Signals

class nengo_dl.signals.TensorSignal(indices, key, dtype, shape, minibatch_size, constant, label='TensorSignal')

Represents a tensor as an indexed view into a base array.

Parameters:

indices : tuple or list or ndarray of int

Indices along the first axis of the base array corresponding to the data for this signal

key : object

Key mapping to the base array that contains the data for this signal

dtype : dtype

dtype of the values represented by this signal

shape : tuple of int

View shape of this signal (may differ from shape of base array)

minibatch_size : int

If not None then this signal contains a minibatch dimension with the given size

constant : callable

A function that returns a TensorFlow constant (will be provided by signals.SignalDict.get_tensor_signal())

label : str, optional

Name for this signal, used to make debugging easier

__getitem__(indices)

Create a new TensorSignal representing a subset (slice or advanced indexing) of the indices of this TensorSignal.

Parameters:

indices : slice or list of int

The desired subset of the indices in this TensorSignal

Returns:

:class:`.signals.TensorSignal`

A new TensorSignal representing the subset of this TensorSignal

reshape(shape)

Create a new TensorSignal representing a reshaped view of the same data in this TensorSignal (size of data must remain unchanged).

Parameters:

shape : tuple of int

New shape for the signal (one dimension can be -1 to indicate an inferred dimension size, as in numpy)

Returns:

:class:`.signals.TensorSignal`

New TensorSignal representing the same data as this signal but with the given shape

broadcast(axis, length)

Add a new dimension by broadcasting this signal along axis for the given length.

Parameters:

axis : 0 or -1

Where to insert the new dimension (currently only supports either the beginning or end of the array)

length : int

The number of times to duplicate signal along the broadcast dimension

Returns:

:class:`.signals.TensorSignal`

TensorSignal with new broadcasted shape

full_shape

Shape including the minibatch dimension.

minibatched

Whether or not this TensorSignal contains a minibatch dimension.

class nengo_dl.signals.SignalDict(dtype, minibatch_size)

Handles the mapping from Signal to tf.Tensor.

Takes care of gather/scatter logic to read/write signals within the base arrays.

Parameters:

dtype : tf.DType

Floating point precision used in signals

minibatch_size : int

Number of items in each minibatch

scatter(dst, val, mode='update')

Updates the base data corresponding to dst.

Parameters:

dst : TensorSignal

Signal indicating the data to be modified in base array

val : tf.Tensor

Update data (same shape as dst, i.e. a dense array <= the size of the base array)

mode : “update” or “inc”

Overwrite/add the data at dst with val

gather(src, force_copy=False)

Fetches the data corresponding to src from the base array.

Parameters:

src : TensorSignal

Signal indicating the data to be read from base array

force_copy : bool, optional

If True, always perform a gather, not a slice (this forces a copy). Note that setting force_copy=False does not guarantee that a copy won’t be performed.

Returns:

``tf.Tensor``

Tensor object corresponding to a dense subset of data from the base array

mark_gather(src)

Marks src as being gathered, but doesn’t actually perform a gather. Used to indicate that some computation relies on src.

Parameters:

src : TensorSignal

Signal indicating the data being read

combine(sigs, label='Combine')

Combines several TensorSignals into one by concatenating along the first axis.

Parameters:

sigs : list of TensorSignal or Signal

Signals to be combined

label : str, optional

Name for combined signal (to help with debugging)

Returns:

:class:`.TensorSignal`

New TensorSignal representing the concatenation of the data in sigs

get_tensor_signal(indices, key, dtype, shape, minibatched, signal=None, label='TensorSignal')

Creates a new TensorSignal with the given properties.

This should be used rather than instantiating a new TensorSignal directly, as it handles some extra book-keeping (e.g., using the custom constant() function).

Parameters:

indices : tuple or list or ndarray of int

Indices along the first axis of the base array corresponding to the data for this signal

key : object

Key mapping to the base array that contains the data for this signal

dtype : dtype

dtype of the values represented by this signal

shape : tuple of int

View shape of this signal (may differ from shape of base array)

minibatched : bool

Whether or not this signal contains a minibatch dimension

signal : Signal, optional

If not None, associate the new TensorSignal with the given Signal in the sig_map

label : str, optional

Name for this signal, used to make debugging easier

Returns:

:class:`.TensorSignal`

A new TensorSignal with the given properties

constant(value, dtype=None, cutoff=33554432)

Returns a constant Tensor containing the given value.

The returned Tensor may be underpinned by a tf.constant op, or a tf.Variable that will be initialized to the constant value. We use the latter in order to avoid storing large constant values in the TensorFlow GraphDef, which has a hard-coded limit of 2GB at the moment.

Parameters:

value : ndarray

Array containing the value of the constant

dtype : tf.DType, optional

The type for the constant (if None, the dtype of value will be used)

cutoff : int, optional

The size of constant (in bytes) for which we will switch from tf.constant to tf.Variable

Returns:

``tf.Tensor``

A tensor representing the given value

op_constant(ops, op_sizes, attr, dtype, ndims=2)

Creates a tensor representing the constant parameters of an op group.

Parameters:

ops : list of object

The operators for some merged group of ops

op_sizes : list of int

The number of constant elements in each op

attr : str

The attribute of the op that describes the constant parameter

dtype : tf.DType

Numeric type of the parameter

ndims : int

Empty dimensions will be added to the end of the returned tensor for all ndims > 1 (in the case that it is not a scalar).

Returns:

``tf.Tensor``

Tensor containing the values of attr for the given ops. This will be a scalar if all the ops have the same parameter value, or an array giving the parameter value for each element in each op.