loading_dataset.py

SAMPLE_SIZE = 2048
print("loading training cat images...")
cat_train_set = np.asarray([pixels_from_path(cat) for cat in glob.glob('cats/*')[:SAMPLE_SIZE]])
print("loading training dog images...")
dog_train_set = np.asarray([pixels_from_path(dog) for dog in glob.glob('dogs/*')[:SAMPLE_SIZE]])

valid_size = 512
print("loading validation cat images...")
cat_valid_set = np.asarray([pixels_from_path(cat) for cat in glob.glob('cats/*')[-valid_size:]])
print("loading validation dog images...")
dog_valid_set = np.asarray([pixels_from_path(dog) for dog in glob.glob('dogs/*')[-valid_size:]])

# generate X and Y (inputs and labels).
x_train = np.concatenate([cat_train_set, dog_train_set])
labels_train = np.asarray([1 for _ in range(SAMPLE_SIZE)]+[0 for _ in range(SAMPLE_SIZE)])

x_valid = np.concatenate([cat_valid_set, dog_valid_set])
labels_valid = np.asarray([1 for _ in range(valid_size)]+[0 for _ in range(valid_size)])