Python tensorflow.python.ops.variables.local_variables_initializer() Examples

def testTFRecordReader(self):
    with self.cached_session():
      [tfrecord_path] = test_utils.create_tfrecord_files(
          tempfile.mkdtemp(), num_files=1)

    key, value = parallel_reader.single_pass_read(
        tfrecord_path, reader_class=io_ops.TFRecordReader)
    init_op = variables.local_variables_initializer()

    with self.cached_session() as sess:
      sess.run(init_op)
      with queues.QueueRunners(sess):
        flowers = 0
        num_reads = 9
        for _ in range(num_reads):
          current_key, _ = sess.run([key, value])
          if 'flowers' in str(current_key):
            flowers += 1
        self.assertGreater(flowers, 0)
        self.assertEqual(flowers, num_reads) 

def _export_graph(graph, saver, checkpoint_path, export_dir,
                  default_graph_signature, named_graph_signatures,
                  exports_to_keep):
  """Exports graph via session_bundle, by creating a Session."""
  with graph.as_default():
    with tf_session.Session('') as session:
      variables.local_variables_initializer()
      lookup_ops.tables_initializer()
      saver.restore(session, checkpoint_path)

      export = exporter.Exporter(saver)
      export.init(
          init_op=control_flow_ops.group(
              variables.local_variables_initializer(),
              lookup_ops.tables_initializer()),
          default_graph_signature=default_graph_signature,
          named_graph_signatures=named_graph_signatures,
          assets_collection=ops.get_collection(ops.GraphKeys.ASSET_FILEPATHS))
      return export.export(export_dir, contrib_variables.get_global_step(),
                           session, exports_to_keep=exports_to_keep) 

def testTFRecordReader(self):
    with self.test_session():
      [tfrecord_path] = test_utils.create_tfrecord_files(
          self.get_temp_dir(), num_files=1)

    key, value = parallel_reader.single_pass_read(
        tfrecord_path, reader_class=io_ops.TFRecordReader)
    init_op = variables.local_variables_initializer()

    with self.test_session() as sess:
      sess.run(init_op)
      with queues.QueueRunners(sess):
        flowers = 0
        num_reads = 9
        for _ in range(num_reads):
          current_key, _ = sess.run([key, value])
          if 'flowers' in str(current_key):
            flowers += 1
        self.assertGreater(flowers, 0)
        self.assertEquals(flowers, num_reads) 

def testTop3(self):
    top_3_fn = eval_metrics._top_k_generator(3)
    probabilities = constant_op.constant([[0.1, 0.2, 0.6, 0.3, 0.5, 0.5],
                                          [0.1, 0.4, 0.7, 0.3, 0.5, 0.2],
                                          [0.1, 0.3, 0.8, 0.7, 0.4, 0.9],
                                          [0.9, 0.8, 0.1, 0.8, 0.2, 0.7],
                                          [0.3, 0.6, 0.9, 0.4, 0.8, 0.6]])
    targets = constant_op.constant([3, 0, 2, 5, 1])
    in_top_3_op, update_op = top_3_fn(probabilities, targets)
    with self.test_session():
      # initializes internal accuracy vars
      variables.local_variables_initializer().run()
      # need to call in order to run the in_top_3_op internal operations because
      # it is a streaming function
      update_op.eval()
      self.assertNear(0.4, in_top_3_op.eval(), 0.0001) 

def _init_local_init_op(self, local_init_op=USE_DEFAULT):
    """Initializes local_init_op.

    Args:
      local_init_op: `Operation` run for every new supervisor instance. If set
      to USE_DEFAULT, use the first op from the GraphKeys.LOCAL_INIT_OP
      collection. If the collection is empty, create an op that initializes
      all local variables and all tables.
    """
    if local_init_op is Supervisor.USE_DEFAULT:
      local_init_op = self._get_first_op_from_collection(
          ops.GraphKeys.LOCAL_INIT_OP)
      if local_init_op is None:
        op_list = [
            variables.local_variables_initializer(),
            lookup_ops.tables_initializer()
        ]
        if op_list:
          local_init_op = control_flow_ops.group(*op_list)
          ops.add_to_collection(ops.GraphKeys.LOCAL_INIT_OP, local_init_op)
    self._local_init_op = local_init_op 

def main_op():
  """Returns a main op to init variables and tables.

  Returns the main op including the group of ops that initializes all
  variables, initializes local variables and initialize all tables.

  Returns:
    The set of ops to be run as part of the main op upon the load operation.
  """
  init = variables.global_variables_initializer()
  init_local = variables.local_variables_initializer()
  init_tables = lookup_ops.tables_initializer()
  return control_flow_ops.group(init, init_local, init_tables)


# TODO(sukritiramesh): Integrate with Saver for complete restore functionality. 

def _export_graph(graph, saver, checkpoint_path, export_dir,
                  default_graph_signature, named_graph_signatures,
                  exports_to_keep):
  """Exports graph via session_bundle, by creating a Session."""
  with graph.as_default():
    with tf_session.Session('') as session:
      variables.local_variables_initializer()
      data_flow_ops.tables_initializer()
      saver.restore(session, checkpoint_path)

      export = exporter.Exporter(saver)
      export.init(init_op=control_flow_ops.group(
          variables.local_variables_initializer(),
          data_flow_ops.tables_initializer()),
                  default_graph_signature=default_graph_signature,
                  named_graph_signatures=named_graph_signatures,
                  assets_collection=ops.get_collection(
                      ops.GraphKeys.ASSET_FILEPATHS))
      return export.export(export_dir, contrib_variables.get_global_step(),
                           session, exports_to_keep=exports_to_keep) 

def _init_local_init_op(self, local_init_op=USE_DEFAULT):
    """Initializes local_init_op.

    Args:
      local_init_op: `Operation` run for every new supervisor instance. If set
      to USE_DEFAULT, use the first op from the GraphKeys.LOCAL_INIT_OP
      collection. If the collection is empty, create an op that initializes
      all local variables and all tables.
    """
    if local_init_op is Supervisor.USE_DEFAULT:
      local_init_op = self._get_first_op_from_collection(
          ops.GraphKeys.LOCAL_INIT_OP)
      if local_init_op is None:
        op_list = [
            variables.local_variables_initializer(),
            lookup_ops.tables_initializer()
        ]
        if op_list:
          local_init_op = control_flow_ops.group(*op_list)
          ops.add_to_collection(ops.GraphKeys.LOCAL_INIT_OP, local_init_op)
    self._local_init_op = local_init_op 

def testRestoredModelPerformance(self):
    checkpoint_path = os.path.join(self.get_temp_dir(), 'model.ckpt')
    log_dir = os.path.join(self.get_temp_dir(), 'log_dir1/')

    # First, save out the current model to a checkpoint:
    init_op = control_flow_ops.group(variables.global_variables_initializer(),
                                     variables.local_variables_initializer())
    saver = saver_lib.Saver(write_version=saver_pb2.SaverDef.V1)
    with self.test_session() as sess:
      sess.run(init_op)
      saver.save(sess, checkpoint_path)

    # Next, determine the metric to evaluate:
    value_op, update_op = metric_ops.streaming_accuracy(self._predictions,
                                                        self._labels)

    # Run the evaluation and verify the results:
    accuracy_value = evaluation.evaluate_once(
        '', checkpoint_path, log_dir, eval_op=update_op, final_op=value_op)
    self.assertAlmostEqual(accuracy_value, self._expected_accuracy) 

def _init_local_init_op(self, local_init_op=USE_DEFAULT):
    """Initializes local_init_op.

    Args:
      local_init_op: `Operation` run for every new supervisor instance. If set
      to USE_DEFAULT, use the first op from the GraphKeys.LOCAL_INIT_OP
      collection. If the collection is empty, create an op that initializes
      all local variables and all tables.
    """
    if local_init_op is Supervisor.USE_DEFAULT:
      local_init_op = self._get_first_op_from_collection(
          ops.GraphKeys.LOCAL_INIT_OP)
      if local_init_op is None:
        op_list = [variables.local_variables_initializer(),
                   data_flow_ops.tables_initializer()]
        if op_list:
          local_init_op = control_flow_ops.group(*op_list)
          ops.add_to_collection(ops.GraphKeys.LOCAL_INIT_OP, local_init_op)
    self._local_init_op = local_init_op 

def testOutOfRangeError(self):
    with self.test_session():
      [tfrecord_path] = test_utils.create_tfrecord_files(
          self.get_temp_dir(), num_files=1)

    key, value = parallel_reader.single_pass_read(
        tfrecord_path, reader_class=io_ops.TFRecordReader)
    init_op = variables.local_variables_initializer()

    with self.test_session() as sess:
      sess.run(init_op)
      with queues.QueueRunners(sess):
        num_reads = 11
        with self.assertRaises(errors_impl.OutOfRangeError):
          for _ in range(num_reads):
            sess.run([key, value]) 

def testRestoredModelPerformance(self):
    checkpoint_path = os.path.join(self.get_temp_dir(), 'model.ckpt')
    log_dir = os.path.join(self.get_temp_dir(), 'log_dir1/')

    # First, save out the current model to a checkpoint:
    init_op = control_flow_ops.group(variables.global_variables_initializer(),
                                     variables.local_variables_initializer())
    saver = saver_lib.Saver(write_version=saver_pb2.SaverDef.V1)
    with self.test_session() as sess:
      sess.run(init_op)
      saver.save(sess, checkpoint_path)

    # Next, determine the metric to evaluate:
    value_op, update_op = metric_ops.streaming_accuracy(self._predictions,
                                                        self._labels)

    # Run the evaluation and verify the results:
    accuracy_value = evaluation.evaluate_once(
        '', checkpoint_path, log_dir, eval_op=update_op, final_op=value_op)
    self.assertAlmostEqual(accuracy_value, self._expected_accuracy) 

def _init_local_init_op(self, local_init_op=USE_DEFAULT):
    """Initializes local_init_op.

    Args:
      local_init_op: `Operation` run for every new supervisor instance. If set
      to USE_DEFAULT, use the first op from the GraphKeys.LOCAL_INIT_OP
      collection. If the collection is empty, create an op that initializes
      all local variables and all tables.
    """
    if local_init_op is Supervisor.USE_DEFAULT:
      local_init_op = self._get_first_op_from_collection(
          ops.GraphKeys.LOCAL_INIT_OP)
      if local_init_op is None:
        op_list = [variables.local_variables_initializer(),
                   data_flow_ops.tables_initializer()]
        if op_list:
          local_init_op = control_flow_ops.group(*op_list)
          ops.add_to_collection(ops.GraphKeys.LOCAL_INIT_OP, local_init_op)
    self._local_init_op = local_init_op 

def testTop3(self):
    top_3_fn = eval_metrics._top_k_generator(3)
    probabilities = constant_op.constant([[0.1, 0.2, 0.6, 0.3, 0.5, 0.5],
                                          [0.1, 0.4, 0.7, 0.3, 0.5, 0.2],
                                          [0.1, 0.3, 0.8, 0.7, 0.4, 0.9],
                                          [0.9, 0.8, 0.1, 0.8, 0.2, 0.7],
                                          [0.3, 0.6, 0.9, 0.4, 0.8, 0.6]])
    targets = constant_op.constant([3, 0, 2, 5, 1])
    in_top_3_op, update_op = top_3_fn(probabilities, targets)
    with self.test_session():
      # initializes internal accuracy vars
      variables.local_variables_initializer().run()
      # need to call in order to run the in_top_3_op internal operations because
      # it is a streaming function
      update_op.eval()
      self.assertNear(0.4, in_top_3_op.eval(), 0.0001) 

def _init_local_init_op(self, local_init_op=USE_DEFAULT):
    """Initializes local_init_op.

    Args:
      local_init_op: `Operation` run for every new supervisor instance. If set
      to USE_DEFAULT, use the first op from the GraphKeys.LOCAL_INIT_OP
      collection. If the collection is empty, create an op that initializes
      all local variables and all tables.
    """
    if local_init_op is Supervisor.USE_DEFAULT:
      local_init_op = self._get_first_op_from_collection(
          ops.GraphKeys.LOCAL_INIT_OP)
      if local_init_op is None:
        op_list = [
            variables.local_variables_initializer(),
            lookup_ops.tables_initializer()
        ]
        if op_list:
          local_init_op = control_flow_ops.group(*op_list)
          ops.add_to_collection(ops.GraphKeys.LOCAL_INIT_OP, local_init_op)
    self._local_init_op = local_init_op 

def _export_graph(graph, saver, checkpoint_path, export_dir,
                  default_graph_signature, named_graph_signatures,
                  exports_to_keep):
  """Exports graph via session_bundle, by creating a Session."""
  with graph.as_default():
    with tf_session.Session('') as session:
      variables.local_variables_initializer()
      data_flow_ops.tables_initializer()
      saver.restore(session, checkpoint_path)

      export = exporter.Exporter(saver)
      export.init(init_op=control_flow_ops.group(
          variables.local_variables_initializer(),
          data_flow_ops.tables_initializer()),
                  default_graph_signature=default_graph_signature,
                  named_graph_signatures=named_graph_signatures,
                  assets_collection=ops.get_collection(
                      ops.GraphKeys.ASSET_FILEPATHS))
      return export.export(export_dir, contrib_variables.get_global_step(),
                           session, exports_to_keep=exports_to_keep) 

def main_op():
  """Returns a main op to init variables and tables.

  Returns the main op including the group of ops that initializes all
  variables, initializes local variables and initialize all tables.

  Returns:
    The set of ops to be run as part of the main op upon the load operation.
  """
  init = variables.global_variables_initializer()
  init_local = variables.local_variables_initializer()
  init_tables = lookup_ops.tables_initializer()
  return control_flow_ops.group(init, init_local, init_tables)


# TODO(sukritiramesh): Integrate with Saver for complete restore functionality. 

def _export_graph(graph, saver, checkpoint_path, export_dir,
                  default_graph_signature, named_graph_signatures,
                  exports_to_keep):
  """Exports graph via session_bundle, by creating a Session."""
  with graph.as_default():
    with tf_session.Session('') as session:
      variables.local_variables_initializer()
      data_flow_ops.initialize_all_tables()
      saver.restore(session, checkpoint_path)

      export = exporter.Exporter(saver)
      export.init(init_op=control_flow_ops.group(
          variables.local_variables_initializer(),
          data_flow_ops.initialize_all_tables()),
                  default_graph_signature=default_graph_signature,
                  named_graph_signatures=named_graph_signatures,
                  assets_collection=ops.get_collection(
                      ops.GraphKeys.ASSET_FILEPATHS))
      return export.export(export_dir, contrib_variables.get_global_step(),
                           session, exports_to_keep=exports_to_keep) 

def _init_local_init_op(self, local_init_op=USE_DEFAULT):
    """Initializes local_init_op.

    Args:
      local_init_op: `Operation` run for every new supervisor instance. If set
      to USE_DEFAULT, use the first op from the GraphKeys.LOCAL_INIT_OP
      collection. If the collection is empty, create an op that initializes
      all local variables and all tables.
    """
    if local_init_op is Supervisor.USE_DEFAULT:
      local_init_op = self._get_first_op_from_collection(
          ops.GraphKeys.LOCAL_INIT_OP)
      if local_init_op is None:
        op_list = [variables.local_variables_initializer(),
                   data_flow_ops.initialize_all_tables()]
        if op_list:
          local_init_op = control_flow_ops.group(*op_list)
          ops.add_to_collection(ops.GraphKeys.LOCAL_INIT_OP, local_init_op)
    self._local_init_op = local_init_op 

def testTFRecordReader(self):
    with self.test_session():
      [tfrecord_path] = test_utils.create_tfrecord_files(
          self.get_temp_dir(), num_files=1)

    key, value = parallel_reader.single_pass_read(
        tfrecord_path, reader_class=io_ops.TFRecordReader)
    init_op = variables.local_variables_initializer()

    with self.test_session() as sess:
      sess.run(init_op)
      with queues.QueueRunners(sess):
        flowers = 0
        num_reads = 9
        for _ in range(num_reads):
          current_key, _ = sess.run([key, value])
          if 'flowers' in str(current_key):
            flowers += 1
        self.assertGreater(flowers, 0)
        self.assertEquals(flowers, num_reads) 

def testValueTensorIsIdempotent(self):
    predictions = random_ops.random_uniform(
        (10, 3), maxval=1, dtype=dtypes.float32, seed=1)
    labels = random_ops.random_uniform(
        (10, 3), maxval=2, dtype=dtypes.int64, seed=2)
    f1, f1_op = classification.f1_score(predictions, labels, num_thresholds=3)

    with self.cached_session() as sess:
      sess.run(variables.local_variables_initializer())

      # Run several updates.
      for _ in range(10):
        sess.run([f1_op])

      # Then verify idempotency.
      initial_f1 = f1.eval()
      for _ in range(10):
        self.assertAllClose(initial_f1, f1.eval()) 

def testOutOfRangeError(self):
    with self.test_session():
      [tfrecord_path] = test_utils.create_tfrecord_files(
          self.get_temp_dir(), num_files=1)

    key, value = parallel_reader.single_pass_read(
        tfrecord_path, reader_class=io_ops.TFRecordReader)
    init_op = variables.local_variables_initializer()

    with self.test_session() as sess:
      sess.run(init_op)
      with queues.QueueRunners(sess):
        num_reads = 11
        with self.assertRaises(errors_impl.OutOfRangeError):
          for _ in range(num_reads):
            sess.run([key, value]) 

def main_op():
  """Returns a main op to init variables and tables.

  Returns the main op including the group of ops that initializes all
  variables, initializes local variables and initialize all tables.

  Returns:
    The set of ops to be run as part of the main op upon the load operation.
  """
  init = variables.global_variables_initializer()
  init_local = variables.local_variables_initializer()
  init_tables = tf_data_flow_ops.tables_initializer()
  return control_flow_ops.group(init, init_local, init_tables) 

def testSeekNextLimitEpochs(self):
    string_list = ["a", "b", "c"]
    with self.test_session() as session:
      elem = input_pipeline_ops.seek_next(string_list, num_epochs=1)
      session.run([
          variables.local_variables_initializer(),
          variables.global_variables_initializer()
      ])
      self._assert_output([b"a", b"b", b"c"], session, elem) 

def _default_local_init_op():
    return control_flow_ops.group(variables.local_variables_initializer(),
                                  data_flow_ops.initialize_all_tables()) 

def _get_local_init_op():
  local_init_op = _get_first_op_from_collection(
      ops.GraphKeys.LOCAL_INIT_OP)
  if local_init_op is None:
    op_list = [variables.local_variables_initializer(),
               data_flow_ops.tables_initializer()]
    if op_list:
      local_init_op = control_flow_ops.group(*op_list)
      ops.add_to_collection(ops.GraphKeys.LOCAL_INIT_OP, local_init_op)
  return local_init_op 

def testInitializedVariableValue(self):
    with self.cached_session() as sess:
      a = variables_lib2.local_variable([0, 0, 0, 0, 0], name='a')
      sess.run(variables_lib.local_variables_initializer())
      self.assertAllEqual(a.eval(), [0] * 5) 

def _prepareCheckpoint(self, checkpoint_path):
    init_op = control_flow_ops.group(variables.global_variables_initializer(),
                                     variables.local_variables_initializer())
    saver = saver_lib.Saver(write_version=saver_pb2.SaverDef.V1)
    with self.cached_session() as sess:
      sess.run(init_op)
      saver.save(sess, checkpoint_path) 

def testWithEpochLimit(self):
    predictions_limited = input.limit_epochs(self._predictions, num_epochs=1)
    labels_limited = input.limit_epochs(self._labels, num_epochs=1)

    value_op, update_op = metrics.accuracy(
        labels=labels_limited, predictions=predictions_limited)

    init_op = control_flow_ops.group(variables.global_variables_initializer(),
                                     variables.local_variables_initializer())
    # Create checkpoint and log directories:
    chkpt_dir = tempfile.mkdtemp('tmp_logs')
    gfile.MakeDirs(chkpt_dir)
    logdir = tempfile.mkdtemp('tmp_logs2')
    gfile.MakeDirs(logdir)

    # Save initialized variables to a checkpoint directory:
    saver = saver_lib.Saver()
    with self.cached_session() as sess:
      init_op.run()
      saver.save(sess, os.path.join(chkpt_dir, 'chkpt'))

    # Now, run the evaluation loop:
    accuracy_value = evaluation.evaluation_loop(
        '', chkpt_dir, logdir, eval_op=update_op, final_op=value_op,
        max_number_of_evaluations=1, num_evals=10000)
    self.assertAlmostEqual(accuracy_value, self._expected_accuracy) 

def testSeekNextLimitEpochsTwo(self):
    string_list = ["a", "b", "c"]
    with self.test_session() as session:
      elem = input_pipeline_ops.seek_next(string_list, num_epochs=2)
      session.run([
          variables.local_variables_initializer(),
          variables.global_variables_initializer()
      ])
      # Expect to see [a, b, c] two times.
      self._assert_output([b"a", b"b", b"c"] * 2, session, elem)