Help protect the Great Barrier Reef with TensorFlow on Kaggle

# 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.

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)

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)

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)

`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 the current TensorArray.

### `concat`

View source

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

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.

View source

### `identity`

View source

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 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 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.

### `size`

View source

Return the size of the TensorArray.

### `split`

View source

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.

### `stack`

View source

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)`.

Args
`name` A name for the operation (optional).

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

### `unstack`

View source

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 all for subsequent operations.

Raises: ValueError: if the shape inference fails.

### `write`

View source

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.

[{ "type": "thumb-down", "id": "missingTheInformationINeed", "label":"Missing the information I need" },{ "type": "thumb-down", "id": "tooComplicatedTooManySteps", "label":"Too complicated / too many steps" },{ "type": "thumb-down", "id": "outOfDate", "label":"Out of date" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"Other" }]
[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"Easy to understand" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"Solved my problem" },{ "type": "thumb-up", "id": "otherUp", "label":"Other" }]