chore: make main more readable
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Skudalen
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5b4fb77ec8
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23f3eab055
@ -139,7 +139,8 @@ def prepare_datasets_sessions(X, y, session_lengths, test_session_index=4, nr_su
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return X_train, X_test, y_train, y_test
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# NOT FUNCTIONAL
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# NOT FUNCTIONAL, HAVE NOT LOCATED ERROR
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# Should be like func above, but with extended flexibility
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def prepare_datasets_new(test_session_indexes, X, y, session_lengths, nr_subjects=5, nr_sessions=4):
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X_list = []
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@ -1044,10 +1045,12 @@ if __name__ == "__main__":
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# X.shape = (2806, 1, 208)
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# y.shape = (2806, nr_subjects)
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# session_lengths.shape = (nr_subjects, nr_sessions)
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'''
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#X_soft, y_soft, session_lengths_soft = load_data_from_json(SOFT_DATA_PATH_MFCC, nr_classes=5)
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#X_hard, y_hard, session_lengths_hard = load_data_from_json(HARD_DATA_PATH_MFCC, nr_classes=5)
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'''
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# Parameters:
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# PARAMS:
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NR_SUBJECTS = 5
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NR_SESSIONS = 4
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BATCH_SIZE = 64
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@ -1063,53 +1066,62 @@ if __name__ == "__main__":
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# X_test.shape = (X_test(from session nr. ?), 1, 208)
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# y_train.shape = (2806-y_test, nr_subjects)
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# y_test.shape = (y_test(from session nr. ?), nr_subjects)
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#X_val, X_train, y_val, y_train = prepare_datasets_sessions(X_soft, y_soft, session_lengths_soft, TEST_SESSION_NR)
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#X_train, X_val, y_train, y_val = reduce_data_set_sizes(X_train, X_val, y_train, y_val, train_reduction=0.5, test_reduction=0)
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#print(X_soft.shape, y_soft.shape)
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#X_train, X_val, y_train, y_val = prepare_datasets_new([0, 1], X_soft, y_soft, session_lengths_soft)
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#print(X_train.shape, X_val.shape, y_train.shape, y_val.shape)
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'''
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X_val, X_train, y_val, y_train = prepare_datasets_sessions(X_soft, y_soft, session_lengths_soft, TEST_SESSION_NR)
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X_train, X_val, y_train, y_val = reduce_data_set_sizes(X_train, X_val, y_train, y_val, train_reduction=0.5, test_reduction=0)
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print(X_soft.shape, y_soft.shape)
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X_train, X_val, y_train, y_val = prepare_datasets_new([0, 1], X_soft, y_soft, session_lengths_soft)
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print(X_train.shape, X_val.shape, y_train.shape, y_val.shape)
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'''
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# ----- Make model ------
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#model_GRU = GRU(input_shape=(1, 208)) # (timestep, 13*16 MFCC coefficients)
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#model_LSTM = LSTM(input_shape=(1, 208)) # (timestep, 13*16 MFCC coefficients)
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#model_CNN_1D = CNN_1D(input_shape=(208, 1)) # (timestep, 13*16 MFCC coefficients)
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'''
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model_GRU = GRU(input_shape=(1, 208)) # (timestep, 13*16 MFCC coefficients)
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model_LSTM = LSTM(input_shape=(1, 208)) # (timestep, 13*16 MFCC coefficients)
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model_CNN_1D = CNN_1D(input_shape=(208, 1)) # (timestep, 13*16 MFCC coefficients)
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#model_GRU.summary()
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#model_LSTM.summary()
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#model_CNN_1D.summary()
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model_GRU.summary()
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model_LSTM.summary()
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model_CNN_1D.summary()
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'''
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# ----- Train network ------
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#history_GRU = train(model_GRU, X_train, y_train, verbose=VERBOSE, batch_size=BATCH_SIZE, epochs=EPOCHS)
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#history_LSTM = train(model_LSTM, X_train, y_train, verbose=VERBOSE, batch_size=BATCH_SIZE, epochs=EPOCHS)
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#history_CNN_1D = train( model_CNN_1D, np.reshape(X_train, (X_train.shape[0], 208, 1)),
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# y_train, X_validation=np.reshape(X_val, (X_val.shape[0], 208, 1)), y_validation=y_val, verbose=VERBOSE,
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# batch_size=BATCH_SIZE, epochs=EPOCHS)
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'''
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history_GRU = train(model_GRU, X_train, y_train, verbose=VERBOSE, batch_size=BATCH_SIZE, epochs=EPOCHS)
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history_LSTM = train(model_LSTM, X_train, y_train, verbose=VERBOSE, batch_size=BATCH_SIZE, epochs=EPOCHS)
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history_CNN_1D = train( model_CNN_1D, np.reshape(X_train, (X_train.shape[0], 208, 1)),
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y_train, X_validation=np.reshape(X_val, (X_val.shape[0], 208, 1)), y_validation=y_val, verbose=VERBOSE,
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batch_size=BATCH_SIZE, epochs=EPOCHS)
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'''
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# ----- Plot train accuracy/error -----
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#plot_train_history(history_CNN_1D, val_data=True)
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'''
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plot_train_history(history_CNN_1D, val_data=True)
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'''
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# ----- Evaluate model on test set ------
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#test_loss, test_acc = model_GRU.evaluate(X_test, y_test, verbose=VERBOSE)
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#print('\nTest accuracy GRU:', test_acc, '\n')
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#test_loss, test_acc = model_LSTM.evaluate(X_test, y_test, verbose=VERBOSE)
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#print('\nTest accuracy LSTM:', test_acc, '\n')
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#test_loss, test_acc = model_CNN_1D.evaluate(np.reshape(X_test, (X_test.shape[0], 208, 1)), y_test, verbose=0)
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#print('\nTest accuracy CNN_1D:', test_acc, '\n')
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'''
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test_loss, test_acc = model_GRU.evaluate(X_test, y_test, verbose=VERBOSE)
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print('\nTest accuracy GRU:', test_acc, '\n')
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test_loss, test_acc = model_LSTM.evaluate(X_test, y_test, verbose=VERBOSE)
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print('\nTest accuracy LSTM:', test_acc, '\n')
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test_loss, test_acc = model_CNN_1D.evaluate(np.reshape(X_test, (X_test.shape[0], 208, 1)), y_test, verbose=0)
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print('\nTest accuracy CNN_1D:', test_acc, '\n')
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'''
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# ----- Store test predictions in CSV ------
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#prediction_csv_logger(np.reshape(X_test, (X_test.shape[0], 208, 1)), y_test, MODEL_NAME, model_CNN_1D, TEST_SESSION_NR)
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'''
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prediction_csv_logger(np.reshape(X_test, (X_test.shape[0], 208, 1)), y_test, MODEL_NAME, model_CNN_1D, TEST_SESSION_NR)
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'''
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# ----- Cross validation ------
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# Trained on three sessions, tested on one
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'''
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# ----- Cross validation ------
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# Trained on three sessions, tested on one
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average_GRU = session_cross_validation('GRU', X, y, session_lengths, nr_sessions=NR_SESSIONS,
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log_to_csv=LOG,
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batch_size=BATCH_SIZE,
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@ -1135,9 +1147,11 @@ if __name__ == "__main__":
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print('\n')
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'''
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'''
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# ----- Inverse cross-validation ------
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# Trained on one session, tested on three
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# Trained on one session, tested on three
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'''
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average_GRU = inverse_session_cross_validation('GRU', X, y, session_lengths, nr_sessions=NR_SESSIONS,
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log_to_csv=LOG,
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batch_size=BATCH_SIZE,
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@ -1164,10 +1178,11 @@ if __name__ == "__main__":
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'''
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# ----- PLOTTING ------
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#plot_comp_spread_single(X, y, session_lengths, NR_SESSIONS, epochs=30)
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#plot_comp_accuracy_single(X_soft, y_soft, session_lengths_soft, NR_SESSIONS, epochs=30)
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#plot_comp_val_SoftHard(X_soft, y_soft, X_hard, y_hard, session_lengths_soft, session_lengths_hard, NR_SESSIONS, epochs=30)
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#plot_comp_SoftHard_3(X_soft, y_soft, X_hard, y_hard, session_lengths_soft, session_lengths_hard, NR_SESSIONS, epochs=30)
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#plot_N_S_val_comp()
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'''
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plot_comp_spread_single(X, y, session_lengths, NR_SESSIONS, epochs=30)
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plot_comp_accuracy_single(X_soft, y_soft, session_lengths_soft, NR_SESSIONS, epochs=30)
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plot_comp_val_SoftHard(X_soft, y_soft, X_hard, y_hard, session_lengths_soft, session_lengths_hard, NR_SESSIONS, epochs=30)
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plot_comp_SoftHard_3(X_soft, y_soft, X_hard, y_hard, session_lengths_soft, session_lengths_hard, NR_SESSIONS, epochs=30)
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plot_N_S_val_comp()
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'''
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