tf.TensorArray

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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) ta.read(1) ta.read(2) ta.stack()

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)

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)

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).

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.

Raises:

• ValueError: if both handle and tensor_array_name are provided.
• TypeError: if handle is provided but is not a Tensor.

Methods

close

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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

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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

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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

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

identity

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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.

read

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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

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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 all for 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

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size(
    name=None
)

Return the size of the TensorArray.

split

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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 all for 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

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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

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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 all for 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

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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 all for 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.