def train_initial(name, n_seq, n_labels, n_dimension, n_hidden_1, n_hidden_2, epochs, save): usage_ratio = 1 # epochs = 150 print '========================= Reading =========================' X_train, y_train, X_test, y_test = test.read_data(name=name, n_seq=n_seq, n_labels=n_labels, n_dimension=n_dimension) data = (X_train, y_train, X_test, y_test) print '========================= Modeling =========================' model = lstm.build_model(n_dimension=n_dimension, n_labels=n_labels, n_seq=n_seq, n_hidden_1=n_hidden_1, n_hidden_2=n_hidden_2) print '========================= Training ==========================' model = lstm.run_network(model=model, data=data, epochs=epochs, usage_ratio=usage_ratio, save=True, save_name=name) print '========================= Testing ==========================' test.test_all_metrics(model, data=data, usage_ratio=usage_ratio)
def train_initial_divide(marker, n_dimension, n_seq, n_hidden_1, n_hidden_2, epochs, save): usage_ratio = 1 # epochs = 150 print '========================= Reading =========================' X_train, y_train, X_test, y_test = pretraining.divide_save( filename = '../data/torque_participants/S01_valid_' + marker + '.csv', savename = marker + '_' + n_dimension + '_' + n_seq, n_seq = n_seq ) data = (X_train, y_train, X_test, y_test) print '========================= Modeling =========================' model = lstm.build_model(n_dimension=n_dimension, n_seq=n_seq, n_hidden_1=n_hidden_1, n_hidden_2=n_hidden_2) print '========================= Training ==========================' model = lstm.run_network(model=model, data=data, epochs=epochs, usage_ratio=usage_ratio, save=True, save_name=marker + '_' + n_dimension + '_' + n_seq) print '========================= Testing ==========================' test.test_all_metrics(model, data=data, usage_ratio=usage_ratio)