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

tf.TensorArray( 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 )

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)>

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. |

Attributes | |
---|---|

`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. |

`close`

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 or an error may be raised. To mark the output as used, call its .mark_used() method.

`concat`

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`

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

`grad`

grad( source, flow=None, name=None )

`identity`

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 for all subsequent operations. |

`read`

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`

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 for all subsequent operations. |

Raises | |
---|---|

`ValueError` | if the shape inference fails. |

Note:The output of this function should be used. If it is not, a warning will be logged or an error may be raised. To mark the output as used, call its .mark_used() method.

`size`

size( name=None )

Return the size of the TensorArray.

`split`

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 for all subsequent operations. |

Raises | |
---|---|

`ValueError` | if the shape inference fails. |

Note:The output of this function should be used. If it is not, a warning will be logged or an error may be raised. To mark the output as used, call its .mark_used() method.

`stack`

stack( name=None )

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`

unstack( value, name=None )

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 for all subsequent operations. |

Raises | |
---|---|

`ValueError` | if the shape inference fails. |

Note:The output of this function should be used. If it is not, a warning will be logged or an error may be raised. To mark the output as used, call its .mark_used() method.

`write`

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 for all subsequent operations. |

Raises | |
---|---|

`ValueError` | if there are more writers than specified. |

Note:The output of this function should be used. If it is not, a warning will be logged or an error may be raised. To mark the output as used, call its .mark_used() method.

© 2020 The TensorFlow Authors. All rights reserved.

Licensed under the Creative Commons Attribution License 3.0.

Code samples licensed under the Apache 2.0 License.

https://www.tensorflow.org/versions/r2.4/api_docs/python/tf/TensorArray