# Class TensorArray

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

# Aliases:

• Class tf.compat.v1.TensorArray
• Class tf.compat.v2.TensorArray

# Used in the guide:

• Betterperformancewith tf.function and AutoGraph

# Used in the tutorials:

• Betterperformancewith tf.function 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.

# init

View source

 __init__(
    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
)

Construct a new TensorArray or wrap an existing TensorArray handle. A note about the parameter name: The name of the TensorArray (even if passed in) is uniquified: each time a new TensorArray is created at runtime it is assigned its own name for the duration of the run. This avoids name collisions if a TensorArray is created within a while_loop.

# Args:

• 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).

# Raises:

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

# Properties

# dtype

The data type of this TensorArray.

# dynamic_size

Python bool; if True the TensorArray can grow dynamically.

# element_shape

tf.TensorShapeThe 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. NOTE The output of this function should be used. If it is not, a warning will be logged. To mark the output as used, call its .mark_used() method.

# 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:

# 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 all for 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.