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.
Note that although the array can be read multiple times and positions can be overwritten, behavior may be undefined when storing multiple references to the same array and clear_after_read is False. In particular, avoid using methods like concat() to convert an intermediate TensorArray to a Tensor, then further modifying the TensorArray, particularly if you need to backprop through it later.
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.
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.
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.
Scatter the values of a Tensor in specific indices of a TensorArray.
Args
indices
A 1-DTensor 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.
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.
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.
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.
