def testWarmStart_BucketizedColumn(self): # Create feature column. real = fc.numeric_column("real") real_bucket = fc.bucketized_column(real, boundaries=[0., 1., 2., 3.]) # Save checkpoint from which to warm-start. _, prev_bucket_val = self._create_prev_run_var( "linear_model/real_bucketized/weights", shape=[5, 1], initializer=norms()) partitioner = lambda shape, dtype: [1] * len(shape) # New graph, new session WITHOUT warm-starting. with ops.Graph().as_default() as g: with self.test_session(graph=g) as sess: cols_to_vars = self._create_linear_model([real_bucket], partitioner) sess.run(variables.global_variables_initializer()) # Without warm-starting, the weights should be initialized using default # initializer (which is init_ops.zeros_initializer). self._assert_cols_to_vars(cols_to_vars, {real_bucket: [np.zeros([5, 1])]}, sess) # New graph, new session with warm-starting. with ops.Graph().as_default() as g: with self.test_session(graph=g) as sess: cols_to_vars = self._create_linear_model([real_bucket], partitioner) ws_util._warm_start( ws_util.WarmStartSettings( self.get_temp_dir(), vars_to_warm_start=".*real_bucketized.*")) sess.run(variables.global_variables_initializer()) # Verify weights were correctly warm-started. self._assert_cols_to_vars(cols_to_vars, {real_bucket: [prev_bucket_val]}, sess)
def testWarmStart_SparseColumnHashed(self): # Create feature column. sc_hash = fc.categorical_column_with_hash_bucket( "sc_hash", hash_bucket_size=15) # Save checkpoint from which to warm-start. _, prev_hash_val = self._create_prev_run_var( "linear_model/sc_hash/weights", shape=[15, 1], initializer=norms()) partitioner = lambda shape, dtype: [1] * len(shape) # New graph, new session WITHOUT warm-starting. with ops.Graph().as_default() as g: with self.test_session(graph=g) as sess: cols_to_vars = self._create_linear_model([sc_hash], partitioner) sess.run(variables.global_variables_initializer()) # Without warm-starting, the weights should be initialized using default # initializer (which is init_ops.zeros_initializer). self._assert_cols_to_vars(cols_to_vars, {sc_hash: [np.zeros([15, 1])]}, sess) # New graph, new session with warm-starting. with ops.Graph().as_default() as g: with self.test_session(graph=g) as sess: cols_to_vars = self._create_linear_model([sc_hash], partitioner) ws_util._warm_start( ws_util.WarmStartSettings( self.get_temp_dir(), vars_to_warm_start=".*sc_hash.*")) sess.run(variables.global_variables_initializer()) # Verify weights were correctly warm-started. self._assert_cols_to_vars(cols_to_vars, {sc_hash: [prev_hash_val]}, sess)
def _model_fn(features, labels, mode, config): """Call the _dnn_linear_combined_model_fn and possibly warm-start.""" estimator_spec = _dnn_linear_combined_model_fn( features=features, labels=labels, mode=mode, head=head_lib. # pylint: disable=protected-access _regression_head_with_mean_squared_error_loss( label_dimension=label_dimension, weight_column=weight_column), linear_feature_columns=linear_feature_columns, linear_optimizer=linear_optimizer, dnn_feature_columns=dnn_feature_columns, dnn_optimizer=dnn_optimizer, dnn_hidden_units=dnn_hidden_units, dnn_activation_fn=dnn_activation_fn, dnn_dropout=dnn_dropout, input_layer_partitioner=input_layer_partitioner, config=config) # pylint: disable=protected-access warm_start_settings = warm_starting_util._get_default_warm_start_settings( warm_start_from) if warm_start_settings: warm_starting_util._warm_start(warm_start_settings) # pylint: enable=protected-access return estimator_spec
def testWarmStartMoreSettingsNoPartitioning(self): # Create old and new vocabs for sparse column "sc_vocab". prev_vocab_path = self._write_vocab(["apple", "banana", "guava", "orange"], "old_vocab") new_vocab_path = self._write_vocab( ["orange", "guava", "banana", "apple", "raspberry", "blueberry"], "new_vocab") # Create feature columns. sc_hash = fc.categorical_column_with_hash_bucket( "sc_hash", hash_bucket_size=15) sc_keys = fc.categorical_column_with_vocabulary_list( "sc_keys", vocabulary_list=["a", "b", "c", "e"]) sc_vocab = fc.categorical_column_with_vocabulary_file( "sc_vocab", vocabulary_file=new_vocab_path, vocabulary_size=6) all_linear_cols = [sc_hash, sc_keys, sc_vocab] # Save checkpoint from which to warm-start. with ops.Graph().as_default() as g: with self.test_session(graph=g) as sess: variable_scope.get_variable( "linear_model/sc_hash/weights", shape=[15, 1], initializer=norms()) sc_keys_weights = variable_scope.get_variable( "some_other_name", shape=[4, 1], initializer=rand()) variable_scope.get_variable( "linear_model/sc_vocab/weights", initializer=[[0.5], [1.], [2.], [3.]]) self._write_checkpoint(sess) prev_keys_val = sess.run(sc_keys_weights) # New graph, new session with warm-starting. with ops.Graph().as_default() as g: with self.test_session(graph=g) as sess: cols_to_vars = self._create_linear_model(all_linear_cols, partitioner=None) vocab_info = ws_util.VocabInfo( new_vocab=sc_vocab.vocabulary_file, new_vocab_size=sc_vocab.vocabulary_size, num_oov_buckets=sc_vocab.num_oov_buckets, old_vocab=prev_vocab_path) ws_settings = ws_util.WarmStartSettings( self.get_temp_dir(), vars_to_warm_start=".*(sc_keys|sc_vocab).*", var_name_to_vocab_info={ ws_util._infer_var_name(cols_to_vars[sc_vocab]): vocab_info }, var_name_to_prev_var_name={ ws_util._infer_var_name(cols_to_vars[sc_keys]): "some_other_name" }) ws_util._warm_start(ws_settings) sess.run(variables.global_variables_initializer()) # Verify weights were correctly warm-started. Var corresponding to # sc_hash should not be warm-started. Var corresponding to sc_vocab # should be correctly warm-started after vocab remapping. self._assert_cols_to_vars(cols_to_vars, { sc_keys: [prev_keys_val], sc_hash: [np.zeros([15, 1])], sc_vocab: [np.array([[3.], [2.], [1.], [0.5], [0.], [0.]])] }, sess)
def testWarmStart_SparseColumnVocabularyConstrainedVocabSizes(self): # Create old vocabulary, and use a size smaller than the total number of # entries. old_vocab_path = self._write_vocab(["apple", "guava", "banana"], "old_vocab") old_vocab_size = 2 # ['apple', 'guava'] # Create new vocab for sparse column "sc_vocab". current_vocab_path = self._write_vocab( ["apple", "banana", "guava", "orange"], "current_vocab") # Create feature column. Only use 2 of the actual entries, resulting in # ['apple', 'banana'] for the new vocabulary. sc_vocab = fc.categorical_column_with_vocabulary_file( "sc_vocab", vocabulary_file=current_vocab_path, vocabulary_size=2) # Save checkpoint from which to warm-start. self._create_prev_run_var( "linear_model/sc_vocab/weights", shape=[2, 1], initializer=ones()) partitioner = lambda shape, dtype: [1] * len(shape) # New graph, new session WITHOUT warm-starting. with ops.Graph().as_default() as g: with self.test_session(graph=g) as sess: cols_to_vars = self._create_linear_model([sc_vocab], partitioner) sess.run(variables.global_variables_initializer()) # Without warm-starting, the weights should be initialized using default # initializer (which is init_ops.zeros_initializer). self._assert_cols_to_vars(cols_to_vars, {sc_vocab: [np.zeros([2, 1])]}, sess) # New graph, new session with warm-starting. with ops.Graph().as_default() as g: with self.test_session(graph=g) as sess: cols_to_vars = self._create_linear_model([sc_vocab], partitioner) vocab_info = ws_util.VocabInfo( new_vocab=sc_vocab.vocabulary_file, new_vocab_size=sc_vocab.vocabulary_size, num_oov_buckets=sc_vocab.num_oov_buckets, old_vocab=old_vocab_path, old_vocab_size=old_vocab_size) warm_start_settings = ws_util.WarmStartSettings( ckpt_to_initialize_from=self.get_temp_dir(), vars_to_warm_start=".*sc_vocab.*", var_name_to_vocab_info={ "linear_model/sc_vocab/weights": vocab_info }) ws_util._warm_start(warm_start_settings) sess.run(variables.global_variables_initializer()) # Verify weights were correctly warm-started. 'banana' isn't in the # first two entries of the old vocabulary, so it's newly initialized. self._assert_cols_to_vars(cols_to_vars, {sc_vocab: [[[1], [0]]]}, sess)
def _model_fn(features, labels, mode, config): """Call the defined shared _linear_model_fn and possibly warm-start.""" estimator_spec = _linear_model_fn(features=features, labels=labels, mode=mode, head=head, feature_columns=tuple( feature_columns or []), optimizer=optimizer, partitioner=partitioner, config=config) # pylint: disable=protected-access warm_start_settings = warm_starting_util._get_default_warm_start_settings( warm_start_from) if warm_start_settings: warm_starting_util._warm_start(warm_start_settings) # pylint: enable=protected-access return estimator_spec
def _model_fn(features, labels, mode, config): """Call the defined shared _linear_model_fn and possibly warm-start.""" estimator_spec = _linear_model_fn( features=features, labels=labels, mode=mode, head=head, feature_columns=tuple(feature_columns or []), optimizer=optimizer, partitioner=partitioner, config=config) # pylint: disable=protected-access warm_start_settings = warm_starting_util._get_default_warm_start_settings( warm_start_from) if warm_start_settings: warm_starting_util._warm_start(warm_start_settings) # pylint: enable=protected-access return estimator_spec
def _model_fn(features, labels, mode, config): """Call the defined shared _dnn_model_fn and possibly warm-start.""" estimator_spec = _dnn_model_fn( features=features, labels=labels, mode=mode, head=head, hidden_units=hidden_units, feature_columns=tuple(feature_columns or []), optimizer=optimizer, activation_fn=activation_fn, dropout=dropout, input_layer_partitioner=input_layer_partitioner, config=config) # pylint: disable=protected-access warm_start_settings = warm_starting_util._get_default_warm_start_settings( warm_start_from) if warm_start_settings: warm_starting_util._warm_start(warm_start_settings) # pylint: enable=protected-access return estimator_spec
def testWarmStart_ExplicitCheckpointFile(self): # Create vocab for sparse column "sc_vocab". vocab_path = self._write_vocab(["apple", "banana", "guava", "orange"], "vocab") # Create feature column. sc_vocab = fc.categorical_column_with_vocabulary_file( "sc_vocab", vocabulary_file=vocab_path, vocabulary_size=4) # Save checkpoint from which to warm-start. _, prev_vocab_val = self._create_prev_run_var( "linear_model/sc_vocab/weights", shape=[4, 1], initializer=ones()) partitioner = lambda shape, dtype: [1] * len(shape) # New graph, new session WITHOUT warm-starting. with ops.Graph().as_default() as g: with self.test_session(graph=g) as sess: cols_to_vars = self._create_linear_model([sc_vocab], partitioner) sess.run(variables.global_variables_initializer()) # Without warm-starting, the weights should be initialized using default # initializer (which is init_ops.zeros_initializer). self._assert_cols_to_vars(cols_to_vars, {sc_vocab: [np.zeros([4, 1])]}, sess) # New graph, new session with warm-starting. with ops.Graph().as_default() as g: with self.test_session(graph=g) as sess: cols_to_vars = self._create_linear_model([sc_vocab], partitioner) # Since old vocab is not explicitly set in WarmStartSettings, the old # vocab is assumed to be same as new vocab. ws_util._warm_start( ws_util.WarmStartSettings( # Explicitly provide the file prefix instead of just the dir. os.path.join(self.get_temp_dir(), "model-0"), vars_to_warm_start=".*sc_vocab.*")) sess.run(variables.global_variables_initializer()) # Verify weights were correctly warm-started. self._assert_cols_to_vars(cols_to_vars, {sc_vocab: [prev_vocab_val]}, sess)
def _train_model(self, input_fn, hooks, saving_listeners): worker_hooks = [] with ops.Graph().as_default() as g, g.device(self._device_fn): random_seed.set_random_seed(self._config.tf_random_seed) global_step_tensor = self._create_and_assert_global_step(g) training_util._get_or_create_global_step_read() # pylint: disable=protected-access features, labels, input_hooks = ( self._get_features_and_labels_from_input_fn( input_fn, model_fn_lib.ModeKeys.TRAIN)) worker_hooks.extend(input_hooks) estimator_spec = self._call_model_fn( features, labels, model_fn_lib.ModeKeys.TRAIN, self.config) if self._warm_start_settings: logging.info('Warm-starting with WarmStartSettings: %s' % (self._warm_start_settings,)) # pylint: disable=protected-access warm_starting_util._warm_start(self._warm_start_settings) # pylint: enable=protected-access # Check if the user created a loss summary, and add one if they didn't. # We assume here that the summary is called 'loss'. If it is not, we will # make another one with the name 'loss' to ensure it shows up in the right # graph in TensorBoard. if not any([x.op.name == 'loss' for x in ops.get_collection(ops.GraphKeys.SUMMARIES)]): summary.scalar('loss', estimator_spec.loss) ops.add_to_collection(ops.GraphKeys.LOSSES, estimator_spec.loss) worker_hooks.extend(hooks) worker_hooks.extend([ training.NanTensorHook(estimator_spec.loss), training.LoggingTensorHook( { 'loss': estimator_spec.loss, 'step': global_step_tensor }, every_n_iter=100) ]) worker_hooks.extend(estimator_spec.training_hooks) if not (estimator_spec.scaffold.saver or ops.get_collection(ops.GraphKeys.SAVERS)): ops.add_to_collection( ops.GraphKeys.SAVERS, training.Saver( sharded=True, max_to_keep=self._config.keep_checkpoint_max, keep_checkpoint_every_n_hours=( self._config.keep_checkpoint_every_n_hours), defer_build=True, save_relative_paths=True)) chief_hooks = [] all_hooks = worker_hooks + list(estimator_spec.training_chief_hooks) saver_hooks = [ h for h in all_hooks if isinstance(h, training.CheckpointSaverHook)] if (self._config.save_checkpoints_secs or self._config.save_checkpoints_steps): if not saver_hooks: chief_hooks = [ training.CheckpointSaverHook( self._model_dir, save_secs=self._config.save_checkpoints_secs, save_steps=self._config.save_checkpoints_steps, scaffold=estimator_spec.scaffold) ] saver_hooks = [chief_hooks[0]] if saving_listeners: if not saver_hooks: raise ValueError( 'There should be a CheckpointSaverHook to use saving_listeners. ' 'Please set one of the RunConfig.save_checkpoints_steps or ' 'RunConfig.save_checkpoints_secs.') else: # It is expected to have one CheckpointSaverHook. If multiple, we pick # up the first one to add listener. saver_hooks[0]._listeners.extend(saving_listeners) # pylint: disable=protected-access with training.MonitoredTrainingSession( master=self._config.master, is_chief=self._config.is_chief, checkpoint_dir=self._model_dir, scaffold=estimator_spec.scaffold, hooks=worker_hooks, chief_only_hooks=( tuple(chief_hooks) + tuple(estimator_spec.training_chief_hooks)), save_checkpoint_secs=0, # Saving is handled by a hook. save_summaries_steps=self._config.save_summary_steps, config=self._session_config, log_step_count_steps=self._config.log_step_count_steps) as mon_sess: loss = None while not mon_sess.should_stop(): _, loss = mon_sess.run([estimator_spec.train_op, estimator_spec.loss]) return loss
def testWarmStartEmbeddingColumnLinearModel(self): # Create old and new vocabs for embedding column "sc_vocab". prev_vocab_path = self._write_vocab(["apple", "banana", "guava", "orange"], "old_vocab") new_vocab_path = self._write_vocab( ["orange", "guava", "banana", "apple", "raspberry", "blueberry"], "new_vocab") # Save checkpoint from which to warm-start. with ops.Graph().as_default() as g: with self.test_session(graph=g) as sess: variable_scope.get_variable( "linear_model/sc_vocab_embedding/embedding_weights", initializer=[[0.5, 0.4], [1., 1.1], [2., 2.2], [3., 3.3]]) variable_scope.get_variable( "linear_model/sc_vocab_embedding/weights", initializer=[[0.69], [0.71]]) self._write_checkpoint(sess) def _partitioner(shape, dtype): # pylint:disable=unused-argument # Partition each var into 2 equal slices. partitions = [1] * len(shape) partitions[0] = min(2, shape[0].value) return partitions # Create feature columns. sc_vocab = fc.categorical_column_with_vocabulary_file( "sc_vocab", vocabulary_file=new_vocab_path, vocabulary_size=6) emb_vocab = fc.embedding_column( categorical_column=sc_vocab, dimension=2) all_deep_cols = [emb_vocab] # New graph, new session with warm-starting. with ops.Graph().as_default() as g: with self.test_session(graph=g) as sess: cols_to_vars = {} with variable_scope.variable_scope("", partitioner=_partitioner): # Create the variables. fc.linear_model( features=self._create_dummy_inputs(), feature_columns=all_deep_cols, cols_to_vars=cols_to_vars) # Construct the vocab_info for the embedding weight. vocab_info = ws_util.VocabInfo( new_vocab=sc_vocab.vocabulary_file, new_vocab_size=sc_vocab.vocabulary_size, num_oov_buckets=sc_vocab.num_oov_buckets, old_vocab=prev_vocab_path, # Can't use constant_initializer with load_and_remap. In practice, # use a truncated normal initializer. backup_initializer=init_ops.random_uniform_initializer( minval=0.42, maxval=0.42)) ws_settings = ws_util.WarmStartSettings( self.get_temp_dir(), vars_to_warm_start=".*sc_vocab.*", var_name_to_vocab_info={ "linear_model/sc_vocab_embedding/embedding_weights": vocab_info }) ws_util._warm_start(ws_settings) sess.run(variables.global_variables_initializer()) # Verify weights were correctly warm-started. Var corresponding to # emb_vocab should be correctly warm-started after vocab remapping. # Missing values are filled in with the EmbeddingColumn's initializer. self._assert_cols_to_vars( cols_to_vars, { emb_vocab: [ # embedding_weights part 0. np.array([[3., 3.3], [2., 2.2], [1., 1.1]]), # embedding_weights part 1. np.array([[0.5, 0.4], [0.42, 0.42], [0.42, 0.42]]), # linear weights part 0. np.array([[0.69]]), # linear weights part 1. np.array([[0.71]]) ] }, sess)
def testWarmStartVarsToWarmstartIsNone(self): # Create old and new vocabs for sparse column "sc_vocab". prev_vocab_path = self._write_vocab(["apple", "banana", "guava", "orange"], "old_vocab") new_vocab_path = self._write_vocab( ["orange", "guava", "banana", "apple", "raspberry", "blueberry"], "new_vocab") # Create feature columns. sc_hash = fc.categorical_column_with_hash_bucket( "sc_hash", hash_bucket_size=15) sc_keys = fc.categorical_column_with_vocabulary_list( "sc_keys", vocabulary_list=["a", "b", "c", "e"]) sc_vocab = fc.categorical_column_with_vocabulary_file( "sc_vocab", vocabulary_file=new_vocab_path, vocabulary_size=6) all_linear_cols = [sc_hash, sc_keys, sc_vocab] # Save checkpoint from which to warm-start. with ops.Graph().as_default() as g: with self.test_session(graph=g) as sess: variable_scope.get_variable( "linear_model/sc_hash/weights", shape=[15, 1], initializer=norms()) variable_scope.get_variable( "some_other_name", shape=[4, 1], initializer=rand()) variable_scope.get_variable( "linear_model/sc_vocab/weights", initializer=[[0.5], [1.], [2.], [3.]]) self._write_checkpoint(sess) def _partitioner(shape, dtype): # pylint:disable=unused-argument # Partition each var into 2 equal slices. partitions = [1] * len(shape) partitions[0] = min(2, shape[0].value) return partitions # New graph, new session with warm-starting. with ops.Graph().as_default() as g: with self.test_session(graph=g) as sess: cols_to_vars = self._create_linear_model(all_linear_cols, _partitioner) vocab_info = ws_util.VocabInfo( new_vocab=sc_vocab.vocabulary_file, new_vocab_size=sc_vocab.vocabulary_size, num_oov_buckets=sc_vocab.num_oov_buckets, old_vocab=prev_vocab_path) ws_settings = ws_util.WarmStartSettings( self.get_temp_dir(), # The special value of None here will ensure that only the variable # specified in var_name_to_vocab_info (sc_vocab embedding) is # warm-started. vars_to_warm_start=None, var_name_to_vocab_info={ ws_util._infer_var_name(cols_to_vars[sc_vocab]): vocab_info }, # Even though this is provided, the None value for # vars_to_warm_start overrides the logic, and this will not be # warm-started. var_name_to_prev_var_name={ ws_util._infer_var_name(cols_to_vars[sc_keys]): "some_other_name" }) ws_util._warm_start(ws_settings) sess.run(variables.global_variables_initializer()) # Verify weights were correctly warm-started. Var corresponding to # sc_vocab should be correctly warm-started after vocab remapping, # and neither of the other two should be warm-started.. self._assert_cols_to_vars(cols_to_vars, { sc_keys: [np.zeros([2, 1]), np.zeros([2, 1])], sc_hash: [np.zeros([8, 1]), np.zeros([7, 1])], sc_vocab: [ np.array([[3.], [2.], [1.]]), np.array([[0.5], [0.], [0.]]) ] }, sess)
def testWarmStart_MultipleCols(self): # Create vocab for sparse column "sc_vocab". vocab_path = self._write_vocab(["apple", "banana", "guava", "orange"], "vocab") # Create feature columns. sc_int = fc.categorical_column_with_identity("sc_int", num_buckets=10) sc_hash = fc.categorical_column_with_hash_bucket( "sc_hash", hash_bucket_size=15) sc_keys = fc.categorical_column_with_vocabulary_list( "sc_keys", vocabulary_list=["a", "b", "c", "e"]) sc_vocab = fc.categorical_column_with_vocabulary_file( "sc_vocab", vocabulary_file=vocab_path, vocabulary_size=4) real = fc.numeric_column("real") real_bucket = fc.bucketized_column(real, boundaries=[0., 1., 2., 3.]) cross = fc.crossed_column([sc_keys, sc_vocab], hash_bucket_size=20) all_linear_cols = [sc_int, sc_hash, sc_keys, sc_vocab, real_bucket, cross] # Save checkpoint from which to warm-start. Also create a bias variable, # so we can check that it's also warm-started. with ops.Graph().as_default() as g: with self.test_session(graph=g) as sess: sc_int_weights = variable_scope.get_variable( "linear_model/sc_int/weights", shape=[10, 1], initializer=ones()) sc_hash_weights = variable_scope.get_variable( "linear_model/sc_hash/weights", shape=[15, 1], initializer=norms()) sc_keys_weights = variable_scope.get_variable( "linear_model/sc_keys/weights", shape=[4, 1], initializer=rand()) sc_vocab_weights = variable_scope.get_variable( "linear_model/sc_vocab/weights", shape=[4, 1], initializer=ones()) real_bucket_weights = variable_scope.get_variable( "linear_model/real_bucketized/weights", shape=[5, 1], initializer=norms()) cross_weights = variable_scope.get_variable( "linear_model/sc_keys_X_sc_vocab/weights", shape=[20, 1], initializer=rand()) bias = variable_scope.get_variable( "linear_model/bias_weights", shape=[1], initializer=rand()) self._write_checkpoint(sess) (prev_int_val, prev_hash_val, prev_keys_val, prev_vocab_val, prev_bucket_val, prev_cross_val, prev_bias_val) = sess.run([ sc_int_weights, sc_hash_weights, sc_keys_weights, sc_vocab_weights, real_bucket_weights, cross_weights, bias ]) partitioner = lambda shape, dtype: [1] * len(shape) # New graph, new session WITHOUT warm-starting. with ops.Graph().as_default() as g: with self.test_session(graph=g) as sess: cols_to_vars = self._create_linear_model(all_linear_cols, partitioner) sess.run(variables.global_variables_initializer()) # Without warm-starting, all weights should be initialized using default # initializer (which is init_ops.zeros_initializer). self._assert_cols_to_vars(cols_to_vars, { sc_int: [np.zeros([10, 1])], sc_hash: [np.zeros([15, 1])], sc_keys: [np.zeros([4, 1])], sc_vocab: [np.zeros([4, 1])], real_bucket: [np.zeros([5, 1])], cross: [np.zeros([20, 1])], }, sess) # New graph, new session with warm-starting. with ops.Graph().as_default() as g: with self.test_session(graph=g) as sess: cols_to_vars = self._create_linear_model(all_linear_cols, partitioner) vocab_info = ws_util.VocabInfo( new_vocab=sc_vocab.vocabulary_file, new_vocab_size=sc_vocab.vocabulary_size, num_oov_buckets=sc_vocab.num_oov_buckets, old_vocab=vocab_path) ws_util._warm_start( ws_util.WarmStartSettings( self.get_temp_dir(), var_name_to_vocab_info={ "linear_model/sc_vocab/weights": vocab_info })) sess.run(variables.global_variables_initializer()) # Verify weights were correctly warm-started. self._assert_cols_to_vars(cols_to_vars, { sc_int: [prev_int_val], sc_hash: [prev_hash_val], sc_keys: [prev_keys_val], sc_vocab: [prev_vocab_val], real_bucket: [prev_bucket_val], cross: [prev_cross_val], "bias": [prev_bias_val], }, sess)