Python v1.tables_initializer方法代碼示例

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import tables_initializer [as 別名]
def testSingleElement(self):
    with tf.Session() as sess:
      with tempfile.NamedTemporaryFile(mode='w+t', delete=False) as vocab:
        vocab.writelines([word + '\n' for word in self.vocab])
        vocab.flush()
        preprocessing_fn = utils.metrics_preprocessing_fn(
            vocab.name, 'text_a', 'lang')
        outputs = preprocessing_fn(self.raw_data)
        tf.tables_initializer().run()
        outputs = sess.run(outputs)

        self.assertEqual(outputs['lang'], 'en')
        self.assertEqual(outputs['num_non_unk_wordpieces'], 7)
        self.assertEqual(outputs['num_preserved_chars'], 20)
        self.assertEqual(outputs['num_dropped_chars'], 3)
        self.assertSequenceAlmostEqual(outputs['wordpieces'].values,
                                       self.expected_wordpieces) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import tables_initializer [as 別名]
def test_string_to_int_mapper(self):
    with tf.Graph().as_default():
      dataset = tf.data.Dataset.from_tensor_slices({
          "s": [["a", "b"], ["c", "d"]]
      })
      dataset = dataset.map(dataset_utils.string_to_int_mapper(
          ["s"], ["a", "c"]))
      dataset = dataset.batch(2)

      self.assertDictEqual(dataset.output_types, {"s": tf.string,
                                                  "s_id": tf.int32})
      iterator = dataset.make_initializable_iterator()
      features = iterator.get_next()

      with tf.Session() as sess:
        sess.run([tf.tables_initializer(), iterator.initializer])
        tf_s, tf_s_id = sess.run([features["s"], features["s_id"]])

      self.assertAllEqual(tf_s, [["a", "b"], ["c", "d"]])
      self.assertAllEqual(tf_s_id, [[0, 2], [1, 2]]) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import tables_initializer [as 別名]
def testExportTokenEmbeddingModule(self):
    export.export_module_from_file(
        embedding_file=self._embedding_file_path,
        export_path=self.get_temp_dir(),
        parse_line_fn=export.parse_line,
        num_oov_buckets=1,
        preprocess_text=False)
    with tf.Graph().as_default():
      hub_module = hub.Module(self.get_temp_dir())
      tokens = tf.constant(["cat", "lizard", "dog"])
      embeddings = hub_module(tokens)
      with tf.Session() as session:
        session.run(tf.tables_initializer())
        session.run(tf.global_variables_initializer())
        self.assertAllClose(
            session.run(embeddings),
            [[1.11, 2.56, 3.45], [0.0, 0.0, 0.0], [1.0, 2.0, 3.0]]) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import tables_initializer [as 別名]
def testExportFulltextEmbeddingModule(self):
    export.export_module_from_file(
        embedding_file=self._embedding_file_path,
        export_path=self.get_temp_dir(),
        parse_line_fn=export.parse_line,
        num_oov_buckets=1,
        preprocess_text=True)
    with tf.Graph().as_default():
      hub_module = hub.Module(self.get_temp_dir())
      tokens = tf.constant(["cat", "cat cat", "lizard. dog", "cat? dog", ""])
      embeddings = hub_module(tokens)
      with tf.Session() as session:
        session.run(tf.tables_initializer())
        session.run(tf.global_variables_initializer())
        self.assertAllClose(
            session.run(embeddings),
            [[1.11, 2.56, 3.45], [1.57, 3.62, 4.88], [0.70, 1.41, 2.12],
             [1.49, 3.22, 4.56], [0.0, 0.0, 0.0]],
            rtol=0.02) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import tables_initializer [as 別名]
def testEmptyInput(self):
    export.export_module_from_file(
        embedding_file=self._embedding_file_path,
        export_path=self.get_temp_dir(),
        parse_line_fn=export.parse_line,
        num_oov_buckets=1,
        preprocess_text=True)
    with tf.Graph().as_default():
      hub_module = hub.Module(self.get_temp_dir())
      tokens = tf.constant(["", "", ""])
      embeddings = hub_module(tokens)
      with tf.Session() as session:
        session.run(tf.tables_initializer())
        session.run(tf.global_variables_initializer())
        self.assertAllClose(
            session.run(embeddings),
            [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
            rtol=0.02) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import tables_initializer [as 別名]
def testEmptyLeading(self):
    export.export_module_from_file(
        embedding_file=self._embedding_file_path,
        export_path=self.get_temp_dir(),
        parse_line_fn=export.parse_line,
        num_oov_buckets=1,
        preprocess_text=True)
    with tf.Graph().as_default():
      hub_module = hub.Module(self.get_temp_dir())
      tokens = tf.constant(["", "cat dog"])
      embeddings = hub_module(tokens)
      with tf.Session() as session:
        session.run(tf.tables_initializer())
        session.run(tf.global_variables_initializer())
        self.assertAllClose(
            session.run(embeddings),
            [[0.0, 0.0, 0.0], [1.49, 3.22, 4.56]],
            rtol=0.02) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import tables_initializer [as 別名]
def _build_eval_graph(self, scope_name=None):
    """Build the evaluation graph.

    Args:
      scope_name: String to filter what summaries are collected. Only summary
        ops whose name contains `scope_name` will be added, which is useful for
        only including evaluation ops.

    Returns:
      A GraphInfo named_tuple containing various useful ops and tensors of the
      evaluation grpah.
    """
    with self._do_eval():
      input_producer_op, enqueue_ops, fetches = self._build_model()
      local_var_init_op = tf.local_variables_initializer()
      table_init_ops = tf.tables_initializer()
      variable_mgr_init_ops = [local_var_init_op]
      if table_init_ops:
        variable_mgr_init_ops.extend([table_init_ops])
      with tf.control_dependencies([local_var_init_op]):
        variable_mgr_init_ops.extend(self.variable_mgr.get_post_init_ops())
      local_var_init_op_group = tf.group(*variable_mgr_init_ops)

      summary_op = tf.summary.merge_all(scope=scope_name)
      # The eval graph has no execution barrier because it doesn't run in
      # distributed mode.
      execution_barrier = None
      # We do not use the global step during evaluation.
      global_step = None
      return GraphInfo(input_producer_op, enqueue_ops, fetches,
                       execution_barrier, global_step, local_var_init_op_group,
                       summary_op)

  # TODO(reedwm): For consistency, we should have a similar
  # "_initialize_train_graph" function. They can likely be the same function. 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import tables_initializer [as 別名]
def compute_data_mean_and_std(data, axis, num_samples):
  """Computes data mean and std."""
  with tf.Session() as sess:
    sess.run([
        tf.global_variables_initializer(),
        tf.local_variables_initializer(),
        tf.tables_initializer()
    ])
    with tf_slim.queues.QueueRunners(sess):
      data_value = np.concatenate(
          [sess.run(data) for _ in range(num_samples)], axis=0)
  mean = np.mean(data_value, axis=tuple(axis), keepdims=True)
  std = np.std(data_value, axis=tuple(axis), keepdims=True)
  return mean, std 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import tables_initializer [as 別名]
def __init__(self, config_dict, model_location, max_padded_length=0,
               num_perturbations=0):
    self.graph_tensor_producer = robust_model.RobustModel(**config_dict)

    self.batch_size = self.graph_tensor_producer.batch_size
    if max_padded_length:
      self.graph_tensor_producer.config.max_padded_length = max_padded_length
    if num_perturbations:
      self.graph_tensor_producer.config.num_perturbations = num_perturbations
    self.graph_tensors = self.graph_tensor_producer()

    network_saver = tf.train.Saver(self.graph_tensor_producer.variables)
    self.open_session = tf.Session()
    self.open_session.run(tf.tables_initializer())
    network_saver.restore(self.open_session, model_location) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import tables_initializer [as 別名]
def testLargerBatchSize(self):
    with tf.Session() as sess:
      with tempfile.NamedTemporaryFile(mode='w+t', delete=False) as vocab:
        raw_data = {
            'label': ['1', '2'],
            'text_a': ['The boy jumped into the air.', 'The cat sat on a hat.'],
            'lang': ['en', 'en'],
        }
        expected_wordpieces = ['The', '[UNK]', 'jumped', 'in', '##to', 'the',
                               'air', '.', 'The', 'cat', 'sat', 'on', 'a', 'h',
                               '##at', '.']
        vocab.writelines([word + '\n' for word in self.vocab])
        vocab.flush()
        preprocessing_fn = utils.metrics_preprocessing_fn(
            vocab.name, 'text_a', 'lang')
        outputs = preprocessing_fn(raw_data)
        tf.tables_initializer().run()
        outputs = sess.run(outputs)

        self.assertSequenceAlmostEqual(outputs['lang'], ['en', 'en'])
        self.assertSequenceAlmostEqual(outputs['num_preserved_chars'], [20, 16])
        self.assertSequenceAlmostEqual(outputs['num_dropped_chars'], [3, 0])
        self.assertSequenceAlmostEqual(outputs['wordpieces'].values,
                                       expected_wordpieces)
        self.assertSequenceAlmostEqual(outputs['num_non_unk_wordpieces'],
                                       [7, 8]) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import tables_initializer [as 別名]
def evaluate():
  """Evaluate a model on the dev set."""
  sess = tf.Session()
  tf.logging.info("Building graph...")

  embeddings = load_embeddings()
  tf_data = load_batched_dataset(False, embeddings)
  it = tf_data.make_initializable_iterator()
  features, labels = it.get_next()

  logits = predict(False, embeddings, features["premise"],
                   features["hypothesis"])
  accuracy, update_ops = tf.metrics.accuracy(
      tf.argmax(logits, 1, output_type=tf.int32), tf.to_int32(labels))

  tf.logging.info("Running initializers...")
  checkpoint_file = FLAGS.checkpoint_file
  if checkpoint_file is not None:
    saver = tf.train.Saver(tf.trainable_variables())
    tf.logging.info("Restoring from checkpoint: " + checkpoint_file)
    saver.restore(sess, checkpoint_file)
  else:
    tf.logging.warning("No checkpoint given, evaling model with random weights")
    sess.run(tf.global_variables_initializer())
  sess.run(tf.local_variables_initializer())
  sess.run(tf.tables_initializer())
  sess.run(it.initializer)

  tf.logging.info("Starting loop....")
  while True:
    try:
      sess.run(update_ops)
    except tf.errors.OutOfRangeError:
      break
  tf.logging.info("Done")

  accuracy = sess.run(accuracy)
  print("Accuracy: %f" % accuracy) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import tables_initializer [as 別名]
def execute_tpu_tf1(self, compute_fn, inputs, graph=None):
    """Executes compute_fn on TPU with Tensorflow 1.X.

    Args:
      compute_fn: a function containing Tensorflow computation that takes a list
        of input numpy tensors, performs computation and returns output numpy
        tensors.
      inputs: a list of numpy arrays to feed input to the `compute_fn`.
      graph: (optional) If not None, provided `graph` is used for computation
        instead of a brand new tf.Graph().

    Returns:
      A list of numpy arrays or a single numpy array.
    """
    with self.session(graph=(graph or tf.Graph())) as sess:
      placeholders = [tf.placeholder_with_default(v, v.shape) for v in inputs]
      def wrap_graph_fn(*args, **kwargs):
        results = compute_fn(*args, **kwargs)
        if (not (isinstance(results, dict) or isinstance(results, tf.Tensor))
            and hasattr(results, '__iter__')):
          results = list(results)
        return results
      tpu_computation = contrib_tpu.rewrite(wrap_graph_fn, placeholders)
      sess.run(contrib_tpu.initialize_system())
      sess.run([tf.global_variables_initializer(), tf.tables_initializer(),
                tf.local_variables_initializer()])
      materialized_results = sess.run(tpu_computation,
                                      feed_dict=dict(zip(placeholders, inputs)))
      sess.run(contrib_tpu.shutdown_system())
    return self.maybe_extract_single_output(materialized_results) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import tables_initializer [as 別名]
def execute_cpu_tf1(self, compute_fn, inputs, graph=None):
    """Executes compute_fn on CPU with Tensorflow 1.X.

    Args:
      compute_fn: a function containing Tensorflow computation that takes a list
        of input numpy tensors, performs computation and returns output numpy
        tensors.
      inputs: a list of numpy arrays to feed input to the `compute_fn`.
      graph: (optional) If not None, provided `graph` is used for computation
        instead of a brand new tf.Graph().

    Returns:
      A list of numpy arrays or a single numpy array.
    """
    if self.is_tf2():
      raise ValueError('Required version Tenforflow 1.X is not available.')
    with self.session(graph=(graph or tf.Graph())) as sess:
      placeholders = [tf.placeholder_with_default(v, v.shape) for v in inputs]
      results = compute_fn(*placeholders)
      if (not (isinstance(results, dict) or isinstance(results, tf.Tensor)) and
          hasattr(results, '__iter__')):
        results = list(results)
      sess.run([tf.global_variables_initializer(), tf.tables_initializer(),
                tf.local_variables_initializer()])
      materialized_results = sess.run(results, feed_dict=dict(zip(placeholders,
                                                                  inputs)))
    return self.maybe_extract_single_output(materialized_results) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import tables_initializer [as 別名]
def main(_):
  tf.gfile.MakeDirs(os.path.dirname(FLAGS.output_tfrecord))
  tokenizer = tokenization.FullTokenizer(vocab_file=FLAGS.vocab_path,
                                         do_lower_case=True)

  annotations_zipfn = os.path.join(FLAGS.data_dir, "vcr1annots.zip")
  images_zipfn = os.path.join(FLAGS.data_dir, "vcr1images.zip")

  # Generate data for all splits:
  for split in ["train", "val", "test"]:
    jsonl_file = split + ".jsonl"
    output_tfrecord = "-".join([FLAGS.output_tfrecord,
                                split,
                                "%05d" % FLAGS.shard,
                                "of",
                                "%05d" % FLAGS.num_shards])
    with tf.python_io.TFRecordWriter(output_tfrecord) as writer:
      with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        sess.run(tf.tables_initializer())
        with zipfile.ZipFile(
            tf.gfile.Open(annotations_zipfn)) as annotations_zip:
          with zipfile.ZipFile(tf.gfile.Open(images_zipfn)) as images_zip:
            with annotations_zip.open(jsonl_file) as jsonl:
              for idx, line in enumerate(jsonl):
                if idx % FLAGS.num_shards != FLAGS.shard:
                  continue
                example = json.loads(line)
                meta_filename = "vcr1images/" + example["metadata_fn"]
                meta = json.loads(images_zip.open(meta_filename).read())
                del meta["segms"]

                try:
                  image_filename = "vcr1images/" + example["img_fn"]
                  tf.logging.info("Reading %s", image_filename)
                  with images_zip.open(image_filename) as image:
                    image_string = image.read()
                except zipfile.BadZipfile as e:
                  tf.logging.error("Bad Zip file: " + str(e))
                  image_string = BLANK_JPEG
                  for box in meta["boxes"]:
                    box[0] = 0.0
                    box[1] = 0.0
                    box[2] = 1.0
                    box[3] = 1.0

                is_test = (split == "test")
                for tf_example in create_tf_examples(tokenizer, example,
                                                     image_string, meta,
                                                     is_test=is_test):
                  writer.write(tf_example.SerializeToString()) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import tables_initializer [as 別名]
def __init__(self, annoy_index_path, unique_strings,
                 use_sentence_piece, module_path):
        self.annoy_index_path = annoy_index_path
        self.unique_strings = unique_strings

        # load the annoy index for mmap speed
        # Length of item vector that will be indexed
        self.annoy_index = AnnoyIndex(512, metric='angular')

        # super fast, will just mmap the file
        self.annoy_index.load(self.annoy_index_path)

        g = tf.Graph()
        with g.as_default():
            # define the module
            module = hub.Module(module_path, trainable=False)

            if use_sentence_piece:
                # build an input placeholder
                self.input_placeholder = tf.sparse_placeholder(
                  tf.int64, shape=[None, None])

                # build an input / output from the placeholders
                self.embeddings = module(inputs=dict(
                    values=self.input_placeholder.values,
                    indices=self.input_placeholder.indices,
                    dense_shape=self.input_placeholder.dense_shape
                  )
                )
            else:
                # build an input placeholder
                self.input_placeholder = tf.placeholder(
                  tf.string, shape=(None))
                self.embeddings = module(self.input_placeholder)

            init_op = tf.group([tf.global_variables_initializer(),
                                tf.tables_initializer()])

        # do not finalize the graph if using sentence piece module
        if not use_sentence_piece:
            g.finalize()

        # define the configuration
        config = tf.ConfigProto(allow_soft_placement=True)
        self.sess = tf.Session(graph=g, config=config)
        self.sess.run(init_op)

        if use_sentence_piece:
            # spm_path now contains a path to the SentencePiece
            # model stored inside the TF-Hub module
            with g.as_default():
                spm_path = self.sess.run(module(signature="spm_path"))
            self.sp = spm.SentencePieceProcessor()
            self.sp.Load(spm_path)

        tf.logging.info('Interactive session is initialized...')