INNER CODE UNIT · Python
__init__
MITDeepLearning/introtodeeplearning · mitdeeplearning/lab2.py:50
def __init__(self, data_path, channels_last=True):
print("Opening {}".format(data_path))
sys.stdout.flush()
self.cache = h5py.File(data_path, "r")
print("Loading data into memory...")
sys.stdout.flush()
self.images = self.cache["images"][:]
self.channels_last = channels_last
self.labels = self.cache["labels"][:].astype(np.float32)
self.image_dims = self.images.shape
n_train_samples = self.image_dims[0]
self.train_inds = np.random.permutation(np.arange(n_train_samples))
self.pos_train_inds = self.train_inds[self.labels[self.train_inds, 0] == 1.0]
self.neg_train_inds = self.train_inds[self.labels[self.train_inds, 0] != 1.0]