WindowDataset

public final class WindowDataset

Combines (nests of) input elements into a dataset of (nests of) windows.

A "window" is a finite dataset of flat elements of size `size` (or possibly fewer if there are not enough input elements to fill the window and `drop_remainder` evaluates to false).

The `shift` argument determines the number of input elements by which the window moves on each iteration. The first element in the `k`th window will be element 

1 + (k-1) * shift
of the input dataset. In particular, the first element of the first window will always be the first element of the input dataset.

If the `stride` parameter is greater than 1, then each window will skip `(stride - 1)` input elements between each element that appears in the window. Output windows will still contain `size` elements regardless of the value of `stride`.

The `stride` argument determines the stride of the input elements, and the `shift` argument determines the shift of the window.

For example, letting `{...}` to represent a Dataset:

- `tf.data.Dataset.range(7).window(2)` produces `{ {0, 1}, {2, 3}, {4, 5}, {6} }` - `tf.data.Dataset.range(7).window(3, 2, 1, True)` produces `{ {0, 1, 2}, {2, 3, 4}, {4, 5, 6} }` - `tf.data.Dataset.range(7).window(3, 1, 2, True)` produces `{ {0, 2, 4}, {1, 3, 5}, {2, 4, 6} }`

Note that when the `window` transformation is applied to a dataset of nested elements, it produces a dataset of nested windows.

For example:

- `tf.data.Dataset.from_tensor_slices((range(4), range(4))).window(2)` produces `{({0, 1}, {0, 1}), ({2, 3}, {2, 3})}` - `tf.data.Dataset.from_tensor_slices({"a": range(4)}).window(2)` produces `{ {"a": {0, 1} }, {"a": {2, 3} } }`

Constants

String OP_NAME The name of this op, as known by TensorFlow core engine

Public Methods

Output<TType>
asOutput()
Returns the symbolic handle of the tensor.
static WindowDataset
create(Scope scope, Operand<?> inputDataset, Operand<TInt64> size, Operand<TInt64> shift, Operand<TInt64> stride, Operand<TBool> dropRemainder, List<Class<? extends TType>> outputTypes, List<Shape> outputShapes)
Factory method to create a class wrapping a new WindowDataset operation.
Output<?>
handle()

Inherited Methods

org.tensorflow.op.RawOp
final boolean
equals(Object obj)
final int
hashCode()
Operation
op()
Return this unit of computation as a single Operation.
final String
toString()
boolean
equals(Object arg0)
final Class<?>
getClass()
int
hashCode()
final void
notify()
final void
notifyAll()
String
toString()
final void
wait(long arg0, int arg1)
final void
wait(long arg0)
final void
wait()
org.tensorflow.op.Op
abstract ExecutionEnvironment
env()
Return the execution environment this op was created in.
abstract Operation
op()
Return this unit of computation as a single Operation.
org.tensorflow.Operand
abstract Output<TType>
asOutput()
Returns the symbolic handle of the tensor.
abstract TType
asTensor()
Returns the tensor at this operand.
abstract Shape
shape()
Returns the (possibly partially known) shape of the tensor referred to by the Output of this operand.
abstract Class<TType>
type()
Returns the tensor type of this operand
org.tensorflow.ndarray.Shaped
abstract int
rank()
abstract Shape
shape()
abstract long
size()
Computes and returns the total size of this container, in number of values.

Constants

public static final String OP_NAME

The name of this op, as known by TensorFlow core engine

Constant Value: "WindowDataset"

Public Methods

public Output<TType> asOutput ()

Returns the symbolic handle of the tensor.

Inputs to TensorFlow operations are outputs of another TensorFlow operation. This method is used to obtain a symbolic handle that represents the computation of the input.

public static WindowDataset create (Scope scope, Operand<?> inputDataset, Operand<TInt64> size, Operand<TInt64> shift, Operand<TInt64> stride, Operand<TBool> dropRemainder, List<Class<? extends TType>> outputTypes, List<Shape> outputShapes)

Factory method to create a class wrapping a new WindowDataset operation.

Parameters
scope current scope
size An integer scalar, representing the number of elements of the input dataset to combine into a window. Must be positive.
shift An integer scalar, representing the number of input elements by which the window moves in each iteration. Defaults to `size`. Must be positive.
stride An integer scalar, representing the stride of the input elements in the sliding window. Must be positive. The default value of 1 means "retain every input element".
dropRemainder A Boolean scalar, representing whether the last window should be dropped if its size is smaller than `window_size`.
Returns
  • a new instance of WindowDataset

public Output<?> handle ()