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()
