diff options
Diffstat (limited to 'tensorflow/g3doc/api_docs/python/nn.md')
| -rw-r--r-- | tensorflow/g3doc/api_docs/python/nn.md | 120 |
1 files changed, 30 insertions, 90 deletions
diff --git a/tensorflow/g3doc/api_docs/python/nn.md b/tensorflow/g3doc/api_docs/python/nn.md index 50c460b68c..b129506107 100644 --- a/tensorflow/g3doc/api_docs/python/nn.md +++ b/tensorflow/g3doc/api_docs/python/nn.md @@ -35,7 +35,6 @@ accepted by [`tf.convert_to_tensor`](framework.md#convert_to_tensor). * [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) @@ -130,17 +129,18 @@ 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 +to the shape of `x`, and only dimensions with `noise_shape[i] == shape(x)[i]` +will make independent decisions. For example, if `shape(x) = [k, l, m, n]` +and `noise_shape = [k, 1, 1, n]`, 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>keep_prob</b>: A Python float. The probability that each element is kept. +* <b>noise_shape</b>: A 1-D `Tensor` of type `int32`, representing the + 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). @@ -247,10 +247,10 @@ 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], - ...] + shape(output) = [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, ...] * @@ -262,7 +262,7 @@ 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: +In the formula for `shape(output)`, the rounding direction depends on padding: * `padding = 'SAME'`: Round down (only full size windows are considered). * `padding = 'VALID'`: Round up (partial windows are included). @@ -411,7 +411,7 @@ In detail, the output is for each tuple of indices `i`. The output shape is - output.shape = (value.shape - ksize + 1) / strides + shape(output) = (shape(value) - ksize + 1) / strides where the rounding direction depends on padding: @@ -722,103 +722,43 @@ and the same dtype (either `float32` or `float64`). ## Embeddings <div class="md-anchor" id="AUTOGENERATED-embeddings">{#AUTOGENERATED-embeddings}</div> -TensorFlow provides several operations that help you compute embeddings. +TensorFlow provides library support for looking up values in embedding +tensors. - - - ### 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". +Looks up `ids` in a list of embedding tensors. -##### 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. +This function is used to perform parallel lookups on the list of +tensors in `params`. It is a generalization of +[`tf.gather()`](array_ops.md#gather), where `params` is interpreted +as a partition of a larger embedding tensor. +If `len(params) > 1`, each element `id` of `ids` is partitioned between +the elements of `params` by computing `p = id % len(params)`, and is +then used to look up the slice `params[p][id // len(params), ...]`. -- - - - -### 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. +The results of the lookup are then concatenated into a dense +tensor. The returned tensor has shape `shape(ids) + shape(params)[1:]`. ##### 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. +* <b>params</b>: A list of tensors with the same shape and type. +* <b>ids</b>: A `Tensor` with type `int32` containing the ids to be looked + up in `params`. +* <b>name</b>: A name for the operation (optional). ##### 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 + A `Tensor` with the same type as the tensors in `params`. ##### 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"}. +* <b>ValueError</b>: If `params` is empty. |