model_train.py

import tflearn
import tensorflow
from data_process import data_process


# # change philosopher name here
# philosopher_name = ""
# [words, labels, training, output] = data_process(
#     "intent", "pikl", philosopher_name)


def model_train(training, output, philosopher_name):

    # tensorflow.reset_default_graph()
    # comment out line 16 until 27 if you want to retrain the model
    # try:
    #     net = tflearn.input_data(shape=[None, len(training[0])])
    #     net = tflearn.fully_connected(net, 8)
    #     net = tflearn.fully_connected(net, 8)
    #     net = tflearn.fully_connected(
    #         net, len(output[0]), activation="softmax")
    #     net = tflearn.regression(net)

    #     model = tflearn.DNN(net)
    #     model.load(philosopher_name + "_model.tflearn")

    # except:
    net = tflearn.input_data(shape=[None, len(training[0])])
    net = tflearn.fully_connected(net, 8)
    net = tflearn.fully_connected(net, 8)
    net = tflearn.fully_connected(
        net, len(output[0]), activation="softmax")
    net = tflearn.regression(net)

    model = tflearn.DNN(net)
    model.fit(training, output, n_epoch=1000,
              batch_size=8, show_metric=True)
    model.save(philosopher_name + "_model.tflearn")

    return model