def __init__(
self,
name,
data='fer2013',
file='train.csv',
):
self.name = name.lower()
self.image = {}
root_dir = os.path.dirname(__file__)
directory_template = '{root_dir}/../../data/{name}/'
self.directory = directory_template.format(root_dir=root_dir,
name=name)
prep = Preprocessing(name=data)
self.data = prep.load_data(name='img_arrays', filename=file)
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 = 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,
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()
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)
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()
n_classes = 7
n_epochs = 10
learning_rate = 0.0001
batch_size = 64
pre = Preprocessing('fer2013')
pre.load_data(filename='train_reduced_norm.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_reduced_{learning_rate}_{batch_size}_{n_epochs}_{n_classes}'
model = CnnDoubleLayer(model_name, d_out=n_classes)
model.train()
train_classifier = TrainClassifier(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)
from utils import ModelImporter, Preprocessing
import torch
if not __name__ == '__main_':
pre = Preprocessing('fer2013_DatasetA')
pre.load_data(filename='test_public_norm.csv', name='test')
X_df = pre.get(name='test').drop(columns=['emotion'])
y_df = pre.get(name='test')['emotion']
n_classes = 7
n_totalepochs = 200
learning_rate = 0.00005
batch_size = 32
epoch_n = 40#for the temp folder
dtype = torch.float
device = torch.device("cpu")
model_name = f'cnn_double_layer_reduced_{learning_rate}_{batch_size}_{n_totalepochs}_{n_classes}'
model_name_bestvalloss = f'{model_name}_epoch150'
m_importer = ModelImporter('fer2013_DatasetA')
model = m_importer.load_nn_model(model_name_bestvalloss)
model.eval()
X_test = model.reshape_data(torch.tensor(X_df.values, device=device, dtype=dtype))
y_test = torch.tensor(y_df.values, device=device, dtype=torch.long)
y_pred = model(X_test).argmax(1)
print(y_pred)
import argparse
if not __name__ == '__main_':
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')
import time
import matplotlib.pyplot as plt
if not __name__ == '__main_':
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()
script_root_dir = os.path.dirname(__file__)
pre = Preprocessing('fer2013', root_dir=script_root_dir)
pre.load_data(filename='train_reduced_norm_centered.csv', name='train')
pre.load_data(filename='test_public_norm_centered.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']
n_classes = 7
n_epochs = 100
learning_rate = 0.001
batch_size = 64
model_name = f'cnn_simple_reduced_bs_{learning_rate}_{batch_size}_{n_epochs}_{n_classes}'
model = CnnSimple(model_name, d_out=n_classes)
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 = 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='DatasetAA.csv', name='train')
pre.load_data(filename='test_public_norm_centered.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_A_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,
import argparse
from models import AnnAutoencoder
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'