Control Flow

Note: Functions taking Tensor arguments can also take anything accepted by tf.convert_to_tensor.

[TOC]

Control Flow Operations

TensorFlow provides several operations and classes that you can use to control the execution of operations and add conditional dependencies to your graph.

`tf.identity(input, name=None)` {#identity}

Return a tensor with the same shape and contents as the input tensor or value.

Args:

input: A Tensor.
name: A name for the operation (optional).

Returns:

A Tensor. Has the same type as input.

`tf.tuple(tensors, name=None, control_inputs=None)` {#tuple}

Group tensors together.

This creates a tuple of tensors with the same values as the tensors argument, except that the value of each tensor is only returned after the values of all tensors have been computed.

control_inputs contains additional ops that have to finish before this op finishes, but whose outputs are not returned.

This can be used as a "join" mechanism for parallel computations: all the argument tensors can be computed in parallel, but the values of any tensor returned by tuple are only available after all the parallel computations are done.

See also group and with_dependencies.

Args:

tensors: A list of Tensors or IndexedSlices, some entries can be None.
name: (optional) A name to use as a name_scope for the operation.
control_inputs: List of additional ops to finish before returning.

Returns:

Same as tensors.

Raises:

ValueError: If tensors does not contain any Tensor or IndexedSlices.
TypeError: If control_inputs is not a list of Operation or Tensor objects.

`tf.group(*inputs, **kwargs)` {#group}

Create an op that groups multiple operations.

When this op finishes, all ops in input have finished. This op has no output.

See also tuple and with_dependencies.

Args:

*inputs: Zero or more tensors to group.
**kwargs: Optional parameters to pass when constructing the NodeDef.
name: A name for this operation (optional).

Returns:

An Operation that executes all its inputs.

Raises:

ValueError: If an unknown keyword argument is provided.

`tf.no_op(name=None)` {#no_op}

Does nothing. Only useful as a placeholder for control edges.

Args:

name: A name for the operation (optional).

Returns:

The created Operation.

`tf.count_up_to(ref, limit, name=None)` {#count_up_to}

Increments 'ref' until it reaches 'limit'.

This operation outputs "ref" after the update is done. This makes it easier to chain operations that need to use the updated value.

Args:

ref: A mutable Tensor. Must be one of the following types: int32, int64. Should be from a scalar Variable node.
limit: An int. If incrementing ref would bring it above limit, instead generates an 'OutOfRange' error.
name: A name for the operation (optional).

Returns:

A Tensor. Has the same type as ref. A copy of the input before increment. If nothing else modifies the input, the values produced will all be distinct.

`tf.cond(pred, fn1, fn2, name=None)` {#cond}

Return either fn1() or fn2() based on the boolean predicate pred.

fn1 and fn2 both return lists of output tensors. fn1 and fn2 must have the same non-zero number and type of outputs.

Note that the conditional execution applies only to the operations defined in fn1 and fn2. Consider the following simple program:

z = tf.mul(a, b)
result = tf.cond(x < y, lambda: tf.add(x, z), lambda: tf.square(y))

If x < y, the tf.add operation will be executed and tf.square operation will not be executed. Since z is needed for at least one branch of the cond, the tf.mul operation is always executed, unconditionally. Although this behavior is consistent with the dataflow model of TensorFlow, it has occasionally surprised some users who expected a lazier semantics.

Args:

pred: A scalar determining whether to return the result of fn1 or fn2.
fn1: The callable to be performed if pred is true.
fn2: The callable to be performed if pref is false.
name: Optional name prefix for the returned tensors.

Returns:

Tensors returned by the call to either fn1 or fn2. If the callables return a singleton list, the element is extracted from the list.

Raises:

TypeError: if fn1 or fn2 is not callable.
ValueError: if fn1 and fn2 do not return the same number of tensors, or return tensors of different types.
Example:

  x = tf.constant(2)
  y = tf.constant(5)
  def f1(): return tf.mul(x, 17)
  def f2(): return tf.add(y, 23)
  r = cond(tf.less(x, y), f1, f2)
  # r is set to f1().
  # Operations in f2 (e.g., tf.add) are not executed.

`tf.case(pred_fn_pairs, default, exclusive=False, name='case')` {#case}

Create a case operation.

The pred_fn_pairs parameter is a dict or list of pairs of size N. Each pair contains a boolean scalar tensor and a python callable that creates the tensors to be returned if the boolean evaluates to True. default is a callable generating a list of tensors. All the callables in pred_fn_pairs as well as default should return the same number and types of tensors.

If exclusive==True, all predicates are evaluated, and a logging operation with an error is returned if more than one of the predicates evaluates to True. If exclusive==False, execution stops are the first predicate which evaluates to True, and the tensors generated by the corresponding function are returned immediately. If none of the predicates evaluate to True, this operation returns the tensors generated by default.

Example 1: Pseudocode:

  if (x < y) return 17;
  else return 23;

Expressions:

  f1 = lambda: tf.constant(17)
  f2 = lambda: tf.constant(23)
  r = case([(tf.less(x, y), f1)], default=f2)

Example 2: Pseudocode:

  if (x < y && x > z) raise OpError("Only one predicate may evaluate true");
  if (x < y) return 17;
  else if (x > z) return 23;
  else return -1;

Expressions:

  x = tf.constant(0)
  y = tf.constant(1)
  z = tf.constant(2)
  def f1(): return tf.constant(17)
  def f2(): return tf.constant(23)
  def f3(): return tf.constant(-1)
  r = case({tf.less(x, y): f1, tf.greater(x, z): f2},
           default=f3, exclusive=True)

Args:

pred_fn_pairs: Dict or list of pairs of a boolean scalar tensor and a callable which returns a list of tensors.
default: A callable that returns a list of tensors.
exclusive: True iff more than one predicate is allowed to evaluate to True.
name: A name for this operation (optional).

Returns:

The tensors returned by the first pair whose predicate evaluated to True, or those returned by default if none does.

Raises:

TypeError: If pred_fn_pairs is not a list/dictionary.
TypeError: If pred_fn_pairs is a list but does not contain 2-tuples.
TypeError: If fns[i] is not callable for any i, or default is not callable.

`tf.while_loop(cond, body, loop_vars, parallel_iterations=10, back_prop=True, swap_memory=False, name=None)` {#while_loop}

Repeat body while the condition cond is true.

cond is a callable returning a boolean scalar tensor. body is a callable returning a list of tensors of the same length and with the same types as loop_vars. loop_vars is a list of tensors that is passed to both cond and body. cond and body both take as many arguments as there are loop_vars.

In addition to regular Tensors or IndexedSlices, the body may accept and return TensorArray objects. The flows of the TensorArray objects will be appropriately forwarded between loops and during gradient calculations.

While cond evaluates to true, body is executed.

while_loop implements non-strict semantics, enabling multiple iterations to run in parallel. The maximum number of parallel iterations can be controlled by parallel_iterations, which gives users some control over memory consumption and execution order. For correct programs, while_loop should return the same result for any parallel_iterations > 0.

For training, TensorFlow remembers the tensors that are produced in the forward inference but needed in back propagation. These tensors can be a main source of memory consumption and often cause OOM problems when training on GPUs. When the flag swap_memory is true, we swap out these tensors from GPU to CPU. This for example allows us to train RNN models with very long sequences and large batches.

Args:

cond: A callable that represents the termination condition of the loop.
body: A callable that represents the loop body.
loop_vars: The list of variable input tensors.
parallel_iterations: The number of iterations allowed to run in parallel.
back_prop: Whether backprop is enabled for this while loop.
swap_memory: Whether GPU-CPU memory swap is enabled for this loop.
name: Optional name prefix for the returned tensors.

Returns:

The output tensors for the loop variables after the loop.

Raises:

TypeError: if cond or body is not callable.
ValueError: if loop_var is empty.
Example:

i = tf.constant(0)
c = lambda i: tf.less(i, 10)
b = lambda i: tf.add(i, 1)
r = tf.while_loop(c, b, [i])

Logical Operators

TensorFlow provides several operations that you can use to add logical operators to your graph.

`tf.logical_and(x, y, name=None)` {#logical_and}

Returns the truth value of x AND y element-wise.

Args:

x: A Tensor of type bool.
y: A Tensor of type bool.
name: A name for the operation (optional).

Returns:

A Tensor of type bool.

`tf.logical_not(x, name=None)` {#logical_not}

Returns the truth value of NOT x element-wise.

Args:

x: A Tensor of type bool.
name: A name for the operation (optional).

Returns:

A Tensor of type bool.

`tf.logical_or(x, y, name=None)` {#logical_or}

Returns the truth value of x OR y element-wise.

Args:

x: A Tensor of type bool.
y: A Tensor of type bool.
name: A name for the operation (optional).

`tf.select(condition, t, e, name=None)` {#select}

Selects elements from t or e, depending on condition.

The t, and e tensors must all have the same shape, and the output will also have that shape. The condition tensor must be a scalar if t and e are scalars. If t and e are vectors or higher rank, then condition must be either a vector with size matching the first dimension of t, or must have the same shape as t.

The condition tensor acts as a mask that chooses, based on the value at each element, whether the corresponding element / row in the output should be taken from t (if true) or e (if false).

If condition is a vector and t and e are higher rank matrices, then it chooses which row (outer dimension) to copy from t and e. If condition has the same shape as t and e, then it chooses which element to copy from t and e.

For example:

# 'condition' tensor is [[True,  False]
#                        [False, True]]
# 't' is [[1, 2],
#         [3, 4]]
# 'e' is [[5, 6],
#         [7, 8]]
select(condition, t, e) ==> [[1, 6],
                             [7, 4]]


# 'condition' tensor is [True, False]
# 't' is [[1, 2],
#         [3, 4]]
# 'e' is [[5, 6],
#         [7, 8]]
select(condition, t, e) ==> [[1, 2],
                             [7, 8]]

Args:

condition: A Tensor of type bool.
t: A Tensor which may have the same shape as condition. If condition is rank 1, t may have higher rank, but its first dimension must match the size of condition.
e: A Tensor with the same type and shape as t.
name: A name for the operation (optional).

Returns:

A Tensor with the same type and shape as t and e.

`tf.where(input, name=None)` {#where}

Returns locations of true values in a boolean tensor.

This operation returns the coordinates of true elements in input. The coordinates are returned in a 2-D tensor where the first dimension (rows) represents the number of true elements, and the second dimension (columns) represents the coordinates of the true elements. Keep in mind, the shape of the output tensor can vary depending on how many true values there are in input. Indices are output in row-major order.

For example:

# 'input' tensor is [[True, False]
#                    [True, False]]
# 'input' has two true values, so output has two coordinates.
# 'input' has rank of 2, so coordinates have two indices.
where(input) ==> [[0, 0],
                  [1, 0]]

# `input` tensor is [[[True, False]
#                     [True, False]]
#                    [[False, True]
#                     [False, True]]
#                    [[False, False]
#                     [False, True]]]
# 'input' has 5 true values, so output has 5 coordinates.
# 'input' has rank of 3, so coordinates have three indices.
where(input) ==> [[0, 0, 0],
                  [0, 1, 0],
                  [1, 0, 1],
                  [1, 1, 1],
                  [2, 1, 1]]

Args:

input: A Tensor of type bool.
name: A name for the operation (optional).

When run, reports an InvalidArgument error if tensor has any values that are not a number (NaN) or infinity (Inf). Otherwise, passes tensor as-is.

Args:

tensor: A Tensor. Must be one of the following types: half, float32, float64.
message: A string. Prefix of the error message.
name: A name for the operation (optional).

Returns:

A Tensor. Has the same type as tensor.

`tf.add_check_numerics_ops()` {#add_check_numerics_ops}

Connect a check_numerics to every floating point tensor.

check_numerics operations themselves are added for each float or double tensor in the graph. For all ops in the graph, the check_numerics op for all of its (float or double) inputs is guaranteed to run before the check_numerics op on any of its outputs.

Returns:

A group op depending on all check_numerics ops added.

`tf.Assert(condition, data, summarize=None, name=None)` {#Assert}

Asserts that the given condition is true.

If condition evaluates to false, print the list of tensors in data. summarize determines how many entries of the tensors to print.

NOTE: To ensure that Assert executes, one usually attaches a dependency:

 # Ensure maximum element of x is smaller or equal to 1
assert_op = tf.Assert(tf.less_equal(tf.reduce_max(x), 1.), [x])
x = tf.with_dependencies([assert_op], x)

Args:

condition: The condition to evaluate.
data: The tensors to print out when condition is false.
summarize: Print this many entries of each tensor.
name: A name for this operation (optional).

Returns:

assert_op: An Operation that, when executed, raises a tf.errors.InvalidArgumentError if condition is not true.

`tf.Print(input_, data, message=None, first_n=None, summarize=None, name=None)` {#Print}

Prints a list of tensors.

This is an identity op with the side effect of printing data when evaluating.

Args:

input_: A tensor passed through this op.
data: A list of tensors to print out when op is evaluated.
message: A string, prefix of the error message.
first_n: Only log first_n number of times. Negative numbers log always; this is the default.
summarize: Only print this many entries of each tensor. If None, then a maximum of 3 elements are printed per input tensor.
name: A name for the operation (optional).

Returns:

Same tensor as input_.

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Control Flow

Control Flow Operations

tf.identity(input, name=None) {#identity}

Args:

Returns:

tf.tuple(tensors, name=None, control_inputs=None) {#tuple}

Args:

Returns:

Raises:

tf.group(*inputs, **kwargs) {#group}

Args:

Returns:

Raises:

tf.no_op(name=None) {#no_op}

Args:

Returns:

tf.count_up_to(ref, limit, name=None) {#count_up_to}

Args:

Returns:

tf.cond(pred, fn1, fn2, name=None) {#cond}

Args:

Returns:

Raises:

tf.case(pred_fn_pairs, default, exclusive=False, name='case') {#case}

Args:

Returns:

Raises:

tf.while_loop(cond, body, loop_vars, parallel_iterations=10, back_prop=True, swap_memory=False, name=None) {#while_loop}

Args:

Returns:

Raises:

Logical Operators

tf.logical_and(x, y, name=None) {#logical_and}

Args:

Returns:

tf.logical_not(x, name=None) {#logical_not}

Args:

Returns:

tf.logical_or(x, y, name=None) {#logical_or}

Args:

Returns:

tf.logical_xor(x, y, name='LogicalXor') {#logical_xor}

Comparison Operators

tf.equal(x, y, name=None) {#equal}

Args:

Returns:

tf.not_equal(x, y, name=None) {#not_equal}

Args:

Returns:

tf.less(x, y, name=None) {#less}

Args:

Returns:

tf.less_equal(x, y, name=None) {#less_equal}

Args:

Returns:

tf.greater(x, y, name=None) {#greater}

Args:

Returns:

tf.greater_equal(x, y, name=None) {#greater_equal}

Args:

Returns:

tf.select(condition, t, e, name=None) {#select}

Args:

Returns:

tf.where(input, name=None) {#where}

Args:

Returns:

Debugging Operations

tf.is_finite(x, name=None) {#is_finite}

Args:

Returns:

tf.is_inf(x, name=None) {#is_inf}

Args:

Returns:

`tf.identity(input, name=None)` {#identity}

`tf.tuple(tensors, name=None, control_inputs=None)` {#tuple}

`tf.group(*inputs, **kwargs)` {#group}

`tf.no_op(name=None)` {#no_op}

`tf.count_up_to(ref, limit, name=None)` {#count_up_to}

`tf.cond(pred, fn1, fn2, name=None)` {#cond}

`tf.case(pred_fn_pairs, default, exclusive=False, name='case')` {#case}

`tf.while_loop(cond, body, loop_vars, parallel_iterations=10, back_prop=True, swap_memory=False, name=None)` {#while_loop}

`tf.logical_and(x, y, name=None)` {#logical_and}

`tf.logical_not(x, name=None)` {#logical_not}

`tf.logical_or(x, y, name=None)` {#logical_or}

`tf.logical_xor(x, y, name='LogicalXor')` {#logical_xor}

`tf.equal(x, y, name=None)` {#equal}

`tf.not_equal(x, y, name=None)` {#not_equal}

`tf.less(x, y, name=None)` {#less}

`tf.less_equal(x, y, name=None)` {#less_equal}

`tf.greater(x, y, name=None)` {#greater}

`tf.greater_equal(x, y, name=None)` {#greater_equal}

`tf.select(condition, t, e, name=None)` {#select}

`tf.where(input, name=None)` {#where}

`tf.is_finite(x, name=None)` {#is_finite}

`tf.is_inf(x, name=None)` {#is_inf}

`tf.is_nan(x, name=None)` {#is_nan}

`tf.verify_tensor_all_finite(t, msg, name=None)` {#verify_tensor_all_finite}

`tf.check_numerics(tensor, message, name=None)` {#check_numerics}

`tf.add_check_numerics_ops()` {#add_check_numerics_ops}

`tf.Assert(condition, data, summarize=None, name=None)` {#Assert}

`tf.Print(input_, data, message=None, first_n=None, summarize=None, name=None)` {#Print}