# 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()