def get_data(PICKLEDIC, PICKLENAME):
# read test data
test_set = load(os.path.join(PICKLEDIC, PICKLENAME))
if test_set is None:
print("No Test data, aborting...")
sys.exit(0)
random.shuffle(test_set) # should be here
with open("../dataset/kFoldDataset/pickles/classes.json") as classesFile:
class_dict = json.load(classesFile)
test_lens = get_class_numbers(test_set, class_dict)
test_data = get_reduced_set(test_set, test_lens, 'min')
rawdata, specdata, _ = get_samples_and_labels(test_data)
return rawdata, specdata
train_set = load(train_dataset_path)
if train_set is None:
print("No Train data, aborting...")
sys.exit(0)
# read train data
test_set = load(test_dataset_path)
if test_set is None:
print("No Test data, aborting...")
sys.exit(0)
random.shuffle(train_set)
random.shuffle(test_set) # should be here
train_lens = get_class_numbers(train_set, class_dict)
train_data = get_reduced_set(train_set, train_lens, 'min')
test_lens = get_class_numbers(test_set, class_dict)
test_data = get_reduced_set(test_set, test_lens, 'min')
_, Xtrain, Ytrain = get_samples_and_labels(train_data)
_, Xtest, Ytest = get_samples_and_labels(test_data)
print("Train size", len(Ytrain))
print("Test size", len(Ytest))
Xtrain = tf.convert_to_tensor(Xtrain, dtype=tf.float32)
Ytrain = tf.convert_to_tensor(Ytrain, dtype=tf.float32)
Xtest = tf.convert_to_tensor(Xtest, dtype=tf.float32)
Ytest = tf.convert_to_tensor(Ytest, dtype=tf.float32)
for i in range(folds):
train_dataset_path = train_path + str(i)
test_dataset_path = test_path + str(i)
raw_path = os.path.join(model_raw, str(i) + '.h5')
spectro_path = os.path.join(model_spectro, str(i) + '.h5')
# read test data
test_set = load(test_dataset_path)
if test_set is None:
print("No Test data, aborting...")
sys.exit(0)
random.shuffle(test_set) # should be here
test_lens = get_class_numbers(test_set, class_dict)
test_data = get_reduced_set(test_set, test_lens, 'min')
Xtest_raw, Xtest_spec, Ytest = get_samples_and_labels(test_data)
size = len(Ytest)
Xtest_raw = tf.convert_to_tensor(Xtest_raw, dtype=tf.float32)
Xtest_spec = tf.convert_to_tensor(Xtest_spec, dtype=tf.float32)
Ytest = tf.convert_to_tensor(Ytest, dtype=tf.float32)
print("Allocating tensors")
test_it = tf.data.Dataset.from_tensor_slices(
(Xtest_raw, Xtest_spec, Ytest))
rawnet = rawCNN()