Python reader.ptb_producer() Examples

The following are 30 code examples of reader.ptb_producer(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module reader , or try the search function .
Example #1
Source File: reader_test.py    From DOTA_models with Apache License 2.0 6 votes vote down vote up 
def testPtbProducer(self):
    raw_data = [4, 3, 2, 1, 0, 5, 6, 1, 1, 1, 1, 0, 3, 4, 1]
    batch_size = 3
    num_steps = 2
    x, y = reader.ptb_producer(raw_data, batch_size, num_steps)
    with self.test_session() as session:
      coord = tf.train.Coordinator()
      tf.train.start_queue_runners(session, coord=coord)
      try:
        xval, yval = session.run([x, y])
        self.assertAllEqual(xval, [[4, 3], [5, 6], [1, 0]])
        self.assertAllEqual(yval, [[3, 2], [6, 1], [0, 3]])
        xval, yval = session.run([x, y])
        self.assertAllEqual(xval, [[2, 1], [1, 1], [3, 4]])
        self.assertAllEqual(yval, [[1, 0], [1, 1], [4, 1]])
      finally:
        coord.request_stop()
        coord.join()
Example #2
Source File: reader_test.py    From g-tensorflow-models with Apache License 2.0 6 votes vote down vote up 
def testPtbProducer(self):
    raw_data = [4, 3, 2, 1, 0, 5, 6, 1, 1, 1, 1, 0, 3, 4, 1]
    batch_size = 3
    num_steps = 2
    x, y = reader.ptb_producer(raw_data, batch_size, num_steps)
    with self.test_session() as session:
      coord = tf.train.Coordinator()
      tf.train.start_queue_runners(session, coord=coord)
      try:
        xval, yval = session.run([x, y])
        self.assertAllEqual(xval, [[4, 3], [5, 6], [1, 0]])
        self.assertAllEqual(yval, [[3, 2], [6, 1], [0, 3]])
        xval, yval = session.run([x, y])
        self.assertAllEqual(xval, [[2, 1], [1, 1], [3, 4]])
        self.assertAllEqual(yval, [[1, 0], [1, 1], [4, 1]])
      finally:
        coord.request_stop()
        coord.join()
Example #3
Source File: reader_test.py    From HumanRecognition with MIT License 6 votes vote down vote up 
def testPtbProducer(self):
    raw_data = [4, 3, 2, 1, 0, 5, 6, 1, 1, 1, 1, 0, 3, 4, 1]
    batch_size = 3
    num_steps = 2
    x, y = reader.ptb_producer(raw_data, batch_size, num_steps)
    with self.test_session() as session:
      coord = tf.train.Coordinator()
      tf.train.start_queue_runners(session, coord=coord)
      try:
        xval, yval = session.run([x, y])
        self.assertAllEqual(xval, [[4, 3], [5, 6], [1, 0]])
        self.assertAllEqual(yval, [[3, 2], [6, 1], [0, 3]])
        xval, yval = session.run([x, y])
        self.assertAllEqual(xval, [[2, 1], [1, 1], [3, 4]])
        self.assertAllEqual(yval, [[1, 0], [1, 1], [4, 1]])
      finally:
        coord.request_stop()
        coord.join()
Example #4
Source File: reader_test.py    From object_detection_with_tensorflow with MIT License 6 votes vote down vote up 
def testPtbProducer(self):
    raw_data = [4, 3, 2, 1, 0, 5, 6, 1, 1, 1, 1, 0, 3, 4, 1]
    batch_size = 3
    num_steps = 2
    x, y = reader.ptb_producer(raw_data, batch_size, num_steps)
    with self.test_session() as session:
      coord = tf.train.Coordinator()
      tf.train.start_queue_runners(session, coord=coord)
      try:
        xval, yval = session.run([x, y])
        self.assertAllEqual(xval, [[4, 3], [5, 6], [1, 0]])
        self.assertAllEqual(yval, [[3, 2], [6, 1], [0, 3]])
        xval, yval = session.run([x, y])
        self.assertAllEqual(xval, [[2, 1], [1, 1], [3, 4]])
        self.assertAllEqual(yval, [[1, 0], [1, 1], [4, 1]])
      finally:
        coord.request_stop()
        coord.join()
Example #5
Source File: reader_test.py    From object_detection_kitti with Apache License 2.0 6 votes vote down vote up 
def testPtbProducer(self):
    raw_data = [4, 3, 2, 1, 0, 5, 6, 1, 1, 1, 1, 0, 3, 4, 1]
    batch_size = 3
    num_steps = 2
    x, y = reader.ptb_producer(raw_data, batch_size, num_steps)
    with self.test_session() as session:
      coord = tf.train.Coordinator()
      tf.train.start_queue_runners(session, coord=coord)
      try:
        xval, yval = session.run([x, y])
        self.assertAllEqual(xval, [[4, 3], [5, 6], [1, 0]])
        self.assertAllEqual(yval, [[3, 2], [6, 1], [0, 3]])
        xval, yval = session.run([x, y])
        self.assertAllEqual(xval, [[2, 1], [1, 1], [3, 4]])
        self.assertAllEqual(yval, [[1, 0], [1, 1], [4, 1]])
      finally:
        coord.request_stop()
        coord.join()
Example #6
Source File: reader_test.py    From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 6 votes vote down vote up 
def testPtbProducer(self):
    raw_data = [4, 3, 2, 1, 0, 5, 6, 1, 1, 1, 1, 0, 3, 4, 1]
    batch_size = 3
    num_steps = 2
    x, y = reader.ptb_producer(raw_data, batch_size, num_steps)
    with self.test_session() as session:
      coord = tf.train.Coordinator()
      tf.train.start_queue_runners(session, coord=coord)
      try:
        xval, yval = session.run([x, y])
        self.assertAllEqual(xval, [[4, 3], [5, 6], [1, 0]])
        self.assertAllEqual(yval, [[3, 2], [6, 1], [0, 3]])
        xval, yval = session.run([x, y])
        self.assertAllEqual(xval, [[2, 1], [1, 1], [3, 4]])
        self.assertAllEqual(yval, [[1, 0], [1, 1], [4, 1]])
      finally:
        coord.request_stop()
        coord.join()
Example #7
Source File: reader_test.py    From ActionRecognition with MIT License 6 votes vote down vote up 
def testPtbProducer(self):
        raw_data = [4, 3, 2, 1, 0, 5, 6, 1, 1, 1, 1, 0, 3, 4, 1]
        batch_size = 3
        num_steps = 2
        x, y = reader.ptb_producer(raw_data, batch_size, num_steps)
        with self.test_session() as session:
            coord = tf.train.Coordinator()
            tf.train.start_queue_runners(session, coord=coord)
            try:
                xval, yval = session.run([x, y])
                self.assertAllEqual(xval, [[4, 3], [5, 6], [1, 0]])
                self.assertAllEqual(yval, [[3, 2], [6, 1], [0, 3]])
                xval, yval = session.run([x, y])
                self.assertAllEqual(xval, [[2, 1], [1, 1], [3, 4]])
                self.assertAllEqual(yval, [[1, 0], [1, 1], [4, 1]])
            finally:
                coord.request_stop()
                coord.join()
Example #8
Source File: reader_test.py    From hands-detection with MIT License 6 votes vote down vote up 
def testPtbProducer(self):
    raw_data = [4, 3, 2, 1, 0, 5, 6, 1, 1, 1, 1, 0, 3, 4, 1]
    batch_size = 3
    num_steps = 2
    x, y = reader.ptb_producer(raw_data, batch_size, num_steps)
    with self.test_session() as session:
      coord = tf.train.Coordinator()
      tf.train.start_queue_runners(session, coord=coord)
      try:
        xval, yval = session.run([x, y])
        self.assertAllEqual(xval, [[4, 3], [5, 6], [1, 0]])
        self.assertAllEqual(yval, [[3, 2], [6, 1], [0, 3]])
        xval, yval = session.run([x, y])
        self.assertAllEqual(xval, [[2, 1], [1, 1], [3, 4]])
        self.assertAllEqual(yval, [[1, 0], [1, 1], [4, 1]])
      finally:
        coord.request_stop()
        coord.join()
Example #9
Source File: reader_test.py    From iss-rnns with Apache License 2.0 6 votes vote down vote up 
def testPtbProducer(self):
    raw_data = [4, 3, 2, 1, 0, 5, 6, 1, 1, 1, 1, 0, 3, 4, 1]
    batch_size = 3
    num_steps = 2
    x, y = reader.ptb_producer(raw_data, batch_size, num_steps)
    with self.test_session() as session:
      coord = tf.train.Coordinator()
      tf.train.start_queue_runners(session, coord=coord)
      try:
        xval, yval = session.run([x, y])
        self.assertAllEqual(xval, [[4, 3], [5, 6], [1, 0]])
        self.assertAllEqual(yval, [[3, 2], [6, 1], [0, 3]])
        xval, yval = session.run([x, y])
        self.assertAllEqual(xval, [[2, 1], [1, 1], [3, 4]])
        self.assertAllEqual(yval, [[1, 0], [1, 1], [4, 1]])
      finally:
        coord.request_stop()
        coord.join()
Example #10
Source File: reader_test.py    From Gun-Detector with Apache License 2.0 6 votes vote down vote up 
def testPtbProducer(self):
    raw_data = [4, 3, 2, 1, 0, 5, 6, 1, 1, 1, 1, 0, 3, 4, 1]
    batch_size = 3
    num_steps = 2
    x, y = reader.ptb_producer(raw_data, batch_size, num_steps)
    with self.test_session() as session:
      coord = tf.train.Coordinator()
      tf.train.start_queue_runners(session, coord=coord)
      try:
        xval, yval = session.run([x, y])
        self.assertAllEqual(xval, [[4, 3], [5, 6], [1, 0]])
        self.assertAllEqual(yval, [[3, 2], [6, 1], [0, 3]])
        xval, yval = session.run([x, y])
        self.assertAllEqual(xval, [[2, 1], [1, 1], [3, 4]])
        self.assertAllEqual(yval, [[1, 0], [1, 1], [4, 1]])
      finally:
        coord.request_stop()
        coord.join()
Example #11
Source File: reader_test.py    From yolo_v2 with Apache License 2.0 6 votes vote down vote up 
def testPtbProducer(self):
    raw_data = [4, 3, 2, 1, 0, 5, 6, 1, 1, 1, 1, 0, 3, 4, 1]
    batch_size = 3
    num_steps = 2
    x, y = reader.ptb_producer(raw_data, batch_size, num_steps)
    with self.test_session() as session:
      coord = tf.train.Coordinator()
      tf.train.start_queue_runners(session, coord=coord)
      try:
        xval, yval = session.run([x, y])
        self.assertAllEqual(xval, [[4, 3], [5, 6], [1, 0]])
        self.assertAllEqual(yval, [[3, 2], [6, 1], [0, 3]])
        xval, yval = session.run([x, y])
        self.assertAllEqual(xval, [[2, 1], [1, 1], [3, 4]])
        self.assertAllEqual(yval, [[1, 0], [1, 1], [4, 1]])
      finally:
        coord.request_stop()
        coord.join()
Example #12
Source File: ptb_word_lm_hd.py    From iss-rnns with Apache License 2.0 5 votes vote down vote up 
def __init__(self, config, data, name=None):
    self.batch_size = batch_size = config.batch_size
    self.num_steps = num_steps = config.num_steps
    self.epoch_size = ((len(data) // batch_size) - 1) // num_steps
    self.input_data, self.targets = reader.ptb_producer(
        data, batch_size, num_steps, name=name)
Example #13
Source File: ptb_word_lm.py    From DOTA_models with Apache License 2.0 5 votes vote down vote up 
def __init__(self, config, data, name=None):
    self.batch_size = batch_size = config.batch_size
    self.num_steps = num_steps = config.num_steps
    self.epoch_size = ((len(data) // batch_size) - 1) // num_steps
    self.input_data, self.targets = reader.ptb_producer(
        data, batch_size, num_steps, name=name)
Example #14
Source File: ptb_word_lm.py    From g-tensorflow-models with Apache License 2.0 5 votes vote down vote up 
def __init__(self, config, data, name=None):
    self.batch_size = batch_size = config.batch_size
    self.num_steps = num_steps = config.num_steps
    self.epoch_size = ((len(data) // batch_size) - 1) // num_steps
    self.input_data, self.targets = reader.ptb_producer(
        data, batch_size, num_steps, name=name)
Example #15
Source File: ptb_word_lm.py    From YellowFin with Apache License 2.0 5 votes vote down vote up 
def __init__(self, config, data, name=None):
    self.batch_size = batch_size = config.batch_size
    self.num_steps = num_steps = config.num_steps
    self.epoch_size = ((len(data) // batch_size) - 1) // num_steps
    self.input_data, self.targets = reader.ptb_producer(
        data, batch_size, num_steps, name=name)
Example #16
Source File: ptb_word_lm.py    From HumanRecognition with MIT License 5 votes vote down vote up 
def __init__(self, config, data, name=None):
    self.batch_size = batch_size = config.batch_size
    self.num_steps = num_steps = config.num_steps
    self.epoch_size = ((len(data) // batch_size) - 1) // num_steps
    self.input_data, self.targets = reader.ptb_producer(
        data, batch_size, num_steps, name=name)
Example #17
Source File: 7_2_LSTM.py    From TensorFlow-HelloWorld with Apache License 2.0 5 votes vote down vote up 
def __init__(self, config, data, name=None):
    self.batch_size = batch_size = config.batch_size
    self.num_steps = num_steps = config.num_steps
    self.epoch_size = ((len(data) // batch_size) - 1) // num_steps
    self.input_data, self.targets = reader.ptb_producer(
        data, batch_size, num_steps, name=name)
Example #18
Source File: ptb_word_lm.py    From yolo_v2 with Apache License 2.0 5 votes vote down vote up 
def __init__(self, config, data, name=None):
    self.batch_size = batch_size = config.batch_size
    self.num_steps = num_steps = config.num_steps
    self.epoch_size = ((len(data) // batch_size) - 1) // num_steps
    self.input_data, self.targets = reader.ptb_producer(
        data, batch_size, num_steps, name=name)
Example #19
Source File: ptb_word_lm.py    From object_detection_with_tensorflow with MIT License 5 votes vote down vote up 
def __init__(self, config, data, name=None):
    self.batch_size = batch_size = config.batch_size
    self.num_steps = num_steps = config.num_steps
    self.epoch_size = ((len(data) // batch_size) - 1) // num_steps
    self.input_data, self.targets = reader.ptb_producer(
        data, batch_size, num_steps, name=name)
Example #20
Source File: ptb_word_lm.py    From object_detection_kitti with Apache License 2.0 5 votes vote down vote up 
def __init__(self, config, data, name=None):
    self.batch_size = batch_size = config.batch_size
    self.num_steps = num_steps = config.num_steps
    self.epoch_size = ((len(data) // batch_size) - 1) // num_steps
    self.input_data, self.targets = reader.ptb_producer(
        data, batch_size, num_steps, name=name)
Example #21
Source File: ptb_word_lm.py    From Gun-Detector with Apache License 2.0 5 votes vote down vote up 
def __init__(self, config, data, name=None):
    self.batch_size = batch_size = config.batch_size
    self.num_steps = num_steps = config.num_steps
    self.epoch_size = ((len(data) // batch_size) - 1) // num_steps
    self.input_data, self.targets = reader.ptb_producer(
        data, batch_size, num_steps, name=name)
Example #22
Source File: ptb_word_lm.py    From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 5 votes vote down vote up 
def __init__(self, config, data, name=None):
    self.batch_size = batch_size = config.batch_size
    self.num_steps = num_steps = config.num_steps
    self.epoch_size = ((len(data) // batch_size) - 1) // num_steps
    self.input_data, self.targets = reader.ptb_producer(
        data, batch_size, num_steps, name=name)
Example #23
Source File: ptb_word_lm.py    From ActionRecognition with MIT License 5 votes vote down vote up 
def __init__(self, config, data, name=None):
        '''
          num_steps: the number of timesteps (or unrolled steps)

        '''
        self.batch_size = batch_size = config.batch_size
        self.num_steps = num_steps = config.num_steps
        self.epoch_size = ((len(data) // batch_size) - 1) // num_steps
        self.input_data, self.targets = reader.ptb_producer(
            data, batch_size, num_steps, name=name)
Example #24
Source File: main.py    From rotational-unit-of-memory with MIT License 5 votes vote down vote up 
def __init__(self, config, data, name=None):
        self.batch_size = batch_size = config.batch_size
        self.num_steps = num_steps = config.num_steps
        self.epoch_size = ((len(data) // batch_size) - 1) // num_steps
        self.input_data, self.targets = reader.ptb_producer(
            data, batch_size, num_steps, name=name)
Example #25
Source File: ptb_word_lm.py    From hands-detection with MIT License 5 votes vote down vote up 
def __init__(self, config, data, name=None):
    self.batch_size = batch_size = config.batch_size
    self.num_steps = num_steps = config.num_steps
    self.epoch_size = ((len(data) // batch_size) - 1) // num_steps
    self.input_data, self.targets = reader.ptb_producer(
        data, batch_size, num_steps, name=name)
Example #26
Source File: bayesian_rnn.py    From BayesianRecurrentNN with MIT License 5 votes vote down vote up 
def __init__(self, config, data, name=None):
		self.batch_size = batch_size = config.batch_size
		self.num_steps = num_steps = config.num_steps
		self.epoch_size = ((len(data) // batch_size) - 1) // num_steps
		self.input_data, self.targets = reader.ptb_producer(
			data, batch_size, num_steps, name=name)
Example #27
Source File: main.py    From Fast-Slow-LSTM with Apache License 2.0 5 votes vote down vote up 
def __init__(self, config, data, name=None):
        self.batch_size = batch_size = config.batch_size
        self.num_steps = num_steps = config.num_steps
        self.epoch_size = ((len(data) // batch_size) - 1) // num_steps
        self.input_data, self.targets = reader.ptb_producer(
            data, batch_size, num_steps, name=name)
Example #28
Source File: ptb_word_lm.py    From iss-rnns with Apache License 2.0 5 votes vote down vote up 
def __init__(self, config, data, name=None):
    self.batch_size = batch_size = config.batch_size
    self.num_steps = num_steps = config.num_steps
    self.epoch_size = ((len(data) // batch_size) - 1) // num_steps
    self.input_data, self.targets = reader.ptb_producer(
        data, batch_size, num_steps, name=name)
Example #29
Source File: ptb_word_lm_heter.py    From iss-rnns with Apache License 2.0 5 votes vote down vote up 
def __init__(self, config, data, name=None):
    self.batch_size = batch_size = config.batch_size
    self.num_steps = num_steps = config.num_steps
    self.epoch_size = ((len(data) // batch_size) - 1) // num_steps
    self.input_data, self.targets = reader.ptb_producer(
        data, batch_size, num_steps, name=name)
Example #30
Source File: PTB-LSTM.py    From tensorpack with Apache License 2.0 4 votes vote down vote up 
def get_config():
    logger.auto_set_dir()

    data3, wd2id = get_PennTreeBank()
    global VOCAB_SIZE
    VOCAB_SIZE = len(wd2id)
    steps_per_epoch = (data3[0].shape[0] // BATCH - 1) // SEQ_LEN

    train_data = TensorInput(
        lambda: ptb_producer(data3[0], BATCH, SEQ_LEN),
        steps_per_epoch)
    val_data = TensorInput(
        lambda: ptb_producer(data3[1], BATCH, SEQ_LEN),
        (data3[1].shape[0] // BATCH - 1) // SEQ_LEN)

    test_data = TensorInput(
        lambda: ptb_producer(data3[2], BATCH, SEQ_LEN),
        (data3[2].shape[0] // BATCH - 1) // SEQ_LEN)

    M = Model()
    return TrainConfig(
        data=train_data,
        model=M,
        callbacks=[
            ModelSaver(),
            HyperParamSetterWithFunc(
                'learning_rate',
                lambda e, x: x * 0.80 if e > 6 else x),
            RunOp(lambda: M.reset_lstm_state()),
            InferenceRunner(val_data, [ScalarStats(['cost'])]),
            RunOp(lambda: M.reset_lstm_state()),
            InferenceRunner(
                test_data,
                [ScalarStats(['cost'], prefix='test')], tower_name='InferenceTowerTest'),
            RunOp(lambda: M.reset_lstm_state()),
            CallbackFactory(
                trigger=lambda self:
                [self.trainer.monitors.put_scalar(
                    'validation_perplexity',
                    np.exp(self.trainer.monitors.get_latest('validation_cost') / SEQ_LEN)),
                 self.trainer.monitors.put_scalar(
                     'test_perplexity',
                     np.exp(self.trainer.monitors.get_latest('test_cost') / SEQ_LEN))]
            ),
        ],
        max_epoch=70,
    )