# Check point
cp_callback = "./models/weights.{epoch:02d}.h5"
checkpoint = ModelCheckpoint(cp_callback,
monitor='val_loss',
verbose=1,
save_best_only=True,
mode='min')
callbacks_list = [checkpoint]
# Tiến hành huấn luyện mô hình
history = model.fit_generator(train_generator,
steps_per_epoch=709595 // BATCH_SIZE,
epochs=N_EPOCHS,
validation_data=validation_generator,
validation_steps=72000 // BATCH_SIZE,
callbacks=callbacks_list)
# lưu mô hình
with open("./models/trongso.json", "w") as json_file:
json_file.write(model.to_json())
# Biểu đồ training & validation accuracy values
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('Model accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show()
if earlyStopFlag:
callbacksList.append(earlyStop)
if reduceLRFlag:
callbacksList.append(reduce_lr)
if modelCheckpointFlag:
callbacksList.append(modelCheckpoint)
history = model.fit_generator(
trainGenerator,
steps_per_epoch=trainSamplesNumber // batchSize * foldAugment,
epochs=epochs,
verbose=1,
callbacks=callbacksList,
validation_data=validationGenerator,
class_weight=getClassWeights(trainGenerator.classes),
shuffle=True,
validation_steps=validateSamplesNumber // batchSize)
score = model.evaluate_generator(testGenerator, testSamplesNumber)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
# serialize model to JSON
model_json = model.to_json()
with open(modelFile, "w") as json_file:
json_file.write(model_json)
# serialize weights to HDF5
model.save_weights(weightsFile)
print("Saved model to disk")
