(self, split)
return ds.repeat(num_epochs)
Returns a fake data set with the correct shapes.
Returns a fake data set with the correct shapes.
Returns a fake data set with the correct shapes .
def _make_fake_dataset(self, split):
    """Returns a fake data set with the correct shapes."""
    np.random.seed(self._seed)
    num_samples_per_epoch = 100
    num_epochs = self.eval_test_samples // 100 if split == "test" else None
    images_shape = [num_samples_per_epoch] + list(self.image_shape)
    images = np.random.uniform(size=images_shape).astype(np.float32)
    labels = np.ones((num_samples_per_epoch,), dtype=np.int32)
    ds = tf.data.Dataset.from_tensor_slices((images, labels))
    return ds.repeat(num_epochs)
def _make_fake_dataset ( self , split ) : np . random . seed ( self . _seed ) num_samples_per_epoch = 100 num_epochs = self . eval_test_samples // 100 if split == "test" else None images_shape = [ num_samples_per_epoch ] + list ( self . image_shape ) images = np . random . uniform ( size = images_shape ) . astype ( np . float32 ) labels = np . ones ( ( num_samples_per_epoch , ) , dtype = np . int32 ) ds = tf . data . Dataset . from_tensor_slices ( ( images , labels ) ) return ds . repeat ( num_epochs )