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='chlorine_concentration', epochs=2000, batch_size=128) summary = model.summary() accuracy, loss , f_score= evaluate_model(model, DATASET_INDEX, dataset_prefix='chlorine_concentration', 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("--- 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())
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 ---" + "\n") print(history.history.keys()) plt.plot(history.history['loss']) plt.xlabel('epoch', fontsize=16) # plt.set_xlim(bottom=0)
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 ---" + "\n") print(history.history.keys()) plt.plot(history.history['loss']) plt.xlabel('epoch', fontsize=16) # plt.set_xlim(bottom=0)
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 = " + str(((time.time() - start_time) / 60.0)) + " minutes ---" + "\n") print(history.history.keys()) plt.plot(history.history['loss']) plt.xlabel('epoch', fontsize=16)
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 = " + str(((time.time() - start_time) / 60.0)) + " minutes ---" + "\n") print(history.history.keys()) plt.plot(history.history['loss']) plt.xlabel('epoch', fontsize=16)
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 ---" + "\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")
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 = " + str(((time.time() - start_time) / 60.0)) + " minutes ---" + "\n") print(history.history.keys()) plt.plot(history.history['loss']) plt.xlabel('epoch', fontsize=16)
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='two_patterns', epochs=2000, batch_size=32) model.summary() accuracy, loss, f_score = evaluate_model(model, DATASET_INDEX, dataset_prefix='two_patterns', 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.set_xlim(bottom=0)
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='refrigertation_devices', epochs=2000, batch_size=64) model.summary() accuracy, loss, f_score = evaluate_model( model, DATASET_INDEX, dataset_prefix='refrigertation_devices', 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)
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='ecg_five_days', epochs=2000, batch_size=128) summary = model.summary() accuracy, loss, f_score = evaluate_model(model, DATASET_INDEX, dataset_prefix='ecg_five_days', 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("--- 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")
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='small_kitchen_appliance', epochs=2000, batch_size=128) model.summary() accuracy, loss, f_score = evaluate_model( model, DATASET_INDEX, dataset_prefix='small_kitchen_appliance', 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)
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_2', epochs=2000, batch_size=64) model.summary() accuracy, loss, f_score = evaluate_model(model, DATASET_INDEX, dataset_prefix='sony_aibo_2', 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.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='word_synonym', epochs=1500, batch_size=64) model.summary() accuracy, loss , f_score= evaluate_model(model, DATASET_INDEX, dataset_prefix='word_synonym', 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.set_xlim(bottom=0) # plt.xlim(left=0)#, right) plt.ylabel('loss',fontsize=16)
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='large_kitchen_appliances', epochs=2000, batch_size=128) model.summary() accuracy, loss, f_score = evaluate_model( model, DATASET_INDEX, dataset_prefix='large_kitchen_appliances', 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)
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 = " + str(((time.time() - start_time) / 60.0)) + " minutes ---" + "\n") print(history.history.keys()) plt.plot(history.history['loss']) plt.xlabel('epoch', fontsize=16)
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='bird_chicken', epochs=8000, batch_size=64) summary = model.summary() accuracy, loss, f_score = evaluate_model(model, DATASET_INDEX, dataset_prefix='bird_chicken', 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.xlim(left=0)
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 ---" + "\n") print(history.history.keys()) plt.plot(history.history['loss']) plt.xlabel('epoch', fontsize=16) plt.ylabel('loss', fontsize=16)
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='cinc_ecg_torso', epochs=500, batch_size=128) summary = model.summary() accuracy, loss, f_score = evaluate_model(model, DATASET_INDEX, dataset_prefix='cinc_ecg_torso', 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("--- 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")
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 = " + str(((time.time() - start_time) / 60.0)) + " minutes ---" + "\n") print(history.history.keys()) plt.plot(history.history['loss']) plt.plot(history.history['loss'])
elif select == '5': model = generate_MLP_model() start_time = time.time() history = train_model(model, DATASET_INDEX, dataset_prefix='cricket_z', epochs=2000, batch_size=64, cutoff=None) summary = model.summary() accuracy, loss, f_score = evaluate_model(model, DATASET_INDEX, dataset_prefix='cricket_z', batch_size=128, cutoff=None) 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'])
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 = " + str(((time.time() - start_time) / 60.0)) + " minutes ---" + "\n") print(history.history.keys()) plt.plot(history.history['loss']) plt.plot(history.history['loss'])
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='lighting7', epochs=3000, batch_size=32, cutoff='pre') model.summary() accuracy, loss, f_score = evaluate_model(model, DATASET_INDEX, dataset_prefix='lighting7', batch_size=32, cutoff='pre') 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)
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") plt.show()
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='uwave_gesture_library_all', epochs=500, batch_size=16) model.summary() accuracy, loss, f_score = evaluate_model( model, DATASET_INDEX, dataset_prefix='uwave_gesture_library_all', 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 ---" + "\n") print(history.history.keys()) plt.plot(history.history['loss']) plt.xlabel('epoch', fontsize=16)
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 = " + str(((time.time() - start_time) / 60.0)) + " minutes ---" + "\n") print(history.history.keys()) plt.plot(history.history['loss']) plt.plot(history.history['val_loss'])
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 = " + str(((time.time() - start_time) / 60.0)) + " minutes ---" + "\n") print(history.history.keys()) plt.plot(history.history['loss']) plt.xlabel('epoch', fontsize=16)
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 = " + str(((time.time() - start_time) / 60.0)) + " minutes ---" + "\n") print(history.history.keys()) plt.plot(history.history['loss']) plt.plot(history.history['val_loss'])
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 = " + str(((time.time() - start_time) / 60.0)) + " minutes ---" + "\n") print(history.history.keys()) plt.plot(history.history['loss']) plt.xlabel('epoch', fontsize=16)
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 ---" + "\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")
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='medical_images', epochs=2000, batch_size=64) model.summary() accuracy, loss, f_score = evaluate_model(model, DATASET_INDEX, dataset_prefix='medical_images', 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") plt.show()