for item in dataset_structure.keys():
X_train, y_train, X_test, y_test = load_ABCDE_datasets(dataset_dir + item)
dataset_data.update(
{item: {
'train': [X_train, y_train],
'test': [X_test, y_test]
}})
# %% DNN Training
for dataset in dataset_structure.keys():
data = dataset_data[dataset]
structure = dataset_structure[dataset]
# loading train and test examples
print('Data Preprocessing...')
X_train = preprocess_data(structure['preprocessing'], data['train'][0])
X_test = preprocess_data(structure['preprocessing'], data['test'][0])
y_train = data['train'][1]
Y_train = to_categorical(y_train)
y_test = data['test'][1]
Y_test = to_categorical(y_test)
# some useful value
nb_classes = len(np.unique(y_train))
model_version = structure['model_version']
# reshaping train and test set
X_train, X_test, input_shape = reshape(X_train, X_test,
*structure['shape'])
for dataset in dataset_structure.keys():
data = {}
# Loading datasets
print('Loading Dataset...')
train, X_train, y_train, X_test, y_test = load_ABCDE_datasets(
dataset_dir + dataset)
__TRAIN__ = (X_train.copy(), y_train.copy())
__TEST__ = (X_test.copy(), y_test.copy())
data.update({'train': [X_train, y_train], 'test': [X_test, y_test]})
data.update(train)
structure = dataset_structure[dataset]
# loading train and test examples
print('Data Preprocessing...')
X_train = preprocess_data(structure['preprocessing'], data['train'][0])
X_test = preprocess_data(structure['preprocessing'], data['test'][0])
y_train = data['train'][1]
Y_train = to_categorical(y_train)
y_test = data['test'][1]
Y_test = to_categorical(y_test)
# some useful value
nb_classes = len(np.unique(np.concatenate((y_train, y_test))))
model_version = structure['model_version']
# reshaping train and test set
X_train, X_test, input_shape = reshape(X_train, X_test,
*structure['shape'])
