| Known Indirect Subclasses |
A statically typed multi-dimensional array.
There are two categories of tensors in TensorFlow Java: typed tensors and
raw tensors. The former maps the tensor native memory to an
n-dimensional typed data space, allowing direct I/O operations from the JVM, while the latter
is only a reference to a native tensor allowing basic operations and flat data access.
WARNING: Resources consumed by the Tensor object must be explicitly freed by
invoking the close() method when the object is no longer needed. For example, using a
try-with-resources block:
try (Tensor t = Tensor.of(...)) {
doSomethingWith(t);
}Instances of a Tensor are not thread-safe.
Public Methods
| abstract RawTensor |
asRawTensor()
Returns a raw (untyped) representation of this tensor
|
| abstract void |
close()
Release resources associated with the Tensor.
|
| abstract DataType | |
| abstract long |
numBytes()
Returns the size, in bytes, of the tensor data.
|
| abstract static <T extends TType> T | |
| abstract static <T extends TType> T | |
| abstract static <T extends TType> T | |
| abstract static <T extends TType> T | |
| abstract static <T extends TType> T |
of(Class<T> type, Shape shape, ByteDataBuffer rawData)
Creates a Tensor of any type from the raw data provided by the given buffer.
|
| abstract Shape |
shape()
Returns the shape of the tensor.
|
Inherited Methods
Public Methods
public abstract void close ()
Release resources associated with the Tensor.
WARNING:This must be invoked for all tensors that were not been produced by an eager operation or memory will be leaked.
The Tensor object is no longer usable after close returns.
public abstract long numBytes ()
Returns the size, in bytes, of the tensor data.
public static abstract T of (Class<T> type, Shape shape, long size, Consumer<T> dataInitializer)
Allocates a tensor of a given datatype, shape and size.
This method is identical to of(Class, Shape, Consumer), except that the final
size for the tensor can be explicitly set instead of being computed from the datatype and shape.
This could be useful for tensor types that stores data but also metadata in the tensor memory, such as the lookup table in a tensor of strings.
Parameters
| type | the tensor type class |
|---|---|
| shape | shape of the tensor |
| size | size in bytes of the tensor or -1 to compute the size from the shape |
| dataInitializer | method receiving accessor to the allocated tensor data for initialization |
Returns
- an allocated and initialized tensor
Throws
| IllegalArgumentException | if size is smaller than the minimum space required to
store the tensor data |
|---|---|
| IllegalArgumentException | if size is set to -1 but elements of the given
type are of variable length (e.g. strings) |
| IllegalArgumentException | if shape is totally or partially
unknown |
| IllegalStateException | if tensor failed to be allocated |
See Also
public static abstract T of (Class<T> type, Shape shape)
Allocates a tensor of a given datatype and shape.
The amount of memory to allocate is derived from the datatype and the shape of the tensor, and is left uninitialized.
Parameters
| type | the tensor type class |
|---|---|
| shape | shape of the tensor |
Returns
- an allocated but uninitialized tensor
Throws
| IllegalArgumentException | if elements of the given type are of variable length
(e.g. strings) |
|---|---|
| IllegalArgumentException | if shape is totally or partially
unknown |
| IllegalStateException | if tensor failed to be allocated |
public static abstract T of (Class<T> type, Shape shape, long size)
Allocates a tensor of a given datatype, shape and size.
This method is identical to of(Class, Shape), except that the final size of the
tensor can be explicitly set instead of computing it from the datatype and shape, which could be
larger than the actual space required to store the data but not smaller.
Parameters
| type | the tensor type class |
|---|---|
| shape | shape of the tensor |
| size | size in bytes of the tensor or -1 to compute the size from the shape |
Returns
- an allocated but uninitialized tensor
Throws
| IllegalArgumentException | if size is smaller than the minimum space required to
store the tensor data |
|---|---|
| IllegalArgumentException | if size is set to -1 but elements of the given
type are of variable length (e.g. strings) |
| IllegalArgumentException | if shape is totally or partially
unknown |
| IllegalStateException | if tensor failed to be allocated |
See Also
public static abstract T of (Class<T> type, Shape shape, Consumer<T> dataInitializer)
Allocates and initialize a tensor of a given datatype and shape.
The amount of memory to allocate is derived from the datatype and the shape of the tensor.
Tensor data is initialized by calling the dataInitializer, which receives in argument
the value returned by ERROR(/#data()) on the allocated tensor. For example:
FloatNdArray data = ...
try (TFloat32 t = Tensor.of(TFloat32.class, Shape.of(2, 2), data::copyTo)) {
...
}If dataInitializer fails and throws an exception, the allocated tensor will be
automatically released before rethrowing the same exception.
Parameters
| type | the tensor type class |
|---|---|
| shape | shape of the tensor |
| dataInitializer | method receiving accessor to the allocated tensor data for initialization |
Returns
- an allocated and initialized tensor
Throws
| IllegalArgumentException | if elements of the given type are of variable length
(e.g. strings) |
|---|---|
| IllegalArgumentException | if shape is totally or partially
unknown |
| IllegalStateException | if tensor failed to be allocated |
public static abstract T of (Class<T> type, Shape shape, ByteDataBuffer rawData)
Creates a Tensor of any type from the raw data provided by the given buffer.
Data must have been encoded into data as per the specification of the TensorFlow C API.
Parameters
| type | the tensor type class |
|---|---|
| shape | the tensor shape. |
| rawData | a buffer containing the tensor raw data. |
Throws
| IllegalArgumentException | if rawData is not large enough to contain the tensor
data |
|---|---|
| IllegalArgumentException | if shape is totally or partially
unknown |
| IllegalStateException | if tensor failed to be allocated with the given parameters |