tf.TensorArray

Class wrapping dynamic-sized, per-time-step, Tensor arrays.

tf.TensorArray(
    dtype,
    size=None,
    dynamic_size=None,
    clear_after_read=None,
    tensor_array_name=None,
    handle=None,
    flow=None,
    infer_shape=True,
    element_shape=None,
    colocate_with_first_write_call=True,
    name=None
)

This class is meant to be used with dynamic iteration primitives such as while_loop and map_fn. It supports gradient back-propagation via special "flow" control flow dependencies.

Note that although the array can be read multiple times and positions can be overwritten, behavior may be undefined when storing multiple references to the same array and clear_after_read is False. In particular, avoid using methods like concat() to convert an intermediate TensorArray to a Tensor, then further modifying the TensorArray, particularly if you need to backprop through it later.

Example 1: Plain reading and writing.

ta = tf.TensorArray(tf.float32, size=0, dynamic_size=True, clear_after_read=False)
ta = ta.write(0, 10)
ta = ta.write(1, 20)
ta = ta.write(2, 30)

ta.read(0)
<tf.Tensor: shape=(), dtype=float32, numpy=10.0>
ta.read(1)
<tf.Tensor: shape=(), dtype=float32, numpy=20.0>
ta.read(2)
<tf.Tensor: shape=(), dtype=float32, numpy=30.0>
ta.stack()
<tf.Tensor: shape=(3,), dtype=float32, numpy=array([10., 20., 30.],
dtype=float32)>

Example 2: Fibonacci sequence algorithm that writes in a loop then returns.

@tf.function
def fibonacci(n):
  ta = tf.TensorArray(tf.float32, size=0, dynamic_size=True)
  ta = ta.unstack([0., 1.])

  for i in range(2, n):
    ta = ta.write(i, ta.read(i - 1) + ta.read(i - 2))

  return ta.stack()

fibonacci(7)
<tf.Tensor: shape=(7,), dtype=float32,
numpy=array([0., 1., 1., 2., 3., 5., 8.], dtype=float32)>

Example 3: A simple loop interacting with a tf.Variable.

v = tf.Variable(1)
@tf.function
def f(x):
  ta = tf.TensorArray(tf.int32, size=0, dynamic_size=True)
  for i in tf.range(x):
    v.assign_add(i)
    ta = ta.write(i, v)
  return ta.stack()
f(5)
<tf.Tensor: shape=(5,), dtype=int32, numpy=array([ 1,  2,  4,  7, 11],
dtype=int32)>

dtype (required) data type of the TensorArray.
size (optional) int32 scalar Tensor: the size of the TensorArray. Required if handle is not provided.
dynamic_size (optional) Python bool: If true, writes to the TensorArray can grow the TensorArray past its initial size. Default: False.
clear_after_read Boolean (optional, default: True). If True, clear TensorArray values after reading them. This disables read-many semantics, but allows early release of memory.
tensor_array_name (optional) Python string: the name of the TensorArray. This is used when creating the TensorArray handle. If this value is set, handle should be None.
handle (optional) A Tensor handle to an existing TensorArray. If this is set, tensor_array_name should be None. Only supported in graph mode.
flow (optional) A float Tensor scalar coming from an existing TensorArray.flow. Only supported in graph mode.
infer_shape (optional, default: True) If True, shape inference is enabled. In this case, all elements must have the same shape.
element_shape (optional, default: None) A TensorShape object specifying the shape constraints of each of the elements of the TensorArray. Need not be fully defined.
colocate_with_first_write_call If True, the TensorArray will be colocated on the same device as the Tensor used on its first write (write operations include write, unstack, and split). If False, the TensorArray will be placed on the device determined by the device context available during its initialization.
name A name for the operation (optional).

ValueError if both handle and tensor_array_name are provided.
TypeError if handle is provided but is not a Tensor.

dtype The data type of this TensorArray.
dynamic_size Python bool; if True the TensorArray can grow dynamically.
element_shape The tf.TensorShape of elements in this TensorArray.
flow The flow Tensor forcing ops leading to this TensorArray state.
handle The reference to the TensorArray.

Methods

close

View source

close(
    name=None
)

Close the current TensorArray.

concat

View source

concat(
    name=None
)

Return the values in the TensorArray as a concatenated Tensor.

All of the values must have been written, their ranks must match, and and their shapes must all match for all dimensions except the first.

Args
name A name for the operation (optional).

Returns
All the tensors in the TensorArray concatenated into one tensor.

gather

View source

gather(
    indices, name=None
)

Return selected values in the TensorArray as a packed Tensor.

All of selected values must have been written and their shapes must all match.

Args
indices A 1-D Tensor taking values in [0, max_value). If the TensorArray is not dynamic, max_value=size().
name A name for the operation (optional).

Returns
The tensors in the TensorArray selected by indices, packed into one tensor.

grad

View source

grad(
    source, flow=None, name=None
)

identity

View source

identity()

Returns a TensorArray with the same content and properties.

Returns
A new TensorArray object with flow that ensures the control dependencies from the contexts will become control dependencies for writes, reads, etc. Use this object for all subsequent operations.

read

View source

read(
    index, name=None
)

Read the value at location index in the TensorArray.

Args
index 0-D. int32 tensor with the index to read from.
name A name for the operation (optional).

Returns
The tensor at index index.

scatter

View source

scatter(
    indices, value, name=None
)

Scatter the values of a Tensor in specific indices of a TensorArray.

Args
indices A 1-D Tensor taking values in [0, max_value). If the TensorArray is not dynamic, max_value=size().
value (N+1)-D. Tensor of type dtype. The Tensor to unpack.
name A name for the operation (optional).

Returns
A new TensorArray object with flow that ensures the scatter occurs. Use this object for all subsequent operations.

Raises
ValueError if the shape inference fails.

size

View source

size(
    name=None
)

Return the size of the TensorArray.

split

View source

split(
    value, lengths, name=None
)

Split the values of a Tensor into the TensorArray.

Args
value (N+1)-D. Tensor of type dtype. The Tensor to split.
lengths 1-D. int32 vector with the lengths to use when splitting value along its first dimension.
name A name for the operation (optional).

Returns
A new TensorArray object with flow that ensures the split occurs. Use this object for all subsequent operations.

Raises
ValueError if the shape inference fails.

stack

View source

stack(
    name=None
)

Return the values in the TensorArray as a stacked Tensor.

All of the values must have been written and their shapes must all match. If input shapes have rank-R, then output shape will have rank-(R+1).

For example:

ta = tf.TensorArray(tf.int32, size=3)
ta.write(0, tf.constant([1, 2]))
ta.write(1, tf.constant([3, 4]))
ta.write(2, tf.constant([5, 6]))
ta.stack()
<tf.Tensor: shape=(3, 2), dtype=int32, numpy=
array([[1, 2],
       [3, 4],
       [5, 6]], dtype=int32)>

Args
name A name for the operation (optional).

Returns
All the tensors in the TensorArray stacked into one tensor.

unstack

View source

unstack(
    value, name=None
)

Unstack the values of a Tensor in the TensorArray.

If input value shapes have rank-R, then the output TensorArray will contain elements whose shapes are rank-(R-1).

Args
value (N+1)-D. Tensor of type dtype. The Tensor to unstack.
name A name for the operation (optional).

Returns
A new TensorArray object with flow that ensures the unstack occurs. Use this object for all subsequent operations.

Raises
ValueError if the shape inference fails.

write

View source

write(
    index, value, name=None
)

Write value into index index of the TensorArray.

Args
index 0-D. int32 scalar with the index to write to.
value N-D. Tensor of type dtype. The Tensor to write to this index.
name A name for the operation (optional).

Returns
A new TensorArray object with flow that ensures the write occurs. Use this object for all subsequent operations.

Raises
ValueError if there are more writers than specified.