elif select == '2': model = generate_LSTM_FCN_model() start_time = time.time() elif select == '3': model = generate_ALSTM_FCN_model() start_time = time.time() elif select == '4': model = generate_FCN_model() start_time = time.time() elif select == '5': model = generate_MLP_model() start_time = time.time() history = train_model(model, DATASET_INDEX, dataset_prefix='sony_aibo', epochs=2000, batch_size=64) model.summary() accuracy, loss, f_score = evaluate_model(model, DATASET_INDEX, dataset_prefix='sony_aibo', batch_size=128) print("--- Run Time = %s seconds ---" % ((time.time() - start_time))) print("--- Run Time = %s minutes ---" % ((time.time() - start_time) / 60.0)) text_file = open("training_time.txt", "w") text_file.write("--- Run Time =" + str(((time.time() - start_time))) + " seconds ---" + "\n" + "--- Run Time = " + str(((time.time() - start_time) / 60.0)) + " minutes ---" +
elif select == '2': model = generate_LSTM_FCN_model() start_time = time.time() elif select == '3': model = generate_ALSTM_FCN_model() start_time = time.time() elif select == '4': model = generate_FCN_model() start_time = time.time() elif select == '5': model = generate_MLP_model() start_time = time.time() history = train_model(model, DATASET_INDEX, dataset_prefix='insect_wingbeat_sound', epochs=1000, batch_size=128) model.summary() accuracy, loss, f_score = evaluate_model( model, DATASET_INDEX, dataset_prefix='insect_wingbeat_sound', batch_size=128) print("--- Run Time = %s seconds ---" % ((time.time() - start_time))) print("--- Run Time = %s minutes ---" % ((time.time() - start_time) / 60.0)) text_file = open("training_time.txt", "w") text_file.write("--- Run Time =" + str(((time.time() - start_time))) + " seconds ---" + "\n" + "--- Run Time = " +
elif select == '2': model = generate_LSTM_FCN_model() start_time = time.time() elif select == '3': model = generate_ALSTM_FCN_model() start_time = time.time() elif select == '4': model = generate_FCN_model() start_time = time.time() elif select == '5': model = generate_MLP_model() start_time = time.time() history = train_model(model, DATASET_INDEX, dataset_prefix='beetle_fly', epochs=8000, batch_size=64) summary = model.summary() accuracy, loss, f_score = evaluate_model(model, DATASET_INDEX, dataset_prefix='beetle_fly', batch_size=64) print("--- Run Time = %s seconds ---" % ((time.time() - start_time))) print("--- Run Time = %s minutes ---" % ((time.time() - start_time) / 60.0)) text_file = open("training_time.txt", "w") text_file.write("--- Run Time =" + str(((time.time() - start_time))) + " seconds ---" + "\n" + "--- Run Time = " + str(((time.time() - start_time) / 60.0)) + " minutes ---" +
elif select == '2': model = generate_LSTM_FCN_model() start_time = time.time() elif select == '3': model = generate_ALSTM_FCN_model() start_time = time.time() elif select == '4': model = generate_FCN_model() start_time = time.time() elif select == '5': model = generate_MLP_model() start_time = time.time() history = train_model(model, DATASET_INDEX, dataset_prefix='synthetic_control', epochs=4000, batch_size=16) model.summary() accuracy, loss, f_score = evaluate_model( model, DATASET_INDEX, dataset_prefix='synthetic_control', batch_size=128) print("--- Run Time = %s seconds ---" % ((time.time() - start_time))) print("--- Run Time = %s minutes ---" % ((time.time() - start_time) / 60.0)) text_file = open("training_time.txt", "w") text_file.write("--- Run Time =" + str(((time.time() - start_time))) + " seconds ---" + "\n" + "--- Run Time = " +
elif select == '2': model = generate_LSTM_FCN_model() start_time = time.time() elif select == '3': model = generate_ALSTM_FCN_model() start_time = time.time() elif select == '4': model = generate_FCN_model() start_time = time.time() elif select == '5': model = generate_MLP_model() start_time = time.time() history = train_model(model, DATASET_INDEX, dataset_prefix='italy_power_demand', epochs=2000, batch_size=64) model.summary() accuracy, loss, f_score = evaluate_model( model, DATASET_INDEX, dataset_prefix='italy_power_demand', batch_size=128) print("--- Run Time = %s seconds ---" % ((time.time() - start_time))) print("--- Run Time = %s minutes ---" % ((time.time() - start_time) / 60.0)) text_file = open("training_time.txt", "w") text_file.write("--- Run Time =" + str(((time.time() - start_time))) + " seconds ---" + "\n" + "--- Run Time = " +
elif select == '2': model = generate_LSTM_FCN_model() start_time = time.time() elif select == '3': model = generate_ALSTM_FCN_model() start_time = time.time() elif select == '4': model = generate_FCN_model() start_time = time.time() elif select == '5': model = generate_MLP_model() start_time = time.time() history = train_model(model, DATASET_INDEX, dataset_prefix='middle_phalanx_age_group', epochs=2000, batch_size=128) model.summary() accuracy, loss, f_score = evaluate_model( model, DATASET_INDEX, dataset_prefix='middle_phalanx_age_group', batch_size=128) print("--- Run Time = %s seconds ---" % ((time.time() - start_time))) print("--- Run Time = %s minutes ---" % ((time.time() - start_time) / 60.0)) text_file = open("training_time.txt", "w") text_file.write("--- Run Time =" + str(((time.time() - start_time))) + " seconds ---" + "\n" + "--- Run Time = " +
model = generate_GRU_FCN_model() start_time = time.time() elif select == '2': model = generate_LSTM_FCN_model() start_time = time.time() elif select == '3': model = generate_ALSTM_FCN_model() start_time = time.time() elif select == '4': model = generate_FCN_model() start_time = time.time() elif select == '5': model = generate_MLP_model() start_time = time.time() history = train_model(model, DATASET_INDEX, dataset_prefix='hand_outlines', epochs=2000, batch_size=64) model.summary() accuracy, loss, f_score = evaluate_model(model, DATASET_INDEX, dataset_prefix='hand_outlines', batch_size=128) print("--- Run Time = %s seconds ---" % ((time.time() - start_time))) print("--- Run Time = %s minutes ---" % ((time.time() - start_time)/60.0)) text_file = open("training_time.txt", "w") text_file.write("--- Run Time ="+ str(((time.time() - start_time)))+" seconds ---" +"\n" +"--- Run Time = "+str(((time.time() - start_time)/60.0))+" minutes ---"+"\n") print(history.history.keys()) plt.plot(history.history['loss']) plt.xlabel('epoch', fontsize=16) plt.ylabel('loss',fontsize=16) plt.savefig("./resulted_plotes/train_loss.jpg")
start_time = time.time() elif select == '2': model = generate_LSTM_FCN_model() start_time = time.time() elif select == '3': model = generate_ALSTM_FCN_model() start_time = time.time() elif select == '4': model = generate_FCN_model() start_time = time.time() elif select == '5': model = generate_MLP_model() start_time = time.time() history = train_model(model, DATASET_INDEX, dataset_prefix='coffee', epochs=500, batch_size=64) summary = model.summary() accuracy, loss, f_score = evaluate_model(model, DATASET_INDEX, dataset_prefix='coffee', batch_size=64) print("--- Run Time = %s seconds ---" % ((time.time() - start_time))) print("--- Run Time = %s minutes ---" % ((time.time() - start_time)/60.0)) text_file = open("training_time.txt", "w") text_file.write("--- Run Time ="+ str(((time.time() - start_time)))+" seconds ---" +"\n" +"--- Run Time = "+str(((time.time() - start_time)/60.0))+" minutes ---"+"\n") print("--- Run Time = %s seconds ---" % ((time.time() - start_time))) print("--- Run Time = %s minutes ---" % ((time.time() - start_time)/60.0)) text_file = open("training_time.txt", "w") text_file.write("--- Run Time ="+ str(((time.time() - start_time)))+" seconds ---" +"\n" +"--- Run Time = "+str(((time.time() - start_time)/60.0))+" minutes ---"+"\n")
elif select == '2': model = generate_LSTM_FCN_model() start_time = time.time() elif select == '3': model = generate_ALSTM_FCN_model() start_time = time.time() elif select == '4': model = generate_FCN_model() start_time = time.time() elif select == '5': model = generate_MLP_model() start_time = time.time() history = train_model(model, DATASET_INDEX, dataset_prefix='swedish_leaf', epochs=2000, batch_size=64) model.summary() accuracy, loss, f_score = evaluate_model(model, DATASET_INDEX, dataset_prefix='swedish_leaf', batch_size=64) print("--- Run Time = %s seconds ---" % ((time.time() - start_time))) print("--- Run Time = %s minutes ---" % ((time.time() - start_time) / 60.0)) text_file = open("training_time.txt", "w") text_file.write("--- Run Time =" + str(((time.time() - start_time))) + " seconds ---" + "\n" + "--- Run Time = " + str(((time.time() - start_time) / 60.0)) + " minutes ---" +
elif select == '2': model = generate_LSTM_FCN_model() start_time = time.time() elif select == '3': model = generate_ALSTM_FCN_model() start_time = time.time() elif select == '4': model = generate_FCN_model() start_time = time.time() elif select == '5': model = generate_MLP_model() start_time = time.time() history = train_model(model, DATASET_INDEX, dataset_prefix='trace', epochs=1000, batch_size=64) model.summary() accuracy, loss, f_score = evaluate_model(model, DATASET_INDEX, dataset_prefix='trace', batch_size=128) print("--- Run Time = %s seconds ---" % ((time.time() - start_time))) print("--- Run Time = %s minutes ---" % ((time.time() - start_time) / 60.0)) text_file = open("training_time.txt", "w") text_file.write("--- Run Time =" + str(((time.time() - start_time))) + " seconds ---" + "\n" + "--- Run Time = " + str(((time.time() - start_time) / 60.0)) + " minutes ---" +
elif select == '2': model = generate_LSTM_FCN_model() start_time = time.time() elif select == '3': model = generate_ALSTM_FCN_model() start_time = time.time() elif select == '4': model = generate_FCN_model() start_time = time.time() elif select == '5': model = generate_MLP_model() start_time = time.time() history = train_model(model, DATASET_INDEX, dataset_prefix='phalanges_outline_correct', epochs=2000, batch_size=64) model.summary() accuracy, loss, f_score = evaluate_model( model, DATASET_INDEX, dataset_prefix='phalanges_outline_correct', batch_size=128) print("--- Run Time = %s seconds ---" % ((time.time() - start_time))) print("--- Run Time = %s minutes ---" % ((time.time() - start_time) / 60.0)) text_file = open("training_time.txt", "w") text_file.write("--- Run Time =" + str(((time.time() - start_time))) + " seconds ---" + "\n" + "--- Run Time = " +
elif select == '2': model = generate_LSTM_FCN_model() start_time = time.time() elif select == '3': model = generate_ALSTM_FCN_model() start_time = time.time() elif select == '4': model = generate_FCN_model() start_time = time.time() elif select == '5': model = generate_MLP_model() start_time = time.time() history = train_model(model, DATASET_INDEX, dataset_prefix='NonInvasiveFatalECG_Thorax1', epochs=2000, batch_size=128) model.summary() accuracy, loss, f_score = evaluate_model( model, DATASET_INDEX, dataset_prefix='NonInvasiveFatalECG_Thorax1', batch_size=128) print("--- Run Time = %s seconds ---" % ((time.time() - start_time))) print("--- Run Time = %s minutes ---" % ((time.time() - start_time) / 60.0)) text_file = open("training_time.txt", "w") text_file.write("--- Run Time =" + str(((time.time() - start_time))) + " seconds ---" + "\n" + "--- Run Time = " +
elif select == '2': model = generate_LSTM_FCN_model() start_time = time.time() elif select == '3': model = generate_ALSTM_FCN_model() start_time = time.time() elif select == '4': model = generate_FCN_model() start_time = time.time() elif select == '5': model = generate_MLP_model() start_time = time.time() history = train_model(model, DATASET_INDEX, dataset_prefix='proximal_phalanx_outline', epochs=2000, batch_size=128) model.summary() accuracy, loss, f_score = evaluate_model( model, DATASET_INDEX, dataset_prefix='proximal_phalanx_outline', batch_size=128) print("--- Run Time = %s seconds ---" % ((time.time() - start_time))) print("--- Run Time = %s minutes ---" % ((time.time() - start_time) / 60.0)) text_file = open("training_time.txt", "w") text_file.write("--- Run Time =" + str(((time.time() - start_time))) + " seconds ---" + "\n" + "--- Run Time = " +
elif select == '2': model = generate_LSTM_FCN_model() start_time = time.time() elif select == '3': model = generate_ALSTM_FCN_model() start_time = time.time() elif select == '4': model = generate_FCN_model() start_time = time.time() elif select == '5': model = generate_MLP_model() start_time = time.time() history = train_model(model, DATASET_INDEX, dataset_prefix='olive_oil', epochs=6000, batch_size=64) model.summary() accuracy, loss, f_score = evaluate_model(model, DATASET_INDEX, dataset_prefix='olive_oil', batch_size=128) print("--- Run Time = %s seconds ---" % ((time.time() - start_time))) print("--- Run Time = %s minutes ---" % ((time.time() - start_time) / 60.0)) text_file = open("training_time.txt", "w") text_file.write("--- Run Time =" + str(((time.time() - start_time))) + " seconds ---" + "\n" + "--- Run Time = " + str(((time.time() - start_time) / 60.0)) + " minutes ---" +
elif select == '2': model = generate_LSTM_FCN_model() start_time = time.time() elif select == '3': model = generate_ALSTM_FCN_model() start_time = time.time() elif select == '4': model = generate_FCN_model() start_time = time.time() elif select == '5': model = generate_MLP_model() start_time = time.time() history = train_model(model, DATASET_INDEX, dataset_prefix='inline_skate', epochs=2000, batch_size=128) accuracy, loss, f_score = evaluate_model(model, DATASET_INDEX, dataset_prefix='inline_skate', batch_size=128) print("--- Run Time = %s seconds ---" % ((time.time() - start_time))) print("--- Run Time = %s minutes ---" % ((time.time() - start_time) / 60.0)) text_file = open("training_time.txt", "w") text_file.write("--- Run Time =" + str(((time.time() - start_time))) + " seconds ---" + "\n" + "--- Run Time = " + str(((time.time() - start_time) / 60.0)) + " minutes ---" +
model = generate_GRU_FCN_model() start_time = time.time() elif select == '2': model = generate_LSTM_FCN_model() start_time = time.time() elif select == '3': model = generate_ALSTM_FCN_model() start_time = time.time() elif select == '4': model = generate_FCN_model() start_time = time.time() elif select == '5': model = generate_MLP_model() start_time = time.time() history = train_model(model, DATASET_INDEX, dataset_prefix='cbf', epochs=2000, batch_size=32) summary = model.summary() accuracy, loss,f_score = evaluate_model(model, DATASET_INDEX, dataset_prefix='cbf', batch_size=128) print("--- Run Time = %s seconds ---" % ((time.time() - start_time))) print("--- Run Time = %s minutes ---" % ((time.time() - start_time)/60.0)) text_file = open("training_time.txt", "w") text_file.write("--- Run Time ="+ str(((time.time() - start_time)))+" seconds ---" +"\n" +"--- Run Time = "+str(((time.time() - start_time)/60.0))+" minutes ---"+"\n") print(history.history.keys()) plt.plot(history.history['loss']) plt.plot(history.history['loss']) plt.xlabel('epoch', fontsize=16) plt.xlim(left=0)
elif select == '2': model = generate_LSTM_FCN_model() start_time = time.time() elif select == '3': model = generate_ALSTM_FCN_model() start_time = time.time() elif select == '4': model = generate_FCN_model() start_time = time.time() elif select == '5': model = generate_MLP_model() start_time = time.time() history = train_model(model, DATASET_INDEX, dataset_prefix='diatom_size_reduction', epochs=2000, batch_size=64) summary = model.summary() accuracy, loss, f_score = evaluate_model( model, DATASET_INDEX, dataset_prefix='diatom_size_reduction', batch_size=64) print("--- Run Time = %s seconds ---" % ((time.time() - start_time))) print("--- Run Time = %s minutes ---" % ((time.time() - start_time) / 60.0)) text_file = open("training_time.txt", "w") text_file.write("--- Run Time =" + str(((time.time() - start_time))) + " seconds ---" + "\n" + "--- Run Time = " +
elif select == '3': model = generate_ALSTM_FCN_model() start_time = time.time() elif select == '4': model = generate_FCN_model() start_time = time.time() elif select == '5': model = generate_MLP_model() start_time = time.time() history = train_model(model, DATASET_INDEX, dataset_prefix='electric_devices', epochs=2000, batch_size=128) summary = model.summary() accuracy, loss, f_score = evaluate_model(model, DATASET_INDEX, dataset_prefix='electric_devices', batch_size=128) print("--- Run Time = %s seconds ---" % ((time.time() - start_time))) print("--- Run Time = %s minutes ---" % ((time.time() - start_time) / 60.0)) text_file = open("training_time.txt", "w") text_file.write("--- Run Time =" + str(((time.time() - start_time))) + " seconds ---" + "\n" + "--- Run Time = " + str(((time.time() - start_time) / 60.0)) + " minutes ---" +
elif select == '2': model = generate_LSTM_FCN_model() start_time = time.time() elif select == '3': model = generate_ALSTM_FCN_model() start_time = time.time() elif select == '4': model = generate_FCN_model() start_time = time.time() elif select == '5': model = generate_MLP_model() start_time = time.time() history = train_model(model, DATASET_INDEX, dataset_prefix='fifty_words', epochs=2000, batch_size=128) model.summary() accuracy, loss, f_score = evaluate_model(model, DATASET_INDEX, dataset_prefix='fifty_words', batch_size=128) print("--- Run Time = %s seconds ---" % ((time.time() - start_time))) print("--- Run Time = %s minutes ---" % ((time.time() - start_time) / 60.0)) text_file = open("training_time.txt", "w") text_file.write("--- Run Time =" + str(((time.time() - start_time))) + " seconds ---" + "\n" + "--- Run Time = " + str(((time.time() - start_time) / 60.0)) + " minutes ---" +
elif select == '2': model = generate_LSTM_FCN_model() start_time = time.time() elif select == '3': model = generate_ALSTM_FCN_model() start_time = time.time() elif select == '4': model = generate_FCN_model() start_time = time.time() elif select == '5': model = generate_MLP_model() start_time = time.time() history = train_model(model, DATASET_INDEX, dataset_prefix='phalanx_outline_timesequence', epochs=2000, batch_size=128) summary = model.summary() accuracy, loss, f_score = evaluate_model( model, DATASET_INDEX, dataset_prefix='phalanx_outline_timesequence', batch_size=128) print("--- Run Time = %s seconds ---" % ((time.time() - start_time))) print("--- Run Time = %s minutes ---" % ((time.time() - start_time) / 60.0)) text_file = open("training_time.txt", "w") text_file.write("--- Run Time =" + str(((time.time() - start_time))) + " seconds ---" + "\n" + "--- Run Time = " +
model = generate_GRU_FCN_model() start_time = time.time() elif select == '2': model = generate_LSTM_FCN_model() start_time = time.time() elif select == '3': model = generate_ALSTM_FCN_model() start_time = time.time() elif select == '4': model = generate_FCN_model() start_time = time.time() elif select == '5': model = generate_MLP_model() start_time = time.time() history = train_model(model, DATASET_INDEX, dataset_prefix='mote_strain', epochs=2000, batch_size=128) model.summary() accuracy, loss, f_score = evaluate_model(model, DATASET_INDEX, dataset_prefix='mote_strain', batch_size=128) print("--- Run Time = %s seconds ---" % ((time.time() - start_time))) print("--- Run Time = %s minutes ---" % ((time.time() - start_time)/60.0)) text_file = open("training_time.txt", "w") text_file.write("--- Run Time ="+ str(((time.time() - start_time)))+" seconds ---" +"\n" +"--- Run Time = "+str(((time.time() - start_time)/60.0))+" minutes ---"+"\n") print(history.history.keys()) plt.plot(history.history['loss']) plt.plot(history.history['val_loss']) plt.legend(['train', 'validation'], loc='upper right', fontsize ='large') plt.xlim(left=0)
elif select == '2': model = generate_LSTM_FCN_model() start_time = time.time() elif select == '3': model = generate_ALSTM_FCN_model() start_time = time.time() elif select == '4': model = generate_FCN_model() start_time = time.time() elif select == '5': model = generate_MLP_model() start_time = time.time() history = train_model(model, DATASET_INDEX, dataset_prefix='gunpoint', epochs=2000, batch_size=128) accuracy, loss, f_score = evaluate_model(model, DATASET_INDEX, dataset_prefix='gunpoint', batch_size=128) print("--- Run Time = %s seconds ---" % ((time.time() - start_time))) print("--- Run Time = %s minutes ---" % ((time.time() - start_time) / 60.0)) text_file = open("training_time.txt", "w") text_file.write("--- Run Time =" + str(((time.time() - start_time))) + " seconds ---" + "\n" + "--- Run Time = " + str(((time.time() - start_time) / 60.0)) + " minutes ---" +
elif select == '2': model = generate_LSTM_FCN_model() start_time = time.time() elif select == '3': model = generate_ALSTM_FCN_model() start_time = time.time() elif select == '4': model = generate_FCN_model() start_time = time.time() elif select == '5': model = generate_MLP_model() start_time = time.time() history = train_model(model, DATASET_INDEX, dataset_prefix='screen_type', epochs=2000, batch_size=64) model.summary() accuracy, loss, f_score = evaluate_model(model, DATASET_INDEX, dataset_prefix='screen_type', batch_size=128) print("--- Run Time = %s seconds ---" % ((time.time() - start_time))) print("--- Run Time = %s minutes ---" % ((time.time() - start_time) / 60.0)) text_file = open("training_time.txt", "w") text_file.write("--- Run Time =" + str(((time.time() - start_time))) + " seconds ---" + "\n" + "--- Run Time = " + str(((time.time() - start_time) / 60.0)) + " minutes ---" +
start_time = time.time() elif select == '2': model = generate_LSTM_FCN_model() start_time = time.time() elif select == '3': model = generate_ALSTM_FCN_model() start_time = time.time() elif select == '4': model = generate_FCN_model() start_time = time.time() elif select == '5': model = generate_MLP_model() start_time = time.time() history = train_model(model, DATASET_INDEX, dataset_prefix='shapes_all', epochs=4000, batch_size=64) model.summary() accuracy, loss , f_score= evaluate_model(model, DATASET_INDEX, dataset_prefix='shapes_all', batch_size=64) print("--- Run Time = %s seconds ---" % ((time.time() - start_time))) print("--- Run Time = %s minutes ---" % ((time.time() - start_time)/60.0)) text_file = open("training_time.txt", "w") text_file.write("--- Run Time ="+ str(((time.time() - start_time)))+" seconds ---" +"\n" +"--- Run Time = "+str(((time.time() - start_time)/60.0))+" minutes ---"+"\n") print(history.history.keys()) plt.plot(history.history['loss']) plt.xlabel('epoch', fontsize=16) plt.ylabel('loss',fontsize=16)
elif select == '2': model = generate_LSTM_FCN_model() start_time = time.time() elif select == '3': model = generate_ALSTM_FCN_model() start_time = time.time() elif select == '4': model = generate_FCN_model() start_time = time.time() elif select == '5': model = generate_MLP_model() start_time = time.time() history = train_model(model, DATASET_INDEX, dataset_prefix='worms_two_class', epochs=1000, batch_size=16) model.summary() accuracy, loss, f_score = evaluate_model(model, DATASET_INDEX, dataset_prefix='worms_two_class', batch_size=16) print("--- Run Time = %s seconds ---" % ((time.time() - start_time))) print("--- Run Time = %s minutes ---" % ((time.time() - start_time) / 60.0)) text_file = open("training_time.txt", "w") text_file.write("--- Run Time =" + str(((time.time() - start_time))) + " seconds ---" + "\n" + "--- Run Time = " + str(((time.time() - start_time) / 60.0)) + " minutes ---" +
elif select == '2': model = generate_LSTM_FCN_model() start_time = time.time() elif select == '3': model = generate_ALSTM_FCN_model() start_time = time.time() elif select == '4': model = generate_FCN_model() start_time = time.time() elif select == '5': model = generate_MLP_model() start_time = time.time() history = train_model(model, DATASET_INDEX, dataset_prefix='phenome', epochs=2000, batch_size=64) model.summary() accuracy, loss, f_score = evaluate_model(model, DATASET_INDEX, dataset_prefix='phenome', batch_size=128) print("--- Run Time = %s seconds ---" % ((time.time() - start_time))) print("--- Run Time = %s minutes ---" % ((time.time() - start_time) / 60.0)) text_file = open("training_time.txt", "w") text_file.write("--- Run Time =" + str(((time.time() - start_time))) + " seconds ---" + "\n" + "--- Run Time = " + str(((time.time() - start_time) / 60.0)) + " minutes ---" +
elif select == '3': model = generate_ALSTM_FCN_model() start_time = time.time() elif select == '4': model = generate_FCN_model() start_time = time.time() elif select == '5': model = generate_MLP_model() start_time = time.time() elif select == '6': model = generate_model_new() start_time = time.time() history = train_model(model, DATASET_INDEX, dataset_prefix='adiac', epochs=4000, batch_size=128) model.summary() accuracy, loss, f_score = evaluate_model(model, DATASET_INDEX, dataset_prefix='adiac', batch_size=128) print("--- Run Time = %s seconds ---" % ((time.time() - start_time))) print("--- Run Time = %s minutes ---" % ((time.time() - start_time) / 60.0)) text_file = open("training_time.txt", "w") text_file.write("--- Run Time =" + str(((time.time() - start_time))) + " seconds ---" + "\n" + "--- Run Time = " + str(((time.time() - start_time) / 60.0)) + " minutes ---" +
elif select == '2': model = generate_LSTM_FCN_model() start_time = time.time() elif select == '3': model = generate_ALSTM_FCN_model() start_time = time.time() elif select == '4': model = generate_FCN_model() start_time = time.time() elif select == '5': model = generate_MLP_model() start_time = time.time() history = train_model(model, DATASET_INDEX, dataset_prefix='toe_segmentation1', epochs=2000, batch_size=64) model.summary() accuracy, loss, f_score = evaluate_model( model, DATASET_INDEX, dataset_prefix='toe_segmentation1', batch_size=128) print("--- Run Time = %s seconds ---" % ((time.time() - start_time))) print("--- Run Time = %s minutes ---" % ((time.time() - start_time) / 60.0)) text_file = open("training_time.txt", "w") text_file.write("--- Run Time =" + str(((time.time() - start_time))) + " seconds ---" + "\n" + "--- Run Time = " +
elif select == '2': model = generate_LSTM_FCN_model() start_time = time.time() elif select == '3': model = generate_ALSTM_FCN_model() start_time = time.time() elif select == '4': model = generate_FCN_model() start_time = time.time() elif select == '5': model = generate_MLP_model() start_time = time.time() history = train_model(model, DATASET_INDEX, dataset_prefix='ecg200', epochs=8000, batch_size=64) summary = model.summary() accuracy, loss, f_score = evaluate_model(model, DATASET_INDEX, dataset_prefix='ecg200', batch_size=64) print("--- Run Time = %s seconds ---" % ((time.time() - start_time))) print("--- Run Time = %s minutes ---" % ((time.time() - start_time) / 60.0)) text_file = open("training_time.txt", "w") text_file.write("--- Run Time =" + str(((time.time() - start_time))) + " seconds ---" + "\n" + "--- Run Time = " + str(((time.time() - start_time) / 60.0)) + " minutes ---" +
elif select == '2': model = generate_LSTM_FCN_model() start_time = time.time() elif select == '3': model = generate_ALSTM_FCN_model() start_time = time.time() elif select == '4': model = generate_FCN_model() start_time = time.time() elif select == '5': model = generate_MLP_model() start_time = time.time() history = train_model(model, DATASET_INDEX, dataset_prefix='shapelet_sim', epochs=2000, batch_size=128) model.summary() accuracy, loss, f_score = evaluate_model(model, DATASET_INDEX, dataset_prefix='shapelet_sim', batch_size=128) print("--- Run Time = %s seconds ---" % ((time.time() - start_time))) print("--- Run Time = %s minutes ---" % ((time.time() - start_time) / 60.0)) text_file = open("training_time.txt", "w") text_file.write("--- Run Time =" + str(((time.time() - start_time))) + " seconds ---" + "\n" + "--- Run Time = " + str(((time.time() - start_time) / 60.0)) + " minutes ---" +