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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 index 98f703f95c..a86e65cd33 100644 --- a/tensorflow/g3doc/api_docs/python/control_flow_ops.md +++ b/tensorflow/g3doc/api_docs/python/control_flow_ops.md @@ -1,73 +1,37 @@ <!-- This file is machine generated: DO NOT EDIT! --> -# Control Flow <a class="md-anchor" id="AUTOGENERATED-control-flow"></a> +# Control Flow Note: Functions taking `Tensor` arguments can also take anything accepted by [`tf.convert_to_tensor`](../../api_docs/python/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) - * [`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) -* [Other Functions and Classes](#AUTOGENERATED-other-functions-and-classes) - * [`class tf.xrange`](#xrange) - - -<!-- TOC-END This section was generated by neural network, THANKS FOR READING! --> - -## Control Flow Operations <a class="md-anchor" id="AUTOGENERATED-control-flow-operations"></a> +[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)` <a class="md-anchor" id="identity"></a> +### `tf.identity(input, name=None)` {#identity} Return a tensor with the same shape and contents as the input tensor or value. -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`input`</b>: A `Tensor`. * <b>`name`</b>: A name for the operation (optional). -##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: A `Tensor`. Has the same type as `input`. - - - -### `tf.tuple(tensors, name=None, control_inputs=None)` <a class="md-anchor" id="tuple"></a> +### `tf.tuple(tensors, name=None, control_inputs=None)` {#tuple} Group tensors together. @@ -85,18 +49,18 @@ are done. See also `group` and `with_dependencies`. -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### 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: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: Same as `tensors`. -##### Raises: <a class="md-anchor" id="AUTOGENERATED-raises-"></a> +##### Raises: * <b>`ValueError`</b>: If `tensors` does not contain any `Tensor` or `IndexedSlices`. @@ -104,7 +68,7 @@ See also `group` and `with_dependencies`. - - - -### `tf.group(*inputs, **kwargs)` <a class="md-anchor" id="group"></a> +### `tf.group(*inputs, **kwargs)` {#group} Create an op that groups multiple operations. @@ -113,18 +77,18 @@ output. See also `tuple` and `with_dependencies`. -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### 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: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: An Operation that executes all its inputs. -##### Raises: <a class="md-anchor" id="AUTOGENERATED-raises-"></a> +##### Raises: * <b>`ValueError`</b>: If an unknown keyword argument is provided, or if there are @@ -133,30 +97,30 @@ See also `tuple` and `with_dependencies`. - - - -### `tf.no_op(name=None)` <a class="md-anchor" id="no_op"></a> +### `tf.no_op(name=None)` {#no_op} Does nothing. Only useful as a placeholder for control edges. -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`name`</b>: A name for the operation (optional). -##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: The created Operation. - - - -### `tf.count_up_to(ref, limit, name=None)` <a class="md-anchor" id="count_up_to"></a> +### `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: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`ref`</b>: A mutable `Tensor`. Must be one of the following types: `int32`, `int64`. @@ -166,7 +130,7 @@ easier to chain operations that need to use the updated value. 'OutOfRange' error. * <b>`name`</b>: A name for the operation (optional). -##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: A `Tensor`. Has the same type as `ref`. A copy of the input before increment. If nothing else modifies the @@ -174,188 +138,188 @@ easier to chain operations that need to use the updated value. -## Logical Operators <a class="md-anchor" id="AUTOGENERATED-logical-operators"></a> +## Logical Operators TensorFlow provides several operations that you can use to add logical operators to your graph. - - - -### `tf.logical_and(x, y, name=None)` <a class="md-anchor" id="logical_and"></a> +### `tf.logical_and(x, y, name=None)` {#logical_and} Returns the truth value of x AND y element-wise. -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### 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 class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: A `Tensor` of type `bool`. - - - -### `tf.logical_not(x, name=None)` <a class="md-anchor" id="logical_not"></a> +### `tf.logical_not(x, name=None)` {#logical_not} Returns the truth value of NOT x element-wise. -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`x`</b>: A `Tensor` of type `bool`. * <b>`name`</b>: A name for the operation (optional). -##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: A `Tensor` of type `bool`. - - - -### `tf.logical_or(x, y, name=None)` <a class="md-anchor" id="logical_or"></a> +### `tf.logical_or(x, y, name=None)` {#logical_or} Returns the truth value of x OR y element-wise. -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### 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 class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: A `Tensor` of type `bool`. - - - -### `tf.logical_xor(x, y, name='LogicalXor')` <a class="md-anchor" id="logical_xor"></a> +### `tf.logical_xor(x, y, name='LogicalXor')` {#logical_xor} x ^ y = (x | y) & ~(x & y). -## Comparison Operators <a class="md-anchor" id="AUTOGENERATED-comparison-operators"></a> +## Comparison Operators TensorFlow provides several operations that you can use to add comparison operators to your graph. - - - -### `tf.equal(x, y, name=None)` <a class="md-anchor" id="equal"></a> +### `tf.equal(x, y, name=None)` {#equal} Returns the truth value of (x == y) element-wise. -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### 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 class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: A `Tensor` of type `bool`. - - - -### `tf.not_equal(x, y, name=None)` <a class="md-anchor" id="not_equal"></a> +### `tf.not_equal(x, y, name=None)` {#not_equal} Returns the truth value of (x != y) element-wise. -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### 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 class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: A `Tensor` of type `bool`. - - - -### `tf.less(x, y, name=None)` <a class="md-anchor" id="less"></a> +### `tf.less(x, y, name=None)` {#less} Returns the truth value of (x < y) element-wise. -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### 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 class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: A `Tensor` of type `bool`. - - - -### `tf.less_equal(x, y, name=None)` <a class="md-anchor" id="less_equal"></a> +### `tf.less_equal(x, y, name=None)` {#less_equal} Returns the truth value of (x <= y) element-wise. -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### 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 class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: A `Tensor` of type `bool`. - - - -### `tf.greater(x, y, name=None)` <a class="md-anchor" id="greater"></a> +### `tf.greater(x, y, name=None)` {#greater} Returns the truth value of (x > y) element-wise. -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### 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 class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: A `Tensor` of type `bool`. - - - -### `tf.greater_equal(x, y, name=None)` <a class="md-anchor" id="greater_equal"></a> +### `tf.greater_equal(x, y, name=None)` {#greater_equal} Returns the truth value of (x >= y) element-wise. -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### 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 class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: A `Tensor` of type `bool`. - - - -### `tf.select(condition, t, e, name=None)` <a class="md-anchor" id="select"></a> +### `tf.select(condition, t, e, name=None)` {#select} Selects elements from `t` or `e`, depending on `condition`. @@ -378,7 +342,7 @@ select(condition, t, e) ==> [[1, 2], [1, 2]] ``` -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`condition`</b>: A `Tensor` of type `bool`. @@ -386,14 +350,14 @@ 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: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: A `Tensor` with the same type and shape as `t` and `e`. - - - -### `tf.where(input, name=None)` <a class="md-anchor" id="where"></a> +### `tf.where(input, name=None)` {#where} Returns locations of true values in a boolean tensor. @@ -429,116 +393,116 @@ where(input) ==> [[0, 0, 0], [2, 1, 1]] ``` -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`input`</b>: A `Tensor` of type `bool`. * <b>`name`</b>: A name for the operation (optional). -##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: A `Tensor` of type `int64`. -## Debugging Operations <a class="md-anchor" id="AUTOGENERATED-debugging-operations"></a> +## Debugging Operations TensorFlow provides several operations that you can use to validate values and debug your graph. - - - -### `tf.is_finite(x, name=None)` <a class="md-anchor" id="is_finite"></a> +### `tf.is_finite(x, name=None)` {#is_finite} Returns which elements of x are finite. -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### 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 class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: A `Tensor` of type `bool`. - - - -### `tf.is_inf(x, name=None)` <a class="md-anchor" id="is_inf"></a> +### `tf.is_inf(x, name=None)` {#is_inf} Returns which elements of x are Inf. -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### 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 class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: A `Tensor` of type `bool`. - - - -### `tf.is_nan(x, name=None)` <a class="md-anchor" id="is_nan"></a> +### `tf.is_nan(x, name=None)` {#is_nan} Returns which elements of x are NaN. -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### 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 class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: A `Tensor` of type `bool`. - - - -### `tf.verify_tensor_all_finite(t, msg, name=None)` <a class="md-anchor" id="verify_tensor_all_finite"></a> +### `tf.verify_tensor_all_finite(t, msg, name=None)` {#verify_tensor_all_finite} Assert that the tensor does not contain any NaN's or Inf's. -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### 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: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: Same tensor as `t`. - - - -### `tf.check_numerics(tensor, message, name=None)` <a class="md-anchor" id="check_numerics"></a> +### `tf.check_numerics(tensor, message, name=None)` {#check_numerics} 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: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### 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 class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: A `Tensor`. Has the same type as `tensor`. - - - -### `tf.add_check_numerics_ops()` <a class="md-anchor" id="add_check_numerics_ops"></a> +### `tf.add_check_numerics_ops()` {#add_check_numerics_ops} Connect a check_numerics to every floating point tensor. @@ -547,21 +511,21 @@ 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: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: A `group` op depending on all `check_numerics` ops added. - - - -### `tf.Assert(condition, data, summarize=None, name=None)` <a class="md-anchor" id="Assert"></a> +### `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. -##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`condition`</b>: The condition to evaluate. @@ -572,14 +536,14 @@ 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)` <a class="md-anchor" id="Print"></a> +### `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: <a class="md-anchor" id="AUTOGENERATED-args-"></a> +##### Args: * <b>`input_`</b>: A tensor passed through this op. @@ -590,16 +554,16 @@ evaluating. * <b>`summarize`</b>: Only print this many entries of each tensor. * <b>`name`</b>: A name for the operation (optional). -##### Returns: <a class="md-anchor" id="AUTOGENERATED-returns-"></a> +##### Returns: Same tensor as `input_`. -## Other Functions and Classes <a class="md-anchor" id="AUTOGENERATED-other-functions-and-classes"></a> +## Other Functions and Classes - - - -### `class tf.xrange` <a class="md-anchor" id="xrange"></a> +### `class tf.xrange` {#xrange} xrange(stop) -> xrange object xrange(start, stop[, step]) -> xrange object |