From 76ad43d753692ed7a956fc0d0cce78c90c959b75 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <nobody@tensorflow.org>
Date: Wed, 25 May 2016 15:01:01 -0800
Subject: [PATCH 01/44] Shards files across several directories when generating
 individual files for each function and class, to avoid hitting the limit for
 files per directory. Change: 123263570

---
 .../{ => shard0}/tf.FIFOQueue.md              |  0
 .../{ => shard0}/tf.QueueBase.from_list.md    |  0
 .../{ => shard0}/tf.ReaderBase.md             |  0
 .../{ => shard0}/tf.batch_fft.md              |  0
 .../{ => shard0}/tf.bytes.md                  |  0
 .../{ => shard0}/tf.cholesky.md               |  0
 .../{ => shard0}/tf.clip_by_global_norm.md    |  0
 .../{ => shard0}/tf.constant.md               |  0
 ...ussian_conjugates_known_sigma_posterior.md |  0
 .../tf.contrib.ffmpeg.encode_audio.md         |  0
 .../tf.contrib.layers.l1_regularizer.md       |  0
 .../tf.contrib.layers.optimize_loss.md        |  0
 ...tf.contrib.layers.summarize_activations.md |  0
 .../tf.contrib.layers.xavier_initializer.md   |  0
 .../{ => shard0}/tf.contrib.learn.ModeKeys.md |  0
 ...f.contrib.learn.TensorFlowRNNClassifier.md |  0
 .../tf.contrib.metrics.set_difference.md      |  0
 .../tf.contrib.metrics.set_intersection.md    |  0
 .../tf.contrib.metrics.streaming_precision.md |  0
 .../tf.contrib.util.make_tensor_proto.md      |  0
 .../{ => shard0}/tf.diag_part.md              |  0
 .../{ => shard0}/tf.erfc.md                   |  0
 .../{ => shard0}/tf.errors.InternalError.md   |  0
 .../tf.errors.InvalidArgumentError.md         |  0
 .../{ => shard0}/tf.get_session_handle.md     |  0
 .../{ => shard0}/tf.identity.md               |  0
 .../{ => shard0}/tf.image.grayscale_to_rgb.md |  0
 .../tf.image.random_saturation.md             |  0
 .../{ => shard0}/tf.is_inf.md                 |  0
 .../{ => shard0}/tf.logical_and.md            |  0
 .../tf.moving_average_variables.md            |  0
 .../{ => shard0}/tf.name_scope.md             |  0
 .../{ => shard0}/tf.nn.avg_pool.md            |  0
 .../{ => shard0}/tf.nn.batch_normalization.md |  0
 .../{ => shard0}/tf.nn.conv3d.md              |  0
 .../{ => shard0}/tf.nn.in_top_k.md            |  0
 .../{ => shard0}/tf.nn.max_pool.md            |  0
 .../tf.nn.sampled_softmax_loss.md             |  0
 .../{ => shard0}/tf.polygamma.md              |  0
 .../{ => shard0}/tf.real.md                   |  0
 .../{ => shard0}/tf.reduce_all.md             |  0
 .../{ => shard0}/tf.reduce_sum.md             |  0
 .../{ => shard0}/tf.scatter_update.md         |  0
 .../{ => shard0}/tf.sparse_placeholder.md     |  0
 .../{ => shard0}/tf.sqrt.md                   |  0
 .../{ => shard0}/tf.stop_gradient.md          |  0
 .../tf.string_to_hash_bucket_fast.md          |  0
 .../{ => shard0}/tf.test.get_temp_dir.md      |  0
 .../{ => shard0}/tf.tile.md                   |  0
 .../{ => shard0}/tf.to_bfloat16.md            |  0
 .../tf.train.MomentumOptimizer.md             |  0
 .../{ => shard0}/tf.train.Supervisor.md       |  0
 .../{ => shard0}/tf.train.add_queue_runner.md |  0
 .../tf.train.replica_device_setter.md         |  0
 .../{ => shard0}/tf.truncated_normal.md       |  0
 .../tf.uniform_unit_scaling_initializer.md    |  0
 .../{ => shard0}/tf.zeros.md                  |  0
 .../{ => shard1}/tf.NoGradient.md             |  0
 .../{ => shard1}/tf.SparseTensor.md           |  0
 .../{ => shard1}/tf.Tensor.md                 |  0
 .../{ => shard1}/tf.VariableScope.md          |  0
 .../{ => shard1}/tf.batch_cholesky.md         |  0
 .../{ => shard1}/tf.batch_fft3d.md            |  0
 .../{ => shard1}/tf.batch_matrix_solve_ls.md  |  0
 .../{ => shard1}/tf.batch_to_space.md         |  0
 .../{ => shard1}/tf.cast.md                   |  0
 .../{ => shard1}/tf.check_numerics.md         |  0
 .../{ => shard1}/tf.clip_by_average_norm.md   |  0
 ...ntrib.copy_graph.copy_variable_to_graph.md |  0
 ...trib.distributions.DirichletMultinomial.md |  0
 ...trib.distributions.DiscreteDistribution.md |  0
 .../tf.contrib.distributions.StudentT.md      |  0
 .../tf.contrib.layers.apply_regularization.md |  0
 .../tf.contrib.layers.summarize_tensors.md    |  0
 .../tf.contrib.learn.extract_pandas_labels.md |  0
 .../tf.contrib.learn.read_batch_examples.md   |  0
 .../tf.contrib.learn.read_batch_features.md   |  0
 .../tf.contrib.metrics.streaming_auc.md       |  0
 ...b.metrics.streaming_mean_relative_error.md |  0
 ...ib.metrics.streaming_mean_squared_error.md |  0
 .../{ => shard1}/tf.decode_json_example.md    |  0
 .../{ => shard1}/tf.dynamic_partition.md      |  0
 .../{ => shard1}/tf.dynamic_stitch.md         |  0
 .../{ => shard1}/tf.floordiv.md               |  0
 .../{ => shard1}/tf.get_session_tensor.md     |  0
 .../{ => shard1}/tf.get_variable.md           |  0
 .../{ => shard1}/tf.ifft.md                   |  0
 .../tf.image.adjust_brightness.md             |  0
 .../{ => shard1}/tf.image.adjust_hue.md       |  0
 .../{ => shard1}/tf.image.encode_jpeg.md      |  0
 .../{ => shard1}/tf.image.flip_up_down.md     |  0
 .../{ => shard1}/tf.image.resize_bilinear.md  |  0
 .../{ => shard1}/tf.image.resize_images.md    |  0
 .../{ => shard1}/tf.image_summary.md          |  0
 .../{ => shard1}/tf.is_non_decreasing.md      |  0
 .../{ => shard1}/tf.is_numeric_tensor.md      |  0
 .../{ => shard1}/tf.is_strictly_increasing.md |  0
 .../tf.load_file_system_library.md            |  0
 .../{ => shard1}/tf.load_op_library.md        |  0
 .../{ => shard1}/tf.map_fn.md                 |  0
 .../{ => shard1}/tf.maximum.md                |  0
 .../{ => shard1}/tf.mod.md                    |  0
 .../{ => shard1}/tf.neg.md                    |  0
 .../{ => shard1}/tf.nn.embedding_lookup.md    |  0
 .../{ => shard1}/tf.nn.relu.md                |  0
 ...tf.nn.sigmoid_cross_entropy_with_logits.md |  0
 .../{ => shard1}/tf.parse_single_example.md   |  0
 .../{ => shard1}/tf.pow.md                    |  0
 .../tf.python_io.TFRecordWriter.md            |  0
 .../{ => shard1}/tf.random_crop.md            |  0
 .../tf.random_uniform_initializer.md          |  0
 .../{ => shard1}/tf.reduce_any.md             |  0
 .../{ => shard1}/tf.reduce_prod.md            |  0
 .../{ => shard1}/tf.scatter_add.md            |  0
 .../{ => shard1}/tf.sparse_softmax.md         |  0
 .../tf.string_to_hash_bucket_strong.md        |  0
 .../{ => shard1}/tf.train.batch.md            |  0
 .../tf.train.get_checkpoint_state.md          |  0
 .../tf.train.import_meta_graph.md             |  0
 .../tf.train.range_input_producer.md          |  0
 .../{ => shard2}/tf.Variable.md               |  0
 .../{ => shard2}/tf.add_check_numerics_ops.md |  0
 .../{ => shard2}/tf.assert_less_equal.md      |  0
 .../{ => shard2}/tf.audio_summary.md          |  0
 .../{ => shard2}/tf.cholesky_solve.md         |  0
 .../tf.contrib.distributions.Exponential.md   |  0
 ...ontrib.distributions.MultivariateNormal.md |  0
 .../tf.contrib.layers.summarize_activation.md |  0
 ...contrib.learn.TensorFlowLinearRegressor.md |  0
 ...tf.contrib.learn.TensorFlowRNNRegressor.md |  0
 .../{ => shard2}/tf.contrib.learn.evaluate.md |  0
 .../{ => shard2}/tf.contrib.learn.infer.md    |  0
 .../{ => shard2}/tf.contrib.learn.run_n.md    |  0
 .../tf.contrib.metrics.confusion_matrix.md    |  0
 ....metrics.streaming_mean_cosine_distance.md |  0
 .../tf.contrib.util.constant_value.md         |  0
 .../tf.contrib.util.make_ndarray.md           |  0
 .../{ => shard2}/tf.errors.DataLossError.md   |  0
 .../tf.errors.UnavailableError.md             |  0
 .../{ => shard2}/tf.greater_equal.md          |  0
 .../{ => shard2}/tf.ifft3d.md                 |  0
 .../{ => shard2}/tf.igamma.md                 |  0
 .../tf.image.adjust_saturation.md             |  0
 .../{ => shard2}/tf.image.hsv_to_rgb.md       |  0
 .../{ => shard2}/tf.image.rgb_to_grayscale.md |  0
 .../{ => shard2}/tf.initialize_variables.md   |  0
 .../{ => shard2}/tf.inv.md                    |  0
 .../{ => shard2}/tf.local_variables.md        |  0
 .../{ => shard2}/tf.matrix_inverse.md         |  0
 .../{ => shard2}/tf.matrix_solve_ls.md        |  0
 .../{ => shard2}/tf.merge_summary.md          |  0
 .../{ => shard2}/tf.nn.l2_loss.md             |  0
 .../{ => shard2}/tf.nn.relu6.md               |  0
 .../{ => shard2}/tf.no_op.md                  |  0
 .../{ => shard2}/tf.ones_initializer.md       |  0
 .../{ => shard2}/tf.pad.md                    |  0
 .../{ => shard2}/tf.range.md                  |  0
 .../{ => shard2}/tf.reverse_sequence.md       |  0
 .../{ => shard2}/tf.saturate_cast.md          |  0
 .../{ => shard2}/tf.scatter_sub.md            |  0
 .../{ => shard2}/tf.space_to_depth.md         |  0
 .../{ => shard2}/tf.sparse_mask.md            |  0
 .../{ => shard2}/tf.sparse_reorder.md         |  0
 .../{ => shard2}/tf.string_to_hash_bucket.md  |  0
 .../tf.train.ExponentialMovingAverage.md      |  0
 .../tf.train.LooperThread.loop.md             |  0
 .../{ => shard2}/tf.train.Optimizer.md        |  0
 .../{ => shard2}/tf.train.Saver.from_proto.md |  0
 .../{ => shard2}/tf.train.Saver.md            |  0
 .../{ => shard2}/tf.train.SummaryWriter.md    |  0
 .../{ => shard2}/tf.train.limit_epochs.md     |  0
 .../tf.train.match_filenames_once.md          |  0
 .../{ => shard2}/tf.zeta.md                   |  0
 .../{ => shard3}/tf.DType.md                  |  0
 .../{ => shard3}/tf.DeviceSpec.from_string.md |  0
 .../{ => shard3}/tf.Graph.md                  |  0
 .../{ => shard3}/tf.GraphKeys.md              |  0
 .../{ => shard3}/tf.RandomShuffleQueue.md     |  0
 .../{ => shard3}/tf.Session.md                |  0
 .../{ => shard3}/tf.WholeFileReader.md        |  0
 .../{ => shard3}/tf.add_to_collection.md      |  0
 .../{ => shard3}/tf.as_dtype.md               |  0
 .../{ => shard3}/tf.assert_integer.md         |  0
 .../{ => shard3}/tf.batch_cholesky_solve.md   |  0
 .../{ => shard3}/tf.batch_ifft3d.md           |  0
 .../{ => shard3}/tf.batch_matrix_diag.md      |  0
 .../{ => shard3}/tf.ceil.md                   |  0
 ...ib.distributions.ContinuousDistribution.md |  0
 .../tf.contrib.distributions.Uniform.md       |  0
 .../tf.contrib.layers.fully_connected.md      |  0
 .../tf.contrib.layers.sum_regularizer.md      |  0
 .../tf.contrib.learn.TensorFlowClassifier.md  |  0
 .../tf.contrib.learn.run_feeds.md             |  0
 .../tf.contrib.metrics.accuracy.md            |  0
 ...trics.streaming_root_mean_squared_error.md |  0
 ...metrics.streaming_sparse_precision_at_k.md |  0
 .../{ => shard3}/tf.decode_csv.md             |  0
 .../{ => shard3}/tf.errors.CancelledError.md  |  0
 .../{ => shard3}/tf.errors.OutOfRangeError.md |  0
 .../tf.errors.ResourceExhaustedError.md       |  0
 .../{ => shard3}/tf.expand_dims.md            |  0
 .../{ => shard3}/tf.gather.md                 |  0
 .../{ => shard3}/tf.invert_permutation.md     |  0
 .../{ => shard3}/tf.listdiff.md               |  0
 .../{ => shard3}/tf.mul.md                    |  0
 .../tf.nn.compute_accidental_hits.md          |  0
 .../tf.nn.embedding_lookup_sparse.md          |  0
 .../{ => shard3}/tf.nn.log_softmax.md         |  0
 .../{ => shard3}/tf.nn.separable_conv2d.md    |  0
 ...tf.nn.softmax_cross_entropy_with_logits.md |  0
 ...f.nn.weighted_cross_entropy_with_logits.md |  0
 .../{ => shard3}/tf.one_hot.md                |  0
 .../{ => shard3}/tf.pack.md                   |  0
 .../{ => shard3}/tf.reduce_mean.md            |  0
 .../{ => shard3}/tf.round.md                  |  0
 .../{ => shard3}/tf.rsqrt.md                  |  0
 .../{ => shard3}/tf.scalar_mul.md             |  0
 .../{ => shard3}/tf.scan.md                   |  0
 .../{ => shard3}/tf.segment_max.md            |  0
 .../{ => shard3}/tf.sparse_merge.md           |  0
 .../{ => shard3}/tf.sparse_segment_mean.md    |  0
 .../{ => shard3}/tf.sparse_to_indicator.md    |  0
 .../{ => shard3}/tf.squeeze.md                |  0
 .../{ => shard3}/tf.test.compute_gradient.md  |  0
 .../{ => shard3}/tf.train.SessionManager.md   |  0
 .../tf.train.latest_checkpoint.md             |  0
 .../{ => shard3}/tf.train.shuffle_batch.md    |  0
 .../tf.train.start_queue_runners.md           |  0
 .../tf.truncated_normal_initializer.md        |  0
 .../tf.verify_tensor_all_finite.md            |  0
 .../{ => shard4}/tf.Assert.md                 |  0
 .../{ => shard4}/tf.Print.md                  |  0
 .../{ => shard4}/tf.TFRecordReader.md         |  0
 .../{ => shard4}/tf.argmin.md                 |  0
 .../{ => shard4}/tf.assert_type.md            |  0
 .../{ => shard4}/tf.batch_ifft.md             |  0
 .../tf.batch_matrix_determinant.md            |  0
 .../{ => shard4}/tf.batch_matrix_diag_part.md |  0
 .../{ => shard4}/tf.case.md                   |  0
 .../{ => shard4}/tf.complex_abs.md            |  0
 .../{ => shard4}/tf.concat.md                 |  0
 .../tf.contrib.copy_graph.copy_op_to_graph.md |  0
 .../tf.contrib.distributions.Chi2.md          |  0
 .../tf.contrib.ffmpeg.decode_audio.md         |  0
 ...ontrib.layers.xavier_initializer_conv2d.md |  0
 ...ontrib.learn.read_batch_record_features.md |  0
 .../tf.contrib.metrics.auc_using_histogram.md |  0
 .../tf.contrib.metrics.streaming_mean.md      |  0
 ...b.metrics.streaming_mean_absolute_error.md |  0
 .../tf.contrib.metrics.streaming_recall.md    |  0
 .../{ => shard4}/tf.convert_to_tensor.md      |  0
 .../{ => shard4}/tf.cos.md                    |  0
 .../{ => shard4}/tf.device.md                 |  0
 .../tf.errors.DeadlineExceededError.md        |  0
 .../{ => shard4}/tf.fft3d.md                  |  0
 .../{ => shard4}/tf.get_variable_scope.md     |  0
 .../{ => shard4}/tf.histogram_summary.md      |  0
 .../{ => shard4}/tf.igammac.md                |  0
 .../{ => shard4}/tf.image.adjust_contrast.md  |  0
 .../tf.image.convert_image_dtype.md           |  0
 .../{ => shard4}/tf.image.resize_area.md      |  0
 .../tf.image.resize_image_with_crop_or_pad.md |  0
 .../{ => shard4}/tf.image.rgb_to_hsv.md       |  0
 .../{ => shard4}/tf.import_graph_def.md       |  0
 .../{ => shard4}/tf.make_template.md          |  0
 .../{ => shard4}/tf.matmul.md                 |  0
 .../tf.matrix_triangular_solve.md             |  0
 .../{ => shard4}/tf.merge_all_summaries.md    |  0
 .../{ => shard4}/tf.nn.elu.md                 |  0
 .../tf.nn.local_response_normalization.md     |  0
 .../{ => shard4}/tf.nn.softmax.md             |  0
 .../tf.nn.sufficient_statistics.md            |  0
 .../{ => shard4}/tf.ones_like.md              |  0
 .../{ => shard4}/tf.rank.md                   |  0
 .../{ => shard4}/tf.read_file.md              |  0
 .../{ => shard4}/tf.reduce_join.md            |  0
 .../tf.register_tensor_conversion_function.md |  0
 .../tf.report_uninitialized_variables.md      |  0
 .../{ => shard4}/tf.select.md                 |  0
 .../{ => shard4}/tf.sparse_segment_sum.md     |  0
 .../{ => shard4}/tf.sparse_tensor_to_dense.md |  0
 .../tf.test.compute_gradient_error.md         |  0
 .../{ => shard4}/tf.train.FtrlOptimizer.md    |  0
 .../tf.train.export_meta_graph.md             |  0
 .../{ => shard4}/tf.where.md                  |  0
 .../{ => shard5}/tf.Dimension.md              |  0
 .../tf.FixedLenSequenceFeature.md             |  0
 .../{ => shard5}/tf.VarLenFeature.md          |  0
 .../{ => shard5}/tf.accumulate_n.md           |  0
 .../{ => shard5}/tf.add_n.md                  |  0
 .../{ => shard5}/tf.assert_rank_at_least.md   |  0
 .../{ => shard5}/tf.batch_matmul.md           |  0
 .../tf.batch_matrix_triangular_solve.md       |  0
 .../{ => shard5}/tf.constant_initializer.md   |  0
 .../tf.contrib.distributions.Gaussian.md      |  0
 .../tf.contrib.layers.l2_regularizer.md       |  0
 .../tf.contrib.learn.RunConfig.md             |  0
 .../tf.contrib.metrics.set_union.md           |  0
 .../{ => shard5}/tf.control_dependencies.md   |  0
 .../{ => shard5}/tf.decode_raw.md             |  0
 .../{ => shard5}/tf.delete_session_tensor.md  |  0
 .../{ => shard5}/tf.edit_distance.md          |  0
 .../{ => shard5}/tf.errors.AbortedError.md    |  0
 .../tf.errors.PermissionDeniedError.md        |  0
 .../{ => shard5}/tf.get_collection.md         |  0
 .../{ => shard5}/tf.get_collection_ref.md     |  0
 .../{ => shard5}/tf.image.extract_glimpse.md  |  0
 .../tf.image.random_brightness.md             |  0
 .../{ => shard5}/tf.image.transpose_image.md  |  0
 .../{ => shard5}/tf.less.md                   |  0
 .../{ => shard5}/tf.lgamma.md                 |  0
 .../{ => shard5}/tf.linspace.md               |  0
 .../{ => shard5}/tf.log.md                    |  0
 .../{ => shard5}/tf.matrix_solve.md           |  0
 .../{ => shard5}/tf.nn.conv2d.md              |  0
 .../tf.nn.fixed_unigram_candidate_sampler.md  |  0
 .../{ => shard5}/tf.not_equal.md              |  0
 .../{ => shard5}/tf.py_func.md                |  0
 .../{ => shard5}/tf.random_shuffle.md         |  0
 .../{ => shard5}/tf.reduce_min.md             |  0
 .../{ => shard5}/tf.reset_default_graph.md    |  0
 .../{ => shard5}/tf.reshape.md                |  0
 .../{ => shard5}/tf.reverse.md                |  0
 .../{ => shard5}/tf.segment_mean.md           |  0
 .../{ => shard5}/tf.segment_min.md            |  0
 .../{ => shard5}/tf.shape_n.md                |  0
 .../{ => shard5}/tf.sigmoid.md                |  0
 .../{ => shard5}/tf.sign.md                   |  0
 .../{ => shard5}/tf.sin.md                    |  0
 .../{ => shard5}/tf.slice.md                  |  0
 .../{ => shard5}/tf.space_to_batch.md         |  0
 .../{ => shard5}/tf.sparse_retain.md          |  0
 .../{ => shard5}/tf.sparse_segment_sqrt_n.md  |  0
 .../tf.sparse_tensor_dense_matmul.md          |  0
 .../{ => shard5}/tf.split.md                  |  0
 .../{ => shard5}/tf.squared_difference.md     |  0
 .../tf.train.GradientDescentOptimizer.md      |  0
 .../{ => shard5}/tf.train.LooperThread.md     |  0
 .../{ => shard5}/tf.train.batch_join.md       |  0
 .../tf.train.exponential_decay.md             |  0
 .../{ => shard5}/tf.train.input_producer.md   |  0
 .../{ => shard5}/tf.train.summary_iterator.md |  0
 .../{ => shard5}/tf.train.write_graph.md      |  0
 .../{ => shard5}/tf.trainable_variables.md    |  0
 .../{ => shard5}/tf.unique.md                 |  0
 .../{ => shard5}/tf.variable_scope.md         |  0
 .../{ => shard5}/tf.zeros_initializer.md      |  0
 .../{ => shard6}/tf.FixedLenFeature.md        |  0
 .../{ => shard6}/tf.Operation.md              |  0
 .../{ => shard6}/tf.Variable.from_proto.md    |  0
 .../{ => shard6}/tf.assert_rank.md            |  0
 .../{ => shard6}/tf.batch_fft2d.md            |  0
 .../{ => shard6}/tf.conj.md                   |  0
 .../tf.contrib.copy_graph.get_copied_op.md    |  0
 ...ssian_congugates_known_sigma_predictive.md |  0
 .../tf.contrib.layers.summarize_collection.md |  0
 .../tf.contrib.learn.Estimator.md             |  0
 ...ontrib.learn.TensorFlowLinearClassifier.md |  0
 .../tf.contrib.metrics.set_size.md            |  0
 ...ntrib.metrics.streaming_percentage_less.md |  0
 .../tf.contrib.util.ops_used_by_graph_def.md  |  0
 .../tf.convert_to_tensor_or_indexed_slices.md |  0
 .../tf.errors.AlreadyExistsError.md           |  0
 .../tf.errors.FailedPreconditionError.md      |  0
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 .../{ => shard6}/tf.errors.UnknownError.md    |  0
 .../{ => shard6}/tf.exp.md                    |  0
 .../{ => shard6}/tf.foldl.md                  |  0
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 .../{ => shard6}/tf.less_equal.md             |  0
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diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/tf.nn.dropout.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.nn.dropout.md
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diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/tf.nn.nce_loss.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.nn.nce_loss.md
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diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/tf.nn.normalize_moments.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.nn.normalize_moments.md
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diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/tf.nn.uniform_candidate_sampler.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.nn.uniform_candidate_sampler.md
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diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/tf.ones.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.ones.md
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diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/tf.size.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.size.md
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diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/tf.sparse_split.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.sparse_split.md
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diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/tf.square.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.square.md
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diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/tf.train.Server.create_local_server.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.train.Server.create_local_server.md
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diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/tf.train.global_step.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.train.global_step.md
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diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/tf.unique_with_counts.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.unique_with_counts.md
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diff --git a/tensorflow/python/framework/docs.py b/tensorflow/python/framework/docs.py
index 12dfc71ffcd..92c6cfb3d92 100644
--- a/tensorflow/python/framework/docs.py
+++ b/tensorflow/python/framework/docs.py
@@ -33,6 +33,8 @@ _always_drop_symbol_re = re.compile("_[_a-zA-Z0-9]")
 _anchor_re = re.compile(r"^[\w.]+$")
 _member_mark = "@@"
 _indiv_dir = "functions_and_classes"
+_num_subdirs = 10
+_subdir_prefix = "shard"
 
 
 class Document(object):
@@ -250,6 +252,24 @@ class Library(Document):
           or self._should_include_member(name)):
         yield name, ("%s.%s" % (cls_name, name), member)
 
+  def shard_dir(self, name):
+    """Returns the path of the doc subdirectory for member `name`.
+
+    When generating individual files for each function and class, we shard
+    the files across several directories to avoid hitting the limit for
+    files per directory. This function determines the subdirectory for
+    a member based on a hash of its name.
+
+    Args:
+      name: string. The name of a function or class.
+
+    Returns:
+      The path to a subdirectory of the api docs directory.
+    """
+    index = hash(name) % _num_subdirs
+    return os.path.join(self.functions_and_classes_dir,
+                        _subdir_prefix + str(index))
+
   def set_functions_and_classes_dir(self, dirname):
     """Sets the name of the directory for function and class markdown files.
 
@@ -400,7 +420,7 @@ class Library(Document):
       # Write an individual file for each function.
       if inspect.isfunction(member):
         indivf = open(
-            os.path.join(self.functions_and_classes_dir, name + ".md"), "w+")
+            os.path.join(self.shard_dir(name), name + ".md"), "w+")
         self._print_function(indivf, prefix, name, member)
     elif inspect.isclass(member):
       print("- - -", file=f)
@@ -414,7 +434,7 @@ class Library(Document):
 
       # Write an individual file for each class.
       indivf = open(
-          os.path.join(self.functions_and_classes_dir, name + ".md"), "w+")
+          os.path.join(self.shard_dir(name), name + ".md"), "w+")
       self._write_class_markdown_to_file(indivf, name, member)
     else:
       raise RuntimeError("Member %s has unknown type %s" % (name, type(member)))
@@ -547,11 +567,17 @@ def write_libraries(output_dir, libraries):
   files = [open(os.path.join(output_dir, k), "w") for k, _ in libraries]
 
   # Set the directory in which to save individual class and function md files,
-  # creating it if it doesn't exist.
+  # creating it if it doesn't exist. Create subdirectories to avoid hitting
+  # the limit for number of files in a directory.
   indiv_dir = os.path.join(output_dir, _indiv_dir)
   if not os.path.exists(indiv_dir):
     os.makedirs(indiv_dir)
 
+  for i in range(0, _num_subdirs):
+    subdir = os.path.join(indiv_dir, _subdir_prefix + str(i))
+    if not os.path.exists(subdir):
+      os.makedirs(subdir)
+
   # Document mentioned symbols for all libraries
   for f, (_, v) in zip(files, libraries):
     v.set_functions_and_classes_dir(indiv_dir)

From cccd6c3b141498156bfdbebe18ce98112e2f11e4 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <nobody@tensorflow.org>
Date: Wed, 25 May 2016 15:02:33 -0800
Subject: [PATCH 02/44] Update ops-related pbtxt files. Change: 123263696

---
 tensorflow/core/ops/ops.pbtxt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index 8cb7d4870be..57e4e716038 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -11482,7 +11482,7 @@ op {
     }
   }
   summary: "Computes the sum of elements across dimensions of a SparseTensor."
-  description: "This Op takes a SparseTensor and is the sparse counterpart to\n`tf.reduce_sum()`.  In particular, this Op also returns a dense `Tensor`\ninstead of a sparse one.\n\nReduces `sp_input` along the dimensions given in `reduction_axes`.  Unless\n`keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in\n`reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained\nwith length 1.\n\nIf `reduction_axes` has no entries, all dimensions are reduced, and a tensor\nwith a single element is returned."
+  description: "This Op takes a SparseTensor and is the sparse counterpart to\n`tf.reduce_sum()`.  In particular, this Op also returns a dense `Tensor`\ninstead of a sparse one.\n\nReduces `sp_input` along the dimensions given in `reduction_axes`.  Unless\n`keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in\n`reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained\nwith length 1.\n\nIf `reduction_axes` has no entries, all dimensions are reduced, and a tensor\nwith a single element is returned.  Additionally, the axes can be negative,\nwhich are interpreted according to the indexing rules in Python."
 }
 op {
   name: "SparseReorder"

From beff79410edb4f1383a8c510b4637df7cf38d641 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <nobody@tensorflow.org>
Date: Wed, 25 May 2016 15:04:58 -0800
Subject: [PATCH 03/44] Update generated Python Op docs. Change: 123264022

---
 .../api_docs/python/contrib.distributions.md  | 92 +++++++++----------
 .../{shard8 => shard0}/tf.OpError.md          |  0
 .../tf.add_check_numerics_ops.md              |  0
 .../{shard8 => shard0}/tf.all_variables.md    |  0
 .../{shard8 => shard0}/tf.argmax.md           |  0
 .../tf.assert_less_equal.md                   |  0
 .../{shard4 => shard0}/tf.assert_type.md      |  0
 .../{shard2 => shard0}/tf.audio_summary.md    |  0
 .../tf.batch_matrix_band_part.md              |  0
 .../tf.batch_matrix_determinant.md            |  0
 .../tf.batch_matrix_diag_part.md              |  0
 .../{shard4 => shard0}/tf.concat.md           |  0
 .../tf.contrib.copy_graph.get_copied_op.md    |  0
 ...rmal_congugates_known_sigma_predictive.md} | 18 ++--
 ...ormal_conjugates_known_sigma_posterior.md} | 18 ++--
 ...ontrib.layers.xavier_initializer_conv2d.md |  0
 ...f.contrib.learn.TensorFlowDNNClassifier.md |  0
 .../tf.contrib.learn.infer.md                 |  0
 ...ontrib.learn.read_batch_record_features.md |  0
 .../tf.contrib.metrics.auc_using_histogram.md |  0
 .../tf.contrib.metrics.set_size.md            |  0
 ...b.metrics.streaming_mean_absolute_error.md |  0
 .../tf.contrib.metrics.streaming_recall.md    |  0
 .../tf.contrib.util.make_ndarray.md           |  0
 .../{shard4 => shard0}/tf.device.md           |  0
 .../{shard6 => shard0}/tf.gradients.md        |  0
 .../tf.image.adjust_saturation.md             |  0
 .../tf.image.flip_left_right.md               |  0
 .../tf.image.pad_to_bounding_box.md           |  0
 .../tf.image.resize_nearest_neighbor.md       |  0
 .../{shard6 => shard0}/tf.logical_xor.md      |  0
 .../tf.matrix_triangular_solve.md             |  0
 .../{shard2 => shard0}/tf.merge_summary.md    |  0
 .../{shard8 => shard0}/tf.multinomial.md      |  0
 .../{shard8 => shard0}/tf.nn.bias_add.md      |  0
 .../tf.nn.depthwise_conv2d_native.md          |  0
 .../tf.nn.max_pool_with_argmax.md             |  0
 .../{shard6 => shard0}/tf.no_regularizer.md   |  0
 .../{shard8 => shard0}/tf.op_scope.md         |  0
 .../{shard8 => shard0}/tf.self_adjoint_eig.md |  0
 .../{shard6 => shard0}/tf.shape.md            |  0
 .../{shard2 => shard0}/tf.space_to_depth.md   |  0
 .../tf.sparse_reset_shape.md                  |  0
 .../tf.sparse_segment_sum.md                  |  0
 .../tf.string_to_hash_bucket.md               |  0
 .../{shard8 => shard0}/tf.to_double.md        |  0
 .../tf.train.ClusterSpec.md                   |  0
 .../{shard2 => shard0}/tf.train.Optimizer.md  |  0
 .../{shard6 => shard0}/tf.truediv.md          |  0
 .../{shard9 => shard1}/tf.DeviceSpec.md       |  0
 .../{shard7 => shard1}/tf.IdentityReader.md   |  0
 .../{shard9 => shard1}/tf.IndexedSlices.md    |  0
 .../tf.RandomShuffleQueue.md                  |  0
 .../{shard3 => shard1}/tf.WholeFileReader.md  |  0
 .../{shard3 => shard1}/tf.as_dtype.md         |  0
 .../{shard7 => shard1}/tf.assert_equal.md     |  0
 .../tf.assert_variables_initialized.md        |  0
 .../{shard3 => shard1}/tf.batch_ifft3d.md     |  0
 .../{shard5 => shard1}/tf.batch_matmul.md     |  0
 .../tf.contrib.layers.convolution2d.md        |  0
 .../tf.contrib.layers.l2_regularizer.md       |  0
 ...ib.metrics.streaming_sparse_recall_at_k.md |  0
 .../{shard7 => shard1}/tf.digamma.md          |  0
 .../tf.errors.AbortedError.md                 |  0
 .../{shard7 => shard1}/tf.fft.md              |  0
 .../{shard7 => shard1}/tf.gather_nd.md        |  0
 .../tf.image.draw_bounding_boxes.md           |  0
 .../tf.image.per_image_whitening.md           |  0
 .../{shard5 => shard1}/tf.less.md             |  0
 .../{shard5 => shard1}/tf.lgamma.md           |  0
 .../{shard5 => shard1}/tf.log.md              |  0
 .../{shard7 => shard1}/tf.logical_or.md       |  0
 .../{shard7 => shard1}/tf.matching_files.md   |  0
 .../{shard5 => shard1}/tf.matrix_solve.md     |  0
 .../tf.nn.conv2d_transpose.md                 |  0
 .../tf.nn.embedding_lookup_sparse.md          |  0
 .../{shard3 => shard1}/tf.nn.log_softmax.md   |  0
 .../{shard7 => shard1}/tf.nn.moments.md       |  0
 .../{shard9 => shard1}/tf.nn.nce_loss.md      |  0
 .../tf.nn.separable_conv2d.md                 |  0
 .../{shard5 => shard1}/tf.reduce_min.md       |  0
 .../{shard3 => shard1}/tf.scan.md             |  0
 .../{shard5 => shard1}/tf.sparse_retain.md    |  0
 .../tf.sparse_segment_sqrt_n.md               |  0
 .../{shard5 => shard1}/tf.split.md            |  0
 .../tf.train.LooperThread.md                  |  0
 .../tf.train.global_step.md                   |  0
 .../tf.train.latest_checkpoint.md             |  0
 .../tf.train.shuffle_batch.md                 |  0
 .../{shard4 => shard2}/tf.Assert.md           |  0
 .../{shard4 => shard2}/tf.Print.md            |  0
 .../{shard4 => shard2}/tf.TFRecordReader.md   |  0
 .../{shard8 => shard2}/tf.TextLineReader.md   |  0
 .../{shard6 => shard2}/tf.batch_fft2d.md      |  0
 .../{shard8 => shard2}/tf.batch_ifft2d.md     |  0
 .../tf.contrib.ffmpeg.encode_audio.md         |  0
 .../{shard0 => shard2}/tf.diag_part.md        |  0
 .../tf.errors.UnauthenticatedError.md         |  0
 .../{shard4 => shard2}/tf.fft3d.md            |  0
 .../tf.get_default_graph.md                   |  0
 .../tf.get_default_session.md                 |  0
 .../tf.histogram_summary.md                   |  0
 .../{shard8 => shard2}/tf.imag.md             |  0
 .../tf.initialize_all_variables.md            |  0
 .../{shard6 => shard2}/tf.less_equal.md       |  0
 .../{shard0 => shard2}/tf.logical_and.md      |  0
 .../tf.nn.batch_normalization.md              |  0
 .../{shard0 => shard2}/tf.nn.conv3d.md        |  0
 .../{shard6 => shard2}/tf.nn.softsign.md      |  0
 .../tf.nn.sufficient_statistics.md            |  0
 .../{shard8 => shard2}/tf.placeholder.md      |  0
 .../{shard4 => shard2}/tf.read_file.md        |  0
 .../{shard0 => shard2}/tf.reduce_all.md       |  0
 .../tf.register_tensor_conversion_function.md |  0
 .../{shard0 => shard2}/tf.scatter_update.md   |  0
 .../{shard6 => shard2}/tf.sparse_concat.md    |  0
 .../tf.test.get_temp_dir.md                   |  0
 .../{shard6 => shard2}/tf.test.main.md        |  0
 .../{shard0 => shard2}/tf.tile.md             |  0
 .../{shard0 => shard2}/tf.to_bfloat16.md      |  0
 .../{shard8 => shard2}/tf.to_float.md         |  0
 .../tf.train.MomentumOptimizer.md             |  0
 .../tf.train.QueueRunner.from_proto.md        |  0
 .../{shard6 => shard2}/tf.transpose.md        |  0
 .../{shard0 => shard2}/tf.truncated_normal.md |  0
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diff --git a/tensorflow/g3doc/api_docs/python/contrib.distributions.md b/tensorflow/g3doc/api_docs/python/contrib.distributions.md
index 760335eafce..d76098b7002 100644
--- a/tensorflow/g3doc/api_docs/python/contrib.distributions.md
+++ b/tensorflow/g3doc/api_docs/python/contrib.distributions.md
@@ -1338,9 +1338,9 @@ Variance of each batch member.
 
 - - -
 
-### `class tf.contrib.distributions.Gaussian` {#Gaussian}
+### `class tf.contrib.distributions.Normal` {#Normal}
 
-The scalar Gaussian distribution with mean and stddev parameters mu, sigma.
+The scalar Normal distribution with mean and stddev parameters mu, sigma.
 
 #### Mathematical details
 
@@ -1353,15 +1353,15 @@ The PDF of this distribution is:
 Examples of initialization of one or a batch of distributions.
 
 ```python
-# Define a single scalar Gaussian distribution.
-dist = tf.contrib.distributions.Gaussian(mu=0, sigma=3)
+# Define a single scalar Normal distribution.
+dist = tf.contrib.distributions.Normal(mu=0, sigma=3)
 
 # Evaluate the cdf at 1, returning a scalar.
 dist.cdf(1)
 
-# Define a batch of two scalar valued Gaussians.
+# Define a batch of two scalar valued Normals.
 # The first has mean 1 and standard deviation 11, the second 2 and 22.
-dist = tf.contrib.distributions.Gaussian(mu=[1, 2.], sigma=[11, 22.])
+dist = tf.contrib.distributions.Normal(mu=[1, 2.], sigma=[11, 22.])
 
 # Evaluate the pdf of the first distribution on 0, and the second on 1.5,
 # returning a length two tensor.
@@ -1374,9 +1374,9 @@ dist.sample(3)
 Arguments are broadcast when possible.
 
 ```python
-# Define a batch of two scalar valued Gaussians.
+# Define a batch of two scalar valued Normals.
 # Both have mean 1, but different standard deviations.
-dist = tf.contrib.distributions.Gaussian(mu=1, sigma=[11, 22.])
+dist = tf.contrib.distributions.Normal(mu=1, sigma=[11, 22.])
 
 # Evaluate the pdf of both distributions on the same point, 3.0,
 # returning a length 2 tensor.
@@ -1384,9 +1384,9 @@ dist.pdf(3.0)
 ```
 - - -
 
-#### `tf.contrib.distributions.Gaussian.__init__(mu, sigma, name=None)` {#Gaussian.__init__}
+#### `tf.contrib.distributions.Normal.__init__(mu, sigma, name=None)` {#Normal.__init__}
 
-Construct Gaussian distributions with mean and stddev `mu` and `sigma`.
+Construct Normal distributions with mean and stddev `mu` and `sigma`.
 
 The parameters `mu` and `sigma` must be shaped in a way that supports
 broadcasting (e.g. `mu + sigma` is a valid operation).
@@ -1407,9 +1407,9 @@ broadcasting (e.g. `mu + sigma` is a valid operation).
 
 - - -
 
-#### `tf.contrib.distributions.Gaussian.cdf(x, name=None)` {#Gaussian.cdf}
+#### `tf.contrib.distributions.Normal.cdf(x, name=None)` {#Normal.cdf}
 
-CDF of observations in `x` under these Gaussian distribution(s).
+CDF of observations in `x` under these Normal distribution(s).
 
 ##### Args:
 
@@ -1425,16 +1425,16 @@ CDF of observations in `x` under these Gaussian distribution(s).
 
 - - -
 
-#### `tf.contrib.distributions.Gaussian.dtype` {#Gaussian.dtype}
+#### `tf.contrib.distributions.Normal.dtype` {#Normal.dtype}
 
 
 
 
 - - -
 
-#### `tf.contrib.distributions.Gaussian.entropy(name=None)` {#Gaussian.entropy}
+#### `tf.contrib.distributions.Normal.entropy(name=None)` {#Normal.entropy}
 
-The entropy of Gaussian distribution(s).
+The entropy of Normal distribution(s).
 
 ##### Args:
 
@@ -1449,16 +1449,16 @@ The entropy of Gaussian distribution(s).
 
 - - -
 
-#### `tf.contrib.distributions.Gaussian.is_reparameterized` {#Gaussian.is_reparameterized}
+#### `tf.contrib.distributions.Normal.is_reparameterized` {#Normal.is_reparameterized}
 
 
 
 
 - - -
 
-#### `tf.contrib.distributions.Gaussian.log_cdf(x, name=None)` {#Gaussian.log_cdf}
+#### `tf.contrib.distributions.Normal.log_cdf(x, name=None)` {#Normal.log_cdf}
 
-Log CDF of observations `x` under these Gaussian distribution(s).
+Log CDF of observations `x` under these Normal distribution(s).
 
 ##### Args:
 
@@ -1474,9 +1474,9 @@ Log CDF of observations `x` under these Gaussian distribution(s).
 
 - - -
 
-#### `tf.contrib.distributions.Gaussian.log_pdf(x, name=None)` {#Gaussian.log_pdf}
+#### `tf.contrib.distributions.Normal.log_pdf(x, name=None)` {#Normal.log_pdf}
 
-Log pdf of observations in `x` under these Gaussian distribution(s).
+Log pdf of observations in `x` under these Normal distribution(s).
 
 ##### Args:
 
@@ -1492,23 +1492,23 @@ Log pdf of observations in `x` under these Gaussian distribution(s).
 
 - - -
 
-#### `tf.contrib.distributions.Gaussian.mean` {#Gaussian.mean}
+#### `tf.contrib.distributions.Normal.mean` {#Normal.mean}
 
 
 
 
 - - -
 
-#### `tf.contrib.distributions.Gaussian.mu` {#Gaussian.mu}
+#### `tf.contrib.distributions.Normal.mu` {#Normal.mu}
 
 
 
 
 - - -
 
-#### `tf.contrib.distributions.Gaussian.pdf(x, name=None)` {#Gaussian.pdf}
+#### `tf.contrib.distributions.Normal.pdf(x, name=None)` {#Normal.pdf}
 
-The PDF of observations in `x` under these Gaussian distribution(s).
+The PDF of observations in `x` under these Normal distribution(s).
 
 ##### Args:
 
@@ -1524,9 +1524,9 @@ The PDF of observations in `x` under these Gaussian distribution(s).
 
 - - -
 
-#### `tf.contrib.distributions.Gaussian.sample(n, seed=None, name=None)` {#Gaussian.sample}
+#### `tf.contrib.distributions.Normal.sample(n, seed=None, name=None)` {#Normal.sample}
 
-Sample `n` observations from the Gaussian Distributions.
+Sample `n` observations from the Normal Distributions.
 
 ##### Args:
 
@@ -1544,7 +1544,7 @@ Sample `n` observations from the Gaussian Distributions.
 
 - - -
 
-#### `tf.contrib.distributions.Gaussian.sigma` {#Gaussian.sigma}
+#### `tf.contrib.distributions.Normal.sigma` {#Normal.sigma}
 
 
 
@@ -2443,26 +2443,26 @@ probability includes a combinatorial coefficient.
 Functions that transform conjugate prior/likelihood pairs to distributions
 representing the posterior or posterior predictive.
 
-### Gaussian likelihood with conjugate prior.
+### Normal likelihood with conjugate prior.
 
 - - -
 
-### `tf.contrib.distributions.gaussian_conjugates_known_sigma_posterior(prior, sigma, s, n)` {#gaussian_conjugates_known_sigma_posterior}
+### `tf.contrib.distributions.normal_conjugates_known_sigma_posterior(prior, sigma, s, n)` {#normal_conjugates_known_sigma_posterior}
 
-Posterior Gaussian distribution with conjugate prior on the mean.
+Posterior Normal distribution with conjugate prior on the mean.
 
 This model assumes that `n` observations (with sum `s`) come from a
-Gaussian with unknown mean `mu` (described by the Gaussian `prior`)
+Normal with unknown mean `mu` (described by the Normal `prior`)
 and known variance `sigma^2`.  The "known sigma posterior" is
 the distribution of the unknown `mu`.
 
-Accepts a prior Gaussian distribution object, having parameters
+Accepts a prior Normal distribution object, having parameters
 `mu0` and `sigma0`, as well as known `sigma` values of the predictive
-distribution(s) (also assumed Gaussian),
+distribution(s) (also assumed Normal),
 and statistical estimates `s` (the sum(s) of the observations) and
 `n` (the number(s) of observations).
 
-Returns a posterior (also Gaussian) distribution object, with parameters
+Returns a posterior (also Normal) distribution object, with parameters
 `(mu', sigma'^2)`, where:
 
 ```
@@ -2477,7 +2477,7 @@ will broadcast in the case of multidimensional sets of parameters.
 ##### Args:
 
 
-*  <b>`prior`</b>: `Gaussian` object of type `dtype`:
+*  <b>`prior`</b>: `Normal` object of type `dtype`:
     the prior distribution having parameters `(mu0, sigma0)`.
 *  <b>`sigma`</b>: tensor of type `dtype`, taking values `sigma > 0`.
     The known stddev parameter(s).
@@ -2486,35 +2486,35 @@ will broadcast in the case of multidimensional sets of parameters.
 
 ##### Returns:
 
-  A new Gaussian posterior distribution object for the unknown observation
+  A new Normal posterior distribution object for the unknown observation
   mean `mu`.
 
 ##### Raises:
 
 
 *  <b>`TypeError`</b>: if dtype of `s` does not match `dtype`, or `prior` is not a
-    Gaussian object.
+    Normal object.
 
 
 - - -
 
-### `tf.contrib.distributions.gaussian_congugates_known_sigma_predictive(prior, sigma, s, n)` {#gaussian_congugates_known_sigma_predictive}
+### `tf.contrib.distributions.normal_congugates_known_sigma_predictive(prior, sigma, s, n)` {#normal_congugates_known_sigma_predictive}
 
-Posterior predictive Gaussian distribution w. conjugate prior on the mean.
+Posterior predictive Normal distribution w. conjugate prior on the mean.
 
 This model assumes that `n` observations (with sum `s`) come from a
-Gaussian with unknown mean `mu` (described by the Gaussian `prior`)
+Normal with unknown mean `mu` (described by the Normal `prior`)
 and known variance `sigma^2`.  The "known sigma predictive"
 is the distribution of new observations, conditioned on the existing
 observations and our prior.
 
-Accepts a prior Gaussian distribution object, having parameters
+Accepts a prior Normal distribution object, having parameters
 `mu0` and `sigma0`, as well as known `sigma` values of the predictive
-distribution(s) (also assumed Gaussian),
+distribution(s) (also assumed Normal),
 and statistical estimates `s` (the sum(s) of the observations) and
 `n` (the number(s) of observations).
 
-Calculates the Gaussian distribution(s) `p(x | sigma^2)`:
+Calculates the Normal distribution(s) `p(x | sigma^2)`:
 
 ```
   p(x | sigma^2) = int N(x | mu, sigma^2) N(mu | prior.mu, prior.sigma^2) dmu
@@ -2536,7 +2536,7 @@ will broadcast in the case of multidimensional sets of parameters.
 ##### Args:
 
 
-*  <b>`prior`</b>: `Gaussian` object of type `dtype`:
+*  <b>`prior`</b>: `Normal` object of type `dtype`:
     the prior distribution having parameters `(mu0, sigma0)`.
 *  <b>`sigma`</b>: tensor of type `dtype`, taking values `sigma > 0`.
     The known stddev parameter(s).
@@ -2545,12 +2545,12 @@ will broadcast in the case of multidimensional sets of parameters.
 
 ##### Returns:
 
-  A new Gaussian predictive distribution object.
+  A new Normal predictive distribution object.
 
 ##### Raises:
 
 
 *  <b>`TypeError`</b>: if dtype of `s` does not match `dtype`, or `prior` is not a
-    Gaussian object.
+    Normal object.
 
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.OpError.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.OpError.md
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diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.gaussian_congugates_known_sigma_predictive.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.normal_congugates_known_sigma_predictive.md
similarity index 70%
rename from tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.gaussian_congugates_known_sigma_predictive.md
rename to tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.normal_congugates_known_sigma_predictive.md
index bfa36e208e0..89e4e5ca3c5 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.gaussian_congugates_known_sigma_predictive.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.normal_congugates_known_sigma_predictive.md
@@ -1,20 +1,20 @@
-### `tf.contrib.distributions.gaussian_congugates_known_sigma_predictive(prior, sigma, s, n)` {#gaussian_congugates_known_sigma_predictive}
+### `tf.contrib.distributions.normal_congugates_known_sigma_predictive(prior, sigma, s, n)` {#normal_congugates_known_sigma_predictive}
 
-Posterior predictive Gaussian distribution w. conjugate prior on the mean.
+Posterior predictive Normal distribution w. conjugate prior on the mean.
 
 This model assumes that `n` observations (with sum `s`) come from a
-Gaussian with unknown mean `mu` (described by the Gaussian `prior`)
+Normal with unknown mean `mu` (described by the Normal `prior`)
 and known variance `sigma^2`.  The "known sigma predictive"
 is the distribution of new observations, conditioned on the existing
 observations and our prior.
 
-Accepts a prior Gaussian distribution object, having parameters
+Accepts a prior Normal distribution object, having parameters
 `mu0` and `sigma0`, as well as known `sigma` values of the predictive
-distribution(s) (also assumed Gaussian),
+distribution(s) (also assumed Normal),
 and statistical estimates `s` (the sum(s) of the observations) and
 `n` (the number(s) of observations).
 
-Calculates the Gaussian distribution(s) `p(x | sigma^2)`:
+Calculates the Normal distribution(s) `p(x | sigma^2)`:
 
 ```
   p(x | sigma^2) = int N(x | mu, sigma^2) N(mu | prior.mu, prior.sigma^2) dmu
@@ -36,7 +36,7 @@ will broadcast in the case of multidimensional sets of parameters.
 ##### Args:
 
 
-*  <b>`prior`</b>: `Gaussian` object of type `dtype`:
+*  <b>`prior`</b>: `Normal` object of type `dtype`:
     the prior distribution having parameters `(mu0, sigma0)`.
 *  <b>`sigma`</b>: tensor of type `dtype`, taking values `sigma > 0`.
     The known stddev parameter(s).
@@ -45,11 +45,11 @@ will broadcast in the case of multidimensional sets of parameters.
 
 ##### Returns:
 
-  A new Gaussian predictive distribution object.
+  A new Normal predictive distribution object.
 
 ##### Raises:
 
 
 *  <b>`TypeError`</b>: if dtype of `s` does not match `dtype`, or `prior` is not a
-    Gaussian object.
+    Normal object.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.gaussian_conjugates_known_sigma_posterior.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.normal_conjugates_known_sigma_posterior.md
similarity index 64%
rename from tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.gaussian_conjugates_known_sigma_posterior.md
rename to tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.normal_conjugates_known_sigma_posterior.md
index f2ff765acb6..ae8eb008903 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.gaussian_conjugates_known_sigma_posterior.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.normal_conjugates_known_sigma_posterior.md
@@ -1,19 +1,19 @@
-### `tf.contrib.distributions.gaussian_conjugates_known_sigma_posterior(prior, sigma, s, n)` {#gaussian_conjugates_known_sigma_posterior}
+### `tf.contrib.distributions.normal_conjugates_known_sigma_posterior(prior, sigma, s, n)` {#normal_conjugates_known_sigma_posterior}
 
-Posterior Gaussian distribution with conjugate prior on the mean.
+Posterior Normal distribution with conjugate prior on the mean.
 
 This model assumes that `n` observations (with sum `s`) come from a
-Gaussian with unknown mean `mu` (described by the Gaussian `prior`)
+Normal with unknown mean `mu` (described by the Normal `prior`)
 and known variance `sigma^2`.  The "known sigma posterior" is
 the distribution of the unknown `mu`.
 
-Accepts a prior Gaussian distribution object, having parameters
+Accepts a prior Normal distribution object, having parameters
 `mu0` and `sigma0`, as well as known `sigma` values of the predictive
-distribution(s) (also assumed Gaussian),
+distribution(s) (also assumed Normal),
 and statistical estimates `s` (the sum(s) of the observations) and
 `n` (the number(s) of observations).
 
-Returns a posterior (also Gaussian) distribution object, with parameters
+Returns a posterior (also Normal) distribution object, with parameters
 `(mu', sigma'^2)`, where:
 
 ```
@@ -28,7 +28,7 @@ will broadcast in the case of multidimensional sets of parameters.
 ##### Args:
 
 
-*  <b>`prior`</b>: `Gaussian` object of type `dtype`:
+*  <b>`prior`</b>: `Normal` object of type `dtype`:
     the prior distribution having parameters `(mu0, sigma0)`.
 *  <b>`sigma`</b>: tensor of type `dtype`, taking values `sigma > 0`.
     The known stddev parameter(s).
@@ -37,12 +37,12 @@ will broadcast in the case of multidimensional sets of parameters.
 
 ##### Returns:
 
-  A new Gaussian posterior distribution object for the unknown observation
+  A new Normal posterior distribution object for the unknown observation
   mean `mu`.
 
 ##### Raises:
 
 
 *  <b>`TypeError`</b>: if dtype of `s` does not match `dtype`, or `prior` is not a
-    Gaussian object.
+    Normal object.
 
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diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.distributions.Gaussian.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.distributions.Normal.md
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index 7fcc6249faa..d15dd93a652 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.distributions.Gaussian.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.distributions.Normal.md
@@ -1,4 +1,4 @@
-The scalar Gaussian distribution with mean and stddev parameters mu, sigma.
+The scalar Normal distribution with mean and stddev parameters mu, sigma.
 
 #### Mathematical details
 
@@ -11,15 +11,15 @@ The PDF of this distribution is:
 Examples of initialization of one or a batch of distributions.
 
 ```python
-# Define a single scalar Gaussian distribution.
-dist = tf.contrib.distributions.Gaussian(mu=0, sigma=3)
+# Define a single scalar Normal distribution.
+dist = tf.contrib.distributions.Normal(mu=0, sigma=3)
 
 # Evaluate the cdf at 1, returning a scalar.
 dist.cdf(1)
 
-# Define a batch of two scalar valued Gaussians.
+# Define a batch of two scalar valued Normals.
 # The first has mean 1 and standard deviation 11, the second 2 and 22.
-dist = tf.contrib.distributions.Gaussian(mu=[1, 2.], sigma=[11, 22.])
+dist = tf.contrib.distributions.Normal(mu=[1, 2.], sigma=[11, 22.])
 
 # Evaluate the pdf of the first distribution on 0, and the second on 1.5,
 # returning a length two tensor.
@@ -32,9 +32,9 @@ dist.sample(3)
 Arguments are broadcast when possible.
 
 ```python
-# Define a batch of two scalar valued Gaussians.
+# Define a batch of two scalar valued Normals.
 # Both have mean 1, but different standard deviations.
-dist = tf.contrib.distributions.Gaussian(mu=1, sigma=[11, 22.])
+dist = tf.contrib.distributions.Normal(mu=1, sigma=[11, 22.])
 
 # Evaluate the pdf of both distributions on the same point, 3.0,
 # returning a length 2 tensor.
@@ -42,9 +42,9 @@ dist.pdf(3.0)
 ```
 - - -
 
-#### `tf.contrib.distributions.Gaussian.__init__(mu, sigma, name=None)` {#Gaussian.__init__}
+#### `tf.contrib.distributions.Normal.__init__(mu, sigma, name=None)` {#Normal.__init__}
 
-Construct Gaussian distributions with mean and stddev `mu` and `sigma`.
+Construct Normal distributions with mean and stddev `mu` and `sigma`.
 
 The parameters `mu` and `sigma` must be shaped in a way that supports
 broadcasting (e.g. `mu + sigma` is a valid operation).
@@ -65,9 +65,9 @@ broadcasting (e.g. `mu + sigma` is a valid operation).
 
 - - -
 
-#### `tf.contrib.distributions.Gaussian.cdf(x, name=None)` {#Gaussian.cdf}
+#### `tf.contrib.distributions.Normal.cdf(x, name=None)` {#Normal.cdf}
 
-CDF of observations in `x` under these Gaussian distribution(s).
+CDF of observations in `x` under these Normal distribution(s).
 
 ##### Args:
 
@@ -83,16 +83,16 @@ CDF of observations in `x` under these Gaussian distribution(s).
 
 - - -
 
-#### `tf.contrib.distributions.Gaussian.dtype` {#Gaussian.dtype}
+#### `tf.contrib.distributions.Normal.dtype` {#Normal.dtype}
 
 
 
 
 - - -
 
-#### `tf.contrib.distributions.Gaussian.entropy(name=None)` {#Gaussian.entropy}
+#### `tf.contrib.distributions.Normal.entropy(name=None)` {#Normal.entropy}
 
-The entropy of Gaussian distribution(s).
+The entropy of Normal distribution(s).
 
 ##### Args:
 
@@ -107,16 +107,16 @@ The entropy of Gaussian distribution(s).
 
 - - -
 
-#### `tf.contrib.distributions.Gaussian.is_reparameterized` {#Gaussian.is_reparameterized}
+#### `tf.contrib.distributions.Normal.is_reparameterized` {#Normal.is_reparameterized}
 
 
 
 
 - - -
 
-#### `tf.contrib.distributions.Gaussian.log_cdf(x, name=None)` {#Gaussian.log_cdf}
+#### `tf.contrib.distributions.Normal.log_cdf(x, name=None)` {#Normal.log_cdf}
 
-Log CDF of observations `x` under these Gaussian distribution(s).
+Log CDF of observations `x` under these Normal distribution(s).
 
 ##### Args:
 
@@ -132,9 +132,9 @@ Log CDF of observations `x` under these Gaussian distribution(s).
 
 - - -
 
-#### `tf.contrib.distributions.Gaussian.log_pdf(x, name=None)` {#Gaussian.log_pdf}
+#### `tf.contrib.distributions.Normal.log_pdf(x, name=None)` {#Normal.log_pdf}
 
-Log pdf of observations in `x` under these Gaussian distribution(s).
+Log pdf of observations in `x` under these Normal distribution(s).
 
 ##### Args:
 
@@ -150,23 +150,23 @@ Log pdf of observations in `x` under these Gaussian distribution(s).
 
 - - -
 
-#### `tf.contrib.distributions.Gaussian.mean` {#Gaussian.mean}
+#### `tf.contrib.distributions.Normal.mean` {#Normal.mean}
 
 
 
 
 - - -
 
-#### `tf.contrib.distributions.Gaussian.mu` {#Gaussian.mu}
+#### `tf.contrib.distributions.Normal.mu` {#Normal.mu}
 
 
 
 
 - - -
 
-#### `tf.contrib.distributions.Gaussian.pdf(x, name=None)` {#Gaussian.pdf}
+#### `tf.contrib.distributions.Normal.pdf(x, name=None)` {#Normal.pdf}
 
-The PDF of observations in `x` under these Gaussian distribution(s).
+The PDF of observations in `x` under these Normal distribution(s).
 
 ##### Args:
 
@@ -182,9 +182,9 @@ The PDF of observations in `x` under these Gaussian distribution(s).
 
 - - -
 
-#### `tf.contrib.distributions.Gaussian.sample(n, seed=None, name=None)` {#Gaussian.sample}
+#### `tf.contrib.distributions.Normal.sample(n, seed=None, name=None)` {#Normal.sample}
 
-Sample `n` observations from the Gaussian Distributions.
+Sample `n` observations from the Normal Distributions.
 
 ##### Args:
 
@@ -202,7 +202,7 @@ Sample `n` observations from the Gaussian Distributions.
 
 - - -
 
-#### `tf.contrib.distributions.Gaussian.sigma` {#Gaussian.sigma}
+#### `tf.contrib.distributions.Normal.sigma` {#Normal.sigma}
 
 
 
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diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.trainable_variables.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.trainable_variables.md
similarity index 100%
rename from tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.trainable_variables.md
rename to tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.trainable_variables.md
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.zeros_initializer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.zeros_initializer.md
similarity index 100%
rename from tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.zeros_initializer.md
rename to tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.zeros_initializer.md
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.zeros_like.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.zeros_like.md
similarity index 100%
rename from tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.zeros_like.md
rename to tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.zeros_like.md
diff --git a/tensorflow/g3doc/api_docs/python/index.md b/tensorflow/g3doc/api_docs/python/index.md
index 1617ebffa9e..1a199d25195 100644
--- a/tensorflow/g3doc/api_docs/python/index.md
+++ b/tensorflow/g3doc/api_docs/python/index.md
@@ -530,10 +530,10 @@
   * [`DiscreteDistribution`](../../api_docs/python/contrib.distributions.md#DiscreteDistribution)
   * [`Exponential`](../../api_docs/python/contrib.distributions.md#Exponential)
   * [`Gamma`](../../api_docs/python/contrib.distributions.md#Gamma)
-  * [`Gaussian`](../../api_docs/python/contrib.distributions.md#Gaussian)
-  * [`gaussian_congugates_known_sigma_predictive`](../../api_docs/python/contrib.distributions.md#gaussian_congugates_known_sigma_predictive)
-  * [`gaussian_conjugates_known_sigma_posterior`](../../api_docs/python/contrib.distributions.md#gaussian_conjugates_known_sigma_posterior)
   * [`MultivariateNormal`](../../api_docs/python/contrib.distributions.md#MultivariateNormal)
+  * [`Normal`](../../api_docs/python/contrib.distributions.md#Normal)
+  * [`normal_congugates_known_sigma_predictive`](../../api_docs/python/contrib.distributions.md#normal_congugates_known_sigma_predictive)
+  * [`normal_conjugates_known_sigma_posterior`](../../api_docs/python/contrib.distributions.md#normal_conjugates_known_sigma_posterior)
   * [`StudentT`](../../api_docs/python/contrib.distributions.md#StudentT)
   * [`Uniform`](../../api_docs/python/contrib.distributions.md#Uniform)
 

From 858f21884ea679bdeaf2253f27b7a10dbd94ae04 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <nobody@tensorflow.org>
Date: Wed, 25 May 2016 15:49:09 -0800
Subject: [PATCH 04/44] Update generated Python Op docs. Change: 123268244

---
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From 87bd228996f471a3800fbf6b07abec58db214a25 Mon Sep 17 00:00:00 2001
From: Nikhil Thorat <nsthorat@gmail.com>
Date: Wed, 25 May 2016 16:03:41 -0800
Subject: [PATCH 05/44] Add run metadata to the mnist_with_summaries.py TF
 example tutorial.

This change also changes some of the name_scope's so that the input metadnode is entirely faded for a stats view. Specifically:
- Move the dropout keep_prob scalar into the dropout metanode.
- Move the input reshape into its own metanode. This is run for every step.
Change: 123269596
---
 .../tutorials/mnist/mnist_with_summaries.py   | 30 ++++++++++++++-----
 1 file changed, 23 insertions(+), 7 deletions(-)

diff --git a/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py b/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py
index f2811b4dca9..a966e7fa493 100644
--- a/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py
+++ b/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py
@@ -52,11 +52,11 @@ def train():
   # Input placehoolders
   with tf.name_scope('input'):
     x = tf.placeholder(tf.float32, [None, 784], name='x-input')
+    y_ = tf.placeholder(tf.float32, [None, 10], name='y-input')
+
+  with tf.name_scope('input_reshape'):
     image_shaped_input = tf.reshape(x, [-1, 28, 28, 1])
     tf.image_summary('input', image_shaped_input, 10)
-    y_ = tf.placeholder(tf.float32, [None, 10], name='y-input')
-    keep_prob = tf.placeholder(tf.float32)
-    tf.scalar_summary('dropout_keep_probability', keep_prob)
 
   # We can't initialize these variables to 0 - the network will get stuck.
   def weight_variable(shape):
@@ -105,7 +105,12 @@ def train():
       return activations
 
   hidden1 = nn_layer(x, 784, 500, 'layer1')
-  dropped = tf.nn.dropout(hidden1, keep_prob)
+
+  with tf.name_scope('dropout'):
+    keep_prob = tf.placeholder(tf.float32)
+    tf.scalar_summary('dropout_keep_probability', keep_prob)
+    dropped = tf.nn.dropout(hidden1, keep_prob)
+
   y = nn_layer(dropped, 500, 10, 'layer2', act=tf.nn.softmax)
 
   with tf.name_scope('cross_entropy'):
@@ -151,9 +156,20 @@ def train():
       summary, acc = sess.run([merged, accuracy], feed_dict=feed_dict(False))
       test_writer.add_summary(summary, i)
       print('Accuracy at step %s: %s' % (i, acc))
-    else:  # Record train set summarieis, and train
-      summary, _ = sess.run([merged, train_step], feed_dict=feed_dict(True))
-      train_writer.add_summary(summary, i)
+    else:  # Record train set summaries, and train
+      if i % 100 == 99:  # Record execution stats
+        run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
+        run_metadata = tf.RunMetadata()
+        summary, _ = sess.run([merged, train_step],
+                              feed_dict=feed_dict(True),
+                              options=run_options,
+                              run_metadata=run_metadata)
+        train_writer.add_run_metadata(run_metadata, 'step%d' % i)
+        train_writer.add_summary(summary, i)
+        print('Adding run metadata for', i)
+      else:  # Record a summary
+        summary, _ = sess.run([merged, train_step], feed_dict=feed_dict(True))
+        train_writer.add_summary(summary, i)
 
 
 def main(_):

From 23bacdbee659bd700f9f599fc4223b337c154aaa Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <nobody@tensorflow.org>
Date: Wed, 25 May 2016 16:19:04 -0800
Subject: [PATCH 06/44] Fixes bug in SquaredLossUpdater::ComputeUpdatedDual
 when example weights are different than 1.0. Change: 123270998

---
 .../linear_optimizer/kernels/squared-loss.h   | 10 ++-
 .../python/kernel_tests/sdca_ops_test.py      | 82 +++++++++++++++----
 2 files changed, 72 insertions(+), 20 deletions(-)

diff --git a/tensorflow/contrib/linear_optimizer/kernels/squared-loss.h b/tensorflow/contrib/linear_optimizer/kernels/squared-loss.h
index fc37a98a4fc..da61e6dbfdb 100644
--- a/tensorflow/contrib/linear_optimizer/kernels/squared-loss.h
+++ b/tensorflow/contrib/linear_optimizer/kernels/squared-loss.h
@@ -26,15 +26,17 @@ namespace tensorflow {
 class SquaredLossUpdater : public DualLossUpdater {
  public:
   // Closed form solution that decreases the dual squared loss.
-  // See page 23 of http://arxiv.org/pdf/1309.2375v2.pdf
+  // See page 23 of http://arxiv.org/pdf/1309.2375v2.pdf for the derivation of
+  // the update rule when the example weights are equal to 1.0.
+  // Note: There is a typo in the formula in the paper: the denominator should
+  // be 1 + ||x_i||^2/(\lambda n) (without the 2 multiplier).
   double ComputeUpdatedDual(const double label, const double example_weight,
                             const double current_dual, const double wx,
                             const double weighted_example_norm,
                             const double primal_loss_unused,
                             const double dual_loss_unused) const final {
-    const double delta_numerator = (label - current_dual - wx) * example_weight;
-    const double delta_denominator =
-        1 + weighted_example_norm * example_weight * example_weight * 0.5;
+    const double delta_numerator = label - current_dual - wx;
+    const double delta_denominator = 1 + weighted_example_norm * example_weight;
     return current_dual + delta_numerator / delta_denominator;
   }
 
diff --git a/tensorflow/contrib/linear_optimizer/python/kernel_tests/sdca_ops_test.py b/tensorflow/contrib/linear_optimizer/python/kernel_tests/sdca_ops_test.py
index e9739465ccb..211df8edd1e 100644
--- a/tensorflow/contrib/linear_optimizer/python/kernel_tests/sdca_ops_test.py
+++ b/tensorflow/contrib/linear_optimizer/python/kernel_tests/sdca_ops_test.py
@@ -455,6 +455,7 @@ class SdcaWithLogisticLossTest(SdcaOptimizerTest):
   # TODO(katsiaspis): add a test for the case when examples at the end of an
   # epoch are repeated, since example id may be duplicated.
 
+
 class SdcaWithLinearLossTest(SdcaOptimizerTest):
   """SDCA optimizer test class for linear (squared) loss."""
 
@@ -488,9 +489,11 @@ class SdcaWithLinearLossTest(SdcaOptimizerTest):
       self.assertAllClose([-20.0 / 3.0, 28.0 / 3.0],
                           predictions.eval(),
                           rtol=0.005)
-      self.assertAllClose(0.01,
+      # Approximate gap should be very close to 0.0. (In fact, because the gap
+      # is only approximate, it is likely that upon convergence the duality gap
+      # can have a tiny negative value).
+      self.assertAllClose(0.00,
                           lr.approximate_duality_gap().eval(),
-                          rtol=1e-2,
                           atol=1e-2)
 
   def testL2Regularization(self):
@@ -580,7 +583,7 @@ class SdcaWithLinearLossTest(SdcaOptimizerTest):
             {'age': [1],
              'gender': [1]}, 14.0, 2.0),
     ]
-    example_weights = [1.0, 1.0]
+    example_weights = [5.0, 3.0]
     with self._single_threaded_test_session():
       examples = make_example_dict(example_protos, example_weights)
 
@@ -597,20 +600,30 @@ class SdcaWithLinearLossTest(SdcaOptimizerTest):
       for _ in xrange(_MAX_ITERATIONS):
         train_op.run()
 
-      # Predictions should be 8/9 of label due to minimizing regularized loss:
-      #   (label - 2 * 2 * weight)^2 / 2 + L2 * 2 * weight^2
-      self.assertAllClose([-10.0 * 8 / 9, 14.0 * 8 / 9],
+      # There are 4 (sparse) variable weights to be learned. 2 for age and 2 for
+      # gender. Let w_1, w_2 be age weights, w_3, w_4 be gender weights, y_1,
+      # y_2 be the labels for examples 1 and 2 respectively and s_1, s_2 the
+      # corresponding *example* weights. With the given feature values, the loss
+      # function is given by:
+      # s_1/2(y_1 + 2w_1 + 2w_3)^2 + s_2/2(y_2 - 2w_2 - 2w_4)^2
+      # + \lambda/2 (w_1^2 + w_2^2 + w_3^2 + w_4^2). Solving for the optimal, it
+      # can be verified that:
+      # w_1* = w_3* = -2.0 s_1 y_1/(\lambda + 8 s_1) and
+      # w_2* = w_4* = 2 \cdot s_2 y_2/(\lambda + 8 s_2). Equivalently, due to
+      # regularization and example weights, the predictions are within:
+      # 8 \cdot s_i /(\lambda + 8 \cdot s_i) of the labels.
+      self.assertAllClose([-10 * 40.0 / 41.0, 14.0 * 24 / 25.0],
                           predictions.eval(),
-                          rtol=0.07)
+                          atol=0.01)
 
-  def testDenseFeatures(self):
+  def testDenseFeaturesWithDefaultWeights(self):
     with self._single_threaded_test_session():
       examples = make_dense_examples_dict(
-          dense_feature_values=[[-2.0, 0.0], [0.0, 2.0]],
+          dense_feature_values=[[1.0, 0.0], [0.0, 1.0]],
           weights=[1.0, 1.0],
-          labels=[-10.0, 14.0])
+          labels=[10.0, -5.0])
       variables = make_dense_variable_dict(2, 2)
-      options = dict(symmetric_l2_regularization=1,
+      options = dict(symmetric_l2_regularization=1.0,
                      symmetric_l1_regularization=0,
                      loss_type='squared_loss')
       lr = SdcaModel(CONTAINER, examples, variables, options)
@@ -621,14 +634,51 @@ class SdcaWithLinearLossTest(SdcaOptimizerTest):
       for _ in xrange(_MAX_ITERATIONS):
         train_op.run()
 
-      # Predictions should be 4/5 of label due to minimizing regularized loss:
-      #   (label - 2 * weight)^2 / 2 + L2 * weight^2
-      self.assertAllClose([-10.0 * 4 / 5, 14.0 * 4 / 5],
+      # The loss function for these particular features is given by:
+      # 1/2(label_1-w_1)^2 + 1/2(label_2-w_2)^2 + \lambda/2 (w_1^2 + w_2^2). So,
+      # differentiating wrt to w_1, w_2 yields the following optimal values:
+      # w_1* = label_1/(\lambda + 1)= 10/2, w_2* =label_2/(\lambda + 1)= -5/2.
+      # In this case the (unnormalized regularized) loss will be:
+      # 1/2(10-5)^2 + 1/2(5-5/2)^2 + 1/2(5^2 + (5/2)^2) = 125.0/4. The actual
+      # loss should be further normalized by the sum of example weights.
+      self.assertAllClose([5.0, -2.5],
                           predictions.eval(),
                           rtol=0.01)
-
       loss = lr.regularized_loss(examples)
-      self.assertAllClose(148.0 / 10.0, loss.eval(), atol=0.01)
+      self.assertAllClose(125.0 / 8.0, loss.eval(), atol=0.01)
+
+  def testDenseFeaturesWithArbitraryWeights(self):
+    with self._single_threaded_test_session():
+      examples = make_dense_examples_dict(
+          dense_feature_values=[[1.0, 0.0], [0.0, 1.0]],
+          weights=[20.0, 10.0],
+          labels=[10.0, -5.0])
+      variables = make_dense_variable_dict(2, 2)
+      options = dict(symmetric_l2_regularization=5.0,
+                     symmetric_l1_regularization=0,
+                     loss_type='squared_loss')
+      lr = SdcaModel(CONTAINER, examples, variables, options)
+      tf.initialize_all_variables().run()
+      predictions = lr.predictions(examples)
+
+      train_op = lr.minimize()
+      for _ in xrange(_MAX_ITERATIONS):
+        train_op.run()
+
+      # The loss function for these particular features is given by:
+      # 1/2 s_1 (label_1-w_1)^2 + 1/2 s_2(label_2-w_2)^2 +
+      # \lambda/2 (w_1^2 + w_2^2) where s_1, s_2 are the *example weights. It
+      # turns out that the optimal (variable) weights are given by:
+      # w_1* = label_1 \cdot s_1/(\lambda + s_1)= 8.0 and
+      # w_2* =label_2 \cdot s_2/(\lambda + s_2)= -10/3.
+      # In this case the (unnormalized regularized) loss will be:
+      # s_1/2(8-10)^2 + s_2/2(5-10/3)^2 + 5.0/2(8^2 + (10/3)^2) = 2175.0/9. The
+      # actual loss should be further normalized by the sum of example weights.
+      self.assertAllClose([8.0, -10.0/3],
+                          predictions.eval(),
+                          rtol=0.01)
+      loss = lr.regularized_loss(examples)
+      self.assertAllClose(2175.0 / 270.0, loss.eval(), atol=0.01)
 
 
 class SdcaWithHingeLossTest(SdcaOptimizerTest):

From 9c7acc69c125252e910966b351847ac9082dccfb Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <nobody@tensorflow.org>
Date: Wed, 25 May 2016 16:35:17 -0800
Subject: [PATCH 07/44] Update generated Python Op docs. Change: 123272356

---
 .../{shard1 => shard0}/tf.DeviceSpec.from_string.md               | 0
 .../functions_and_classes/{shard9 => shard0}/tf.Dimension.md      | 0
 .../{shard9 => shard0}/tf.RegisterGradient.md                     | 0
 .../functions_and_classes/{shard1 => shard0}/tf.RegisterShape.md  | 0
 .../{shard7 => shard0}/tf.WholeFileReader.md                      | 0
 .../python/functions_and_classes/{shard7 => shard0}/tf.add_n.md   | 0
 .../python/functions_and_classes/{shard3 => shard0}/tf.ceil.md    | 0
 .../functions_and_classes/{shard3 => shard0}/tf.clip_by_value.md  | 0
 .../tf.contrib.distributions.BaseDistribution.md                  | 0
 .../{shard3 => shard0}/tf.contrib.layers.l2_regularizer.md        | 0
 .../{shard3 => shard0}/tf.contrib.learn.RunConfig.md              | 0
 .../{shard1 => shard0}/tf.contrib.learn.TensorFlowClassifier.md   | 0
 .../{shard5 => shard0}/tf.contrib.learn.read_batch_examples.md    | 0
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From 041241882c6b33f94e0f1efd1f18d4e9b6cfcc09 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <nobody@tensorflow.org>
Date: Wed, 25 May 2016 17:19:40 -0800
Subject: [PATCH 08/44] Update generated Python Op docs. Change: 123275589

---
 .../{shard7 => shard0}/tf.FixedLenFeature.md                      | 0
 .../{shard5 => shard0}/tf.InteractiveSession.md                   | 0
 .../functions_and_classes/{shard5 => shard0}/tf.Operation.md      | 0
 .../functions_and_classes/{shard3 => shard0}/tf.TextLineReader.md | 0
 .../functions_and_classes/{shard5 => shard0}/tf.Variable.md       | 0
 .../python/functions_and_classes/{shard3 => shard0}/tf.argmax.md  | 0
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 .../functions_and_classes/{shard7 => shard0}/tf.batch_ifft.md     | 0
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 .../{shard3 => shard0}/tf.contrib.layers.l1_regularizer.md        | 0
 .../{shard9 => shard0}/tf.contrib.layers.xavier_initializer.md    | 0
 .../{shard3 => shard0}/tf.contrib.learn.Estimator.md              | 0
 .../{shard5 => shard0}/tf.convert_to_tensor.md                    | 0
 .../functions_and_classes/{shard1 => shard0}/tf.diag_part.md      | 0
 .../{shard1 => shard0}/tf.errors.UnavailableError.md              | 0
 .../python/functions_and_classes/{shard9 => shard0}/tf.foldr.md   | 0
 .../{shard3 => shard0}/tf.image.hsv_to_rgb.md                     | 0
 .../{shard5 => shard0}/tf.image.resize_nearest_neighbor.md        | 0
 .../{shard1 => shard0}/tf.image.rgb_to_grayscale.md               | 0
 .../functions_and_classes/{shard5 => shard0}/tf.logical_xor.md    | 0
 .../{shard5 => shard0}/tf.moving_average_variables.md             | 0
 .../functions_and_classes/{shard1 => shard0}/tf.name_scope.md     | 0
 .../functions_and_classes/{shard1 => shard0}/tf.nn.bias_add.md    | 0
 .../functions_and_classes/{shard3 => shard0}/tf.nn.in_top_k.md    | 0
 .../functions_and_classes/{shard1 => shard0}/tf.nn.softsign.md    | 0
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 .../functions_and_classes/{shard1 => shard0}/tf.random_uniform.md | 0
 .../python/functions_and_classes/{shard7 => shard0}/tf.range.md   | 0
 .../python/functions_and_classes/{shard9 => shard0}/tf.rank.md    | 0
 .../functions_and_classes/{shard3 => shard0}/tf.read_file.md      | 0
 .../functions_and_classes/{shard7 => shard0}/tf.space_to_depth.md | 0
 .../functions_and_classes/{shard7 => shard0}/tf.sparse_concat.md  | 0
 .../{shard7 => shard0}/tf.sparse_placeholder.md                   | 0
 .../functions_and_classes/{shard7 => shard0}/tf.stop_gradient.md  | 0
 .../python/functions_and_classes/{shard1 => shard0}/tf.tile.md    | 0
 .../functions_and_classes/{shard5 => shard0}/tf.to_bfloat16.md    | 0
 .../{shard5 => shard0}/tf.train.Supervisor.md                     | 0
 .../{shard3 => shard0}/tf.train.match_filenames_once.md           | 0
 .../functions_and_classes/{shard7 => shard0}/tf.transpose.md      | 0
 .../python/functions_and_classes/{shard9 => shard0}/tf.where.md   | 0
 .../python/functions_and_classes/{shard7 => shard0}/tf.zeros.md   | 0
 .../functions_and_classes/{shard8 => shard1}/tf.DeviceSpec.md     | 0
 .../functions_and_classes/{shard6 => shard1}/tf.NoGradient.md     | 0
 .../{shard0 => shard1}/tf.RegisterGradient.md                     | 0
 .../python/functions_and_classes/{shard4 => shard1}/tf.abs.md     | 0
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 .../functions_and_classes/{shard2 => shard1}/tf.assert_equal.md   | 0
 .../{shard2 => shard1}/tf.assert_non_positive.md                  | 0
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 .../{shard4 => shard1}/tf.batch_cholesky_solve.md                 | 0
 .../{shard6 => shard1}/tf.batch_matrix_inverse.md                 | 0
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 .../{shard0 => shard1}/tf.contrib.learn.RunConfig.md              | 0
 .../{shard0 => shard1}/tf.contrib.learn.TensorFlowClassifier.md   | 0
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 .../{shard2 => shard1}/tf.histogram_fixed_width.md                | 0
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 .../{shard0 => shard1}/tf.image.adjust_brightness.md              | 0
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 .../{shard4 => shard1}/tf.image.decode_png.md                     | 0
 .../{shard4 => shard1}/tf.image.random_hue.md                     | 0
 .../python/functions_and_classes/{shard4 => shard1}/tf.lbeta.md   | 0
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 .../{shard6 => shard1}/tf.train.AdamOptimizer.md                  | 0
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 .../{shard8 => shard1}/tf.train.summary_iterator.md               | 0
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 .../{shard0 => shard1}/tf.zeros_initializer.md                    | 0
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 .../{shard1 => shard2}/tf.AggregationMethod.md                    | 0
 .../{shard7 => shard2}/tf.QueueBase.from_list.md                  | 0
 .../functions_and_classes/{shard5 => shard2}/tf.TFRecordReader.md | 0
 .../functions_and_classes/{shard1 => shard2}/tf.batch_ifft2d.md   | 0
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 .../python/functions_and_classes/{shard9 => shard2}/tf.bytes.md   | 0
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 .../python/functions_and_classes/{shard1 => shard2}/tf.conj.md    | 0
 .../{shard5 => shard2}/tf.contrib.copy_graph.copy_op_to_graph.md  | 0
 ...trib.distributions.normal_congugates_known_sigma_predictive.md | 0
 .../{shard3 => shard2}/tf.contrib.ffmpeg.encode_audio.md          | 0
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From 2b744876db969020242e3d70f7d77d9128f94c6e Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <nobody@tensorflow.org>
Date: Wed, 25 May 2016 18:04:40 -0800
Subject: [PATCH 09/44] Update generated Python Op docs. Change: 123278130

---
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diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.random_crop.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.random_crop.md
similarity index 100%
rename from tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.random_crop.md
rename to tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.random_crop.md
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.random_normal_initializer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.random_normal_initializer.md
similarity index 100%
rename from tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.random_normal_initializer.md
rename to tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.random_normal_initializer.md
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.random_shuffle.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.random_shuffle.md
similarity index 100%
rename from tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.random_shuffle.md
rename to tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.random_shuffle.md
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.sparse_segment_mean.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.sparse_segment_mean.md
similarity index 100%
rename from tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.sparse_segment_mean.md
rename to tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.sparse_segment_mean.md
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.sparse_segment_sqrt_n.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.sparse_segment_sqrt_n.md
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rename from tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.sparse_segment_sqrt_n.md
rename to tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.sparse_segment_sqrt_n.md
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.sparse_softmax.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.sparse_softmax.md
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rename from tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.sparse_softmax.md
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diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.split.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.split.md
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rename from tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.split.md
rename to tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.split.md
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.train.GradientDescentOptimizer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.train.GradientDescentOptimizer.md
similarity index 100%
rename from tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.train.GradientDescentOptimizer.md
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diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.train.get_checkpoint_state.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.train.get_checkpoint_state.md
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rename from tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.train.get_checkpoint_state.md
rename to tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.train.get_checkpoint_state.md
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.train.global_step.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.train.global_step.md
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rename from tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.train.global_step.md
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diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.trainable_variables.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.trainable_variables.md
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From 710edb74e65ae2593040c8fff4934eeeb49e676f Mon Sep 17 00:00:00 2001
From: Illia Polosukhin <ilblackdragon@gmail.com>
Date: Wed, 25 May 2016 18:06:43 -0800
Subject: [PATCH 10/44] tf.learn: Added functionality to run over data only
 once (num_epochs=1).

Speed up read_batch_example by adding more reading ops before queueing (if reading is slow operation, before it would create a bottleneck on one op).

Speed up read_batch_features by adding parsing_num_threads of parsing ops and queue for aggregating them (currently only one parsing op was used syncronously with training).
Change: 123278248
---
 .../contrib/learn/python/learn/io/graph_io.py |  88 ++++++++----
 .../learn/python/learn/io/graph_io_test.py    | 135 ++++++++++++++----
 2 files changed, 167 insertions(+), 56 deletions(-)

diff --git a/tensorflow/contrib/learn/python/learn/io/graph_io.py b/tensorflow/contrib/learn/python/learn/io/graph_io.py
index b9fffb2fb0c..bd1f4f3c0e6 100644
--- a/tensorflow/contrib/learn/python/learn/io/graph_io.py
+++ b/tensorflow/contrib/learn/python/learn/io/graph_io.py
@@ -26,8 +26,9 @@ from tensorflow.python.training import input as input_ops
 
 
 def read_batch_examples(file_pattern, batch_size, reader,
-                        randomize_input=True, queue_capacity=10000,
-                        num_threads=1, name='dequeue_examples'):
+                        randomize_input=True, num_epochs=None,
+                        queue_capacity=10000, num_threads=1,
+                        name=None):
   """Adds operations to read, queue, batch `Example` protos.
 
   Given file pattern (or list of files), will setup a queue for file names,
@@ -46,6 +47,10 @@ def read_batch_examples(file_pattern, batch_size, reader,
     reader: A function or class that returns an object with
       `read` method, (filename tensor) -> (example tensor).
     randomize_input: Whether the input should be randomized.
+    num_epochs: Integer specifying the number of times to read through the
+      dataset. If `None`, cycles through the dataset forever.
+      NOTE - If specified, creates a variable that must be initialized, so call
+      `tf.initialize_all_variables()` as shown in the tests.
     queue_capacity: Capacity for input queue.
     num_threads: The number of threads enqueuing examples.
     name: Name of resulting op.
@@ -82,39 +87,47 @@ def read_batch_examples(file_pattern, batch_size, reader,
         (batch_size, queue_capacity))
   if (not num_threads) or (num_threads <= 0):
     raise ValueError('Invalid num_threads %s.' % num_threads)
+  if (num_epochs is not None) and (num_epochs <= 0):
+    raise ValueError('Invalid num_epochs %s.' % num_epochs)
 
-  with ops.name_scope(name) as scope:
+  with ops.op_scope([file_pattern], name, 'read_batch_examples') as scope:
     # Setup filename queue with shuffling.
     with ops.name_scope('file_name_queue') as file_name_queue_scope:
       file_name_queue = input_ops.string_input_producer(
           constant_op.constant(file_names, name='input'),
-          shuffle=randomize_input, name=file_name_queue_scope)
+          shuffle=randomize_input, num_epochs=num_epochs,
+          name=file_name_queue_scope)
 
-    # Create reader and set it to read from filename queue.
+    # Create readers, one per thread and set them to read from filename queue.
     with ops.name_scope('read'):
-      _, example_proto = reader().read(file_name_queue)
+      example_list = []
+      for _ in range(num_threads):
+        _, example_proto = reader().read(file_name_queue)
+        example_list.append([example_proto])
 
-    # Setup batching queue.
+    # Setup batching queue given list of read example tensors.
     if randomize_input:
       if isinstance(batch_size, ops.Tensor):
         min_after_dequeue = int(queue_capacity * 0.4)
       else:
         min_after_dequeue = max(queue_capacity - (3 * batch_size), batch_size)
-      examples = input_ops.shuffle_batch(
-          [example_proto], batch_size, capacity=queue_capacity,
-          num_threads=num_threads, min_after_dequeue=min_after_dequeue,
+      examples = input_ops.shuffle_batch_join(
+          example_list, batch_size, capacity=queue_capacity,
+          min_after_dequeue=min_after_dequeue,
           name=scope)
     else:
-      examples = input_ops.batch(
-          [example_proto], batch_size, capacity=queue_capacity,
-          num_threads=num_threads, name=scope)
+      examples = input_ops.batch_join(
+          example_list, batch_size, capacity=queue_capacity,
+          name=scope)
 
     return examples
 
 
 def read_batch_features(file_pattern, batch_size, features, reader,
-                        randomize_input=True, queue_capacity=10000,
-                        num_threads=1, name='dequeue_examples'):
+                        randomize_input=True, num_epochs=None,
+                        queue_capacity=10000, reader_num_threads=1,
+                        parser_num_threads=1,
+                        name=None):
   """Adds operations to read, queue, batch and parse `Example` protos.
 
   Given file pattern (or list of files), will setup a queue for file names,
@@ -136,8 +149,13 @@ def read_batch_features(file_pattern, batch_size, features, reader,
     reader: A function or class that returns an object with
       `read` method, (filename tensor) -> (example tensor).
     randomize_input: Whether the input should be randomized.
+    num_epochs: Integer specifying the number of times to read through the
+      dataset. If None, cycles through the dataset forever. NOTE - If specified,
+      creates a variable that must be initialized, so call
+      tf.initialize_all_variables() as shown in the tests.
     queue_capacity: Capacity for input queue.
-    num_threads: The number of threads enqueuing examples.
+    reader_num_threads: The number of threads to read examples.
+    parser_num_threads: The number of threads to parse examples.
     name: Name of resulting op.
 
   Returns:
@@ -146,17 +164,29 @@ def read_batch_features(file_pattern, batch_size, features, reader,
   Raises:
     ValueError: for invalid inputs.
   """
-  examples = read_batch_examples(
-      file_pattern, batch_size, reader, randomize_input,
-      queue_capacity, num_threads, name=name)
+  with ops.op_scope([file_pattern], name, 'read_batch_features') as scope:
+    examples = read_batch_examples(
+        file_pattern, batch_size, reader, randomize_input=randomize_input,
+        num_epochs=num_epochs, queue_capacity=queue_capacity,
+        num_threads=reader_num_threads, name=scope)
 
-  # Parse features into tensors.
-  return parsing_ops.parse_example(examples, features)
+    # Parse features into tensors in many threads and put on the queue.
+    features_list = []
+    for _ in range(parser_num_threads):
+      features_list.append(parsing_ops.parse_example(examples, features))
+    return input_ops.batch_join(
+        features_list,
+        batch_size=batch_size,
+        capacity=queue_capacity,
+        enqueue_many=True,
+        name='parse_example_batch_join')
 
 
 def read_batch_record_features(file_pattern, batch_size, features,
-                               randomize_input=True, queue_capacity=10000,
-                               num_threads=1, name='dequeue_record_examples'):
+                               randomize_input=True, num_epochs=None,
+                               queue_capacity=10000, reader_num_threads=1,
+                               parser_num_threads=1,
+                               name='dequeue_record_examples'):
   """Reads TFRecord, queues, batches and parses `Example` proto.
 
   See more detailed description in `read_examples`.
@@ -168,8 +198,13 @@ def read_batch_record_features(file_pattern, batch_size, features,
     features: A `dict` mapping feature keys to `FixedLenFeature` or
       `VarLenFeature` values.
     randomize_input: Whether the input should be randomized.
+    num_epochs: Integer specifying the number of times to read through the
+      dataset. If None, cycles through the dataset forever. NOTE - If specified,
+      creates a variable that must be initialized, so call
+      tf.initialize_all_variables() as shown in the tests.
     queue_capacity: Capacity for input queue.
-    num_threads: The number of threads enqueuing examples.
+    reader_num_threads: The number of threads to read examples.
+    parser_num_threads: The number of threads to parse examples.
     name: Name of resulting op.
 
   Returns:
@@ -181,5 +216,6 @@ def read_batch_record_features(file_pattern, batch_size, features,
   return read_batch_features(
       file_pattern=file_pattern, batch_size=batch_size, features=features,
       reader=io_ops.TFRecordReader,
-      randomize_input=randomize_input,
-      queue_capacity=queue_capacity, num_threads=num_threads, name=name)
+      randomize_input=randomize_input, num_epochs=num_epochs,
+      queue_capacity=queue_capacity, reader_num_threads=reader_num_threads,
+      parser_num_threads=parser_num_threads, name=name)
diff --git a/tensorflow/contrib/learn/python/learn/io/graph_io_test.py b/tensorflow/contrib/learn/python/learn/io/graph_io_test.py
index 175c29ac4ed..a3d70164ca5 100644
--- a/tensorflow/contrib/learn/python/learn/io/graph_io_test.py
+++ b/tensorflow/contrib/learn/python/learn/io/graph_io_test.py
@@ -17,10 +17,13 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import os
 import random
+import tempfile
 
 import tensorflow as tf
 
+from tensorflow.python.framework import errors
 from tensorflow.python.framework import test_util
 from tensorflow.python.platform import gfile
 
@@ -55,44 +58,83 @@ class GraphIOTest(tf.test.TestCase):
 
     self.assertRaisesRegexp(
         ValueError, "No files match",
-        tf.contrib.learn.io.read_batch_features,
-        _INVALID_FILE_PATTERN, default_batch_size, None, tf.TFRecordReader,
-        False, queue_capacity,
-        num_threads, name)
+        tf.contrib.learn.io.read_batch_examples,
+        _INVALID_FILE_PATTERN, default_batch_size, tf.TFRecordReader,
+        False, num_epochs=None, queue_capacity=queue_capacity,
+        num_threads=num_threads, name=name)
     self.assertRaisesRegexp(
         ValueError, "Invalid batch_size",
-        tf.contrib.learn.io.read_batch_features,
-        _VALID_FILE_PATTERN, None, None, tf.TFRecordReader,
-        False, queue_capacity, num_threads, name)
+        tf.contrib.learn.io.read_batch_examples,
+        _VALID_FILE_PATTERN, None, tf.TFRecordReader,
+        False, num_epochs=None, queue_capacity=queue_capacity,
+        num_threads=num_threads, name=name)
     self.assertRaisesRegexp(
         ValueError, "Invalid batch_size",
-        tf.contrib.learn.io.read_batch_features,
-        _VALID_FILE_PATTERN, -1, None, tf.TFRecordReader,
-        False, queue_capacity, num_threads, name)
+        tf.contrib.learn.io.read_batch_examples,
+        _VALID_FILE_PATTERN, -1, tf.TFRecordReader,
+        False, num_epochs=None, queue_capacity=queue_capacity,
+        num_threads=num_threads, name=name)
     self.assertRaisesRegexp(
         ValueError, "Invalid queue_capacity",
-        tf.contrib.learn.io.read_batch_features,
-        _VALID_FILE_PATTERN, default_batch_size, None, tf.TFRecordReader,
-        False, None, num_threads, name)
+        tf.contrib.learn.io.read_batch_examples,
+        _VALID_FILE_PATTERN, default_batch_size, tf.TFRecordReader,
+        False, num_epochs=None, queue_capacity=None,
+        num_threads=num_threads, name=name)
     self.assertRaisesRegexp(
         ValueError, "Invalid num_threads",
-        tf.contrib.learn.io.read_batch_features,
-        _VALID_FILE_PATTERN, default_batch_size, None, tf.TFRecordReader,
-        False, queue_capacity, None,
-        name)
+        tf.contrib.learn.io.read_batch_examples,
+        _VALID_FILE_PATTERN, default_batch_size, tf.TFRecordReader,
+        False, num_epochs=None, queue_capacity=queue_capacity,
+        num_threads=None, name=name)
     self.assertRaisesRegexp(
         ValueError, "Invalid num_threads",
-        tf.contrib.learn.io.read_batch_features,
-        _VALID_FILE_PATTERN, default_batch_size, None, tf.TFRecordReader,
-        False, queue_capacity, -1,
-        name)
+        tf.contrib.learn.io.read_batch_examples,
+        _VALID_FILE_PATTERN, default_batch_size, tf.TFRecordReader,
+        False, num_epochs=None, queue_capacity=queue_capacity,
+        num_threads=-1, name=name)
     self.assertRaisesRegexp(
         ValueError, "Invalid batch_size",
-        tf.contrib.learn.io.read_batch_features,
-        _VALID_FILE_PATTERN, queue_capacity + 1, None, tf.TFRecordReader,
-        False, queue_capacity, 1, name)
+        tf.contrib.learn.io.read_batch_examples,
+        _VALID_FILE_PATTERN, queue_capacity + 1, tf.TFRecordReader,
+        False, num_epochs=None, queue_capacity=queue_capacity,
+        num_threads=1, name=name)
+    self.assertRaisesRegexp(
+        ValueError, "Invalid num_epochs",
+        tf.contrib.learn.io.read_batch_examples,
+        _VALID_FILE_PATTERN, default_batch_size, tf.TFRecordReader,
+        False, num_epochs=-1, queue_capacity=queue_capacity, num_threads=1,
+        name=name)
 
-  def test_batch_tf_record(self):
+  def test_batch_record_features(self):
+    batch_size = 17
+    queue_capacity = 1234
+    name = "my_batch"
+    features = {"feature": tf.FixedLenFeature(shape=[0], dtype=tf.float32)}
+
+    with tf.Graph().as_default() as g, self.test_session(graph=g) as sess:
+      features = tf.contrib.learn.io.read_batch_record_features(
+          _VALID_FILE_PATTERN, batch_size, features, randomize_input=False,
+          queue_capacity=queue_capacity, reader_num_threads=2,
+          parser_num_threads=2, name=name)
+      self.assertEquals("%s/parse_example_batch_join:0" % name,
+                        features["feature"].name)
+      file_name_queue_name = "%s/file_name_queue" % name
+      file_names_name = "%s/input" % file_name_queue_name
+      example_queue_name = "%s/fifo_queue" % name
+      parse_example_queue_name = "%s/parse_example_batch_join" % name
+      op_nodes = test_util.assert_ops_in_graph({
+          file_names_name: "Const",
+          file_name_queue_name: "FIFOQueue",
+          "%s/read/TFRecordReader" % name: "TFRecordReader",
+          example_queue_name: "FIFOQueue",
+          parse_example_queue_name: "QueueDequeueMany",
+          name: "QueueDequeueMany"
+      }, g)
+      self.assertAllEqual(_FILE_NAMES, sess.run(["%s:0" % file_names_name])[0])
+      self.assertEqual(
+          queue_capacity, op_nodes[example_queue_name].attr["capacity"].i)
+
+  def test_one_epoch(self):
     batch_size = 17
     queue_capacity = 1234
     name = "my_batch"
@@ -100,20 +142,25 @@ class GraphIOTest(tf.test.TestCase):
     with tf.Graph().as_default() as g, self.test_session(graph=g) as sess:
       inputs = tf.contrib.learn.io.read_batch_examples(
           _VALID_FILE_PATTERN, batch_size,
-          reader=tf.TFRecordReader, randomize_input=False,
+          reader=tf.TFRecordReader, randomize_input=True,
+          num_epochs=1,
           queue_capacity=queue_capacity, name=name)
       self.assertEquals("%s:0" % name, inputs.name)
       file_name_queue_name = "%s/file_name_queue" % name
+      file_name_queue_limit_name = (
+          "%s/limit_epochs/epochs" % file_name_queue_name)
       file_names_name = "%s/input" % file_name_queue_name
-      example_queue_name = "%s/fifo_queue" % name
+      example_queue_name = "%s/random_shuffle_queue" % name
       op_nodes = test_util.assert_ops_in_graph({
           file_names_name: "Const",
           file_name_queue_name: "FIFOQueue",
           "%s/read/TFRecordReader" % name: "TFRecordReader",
-          example_queue_name: "FIFOQueue",
-          name: "QueueDequeueMany"
+          example_queue_name: "RandomShuffleQueue",
+          name: "QueueDequeueMany",
+          file_name_queue_limit_name: "Variable"
       }, g)
-      self.assertAllEqual(_FILE_NAMES, sess.run(["%s:0" % file_names_name])[0])
+      self.assertEqual(
+          set(_FILE_NAMES), set(sess.run(["%s:0" % file_names_name])[0]))
       self.assertEqual(
           queue_capacity, op_nodes[example_queue_name].attr["capacity"].i)
 
@@ -143,6 +190,34 @@ class GraphIOTest(tf.test.TestCase):
       self.assertEqual(
           queue_capacity, op_nodes[example_queue_name].attr["capacity"].i)
 
+  def test_read_csv(self):
+    gfile.Glob = self._orig_glob
+    tempdir = tempfile.mkdtemp()
+    filename = os.path.join(tempdir, "file.csv")
+    gfile.Open(filename, "w").write("ABC\nDEF\nGHK\n")
+
+    batch_size = 1
+    queue_capacity = 5
+    name = "my_batch"
+
+    with tf.Graph().as_default() as g, self.test_session(graph=g) as session:
+      inputs = tf.contrib.learn.io.read_batch_examples(
+          filename, batch_size,
+          reader=tf.TextLineReader, randomize_input=False,
+          num_epochs=1, queue_capacity=queue_capacity, name=name)
+      session.run(tf.initialize_all_variables())
+
+      coord = tf.train.Coordinator()
+      tf.train.start_queue_runners(session, coord=coord)
+
+      self.assertEqual(session.run(inputs), "ABC")
+      self.assertEqual(session.run(inputs), "DEF")
+      self.assertEqual(session.run(inputs), "GHK")
+      with self.assertRaises(errors.OutOfRangeError):
+        session.run(inputs)
+
+      coord.request_stop()
+
 
 if __name__ == "__main__":
   tf.test.main()

From 0f79008185afa8e426b937f8edcfa9df663bdcfc Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <nobody@tensorflow.org>
Date: Wed, 25 May 2016 18:25:51 -0800
Subject: [PATCH 11/44] Adding add_loss, get_losses, get_total_loss to
 loss_ops.py Change: 123279011

---
 .../contrib/losses/python/losses/__init__.py  |  3 +
 .../contrib/losses/python/losses/loss_ops.py  | 59 ++++++++++++++++++-
 2 files changed, 61 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/losses/python/losses/__init__.py b/tensorflow/contrib/losses/python/losses/__init__.py
index 0a549a6bc27..05bad60cabf 100644
--- a/tensorflow/contrib/losses/python/losses/__init__.py
+++ b/tensorflow/contrib/losses/python/losses/__init__.py
@@ -19,7 +19,10 @@ from __future__ import division
 from __future__ import print_function
 
 from tensorflow.contrib.losses.python.losses.loss_ops import absolute_difference
+from tensorflow.contrib.losses.python.losses.loss_ops import add_loss
 from tensorflow.contrib.losses.python.losses.loss_ops import cosine_distance
+from tensorflow.contrib.losses.python.losses.loss_ops import get_losses
+from tensorflow.contrib.losses.python.losses.loss_ops import get_total_loss
 from tensorflow.contrib.losses.python.losses.loss_ops import log
 from tensorflow.contrib.losses.python.losses.loss_ops import sigmoid_cross_entropy
 from tensorflow.contrib.losses.python.losses.loss_ops import softmax_cross_entropy
diff --git a/tensorflow/contrib/losses/python/losses/loss_ops.py b/tensorflow/contrib/losses/python/losses/loss_ops.py
index c9947a4ec2b..2c22fb73d0e 100644
--- a/tensorflow/contrib/losses/python/losses/loss_ops.py
+++ b/tensorflow/contrib/losses/python/losses/loss_ops.py
@@ -104,9 +104,11 @@ weighted average over the individual prediction errors:
   weight = tf.div(weight, tf.size(weight))
   loss = tf.contrib.losses.sum_of_squares(predictions, depths, weight)
 
-
 @@absolute_difference
+@@add_loss
 @@cosine_distance
+@@get_losses
+@@get_total_loss
 @@log
 @@sigmoid_cross_entropy
 @@softmax_cross_entropy
@@ -252,6 +254,61 @@ def _num_present(losses, weight, per_batch=False):
   return num_per_batch if per_batch else math_ops.reduce_sum(num_per_batch)
 
 
+def add_loss(loss):
+  """Adds a externally defined loss to collection of losses.
+
+  Args:
+    loss: A loss `Tensor`.
+  """
+  ops.add_to_collection(ops.GraphKeys.LOSSES, loss)
+
+
+def get_losses(scope=None):
+  """Gets the list of loss variables.
+
+  Args:
+    scope: an optional scope for filtering the losses to return.
+
+  Returns:
+    a list of loss variables.
+  """
+  return ops.get_collection(ops.GraphKeys.LOSSES, scope)
+
+
+def get_regularization_losses(scope=None):
+  """Gets the regularization losses.
+
+  Args:
+    scope: an optional scope for filtering the losses to return.
+
+  Returns:
+    A list of loss variables.
+  """
+  return ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES, scope)
+
+
+def get_total_loss(add_regularization_losses=True, name="total_loss"):
+  """Returns a tensor whose value represents the total loss.
+
+  Notice that the function adds the given losses to the regularization losses.
+
+  Args:
+    add_regularization_losses: A boolean indicating whether or not to use the
+      regularization losses in the sum.
+    name: The name of the returned tensor.
+
+  Returns:
+    A `Tensor` whose value represents the total loss.
+
+  Raises:
+    ValueError: if `losses` is not iterable.
+  """
+  losses = get_losses()
+  if add_regularization_losses:
+    losses += get_regularization_losses()
+  return math_ops.add_n(losses, name=name)
+
+
 def absolute_difference(predictions, targets, weight=1.0, scope=None):
   """Adds an Absolute Difference loss to the training procedure.
 

From 779c8534b3de27303808ff28dc581acc38e3e59f Mon Sep 17 00:00:00 2001
From: Yuan Yu <yuanbyu@google.com>
Date: Wed, 25 May 2016 18:27:49 -0800
Subject: [PATCH 12/44] Make sure we always add nodes to the session graph for
 persistent tensors. Change: 123279086

---
 tensorflow/python/ops/session_ops.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tensorflow/python/ops/session_ops.py b/tensorflow/python/ops/session_ops.py
index 4e43073e9e6..f97e5f5b564 100644
--- a/tensorflow/python/ops/session_ops.py
+++ b/tensorflow/python/ops/session_ops.py
@@ -207,7 +207,7 @@ def _get_handle_reader(graph, handle, dtype):
   if result is None:
     # Create reader if we haven't done it.
     handle_device = TensorHandle._get_device_name(handle)
-    with ops.device(handle_device):
+    with graph.as_default(), graph.device(handle_device):
       holder = array_ops.placeholder(dtypes.string)
       _register_handle_feeder(holder.graph, holder, dtype)
       reader = gen_data_flow_ops._get_session_tensor(holder, dtype)
@@ -234,7 +234,7 @@ def _get_handle_mover(graph, feeder, handle):
   if result is None:
     # Create mover if we haven't done it.
     holder, reader = _get_handle_reader(graph, handle, dtype)
-    with ops.device(feeder.op.device):
+    with graph.as_default(), graph.device(feeder.op.device):
       mover = gen_data_flow_ops._get_session_handle(reader)
     result = (holder, mover)
     graph._handle_movers[graph_key] = result
@@ -248,7 +248,7 @@ def _get_handle_deleter(graph, handle):
   if result is None:
     # Create deleter if we haven't done it.
     handle_device = TensorHandle._get_device_name(handle)
-    with ops.device(handle_device):
+    with graph.as_default(), graph.device(handle_device):
       holder = array_ops.placeholder(dtypes.string)
       deleter = gen_data_flow_ops._delete_session_tensor(holder)
     result = (holder, deleter)

From c41a19137ab926d569507a05313e898aa5d395bb Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <nobody@tensorflow.org>
Date: Wed, 25 May 2016 18:50:22 -0800
Subject: [PATCH 13/44] Update generated Python Op docs. Change: 123279971

---
 .../g3doc/api_docs/python/contrib.learn.md    | 24 +++++++++++++++----
 .../{shard5 => shard0}/tf.DeviceSpec.md       |  0
 .../tf.FixedLengthRecordReader.md             |  0
 .../tf.RandomShuffleQueue.md                  |  0
 .../{shard5 => shard0}/tf.RegisterShape.md    |  0
 .../tf.add_to_collection.md                   |  0
 .../{shard7 => shard0}/tf.assert_less.md      |  0
 .../tf.assert_non_negative.md                 |  0
 .../{shard7 => shard0}/tf.batch_ifft3d.md     |  0
 .../tf.batch_matrix_inverse.md                |  0
 .../{shard7 => shard0}/tf.bitcast.md          |  0
 .../{shard9 => shard0}/tf.cast.md             |  0
 .../{shard1 => shard0}/tf.ceil.md             |  0
 ...trib.distributions.DirichletMultinomial.md |  0
 .../tf.contrib.layers.sum_regularizer.md      |  0
 .../tf.contrib.learn.TensorFlowRegressor.md   |  0
 .../tf.contrib.learn.read_batch_features.md   |  9 +++++--
 ...b.metrics.streaming_mean_relative_error.md |  0
 ...f.contrib.metrics.streaming_recall_at_k.md |  0
 .../{shard7 => shard0}/tf.cross.md            |  0
 .../{shard3 => shard0}/tf.decode_raw.md       |  0
 .../{shard1 => shard0}/tf.diag.md             |  0
 .../tf.dynamic_partition.md                   |  0
 .../tf.errors.CancelledError.md               |  0
 .../tf.errors.PermissionDeniedError.md        |  0
 .../tf.errors.ResourceExhaustedError.md       |  0
 .../{shard7 => shard0}/tf.floordiv.md         |  0
 .../{shard5 => shard0}/tf.image.adjust_hue.md |  0
 .../tf.image.random_brightness.md             |  0
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diff --git a/tensorflow/g3doc/api_docs/python/contrib.learn.md b/tensorflow/g3doc/api_docs/python/contrib.learn.md
index 70aff96a846..af57392deb9 100644
--- a/tensorflow/g3doc/api_docs/python/contrib.learn.md
+++ b/tensorflow/g3doc/api_docs/python/contrib.learn.md
@@ -3396,7 +3396,7 @@ Extracts numpy matrix from pandas DataFrame.
 
 - - -
 
-### `tf.contrib.learn.read_batch_examples(file_pattern, batch_size, reader, randomize_input=True, queue_capacity=10000, num_threads=1, name='dequeue_examples')` {#read_batch_examples}
+### `tf.contrib.learn.read_batch_examples(file_pattern, batch_size, reader, randomize_input=True, num_epochs=None, queue_capacity=10000, num_threads=1, name=None)` {#read_batch_examples}
 
 Adds operations to read, queue, batch `Example` protos.
 
@@ -3418,6 +3418,10 @@ All ops are added to the default graph.
 *  <b>`reader`</b>: A function or class that returns an object with
     `read` method, (filename tensor) -> (example tensor).
 *  <b>`randomize_input`</b>: Whether the input should be randomized.
+*  <b>`num_epochs`</b>: Integer specifying the number of times to read through the
+    dataset. If `None`, cycles through the dataset forever.
+    NOTE - If specified, creates a variable that must be initialized, so call
+    `tf.initialize_all_variables()` as shown in the tests.
 *  <b>`queue_capacity`</b>: Capacity for input queue.
 *  <b>`num_threads`</b>: The number of threads enqueuing examples.
 *  <b>`name`</b>: Name of resulting op.
@@ -3434,7 +3438,7 @@ All ops are added to the default graph.
 
 - - -
 
-### `tf.contrib.learn.read_batch_features(file_pattern, batch_size, features, reader, randomize_input=True, queue_capacity=10000, num_threads=1, name='dequeue_examples')` {#read_batch_features}
+### `tf.contrib.learn.read_batch_features(file_pattern, batch_size, features, reader, randomize_input=True, num_epochs=None, queue_capacity=10000, reader_num_threads=1, parser_num_threads=1, name=None)` {#read_batch_features}
 
 Adds operations to read, queue, batch and parse `Example` protos.
 
@@ -3459,8 +3463,13 @@ All ops are added to the default graph.
 *  <b>`reader`</b>: A function or class that returns an object with
     `read` method, (filename tensor) -> (example tensor).
 *  <b>`randomize_input`</b>: Whether the input should be randomized.
+*  <b>`num_epochs`</b>: Integer specifying the number of times to read through the
+    dataset. If None, cycles through the dataset forever. NOTE - If specified,
+    creates a variable that must be initialized, so call
+    tf.initialize_all_variables() as shown in the tests.
 *  <b>`queue_capacity`</b>: Capacity for input queue.
-*  <b>`num_threads`</b>: The number of threads enqueuing examples.
+*  <b>`reader_num_threads`</b>: The number of threads to read examples.
+*  <b>`parser_num_threads`</b>: The number of threads to parse examples.
 *  <b>`name`</b>: Name of resulting op.
 
 ##### Returns:
@@ -3475,7 +3484,7 @@ All ops are added to the default graph.
 
 - - -
 
-### `tf.contrib.learn.read_batch_record_features(file_pattern, batch_size, features, randomize_input=True, queue_capacity=10000, num_threads=1, name='dequeue_record_examples')` {#read_batch_record_features}
+### `tf.contrib.learn.read_batch_record_features(file_pattern, batch_size, features, randomize_input=True, num_epochs=None, queue_capacity=10000, reader_num_threads=1, parser_num_threads=1, name='dequeue_record_examples')` {#read_batch_record_features}
 
 Reads TFRecord, queues, batches and parses `Example` proto.
 
@@ -3490,8 +3499,13 @@ See more detailed description in `read_examples`.
 *  <b>`features`</b>: A `dict` mapping feature keys to `FixedLenFeature` or
     `VarLenFeature` values.
 *  <b>`randomize_input`</b>: Whether the input should be randomized.
+*  <b>`num_epochs`</b>: Integer specifying the number of times to read through the
+    dataset. If None, cycles through the dataset forever. NOTE - If specified,
+    creates a variable that must be initialized, so call
+    tf.initialize_all_variables() as shown in the tests.
 *  <b>`queue_capacity`</b>: Capacity for input queue.
-*  <b>`num_threads`</b>: The number of threads enqueuing examples.
+*  <b>`reader_num_threads`</b>: The number of threads to read examples.
+*  <b>`parser_num_threads`</b>: The number of threads to parse examples.
 *  <b>`name`</b>: Name of resulting op.
 
 ##### Returns:
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diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.learn.read_batch_features.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.learn.read_batch_features.md
similarity index 69%
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index 1008bf64223..75b40f7e753 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.learn.read_batch_features.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.learn.read_batch_features.md
@@ -1,4 +1,4 @@
-### `tf.contrib.learn.read_batch_features(file_pattern, batch_size, features, reader, randomize_input=True, queue_capacity=10000, num_threads=1, name='dequeue_examples')` {#read_batch_features}
+### `tf.contrib.learn.read_batch_features(file_pattern, batch_size, features, reader, randomize_input=True, num_epochs=None, queue_capacity=10000, reader_num_threads=1, parser_num_threads=1, name=None)` {#read_batch_features}
 
 Adds operations to read, queue, batch and parse `Example` protos.
 
@@ -23,8 +23,13 @@ All ops are added to the default graph.
 *  <b>`reader`</b>: A function or class that returns an object with
     `read` method, (filename tensor) -> (example tensor).
 *  <b>`randomize_input`</b>: Whether the input should be randomized.
+*  <b>`num_epochs`</b>: Integer specifying the number of times to read through the
+    dataset. If None, cycles through the dataset forever. NOTE - If specified,
+    creates a variable that must be initialized, so call
+    tf.initialize_all_variables() as shown in the tests.
 *  <b>`queue_capacity`</b>: Capacity for input queue.
-*  <b>`num_threads`</b>: The number of threads enqueuing examples.
+*  <b>`reader_num_threads`</b>: The number of threads to read examples.
+*  <b>`parser_num_threads`</b>: The number of threads to parse examples.
 *  <b>`name`</b>: Name of resulting op.
 
 ##### Returns:
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diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.learn.read_batch_examples.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.learn.read_batch_examples.md
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index 7a3c3d7addb..c5cec0542aa 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.learn.read_batch_examples.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.learn.read_batch_examples.md
@@ -1,4 +1,4 @@
-### `tf.contrib.learn.read_batch_examples(file_pattern, batch_size, reader, randomize_input=True, queue_capacity=10000, num_threads=1, name='dequeue_examples')` {#read_batch_examples}
+### `tf.contrib.learn.read_batch_examples(file_pattern, batch_size, reader, randomize_input=True, num_epochs=None, queue_capacity=10000, num_threads=1, name=None)` {#read_batch_examples}
 
 Adds operations to read, queue, batch `Example` protos.
 
@@ -20,6 +20,10 @@ All ops are added to the default graph.
 *  <b>`reader`</b>: A function or class that returns an object with
     `read` method, (filename tensor) -> (example tensor).
 *  <b>`randomize_input`</b>: Whether the input should be randomized.
+*  <b>`num_epochs`</b>: Integer specifying the number of times to read through the
+    dataset. If `None`, cycles through the dataset forever.
+    NOTE - If specified, creates a variable that must be initialized, so call
+    `tf.initialize_all_variables()` as shown in the tests.
 *  <b>`queue_capacity`</b>: Capacity for input queue.
 *  <b>`num_threads`</b>: The number of threads enqueuing examples.
 *  <b>`name`</b>: Name of resulting op.
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diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.learn.read_batch_record_features.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.read_batch_record_features.md
similarity index 57%
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index f7aeb003daa..aa4e964be14 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.learn.read_batch_record_features.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.read_batch_record_features.md
@@ -1,4 +1,4 @@
-### `tf.contrib.learn.read_batch_record_features(file_pattern, batch_size, features, randomize_input=True, queue_capacity=10000, num_threads=1, name='dequeue_record_examples')` {#read_batch_record_features}
+### `tf.contrib.learn.read_batch_record_features(file_pattern, batch_size, features, randomize_input=True, num_epochs=None, queue_capacity=10000, reader_num_threads=1, parser_num_threads=1, name='dequeue_record_examples')` {#read_batch_record_features}
 
 Reads TFRecord, queues, batches and parses `Example` proto.
 
@@ -13,8 +13,13 @@ See more detailed description in `read_examples`.
 *  <b>`features`</b>: A `dict` mapping feature keys to `FixedLenFeature` or
     `VarLenFeature` values.
 *  <b>`randomize_input`</b>: Whether the input should be randomized.
+*  <b>`num_epochs`</b>: Integer specifying the number of times to read through the
+    dataset. If None, cycles through the dataset forever. NOTE - If specified,
+    creates a variable that must be initialized, so call
+    tf.initialize_all_variables() as shown in the tests.
 *  <b>`queue_capacity`</b>: Capacity for input queue.
-*  <b>`num_threads`</b>: The number of threads enqueuing examples.
+*  <b>`reader_num_threads`</b>: The number of threads to read examples.
+*  <b>`parser_num_threads`</b>: The number of threads to parse examples.
 *  <b>`name`</b>: Name of resulting op.
 
 ##### Returns:
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From 8452d29fa834b1b872dcdbe1fd23c720c90700a2 Mon Sep 17 00:00:00 2001
From: Xiaoqiang Zheng <zhengxq@google.com>
Date: Wed, 25 May 2016 19:27:10 -0800
Subject: [PATCH 14/44] In case of memory allocation failure, return an error
 status to stream executor. Change: 123281933

---
 tensorflow/core/kernels/conv_ops_gpu.h | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/tensorflow/core/kernels/conv_ops_gpu.h b/tensorflow/core/kernels/conv_ops_gpu.h
index 14e7d033eb9..37c3711e4aa 100644
--- a/tensorflow/core/kernels/conv_ops_gpu.h
+++ b/tensorflow/core/kernels/conv_ops_gpu.h
@@ -64,8 +64,7 @@ class CudnnScratchAllocator : public perftools::gputools::ScratchAllocator {
         AllocatorAttributes(), allocation_attr));
     if (!allocation_status.ok()) {
       return perftools::gputools::port::StatusOr<
-          perftools::gputools::DeviceMemory<uint8>>(
-          AsDeviceMemory<uint8>(nullptr, 0));
+          perftools::gputools::DeviceMemory<uint8>>();
     }
     // Hold the reference of the allocated tensors until the end of the
     // allocator.

From d89e30e61fad5176f4492daab0345ac0ff703c38 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <nobody@tensorflow.org>
Date: Wed, 25 May 2016 19:35:32 -0800
Subject: [PATCH 15/44] Update generated Python Op docs. Change: 123282322

---
 .../functions_and_classes/{shard8 => shard0}/tf.GraphKeys.md      | 0
 .../{shard8 => shard0}/tf.RegisterGradient.md                     | 0
 .../python/functions_and_classes/{shard8 => shard0}/tf.add_n.md   | 0
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 .../{shard8 => shard0}/tf.assert_rank_at_least.md                 | 0
 .../{shard4 => shard0}/tf.assert_variables_initialized.md         | 0
 .../functions_and_classes/{shard4 => shard0}/tf.batch_cholesky.md | 0
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 .../{shard2 => shard0}/tf.contrib.distributions.Uniform.md        | 0
 .../{shard8 => shard0}/tf.contrib.layers.convolution2d.md         | 0
 .../{shard2 => shard0}/tf.contrib.learn.extract_dask_labels.md    | 0
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 .../{shard6 => shard0}/tf.sparse_to_indicator.md                  | 0
 .../python/functions_and_classes/{shard2 => shard0}/tf.square.md  | 0
 .../{shard8 => shard0}/tf.string_to_number.md                     | 0
 .../python/functions_and_classes/{shard4 => shard0}/tf.tanh.md    | 0
 .../functions_and_classes/{shard2 => shard0}/tf.train.batch.md    | 0
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From 0ef35580070f910ea7286660bf5893d3227b3609 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <nobody@tensorflow.org>
Date: Wed, 25 May 2016 20:20:26 -0800
Subject: [PATCH 16/44] Update generated Python Op docs. Change: 123284722

---
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From d29bdd874fc1775a2394535eaa84911ba64e2ff4 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <nobody@tensorflow.org>
Date: Wed, 25 May 2016 21:04:29 -0800
Subject: [PATCH 17/44] Update generated Python Op docs. Change: 123286647

---
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From 3a707b1302ad93050b0fc08789e546587914adfd Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <nobody@tensorflow.org>
Date: Wed, 25 May 2016 21:52:18 -0800
Subject: [PATCH 18/44] Update generated Python Op docs. Change: 123288849

---
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From 05488ebac7731c82b4ee0d076fc27741e6beb0a4 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <nobody@tensorflow.org>
Date: Wed, 25 May 2016 22:35:47 -0800
Subject: [PATCH 19/44] Update generated Python Op docs. Change: 123290850

---
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From 4bf1da61839c7587b3f93a7bb5e305d6368e75bc Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <nobody@tensorflow.org>
Date: Wed, 25 May 2016 23:21:01 -0800
Subject: [PATCH 20/44] Update generated Python Op docs. Change: 123293094

---
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From 37adf1eaae7483410e09ab32d5df5a373e7e719a Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <nobody@tensorflow.org>
Date: Thu, 26 May 2016 00:06:30 -0800
Subject: [PATCH 21/44] Update generated Python Op docs. Change: 123295275

---
 .../functions_and_classes/{shard9 => shard0}/tf.Dimension.md      | 0
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 .../{shard6 => shard1}/tf.truncated_normal.md                     | 0
 .../{shard2 => shard1}/tf.variable_op_scope.md                    | 0
 .../{shard9 => shard2}/tf.FixedLengthRecordReader.md              | 0
 .../python/functions_and_classes/{shard1 => shard2}/tf.Graph.md   | 0
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rename from tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.while_loop.md
rename to tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.while_loop.md

From 9be296aa4ba863d2c8d5158867de472ce75e9bbe Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <nobody@tensorflow.org>
Date: Thu, 26 May 2016 00:51:29 -0800
Subject: [PATCH 22/44] Update generated Python Op docs. Change: 123297766

---
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From c963048b536b8905729ee617dfb78f21a0abd4d7 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <nobody@tensorflow.org>
Date: Thu, 26 May 2016 01:20:46 -0800
Subject: [PATCH 23/44] Minor update to example script to produce the reported
 output. Change: 123299440

---
 tensorflow/g3doc/get_started/basic_usage.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tensorflow/g3doc/get_started/basic_usage.md b/tensorflow/g3doc/get_started/basic_usage.md
index ad95472574f..b4289a986d1 100644
--- a/tensorflow/g3doc/get_started/basic_usage.md
+++ b/tensorflow/g3doc/get_started/basic_usage.md
@@ -274,9 +274,9 @@ example we fetched the single node `state`, but you can also fetch multiple
 tensors:
 
 ```python
-input1 = tf.constant(3.0)
-input2 = tf.constant(2.0)
-input3 = tf.constant(5.0)
+input1 = tf.constant([3.0])
+input2 = tf.constant([2.0])
+input3 = tf.constant([5.0])
 intermed = tf.add(input2, input3)
 mul = tf.mul(input1, intermed)
 

From 017175783ec4eead6c6c98a0e0de1e5031d45845 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <nobody@tensorflow.org>
Date: Thu, 26 May 2016 01:36:58 -0800
Subject: [PATCH 24/44] Update generated Python Op docs. Change: 123300300

---
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From 7eb068ca43b31c9e84e552696cbb1e13c82a533d Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <nobody@tensorflow.org>
Date: Thu, 26 May 2016 02:21:09 -0800
Subject: [PATCH 25/44] Update generated Python Op docs. Change: 123303179

---
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 .../functions_and_classes/{shard4 => shard0}/tf.IdentityReader.md | 0
 .../{shard4 => shard0}/tf.SparseTensorValue.md                    | 0
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From 9819e968a114ad6da13dabf9f608c10c6a25f2dd Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <nobody@tensorflow.org>
Date: Thu, 26 May 2016 03:06:20 -0800
Subject: [PATCH 26/44] Update generated Python Op docs. Change: 123305713

---
 .../{shard4 => shard0}/tf.FixedLenSequenceFeature.md              | 0
 .../python/functions_and_classes/{shard8 => shard0}/tf.Graph.md   | 0
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From 910963836ae5b7976cda50e77e3bebe17edc78ea Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <nobody@tensorflow.org>
Date: Thu, 26 May 2016 03:50:02 -0800
Subject: [PATCH 27/44] Update generated Python Op docs. Change: 123307673

---
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From 2767aa0aef782b7707cf28d032bb3c91b6ccf0e8 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <nobody@tensorflow.org>
Date: Thu, 26 May 2016 04:35:25 -0800
Subject: [PATCH 28/44] Update generated Python Op docs. Change: 123310315

---
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From c38fbcc6a66cc88e9032c84084ac3d8bc1de8c61 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <nobody@tensorflow.org>
Date: Thu, 26 May 2016 05:20:40 -0800
Subject: [PATCH 29/44] Update generated Python Op docs. Change: 123313010

---
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 .../python/functions_and_classes/{shard1 => shard0}/tf.Assert.md  | 0
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rename to tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.train.update_checkpoint_state.md

From 67782fae2bb06d04c537ee430e62b66d689ef009 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <nobody@tensorflow.org>
Date: Thu, 26 May 2016 06:06:18 -0800
Subject: [PATCH 30/44] Update generated Python Op docs. Change: 123315764

---
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From 0bb913604ab8c8465a0ef7c203d699b36e7d157e Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <nobody@tensorflow.org>
Date: Thu, 26 May 2016 06:50:34 -0800
Subject: [PATCH 31/44] Update generated Python Op docs. Change: 123318524

---
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 .../{shard9 => shard0}/tf.contrib.ffmpeg.encode_audio.md          | 0
 .../{shard5 => shard0}/tf.contrib.layers.summarize_activation.md  | 0
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From a6901e988401328be0d7756ccd53c2b7c3d19a34 Mon Sep 17 00:00:00 2001
From: Derek Murray <mrry@google.com>
Date: Thu, 26 May 2016 07:17:29 -0800
Subject: [PATCH 32/44] Improves shape functions for tf.space_to_batch() and
 related ops.

Also fixes tf.batch_to_space(), tf.depth_to_space(), and tf.space_to_depth().

The shape functions now tolerate computed inputs for their paddings argument (whereas before they used the evaluated constant_value, which made them only work with static inputs).

Fixes #2473.
Change: 123320634
---
 .../kernel_tests/batchtospace_op_test.py      |  5 +++
 .../kernel_tests/depthtospace_op_test.py      |  4 +++
 .../kernel_tests/spacetobatch_op_test.py      |  5 +++
 .../kernel_tests/spacetodepth_op_test.py      |  4 +++
 tensorflow/python/ops/array_ops.py            | 35 ++++++++++++-------
 5 files changed, 40 insertions(+), 13 deletions(-)

diff --git a/tensorflow/python/kernel_tests/batchtospace_op_test.py b/tensorflow/python/kernel_tests/batchtospace_op_test.py
index df46c446fc6..2bc1fd5e10c 100644
--- a/tensorflow/python/kernel_tests/batchtospace_op_test.py
+++ b/tensorflow/python/kernel_tests/batchtospace_op_test.py
@@ -88,6 +88,11 @@ class BatchToSpaceErrorHandlingTest(tf.test.TestCase):
     with self.assertRaises(IndexError):
       _ = tf.batch_to_space(x_np, crops, block_size)
 
+  def testUnknownShape(self):
+    t = tf.batch_to_space(tf.placeholder(tf.float32), tf.placeholder(tf.int32),
+                          block_size=4)
+    self.assertEqual(4, t.get_shape().ndims)
+
 
 class BatchToSpaceGradientTest(tf.test.TestCase):
 
diff --git a/tensorflow/python/kernel_tests/depthtospace_op_test.py b/tensorflow/python/kernel_tests/depthtospace_op_test.py
index e9e66e0617d..3bd6fa2c97b 100644
--- a/tensorflow/python/kernel_tests/depthtospace_op_test.py
+++ b/tensorflow/python/kernel_tests/depthtospace_op_test.py
@@ -180,6 +180,10 @@ class DepthToSpaceTest(tf.test.TestCase):
     with self.assertRaises(IndexError):
       _ = tf.space_to_depth(x_np, block_size)
 
+  def testUnknownShape(self):
+    t = tf.depth_to_space(tf.placeholder(tf.float32), block_size=4)
+    self.assertEqual(4, t.get_shape().ndims)
+
 
 class DepthToSpaceGradientTest(tf.test.TestCase):
 
diff --git a/tensorflow/python/kernel_tests/spacetobatch_op_test.py b/tensorflow/python/kernel_tests/spacetobatch_op_test.py
index 2cb6f5e1048..690c9371cc0 100644
--- a/tensorflow/python/kernel_tests/spacetobatch_op_test.py
+++ b/tensorflow/python/kernel_tests/spacetobatch_op_test.py
@@ -195,6 +195,11 @@ class SpaceToBatchErrorHandlingTest(tf.test.TestCase):
     with self.assertRaises(IndexError):
       _ = tf.space_to_batch(x_np, paddings, block_size)
 
+  def testUnknownShape(self):
+    t = tf.space_to_batch(tf.placeholder(tf.float32), tf.placeholder(tf.int32),
+                          block_size=4)
+    self.assertEqual(4, t.get_shape().ndims)
+
 
 class SpaceToBatchGradientTest(tf.test.TestCase):
 
diff --git a/tensorflow/python/kernel_tests/spacetodepth_op_test.py b/tensorflow/python/kernel_tests/spacetodepth_op_test.py
index 88e650840e5..82612f6fbaa 100644
--- a/tensorflow/python/kernel_tests/spacetodepth_op_test.py
+++ b/tensorflow/python/kernel_tests/spacetodepth_op_test.py
@@ -198,6 +198,10 @@ class SpaceToDepthTest(tf.test.TestCase):
     with self.assertRaises(IndexError):
       _ = tf.space_to_depth(x_np, block_size)
 
+  def testUnknownShape(self):
+    t = tf.space_to_depth(tf.placeholder(tf.float32), block_size=4)
+    self.assertEqual(4, t.get_shape().ndims)
+
 
 class SpaceToDepthGradientTest(tf.test.TestCase):
 
diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py
index c19d93829a7..b8aa311951a 100644
--- a/tensorflow/python/ops/array_ops.py
+++ b/tensorflow/python/ops/array_ops.py
@@ -1723,15 +1723,19 @@ def _SpaceToBatchShape(op):
     raise ValueError("Attribute block_size has to be > 1.")
 
   paddings = tensor_util.constant_value(op.inputs[1])
-  if (paddings[0, 0] < 0 or paddings[0, 1] < 0 or
-      paddings[1, 0] < 0 or paddings[1, 1] < 0):
-    raise ValueError("paddings cannot be negative.")
+  if paddings is not None:
+    if (paddings[0, 0] < 0 or paddings[0, 1] < 0 or
+        paddings[1, 0] < 0 or paddings[1, 1] < 0):
+      raise ValueError("paddings cannot be negative.")
 
-  input_height = input_shape[1] + paddings[0, 0] + paddings[0, 1]
-  input_width = input_shape[2] + paddings[1, 0] + paddings[1, 1]
+    input_height = input_shape[1] + paddings[0, 0] + paddings[0, 1]
+    input_width = input_shape[2] + paddings[1, 0] + paddings[1, 1]
 
-  if input_height % block_size > 0 or input_width % block_size > 0:
-    raise IndexError("block_size needs to divide both width and height.")
+    if input_height % block_size > 0 or input_width % block_size > 0:
+      raise IndexError("block_size needs to divide both width and height.")
+  else:
+    input_height = tensor_shape.Dimension(None)
+    input_width = tensor_shape.Dimension(None)
 
   batch = input_shape[0] * block_size * block_size
   height = input_height // block_size
@@ -1792,8 +1796,9 @@ def _BatchToSpaceShape(op):
         "tf.space_to_batch() requires input crops with shape [2, 2].")
 
   crops = tensor_util.constant_value(op.inputs[1])
-  if (crops[0, 0] < 0 or crops[0, 1] < 0 or
-      crops[1, 0] < 0 or crops[1, 1] < 0):
+  if (crops is not None and
+      (crops[0, 0] < 0 or crops[0, 1] < 0 or
+       crops[1, 0] < 0 or crops[1, 1] < 0)):
     raise ValueError("crops cannot be negative.")
 
   block_size = op.get_attr("block_size")
@@ -1805,10 +1810,14 @@ def _BatchToSpaceShape(op):
     raise IndexError("input batch must be divisible by block_size*block_size.")
   batch = input_batch // (block_size * block_size)
 
-  height = input_shape[1] * block_size - crops[0, 0] - crops[0, 1]
-  width = input_shape[2] * block_size - crops[1, 0] - crops[1, 1]
-  if height <= 0 or width <= 0:
-    raise ValueError("Output height or width is not positive.")
+  if crops is not None:
+    height = input_shape[1] * block_size - crops[0, 0] - crops[0, 1]
+    width = input_shape[2] * block_size - crops[1, 0] - crops[1, 1]
+    if height <= 0 or width <= 0:
+      raise ValueError("Output height or width is not positive.")
+  else:
+    height = tensor_shape.Dimension(None)
+    width = tensor_shape.Dimension(None)
   depth = input_shape[3]
 
   return [tensor_shape.TensorShape([batch, height, width, depth])]

From 334618293be485912d1a8b126a4e3c29286cebb1 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <nobody@tensorflow.org>
Date: Thu, 26 May 2016 07:36:33 -0800
Subject: [PATCH 33/44] Update generated Python Op docs. Change: 123322073

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rename from tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.squeeze.md
rename to tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.squeeze.md
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.test.assert_equal_graph_def.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.test.assert_equal_graph_def.md
similarity index 100%
rename from tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.test.assert_equal_graph_def.md
rename to tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.test.assert_equal_graph_def.md
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.train.import_meta_graph.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.train.import_meta_graph.md
similarity index 100%
rename from tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.train.import_meta_graph.md
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diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.train.shuffle_batch.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.train.shuffle_batch.md
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rename from tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.train.shuffle_batch.md
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diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.train.shuffle_batch_join.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.train.shuffle_batch_join.md
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diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.train.start_queue_runners.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.train.start_queue_runners.md
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rename from tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.train.start_queue_runners.md
rename to tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.train.start_queue_runners.md
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.zeros_like.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.zeros_like.md
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From a9112b4a0a189f897dea8cf25c76c3048a95e8ff Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <nobody@tensorflow.org>
Date: Thu, 26 May 2016 07:57:34 -0800
Subject: [PATCH 34/44] Allow the output of the separable_conv2d to have the
 same number of channels as the corresponding input. Change: 123323605

---
 .../python/kernel_tests/conv_ops_test.py      | 46 +++++++++++++++++--
 tensorflow/python/ops/nn.py                   | 13 +++++-
 2 files changed, 54 insertions(+), 5 deletions(-)

diff --git a/tensorflow/python/kernel_tests/conv_ops_test.py b/tensorflow/python/kernel_tests/conv_ops_test.py
index 74e91b826a7..50bb643402d 100644
--- a/tensorflow/python/kernel_tests/conv_ops_test.py
+++ b/tensorflow/python/kernel_tests/conv_ops_test.py
@@ -1059,9 +1059,9 @@ class SeparableConv2DTest(tf.test.TestCase):
 
   def testSeparableConv2D(self):
     # The output is the result of two convolutions:
-    # First with tensor_in[1, 4, 4, 3] * filter1[2, 2, 3, 3].
-    # Second with intermediate_out[4, 4, 3, 3] * filter2[1, 1, 3, 6].
-    # Complexity is O(3*3*2*2 + 3*6*1*1] as opposed to O(3*6*2*2).
+    # First with tensor_in[1, 4, 4, 2] * filter1[2, 2, 2, 3].
+    # Second with intermediate_out[1, 4, 4, 6] * filter2[1, 1, 6, 7].
+    # Complexity is O(2*3*2*2 + 6*7*1*1) as opposed to O(2*7*2*2).
     expected_output = [
         6644.5, 6971.5, 7298.5, 7625.5, 7952.5, 8279.5, 8606.5, 8154.5, 8556.5,
         8958.5, 9360.5, 9762.5, 10164.5, 10566.5, 9664.5, 10141.5, 10618.5,
@@ -1084,6 +1084,46 @@ class SeparableConv2DTest(tf.test.TestCase):
                        stride=1, padding="SAME",
                        expected=expected_output)
 
+  def testSeparableConv2DEqualInputOutputDepth(self):
+    # The output is the result of two convolutions:
+    # First with tensor_in[1, 4, 4, 2] * filter1[2, 2, 3, 3].
+    # Second with intermediate_out[1, 4, 4, 6] * filter2[1, 1, 6, 6].
+    # Complexity is O(2*3*2*2 + 6*6*1*1) as opposed to O(2*6*2*2).
+    expected_output = [
+        5742.0, 6069.0, 6396.0, 6723.0, 7050.0, 7377.0,
+        7047.0, 7449.0, 7851.0, 8253.0, 8655.0, 9057.0,
+        8352.0, 8829.0, 9306.0, 9783.0, 10260.0, 10737.0,
+        3582.0, 3783.0, 3984.0, 4185.0, 4386.0, 4587.0,
+        10962.0, 11589.0, 12216.0, 12843.0, 13470.0, 14097.0,
+        12267.0, 12969.0, 13671.0, 14373.0, 15075.0, 15777.0,
+        13572.0, 14349.0, 15126.0, 15903.0, 16680.0, 17457.0,
+        5616.0, 5931.0, 6246.0, 6561.0, 6876.0, 7191.0,
+        16182.0, 17109.0, 18036.0, 18963.0, 19890.0, 20817.0,
+        17487.0, 18489.0, 19491.0, 20493.0, 21495.0, 22497.0,
+        18792.0, 19869.0, 20946.0, 22023.0, 23100.0, 24177.0,
+        7650.0, 8079.0, 8508.0, 8937.0, 9366.0, 9795.0,
+        4963.5, 5227.5, 5491.5, 5755.5, 6019.5, 6283.5,
+        5328.0, 5611.5, 5895.0, 6178.5, 6462.0, 6745.5,
+        5692.5, 5995.5, 6298.5, 6601.5, 6904.5, 7207.5,
+        1757.25, 1840.5, 1923.75, 2007.0, 2090.25, 2173.5]
+
+    self._VerifyValues(tensor_in_sizes=[1, 4, 4, 2],
+                       depthwise_filter_in_sizes=[2, 2, 2, 3],
+                       pointwise_filter_in_sizes=[1, 1, 6, 6],
+                       stride=1, padding="SAME",
+                       expected=expected_output)
+
+  def testSeparableConv2DIllegalCases(self):
+    # Output depth less then input depth.
+    with self.assertRaisesRegexp(
+        ValueError,
+        "Refusing to perform an overparameterized separable convolution"):
+      self._VerifyValues(tensor_in_sizes=[1, 4, 4, 2],
+                         depthwise_filter_in_sizes=[2, 2, 2, 3],
+                         pointwise_filter_in_sizes=[1, 1, 6, 5],
+                         stride=1, padding="SAME",
+                         expected=None)
+
 
 def GetInceptionFwdTest(input_size, filter_size, stride, padding):
   def Test(self):
diff --git a/tensorflow/python/ops/nn.py b/tensorflow/python/ops/nn.py
index d47b03db5b6..e6b8bb664fd 100644
--- a/tensorflow/python/ops/nn.py
+++ b/tensorflow/python/ops/nn.py
@@ -559,6 +559,10 @@ def separable_conv2d(input, depthwise_filter, pointwise_filter, strides,
 
   Returns:
     A 4-D `Tensor` of shape `[batch, out_height, out_width, out_channels]`.
+
+  Raises:
+    ValueError: If channel_multiplier * in_channels > out_channels,
+      which means that the separable convolution is overparameterized.
   """
   with ops.op_scope([input, depthwise_filter, pointwise_filter],
                    name, "separable_conv2d") as name:
@@ -576,8 +580,13 @@ def separable_conv2d(input, depthwise_filter, pointwise_filter, strides,
         channel_multiplier = depthwise_filter.get_shape()[3]
         in_channels = input.get_shape()[3]
         out_channels = pointwise_filter.get_shape()[3]
-        # This would mean the separable convolutions is over-parametrized.
-        assert channel_multiplier * in_channels < out_channels
+        if channel_multiplier * in_channels > out_channels:
+          raise ValueError(
+              ("Refusing to perform an overparameterized separable "
+               "convolution: channel_multiplier * in_channels = "
+               "%d * %d = %d > %d = out_channels" %
+               (channel_multiplier, in_channels,
+                channel_multiplier * in_channels, out_channels)))
     # The layout of the ops in the graph are expected to be as follows:
     # depthwise_conv2d  // Conv2D op corresponding to native deptwise conv.
     # separable_conv2d  // Conv2D op corresponding to the pointwise conv.

From 037bfaf05ac75ae7d7c83d23bfb231b8eaeebef8 Mon Sep 17 00:00:00 2001
From: Illia Polosukhin <ilblackdragon@gmail.com>
Date: Thu, 26 May 2016 08:04:08 -0800
Subject: [PATCH 35/44] tf.learn: Adding update op to
 tf.contrib.layers.optimize_loss to support batch_norm like layers. Change:
 123324232

---
 .../layers/python/layers/optimizers.py        | 12 ++++++++++++
 .../layers/python/layers/optimizers_test.py   | 19 +++++++++++++++++++
 2 files changed, 31 insertions(+)

diff --git a/tensorflow/contrib/layers/python/layers/optimizers.py b/tensorflow/contrib/layers/python/layers/optimizers.py
index 1485cf958cb..3edd9e70105 100644
--- a/tensorflow/contrib/layers/python/layers/optimizers.py
+++ b/tensorflow/contrib/layers/python/layers/optimizers.py
@@ -53,6 +53,7 @@ def optimize_loss(loss,
                   clip_gradients=None,
                   moving_average_decay=0.9,
                   learning_rate_decay_fn=None,
+                  update_ops=None,
                   variables=None,
                   name=None):
   """Given loss and parameters for optimizer, returns a training op.
@@ -81,6 +82,8 @@ def optimize_loss(loss,
                             Can be used to implement any learning rate decay
                             functions.
                             For example: tf.train.exponential_decay.
+    update_ops: list of update `Operation`s to execute at each step. If `None`,
+                uses elements of UPDATE_OPS collection.
     variables: list of variables to optimize or
                `None` to use all trainable variables.
     name: The name for this operation is used to scope operations and summaries.
@@ -92,6 +95,15 @@ def optimize_loss(loss,
     ValueError: if optimizer is wrong type.
   """
   with vs.variable_op_scope([loss, global_step], name, "OptimizeLoss"):
+    # Update ops take UPDATE_OPS collection if not provided.
+    update_ops = (set(update_ops or []) or
+                  set(ops.get_collection(ops.GraphKeys.UPDATE_OPS)))
+    # Make sure update ops are ran before computing loss.
+    if update_ops:
+      with ops.control_dependencies(update_ops):
+        barrier = control_flow_ops.no_op(name="update_barrier")
+      loss = control_flow_ops.with_dependencies([barrier], loss)
+
     # Moving average of the loss with decay.
     if moving_average_decay is not None:
       # Generate moving averages of the loss.
diff --git a/tensorflow/contrib/layers/python/layers/optimizers_test.py b/tensorflow/contrib/layers/python/layers/optimizers_test.py
index 0f0bfe568b8..49baffb6f95 100644
--- a/tensorflow/contrib/layers/python/layers/optimizers_test.py
+++ b/tensorflow/contrib/layers/python/layers/optimizers_test.py
@@ -132,6 +132,25 @@ class OptimizersTest(tf.test.TestCase):
     tf.contrib.layers.optimize_loss(
         loss, global_step, learning_rate=0.1, optimizer="SGD")
 
+  def testUpdateOp(self):
+    optimizers = ["SGD", tf.train.GradientDescentOptimizer,
+                  tf.train.GradientDescentOptimizer(learning_rate=0.1)]
+    for optimizer in optimizers:
+      with tf.Graph().as_default() as g:
+        with self.test_session(graph=g) as session:
+          x, var, loss, global_step = _setup_model()
+          update_op = tf.assign(var, 20)
+          train = tf.contrib.layers.optimize_loss(loss,
+                                                  global_step,
+                                                  learning_rate=0.1,
+                                                  optimizer=optimizer,
+                                                  update_ops=[update_op])
+          tf.initialize_all_variables().run()
+          session.run(train, feed_dict={x: 5})
+          var_value, global_step_value = session.run([var, global_step])
+          # 19.5, due to update of var to 20 before loss computation.
+          self.assertEqual(var_value, 19.5)
+          self.assertEqual(global_step_value, 1)
 
 if __name__ == "__main__":
   tf.test.main()

From 63013956e3d13088ea1eb5cbda6b8b9a4c2cd2e6 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <nobody@tensorflow.org>
Date: Thu, 26 May 2016 08:21:17 -0800
Subject: [PATCH 36/44] Update generated Python Op docs. Change: 123325658

---
 tensorflow/g3doc/api_docs/python/contrib.layers.md          | 4 +++-
 .../functions_and_classes/{shard1 => shard0}/tf.DType.md    | 0
 .../{shard7 => shard0}/tf.DeviceSpec.md                     | 0
 .../{shard1 => shard0}/tf.accumulate_n.md                   | 0
 .../{shard3 => shard0}/tf.assert_equal.md                   | 0
 .../functions_and_classes/{shard9 => shard0}/tf.ceil.md     | 0
 .../{shard5 => shard0}/tf.clip_by_norm.md                   | 0
 .../tf.contrib.copy_graph.copy_variable_to_graph.md         | 0
 .../{shard1 => shard0}/tf.contrib.layers.convolution2d.md   | 0
 .../{shard5 => shard0}/tf.contrib.layers.l2_regularizer.md  | 0
 .../{shard7 => shard0}/tf.contrib.learn.RunConfig.md        | 0
 .../tf.contrib.learn.extract_dask_labels.md                 | 0
 .../{shard5 => shard0}/tf.contrib.learn.run_feeds.md        | 0
 .../{shard3 => shard0}/tf.contrib.metrics.set_union.md      | 0
 .../tf.contrib.metrics.streaming_mean_squared_error.md      | 0
 .../tf.contrib.metrics.streaming_recall_at_k.md             | 0
 .../tf.contrib.metrics.streaming_sparse_recall_at_k.md      | 0
 .../{shard5 => shard0}/tf.decode_raw.md                     | 0
 .../{shard9 => shard0}/tf.delete_session_tensor.md          | 0
 .../{shard9 => shard0}/tf.expand_dims.md                    | 0
 .../functions_and_classes/{shard7 => shard0}/tf.floor.md    | 0
 .../functions_and_classes/{shard5 => shard0}/tf.floordiv.md | 0
 .../{shard3 => shard0}/tf.gather_nd.md                      | 0
 .../{shard7 => shard0}/tf.get_variable.md                   | 0
 .../{shard1 => shard0}/tf.global_norm.md                    | 0
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diff --git a/tensorflow/g3doc/api_docs/python/contrib.layers.md b/tensorflow/g3doc/api_docs/python/contrib.layers.md
index cb03110c9b0..8ba74c6faa1 100644
--- a/tensorflow/g3doc/api_docs/python/contrib.layers.md
+++ b/tensorflow/g3doc/api_docs/python/contrib.layers.md
@@ -339,7 +339,7 @@ Optimize weights given a loss.
 
 - - -
 
-### `tf.contrib.layers.optimize_loss(loss, global_step, learning_rate, optimizer, gradient_noise_scale=None, gradient_multipliers=None, clip_gradients=None, moving_average_decay=0.9, learning_rate_decay_fn=None, variables=None, name=None)` {#optimize_loss}
+### `tf.contrib.layers.optimize_loss(loss, global_step, learning_rate, optimizer, gradient_noise_scale=None, gradient_multipliers=None, clip_gradients=None, moving_average_decay=0.9, learning_rate_decay_fn=None, update_ops=None, variables=None, name=None)` {#optimize_loss}
 
 Given loss and parameters for optimizer, returns a training op.
 
@@ -369,6 +369,8 @@ Given loss and parameters for optimizer, returns a training op.
                           Can be used to implement any learning rate decay
                           functions.
                           For example: tf.train.exponential_decay.
+*  <b>`update_ops`</b>: list of update `Operation`s to execute at each step. If `None`,
+              uses elements of UPDATE_OPS collection.
 *  <b>`variables`</b>: list of variables to optimize or
              `None` to use all trainable variables.
 *  <b>`name`</b>: The name for this operation is used to scope operations and summaries.
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diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.layers.optimize_loss.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.layers.optimize_loss.md
similarity index 90%
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index db0b01186a2..8e6072532be 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.layers.optimize_loss.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.layers.optimize_loss.md
@@ -1,4 +1,4 @@
-### `tf.contrib.layers.optimize_loss(loss, global_step, learning_rate, optimizer, gradient_noise_scale=None, gradient_multipliers=None, clip_gradients=None, moving_average_decay=0.9, learning_rate_decay_fn=None, variables=None, name=None)` {#optimize_loss}
+### `tf.contrib.layers.optimize_loss(loss, global_step, learning_rate, optimizer, gradient_noise_scale=None, gradient_multipliers=None, clip_gradients=None, moving_average_decay=0.9, learning_rate_decay_fn=None, update_ops=None, variables=None, name=None)` {#optimize_loss}
 
 Given loss and parameters for optimizer, returns a training op.
 
@@ -28,6 +28,8 @@ Given loss and parameters for optimizer, returns a training op.
                           Can be used to implement any learning rate decay
                           functions.
                           For example: tf.train.exponential_decay.
+*  <b>`update_ops`</b>: list of update `Operation`s to execute at each step. If `None`,
+              uses elements of UPDATE_OPS collection.
 *  <b>`variables`</b>: list of variables to optimize or
              `None` to use all trainable variables.
 *  <b>`name`</b>: The name for this operation is used to scope operations and summaries.
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diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.nn.separable_conv2d.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.separable_conv2d.md
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index f4be03303fc..a88c2112efe 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.nn.separable_conv2d.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.separable_conv2d.md
@@ -38,3 +38,9 @@ horizontal and vertical strides, `strides = [1, stride, stride, 1]`.
 
   A 4-D `Tensor` of shape `[batch, out_height, out_width, out_channels]`.
 
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If channel_multiplier * in_channels > out_channels,
+    which means that the separable convolution is overparameterized.
+
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diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.reverse_sequence.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.reverse_sequence.md
similarity index 100%
rename from tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.reverse_sequence.md
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diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.select.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.select.md
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rename from tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.select.md
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diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.sparse_mask.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.sparse_mask.md
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rename from tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.sparse_mask.md
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diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.sparse_placeholder.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.sparse_placeholder.md
similarity index 100%
rename from tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.sparse_placeholder.md
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diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.train.AdadeltaOptimizer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.train.AdadeltaOptimizer.md
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diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.train.ExponentialMovingAverage.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.train.ExponentialMovingAverage.md
similarity index 100%
rename from tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.train.ExponentialMovingAverage.md
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diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.train.LooperThread.loop.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.train.LooperThread.loop.md
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diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.train.Supervisor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.train.Supervisor.md
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diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.transpose.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.transpose.md
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diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.truncated_normal.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.truncated_normal.md
similarity index 100%
rename from tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.truncated_normal.md
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diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.zeta.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.zeta.md
similarity index 100%
rename from tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.zeta.md
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diff --git a/tensorflow/g3doc/api_docs/python/nn.md b/tensorflow/g3doc/api_docs/python/nn.md
index 8254810db8a..e92d5b94108 100644
--- a/tensorflow/g3doc/api_docs/python/nn.md
+++ b/tensorflow/g3doc/api_docs/python/nn.md
@@ -418,6 +418,12 @@ horizontal and vertical strides, `strides = [1, stride, stride, 1]`.
 
   A 4-D `Tensor` of shape `[batch, out_height, out_width, out_channels]`.
 
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If channel_multiplier * in_channels > out_channels,
+    which means that the separable convolution is overparameterized.
+
 
 - - -
 

From c1b5075d1f3f28ff8c5ed20334dc92ac7c9751f6 Mon Sep 17 00:00:00 2001
From: Derek Murray <mrry@google.com>
Date: Thu, 26 May 2016 08:33:03 -0800
Subject: [PATCH 37/44] Fix broken Python 3 test:
 //tensorflow/contrib/learn:graph_io_test Change: 123326664

---
 tensorflow/contrib/learn/python/learn/io/graph_io_test.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tensorflow/contrib/learn/python/learn/io/graph_io_test.py b/tensorflow/contrib/learn/python/learn/io/graph_io_test.py
index a3d70164ca5..eaf62f003af 100644
--- a/tensorflow/contrib/learn/python/learn/io/graph_io_test.py
+++ b/tensorflow/contrib/learn/python/learn/io/graph_io_test.py
@@ -210,9 +210,9 @@ class GraphIOTest(tf.test.TestCase):
       coord = tf.train.Coordinator()
       tf.train.start_queue_runners(session, coord=coord)
 
-      self.assertEqual(session.run(inputs), "ABC")
-      self.assertEqual(session.run(inputs), "DEF")
-      self.assertEqual(session.run(inputs), "GHK")
+      self.assertAllEqual(session.run(inputs), [b"ABC"])
+      self.assertAllEqual(session.run(inputs), [b"DEF"])
+      self.assertAllEqual(session.run(inputs), [b"GHK"])
       with self.assertRaises(errors.OutOfRangeError):
         session.run(inputs)
 

From cafe948be40c7883ce116e2516c4c67a2045558b Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <nobody@tensorflow.org>
Date: Thu, 26 May 2016 08:54:00 -0800
Subject: [PATCH 38/44] Add gradients for batch_cholesky. Change: 123328520

---
 tensorflow/core/kernels/cholesky_grad.cc      | 119 +++++++++---------
 tensorflow/core/ops/linalg_ops.cc             |  31 ++++-
 .../python/kernel_tests/cholesky_op_test.py   |  57 ++++++---
 tensorflow/python/ops/linalg_grad.py          |  11 +-
 tensorflow/python/ops/linalg_ops.py           |   6 +-
 5 files changed, 136 insertions(+), 88 deletions(-)

diff --git a/tensorflow/core/kernels/cholesky_grad.cc b/tensorflow/core/kernels/cholesky_grad.cc
index 4fefcee55e4..7a1c44da426 100644
--- a/tensorflow/core/kernels/cholesky_grad.cc
+++ b/tensorflow/core/kernels/cholesky_grad.cc
@@ -13,75 +13,68 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#include "tensorflow/core/framework/op.h"
 #include "third_party/eigen3/Eigen/Core"
-
+#include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/framework/op_kernel.h"
-
-#include "tensorflow/core/kernels/linalg_ops_common.h"
-#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
 #include "tensorflow/core/framework/tensor_types.h"
 #include "tensorflow/core/framework/types.h"
+#include "tensorflow/core/kernels/binary_linalg_ops_common.h"
 
 namespace tensorflow {
 
-template <typename T>
-class CholeskyGrad : public OpKernel {
+template <typename Scalar, bool SupportsBatchOperationT>
+class CholeskyGrad
+    : public BinaryLinearAlgebraOp<Scalar, SupportsBatchOperationT> {
  public:
-  explicit CholeskyGrad(OpKernelConstruction* context) : OpKernel(context) {}
+  explicit CholeskyGrad(OpKernelConstruction* context)
+      : BinaryLinearAlgebraOp<Scalar, SupportsBatchOperationT>(context) {}
+  ~CholeskyGrad() override {}
+
   using Matrix =
-      Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>;
+      Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>;
   using ConstMatrixMap = Eigen::Map<const Matrix>;
   using MatrixMap = Eigen::Map<Matrix>;
   using ConstRef = Eigen::Ref<const Matrix>;
   using Ref = Eigen::Ref<Matrix>;
 
-  void Compute(OpKernelContext* context) override {
-    const Tensor& input_tensor_l = context->input(0);
-    const Tensor& input_tensor_grad = context->input(1);
-    // Check that input tensors represent a matrix.
-    OP_REQUIRES(context, TensorShapeUtils::IsMatrix(input_tensor_l.shape()),
-                errors::InvalidArgument("In[0] is not a matrix"));
-    OP_REQUIRES(context, TensorShapeUtils::IsMatrix(input_tensor_grad.shape()),
-                errors::InvalidArgument("In[1] is not a matrix"));
-    // Check that input tensors are square.
-    OP_REQUIRES(context,
-                input_tensor_l.dim_size(0) == input_tensor_l.dim_size(1),
-                errors::InvalidArgument("Input matrix must be square."));
-    OP_REQUIRES(context,
-                input_tensor_grad.dim_size(0) == input_tensor_grad.dim_size(1),
-                errors::InvalidArgument("Input matrix must be square."));
+  TensorShape GetOutputMatrixShape(
+      const TensorShape& input_matrix_l_full_shape,
+      const TensorShape& input_matrix_grad_shape) override {
+    return input_matrix_l_full_shape;
+  }
 
-    // Check that input tensors are of same size.
-    OP_REQUIRES(context,
-                input_tensor_l.dim_size(0) == input_tensor_grad.dim_size(0),
-                errors::InvalidArgument("Input matrices must be same size."));
-
-    // Create an output tensor
-    Tensor* output_tensor = NULL;
-    OP_REQUIRES_OK(context, context->allocate_output(
-                                0, input_tensor_grad.shape(), &output_tensor));
-
-    if (output_tensor->NumElements() == 0) {
-      // the output shape is a 0-element matrix, so there is nothing to do.
-      return;
+  int64 GetCostPerUnit(const TensorShape& input_matrix_shape,
+                       const TensorShape& rhs_matrix_shape) override {
+    const int64 rows = input_matrix_shape.dim_size(0);
+    if (rows > (1LL << 20)) {
+      // A big number to cap the cost in case overflow.
+      return kint64max;
+    } else {
+      return rows * rows * rows;
     }
-    // The next lines are necessary to get Eigen matrix behaviour.
-    const ConstMatrixMap input_matrix_l_full(input_tensor_l.flat<T>().data(),
-                                             input_tensor_l.dim_size(0),
-                                             input_tensor_l.dim_size(1));
-    const ConstMatrixMap input_matrix_grad(input_tensor_grad.flat<T>().data(),
-                                           input_tensor_grad.dim_size(0),
-                                           input_tensor_grad.dim_size(1));
-    MatrixMap output_matrix(output_tensor->template flat<T>().data(),
-                            input_tensor_l.dim_size(0),
-                            input_tensor_l.dim_size(1));
+  }
 
-    // Algorithm only depends on lower triangular half on input_tensor_l.
+  void ComputeMatrix(OpKernelContext* context,
+                     const ConstMatrixMap& input_matrix_l_full,
+                     const ConstMatrixMap& input_matrix_grad,
+                     MatrixMap* output_matrix) override {
+    OP_REQUIRES(context,
+                input_matrix_l_full.rows() == input_matrix_l_full.cols(),
+                errors::InvalidArgument("Input matrix must be square."));
+    OP_REQUIRES(
+        context, input_matrix_l_full.cols() == input_matrix_grad.cols(),
+        errors::InvalidArgument(
+            "Input matrix and gradient must have same number of cols."));
+    OP_REQUIRES(
+        context, input_matrix_l_full.rows() == input_matrix_grad.rows(),
+        errors::InvalidArgument(
+            "Input matrix and gradient must have same number of rows."));
+
+    // Algorithm only depends on lower triangular half on input_matrix_l.
     const Matrix input_matrix_l =
         input_matrix_l_full.template triangularView<Eigen::Lower>();
     // Algorithm only depends on lower triangular half on input_matrix_grad.
-    output_matrix = input_matrix_grad.template triangularView<Eigen::Lower>();
+    *output_matrix = input_matrix_grad.template triangularView<Eigen::Lower>();
 
     const int64 kMatrixSize = input_matrix_l.rows();
     const int64 kMaxBlockSize = 32;
@@ -104,20 +97,21 @@ class CholeskyGrad : public OpKernel {
 
       auto B = input_matrix_l.block(block_end, 0, trailing_size, block_begin);
       auto B_bar =
-          output_matrix.block(block_end, 0, trailing_size, block_begin);
+          output_matrix->block(block_end, 0, trailing_size, block_begin);
 
       auto C = input_matrix_l.block(block_end, block_begin, trailing_size,
                                     block_size);
-      auto C_bar = output_matrix.block(block_end, block_begin, trailing_size,
-                                       block_size);
+      auto C_bar = output_matrix->block(block_end, block_begin, trailing_size,
+                                        block_size);
 
       auto D = input_matrix_l.block(block_begin, block_begin, block_size,
                                     block_size);
-      auto D_bar =
-          output_matrix.block(block_begin, block_begin, block_size, block_size);
+      auto D_bar = output_matrix->block(block_begin, block_begin, block_size,
+                                        block_size);
 
       auto R = input_matrix_l.block(block_begin, 0, block_size, block_begin);
-      auto R_bar = output_matrix.block(block_begin, 0, block_size, block_begin);
+      auto R_bar =
+          output_matrix->block(block_begin, 0, block_size, block_begin);
 
       C_bar = D.adjoint().template triangularView<Eigen::Upper>()
           .solve(C_bar.adjoint()).adjoint();
@@ -127,9 +121,11 @@ class CholeskyGrad : public OpKernel {
       CholeskyGradUnblocked(D, D_bar);
       R_bar -= (D_bar + D_bar.adjoint()) * R;
     }
-    output_matrix = (0.5 * (output_matrix + output_matrix.transpose())).eval();
+    *output_matrix =
+        (0.5 * (*output_matrix + output_matrix->transpose())).eval();
   }
-  void CholeskyGradUnblocked(const ConstRef l_block, Ref grad_block) {
+
+  void CholeskyGradUnblocked(const ConstRef& l_block, Ref grad_block) {
     const int64 kMatrixSize = l_block.rows();
     for (int64 k = kMatrixSize - 1; k >= 0; k--) {
       /* This shows the block structure.
@@ -166,6 +162,11 @@ class CholeskyGrad : public OpKernel {
   }
 };
 
-REGISTER_LINALG_OP("CholeskyGrad", (CholeskyGrad<float>), float);
-REGISTER_LINALG_OP("CholeskyGrad", (CholeskyGrad<double>), double);
+REGISTER_BINARY_LINALG_OP("CholeskyGrad", (CholeskyGrad<float, false>), float);
+REGISTER_BINARY_LINALG_OP("CholeskyGrad", (CholeskyGrad<double, false>),
+                          double);
+REGISTER_BINARY_LINALG_OP("BatchCholeskyGrad", (CholeskyGrad<float, true>),
+                          float);
+REGISTER_BINARY_LINALG_OP("BatchCholeskyGrad", (CholeskyGrad<double, true>),
+                          double);
 }  // namespace tensorflow
diff --git a/tensorflow/core/ops/linalg_ops.cc b/tensorflow/core/ops/linalg_ops.cc
index edd60df8ef9..be87022c0a8 100644
--- a/tensorflow/core/ops/linalg_ops.cc
+++ b/tensorflow/core/ops/linalg_ops.cc
@@ -129,11 +129,34 @@ REGISTER_OP("CholeskyGrad")
     .Doc(R"doc(
 Calculates the reverse mode backpropagated gradient of the Cholesky algorithm.
 
-For an explanation see "Differentiation of the Cholesky algorithm" by Iain Murray http://arxiv.org/abs/1602.07527.
+For an explanation see "Differentiation of the Cholesky algorithm" by
+Iain Murray http://arxiv.org/abs/1602.07527.
 
-l: Output of Cholesky algorithm l = chol(A). Shape is `[M, M]`. Algorithm depends only on lower triangular part of this matrix.
-grad: df/dl where f is some scalar function. Shape is `[M, M]'. Algorithm depends only on lower triangular part of this matrix.
-output: Symmetrized version of df/dA . Shape is `[M, M]'
+l: Output of Cholesky algorithm l = chol(A). Shape is `[M, M]`.
+  Algorithm depends only on lower triangular part of this matrix.
+grad: df/dl where f is some scalar function. Shape is `[M, M]'.
+  Algorithm depends only on lower triangular part of this matrix.
+output: Symmetrized version of df/dA . Shape is `[M, M]'.
+)doc");
+
+REGISTER_OP("BatchCholeskyGrad")
+    .Input("l: T")
+    .Input("grad: T")
+    .Output("output: T")
+    .Attr("T: {float, double}")
+    .Doc(R"doc(
+Calculates the reverse mode backpropagated gradient of the Cholesky algorithm.
+
+For an explanation see "Differentiation of the Cholesky algorithm" by
+Iain Murray http://arxiv.org/abs/1602.07527.
+
+l: Output of batch Cholesky algorithm l = batch_cholesky(A). Shape is `[..., M, M]`.
+  Algorithm depends only on lower triangular part of the innermost matrices of
+  this tensor.
+grad: df/dl where f is some scalar function. Shape is `[..., M, M]'.
+  Algorithm depends only on lower triangular part of the innermost matrices of
+  this tensor.
+output: Symmetrized version of df/dA . Shape is `[..., M, M]'
 )doc");
 
 REGISTER_OP("SelfAdjointEig")
diff --git a/tensorflow/python/kernel_tests/cholesky_op_test.py b/tensorflow/python/kernel_tests/cholesky_op_test.py
index 199b54512e0..0189280e248 100644
--- a/tensorflow/python/kernel_tests/cholesky_op_test.py
+++ b/tensorflow/python/kernel_tests/cholesky_op_test.py
@@ -71,18 +71,23 @@ class CholeskyOpTest(tf.test.TestCase):
   def testNonSquareMatrix(self):
     with self.assertRaises(ValueError):
       tf.cholesky(np.array([[1., 2., 3.], [3., 4., 5.]]))
+    with self.assertRaises(ValueError):
+      tf.batch_cholesky(np.array([[[1., 2., 3.], [3., 4., 5.]],
+                                  [[1., 2., 3.], [3., 4., 5.]]]))
 
   def testWrongDimensions(self):
     tensor3 = tf.constant([1., 2.])
     with self.assertRaises(ValueError):
       tf.cholesky(tensor3)
+    with self.assertRaises(ValueError):
+      tf.batch_cholesky(tensor3)
 
   def testNotInvertible(self):
-     # The input should be invertible.
+    # The input should be invertible.
     with self.test_session():
       with self.assertRaisesOpError("LLT decomposition was not successful. The"
                                     " input might not be valid."):
-         # All rows of the matrix below add to zero
+        # All rows of the matrix below add to zero
         self._verifyCholesky(np.array([[1., -1., 0.], [-1., 1., -1.], [0., -1.,
                                                                        1.]]))
 
@@ -122,24 +127,36 @@ class CholeskyGradTest(tf.test.TestCase):
                            scalarTest=False):
     with self.test_session(use_gpu=False):
       for shape in shapes:
-        for dtype in dtypes:
-          if not(scalarTest):
-            x = tf.constant(np.random.randn(shape[0], shape[1]), dtype)
-            K = tf.matmul(x, tf.transpose(x)) / shape[0]  # K is posdef
-            y = tf.cholesky(K)
-          else:  # This is designed to be a faster test for larger matrices.
-            x = tf.constant(np.random.randn(), dtype)
-            R = tf.constant(np.random.randn(shape[0], shape[1]), dtype)
-            e = tf.mul(R, x)
-            K = tf.matmul(e, tf.transpose(e)) / shape[0]  # K is posdef
-            y = tf.reduce_mean(tf.cholesky(K))
-          error = tf.test.compute_gradient_error(x, x._shape_as_list(),
-                                                 y, y._shape_as_list())
-          tf.logging.info("error = %f", error)
-          if dtype == tf.float64:
-            self.assertLess(error, 1e-5)
-          else:
-            self.assertLess(error, 2e-3)
+        for batch in False, True:
+          for dtype in dtypes:
+            if not scalarTest:
+              x = tf.constant(np.random.randn(shape[0], shape[1]), dtype)
+              tensor = tf.matmul(x, tf.transpose(x)) / shape[0]
+            else:
+              # This is designed to be a faster test for larger matrices.
+              x = tf.constant(np.random.randn(), dtype)
+              R = tf.constant(np.random.randn(shape[0], shape[1]), dtype)
+              e = tf.mul(R, x)
+              tensor = tf.matmul(e, tf.transpose(e)) / shape[0]
+
+            # Inner-most matrices in tensor are positive definite.
+            if batch:
+              tensor = tf.tile(tf.expand_dims(tensor, 0), [4, 1, 1])
+              op = tf.batch_cholesky
+            else:
+              op = tf.cholesky
+
+            if not (scalarTest):
+              y = op(tensor)
+            else:
+              y = tf.reduce_mean(op(tensor))
+            error = tf.test.compute_gradient_error(x, x._shape_as_list(), y,
+                                                   y._shape_as_list())
+            tf.logging.info("error = %f", error)
+            if dtype == tf.float64:
+              self.assertLess(error, 1e-5)
+            else:
+              self.assertLess(error, 3e-3)
 
 if __name__ == "__main__":
   tf.test.main()
diff --git a/tensorflow/python/ops/linalg_grad.py b/tensorflow/python/ops/linalg_grad.py
index 92911009eb7..36c82278584 100644
--- a/tensorflow/python/ops/linalg_grad.py
+++ b/tensorflow/python/ops/linalg_grad.py
@@ -32,6 +32,9 @@ from tensorflow.python.ops import constant_op
 from tensorflow.python.ops import linalg_ops
 from tensorflow.python.ops import math_ops
 
+ops.NoGradient("CholeskyGrad")
+ops.NoGradient("BatchCholeskyGrad")
+
 
 @ops.RegisterGradient("MatrixInverse")
 def _MatrixInverseGrad(op, grad):
@@ -76,11 +79,17 @@ def _BatchMatrixDeterminantGrad(op, grad):
 
 
 @ops.RegisterGradient("Cholesky")
-def _cholesky_grad(op, grad):
+def _CholeskyGrad(op, grad):
   """Gradient for Cholesky."""
   return linalg_ops.cholesky_grad(op.outputs[0], grad)
 
 
+@ops.RegisterGradient("BatchCholesky")
+def _BatchCholeskyGrad(op, grad):
+  """Gradient for BatchCholesky."""
+  return linalg_ops.batch_cholesky_grad(op.outputs[0], grad)
+
+
 @ops.RegisterGradient("MatrixSolve")
 def _MatrixSolveGrad(op, grad):
   """Gradients for MatrixSolve."""
diff --git a/tensorflow/python/ops/linalg_ops.py b/tensorflow/python/ops/linalg_ops.py
index 983851e09e4..66a64a998ba 100644
--- a/tensorflow/python/ops/linalg_ops.py
+++ b/tensorflow/python/ops/linalg_ops.py
@@ -28,6 +28,7 @@ from tensorflow.python.ops.gen_linalg_ops import *
 
 
 @ops.RegisterShape("Cholesky")
+@ops.RegisterShape("CholeskyGrad")
 @ops.RegisterShape("MatrixInverse")
 def _UnchangedSquare(op):
   input_shape = op.inputs[0].get_shape().with_rank(2)
@@ -37,6 +38,7 @@ def _UnchangedSquare(op):
 
 
 @ops.RegisterShape("BatchCholesky")
+@ops.RegisterShape("BatchCholeskyGrad")
 @ops.RegisterShape("BatchMatrixInverse")
 def _BatchUnchangedSquare(op):
   input_shape = op.inputs[0].get_shape().with_rank_at_least(2)
@@ -44,10 +46,6 @@ def _BatchUnchangedSquare(op):
   input_shape[-1].assert_is_compatible_with(input_shape[-2])
   return [input_shape]
 
-@ops.RegisterShape("CholeskyGrad")
-def _cholesky_grad_shape(op):
-  return [op.inputs[0].get_shape()]
-
 @ops.RegisterShape("MatrixDeterminant")
 def _MatrixDeterminantShape(op):
   input_shape = op.inputs[0].get_shape().with_rank(2)

From a2009115dfa777243f277d8abb702aec42c4178a Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <nobody@tensorflow.org>
Date: Thu, 26 May 2016 09:03:01 -0800
Subject: [PATCH 39/44] Update ops-related pbtxt files. Change: 123329408

---
 .../core/ops/compat/ops_history.v0.pbtxt      | 25 ++++++++++++
 tensorflow/core/ops/ops.pbtxt                 | 38 +++++++++++++++++--
 2 files changed, 59 insertions(+), 4 deletions(-)

diff --git a/tensorflow/core/ops/compat/ops_history.v0.pbtxt b/tensorflow/core/ops/compat/ops_history.v0.pbtxt
index 1ab52b252aa..0a15d861c2c 100644
--- a/tensorflow/core/ops/compat/ops_history.v0.pbtxt
+++ b/tensorflow/core/ops/compat/ops_history.v0.pbtxt
@@ -3175,6 +3175,31 @@ op {
     }
   }
 }
+op {
+  name: "BatchCholeskyGrad"
+  input_arg {
+    name: "l"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "grad"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "output"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+      }
+    }
+  }
+}
 op {
   name: "BatchFFT"
   input_arg {
diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index 57e4e716038..2c0c6f30d3c 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -1397,6 +1397,36 @@ op {
   summary: "Calculates the Cholesky decomposition of a batch of square matrices."
   description: "The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions\nform square matrices, with the same constraints as the single matrix Cholesky\ndecomposition above. The output is a tensor of the same shape as the input\ncontaining the Cholesky decompositions for all input submatrices `[..., :, :]`."
 }
+op {
+  name: "BatchCholeskyGrad"
+  input_arg {
+    name: "l"
+    description: "Output of batch Cholesky algorithm l = batch_cholesky(A). Shape is `[..., M, M]`.\nAlgorithm depends only on lower triangular part of the innermost matrices of\nthis tensor."
+    type_attr: "T"
+  }
+  input_arg {
+    name: "grad"
+    description: "df/dl where f is some scalar function. Shape is `[..., M, M]\'.\nAlgorithm depends only on lower triangular part of the innermost matrices of\nthis tensor."
+    type_attr: "T"
+  }
+  output_arg {
+    name: "output"
+    description: "Symmetrized version of df/dA . Shape is `[..., M, M]\'"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+      }
+    }
+  }
+  summary: "Calculates the reverse mode backpropagated gradient of the Cholesky algorithm."
+  description: "For an explanation see \"Differentiation of the Cholesky algorithm\" by\nIain Murray http://arxiv.org/abs/1602.07527."
+}
 op {
   name: "BatchFFT"
   input_arg {
@@ -2482,17 +2512,17 @@ op {
   name: "CholeskyGrad"
   input_arg {
     name: "l"
-    description: "Output of Cholesky algorithm l = chol(A). Shape is `[M, M]`. Algorithm depends only on lower triangular part of this matrix."
+    description: "Output of Cholesky algorithm l = chol(A). Shape is `[M, M]`.\nAlgorithm depends only on lower triangular part of this matrix."
     type_attr: "T"
   }
   input_arg {
     name: "grad"
-    description: "df/dl where f is some scalar function. Shape is `[M, M]\'. Algorithm depends only on lower triangular part of this matrix."
+    description: "df/dl where f is some scalar function. Shape is `[M, M]\'.\nAlgorithm depends only on lower triangular part of this matrix."
     type_attr: "T"
   }
   output_arg {
     name: "output"
-    description: "Symmetrized version of df/dA . Shape is `[M, M]\'"
+    description: "Symmetrized version of df/dA . Shape is `[M, M]\'."
     type_attr: "T"
   }
   attr {
@@ -2506,7 +2536,7 @@ op {
     }
   }
   summary: "Calculates the reverse mode backpropagated gradient of the Cholesky algorithm."
-  description: "For an explanation see \"Differentiation of the Cholesky algorithm\" by Iain Murray http://arxiv.org/abs/1602.07527."
+  description: "For an explanation see \"Differentiation of the Cholesky algorithm\" by\nIain Murray http://arxiv.org/abs/1602.07527."
 }
 op {
   name: "Complex"

From a5b8693369e7e6be16f4cd769b62a6d6330fdefa Mon Sep 17 00:00:00 2001
From: Benoit Steiner <benoit.steiner.goog@gmail.com>
Date: Thu, 26 May 2016 09:06:34 -0800
Subject: [PATCH 40/44] Made the pooling code fp16 friendly Change: 123329817

---
 tensorflow/core/kernels/pooling_ops_common.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/core/kernels/pooling_ops_common.h b/tensorflow/core/kernels/pooling_ops_common.h
index 4bdeb6bf9c5..f38e8e751c5 100644
--- a/tensorflow/core/kernels/pooling_ops_common.h
+++ b/tensorflow/core/kernels/pooling_ops_common.h
@@ -305,7 +305,7 @@ void SpatialAvgPool(OpKernelContext* context, Tensor* output,
             const int out_offset =
                 (b * params.out_height + ph) * params.out_width + pw;
             out_mat.col(out_offset) += in_mat.col(in_offset);
-            out_count(out_offset)++;
+            out_count(out_offset) += T(1);
           }
         }
       }

From afa0112782edafca2f1df27fe1f51e9bb91eb4f2 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <nobody@tensorflow.org>
Date: Thu, 26 May 2016 09:07:01 -0800
Subject: [PATCH 41/44] Update generated Python Op docs. Change: 123329865

---
 .../python/functions_and_classes/{shard7 => shard0}/tf.OpError.md | 0
 .../functions_and_classes/{shard3 => shard0}/tf.ReaderBase.md     | 0
 .../functions_and_classes/{shard1 => shard0}/tf.TFRecordReader.md | 0
 .../functions_and_classes/{shard9 => shard0}/tf.Variable.md       | 0
 .../python/functions_and_classes/{shard5 => shard0}/tf.argmin.md  | 0
 .../{shard1 => shard0}/tf.assert_less_equal.md                    | 0
 .../functions_and_classes/{shard7 => shard0}/tf.assert_rank.md    | 0
 .../functions_and_classes/{shard7 => shard0}/tf.batch_fft.md      | 0
 .../{shard9 => shard0}/tf.batch_matrix_band_part.md               | 0
 .../functions_and_classes/{shard1 => shard0}/tf.complex_abs.md    | 0
 .../{shard7 => shard0}/tf.contrib.copy_graph.copy_op_to_graph.md  | 0
 .../{shard9 => shard0}/tf.contrib.distributions.Exponential.md    | 0
 .../tf.contrib.distributions.MultivariateNormal.md                | 0
 ...ntrib.distributions.normal_conjugates_known_sigma_posterior.md | 0
 .../{shard7 => shard0}/tf.contrib.layers.xavier_initializer.md    | 0
 .../tf.contrib.learn.TensorFlowLinearRegressor.md                 | 0
 .../{shard3 => shard0}/tf.contrib.learn.extract_dask_data.md      | 0
 .../tf.contrib.learn.read_batch_record_features.md                | 0
 .../{shard1 => shard0}/tf.contrib.learn.train.md                  | 0
 .../{shard7 => shard0}/tf.contrib.metrics.set_difference.md       | 0
 .../tf.contrib.metrics.streaming_mean_absolute_error.md           | 0
 .../tf.contrib.metrics.streaming_mean_cosine_distance.md          | 0
 .../functions_and_classes/{shard5 => shard0}/tf.diag_part.md      | 0
 .../python/functions_and_classes/{shard7 => shard0}/tf.erf.md     | 0
 .../functions_and_classes/{shard3 => shard0}/tf.greater_equal.md  | 0
 .../python/functions_and_classes/{shard5 => shard0}/tf.group.md   | 0
 .../{shard1 => shard0}/tf.image.adjust_contrast.md                | 0
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 .../{shard9 => shard0}/tf.import_graph_def.md                     | 0
 .../{shard3 => shard0}/tf.initialize_local_variables.md           | 0
 .../{shard7 => shard0}/tf.initialize_variables.md                 | 0
 .../python/functions_and_classes/{shard7 => shard0}/tf.inv.md     | 0
 .../{shard9 => shard0}/tf.local_variables.md                      | 0
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 .../{shard9 => shard0}/tf.nn.sufficient_statistics.md             | 0
 .../{shard9 => shard0}/tf.ones_initializer.md                     | 0
 .../functions_and_classes/{shard1 => shard0}/tf.placeholder.md    | 0
 .../functions_and_classes/{shard3 => shard0}/tf.polygamma.md      | 0
 .../python/functions_and_classes/{shard9 => shard0}/tf.range.md   | 0
 .../{shard1 => shard0}/tf.register_tensor_conversion_function.md  | 0
 .../{shard9 => shard0}/tf.reverse_sequence.md                     | 0
 .../functions_and_classes/{shard3 => shard0}/tf.segment_sum.md    | 0
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 .../python/functions_and_classes/{shard1 => shard0}/tf.sqrt.md    | 0
 .../functions_and_classes/{shard1 => shard0}/tf.stop_gradient.md  | 0
 .../{shard1 => shard0}/tf.test.compute_gradient_error.md          | 0
 .../functions_and_classes/{shard3 => shard0}/tf.to_int32.md       | 0
 .../python/functions_and_classes/{shard3 => shard0}/tf.trace.md   | 0
 .../{shard3 => shard0}/tf.train.Optimizer.md                      | 0
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 .../functions_and_classes/{shard0 => shard3}/tf.variable_scope.md | 0
 .../functions_and_classes/{shard9 => shard4}/tf.FIFOQueue.md      | 0
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From e8948a2d9d4e8d69d6fd925ca3824fca26e14fee Mon Sep 17 00:00:00 2001
From: Vijay Vasudevan <vrv@google.com>
Date: Thu, 26 May 2016 09:16:41 -0800
Subject: [PATCH 42/44] TensorFlow: Add additional debugging info to error
 messages when a node cannot be placed on a device, specifically when the
 cause is due to colocation constraints.

For example, node colocation can cause a group of nodes to be
colocated with each other in an unsatisfiable way.  For example, if we
have three ops, A, B, C, where A supports GPU and CPU, B supports only
GPU, and C supports only CPU, a colocation group has no satisfiable
assignment.  In these cases, the cause is not just the lack of a
kernel for the op that failed to place, but possibly due the set of
ops in the colocation group.

This change adds additional logging to the error message that lists
the op types and their supported devices, so a user can figure out
which combinations of ops are problematic.

Fixes #2508 in that the error message should be clearer now.
Change: 123330848
---
 .../core/common_runtime/simple_placer.cc      | 67 +++++++++++++++++--
 .../core/common_runtime/simple_placer_test.cc |  6 ++
 2 files changed, 68 insertions(+), 5 deletions(-)

diff --git a/tensorflow/core/common_runtime/simple_placer.cc b/tensorflow/core/common_runtime/simple_placer.cc
index c14c37a4c32..0c2ee20670f 100644
--- a/tensorflow/core/common_runtime/simple_placer.cc
+++ b/tensorflow/core/common_runtime/simple_placer.cc
@@ -16,6 +16,7 @@ limitations under the License.
 #include "tensorflow/core/common_runtime/simple_placer.h"
 
 #include <memory>
+#include <set>
 #include <utility>
 #include <vector>
 
@@ -182,6 +183,7 @@ class ColocationGraph {
   Status ColocateNodes(const Node& x, const Node& y) {
     int x_root = FindRoot(x.id());
     int y_root = FindRoot(y.id());
+
     Status s;
     if (x_root != y_root) {
       // Merge the sets by swinging the parent pointer of the smaller
@@ -229,6 +231,12 @@ class ColocationGraph {
                                        s.error_message());
       }
 
+      // Transfer ids in the old group to the new one.
+      members_[new_root].ids_in_group.insert(
+          members_[old_root].ids_in_group.begin(),
+          members_[old_root].ids_in_group.end());
+      members_[old_root].ids_in_group.clear();
+
       // Ensure that the common root has at least one supported device
       // type, by computing the intersection of
       // members_[new_root].supported_device_types and
@@ -267,6 +275,9 @@ class ColocationGraph {
       return Status::OK();
     }
 
+    // String containing additional debugging info on failures.
+    string debug_info;
+
     // We have not yet computed the possible devices for the
     // colocated node set containing 'node', so we do so now using the
     // constraints on the root node.
@@ -310,6 +321,8 @@ class ColocationGraph {
         // Return an error when a physical device that matches an explicit
         // device specification is not found. This ensures that we don't
         // assign a node to GPU when the user wanted to force it on CPU.
+        AddDebugInfo(node_root, &debug_info);
+
         DeviceNameUtils::ParsedName specified_device_name;
         if (DeviceNameUtils::ParseFullName(node->def().device(),
                                            &specified_device_name) &&
@@ -334,16 +347,17 @@ class ColocationGraph {
                 node->def().device(),
                 "' because no devices matching that specification "
                 "are registered in this process; available devices: ",
-                str_util::Join(device_names, ", "));
+                str_util::Join(device_names, ", "), debug_info);
           } else if (specified_device_name.has_type) {
             return errors::InvalidArgument(
                 "Could not satisfy explicit device specification '",
                 node->def().device(), "' because no supported kernel for ",
-                specified_device_name.type, " devices is available");
+                specified_device_name.type, " devices is available.",
+                debug_info);
           } else {
             return errors::InvalidArgument(
                 "Could not satisfy explicit device specification '",
-                node->def().device());
+                node->def().device(), debug_info);
           }
         } else {
           // The specified device may be a valid device but the
@@ -355,7 +369,7 @@ class ColocationGraph {
               "required incompatible device '",
               DeviceNameUtils::ParsedNameToString(
                   members_[node_root].device_name),
-              "'");
+              "'", debug_info);
         }
       }
     } else {
@@ -368,10 +382,11 @@ class ColocationGraph {
           device_set_->devices(), members_[node_root].supported_device_types);
 
       if (devices.empty()) {
+        AddDebugInfo(node_root, &debug_info);
         return errors::InvalidArgument(
             "Node had no OpKernel registered to support this operation: ",
             "Operation was ", node->type_string(), " and inputs were ",
-            DataTypeVectorString(node->input_types()));
+            DataTypeVectorString(node->input_types()), debug_info);
       }
     }
 
@@ -390,6 +405,15 @@ class ColocationGraph {
     // id if it is a root. parent <= 0 indicates that this member is invalid.
     int parent = -1;
 
+    // The set of ids that are part of the disjoint node set forest.
+    //
+    // This is only fully specified in the root of a disjoint
+    // node set forest.
+    std::set<int> ids_in_group;
+
+    // The type of the op for this node.
+    string op_type;
+
     // A proxy for the depth of the tree that is used to prefer
     // connecting smaller trees to larger trees when merging disjoint
     // sets.
@@ -410,8 +434,41 @@ class ColocationGraph {
     std::vector<Device*> possible_devices;
   };
 
+  // Adds debugging info to 'output' for the node referred to by
+  // 'node_root'.
+  void AddDebugInfo(const int node_root, string* output) {
+    if (members_[node_root].ids_in_group.size() > 1) {
+      strings::StrAppend(output, "\nColocation Debug Info:\n");
+
+      // If this node is part of a colocation group, then we want to
+      // collect the mapping of ops to supported devices, so that
+      // the user can see why an unsatisfiable placement occurred.
+      strings::StrAppend(
+          output, "Colocation group had the following types and devices: ");
+
+      std::unordered_map<string, string> type_to_devices;
+      for (const int id : members_[node_root].ids_in_group) {
+        const string& op_type = members_[id].op_type;
+        string devices_registered;
+        for (const auto& device_type : members_[id].supported_device_types) {
+          strings::StrAppend(&devices_registered, DeviceTypeString(device_type),
+                             " ");
+        }
+
+        type_to_devices[op_type] = devices_registered;
+      }
+
+      for (const auto& td : type_to_devices) {
+        strings::StrAppend(output, "\n", td.first, ": ", td.second);
+      }
+    }
+  }
+
   Status InitializeMember(const Node& node, Member* member) {
     const int id = node.id();
+    member->ids_in_group.insert(id);
+    member->op_type = node.type_string();
+
     if (id < 0) {
       return errors::InvalidArgument("Node id was not positive: ", id);
     }
diff --git a/tensorflow/core/common_runtime/simple_placer_test.cc b/tensorflow/core/common_runtime/simple_placer_test.cc
index cc726f376c4..1d1217bed44 100644
--- a/tensorflow/core/common_runtime/simple_placer_test.cc
+++ b/tensorflow/core/common_runtime/simple_placer_test.cc
@@ -729,6 +729,12 @@ TEST_F(SimplePlacerTest, TestHeterogeneousDeviceSetFailure) {
   EXPECT_TRUE(StringPiece(s.error_message())
                   .contains("colocated with a group of nodes that required "
                             "incompatible device"));
+
+  // The error message should contain information that indicates which
+  // op types have which registered device types.
+  EXPECT_TRUE(StringPiece(s.error_message()).contains("VariableGPU: GPU")) << s;
+  EXPECT_TRUE(StringPiece(s.error_message()).contains("TestAssign: GPU CPU"))
+      << s;
 }
 
 // Test that placement fails when an unknown device is requested.

From 6b8de8ac69d02ab920a64be27014107db8e42e23 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <nobody@tensorflow.org>
Date: Thu, 26 May 2016 09:28:51 -0800
Subject: [PATCH 43/44] In graph_actions, makes sure monitors run only in
 chief. Change: 123332078

---
 tensorflow/contrib/learn/python/learn/graph_actions.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/learn/python/learn/graph_actions.py b/tensorflow/contrib/learn/python/learn/graph_actions.py
index 898590e52ff..1a69e291d1c 100644
--- a/tensorflow/contrib/learn/python/learn/graph_actions.py
+++ b/tensorflow/contrib/learn/python/learn/graph_actions.py
@@ -195,7 +195,10 @@ def train(graph,
     raise ValueError('No "global_step" was provided or found in the graph.')
 
   # TODO(ipolosukhin): Replace all functionality of Supervisor with Monitors.
-  if not monitors:
+  if not supervisor_is_chief:
+    # monitors should run only in supervisor.
+    monitors = []
+  elif not monitors:
     monitors = monitors_lib.get_default_monitors(
         loss_op=loss_op,
         summary_op=logging_ops.get_summary_op(),

From 008d41f4cc9f80fc4f29fc90da5b0ae361624fed Mon Sep 17 00:00:00 2001
From: Zongheng Yang <zongheng.y@gmail.com>
Date: Thu, 26 May 2016 09:37:42 -0800
Subject: [PATCH 44/44] Implement gradient for SparseSoftmax.

Essentially the same calculation as Softmax, adapted to sparse inputs/outputs.
Change: 123332988
---
 .../python/kernel_tests/sparse_ops_test.py    | 11 +++++++
 tensorflow/python/ops/sparse_grad.py          | 33 +++++++++++++++++--
 2 files changed, 41 insertions(+), 3 deletions(-)

diff --git a/tensorflow/python/kernel_tests/sparse_ops_test.py b/tensorflow/python/kernel_tests/sparse_ops_test.py
index 037d1f2c3eb..f6474f58458 100644
--- a/tensorflow/python/kernel_tests/sparse_ops_test.py
+++ b/tensorflow/python/kernel_tests/sparse_ops_test.py
@@ -637,6 +637,17 @@ class SparseSoftmaxTest(test_util.TensorFlowTestCase):
         self.assertAllEqual(sp_t.indices.eval(), result.indices)
         self.assertAllEqual(shape, result.shape)
 
+  def testGradient(self):
+    x_shape = [2, 5, 10]
+    with self.test_session(use_gpu=False):
+      for dtype in [np.float32, np.float64]:
+        x_np = np.random.randn(*x_shape).astype(dtype)
+        x_tf, nnz = _sparsify(x_np)
+        y_tf = tf.sparse_softmax(x_tf)
+        err = tf.test.compute_gradient_error(x_tf.values, (nnz,), y_tf.values,
+                                             (nnz,))
+        self.assertLess(err, 1e-4)
+
 
 if __name__ == "__main__":
   googletest.main()
diff --git a/tensorflow/python/ops/sparse_grad.py b/tensorflow/python/ops/sparse_grad.py
index d2e1ae20967..16c45aba544 100644
--- a/tensorflow/python/ops/sparse_grad.py
+++ b/tensorflow/python/ops/sparse_grad.py
@@ -227,6 +227,33 @@ def _SparseDenseCwiseDivGrad(op, grad):
 
 
 @ops.RegisterGradient("SparseSoftmax")
-def _SparseSoftmaxGrad(unused_op, unused_grad):
-  raise NotImplementedError("SparseSoftmax op doesn't have its gradient"
-                            "implemented yet")
+def _SparseSoftmaxGrad(op, grad):
+  """Gradients for SparseSoftmax.
+
+  The calculation is the same as SoftmaxGrad:
+
+    grad_x = grad_softmax * softmax - sum(grad_softmax * softmax) * softmax
+
+  where we now only operate on the non-zero values present in the SparseTensors.
+
+  Args:
+    op: the SparseSoftmax op.
+    grad: the upstream gradient w.r.t. the non-zero SparseSoftmax output values.
+
+  Returns:
+    Gradients w.r.t. the input (sp_indices, sp_values, sp_shape).
+  """
+  indices, shape = op.inputs[0], op.inputs[2]
+  out_vals = op.outputs[0]
+  sp_output = ops.SparseTensor(indices, out_vals, shape)
+  sp_grad = ops.SparseTensor(indices, grad, shape)
+  sp_product = ops.SparseTensor(
+      indices, sp_output.values * sp_grad.values, shape)
+
+  # [..., B, 1], dense.
+  sum_reduced = -sparse_ops.sparse_reduce_sum(sp_product, [-1], keep_dims=True)
+  # sparse [..., B, C] + dense [..., B, 1] with broadcast; outputs sparse.
+  sp_sum = sparse_ops.sparse_dense_cwise_add(sp_grad, sum_reduced)
+
+  grad_x = sp_sum.values * sp_output.values
+  return [None, grad_x, None]