TensorArray Operations

Note: Functions taking Tensor arguments can also take anything accepted by tf.convert_to_tensor.

[TOC]

TensorArray operations.

Classes containing dynamically sized arrays of Tensors.

class tf.TensorArray

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

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.

tf.TensorArray.handle

The reference to the TensorArray.

tf.TensorArray.flow

The flow Tensor forcing ops leading to this TensorArray state.

tf.TensorArray.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.

tf.TensorArray.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 in the TensorArray selected by indices, packed into one tensor.

tf.TensorArray.pack(name=None)

Return the values in the TensorArray as a packed Tensor.

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

Args:
  • name: A name for the operation (optional).
Returns:

All the tensors in the TensorArray packed into one tensor.

tf.TensorArray.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.

tf.TensorArray.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 all for subsequent operations.

Raises:
  • ValueError: if there are more writers than specified.

tf.TensorArray.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 all for subsequent operations.

Raises:
  • ValueError: if the shape inference fails.

tf.TensorArray.unpack(value, name=None)

Pack the values of a Tensor in the TensorArray.

Args:
  • 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 unpack occurs. Use this object all for subsequent operations.

Raises:
  • ValueError: if the shape inference fails.

tf.TensorArray.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 all for subsequent operations.

Raises:
  • ValueError: if the shape inference fails.

tf.TensorArray.grad(source, flow=None, name=None)

Other Methods

tf.TensorArray.__init__(dtype, size=None, dynamic_size=None, clear_after_read=None, tensor_array_name=None, handle=None, flow=None, infer_shape=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.
  • flow: (optional) A float Tensor scalar coming from an existing TensorArray.flow.
  • infer_shape: (optional, default: True) If True, shape inference is enabled. In this case, all elements must have the same shape.
  • 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.

tf.TensorArray.close(name=None)

Close the current TensorArray.

tf.TensorArray.dtype

The data type of this TensorArray.

tf.TensorArray.size(name=None)

Return the size of the TensorArray.

results matching ""

    No results matching ""