Python datasets.load_data() Examples

The following are 9 code examples of datasets.load_data(). 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 datasets , or try the search function .
Example #1
Source File: rebar_train.py    From yolo_v2 with Apache License 2.0 5 votes vote down vote up 
def main():
  # Parse hyperparams
  hparams = rebar.default_hparams
  hparams.parse(FLAGS.hparams)
  print(hparams.values())

  train_xs, valid_xs, test_xs = datasets.load_data(hparams)
  mean_xs = np.mean(train_xs, axis=0)  # Compute mean centering on training

  training_steps = 2000000
  model = getattr(rebar, hparams.model)
  sbn = model(hparams, mean_xs=mean_xs)

  scores = train(sbn, train_xs, valid_xs, test_xs,
                 training_steps=training_steps, debug=False)
Example #2
Source File: rebar_train.py    From Gun-Detector with Apache License 2.0 5 votes vote down vote up 
def main():
  # Parse hyperparams
  hparams = rebar.default_hparams
  hparams.parse(FLAGS.hparams)
  print(hparams.values())

  train_xs, valid_xs, test_xs = datasets.load_data(hparams)
  mean_xs = np.mean(train_xs, axis=0)  # Compute mean centering on training

  training_steps = 2000000
  model = getattr(rebar, hparams.model)
  sbn = model(hparams, mean_xs=mean_xs)

  scores = train(sbn, train_xs, valid_xs, test_xs,
                 training_steps=training_steps, debug=False)
Example #3
Source File: main.py    From DEC-DA with MIT License 5 votes vote down vote up 
def _get_data_and_model(args):
    # prepare dataset
    if args.method in ['FcDEC', 'FcIDEC', 'FcDEC-DA', 'FcIDEC-DA']:
        x, y = load_data(args.dataset)
    elif args.method in ['ConvDEC', 'ConvIDEC', 'ConvDEC-DA', 'ConvIDEC-DA']:
        x, y = load_data_conv(args.dataset)
    else:
        raise ValueError("Invalid value for method, which can only be in ['FcDEC', 'FcIDEC', 'ConvDEC', 'ConvIDEC', "
                         "'FcDEC-DA', 'FcIDEC-DA', 'ConvDEC-DA', 'ConvIDEC-DA']")

    # prepare optimizer
    if args.optimizer in ['sgd', 'SGD']:
        optimizer = SGD(args.lr, 0.9)
    else:
        optimizer = Adam()

    # prepare the model
    n_clusters = len(np.unique(y))
    if 'FcDEC' in args.method:
        model = FcDEC(dims=[x.shape[-1], 500, 500, 2000, 10], n_clusters=n_clusters)
        model.compile(optimizer=optimizer, loss='kld')
    elif 'FcIDEC' in args.method:
        model = FcIDEC(dims=[x.shape[-1], 500, 500, 2000, 10], n_clusters=n_clusters)
        model.compile(optimizer=optimizer, loss=['kld', 'mse'], loss_weights=[0.1, 1.0])
    elif 'ConvDEC' in args.method:
        model = ConvDEC(input_shape=x.shape[1:], filters=[32, 64, 128, 10], n_clusters=n_clusters)
        model.compile(optimizer=optimizer, loss='kld')
    elif 'ConvIDEC' in args.method:
        model = ConvIDEC(input_shape=x.shape[1:], filters=[32, 64, 128, 10], n_clusters=n_clusters)
        model.compile(optimizer=optimizer, loss=['kld', 'mse'], loss_weights=[0.1, 1.0])
    else:
        raise ValueError("Invalid value for method, which can only be in ['FcDEC', 'FcIDEC', 'ConvDEC', 'ConvIDEC', "
                         "'FcDEC-DA', 'FcIDEC-DA', 'ConvDEC-DA', 'ConvIDEC-DA']")

    # if -DA method, we'll force aug_pretrain and aug_cluster is True
    if '-DA' in args.method:
        args.aug_pretrain = True
        args.aug_cluster = True

    return (x, y), model
Example #4
Source File: rebar_train.py    From hands-detection with MIT License 5 votes vote down vote up 
def main():
  # Parse hyperparams
  hparams = rebar.default_hparams
  hparams.parse(FLAGS.hparams)
  print(hparams.values())

  train_xs, valid_xs, test_xs = datasets.load_data(hparams)
  mean_xs = np.mean(train_xs, axis=0)  # Compute mean centering on training

  training_steps = 2000000
  model = getattr(rebar, hparams.model)
  sbn = model(hparams, mean_xs=mean_xs)

  scores = train(sbn, train_xs, valid_xs, test_xs,
                 training_steps=training_steps, debug=False)
Example #5
Source File: rebar_train.py    From object_detection_kitti with Apache License 2.0 5 votes vote down vote up 
def main():
  # Parse hyperparams
  hparams = rebar.default_hparams
  hparams.parse(FLAGS.hparams)
  print(hparams.values())

  train_xs, valid_xs, test_xs = datasets.load_data(hparams)
  mean_xs = np.mean(train_xs, axis=0)  # Compute mean centering on training

  training_steps = 2000000
  model = getattr(rebar, hparams.model)
  sbn = model(hparams, mean_xs=mean_xs)

  scores = train(sbn, train_xs, valid_xs, test_xs,
                 training_steps=training_steps, debug=False)
Example #6
Source File: rebar_train.py    From object_detection_with_tensorflow with MIT License 5 votes vote down vote up 
def main():
  # Parse hyperparams
  hparams = rebar.default_hparams
  hparams.parse(FLAGS.hparams)
  print(hparams.values())

  train_xs, valid_xs, test_xs = datasets.load_data(hparams)
  mean_xs = np.mean(train_xs, axis=0)  # Compute mean centering on training

  training_steps = 2000000
  model = getattr(rebar, hparams.model)
  sbn = model(hparams, mean_xs=mean_xs)

  scores = train(sbn, train_xs, valid_xs, test_xs,
                 training_steps=training_steps, debug=False)
Example #7
Source File: rebar_train.py    From g-tensorflow-models with Apache License 2.0 5 votes vote down vote up 
def main():
  # Parse hyperparams
  hparams = rebar.default_hparams
  hparams.parse(FLAGS.hparams)
  print(hparams.values())

  train_xs, valid_xs, test_xs = datasets.load_data(hparams)
  mean_xs = np.mean(train_xs, axis=0)  # Compute mean centering on training

  training_steps = 2000000
  model = getattr(rebar, hparams.model)
  sbn = model(hparams, mean_xs=mean_xs)

  scores = train(sbn, train_xs, valid_xs, test_xs,
                 training_steps=training_steps, debug=False)
Example #8
Source File: rebar_train.py    From models with Apache License 2.0 5 votes vote down vote up 
def main():
  # Parse hyperparams
  hparams = rebar.default_hparams
  hparams.parse(FLAGS.hparams)
  print(hparams.values())

  train_xs, valid_xs, test_xs = datasets.load_data(hparams)
  mean_xs = np.mean(train_xs, axis=0)  # Compute mean centering on training

  training_steps = 2000000
  model = getattr(rebar, hparams.model)
  sbn = model(hparams, mean_xs=mean_xs)

  scores = train(sbn, train_xs, valid_xs, test_xs,
                 training_steps=training_steps, debug=False)
Example #9
Source File: rebar_train.py    From multilabel-image-classification-tensorflow with MIT License 5 votes vote down vote up 
def main():
  # Parse hyperparams
  hparams = rebar.default_hparams
  hparams.parse(FLAGS.hparams)
  print(hparams.values())

  train_xs, valid_xs, test_xs = datasets.load_data(hparams)
  mean_xs = np.mean(train_xs, axis=0)  # Compute mean centering on training

  training_steps = 2000000
  model = getattr(rebar, hparams.model)
  sbn = model(hparams, mean_xs=mean_xs)

  scores = train(sbn, train_xs, valid_xs, test_xs,
                 training_steps=training_steps, debug=False)