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- 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()
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