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diff --git a/tensorflow/g3doc/api_docs/python/control_flow_ops.md b/tensorflow/g3doc/api_docs/python/control_flow_ops.md new file mode 100644 index 0000000000..ad4321f01b --- /dev/null +++ b/tensorflow/g3doc/api_docs/python/control_flow_ops.md @@ -0,0 +1,590 @@ +<!-- This file is machine generated: DO NOT EDIT! --> + +# Control Flow +<!-- TOC-BEGIN This section is generated by neural network: DO NOT EDIT! --> +## Contents +* [Control Flow Operations](#AUTOGENERATED-control-flow-operations) + * [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) +* [Logical Operators](#AUTOGENERATED-logical-operators) + * [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) +* [Comparison Operators](#AUTOGENERATED-comparison-operators) + * [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) +* [Debugging Operations](#AUTOGENERATED-debugging-operations) + * [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) + + +<!-- TOC-END This section was generated by neural network, THANKS FOR READING! --> + +## Control Flow Operations <div class="md-anchor" id="AUTOGENERATED-control-flow-operations">{#AUTOGENERATED-control-flow-operations}</div> + +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) <div class="md-anchor" id="identity">{#identity}</div> + +Return a tensor with the same shape and contents as the input tensor or value. + +##### Args: + + +* <b>input</b>: A `Tensor`. +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + A `Tensor`. Has the same type as `input`. + + +- - - + +### tf.tuple(tensors, name=None, control_inputs=None) <div class="md-anchor" id="tuple">{#tuple}</div> + +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: + + +* <b>tensors</b>: A list of `Tensor`s or `IndexedSlices`, some entries can be `None`. +* <b>name</b>: (optional) A name to use as a `name_scope` for the operation. +* <b>control_inputs</b>: List of additional ops to finish before returning. + +##### Returns: + + Same as `tensors`. + +##### Raises: + + +* <b>ValueError</b>: If `tensors` does not contain any `Tensor` or `IndexedSlices`. + + +- - - + +### tf.group(*inputs, **kwargs) <div class="md-anchor" id="group">{#group}</div> + +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: + + +* <b>*inputs</b>: One or more tensors to group. +* <b>**kwargs</b>: Optional parameters to pass when constructing the NodeDef. +* <b>name</b>: A name for this operation (optional). + +##### Returns: + + An Operation that executes all its inputs. + +##### Raises: + + +* <b>ValueError</b>: If an unknown keyword argument is provided, or if there are + no inputs. + + +- - - + +### tf.no_op(name=None) <div class="md-anchor" id="no_op">{#no_op}</div> + +Does nothing. Only useful as a placeholder for control edges. + +##### Args: + + +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + The created Operation. + + +- - - + +### tf.count_up_to(ref, limit, name=None) <div class="md-anchor" id="count_up_to">{#count_up_to}</div> + +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: + + +* <b>ref</b>: A mutable `Tensor`. Must be one of the following types: `int32`, `int64`. + Should be from a scalar `Variable` node. +* <b>limit</b>: An `int`. + If incrementing ref would bring it above limit, instead generates an + 'OutOfRange' error. +* <b>name</b>: 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. + + + +## Logical Operators <div class="md-anchor" id="AUTOGENERATED-logical-operators">{#AUTOGENERATED-logical-operators}</div> + +TensorFlow provides several operations that you can use to add logical operators +to your graph. + +- - - + +### tf.logical_and(x, y, name=None) <div class="md-anchor" id="logical_and">{#logical_and}</div> + +Returns the truth value of x AND y element-wise. + +##### Args: + + +* <b>x</b>: A `Tensor` of type `bool`. +* <b>y</b>: A `Tensor` of type `bool`. +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + A `Tensor` of type `bool`. + + +- - - + +### tf.logical_not(x, name=None) <div class="md-anchor" id="logical_not">{#logical_not}</div> + +Returns the truth value of NOT x element-wise. + +##### Args: + + +* <b>x</b>: A `Tensor` of type `bool`. +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + A `Tensor` of type `bool`. + + +- - - + +### tf.logical_or(x, y, name=None) <div class="md-anchor" id="logical_or">{#logical_or}</div> + +Returns the truth value of x OR y element-wise. + +##### Args: + + +* <b>x</b>: A `Tensor` of type `bool`. +* <b>y</b>: A `Tensor` of type `bool`. +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + A `Tensor` of type `bool`. + + +- - - + +### tf.logical_xor(x, y, name='LogicalXor') <div class="md-anchor" id="logical_xor">{#logical_xor}</div> + +x ^ y = (x | y) & ~(x & y). + + + +## Comparison Operators <div class="md-anchor" id="AUTOGENERATED-comparison-operators">{#AUTOGENERATED-comparison-operators}</div> + +TensorFlow provides several operations that you can use to add comparison +operators to your graph. + +- - - + +### tf.equal(x, y, name=None) <div class="md-anchor" id="equal">{#equal}</div> + +Returns the truth value of (x == y) element-wise. + +##### Args: + + +* <b>x</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`, `complex64`, `quint8`, `qint8`, `qint32`. +* <b>y</b>: A `Tensor`. Must have the same type as `x`. +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + A `Tensor` of type `bool`. + + +- - - + +### tf.not_equal(x, y, name=None) <div class="md-anchor" id="not_equal">{#not_equal}</div> + +Returns the truth value of (x != y) element-wise. + +##### Args: + + +* <b>x</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`, `complex64`, `quint8`, `qint8`, `qint32`. +* <b>y</b>: A `Tensor`. Must have the same type as `x`. +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + A `Tensor` of type `bool`. + + +- - - + +### tf.less(x, y, name=None) <div class="md-anchor" id="less">{#less}</div> + +Returns the truth value of (x < y) element-wise. + +##### Args: + + +* <b>x</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`. +* <b>y</b>: A `Tensor`. Must have the same type as `x`. +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + A `Tensor` of type `bool`. + + +- - - + +### tf.less_equal(x, y, name=None) <div class="md-anchor" id="less_equal">{#less_equal}</div> + +Returns the truth value of (x <= y) element-wise. + +##### Args: + + +* <b>x</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`. +* <b>y</b>: A `Tensor`. Must have the same type as `x`. +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + A `Tensor` of type `bool`. + + +- - - + +### tf.greater(x, y, name=None) <div class="md-anchor" id="greater">{#greater}</div> + +Returns the truth value of (x > y) element-wise. + +##### Args: + + +* <b>x</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`. +* <b>y</b>: A `Tensor`. Must have the same type as `x`. +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + A `Tensor` of type `bool`. + + +- - - + +### tf.greater_equal(x, y, name=None) <div class="md-anchor" id="greater_equal">{#greater_equal}</div> + +Returns the truth value of (x >= y) element-wise. + +##### Args: + + +* <b>x</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`. +* <b>y</b>: A `Tensor`. Must have the same type as `x`. +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + A `Tensor` of type `bool`. + + +- - - + +### tf.select(condition, t, e, name=None) <div class="md-anchor" id="select">{#select}</div> + +Selects elements from `t` or `e`, depending on `condition`. + +The `condition`, `t`, and `e` tensors must all have the same shape, +and the output will also have that shape. The `condition` tensor acts +as an element-wise mask that chooses, based on the value at each +element, whether the corresponding element in the output should be +taken from `t` (if true) or `e` (if false). For example: + +For example: + +```prettyprint +# 'condition' tensor is [[True, False] +# [True, False]] +# 't' is [[1, 1], +# [1, 1]] +# 'e' is [[2, 2], +# [2, 2]] +select(condition, t, e) ==> [[1, 2], + [1, 2]] +``` + +##### Args: + + +* <b>condition</b>: A `Tensor` of type `bool`. +* <b>t</b>: A `Tensor` with the same shape as `condition`. +* <b>e</b>: A `Tensor` with the same type and shape as `t`. +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + A `Tensor` with the same type and shape as `t` and `e`. + + +- - - + +### tf.where(input, name=None) <div class="md-anchor" id="where">{#where}</div> + +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: + +```prettyprint +# '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: + + +* <b>input</b>: A `Tensor` of type `bool`. +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + A `Tensor` of type `int64`. + + + +## Debugging Operations <div class="md-anchor" id="AUTOGENERATED-debugging-operations">{#AUTOGENERATED-debugging-operations}</div> + +TensorFlow provides several operations that you can use to validate values and +debug your graph. + +- - - + +### tf.is_finite(x, name=None) <div class="md-anchor" id="is_finite">{#is_finite}</div> + +Returns which elements of x are finite. + +##### Args: + + +* <b>x</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`. +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + A `Tensor` of type `bool`. + + +- - - + +### tf.is_inf(x, name=None) <div class="md-anchor" id="is_inf">{#is_inf}</div> + +Returns which elements of x are Inf. + +##### Args: + + +* <b>x</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`. +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + A `Tensor` of type `bool`. + + +- - - + +### tf.is_nan(x, name=None) <div class="md-anchor" id="is_nan">{#is_nan}</div> + +Returns which elements of x are NaN. + +##### Args: + + +* <b>x</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`. +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + A `Tensor` of type `bool`. + + +- - - + +### tf.verify_tensor_all_finite(t, msg, name=None) <div class="md-anchor" id="verify_tensor_all_finite">{#verify_tensor_all_finite}</div> + +Assert that the tensor does not contain any NaN's or Inf's. + +##### Args: + + +* <b>t</b>: Tensor to check. +* <b>msg</b>: Message to log on failure. +* <b>name</b>: A name for this operation (optional). + +##### Returns: + + Same tensor as `t`. + + +- - - + +### tf.check_numerics(tensor, message, name=None) <div class="md-anchor" id="check_numerics">{#check_numerics}</div> + +Checks a tensor for NaN and Inf values. + +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: + + +* <b>tensor</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`. +* <b>message</b>: A `string`. Prefix of the error message. +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + A `Tensor`. Has the same type as `tensor`. + + +- - - + +### tf.add_check_numerics_ops() <div class="md-anchor" id="add_check_numerics_ops">{#add_check_numerics_ops}</div> + +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) <div class="md-anchor" id="Assert">{#Assert}</div> + +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. + +##### Args: + + +* <b>condition</b>: The condition to evaluate. +* <b>data</b>: The tensors to print out when condition is false. +* <b>summarize</b>: Print this many entries of each tensor. +* <b>name</b>: A name for this operation (optional). + + +- - - + +### tf.Print(input_, data, message=None, first_n=None, summarize=None, name=None) <div class="md-anchor" id="Print">{#Print}</div> + +Prints a list of tensors. + +This is an identity op with the side effect of printing `data` when +evaluating. + +##### Args: + + +* <b>input_</b>: A tensor passed through this op. +* <b>data</b>: A list of tensors to print out when op is evaluated. +* <b>message</b>: A string, prefix of the error message. +* <b>first_n</b>: Only log `first_n` number of times. Negative numbers log always; + this is the default. +* <b>summarize</b>: Only print this many entries of each tensor. +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + Same tensor as `input_`. + + |