def get_all_data_no_split():
import keras
##Grabbing
dat, lab = data_grab.all_data(currentdir, data_dir)
lab = [0 if x == -1 else 1 for x in lab]
dat_n = np.array(dat)
lab_n = np.array(lab)
lab_n = np.expand_dims(lab_n, axis=1)
return dat_n, lab_n
def get_datasets(test=True):
import keras
##Grabbing
dat, lab = data_grab.all_data(currentdir, data_dir)
lab = [0 if x == -1 else 1 for x in lab]
dat_n = np.array(dat)
lab_n = np.array(lab)
lab_n = np.expand_dims(lab_n, axis=1)
## Shuffle at subject level - not slice level
dat_index = np.arange(len(dat))
dat_index = np.expand_dims(dat_index, axis=1)
if test:
X_train_idx, X_test_idx, y_train, y_test = train_test_split(
dat_index, lab_n, test_size=0.3, stratify=lab_n)
x_train = dat_n[X_train_idx]
x_test = dat_n[X_test_idx]
x_train = x_train.reshape(x_train.shape[0], 100, 100, 100, 1)
x_test = x_test.reshape(x_test.shape[0], 100, 100, 100, 1)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
# Convert class vectors to binary class matrices.
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
else:
x_train = dat_n
x_train = x_train.reshape(x_train.shape[0], 100, 100, 100, 1)
x_train = x_train.astype('float32')
x_train /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
y_train = keras.utils.to_categorical(lab_n, num_classes)
x_test = None
y_test = None
# Convert class vectors to binary class matrices.
#y_train = keras.utils.to_categorical(y_train, num_classes)
return x_train, y_train, x_test, y_test
def get_datasets():
def gen_portion(indexes, data, portion=3):
ratio = int(indexes.shape[0] / portion)
train = data[ratio:, :]
test = data[:ratio, :]
return train, test
##Grabbing
dat, lab = data_grab.all_data(currentdir, data_dir)
lab = [0 if x==-1 else 1 for x in lab]
dat_n = np.array(dat)
lab_n = np.array(lab)
lab_n = np.expand_dims(lab_n, axis=1)
## Shuffle at subject level - not slice level
dat_index = np.arange(len(dat))
dat_index = np.expand_dims(dat_index, axis=1)
X_train_idx, X_test_idx, y_train, y_test = train_test_split(dat_index, lab_n, test_size=0.33, stratify=lab_n)
x_train = dat_n[X_train_idx]
x_test = dat_n[X_test_idx]
x_train = x_train.reshape(x_train.shape[0], 80, 80, 80, 1)
x_test = x_test.reshape(x_test.shape[0], 80, 80, 80, 1)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train/=255
x_test/=255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
# Convert class vectors to binary class matrices.
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
return x_train, y_train, x_test,y_test
num_classes = 2
epochs = 100
data_augmentation = False
save_dir = os.path.join(os.getcwd(), 'saved_models')
model_name = 'keras_deepmriqc_cnnv13D_trained_model.h5'
def gen_portion(indexes, data, portion=3):
ratio = int(indexes.shape[0] / portion)
train = data[ratio:, :]
test = data[:ratio, :]
return train, test
##Grabbing
dat, lab = data_grab.all_data()
lab = [0 if x == -1 else 1 for x in lab]
dat_n = np.array(dat)
lab_n = np.array(lab)
lab_n = np.expand_dims(lab_n, axis=1)
## Shuffle at subject level - not slice level
dat_index = np.arange(len(dat))
dat_index = np.expand_dims(dat_index, axis=1)
X_train_idx, X_test_idx, y_train, y_test = train_test_split(dat_index,
lab_n,
test_size=0.33,
stratify=lab_n)
#indexes_train_subjects, indexes_test_subjects = gen_portion(dat_index, dat_index)