as input data (e.g. sequence-to-sequence)
>>> s = C.input_variable((3,2)) # not a sequence, e.g. a final encoder hidden state
>>> s0 = np.reshape(np.arange(6,dtype=np.float32)/2,(1,3,2))
>>> s0
array([[[ 0. , 0.5],
[ 1. , 1.5],
[ 2. , 2.5]]], dtype=float32)
>>> y = C.sequence.past_value(x, initial_state=s)
>>> y.eval({x:x0, s:s0}) # same as the previous example except for the first time step
[array([[[ 0. , 0.5],
[ 1. , 1.5],
[ 2. , 2.5]],
<BLANKLINE>
[[ 0. , 1. ],
[ 2. , 3. ],
[ 4. , 5. ]],
<BLANKLINE>
[[ 6. , 7. ],
[ 8. , 9. ],
[ 10. , 11. ]],
<BLANKLINE>
[[ 12. , 13. ],
[ 14. , 15. ],
[ 16. , 17. ]]], dtype=float32)]
Args:
x: the tensor (or its name) from which the past value is obtained
initial_state: tensor or scalar representing the initial value to be used when the input tensor is shifted in time.
time_step (int): the number of time steps to look into the past (default 1)
name (str, optional): the name of the Function instance in the network
Returns:
:class:`~cntk.ops.functions.Function`
'''
from cntk.internal import sanitize_dtype_cntk
from cntk.cntk_py import Constant, past_value
if initial_state is None:
initial_state = Constant.scalar(sanitize_dtype_cntk(x.dtype), 0.0)
else:
initial_state = sanitize_input(initial_state)
x = sanitize_input(x)
return past_value(x, initial_state, time_step, name)