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train.py
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train.py
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from __future__ import print_function
import numpy as np
import tensorflow as tf
import argparse
import time
import os
from six.moves import cPickle
from utils import TextLoader
from model import Model
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--data_dir', type=str, default='data/elonmusk',
help='data directory containing input.txt')
parser.add_argument('--input_encoding', type=str, default=None,
help='character encoding of input.txt, from https://docs.python.org/3/library/codecs.html#standard-encodings')
parser.add_argument('--log_dir', type=str, default='logs',
help='directory containing tensorboard logs')
parser.add_argument('--save_dir', type=str, default='save',
help='directory to store checkpointed models')
parser.add_argument('--rnn_size', type=int, default=256,
help='size of RNN hidden state')
parser.add_argument('--num_layers', type=int, default=2,
help='number of layers in the RNN')
parser.add_argument('--model', type=str, default='lstm',
help='rnn, gru, or lstm')
parser.add_argument('--batch_size', type=int, default=50,
help='minibatch size')
parser.add_argument('--seq_length', type=int, default=25,
help='RNN sequence length')
parser.add_argument('--num_epochs', type=int, default=50,
help='number of epochs')
parser.add_argument('--save_every', type=int, default=1000,
help='save frequency')
parser.add_argument('--grad_clip', type=float, default=5.,
help='clip gradients at this value')
parser.add_argument('--learning_rate', type=float, default=0.002,
help='learning rate')
parser.add_argument('--decay_rate', type=float, default=0.97,
help='decay rate for rmsprop')
parser.add_argument('--gpu_mem', type=float, default=0.666,
help='%% of gpu memory to be allocated to this process. Default is 66.6%%')
parser.add_argument('--init_from', type=str, default=None,
help="""continue training from saved model at this path. Path must contain files saved by previous training process:
'config.pkl' : configuration;
'words_vocab.pkl' : vocabulary definitions;
'checkpoint' : paths to model file(s) (created by tf).
Note: this file contains absolute paths, be careful when moving files around;
'model.ckpt-*' : file(s) with model definition (created by tf)
""")
args = parser.parse_args()
train(args)
def train(args):
data_loader = TextLoader(args.data_dir, args.batch_size, args.seq_length, args.input_encoding)
args.vocab_size = data_loader.vocab_size
# check compatibility if training is continued from previously saved model
if args.init_from is not None:
# check if all necessary files exist
assert os.path.isdir(args.init_from)," %s must be a path" % args.init_from
assert os.path.isfile(os.path.join(args.init_from,"config.pkl")),"config.pkl file does not exist in path %s"%args.init_from
assert os.path.isfile(os.path.join(args.init_from,"words_vocab.pkl")),"words_vocab.pkl.pkl file does not exist in path %s" % args.init_from
ckpt = tf.train.get_checkpoint_state(args.init_from)
assert ckpt,"No checkpoint found"
assert ckpt.model_checkpoint_path,"No model path found in checkpoint"
# open old config and check if models are compatible
with open(os.path.join(args.init_from, 'config.pkl'), 'rb') as f:
saved_model_args = cPickle.load(f)
need_be_same=["model","rnn_size","num_layers","seq_length"]
for checkme in need_be_same:
assert vars(saved_model_args)[checkme]==vars(args)[checkme],"Command line argument and saved model disagree on '%s' "%checkme
# open saved vocab/dict and check if vocabs/dicts are compatible
with open(os.path.join(args.init_from, 'words_vocab.pkl'), 'rb') as f:
saved_words, saved_vocab = cPickle.load(f)
assert saved_words==data_loader.words, "Data and loaded model disagree on word set!"
assert saved_vocab==data_loader.vocab, "Data and loaded model disagree on dictionary mappings!"
with open(os.path.join(args.save_dir, 'config.pkl'), 'wb') as f:
cPickle.dump(args, f)
with open(os.path.join(args.save_dir, 'words_vocab.pkl'), 'wb') as f:
cPickle.dump((data_loader.words, data_loader.vocab), f)
model = Model(args)
merged = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(args.log_dir)
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=args.gpu_mem)
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
train_writer.add_graph(sess.graph)
tf.global_variables_initializer().run()
saver = tf.train.Saver(tf.global_variables())
# restore model
if args.init_from is not None:
saver.restore(sess, ckpt.model_checkpoint_path)
for e in range(model.epoch_pointer.eval(), args.num_epochs):
sess.run(tf.assign(model.lr, args.learning_rate * (args.decay_rate ** e)))
data_loader.reset_batch_pointer()
state = sess.run(model.initial_state)
speed = 0
if args.init_from is None:
assign_op = model.epoch_pointer.assign(e)
sess.run(assign_op)
if args.init_from is not None:
data_loader.pointer = model.batch_pointer.eval()
args.init_from = None
for b in range(data_loader.pointer, data_loader.num_batches):
start = time.time()
x, y = data_loader.next_batch()
feed = {model.input_data: x, model.targets: y, model.initial_state: state,
model.batch_time: speed}
summary, train_loss, state, _, _ = sess.run([merged, model.cost, model.final_state,
model.train_op, model.inc_batch_pointer_op], feed)
train_writer.add_summary(summary, e * data_loader.num_batches + b)
speed = time.time() - start
if (e * data_loader.num_batches + b) % args.batch_size == 0:
print("{}/{} (epoch {}), train_loss = {:.3f}, time/batch = {:.3f}" \
.format(e * data_loader.num_batches + b,
args.num_epochs * data_loader.num_batches,
e, train_loss, speed))
if (e * data_loader.num_batches + b) % args.save_every == 0 \
or (e==args.num_epochs-1 and b == data_loader.num_batches-1): # save for the last result
checkpoint_path = os.path.join(args.save_dir, 'model.ckpt')
saver.save(sess, checkpoint_path, global_step = e * data_loader.num_batches + b)
print("model saved to {}".format(checkpoint_path))
train_writer.close()
if __name__ == '__main__':
main()