# Copyright 2015 The TensorFlow Authors. 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. # ============================================================================== """Wrappers for candidate sampling operations.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow.python.framework import random_seed from tensorflow.python.ops import array_ops # pylint: disable=unused-import from tensorflow.python.ops import gen_candidate_sampling_ops from tensorflow.python.ops import math_ops # pylint: disable=unused-import from tensorflow.python.util import deprecation from tensorflow.python.util import dispatch from tensorflow.python.util.tf_export import tf_export @tf_export( 'random.uniform_candidate_sampler', v1=['random.uniform_candidate_sampler', 'nn.uniform_candidate_sampler']) @dispatch.add_dispatch_support @deprecation.deprecated_endpoints('nn.uniform_candidate_sampler') def uniform_candidate_sampler(true_classes, num_true, num_sampled, unique, range_max, seed=None, name=None): """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: true_classes: A `Tensor` of type `int64` and shape `[batch_size, num_true]`. The target classes. num_true: An `int`. The number of target classes per training example. num_sampled: An `int`. The number of classes to randomly sample. The `sampled_candidates` return value will have shape `[num_sampled]`. If `unique=True`, `num_sampled` must be less than or equal to `range_max`. unique: A `bool`. Determines whether all sampled classes in a batch are unique. range_max: An `int`. The number of possible classes. seed: An `int`. An operation-specific seed. Default is 0. name: A name for the operation (optional). Returns: sampled_candidates: A tensor of type `int64` and shape `[num_sampled]`. The sampled classes, either with possible duplicates (`unique=False`) or all unique (`unique=True`). In either case, `sampled_candidates` is independent of the true classes. true_expected_count: A tensor of type `float`. Same shape as `true_classes`. The expected counts under the sampling distribution of each of `true_classes`. sampled_expected_count: A tensor of type `float`. Same shape as `sampled_candidates`. The expected counts under the sampling distribution of each of `sampled_candidates`. """ seed1, seed2 = random_seed.get_seed(seed) return gen_candidate_sampling_ops.uniform_candidate_sampler( true_classes, num_true, num_sampled, unique, range_max, seed=seed1, seed2=seed2, name=name) @tf_export( 'random.log_uniform_candidate_sampler', v1=[ 'random.log_uniform_candidate_sampler', 'nn.log_uniform_candidate_sampler' ]) @dispatch.add_dispatch_support @deprecation.deprecated_endpoints('nn.log_uniform_candidate_sampler') def log_uniform_candidate_sampler(true_classes, num_true, num_sampled, unique, range_max, seed=None, name=None): """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: true_classes: A `Tensor` of type `int64` and shape `[batch_size, num_true]`. The target classes. num_true: An `int`. The number of target classes per training example. num_sampled: An `int`. The number of classes to randomly sample. unique: A `bool`. Determines whether all sampled classes in a batch are unique. range_max: An `int`. The number of possible classes. seed: An `int`. An operation-specific seed. Default is 0. name: A name for the operation (optional). Returns: sampled_candidates: A tensor of type `int64` and shape `[num_sampled]`. The sampled classes. true_expected_count: A tensor of type `float`. Same shape as `true_classes`. The expected counts under the sampling distribution of each of `true_classes`. sampled_expected_count: A tensor of type `float`. Same shape as `sampled_candidates`. The expected counts under the sampling distribution of each of `sampled_candidates`. """ seed1, seed2 = random_seed.get_seed(seed) return gen_candidate_sampling_ops.log_uniform_candidate_sampler( true_classes, num_true, num_sampled, unique, range_max, seed=seed1, seed2=seed2, name=name) @tf_export( 'random.learned_unigram_candidate_sampler', 'nn.learned_unigram_candidate_sampler') @dispatch.add_dispatch_support @deprecation.deprecated_endpoints(['nn.learned_unigram_candidate_sampler']) def learned_unigram_candidate_sampler(true_classes, num_true, num_sampled, unique, range_max, seed=None, name=None): """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: true_classes: A `Tensor` of type `int64` and shape `[batch_size, num_true]`. The target classes. num_true: An `int`. The number of target classes per training example. num_sampled: An `int`. The number of classes to randomly sample. unique: A `bool`. Determines whether all sampled classes in a batch are unique. range_max: An `int`. The number of possible classes. seed: An `int`. An operation-specific seed. Default is 0. name: A name for the operation (optional). Returns: sampled_candidates: A tensor of type `int64` and shape `[num_sampled]`. The sampled classes. true_expected_count: A tensor of type `float`. Same shape as `true_classes`. The expected counts under the sampling distribution of each of `true_classes`. sampled_expected_count: A tensor of type `float`. Same shape as `sampled_candidates`. The expected counts under the sampling distribution of each of `sampled_candidates`. """ seed1, seed2 = random_seed.get_seed(seed) return gen_candidate_sampling_ops.learned_unigram_candidate_sampler( true_classes, num_true, num_sampled, unique, range_max, seed=seed1, seed2=seed2, name=name) @tf_export('random.fixed_unigram_candidate_sampler', 'nn.fixed_unigram_candidate_sampler') @dispatch.add_dispatch_support def fixed_unigram_candidate_sampler(true_classes, num_true, num_sampled, unique, range_max, vocab_file='', distortion=1.0, num_reserved_ids=0, num_shards=1, shard=0, unigrams=(), seed=None, name=None): """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: true_classes: A `Tensor` of type `int64` and shape `[batch_size, num_true]`. The target classes. num_true: An `int`. The number of target classes per training example. num_sampled: An `int`. The number of classes to randomly sample. unique: A `bool`. Determines whether all sampled classes in a batch are unique. range_max: An `int`. The number of possible classes. vocab_file: 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. distortion: 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. num_reserved_ids: 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. num_shards: 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. shard: 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. unigrams: 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. seed: An `int`. An operation-specific seed. Default is 0. name: A name for the operation (optional). Returns: sampled_candidates: A tensor of type `int64` and shape `[num_sampled]`. The sampled classes. true_expected_count: A tensor of type `float`. Same shape as `true_classes`. The expected counts under the sampling distribution of each of `true_classes`. sampled_expected_count: A tensor of type `float`. Same shape as `sampled_candidates`. The expected counts under the sampling distribution of each of `sampled_candidates`. """ seed1, seed2 = random_seed.get_seed(seed) return gen_candidate_sampling_ops.fixed_unigram_candidate_sampler( true_classes, num_true, num_sampled, unique, range_max, vocab_file=vocab_file, distortion=distortion, num_reserved_ids=num_reserved_ids, num_shards=num_shards, shard=shard, unigrams=unigrams, seed=seed1, seed2=seed2, name=name) @tf_export('random.all_candidate_sampler', 'nn.all_candidate_sampler') def all_candidate_sampler(true_classes, num_true, num_sampled, unique, seed=None, name=None): """Generate the set of all classes. Deterministically generates and returns the set of all possible classes. For testing purposes. There is no need to use this, since you might as well use full softmax or full logistic regression. Args: true_classes: A `Tensor` of type `int64` and shape `[batch_size, num_true]`. The target classes. num_true: An `int`. The number of target classes per training example. num_sampled: An `int`. The number of possible classes. unique: A `bool`. Ignored. unique. seed: An `int`. An operation-specific seed. Default is 0. name: A name for the operation (optional). Returns: sampled_candidates: A tensor of type `int64` and shape `[num_sampled]`. This operation deterministically returns the entire range `[0, num_sampled]`. true_expected_count: A tensor of type `float`. Same shape as `true_classes`. The expected counts under the sampling distribution of each of `true_classes`. All returned values are 1.0. sampled_expected_count: A tensor of type `float`. Same shape as `sampled_candidates`. The expected counts under the sampling distribution of each of `sampled_candidates`. All returned values are 1.0. """ seed1, seed2 = random_seed.get_seed(seed) return gen_candidate_sampling_ops.all_candidate_sampler( true_classes, num_true, num_sampled, unique, seed=seed1, seed2=seed2, name=name) @tf_export('nn.compute_accidental_hits') @dispatch.add_dispatch_support def compute_accidental_hits(true_classes, sampled_candidates, num_true, seed=None, name=None): """Compute the position ids 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: true_classes: A `Tensor` of type `int64` and shape `[batch_size, num_true]`. The target classes. sampled_candidates: A tensor of type `int64` and shape `[num_sampled]`. The sampled_candidates output of CandidateSampler. num_true: An `int`. The number of target classes per training example. seed: An `int`. An operation-specific seed. Default is 0. name: A name for the operation (optional). Returns: indices: A `Tensor` of type `int32` and shape `[num_accidental_hits]`. Values indicate rows in `true_classes`. ids: A `Tensor` of type `int64` and shape `[num_accidental_hits]`. Values indicate positions in `sampled_candidates`. weights: A `Tensor` of type `float` and shape `[num_accidental_hits]`. Each value is `-FLOAT_MAX`. """ seed1, seed2 = random_seed.get_seed(seed) return gen_candidate_sampling_ops.compute_accidental_hits( true_classes, sampled_candidates, num_true, seed=seed1, seed2=seed2, name=name)