Java Code Examples for org.tensorflow.Tensor#of()

/**
 * Allocates a new tensor for storing a vector of floats.
 *
 * @param values floats to store in the new tensor
 * @return the new tensor
 */
static Tensor<TBfloat16> vectorOf(float... values) {
  if (values == null) {
    throw new IllegalArgumentException();
  }
  return Tensor.of(DTYPE, Shape.of(values.length), data -> StdArrays.copyTo(data, values));
}
 

/**
 * Allocates a new tensor for storing a vector of floats.
 *
 * @param values floats to store in the new tensor
 * @return the new tensor
 */
static Tensor<TFloat32> vectorOf(float... values) {
  if (values == null) {
    throw new IllegalArgumentException();
  }
  return Tensor.of(DTYPE, Shape.of(values.length), data -> StdArrays.copyTo(data, values));
}
 

/**
 * Allocates a new tensor for storing a vector of longs.
 *
 * @param values longs to store in the new tensor
 * @return the new tensor
 */
static Tensor<TInt64> vectorOf(long... values) {
  if (values == null) {
    throw new IllegalArgumentException();
  }
  return Tensor.of(DTYPE, Shape.of(values.length), data -> StdArrays.copyTo(data, values));
}
 

/**
 * Allocates a new tensor for storing a vector of floats.
 *
 * @param values floats to store in the new tensor
 * @return the new tensor
 */
static Tensor<TFloat16> vectorOf(float... values) {
  if (values == null) {
    throw new IllegalArgumentException();
  }
  return Tensor.of(DTYPE, Shape.of(values.length), data -> StdArrays.copyTo(data, values));
}
 

/**
 * Allocates a new tensor for storing a vector of bytes.
 *
 * @param values bytes to store in the new tensor
 * @return the new tensor
 */
static Tensor<TUint8> vectorOf(byte... values) {
  if (values == null) {
    throw new IllegalArgumentException();
  }
  return Tensor.of(DTYPE, Shape.of(values.length), data -> StdArrays.copyTo(data, values));
}
 

TfNDArray(NDManager manager, Shape shape, ByteBuffer data) {
    this(manager, Tensor.of(TUint8.DTYPE, toTfShape(shape), DataBuffers.of(data)));
}
 

/**
 * Allocates a new tensor of the given shape, initialized with the provided data.
 *
 * @param shape shape of the tensor to allocate
 * @param data buffer of floats to initialize the tensor with
 * @return the new tensor
 */
static Tensor<TFloat32> tensorOf(Shape shape, FloatDataBuffer data) {
  return Tensor.of(DTYPE, shape, d -> d.write(data));
}
 

/**
 * Allocates a new tensor which is a copy of a given array of bytes.
 *
 * <p>The tensor will have the same shape as the source array and its data will be copied.
 *
 * @param src the source array giving the shape and data to the new tensor
 * @return the new tensor
 */
static Tensor<TUint8> tensorOf(NdArray<Byte> src) {
  return Tensor.of(DTYPE, src.shape(), src::copyTo);
}
 

/**
 * Allocates a new tensor of the given shape and initialize its data.
 *
 * @param shape shape of the tensor to allocate
 * @param dataInit tensor data initializer
 * @return the new tensor
 * @throws TensorFlowException if the tensor cannot be allocated or initialized
 */
static Tensor<TFloat32> tensorOf(Shape shape, Consumer<TFloat32> dataInit) {
  return Tensor.of(DTYPE, shape, dataInit);
}
 

/**
 * Allocates a new tensor of the given shape.
 *
 * @param shape shape of the tensor to allocate
 * @return the new tensor
 */
static Tensor<TInt32> tensorOf(Shape shape) {
  return Tensor.of(DTYPE, shape);
}
 

/**
 * Allocates a new tensor of the given shape and initialize its data.
 *
 * @param shape shape of the tensor to allocate
 * @param dataInit tensor data initializer
 * @return the new tensor
 * @throws TensorFlowException if the tensor cannot be allocated or initialized
 */
static Tensor<TFloat64> tensorOf(Shape shape, Consumer<TFloat64> dataInit) {
  return Tensor.of(DTYPE, shape, dataInit);
}
 

/**
 * Allocates a new tensor of the given shape.
 *
 * @param shape shape of the tensor to allocate
 * @return the new tensor
 */
static Tensor<TFloat32> tensorOf(Shape shape) {
  return Tensor.of(DTYPE, shape);
}
 

/**
 * Allocates a new tensor for storing a single float value.
 *
 * @param value float to store in the new tensor
 * @return the new tensor
 */
static Tensor<TFloat16> scalarOf(float value) {
  return Tensor.of(DTYPE, Shape.scalar(), data -> data.setFloat(value));
}
 

/**
 * Allocates a new tensor which is a copy of a given array of longs.
 *
 * <p>The tensor will have the same shape as the source array and its data will be copied.
 *
 * @param src the source array giving the shape and data to the new tensor
 * @return the new tensor
 */
static Tensor<TInt64> tensorOf(NdArray<Long> src) {
  return Tensor.of(DTYPE, src.shape(), src::copyTo);
}
 

/**
 * Allocates a new tensor for storing a single float value.
 *
 * @param value float to store in the new tensor
 * @return the new tensor
 */
static Tensor<TBfloat16> scalarOf(float value) {
  return Tensor.of(DTYPE, Shape.scalar(), data -> data.setFloat(value));
}
 

/**
 * Allocates a new tensor of the given shape and initialize its data.
 *
 * @param shape shape of the tensor to allocate
 * @param dataInit tensor data initializer
 * @return the new tensor
 * @throws TensorFlowException if the tensor cannot be allocated or initialized
 */
static Tensor<TBfloat16> tensorOf(Shape shape, Consumer<TBfloat16> dataInit) {
  return Tensor.of(DTYPE, shape, dataInit);
}
 

/**
 * Allocates a new tensor for storing a single double value.
 *
 * @param value double to store in the new tensor
 * @return the new tensor
 */
static Tensor<TFloat64> scalarOf(double value) {
  return Tensor.of(DTYPE, Shape.scalar(), data -> data.setDouble(value));
}
 

/**
 * Allocates a new tensor which is a copy of a given array of floats.
 *
 * <p>The tensor will have the same shape as the source array and its data will be copied.
 *
 * @param src the source array giving the shape and data to the new tensor
 * @return the new tensor
 */
static Tensor<TFloat16> tensorOf(NdArray<Float> src) {
  return Tensor.of(DTYPE, src.shape(), src::copyTo);
}
 

/**
 * Allocates a new tensor for storing a single float value.
 *
 * @param value float to store in the new tensor
 * @return the new tensor
 */
static Tensor<TFloat32> scalarOf(float value) {
  return Tensor.of(DTYPE, Shape.scalar(), data -> data.setFloat(value));
}
 

/**
 * Allocates a new tensor which is a copy of a given array of booleans.
 *
 * <p>The tensor will have the same shape as the source array and its data will be copied.
 *
 * @param src the source array giving the shape and data to the new tensor
 * @return the new tensor
 */
static Tensor<TBool> tensorOf(NdArray<Boolean> src) {
  return Tensor.of(DTYPE, src.shape(), src::copyTo);
}