def modify(y_tr, x_tr, x_tst, y_tst, simple_N, noise_pattern, noise_ratio):
idx0123 = (np.argmax(y_tr, axis=1) <= 3)
simple_index = range(simple_N)
x_tr = x_tr[idx0123, :][simple_index, :]
y_tr = y_tr[idx0123, 0:4][simple_index, 0:4]
idx0123_test = (np.argmax(y_tst, axis=1) <= 3)
x_tst = x_tst[idx0123_test, :]
y_tst = y_tst[idx0123_test, 0:4]
y_tr_noisy = data.flip_label(y_tr,
pattern=noise_pattern,
ratio=noise_ratio,
one_hot=True)
return (x_tr, y_tr, x_tst, y_tst, y_tr_noisy)
## DONE
#################################################################################################################################
""" Data preparation """
if dataset == 'cifar10':
x_train, y_train, _, _, x_test, y_test = data.prepare_cifar10_data(
data_dir='data/cifar-10-batches-py'
) ## forms the training and testing data
## DONE
Num_top = 1 #using top k prediction
## DONE
y_train_noisy = data.flip_label(
y_train, pattern=noise_pattern, ratio=noise_ratio, one_hot=True
) ## y_train_noisy is the noisy labels where we randomly change labels of some examples
input_shape = list(
x_train.shape[1:]) ## shape of the given as input to the models (IMP)
n_classes = y_train.shape[1]
n_train = x_train.shape[0]
np.save('y_train_total.npy', y_train)
np.save('y_train_noisy_total.npy', y_train_noisy)
clean_index = np.array([
(y_train_noisy[i, :] == y_train[i, :]).all() for i in range(n_train)
]) # For tracking only, unused during training
noisy_index = np.array([not i for i in clean_index])
# we make an array which tells which of the indices are noisy and which ones are clean.
## DONE
INCV_iter = 4
epochs = 200
INCV_name = 'INCV_ResNet32'
save_dir = 'saved_model'
if not os.path.isdir(save_dir):
os.makedirs(save_dir)
filepath_INCV = os.path.join(save_dir,INCV_name+'-noisy.h5')
#################################################################################################################################
""" Data preparation """
if dataset=='cifar10':
x_train, y_train, _, _, x_test, y_test = data.prepare_cifar10_data(data_dir='data/cifar-10-batches-py')
Num_top = 1 #using top k prediction
y_train_noisy = data.flip_label(y_train, pattern=noise_pattern, ratio=noise_ratio, one_hot=True)
input_shape = list(x_train.shape[1:])
n_classes = y_train.shape[1]
n_train = x_train.shape[0]
np.save('y_train_total.npy',y_train)
np.save('y_train_noisy_total.npy',y_train_noisy)
clean_index = np.array([(y_train_noisy[i,:]==y_train[i,:]).all() for i in range(n_train)])# For tracking only, unused during training
noisy_index = np.array([not i for i in clean_index])
# Generator for data augmantation
datagen = ImageDataGenerator(width_shift_range=4./32, # randomly shift images horizontally (fraction of total width)
height_shift_range=4./32, # randomly shift images vertically (fraction of total height)
horizontal_flip=True) # randomly flip images
#################################################################################################################################
""" Build model """
