Python v2.string方法代码示例

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import string [as 别名]
def _info(self):
        return tfds.core.DatasetInfo(
            builder=self,
            description=_DESCRIPTION,
            features=tfds.features.FeaturesDict(
                {
                    "speech": tfds.features.Audio(),
                    "text": tfds.features.Text(
                        encoder_config=self.builder_config.text_encoder_config
                    ),
                    "speaker_id": tf.int64,
                    "chapter_id": tf.int64,
                    "id": tf.string,
                    "label": tfds.features.ClassLabel(names=_LABELS),
                }
            ),
            supervised_keys=("speech", "label"),
            homepage=_URL,
            citation=_CITATION,
            metadata=tfds.core.MetadataDict(sample_rate=16000,),
        ) 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import string [as 别名]
def _info(self):
    return tfds.core.DatasetInfo(
        builder=self,
        description=_DESCRIPTION,
        features=tfds.features.FeaturesDict({
            "data":
                collections.OrderedDict([
                    ("marketplace", tf.string), ("customer_id", tf.string),
                    ("review_id", tf.string), ("product_id", tf.string),
                    ("product_parent", tf.string), ("product_title", tf.string),
                    ("product_category", tf.string), ("star_rating", tf.int32),
                    ("helpful_votes", tf.int32), ("total_votes", tf.int32),
                    ("vine", tfds.features.ClassLabel(names=["Y", "N"])),
                    ("verified_purchase",
                     tfds.features.ClassLabel(names=["Y", "N"])),
                    ("review_headline", tf.string), ("review_body", tf.string),
                    ("review_date", tf.string)
                ])
        }),
        supervised_keys=None,
        homepage="https://s3.amazonaws.com/amazon-reviews-pds/readme.html",
        citation=_CITATION,
    ) 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import string [as 别名]
def _info(self):

    return tfds.core.DatasetInfo(
        builder=self,
        description=_DESCRIPTION,
        features=tfds.features.FeaturesDict({
            'book_text': tfds.features.Text(),
            'book_id': tf.int32,
            'book_title': tf.string,
            'publication_date': tf.string,
            'book_link': tf.string
        }),
        supervised_keys=None,
        homepage='https://github.com/deepmind/pg19',
        citation=_CITATION,
    ) 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import string [as 别名]
def _info(self) -> tfds.core.DatasetInfo:
    config = self.builder_config
    return tfds.core.DatasetInfo(
        builder=self,
        description=_DESCRIPTION,
        features=tfds.features.FeaturesDict({
            config.name_key:
                tf.string,
            config.id_key:
                tf.string,
            config.summary_key:
                tf.string,
            config.opinions_key:
                tfds.features.Sequence(
                    tfds.features.FeaturesDict({
                        "key": tf.string,
                        "value": tf.string
                    })),
        }),
        supervised_keys=(config.opinions_key, config.summary_key),
        homepage="http://www.ccs.neu.edu/home/luwang/data.html",
        citation=_CITATION,
    ) 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import string [as 别名]
def _parse_single_image(self, example_proto):
    """Parses single video from the input tfrecords.

    Args:
      example_proto: tfExample proto with a single video.

    Returns:
      dict with all frames, positions and actions.
    """

    feature_map = {
        "image": tf.io.FixedLenFeature(shape=[], dtype=tf.string),
        "filename": tf.io.FixedLenFeature(shape=[], dtype=tf.string),
        "label": tf.io.FixedLenFeature(shape=[], dtype=tf.int64),
    }

    parse_single = tf.io.parse_single_example(example_proto, feature_map)

    return parse_single 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import string [as 别名]
def _info(self):
    return tfds.core.DatasetInfo(
        builder=self,
        description=_DESCRIPTION,
        features=tfds.features.FeaturesDict({
            "speech":
                tfds.features.Audio(sample_rate=16000),
            "text":
                tfds.features.Text(
                    encoder_config=self.builder_config.text_encoder_config),
            "speaker_id":
                tf.int64,
            "chapter_id":
                tf.int64,
            "id":
                tf.string,
        }),
        supervised_keys=("speech", "text"),
        homepage=_URL,
        citation=_CITATION,
        metadata=tfds.core.MetadataDict(sample_rate=16000,),
    ) 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import string [as 别名]
def _info(self):
    return tfds.core.DatasetInfo(
        builder=self,
        description=self.builder_config.description,
        features=tfds.features.FeaturesDict({
            "speech":
                tfds.features.Audio(sample_rate=16000),
            "text":
                tfds.features.Text(),
            "speaker_id":
                tf.string,
            "gender":
                tfds.features.ClassLabel(names=["unknown", "female", "male"]),
            "id":
                tf.string,
        }),
        supervised_keys=("speech", "text"),
        homepage=self.builder_config.url,
        citation=self.builder_config.citation,
        metadata=tfds.core.MetadataDict(sample_rate=16000,),
    ) 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import string [as 别名]
def _info(self):
    features_dict = {
        "id": tf.string,
        "drummer":
            tfds.features.ClassLabel(
                names=["drummer%d" % i for i in range(1, 11)]),
        "type": tfds.features.ClassLabel(names=["beat", "fill"]),
        "bpm": tf.int32,
        "time_signature": tfds.features.ClassLabel(names=_TIME_SIGNATURES),
        "style": {
            "primary": tfds.features.ClassLabel(names=_PRIMARY_STYLES),
            "secondary": tf.string,
        },
        "midi": tf.string
    }
    if self.builder_config.include_audio:
      features_dict["audio"] = tfds.features.Audio(
          dtype=tf.float32, sample_rate=self.builder_config.audio_rate)
    return tfds.core.DatasetInfo(
        builder=self,
        description=_DESCRIPTION,
        features=tfds.features.FeaturesDict(features_dict),
        homepage="https://g.co/magenta/groove-dataset",
        citation=_CITATION,
    ) 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import string [as 别名]
def _info(self):
    return tfds.core.DatasetInfo(
        builder=self,
        description=_DESCRIPTION,
        features=tfds.features.FeaturesDict({
            "speech": tfds.features.Audio(
                file_format="wav", sample_rate=24000),
            "text_original": tfds.features.Text(),
            "text_normalized": tfds.features.Text(),
            "speaker_id": tf.int64,
            "chapter_id": tf.int64,
            "id": tf.string,
        }),
        supervised_keys=("text_normalized", "speech"),
        homepage=_URL,
        citation=_CITATION,
        metadata=tfds.core.MetadataDict(sample_rate=24000,),
    ) 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import string [as 别名]
def _info(self):
    return tfds.core.DatasetInfo(
        builder=self,
        description=_DESCRIPTION,
        features=tfds.features.FeaturesDict({
            "id":
                tf.string,
            "title":
                tfds.features.Text(),
            "context":
                tfds.features.Text(),
            "question":
                tfds.features.Text(),
            "answers":
                tfds.features.Sequence({
                    "text": tfds.features.Text(),
                    "answer_start": tf.int32,
                }),
        }),
        # No default supervised_keys (as we have to pass both question
        # and context as input).
        supervised_keys=None,
        homepage="https://rajpurkar.github.io/SQuAD-explorer/",
        citation=_CITATION,
    ) 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import string [as 别名]
def placeholder_for_type(context,
                         type_spec,
                         name = None):
  """Produce a Tensorflow placeholder for this type_spec.

  Args:
    context: a NeuralQueryContext
    type_spec: a single type_spec (see tuple_dataset)
    name: a name to use for the placeholder

  Returns:
    a Tensorflow placeholder

    Raises:
      ValueError, if the type_spec is invalid
  """
  if type_spec == str:
    return tf.compat.v1.placeholder(tf.string, shape=[None], name=name)
  elif isinstance(type_spec, str) and context.is_type(type_spec):
    name = name or ('%s_ph' % type_spec)
    return context.placeholder(name, type_spec).tf
  else:
    raise ValueError('bad type spec %r' % type_spec) 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import string [as 别名]
def testReadWriteSpecs(self):
    logdir = FLAGS.test_tmpdir
    specs = {
        'a': tf.TensorSpec(shape=(2, 3), dtype=tf.float32),
        'b': {
            'b_1': tf.TensorSpec(shape=(5,), dtype=tf.string),
            'b_2': tf.TensorSpec(shape=(5, 6), dtype=tf.int32),
        }
    }
    utils.write_specs(logdir, specs)
    # Now read and verify
    specs_read = utils.read_specs(logdir)

    def _check_equal(sp1, sp2):
      self.assertEqual(sp1, sp2)

    tf.nest.map_structure(_check_equal, specs, specs_read) 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import string [as 别名]
def test_run_eval_actor_once(self):
    hparams = {}
    hparams['max_iter'] = 1
    hparams['num_episodes_per_iter'] = 5
    hparams['logdir'] = os.path.join(FLAGS.test_tmpdir, 'model')

    mock_problem = testing_utils.MockProblem(unroll_length=FLAGS.unroll_length)
    agent = mock_problem.get_agent()
    ckpt_manager = _get_ckpt_manager(hparams['logdir'], agent=agent)
    ckpt_manager.save(checkpoint_number=0)

    # Create a no-op gRPC server that responds to Aggregator RPCs.
    server_address = 'unix:/tmp/eval_actor_test_grpc'
    server = grpc.Server([server_address])

    @tf.function(input_signature=[tf.TensorSpec(shape=(), dtype=tf.string)])
    def eval_enqueue(_):
      return []

    server.bind(eval_enqueue, batched=False)

    server.start()

    eval_actor.run_with_aggregator(mock_problem, server_address, hparams) 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import string [as 别名]
def __init__(self, state_space_size, unroll_length=1):
    self._state_space_size = state_space_size
    # Creates simple dynamics (T stands for transition):
    #   states = [0, 1, ... len(state_space_size - 1)] + [STOP]
    #   actions = [-1, 1]
    #   T(s, a) = s + a  iff (s + a) is a valid state
    #           = STOP   otherwise
    self._action_space = [-1, 1]
    self._current_state = None
    self._env_spec = common.EnvOutput(
        reward=tf.TensorSpec(shape=[unroll_length + 1], dtype=tf.float32),
        done=tf.TensorSpec(shape=[unroll_length + 1], dtype=tf.bool),
        observation={
            'f1':
                tf.TensorSpec(
                    shape=[unroll_length + 1, 4, 10], dtype=tf.float32),
            'f2':
                tf.TensorSpec(
                    shape=[unroll_length + 1, 7, 10, 2], dtype=tf.float32)
        },
        info=tf.TensorSpec(shape=[unroll_length + 1], dtype=tf.string)) 

# 需要导入模块: from tensorflow.compat import v2 [as 别名]
# 或者: from tensorflow.compat.v2 import string [as 别名]
def testDenseFeaturesInKeras(self):
    features = {
        "text": np.array(["hello world", "pair-programming"]),
    }
    label = np.int64([0, 1])
    feature_columns = [
        hub.text_embedding_column_v2("text", self.model, trainable=True),
    ]
    input_features = dict(
        text=tf.keras.layers.Input(name="text", shape=[None], dtype=tf.string))
    dense_features = tf.keras.layers.DenseFeatures(feature_columns)
    x = dense_features(input_features)
    x = tf.keras.layers.Dense(16, activation="relu")(x)
    logits = tf.keras.layers.Dense(1, activation="linear")(x)
    model = tf.keras.Model(inputs=input_features, outputs=logits)
    model.compile(
        optimizer="rmsprop", loss="binary_crossentropy", metrics=["accuracy"])
    model.fit(x=features, y=label, epochs=10)
    self.assertAllEqual(model.predict(features["text"]).shape, [2, 1])