diff options
Diffstat (limited to 'tensorflow/python/ops/nn.py')
| -rw-r--r-- | tensorflow/python/ops/nn.py | 45 |
1 files changed, 30 insertions, 15 deletions
diff --git a/tensorflow/python/ops/nn.py b/tensorflow/python/ops/nn.py index 925ae76b98..749faaf73a 100644 --- a/tensorflow/python/ops/nn.py +++ b/tensorflow/python/ops/nn.py @@ -1,3 +1,18 @@ +# Copyright 2015 Google Inc. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + # pylint: disable=wildcard-import,unused-import,g-bad-import-order """## Activation Functions @@ -618,7 +633,7 @@ def _compute_sampled_logits(weights, biases, inputs, labels, num_sampled, sum to 1 per-example. Args: - weights: tensor of label embeddings with shape = [num_classes, dim] + weights: tensor of label embeddings with shape = [num_classes, dim]. biases: tensor of num_classes label biases inputs: tensor with shape = [batch_size, dim] corresponding to forward activations of the input network @@ -626,21 +641,21 @@ def _compute_sampled_logits(weights, biases, inputs, labels, num_sampled, num_sampled: number of label classes to sample per batch num_classes: number of possible label classes in the data (e.g. vocab size) num_true: number of target classes per example (default: 1) - sampled_values: a tuple of (sampled_candidates, true_expected_count, - sampled_expected_count) returned by a *CandidateSampler function to use - (if None, we default to LogUniformCandidateSampler) + sampled_values: a tuple of (`sampled_candidates`, `true_expected_count`, + `sampled_expected_count`) returned by a `*_candidate_sampler` function + to use (if None, we default to `log_uniform_candidate_sampler`) subtract_log_q: subtract the log expected count of the labels in the sample to get the logits of the true labels (default: True) Turn off for Negative Sampling. remove_accidental_hits: whether to remove "accidental hits" where a sampled label equals the true labels (bool, default: False) - name: name for this op + name: A name for the operation (optional). Returns: - out_logits, out_labels: tensors with shape [batch_size, num_true + - num_sampled] for passing to either SigmoidCrossEntropyWithLogits (NCE) - or SoftmaxCrossEntropyWithLogits (sampled softmax). - + out_logits, out_labels: tensors with shape + `[batch_size, num_true + num_sampled]` for passing to either + `sigmoid_cross_entropy_with_logits` (NCE) + or `softmax_cross_entropy_with_logits` (sampled softmax). """ with ops.op_scope( @@ -751,8 +766,8 @@ def nce_loss(weights, biases, inputs, labels, num_sampled, num_classes, 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 + 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 @@ -772,8 +787,8 @@ def nce_loss(weights, biases, inputs, labels, num_sampled, num_classes, num_classes: An `int`. The number of possible classes. num_true: An `int`. The number of target classes per training example. sampled_values: a tuple of `(sampled_candidates, true_expected_count, - sampled_expected_count)` returned by a *_candidate_sampler function. - (if None, we default to LogUniformCandidateSampler) + sampled_expected_count)` returned by a `*_candidate_sampler` function. + (if None, we default to `log_uniform_candidate_sampler`) remove_accidental_hits: 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 @@ -834,8 +849,8 @@ def sampled_softmax_loss(weights, biases, inputs, labels, num_sampled, num_classes: An `int`. The number of possible classes. num_true: An `int`. The number of target classes per training example. sampled_values: a tuple of `(sampled_candidates, true_expected_count, - sampled_expected_count)` returned by a *_candidate_sampler function. - (if None, we default to LogUniformCandidateSampler) + sampled_expected_count)` returned by a `*_candidate_sampler` function. + (if None, we default to `log_uniform_candidate_sampler`) remove_accidental_hits: A `bool`. whether to remove "accidental hits" where a sampled class equals one of the target classes. Default is True. |