def cross_validate_normalized(hyperparams, cur_model):
nn_simple = MyNeuralNets(input_dim=train.shape[1] - 1,
n_classes=len(np.unique(train['Class'])),
hyperparams=hyperparams,
model_setup=cur_model)
scores = nn_simple.cross_validate(train_normalized.iloc[:, :-1],
train_normalized.iloc[:, -1],
cv=3,
architecture_type='dynamic')
return scores
def create_submission(hyperparams, name_string, model_setup={}):
nn_simple = MyNeuralNets(input_dim=train.shape[1] - 1,
n_classes=len(np.unique(train['Class'])),
hyperparams=hyperparams,
model_setup=model_setup)
nn_simple.build_model()
nn_simple.fit(train.iloc[:, :-1],
np.array(train.iloc[:, -1]).reshape((-1, 1)))
predicted_labels = nn_simple.predict(test)
predicted_labels = pd.DataFrame(predicted_labels,
index=test.index,
columns=[
'class1', 'class2', 'class3', 'class4',
'class5', 'class6', 'class7', 'class8',
'class9'
])
predicted_labels.to_csv('data/predicted_labels/' + name_string + '.csv')