path: root/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.TensorArray.md

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.

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#### `tf.TensorArray.handle` {#TensorArray.handle}

The reference to the TensorArray.


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#### `tf.TensorArray.flow` {#TensorArray.flow}

The flow `Tensor` forcing ops leading to this TensorArray state.


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#### `tf.TensorArray.dtype` {#TensorArray.dtype}

The data type of this TensorArray.



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#### `tf.TensorArray.read(index, name=None)` {#TensorArray.read}

Read the value at location `index` in the TensorArray.

##### Args:


*  <b>`index`</b>: 0-D.  int32 tensor with the index to read from.
*  <b>`name`</b>: A name for the operation (optional).

##### Returns:

  The tensor at index `index`.


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#### `tf.TensorArray.gather(indices, name=None)` {#TensorArray.gather}

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:


*  <b>`indices`</b>: A `1-D` `Tensor` taking values in `[0, max_value)`.  If
    the `TensorArray` is not dynamic, `max_value=size()`.
*  <b>`name`</b>: A name for the operation (optional).

##### Returns:

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


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#### `tf.TensorArray.stack(name=None)` {#TensorArray.stack}

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:


*  <b>`name`</b>: A name for the operation (optional).

##### Returns:

  All the tensors in the TensorArray stacked into one tensor.


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#### `tf.TensorArray.concat(name=None)` {#TensorArray.concat}

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:


*  <b>`name`</b>: A name for the operation (optional).

##### Returns:

  All the tensors in the TensorArray concatenated into one tensor.



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#### `tf.TensorArray.write(index, value, name=None)` {#TensorArray.write}

Write `value` into index `index` of the TensorArray.

##### Args:


*  <b>`index`</b>: 0-D.  int32 scalar with the index to write to.
*  <b>`value`</b>: N-D.  Tensor of type `dtype`.  The Tensor to write to this index.
*  <b>`name`</b>: 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:


*  <b>`ValueError`</b>: if there are more writers than specified.


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#### `tf.TensorArray.scatter(indices, value, name=None)` {#TensorArray.scatter}

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

##### Args:


*  <b>`indices`</b>: A `1-D` `Tensor` taking values in `[0, max_value)`.  If
    the `TensorArray` is not dynamic, `max_value=size()`.
*  <b>`value`</b>: (N+1)-D.  Tensor of type `dtype`.  The Tensor to unpack.
*  <b>`name`</b>: 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:


*  <b>`ValueError`</b>: if the shape inference fails.


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#### `tf.TensorArray.unstack(value, name=None)` {#TensorArray.unstack}

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:


*  <b>`value`</b>: (N+1)-D.  Tensor of type `dtype`.  The Tensor to unstack.
*  <b>`name`</b>: 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:


*  <b>`ValueError`</b>: if the shape inference fails.


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#### `tf.TensorArray.split(value, lengths, name=None)` {#TensorArray.split}

Split the values of a `Tensor` into the TensorArray.

##### Args:


*  <b>`value`</b>: (N+1)-D.  Tensor of type `dtype`.  The Tensor to split.
*  <b>`lengths`</b>: 1-D.  int32 vector with the lengths to use when splitting
    `value` along its first dimension.
*  <b>`name`</b>: 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:


*  <b>`ValueError`</b>: if the shape inference fails.



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#### `tf.TensorArray.identity()` {#TensorArray.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.



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#### `tf.TensorArray.grad(source, flow=None, name=None)` {#TensorArray.grad}





#### Other Methods
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#### `tf.TensorArray.__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, name=None)` {#TensorArray.__init__}

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:


*  <b>`dtype`</b>: (required) data type of the TensorArray.
*  <b>`size`</b>: (optional) int32 scalar `Tensor`: the size of the TensorArray.
    Required if handle is not provided.
*  <b>`dynamic_size`</b>: (optional) Python bool: If true, writes to the TensorArray
    can grow the TensorArray past its initial size.  Default: False.
*  <b>`clear_after_read`</b>: Boolean (optional, default: True).  If True, clear
    TensorArray values after reading them.  This disables read-many
    semantics, but allows early release of memory.
*  <b>`tensor_array_name`</b>: (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.
*  <b>`handle`</b>: (optional) A `Tensor` handle to an existing TensorArray.  If this
    is set, tensor_array_name should be None.
*  <b>`flow`</b>: (optional) A float `Tensor` scalar coming from an existing
    `TensorArray.flow`.
*  <b>`infer_shape`</b>: (optional, default: True) If True, shape inference
    is enabled.  In this case, all elements must have the same shape.
*  <b>`element_shape`</b>: (optional, default: None) A `TensorShape` object specifying
    the shape constraints of each of the elements of the TensorArray.
    Need not be fully defined.
*  <b>`name`</b>: A name for the operation (optional).

##### Raises:


*  <b>`ValueError`</b>: if both handle and tensor_array_name are provided.
*  <b>`TypeError`</b>: if handle is provided but is not a Tensor.


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#### `tf.TensorArray.close(name=None)` {#TensorArray.close}

Close the current TensorArray.


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#### `tf.TensorArray.size(name=None)` {#TensorArray.size}

Return the size of the TensorArray.