diff options
Diffstat (limited to 'tensorflow/g3doc/api_docs/python/control_flow_ops.md')
| -rw-r--r-- | tensorflow/g3doc/api_docs/python/control_flow_ops.md | 157 |
1 files changed, 79 insertions, 78 deletions
diff --git a/tensorflow/g3doc/api_docs/python/control_flow_ops.md b/tensorflow/g3doc/api_docs/python/control_flow_ops.md index 4d96984f59..f3245e6957 100644 --- a/tensorflow/g3doc/api_docs/python/control_flow_ops.md +++ b/tensorflow/g3doc/api_docs/python/control_flow_ops.md @@ -1,12 +1,13 @@ <!-- This file is machine generated: DO NOT EDIT! --> -# Control Flow +# Control Flow <a class="md-anchor" id="AUTOGENERATED-control-flow"></a> Note: Functions taking `Tensor` arguments can also take anything accepted by [`tf.convert_to_tensor`](framework.md#convert_to_tensor). <!-- TOC-BEGIN This section is generated by neural network: DO NOT EDIT! --> ## Contents +### [Control Flow](#AUTOGENERATED-control-flow) * [Control Flow Operations](#AUTOGENERATED-control-flow-operations) * [tf.identity(input, name=None)](#identity) * [tf.tuple(tensors, name=None, control_inputs=None)](#tuple) @@ -40,31 +41,31 @@ accepted by [`tf.convert_to_tensor`](framework.md#convert_to_tensor). <!-- 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> +## Control Flow Operations <a class="md-anchor" id="AUTOGENERATED-control-flow-operations"></a> 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> +### tf.identity(input, name=None) <a class="md-anchor" id="identity"></a> Return a tensor with the same shape and contents as the input tensor or value. -##### Args: +##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> * <b>input</b>: A `Tensor`. * <b>name</b>: A name for the operation (optional). -##### Returns: +##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> A `Tensor`. Has the same type as `input`. - - - -### tf.tuple(tensors, name=None, control_inputs=None) <div class="md-anchor" id="tuple">{#tuple}</div> +### tf.tuple(tensors, name=None, control_inputs=None) <a class="md-anchor" id="tuple"></a> Group tensors together. @@ -82,18 +83,18 @@ are done. See also `group` and `with_dependencies`. -##### Args: +##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> * <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: +##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> Same as `tensors`. -##### Raises: +##### Raises: <a class="md-anchor" id="AUTOGENERATED-raises-"></a> * <b>ValueError</b>: If `tensors` does not contain any `Tensor` or `IndexedSlices`. @@ -101,7 +102,7 @@ See also `group` and `with_dependencies`. - - - -### tf.group(*inputs, **kwargs) <div class="md-anchor" id="group">{#group}</div> +### tf.group(*inputs, **kwargs) <a class="md-anchor" id="group"></a> Create an op that groups multiple operations. @@ -110,18 +111,18 @@ output. See also `tuple` and `with_dependencies`. -##### Args: +##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> * <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: +##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> An Operation that executes all its inputs. -##### Raises: +##### Raises: <a class="md-anchor" id="AUTOGENERATED-raises-"></a> * <b>ValueError</b>: If an unknown keyword argument is provided, or if there are @@ -130,30 +131,30 @@ See also `tuple` and `with_dependencies`. - - - -### tf.no_op(name=None) <div class="md-anchor" id="no_op">{#no_op}</div> +### tf.no_op(name=None) <a class="md-anchor" id="no_op"></a> Does nothing. Only useful as a placeholder for control edges. -##### Args: +##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> * <b>name</b>: A name for the operation (optional). -##### Returns: +##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> The created Operation. - - - -### tf.count_up_to(ref, limit, name=None) <div class="md-anchor" id="count_up_to">{#count_up_to}</div> +### tf.count_up_to(ref, limit, name=None) <a class="md-anchor" id="count_up_to"></a> 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: +##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> * <b>ref</b>: A mutable `Tensor`. Must be one of the following types: `int32`, `int64`. @@ -163,7 +164,7 @@ easier to chain operations that need to use the updated value. 'OutOfRange' error. * <b>name</b>: A name for the operation (optional). -##### Returns: +##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> A `Tensor`. Has the same type as `ref`. A copy of the input before increment. If nothing else modifies the @@ -171,188 +172,188 @@ easier to chain operations that need to use the updated value. -## Logical Operators <div class="md-anchor" id="AUTOGENERATED-logical-operators">{#AUTOGENERATED-logical-operators}</div> +## Logical Operators <a class="md-anchor" id="AUTOGENERATED-logical-operators"></a> 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> +### tf.logical_and(x, y, name=None) <a class="md-anchor" id="logical_and"></a> Returns the truth value of x AND y element-wise. -##### Args: +##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> * <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: +##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> A `Tensor` of type `bool`. - - - -### tf.logical_not(x, name=None) <div class="md-anchor" id="logical_not">{#logical_not}</div> +### tf.logical_not(x, name=None) <a class="md-anchor" id="logical_not"></a> Returns the truth value of NOT x element-wise. -##### Args: +##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> * <b>x</b>: A `Tensor` of type `bool`. * <b>name</b>: A name for the operation (optional). -##### Returns: +##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> A `Tensor` of type `bool`. - - - -### tf.logical_or(x, y, name=None) <div class="md-anchor" id="logical_or">{#logical_or}</div> +### tf.logical_or(x, y, name=None) <a class="md-anchor" id="logical_or"></a> Returns the truth value of x OR y element-wise. -##### Args: +##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> * <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: +##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> A `Tensor` of type `bool`. - - - -### tf.logical_xor(x, y, name='LogicalXor') <div class="md-anchor" id="logical_xor">{#logical_xor}</div> +### tf.logical_xor(x, y, name='LogicalXor') <a class="md-anchor" id="logical_xor"></a> x ^ y = (x | y) & ~(x & y). -## Comparison Operators <div class="md-anchor" id="AUTOGENERATED-comparison-operators">{#AUTOGENERATED-comparison-operators}</div> +## Comparison Operators <a class="md-anchor" id="AUTOGENERATED-comparison-operators"></a> 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> +### tf.equal(x, y, name=None) <a class="md-anchor" id="equal"></a> Returns the truth value of (x == y) element-wise. -##### Args: +##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> * <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: +##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> A `Tensor` of type `bool`. - - - -### tf.not_equal(x, y, name=None) <div class="md-anchor" id="not_equal">{#not_equal}</div> +### tf.not_equal(x, y, name=None) <a class="md-anchor" id="not_equal"></a> Returns the truth value of (x != y) element-wise. -##### Args: +##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> * <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: +##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> A `Tensor` of type `bool`. - - - -### tf.less(x, y, name=None) <div class="md-anchor" id="less">{#less}</div> +### tf.less(x, y, name=None) <a class="md-anchor" id="less"></a> Returns the truth value of (x < y) element-wise. -##### Args: +##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> * <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: +##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> A `Tensor` of type `bool`. - - - -### tf.less_equal(x, y, name=None) <div class="md-anchor" id="less_equal">{#less_equal}</div> +### tf.less_equal(x, y, name=None) <a class="md-anchor" id="less_equal"></a> Returns the truth value of (x <= y) element-wise. -##### Args: +##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> * <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: +##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> A `Tensor` of type `bool`. - - - -### tf.greater(x, y, name=None) <div class="md-anchor" id="greater">{#greater}</div> +### tf.greater(x, y, name=None) <a class="md-anchor" id="greater"></a> Returns the truth value of (x > y) element-wise. -##### Args: +##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> * <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: +##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> A `Tensor` of type `bool`. - - - -### tf.greater_equal(x, y, name=None) <div class="md-anchor" id="greater_equal">{#greater_equal}</div> +### tf.greater_equal(x, y, name=None) <a class="md-anchor" id="greater_equal"></a> Returns the truth value of (x >= y) element-wise. -##### Args: +##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> * <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: +##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> A `Tensor` of type `bool`. - - - -### tf.select(condition, t, e, name=None) <div class="md-anchor" id="select">{#select}</div> +### tf.select(condition, t, e, name=None) <a class="md-anchor" id="select"></a> Selects elements from `t` or `e`, depending on `condition`. @@ -375,7 +376,7 @@ select(condition, t, e) ==> [[1, 2], [1, 2]] ``` -##### Args: +##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> * <b>condition</b>: A `Tensor` of type `bool`. @@ -383,14 +384,14 @@ select(condition, t, e) ==> [[1, 2], * <b>e</b>: A `Tensor` with the same type and shape as `t`. * <b>name</b>: A name for the operation (optional). -##### Returns: +##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> 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> +### tf.where(input, name=None) <a class="md-anchor" id="where"></a> Returns locations of true values in a boolean tensor. @@ -426,116 +427,116 @@ where(input) ==> [[0, 0, 0], [2, 1, 1]] ``` -##### Args: +##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> * <b>input</b>: A `Tensor` of type `bool`. * <b>name</b>: A name for the operation (optional). -##### Returns: +##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> A `Tensor` of type `int64`. -## Debugging Operations <div class="md-anchor" id="AUTOGENERATED-debugging-operations">{#AUTOGENERATED-debugging-operations}</div> +## Debugging Operations <a class="md-anchor" id="AUTOGENERATED-debugging-operations"></a> 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> +### tf.is_finite(x, name=None) <a class="md-anchor" id="is_finite"></a> Returns which elements of x are finite. -##### Args: +##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> * <b>x</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`. * <b>name</b>: A name for the operation (optional). -##### Returns: +##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> A `Tensor` of type `bool`. - - - -### tf.is_inf(x, name=None) <div class="md-anchor" id="is_inf">{#is_inf}</div> +### tf.is_inf(x, name=None) <a class="md-anchor" id="is_inf"></a> Returns which elements of x are Inf. -##### Args: +##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> * <b>x</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`. * <b>name</b>: A name for the operation (optional). -##### Returns: +##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> A `Tensor` of type `bool`. - - - -### tf.is_nan(x, name=None) <div class="md-anchor" id="is_nan">{#is_nan}</div> +### tf.is_nan(x, name=None) <a class="md-anchor" id="is_nan"></a> Returns which elements of x are NaN. -##### Args: +##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> * <b>x</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`. * <b>name</b>: A name for the operation (optional). -##### Returns: +##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> 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> +### tf.verify_tensor_all_finite(t, msg, name=None) <a class="md-anchor" id="verify_tensor_all_finite"></a> Assert that the tensor does not contain any NaN's or Inf's. -##### Args: +##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> * <b>t</b>: Tensor to check. * <b>msg</b>: Message to log on failure. * <b>name</b>: A name for this operation (optional). -##### Returns: +##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> Same tensor as `t`. - - - -### tf.check_numerics(tensor, message, name=None) <div class="md-anchor" id="check_numerics">{#check_numerics}</div> +### tf.check_numerics(tensor, message, name=None) <a class="md-anchor" id="check_numerics"></a> 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: +##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> * <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: +##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> 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> +### tf.add_check_numerics_ops() <a class="md-anchor" id="add_check_numerics_ops"></a> Connect a check_numerics to every floating point tensor. @@ -544,21 +545,21 @@ 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: +##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> 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> +### tf.Assert(condition, data, summarize=None, name=None) <a class="md-anchor" id="Assert"></a> 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: +##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> * <b>condition</b>: The condition to evaluate. @@ -569,14 +570,14 @@ If `condition` evaluates to false, print the list of tensors in `data`. - - - -### tf.Print(input_, data, message=None, first_n=None, summarize=None, name=None) <div class="md-anchor" id="Print">{#Print}</div> +### tf.Print(input_, data, message=None, first_n=None, summarize=None, name=None) <a class="md-anchor" id="Print"></a> Prints a list of tensors. This is an identity op with the side effect of printing `data` when evaluating. -##### Args: +##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> * <b>input_</b>: A tensor passed through this op. @@ -587,7 +588,7 @@ evaluating. * <b>summarize</b>: Only print this many entries of each tensor. * <b>name</b>: A name for the operation (optional). -##### Returns: +##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> Same tensor as `input_`. |