Python util.count_parameters() Examples

The following are 3 code examples of util.count_parameters(). 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 util , or try the search function .
Example #1
Source File: model.py    From g-tensorflow-models with Apache License 2.0 4 votes vote down vote up 
def __init__(self,
               data_dir=None,
               is_training=True,
               learning_rate=0.0002,
               beta1=0.9,
               reconstr_weight=0.85,
               smooth_weight=0.05,
               ssim_weight=0.15,
               icp_weight=0.0,
               batch_size=4,
               img_height=128,
               img_width=416,
               seq_length=3,
               legacy_mode=False):
    self.data_dir = data_dir
    self.is_training = is_training
    self.learning_rate = learning_rate
    self.reconstr_weight = reconstr_weight
    self.smooth_weight = smooth_weight
    self.ssim_weight = ssim_weight
    self.icp_weight = icp_weight
    self.beta1 = beta1
    self.batch_size = batch_size
    self.img_height = img_height
    self.img_width = img_width
    self.seq_length = seq_length
    self.legacy_mode = legacy_mode

    logging.info('data_dir: %s', data_dir)
    logging.info('learning_rate: %s', learning_rate)
    logging.info('beta1: %s', beta1)
    logging.info('smooth_weight: %s', smooth_weight)
    logging.info('ssim_weight: %s', ssim_weight)
    logging.info('icp_weight: %s', icp_weight)
    logging.info('batch_size: %s', batch_size)
    logging.info('img_height: %s', img_height)
    logging.info('img_width: %s', img_width)
    logging.info('seq_length: %s', seq_length)
    logging.info('legacy_mode: %s', legacy_mode)

    if self.is_training:
      self.reader = reader.DataReader(self.data_dir, self.batch_size,
                                      self.img_height, self.img_width,
                                      self.seq_length, NUM_SCALES)
      self.build_train_graph()
    else:
      self.build_depth_test_graph()
      self.build_egomotion_test_graph()

    # At this point, the model is ready.  Print some info on model params.
    util.count_parameters()
Example #2
Source File: model.py    From models with Apache License 2.0 4 votes vote down vote up 
def __init__(self,
               data_dir=None,
               is_training=True,
               learning_rate=0.0002,
               beta1=0.9,
               reconstr_weight=0.85,
               smooth_weight=0.05,
               ssim_weight=0.15,
               icp_weight=0.0,
               batch_size=4,
               img_height=128,
               img_width=416,
               seq_length=3,
               legacy_mode=False):
    self.data_dir = data_dir
    self.is_training = is_training
    self.learning_rate = learning_rate
    self.reconstr_weight = reconstr_weight
    self.smooth_weight = smooth_weight
    self.ssim_weight = ssim_weight
    self.icp_weight = icp_weight
    self.beta1 = beta1
    self.batch_size = batch_size
    self.img_height = img_height
    self.img_width = img_width
    self.seq_length = seq_length
    self.legacy_mode = legacy_mode

    logging.info('data_dir: %s', data_dir)
    logging.info('learning_rate: %s', learning_rate)
    logging.info('beta1: %s', beta1)
    logging.info('smooth_weight: %s', smooth_weight)
    logging.info('ssim_weight: %s', ssim_weight)
    logging.info('icp_weight: %s', icp_weight)
    logging.info('batch_size: %s', batch_size)
    logging.info('img_height: %s', img_height)
    logging.info('img_width: %s', img_width)
    logging.info('seq_length: %s', seq_length)
    logging.info('legacy_mode: %s', legacy_mode)

    if self.is_training:
      self.reader = reader.DataReader(self.data_dir, self.batch_size,
                                      self.img_height, self.img_width,
                                      self.seq_length, NUM_SCALES)
      self.build_train_graph()
    else:
      self.build_depth_test_graph()
      self.build_egomotion_test_graph()

    # At this point, the model is ready.  Print some info on model params.
    util.count_parameters()
Example #3
Source File: model.py    From multilabel-image-classification-tensorflow with MIT License 4 votes vote down vote up 
def __init__(self,
               data_dir=None,
               is_training=True,
               learning_rate=0.0002,
               beta1=0.9,
               reconstr_weight=0.85,
               smooth_weight=0.05,
               ssim_weight=0.15,
               icp_weight=0.0,
               batch_size=4,
               img_height=128,
               img_width=416,
               seq_length=3,
               legacy_mode=False):
    self.data_dir = data_dir
    self.is_training = is_training
    self.learning_rate = learning_rate
    self.reconstr_weight = reconstr_weight
    self.smooth_weight = smooth_weight
    self.ssim_weight = ssim_weight
    self.icp_weight = icp_weight
    self.beta1 = beta1
    self.batch_size = batch_size
    self.img_height = img_height
    self.img_width = img_width
    self.seq_length = seq_length
    self.legacy_mode = legacy_mode

    logging.info('data_dir: %s', data_dir)
    logging.info('learning_rate: %s', learning_rate)
    logging.info('beta1: %s', beta1)
    logging.info('smooth_weight: %s', smooth_weight)
    logging.info('ssim_weight: %s', ssim_weight)
    logging.info('icp_weight: %s', icp_weight)
    logging.info('batch_size: %s', batch_size)
    logging.info('img_height: %s', img_height)
    logging.info('img_width: %s', img_width)
    logging.info('seq_length: %s', seq_length)
    logging.info('legacy_mode: %s', legacy_mode)

    if self.is_training:
      self.reader = reader.DataReader(self.data_dir, self.batch_size,
                                      self.img_height, self.img_width,
                                      self.seq_length, NUM_SCALES)
      self.build_train_graph()
    else:
      self.build_depth_test_graph()
      self.build_egomotion_test_graph()

    # At this point, the model is ready.  Print some info on model params.
    util.count_parameters()