help='save patterns images')
args = parser.parse_args()
n_classes = 7
n_epochs = 200
learning_rate = 0.001
batch_size = 64
current_working_dir = os.getcwd()
print('current_working_dir: ', current_working_dir)
pre = Preprocessing('fer2013', root_dir=current_working_dir)
pre.load_data(filename='DatasetD.csv', name='train')
pre.load_data(filename='test_public_norm.csv', name='validate')
X_train_df = pre.get(name='train').drop(columns=['emotion'])
y_train_df = pre.get(name='train')['emotion']
X_val_df = pre.get(name='validate').drop(columns=['emotion'])
y_val_df = pre.get(name='validate')['emotion']
dtype = torch.float
model_name = f'cnn_triple_layer_D_bs_{learning_rate}_{batch_size}_{n_epochs}_{n_classes}'
model = CnnTripleLayer(model_name, d_out=n_classes)
model.train()
train_classifier = TrainClassifier2(model,
X_train_df,
y_train_df,
X_val_df,
y_val_df,
print('train learning transfer VGG16 DatasetA')
parser = argparse.ArgumentParser(description='fer2013')
parser.add_argument('--s_model', default=True, help='save trained model')
parser.add_argument('--s_patterns', default=False, help='save patterns images')
args=parser.parse_args()
current_working_dir = os.getcwd()
print('current_working_dir: ', current_working_dir)
pre = Preprocessing('fer2013', root_dir=current_working_dir)
pre.load_data('train_reduced_norm.csv.gz', name='train')
pre.load_data('test_public_norm.csv.gz', name='val')
X = pre.get('val').drop(columns=['emotion'])
y = pre.get('val')['emotion']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 42)
val = pd.DataFrame(X_test)
val['emotion'] = y_test
pre.set(name='val', value=val)
print(pre.get(name='val').head())
train_pixels = pre.get(name='train').drop(columns=['emotion'])
val_pixels = pre.get(name='val').drop(columns=['emotion'])
print('data loaded')
img_conv = ImageConverter()
parser.add_argument('--s_model', default=True, help='save trained model')
parser.add_argument('--s_patterns',
default=False,
help='save patterns images')
args = parser.parse_args()
n_classes = 7
n_epochs = 300
learning_rate = 0.0001
batch_size = 32
pre = Preprocessing('fer2013')
pre.load_data(filename='DatasetD.csv', name='train')
X_df = pre.get(name='train').drop(columns=['emotion'])
y_df = pre.get(name='train')['emotion']
dtype = torch.float
device = torch.device("cpu")
model_name = f'cnn_double_layer_D_bs_{learning_rate}_{batch_size}_{n_epochs}_{n_classes}'
model = CnnDoubleLayer(model_name, d_out=n_classes)
model.train()
train_classifier = TrainClassifier2(model, X_df, y_df)
t = time.time()
trained_model, optimizer, criterion, loss_hist, loss_val_hist, f1_val_hist = train_classifier.run_train(
n_epochs=n_epochs, lr=learning_rate, batch_size=batch_size)
print(f'trained in {time.time() - t} sec')
pre.save_results(loss_hist, loss_val_hist, f1_val_hist, f'{model_name}')
import matplotlib.pyplot as plt
if not __name__ == '__main_':
parser = argparse.ArgumentParser(description='fer2013_DatasetA')
parser.add_argument('--s_model', default=True, help='save trained model')
args = parser.parse_args()
n_epochs = 100
pre = Preprocessing('fer2013_DatasetA')
pre.load_data(filename='DatasetA.csv', name='train')
X_train_df = pre.get(name='train').drop(columns=['emotion'])
y_train_df = pre.get(name='train')['emotion']
dtype = torch.float
device = torch.device("cpu")
H1 = 1764
n_features = len(X_train_df.columns)
n_features_encoded = 1296
print(f'features {n_features}')
print(f'H1 {H1}')
print(f'n_features_encoded {n_features_encoded}')
model_name = 'ann_encoder'
model = AnnAutoencoder(model_name, n_features, H1, n_features_encoded)
