Пример #1
0
  def _init_data(self):
    hparams = self.hparams
    batch_size = hparams.batch_size
    if hparams.dataset == 'mnist':
      # Get MNIST test data
      X_train, Y_train, X_test, Y_test = data_mnist(
          train_start=hparams.train_start,
          train_end=hparams.train_end,
          test_start=hparams.test_start,
          test_end=hparams.test_end)
      input_shape = (batch_size, 28, 28, 1)
      preproc_func = None
    elif hparams.dataset == 'cifar10':
      X_train, Y_train, X_test, Y_test = cifar_input.read_CIFAR10(
          os.path.join(hparams.data_path, hparams.dataset))
      input_shape = (batch_size, 32, 32, 3)
      preproc_func = cifar_input.cifar_tf_preprocess
    elif hparams.dataset == 'svhn':
      X_train, Y_train, X_test, Y_test = svhn_input.read_SVHN(
          os.path.join(hparams.data_path, hparams.dataset))
      input_shape = (batch_size, 32, 32, 3)
      preproc_func = svhn_input.svhn_tf_preprocess

    # Use label smoothing
    assert Y_train.shape[1] == 10.
    label_smooth = .1
    Y_train = Y_train.clip(label_smooth / 9., 1. - label_smooth)

    self.X_train = X_train
    self.Y_train = Y_train
    self.X_test = X_test
    self.Y_test = Y_test
    self.data = (X_train, Y_train, X_test, Y_test)
    self.input_shape = input_shape
    self.preproc_func = preproc_func
Пример #2
0
    def _init_data(self):
        hparams = self.hparams
        batch_size = hparams.batch_size
        if hparams.dataset == 'mnist':
            # Get MNIST test data
            X_train, Y_train, X_test, Y_test = data_mnist(
                train_start=hparams.train_start,
                train_end=hparams.train_end,
                test_start=hparams.test_start,
                test_end=hparams.test_end)
            input_shape = (batch_size, 28, 28, 1)
            preproc_func = None
        elif hparams.dataset == 'cifar10':
            X_train, Y_train, X_test, Y_test = cifar_input.read_CIFAR10(
                os.path.join(hparams.data_path, hparams.dataset))
            input_shape = (batch_size, 32, 32, 3)
            preproc_func = cifar_input.cifar_tf_preprocess
        elif hparams.dataset == 'svhn':
            X_train, Y_train, X_test, Y_test = svhn_input.read_SVHN(
                os.path.join(hparams.data_path, hparams.dataset))
            input_shape = (batch_size, 32, 32, 3)
            preproc_func = svhn_input.svhn_tf_preprocess

        # Use label smoothing
        assert Y_train.shape[1] == 10.
        label_smooth = .1
        Y_train = Y_train.clip(label_smooth / 9., 1. - label_smooth)

        self.X_train = X_train
        self.Y_train = Y_train
        self.X_test = X_test
        self.Y_test = Y_test
        self.data = (X_train, Y_train, X_test, Y_test)
        self.input_shape = input_shape
        self.preproc_func = preproc_func