path: root/tensorflow/g3doc/api_docs/python/nn.md

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# Neural Network
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## Contents
* [Activation Functions](#AUTOGENERATED-activation-functions)
  * [tf.nn.relu(features, name=None)](#relu)
  * [tf.nn.relu6(features, name=None)](#relu6)
  * [tf.nn.softplus(features, name=None)](#softplus)
  * [tf.nn.dropout(x, keep_prob, noise_shape=None, seed=None, name=None)](#dropout)
  * [tf.nn.bias_add(value, bias, name=None)](#bias_add)
  * [tf.sigmoid(x, name=None)](#sigmoid)
  * [tf.tanh(x, name=None)](#tanh)
* [Convolution](#AUTOGENERATED-convolution)
  * [tf.nn.conv2d(input, filter, strides, padding, use_cudnn_on_gpu=None, name=None)](#conv2d)
  * [tf.nn.depthwise_conv2d(input, filter, strides, padding, name=None)](#depthwise_conv2d)
  * [tf.nn.separable_conv2d(input, depthwise_filter, pointwise_filter, strides, padding, name=None)](#separable_conv2d)
* [Pooling](#AUTOGENERATED-pooling)
  * [tf.nn.avg_pool(value, ksize, strides, padding, name=None)](#avg_pool)
  * [tf.nn.max_pool(value, ksize, strides, padding, name=None)](#max_pool)
  * [tf.nn.max_pool_with_argmax(input, ksize, strides, padding, Targmax=None, name=None)](#max_pool_with_argmax)
* [Normalization](#AUTOGENERATED-normalization)
  * [tf.nn.l2_normalize(x, dim, epsilon=1e-12, name=None)](#l2_normalize)
  * [tf.nn.local_response_normalization(input, depth_radius=None, bias=None, alpha=None, beta=None, name=None)](#local_response_normalization)
  * [tf.nn.moments(x, axes, name=None)](#moments)
* [Losses](#AUTOGENERATED-losses)
  * [tf.nn.l2_loss(t, name=None)](#l2_loss)
* [Classification](#AUTOGENERATED-classification)
  * [tf.nn.sigmoid_cross_entropy_with_logits(logits, targets, name=None)](#sigmoid_cross_entropy_with_logits)
  * [tf.nn.softmax(logits, name=None)](#softmax)
  * [tf.nn.softmax_cross_entropy_with_logits(logits, labels, name=None)](#softmax_cross_entropy_with_logits)
* [Embeddings](#AUTOGENERATED-embeddings)
  * [tf.nn.embedding_lookup(params, ids, name=None)](#embedding_lookup)
  * [tf.nn.embedding_lookup_sparse(params, sp_ids, sp_weights, name=None, combiner='mean')](#embedding_lookup_sparse)
* [Evaluation](#AUTOGENERATED-evaluation)
  * [tf.nn.top_k(input, k, name=None)](#top_k)
  * [tf.nn.in_top_k(predictions, targets, k, name=None)](#in_top_k)
* [Candidate Sampling](#AUTOGENERATED-candidate-sampling)
  * [Sampled Loss Functions](#AUTOGENERATED-sampled-loss-functions)
  * [tf.nn.nce_loss(weights, biases, inputs, labels, num_sampled, num_classes, num_true=1, sampled_values=None, remove_accidental_hits=False, name='nce_loss')](#nce_loss)
  * [tf.nn.sampled_softmax_loss(weights, biases, inputs, labels, num_sampled, num_classes, num_true=1, sampled_values=None, remove_accidental_hits=True, name='sampled_softmax_loss')](#sampled_softmax_loss)
  * [Candidate Samplers](#AUTOGENERATED-candidate-samplers)
  * [tf.nn.uniform_candidate_sampler(true_classes, num_true, num_sampled, unique, range_max, seed=None, name=None)](#uniform_candidate_sampler)
  * [tf.nn.log_uniform_candidate_sampler(true_classes, num_true, num_sampled, unique, range_max, seed=None, name=None)](#log_uniform_candidate_sampler)
  * [tf.nn.learned_unigram_candidate_sampler(true_classes, num_true, num_sampled, unique, range_max, seed=None, name=None)](#learned_unigram_candidate_sampler)
  * [tf.nn.fixed_unigram_candidate_sampler(true_classes, num_true, num_sampled, unique, range_max, vocab_file='', distortion=0.0, num_reserved_ids=0, num_shards=1, shard=0, unigrams=[], seed=None, name=None)](#fixed_unigram_candidate_sampler)
  * [Miscellaneous candidate sampling utilities](#AUTOGENERATED-miscellaneous-candidate-sampling-utilities)
  * [tf.nn.compute_accidental_hits(true_classes, sampled_candidates, num_true, seed=None, name=None)](#compute_accidental_hits)


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## Activation Functions <div class="md-anchor" id="AUTOGENERATED-activation-functions">{#AUTOGENERATED-activation-functions}</div>

The activation ops provide different types of nonlinearities for use in
neural networks.  These include smooth nonlinearities (`sigmoid`,
`tanh`, and `softplus`), continuous but not everywhere differentiable
functions (`relu`, `relu6`, and `relu_x`), and random regularization
(`dropout`).

All activation ops apply componentwise, and produce a tensor of the same
shape as the input tensor.

- - -

### tf.nn.relu(features, name=None) <div class="md-anchor" id="relu">{#relu}</div>

Computes rectified linear: `max(features, 0)`.

##### Args:


*  <b>features</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`, `uint8`, `int16`, `int8`.
*  <b>name</b>: A name for the operation (optional).

##### Returns:

  A `Tensor`. Has the same type as `features`.


- - -

### tf.nn.relu6(features, name=None) <div class="md-anchor" id="relu6">{#relu6}</div>

Computes Rectified Linear 6: `min(max(features, 0), 6)`.

##### Args:


*  <b>features</b>: A `Tensor` with type `float`, `double`, `int32`, `int64`, `uint8`,
    `int16`, or `int8`.
*  <b>name</b>: A name for the operation (optional).

##### Returns:

  A `Tensor` with the same type as `features`.


- - -

### tf.nn.softplus(features, name=None) <div class="md-anchor" id="softplus">{#softplus}</div>

Computes softplus: `log(exp(features) + 1)`.

##### Args:


*  <b>features</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`, `uint8`, `int16`, `int8`.
*  <b>name</b>: A name for the operation (optional).

##### Returns:

  A `Tensor`. Has the same type as `features`.


- - -

### tf.nn.dropout(x, keep_prob, noise_shape=None, seed=None, name=None) <div class="md-anchor" id="dropout">{#dropout}</div>

Computes dropout.

With probability `keep_prob`, outputs the input element scaled up by
`1 / keep_prob`, otherwise outputs `0`.  The scaling is so that the expected
sum is unchanged.

By default, each element is kept or dropped independently.  If `noise_shape`
is specified, it must be
[broadcastable](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
to the shape of `x`, and only dimensions with `noise_shape[i] == x.shape[i]`
will make independent decisions.  For example, if `x.shape = [b, x, y, c]` and
`noise_shape = [b, 1, 1, c]`, each batch and channel component will be
kept independently and each row and column will be kept or not kept together.

##### Args:


*  <b>x</b>: A tensor.
*  <b>keep_prob</b>: Float probability that each element is kept.
*  <b>noise_shape</b>: Shape for randomly generated keep/drop flags.
*  <b>seed</b>: A Python integer. Used to create a random seed.
    See [`set_random_seed`](constant_op.md#set_random_seed) for behavior.
*  <b>name</b>: A name for this operation (optional).

##### Returns:

  A Tensor of the same shape of `x`.

##### Raises:


*  <b>ValueError</b>: If `keep_prob` is not in `(0, 1]`.


- - -

### tf.nn.bias_add(value, bias, name=None) <div class="md-anchor" id="bias_add">{#bias_add}</div>

Adds `bias` to `value`.

This is (mostly) a special case of `tf.add` where `bias` is restricted to 1-D.
Broadcasting is supported, so `value` may have any number of dimensions.
Unlike `tf.add`, the type of `bias` is allowed to differ from `value` in the
case where both types are quantized.

##### Args:


*  <b>value</b>: A `Tensor` with type `float`, `double`, `int64`, `int32`, `uint8`,
    `int16`, `int8`, or `complex64`.
*  <b>bias</b>: A 1-D `Tensor` with size matching the last dimension of `value`.
    Must be the same type as `value` unless `value` is a quantized type,
    in which case a different quantized type may be used.
*  <b>name</b>: A name for the operation (optional).

##### Returns:

  A `Tensor` with the same type as `value`.


- - -

### tf.sigmoid(x, name=None) <div class="md-anchor" id="sigmoid">{#sigmoid}</div>

Computes sigmoid of `x` element-wise.

Specifically, `y = 1 / (1 + exp(-x))`.

##### Args:


*  <b>x</b>: A Tensor with type `float`, `double`, `int32`, `complex64`, `int64`,
    or `qint32`.
*  <b>name</b>: A name for the operation (optional).

##### Returns:

  A Tensor with the same type as `x` if `x.dtype != qint32`
    otherwise the return type is `quint8`.


- - -

### tf.tanh(x, name=None) <div class="md-anchor" id="tanh">{#tanh}</div>

Computes hyperbolic tangent of `x` element-wise.

##### Args:


*  <b>x</b>: A Tensor with type `float`, `double`, `int32`, `complex64`, `int64`,
    or `qint32`.
*  <b>name</b>: A name for the operation (optional).

##### Returns:

  A Tensor with the same type as `x` if `x.dtype != qint32` otherwise
    the return type is `quint8`.



## Convolution <div class="md-anchor" id="AUTOGENERATED-convolution">{#AUTOGENERATED-convolution}</div>

The convolution ops sweep a 2-D filter over a batch of images, applying the
filter to each window of each image of the appropriate size.  The different
ops trade off between generic vs. specific filters:

* `conv2d`: Arbitrary filters that can mix channels together.
* `depthwise_conv2d`: Filters that operate on each channel independently.
* `separable_conv2d`: A depthwise spatial filter followed by a pointwise filter.

Note that although these ops are called "convolution", they are strictly
speaking "cross-correlation" since the filter is combined with an input window
without reversing the filter.  For details, see [the properties of
cross-correlation](https://en.wikipedia.org/wiki/Cross-correlation#Properties).

The filter is applied to image patches of the same size as the filter and
strided according to the `strides` argument.  `strides = [1, 1, 1, 1]` applies
the filter to a patch at every offset, `strides = [1, 2, 2, 1]` applies the
filter to every other image patch in each dimension, etc.

Ignoring channels for the moment, the spatial semantics of the convolution ops
are as follows.  If the 4-D `input` has shape
`[batch, in_height, in_width, ...]` and the 4-D `filter` has shape
`[filter_height, filter_width, ...]`, then

    output.shape = [batch,
                    (in_height - filter_height + 1) / strides[1],
                    (in_width - filter_width + 1) / strides[2],
                    ...]

    output[b, i, j, :] =
        sum_{di, dj} input[b, strides[1] * i + di, strides[2] * j + dj, ...] *
                     filter[di, dj, ...]

Since `input` is 4-D, each `input[b, i, j, :]` is a vector.  For `conv2d`, these
vectors are multiplied by the `filter[di, dj, :, :]` matrices to produce new
vectors.  For `depthwise_conv_2d`, each scalar component `input[b, i, j, k]`
is multiplied by a vector `filter[di, dj, k]`, and all the vectors are
concatenated.

In the formula for `output.shape`, the rounding direction depends on padding:

* `padding = 'SAME'`: Round down (only full size windows are considered).
* `padding = 'VALID'`: Round up (partial windows are included).

- - -

### tf.nn.conv2d(input, filter, strides, padding, use_cudnn_on_gpu=None, name=None) <div class="md-anchor" id="conv2d">{#conv2d}</div>

Computes a 2-D convolution given 4-D `input` and `filter` tensors.

Given an input tensor of shape `[batch, in_height, in_width, in_channels]`
and a filter / kernel tensor of shape
`[filter_height, filter_width, in_channels, out_channels]`, this op
performs the following:

1. Flattens the filter to a 2-D matrix with shape
   `[filter_height * filter_width * in_channels, output_channels]`.
2. Extracts image patches from the the input tensor to form a *virtual*
   tensor of shape `[batch, out_height, out_width,
   filter_height * filter_width * in_channels]`.
3. For each patch, right-multiplies the filter matrix and the image patch
   vector.

In detail,

    output[b, i, j, k] =
        sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] *
                        filter[di, dj, q, k]

Must have `strides[0] = strides[3] = 1`.  For the most common case of the same
horizontal and vertices strides, `strides = [1, stride, stride, 1]`.

##### Args:


*  <b>input</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`.
*  <b>filter</b>: A `Tensor`. Must have the same type as `input`.
*  <b>strides</b>: A list of `ints`.
    1-D of length 4.  The stride of the sliding window for each dimension
    of `input`.
*  <b>padding</b>: A `string` from: `"SAME", "VALID"`.
    The type of padding algorithm to use.
*  <b>use_cudnn_on_gpu</b>: An optional `bool`. Defaults to `True`.
*  <b>name</b>: A name for the operation (optional).

##### Returns:

  A `Tensor`. Has the same type as `input`.


- - -

### tf.nn.depthwise_conv2d(input, filter, strides, padding, name=None) <div class="md-anchor" id="depthwise_conv2d">{#depthwise_conv2d}</div>

Depthwise 2-D convolution.

Given an input tensor of shape `[batch, in_height, in_width, in_channels]`
and a filter tensor of shape
`[filter_height, filter_width, in_channels, channel_multiplier]`
containing `in_channels` convolutional filters of depth 1, `depthwise_conv2d`
applies a different filter to each input channel (expanding from 1 channel
to `channel_multiplier` channels for each), then concatenates the results
together.  The output has `in_channels * channel_multiplier` channels.

In detail,

    output[b, i, j, k * channel_multiplier + q] =
        sum_{di, dj} input[b, strides[1] * i + di, strides[2] * j + dj, k] *
                     filter[di, dj, k, q]

Must have `strides[0] = strides[3] = 1`.  For the most common case of the
same horizontal and vertical strides, `strides = [1, stride, stride, 1]`.

##### Args:


*  <b>input</b>: 4-D with shape `[batch, in_height, in_width, in_channels]`.
*  <b>filter</b>: 4-D with shape
    `[filter_height, filter_width, in_channels, channel_multiplier]`.
*  <b>strides</b>: 1-D of size 4.  The stride of the sliding window for each
    dimension of `input`.
*  <b>padding</b>: A string, either `'VALID'` or `'SAME'`.  The padding algorithm.
*  <b>name</b>: A name for this operation (optional).

##### Returns:

  A 4-D `Tensor` of shape
  `[batch, out_height, out_width, in_channels * channel_multiplier].`


- - -

### tf.nn.separable_conv2d(input, depthwise_filter, pointwise_filter, strides, padding, name=None) <div class="md-anchor" id="separable_conv2d">{#separable_conv2d}</div>

2-D convolution with separable filters.

Performs a depthwise convolution that acts separately on channels followed by
a pointwise convolution that mixes channels.  Note that this is separability
between dimensions `[1, 2]` and `3`, not spatial separability between
dimensions `1` and `2`.

In detail,

    output[b, i, j, k] = sum_{di, dj, q, r]
        input[b, strides[1] * i + di, strides[2] * j + dj, q] *
        depthwise_filter[di, dj, q, r] *
        pointwise_filter[0, 0, q * channel_multiplier + r, k]

`strides` controls the strides for the depthwise convolution only, since
the pointwise convolution has implicit strides of `[1, 1, 1, 1]`.  Must have
`strides[0] = strides[3] = 1`.  For the most common case of the same
horizontal and vertical strides, `strides = [1, stride, stride, 1]`.

##### Args:


*  <b>input</b>: 4-D `Tensor` with shape `[batch, in_height, in_width, in_channels]`.
*  <b>depthwise_filter</b>: 4-D `Tensor` with shape
    `[filter_height, filter_width, in_channels, channel_multiplier]`.
    Contains `in_channels` convolutional filters of depth 1.
*  <b>pointwise_filter</b>: 4-D `Tensor` with shape
    `[1, 1, channel_multiplier * in_channels, out_channels]`.  Pointwise
    filter to mix channels after `depthwise_filter` has convolved spatially.
*  <b>strides</b>: 1-D of size 4.  The strides for the depthwise convolution for
    each dimension of `input`.
*  <b>padding</b>: A string, either `'VALID'` or `'SAME'`.  The padding algorithm.
*  <b>name</b>: A name for this operation (optional).

##### Returns:

  A 4-D `Tensor` of shape `[batch, out_height, out_width, out_channels]`.



## Pooling <div class="md-anchor" id="AUTOGENERATED-pooling">{#AUTOGENERATED-pooling}</div>

The pooling ops sweep a rectangular window over the input tensor, computing a
reduction operation for each window (average, max, or max with argmax).  Each
pooling op uses rectangular windows of size `ksize` separated by offset
`strides`.  For example, if `strides` is all ones every window is used, if
`strides` is all twos every other window is used in each dimension, etc.

In detail, the output is

    output[i] = reduce(value[strides * i:strides * i + ksize])

for each tuple of indices `i`.  The output shape is

    output.shape = (value.shape - ksize + 1) / strides

where the rounding direction depends on padding:

* `padding = 'SAME'`: Round down (only full size windows are considered).
* `padding = 'VALID'`: Round up (partial windows are included).

- - -

### tf.nn.avg_pool(value, ksize, strides, padding, name=None) <div class="md-anchor" id="avg_pool">{#avg_pool}</div>

Performs the average pooling on the input.

Each entry in `output` is the mean of the corresponding size `ksize`
window in `value`.

##### Args:


*  <b>value</b>: A 4-D `Tensor` of shape `[batch, height, width, channels]` and type
    `float32`, `float64`, `qint8`, `quint8`, or `qint32`.
*  <b>ksize</b>: A list of ints that has length >= 4.
    The size of the window for each dimension of the input tensor.
*  <b>strides</b>: A list of ints that has length >= 4.
    The stride of the sliding window for each dimension of the
    input tensor.
*  <b>padding</b>: A string, either `'VALID'` or `'SAME'`. The padding algorithm.
*  <b>name</b>: Optional name for the operation.

##### Returns:

  A `Tensor` with the same type as `value`.  The average pooled output tensor.


- - -

### tf.nn.max_pool(value, ksize, strides, padding, name=None) <div class="md-anchor" id="max_pool">{#max_pool}</div>

Performs the max pooling on the input.

##### Args:


*  <b>value</b>: A 4-D `Tensor` with shape `[batch, height, width, channels]` and
    type `float32`, `float64`, `qint8`, `quint8`, `qint32`.
*  <b>ksize</b>: A list of ints that has length >= 4.  The size of the window for
    each dimension of the input tensor.
*  <b>strides</b>: A list of ints that has length >= 4.  The stride of the sliding
    window for each dimension of the input tensor.
*  <b>padding</b>: A string, either `'VALID'` or `'SAME'`. The padding algorithm.
*  <b>name</b>: Optional name for the operation.

##### Returns:

  A `Tensor` with the same type as `value`.  The max pooled output tensor.


- - -

### tf.nn.max_pool_with_argmax(input, ksize, strides, padding, Targmax=None, name=None) <div class="md-anchor" id="max_pool_with_argmax">{#max_pool_with_argmax}</div>

Performs max pooling on the input and outputs both max values and indices.

The indices in `argmax` are flattened, so that a maximum value at position
`[b, y, x, c]` becomes flattened index
`((b * height + y) * width + x) * channels + c`.

##### Args:


*  <b>input</b>: A `Tensor` of type `float32`.
    4-D with shape `[batch, height, width, channels]`.  Input to pool over.
*  <b>ksize</b>: A list of `ints` that has length `>= 4`.
    The size of the window for each dimension of the input tensor.
*  <b>strides</b>: A list of `ints` that has length `>= 4`.
    The stride of the sliding window for each dimension of the
    input tensor.
*  <b>padding</b>: A `string` from: `"SAME", "VALID"`.
    The type of padding algorithm to use.
*  <b>Targmax</b>: An optional `tf.DType` from: `tf.int32, tf.int64`. Defaults to `tf.int64`.
*  <b>name</b>: A name for the operation (optional).

##### Returns:

  A tuple of `Tensor` objects (output, argmax).

*  <b>output</b>: A `Tensor` of type `float32`. The max pooled output tensor.
*  <b>argmax</b>: A `Tensor` of type `Targmax`. 4-D.  The flattened indices of the max values chosen for each output.



## Normalization <div class="md-anchor" id="AUTOGENERATED-normalization">{#AUTOGENERATED-normalization}</div>

Normalization is useful to prevent neurons from saturating when inputs may
have varying scale, and to aid generalization.

- - -

### tf.nn.l2_normalize(x, dim, epsilon=1e-12, name=None) <div class="md-anchor" id="l2_normalize">{#l2_normalize}</div>

Normalizes along dimension `dim` using an L2 norm.

For a 1-D tensor with `dim = 0`, computes

    output = x / sqrt(max(sum(x**2), epsilon))

For `x` with more dimensions, independently normalizes each 1-D slice along
dimension `dim`.

##### Args:


*  <b>x</b>: A `Tensor`.
*  <b>dim</b>: Dimension along which to normalize.
*  <b>epsilon</b>: A lower bound value for the norm. Will use `sqrt(epsilon)` as the
    divisor if `norm < sqrt(epsilon)`.
*  <b>name</b>: A name for this operation (optional).

##### Returns:

  A `Tensor` with the same shape as `x`.


- - -

### tf.nn.local_response_normalization(input, depth_radius=None, bias=None, alpha=None, beta=None, name=None) <div class="md-anchor" id="local_response_normalization">{#local_response_normalization}</div>

Local Response Normalization.

The 4-D `input` tensor is treated as a 3-D array of 1-D vectors (along the last
dimension), and each vector is normalized independently.  Within a given vector,
each component is divided by the weighted, squared sum of inputs within
`depth_radius`.  In detail,

    sqr_sum[a, b, c, d] =
        sum(input[a, b, c, d - depth_radius : d + depth_radius + 1] ** 2)
    output = input / (bias + alpha * sqr_sum ** beta)

For details, see [Krizhevsky et al., ImageNet classification with deep
convolutional neural networks (NIPS 2012)]
(http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks).

##### Args:


*  <b>input</b>: A `Tensor` of type `float32`. 4-D.
*  <b>depth_radius</b>: An optional `int`. Defaults to `5`.
    0-D.  Half-width of the 1-D normalization window.
*  <b>bias</b>: An optional `float`. Defaults to `1`.
    An offset (usually positive to avoid dividing by 0).
*  <b>alpha</b>: An optional `float`. Defaults to `1`.
    A scale factor, usually positive.
*  <b>beta</b>: An optional `float`. Defaults to `0.5`. An exponent.
*  <b>name</b>: A name for the operation (optional).

##### Returns:

  A `Tensor` of type `float32`.


- - -

### tf.nn.moments(x, axes, name=None) <div class="md-anchor" id="moments">{#moments}</div>

Calculate the mean and variance of `x`.

The mean and variance are calculated by aggregating the contents of `x`
across `axes`.  If `x` is 1-D and `axes = [0]` this is just the mean
and variance of a vector.

For so-called "global normalization" needed for convolutional filters pass
`axes=[0, 1, 2]` (batch, height, width).  For batch normalization pass
`axes=[0]` (batch).

##### Args:


*  <b>x</b>: A `Tensor`.
*  <b>axes</b>: array of ints.  Axes along which to compute mean and
    variance.
*  <b>name</b>: Name used to scope the operations that compute the moments.

##### Returns:

  Two `Tensors`: `mean` and `variance`.



## Losses <div class="md-anchor" id="AUTOGENERATED-losses">{#AUTOGENERATED-losses}</div>

The loss ops measure error between two tensors, or between a tensor and zero.
These can be used for measuring accuracy of a network in a regression task
or for regularization purposes (weight decay).

- - -

### tf.nn.l2_loss(t, name=None) <div class="md-anchor" id="l2_loss">{#l2_loss}</div>

L2 Loss.

Computes half the L2 norm of a tensor without the `sqrt`:

    output = sum(t ** 2) / 2

##### Args:


*  <b>t</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `qint8`, `quint8`, `qint32`.
    Typically 2-D, but may have any dimensions.
*  <b>name</b>: A name for the operation (optional).

##### Returns:

  A `Tensor`. Has the same type as `t`. 0-D.



## Classification <div class="md-anchor" id="AUTOGENERATED-classification">{#AUTOGENERATED-classification}</div>

TensorFlow provides several operations that help you perform classification.

- - -

### tf.nn.sigmoid_cross_entropy_with_logits(logits, targets, name=None) <div class="md-anchor" id="sigmoid_cross_entropy_with_logits">{#sigmoid_cross_entropy_with_logits}</div>

Computes sigmoid cross entropy given `logits`.

Measures the probability error in discrete classification tasks in which each
class is independent and not mutually exclusive.  For instance, one could
perform multilabel classification where a picture can contain both an elephant
and a dog at the same time.

For brevity, let `x = logits`, `z = targets`.  The logistic loss is

    x - x * z + log(1 + exp(-x))

To ensure stability and avoid overflow, the implementation uses

    max(x, 0) - x * z + log(1 + exp(-abs(x)))

`logits` and `targets` must have the same type and shape.

##### Args:


*  <b>logits</b>: A `Tensor` of type `float32` or `float64`.
*  <b>targets</b>: A `Tensor` of the same type and shape as `logits`.
*  <b>name</b>: A name for the operation (optional).

##### Returns:

  A `Tensor` of the same shape as `logits` with the componentwise
  logistic losses.


- - -

### tf.nn.softmax(logits, name=None) <div class="md-anchor" id="softmax">{#softmax}</div>

Computes softmax activations.

For each batch `i` and class `j` we have

    softmax[i, j] = exp(logits[i, j]) / sum(exp(logits[i]))

##### Args:


*  <b>logits</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`.
    2-D with shape `[batch_size, num_classes]`.
*  <b>name</b>: A name for the operation (optional).

##### Returns:

  A `Tensor`. Has the same type as `logits`. Same shape as `logits`.


- - -

### tf.nn.softmax_cross_entropy_with_logits(logits, labels, name=None) <div class="md-anchor" id="softmax_cross_entropy_with_logits">{#softmax_cross_entropy_with_logits}</div>

Computes softmax cross entropy between `logits` and `labels`.

Measures the probability error in discrete classification tasks in which the
classes are mutually exclusive (each entry is in exactly one class).  For
example, each CIFAR-10 image is labeled with one and only one label: an image
can be a dog or a truck, but not both.

**WARNING:** This op expects unscaled logits, since it performs a `softmax`
on `logits` internally for efficiency.  Do not call this op with the
output of `softmax`, as it will produce incorrect results.

`logits` and `labels` must have the same shape `[batch_size, num_classes]`
and the same dtype (either `float32` or `float64`).

##### Args:


*  <b>logits</b>: Unscaled log probabilities.
*  <b>labels</b>: Each row `labels[i]` must be a valid probability distribution.
*  <b>name</b>: A name for the operation (optional).

##### Returns:

  A 1-D `Tensor` of length `batch_size` of the same type as `logits` with the
  softmax cross entropy loss.



## Embeddings <div class="md-anchor" id="AUTOGENERATED-embeddings">{#AUTOGENERATED-embeddings}</div>

TensorFlow provides several operations that help you compute embeddings.

- - -

### tf.nn.embedding_lookup(params, ids, name=None) <div class="md-anchor" id="embedding_lookup">{#embedding_lookup}</div>

Return a tensor of embedding values by looking up "ids" in "params".

##### Args:


*  <b>params</b>: List of tensors of the same shape.  A single tensor is
          treated as a singleton list.
*  <b>ids</b>: Tensor of integers containing the ids to be looked up in
       'params'.  Let P be len(params).  If P > 1, then the ids are
       partitioned by id % P, and we do separate lookups in params[p]
       for 0 <= p < P, and then stitch the results back together into
       a single result tensor.
*  <b>name</b>: Optional name for the op.

##### Returns:

  A tensor of shape ids.shape + params[0].shape[1:] containing the
  values params[i % P][i] for each i in ids.

##### Raises:


*  <b>ValueError</b>: if some parameters are invalid.


- - -

### tf.nn.embedding_lookup_sparse(params, sp_ids, sp_weights, name=None, combiner='mean') <div class="md-anchor" id="embedding_lookup_sparse">{#embedding_lookup_sparse}</div>

Computes embeddings for the given ids and weights.

This op assumes that there is at least one id for each row in the dense tensor
represented by sp_ids (i.e. there are no rows with empty features), and that
all the indices of sp_ids are in canonical row-major order.

It also assumes that all id values lie in the range [0, p0), where p0
is the sum of the size of params along dimension 0.

##### Args:


*  <b>params</b>: A single tensor representing the complete embedding tensor,
    or a list of P tensors all of same shape except for the first dimension,
    representing sharded embedding tensors. In the latter case, the ids are
    partitioned by id % P, and we do separate lookups in params[p] for
    0 <= p < P, and then stitch the results back together into a single
    result tensor. The first dimension is allowed to vary as the vocab
    size is not necessarily a multiple of P.
*  <b>sp_ids</b>: N x M SparseTensor of int64 ids (typically from FeatureValueToId),
    where N is typically batch size and M is arbitrary.
*  <b>sp_weights</b>: either a SparseTensor of float / double weights, or None to
    indicate all weights should be taken to be 1. If specified, sp_weights
    must have exactly the same shape and indices as sp_ids.
*  <b>name</b>: Optional name for the op.
*  <b>combiner</b>: A string specifying the reduction op. Currently "mean" and "sum"
    are supported.
    "sum" computes the weighted sum of the embedding results for each row.
    "mean" is the weighted sum divided by the total weight.

##### Returns:

  A dense tensor representing the combined embeddings for the
  sparse ids. For each row in the dense tensor represented by sp_ids, the op
  looks up the embeddings for all ids in that row, multiplies them by the
  corresponding weight, and combines these embeddings as specified.

  In other words, if
    shape(combined params) = [p0, p1, ..., pm]
  and
    shape(sp_ids) = shape(sp_weights) = [d0, d1, ..., dn]
  then
    shape(output) = [d0, d1, ..., dn-1, p1, ..., pm].

  For instance, if params is a 10x20 matrix, and sp_ids / sp_weights are

    [0, 0]: id 1, weight 2.0
    [0, 1]: id 3, weight 0.5
    [1, 0]: id 0, weight 1.0
    [2, 3]: id 1, weight 3.0

  with combiner="mean", then the output will be a 3x20 matrix where
    output[0, :] = (params[1, :] * 2.0 + params[3, :] * 0.5) / (2.0 + 0.5)
    output[1, :] = params[0, :] * 1.0
    output[2, :] = params[1, :] * 3.0

##### Raises:


*  <b>TypeError</b>: If sp_ids is not a SparseTensor, or if sp_weights is neither
    None nor SparseTensor.
*  <b>ValueError</b>: If combiner is not one of {"mean", "sum"}.



## Evaluation <div class="md-anchor" id="AUTOGENERATED-evaluation">{#AUTOGENERATED-evaluation}</div>

The evaluation ops are useful for measuring the performance of a network.
Since they are nondifferentiable, they are typically used at evaluation time.

- - -

### tf.nn.top_k(input, k, name=None) <div class="md-anchor" id="top_k">{#top_k}</div>

Returns the values and indices of the k largest elements for each row.

\\(values_{i, j}\\) represents the j-th largest element in \\(input_i\\).

\\(indices_{i, j}\\) gives the column index of the corresponding element,
such that \\(input_{i, indices_{i, j}} = values_{i, j}\\). If two
elements are equal, the lower-index element appears first.

##### Args:


*  <b>input</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`, `uint8`, `int16`, `int8`.
    A batch_size x classes tensor
*  <b>k</b>: An `int` that is `>= 1`.
    Number of top elements to look for within each row
*  <b>name</b>: A name for the operation (optional).

##### Returns:

  A tuple of `Tensor` objects (values, indices).

*  <b>values</b>: A `Tensor`. Has the same type as `input`. A batch_size x k tensor with the k largest elements for each row,
    sorted in descending order
*  <b>indices</b>: A `Tensor` of type `int32`. A batch_size x k tensor with the index of each value within each row


- - -

### tf.nn.in_top_k(predictions, targets, k, name=None) <div class="md-anchor" id="in_top_k">{#in_top_k}</div>

Says whether the targets are in the top K predictions.

This outputs a batch_size bool array, an entry out[i] is true if the
prediction for the target class is among the top k predictions among
all predictions for example i. Note that the behavior of InTopK differs
from the TopK op in its handling of ties; if multiple classes have the
same prediction value and straddle the top-k boundary, all of those
classes are considered to be in the top k.

More formally, let

  \\(predictions_i\\) be the predictions for all classes for example i,
  \\(targets_i\\) be the target class for example i,
  \\(out_i\\) be the output for example i,

$$out_i = predictions_{i, targets_i} \in TopKIncludingTies(predictions_i)$$

##### Args:


*  <b>predictions</b>: A `Tensor` of type `float32`. A batch_size x classes tensor
*  <b>targets</b>: A `Tensor` of type `int32`. A batch_size vector of class ids
*  <b>k</b>: An `int`. Number of top elements to look at for computing precision
*  <b>name</b>: A name for the operation (optional).

##### Returns:

  A `Tensor` of type `bool`. Computed Precision at k as a bool Tensor



## Candidate Sampling <div class="md-anchor" id="AUTOGENERATED-candidate-sampling">{#AUTOGENERATED-candidate-sampling}</div>

Do you want to train a multiclass or multilabel model with thousands
or millions of output classes (for example, a language model with a
large vocabulary)?  Training with a full Softmax is slow in this case,
since all of the classes are evaluated for every training example.
Candidate Sampling training algorithms can speed up your step times by
only considering a small randomly-chosen subset of contrastive classes
(called candidates) for each batch of training examples.

See our [Candidate Sampling Algorithms Reference]
(http://www.tensorflow.org/extras/candidate_sampling.pdf)

### Sampled Loss Functions <div class="md-anchor" id="AUTOGENERATED-sampled-loss-functions">{#AUTOGENERATED-sampled-loss-functions}</div>

TensorFlow provides the following sampled loss functions for faster training.

- - -

### tf.nn.nce_loss(weights, biases, inputs, labels, num_sampled, num_classes, num_true=1, sampled_values=None, remove_accidental_hits=False, name='nce_loss') <div class="md-anchor" id="nce_loss">{#nce_loss}</div>

Computes and returns the noise-contrastive estimation training loss.

See [Noise-contrastive estimation: A new estimation principle for
unnormalized statistical models]
(http://www.jmlr.org/proceedings/papers/v9/gutmann10a/gutmann10a.pdf).
Also see our [Candidate Sampling Algorithms Reference]
(http://www.tensorflow.org/extras/candidate_sampling.pdf)

Note: In the case where num_true > 1, we assign to each target class
the target probability 1 / num_true so that the target probabilities
sum to 1 per-example.

Note: It would be useful to allow a variable number of target classes per
example.  We hope to provide this functionality in a future release.
For now, if you have a variable number of target classes, you can pad them
out to a constant number by either repeating them or by padding
with an otherwise unused class.

##### Args:


*  <b>weights</b>: A `Tensor` of shape [num_classes, dim].  The class embeddings.
*  <b>biases</b>: A `Tensor` of shape [num_classes].  The class biases.
*  <b>inputs</b>: A `Tensor` of shape [batch_size, dim].  The forward
      activations of the input network.
*  <b>labels</b>: A `Tensor` of type `int64` and shape `[batch_size,
    num_true]`. The target classes.
*  <b>num_sampled</b>: An `int`.  The number of classes to randomly sample per batch.
*  <b>num_classes</b>: An `int`. The number of possible classes.
*  <b>num_true</b>: An `int`.  The number of target classes per training example.
*  <b>sampled_values</b>: a tuple of `(sampled_candidates, true_expected_count,
      sampled_expected_count)` returned by a *_candidate_sampler function.
      (if None, we default to LogUniformCandidateSampler)
*  <b>remove_accidental_hits</b>: A `bool`.  Whether to remove "accidental hits"
      where a sampled class equals one of the target classes.  If set to
      `True`, this is a "Sampled Logistic" loss instead of NCE, and we are
      learning to generate log-odds instead of log probabilities.  See
      our [Candidate Sampling Algorithms Reference]
      (http://www.tensorflow.org/extras/candidate_sampling.pdf).
      Default is False.
*  <b>name</b>: A name for the operation (optional).

##### Returns:

  A batch_size 1-D tensor of per-example NCE losses.


- - -

### tf.nn.sampled_softmax_loss(weights, biases, inputs, labels, num_sampled, num_classes, num_true=1, sampled_values=None, remove_accidental_hits=True, name='sampled_softmax_loss') <div class="md-anchor" id="sampled_softmax_loss">{#sampled_softmax_loss}</div>

Computes and returns the sampled softmax training loss.

This is a faster way to train a softmax classifier over a huge number of
classes.

This operation is for training only.  It is generally an underestimate of
the full softmax loss.

At inference time, you can compute full softmax probabilities with the
expression `tf.nn.softmax(tf.matmul(inputs, weights) + biases)`.

See our [Candidate Sampling Algorithms Reference]
(http://www.tensorflow.org/extras/candidate_sampling.pdf)

Also see Section 3 of http://arxiv.org/abs/1412.2007 for the math.

##### Args:


*  <b>weights</b>: A `Tensor` of shape [num_classes, dim].  The class embeddings.
*  <b>biases</b>: A `Tensor` of shape [num_classes].  The class biases.
*  <b>inputs</b>: A `Tensor` of shape [batch_size, dim].  The forward
      activations of the input network.
*  <b>labels</b>: A `Tensor` of type `int64` and shape `[batch_size,
    num_true]`. The target classes.  Note that this format differs from
    the `labels` argument of `nn.softmax_cross_entropy_with_logits`.
*  <b>num_sampled</b>: An `int`.  The number of classes to randomly sample per batch.
*  <b>num_classes</b>: An `int`. The number of possible classes.
*  <b>num_true</b>: An `int`.  The number of target classes per training example.
*  <b>sampled_values</b>: a tuple of `(sampled_candidates, true_expected_count,
      sampled_expected_count)` returned by a *_candidate_sampler function.
      (if None, we default to LogUniformCandidateSampler)
*  <b>remove_accidental_hits</b>: A `bool`.  whether to remove "accidental hits"
      where a sampled class equals one of the target classes.  Default is
      True.
*  <b>name</b>: A name for the operation (optional).

##### Returns:

  A batch_size 1-D tensor of per-example sampled softmax losses.



### Candidate Samplers <div class="md-anchor" id="AUTOGENERATED-candidate-samplers">{#AUTOGENERATED-candidate-samplers}</div>

TensorFlow provides the following samplers for randomly sampling candidate
classes when using one of the sampled loss functions above.

- - -

### tf.nn.uniform_candidate_sampler(true_classes, num_true, num_sampled, unique, range_max, seed=None, name=None) <div class="md-anchor" id="uniform_candidate_sampler">{#uniform_candidate_sampler}</div>

Samples a set of classes using a uniform base distribution.

This operation randomly samples a tensor of sampled classes
(`sampled_candidates`) from the range of integers `[0, range_max]`.

The elements of `sampled_candidates` are drawn without replacement
(if `unique=True`) or with replacement (if `unique=False`) from
the base distribution.

The base distribution for this operation is the uniform distribution
over the range of integers `[0, range_max]`.

In addition, this operation returns tensors `true_expected_count`
and `sampled_expected_count` representing the number of times each
of the target classes (`true_classes`) and the sampled
classes (`sampled_candidates`) is expected to occur in an average
tensor of sampled classes.  These values correspond to `Q(y|x)`
defined in [this
document](http://www.tensorflow.org/extras/candidate_sampling.pdf).
If `unique=True`, then these are post-rejection probabilities and we
compute them approximately.

##### Args:


*  <b>true_classes</b>: A `Tensor` of type `int64` and shape `[batch_size,
    num_true]`. The target classes.
*  <b>num_true</b>: An `int`.  The number of target classes per training example.
*  <b>num_sampled</b>: An `int`.  The number of classes to randomly sample per batch.
*  <b>unique</b>: A `bool`. Determines whether all sampled classes in a batch are
    unique.
*  <b>range_max</b>: An `int`. The number of possible classes.
*  <b>seed</b>: An `int`. An operation-specific seed. Default is 0.
*  <b>name</b>: A name for the operation (optional).

##### Returns:


*  <b>sampled_candidates</b>: A tensor of type `int64` and shape `[num_sampled]`.
    The sampled classes.
*  <b>true_expected_count</b>: A tensor of type `float`.  Same shape as
    `true_classes`. The expected counts under the sampling distribution
    of each of `true_classes`.
*  <b>sampled_expected_count</b>: A tensor of type `float`. Same shape as
    `sampled_candidates`. The expected counts under the sampling distribution
    of each of `sampled_candidates`.


- - -

### tf.nn.log_uniform_candidate_sampler(true_classes, num_true, num_sampled, unique, range_max, seed=None, name=None) <div class="md-anchor" id="log_uniform_candidate_sampler">{#log_uniform_candidate_sampler}</div>

Samples a set of classes using a log-uniform (Zipfian) base distribution.

This operation randomly samples a tensor of sampled classes
(`sampled_candidates`) from the range of integers `[0, range_max]`.

The elements of `sampled_candidates` are drawn without replacement
(if `unique=True`) or with replacement (if `unique=False`) from
the base distribution.

The base distribution for this operation is an approximately log-uniform
or Zipfian distribution:

`P(class) = (log(class + 2) - log(class + 1)) / log(range_max + 1)`

This sampler is useful when the target classes approximately follow such
a distribution - for example, if the classes represent words in a lexicon
sorted in decreasing order of frequency. If your classes are not ordered by
decreasing frequency, do not use this op.

In addition, this operation returns tensors `true_expected_count`
and `sampled_expected_count` representing the number of times each
of the target classes (`true_classes`) and the sampled
classes (`sampled_candidates`) is expected to occur in an average
tensor of sampled classes.  These values correspond to `Q(y|x)`
defined in [this
document](http://www.tensorflow.org/extras/candidate_sampling.pdf).
If `unique=True`, then these are post-rejection probabilities and we
compute them approximately.

##### Args:


*  <b>true_classes</b>: A `Tensor` of type `int64` and shape `[batch_size,
    num_true]`. The target classes.
*  <b>num_true</b>: An `int`.  The number of target classes per training example.
*  <b>num_sampled</b>: An `int`.  The number of classes to randomly sample per batch.
*  <b>unique</b>: A `bool`. Determines whether all sampled classes in a batch are
    unique.
*  <b>range_max</b>: An `int`. The number of possible classes.
*  <b>seed</b>: An `int`. An operation-specific seed. Default is 0.
*  <b>name</b>: A name for the operation (optional).

##### Returns:


*  <b>sampled_candidates</b>: A tensor of type `int64` and shape `[num_sampled]`.
    The sampled classes.
*  <b>true_expected_count</b>: A tensor of type `float`.  Same shape as
    `true_classes`. The expected counts under the sampling distribution
    of each of `true_classes`.
*  <b>sampled_expected_count</b>: A tensor of type `float`. Same shape as
    `sampled_candidates`. The expected counts under the sampling distribution
    of each of `sampled_candidates`.


- - -

### tf.nn.learned_unigram_candidate_sampler(true_classes, num_true, num_sampled, unique, range_max, seed=None, name=None) <div class="md-anchor" id="learned_unigram_candidate_sampler">{#learned_unigram_candidate_sampler}</div>

Samples a set of classes from a distribution learned during training.

This operation randomly samples a tensor of sampled classes
(`sampled_candidates`) from the range of integers `[0, range_max]`.

The elements of `sampled_candidates` are drawn without replacement
(if `unique=True`) or with replacement (if `unique=False`) from
the base distribution.

The base distribution for this operation is constructed on the fly
during training.  It is a unigram distribution over the target
classes seen so far during training.  Every integer in `[0, range_max]`
begins with a weight of 1, and is incremented by 1 each time it is
seen as a target class.  The base distribution is not saved to checkpoints,
so it is reset when the model is reloaded.

In addition, this operation returns tensors `true_expected_count`
and `sampled_expected_count` representing the number of times each
of the target classes (`true_classes`) and the sampled
classes (`sampled_candidates`) is expected to occur in an average
tensor of sampled classes.  These values correspond to `Q(y|x)`
defined in [this
document](http://www.tensorflow.org/extras/candidate_sampling.pdf).
If `unique=True`, then these are post-rejection probabilities and we
compute them approximately.

##### Args:


*  <b>true_classes</b>: A `Tensor` of type `int64` and shape `[batch_size,
    num_true]`. The target classes.
*  <b>num_true</b>: An `int`.  The number of target classes per training example.
*  <b>num_sampled</b>: An `int`.  The number of classes to randomly sample per batch.
*  <b>unique</b>: A `bool`. Determines whether all sampled classes in a batch are
    unique.
*  <b>range_max</b>: An `int`. The number of possible classes.
*  <b>seed</b>: An `int`. An operation-specific seed. Default is 0.
*  <b>name</b>: A name for the operation (optional).

##### Returns:


*  <b>sampled_candidates</b>: A tensor of type `int64` and shape `[num_sampled]`.
    The sampled classes.
*  <b>true_expected_count</b>: A tensor of type `float`.  Same shape as
    `true_classes`. The expected counts under the sampling distribution
    of each of `true_classes`.
*  <b>sampled_expected_count</b>: A tensor of type `float`. Same shape as
    `sampled_candidates`. The expected counts under the sampling distribution
    of each of `sampled_candidates`.


- - -

### tf.nn.fixed_unigram_candidate_sampler(true_classes, num_true, num_sampled, unique, range_max, vocab_file='', distortion=0.0, num_reserved_ids=0, num_shards=1, shard=0, unigrams=[], seed=None, name=None) <div class="md-anchor" id="fixed_unigram_candidate_sampler">{#fixed_unigram_candidate_sampler}</div>

Samples a set of classes using the provided (fixed) base distribution.

This operation randomly samples a tensor of sampled classes
(`sampled_candidates`) from the range of integers `[0, range_max]`.

The elements of `sampled_candidates` are drawn without replacement
(if `unique=True`) or with replacement (if `unique=False`) from
the base distribution.

The base distribution is read from a file or passed in as an
in-memory array. There is also an option to skew the distribution by
applying a distortion power to the weights.

In addition, this operation returns tensors `true_expected_count`
and `sampled_expected_count` representing the number of times each
of the target classes (`true_classes`) and the sampled
classes (`sampled_candidates`) is expected to occur in an average
tensor of sampled classes.  These values correspond to `Q(y|x)`
defined in [this
document](http://www.tensorflow.org/extras/candidate_sampling.pdf).
If `unique=True`, then these are post-rejection probabilities and we
compute them approximately.

##### Args:


*  <b>true_classes</b>: A `Tensor` of type `int64` and shape `[batch_size,
    num_true]`. The target classes.
*  <b>num_true</b>: An `int`.  The number of target classes per training example.
*  <b>num_sampled</b>: An `int`.  The number of classes to randomly sample per batch.
*  <b>unique</b>: A `bool`. Determines whether all sampled classes in a batch are
    unique.
*  <b>range_max</b>: An `int`. The number of possible classes.
*  <b>vocab_file</b>: Each valid line in this file (which should have a CSV-like
    format) corresponds to a valid word ID. IDs are in sequential order,
    starting from num_reserved_ids. The last entry in each line is expected
    to be a value corresponding to the count or relative probability. Exactly
    one of `vocab_file` and `unigrams` needs to be passed to this operation.
*  <b>distortion</b>: The distortion is used to skew the unigram probability
    distribution.  Each weight is first raised to the distortion's power
    before adding to the internal unigram distribution. As a result,
    `distortion = 1.0` gives regular unigram sampling (as defined by the vocab
    file), and `distortion = 0.0` gives a uniform distribution.
*  <b>num_reserved_ids</b>: Optionally some reserved IDs can be added in the range
    `[0, num_reserved_ids]` by the users. One use case is that a special
    unknown word token is used as ID 0. These IDs will have a sampling
    probability of 0.
*  <b>num_shards</b>: A sampler can be used to sample from a subset of the original
    range in order to speed up the whole computation through parallelism. This
    parameter (together with `shard`) indicates the number of partitions that
    are being used in the overall computation.
*  <b>shard</b>: A sampler can be used to sample from a subset of the original range
    in order to speed up the whole computation through parallelism. This
    parameter (together with `num_shards`) indicates the particular partition
    number of the operation, when partitioning is being used.
*  <b>unigrams</b>: A list of unigram counts or probabilities, one per ID in
    sequential order. Exactly one of `vocab_file` and `unigrams` should be
    passed to this operation.
*  <b>seed</b>: An `int`. An operation-specific seed. Default is 0.
*  <b>name</b>: A name for the operation (optional).

##### Returns:


*  <b>sampled_candidates</b>: A tensor of type `int64` and shape `[num_sampled]`.
    The sampled classes.
*  <b>true_expected_count</b>: A tensor of type `float`.  Same shape as
    `true_classes`. The expected counts under the sampling distribution
    of each of `true_classes`.
*  <b>sampled_expected_count</b>: A tensor of type `float`. Same shape as
    `sampled_candidates`. The expected counts under the sampling distribution
    of each of `sampled_candidates`.



### Miscellaneous candidate sampling utilities <div class="md-anchor" id="AUTOGENERATED-miscellaneous-candidate-sampling-utilities">{#AUTOGENERATED-miscellaneous-candidate-sampling-utilities}</div>

- - -

### tf.nn.compute_accidental_hits(true_classes, sampled_candidates, num_true, seed=None, name=None) <div class="md-anchor" id="compute_accidental_hits">{#compute_accidental_hits}</div>

Compute the ids of positions in sampled_candidates matching true_classes.

In Candidate Sampling, this operation facilitates virtually removing
sampled classes which happen to match target classes.  This is done
in Sampled Softmax and Sampled Logistic.

See our [Candidate Sampling Algorithms
Reference](http://www.tensorflow.org/extras/candidate_sampling.pdf).

We presuppose that the `sampled_candidates` are unique.

We call it an 'accidental hit' when one of the target classes
matches one of the sampled classes.  This operation reports
accidental hits as triples `(index, id, weight)`, where `index`
represents the row number in `true_classes`, `id` represents the
position in `sampled_candidates`, and weight is `-FLOAT_MAX`.

The result of this op should be passed through a `sparse_to_dense`
operation, then added to the logits of the sampled classes. This
removes the contradictory effect of accidentally sampling the true
target classes as noise classes for the same example.

##### Args:


*  <b>true_classes</b>: A `Tensor` of type `int64` and shape `[batch_size,
    num_true]`. The target classes.
*  <b>sampled_candidates</b>: A tensor of type `int64` and shape `[num_sampled]`.
    The sampled_candidates output of CandidateSampler.
*  <b>num_true</b>: An `int`.  The number of target classes per training example.
*  <b>seed</b>: An `int`. An operation-specific seed. Default is 0.
*  <b>name</b>: A name for the operation (optional).

##### Returns:


*  <b>indices</b>: A `Tensor` of type `int32` and shape `[num_accidental_hits]`.
    Values indicate rows in `true_classes`.
*  <b>ids</b>: A `Tensor` of type `int64` and shape `[num_accidental_hits]`.
    Values indicate positions in `sampled_candidates`.
*  <b>weights</b>: A `Tensor` of type `float` and shape `[num_accidental_hits]`.
    Each value is `-FLOAT_MAX`.