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# tf.TensorArray

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

### Used in the notebooks

Used in the guide Used in the tutorials

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)`

`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 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 for all 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. </