PoemGenerator/train.py

import os
import numpy as np
import tensorflow as tf
from poem.model import rnn_model
from poem.poem import process_poem, generate_batch

tf.app.flags.DEFINE_integer('batch_size', 64, 'batch size.')
tf.app.flags.DEFINE_float('learning_rate', 0.01, 'learning rate.')
tf.app.flags.DEFINE_string('model_dir', os.path.abspath('./model'), 'model save path.')
tf.app.flags.DEFINE_string('file_path', os.path.abspath('./data/poem.txt'), 'file name of poem.')
tf.app.flags.DEFINE_string('model_prefix', 'poem', 'model save prefix.')
tf.app.flags.DEFINE_integer('epochs', 50, 'train how many epochs.')

FLAGS = tf.app.flags.FLAGS


def run_training():
    if not os.path.exists(FLAGS.model_dir):
        os.makedirs(FLAGS.model_dir)

    poem_vector, word_to_int, vocabularies = process_poem(FLAGS.file_path)
    batches_inputs, batches_outputs = generate_batch(FLAGS.batch_size, poem_vector, word_to_int)

    input_data = tf.placeholder(tf.int32, [FLAGS.batch_size, None])
    output_targets = tf.placeholder(tf.int32, [FLAGS.batch_size, None])

    end_points = rnn_model(model='lstm', input_data=input_data, output_data=output_targets, vocab_size=len(
        vocabularies), rnn_size=128, num_layers=2, batch_size=64, learning_rate=FLAGS.learning_rate)

    saver = tf.train.Saver(tf.global_variables())
    init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())
    with tf.Session() as sess:
        # sess = tf_debug.LocalCLIDebugWrapperSession(sess=sess)
        # sess.add_tensor_filter("has_inf_or_nan", tf_debug.has_inf_or_nan)
        sess.run(init_op)

        start_epoch = 0
        checkpoint = tf.train.latest_checkpoint(FLAGS.model_dir)
        if checkpoint:
            saver.restore(sess, checkpoint)
            print("## restore from the checkpoint {0}".format(checkpoint))
            start_epoch += int(checkpoint.split('-')[-1])
        print('## start training...')
        try:
            for epoch in range(start_epoch, FLAGS.epochs):
                n = 0
                n_chunk = len(poem_vector) // FLAGS.batch_size
                for batch in range(n_chunk):
                    loss, _, _ = sess.run([
                        end_points['total_loss'],
                        end_points['last_state'],
                        end_points['train_op']
                    ], feed_dict={input_data: batches_inputs[n], output_targets: batches_outputs[n]})
                    n += 1
                    print('Epoch: %d, batch: %d, training loss: %.6f' % (epoch, batch, loss))
                if epoch % 6 == 0:
                    saver.save(sess, os.path.join(FLAGS.model_dir, FLAGS.model_prefix), global_step=epoch)
        except KeyboardInterrupt:
            print('## Interrupt manually, try saving checkpoint for now...')
            saver.save(sess, os.path.join(FLAGS.model_dir, FLAGS.model_prefix), global_step=epoch)
            print('## Last epoch were saved, next time will start from epoch {}.'.format(epoch))


def main(_):
    run_training()


if __name__ == '__main__':
    tf.app.run()