Python utils.TextLoader() Examples

The following are 4 code examples of utils.TextLoader(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module utils , or try the search function .
Example #1
Source File: test_train.py    From word-rnn-tensorflow with MIT License 5 votes vote down vote up 
def setUp(self):
        self.data_loader = TextLoader("tests/test_data", batch_size=2, seq_length=5)
Example #2
Source File: test_utils.py    From word-rnn-tensorflow with MIT License 5 votes vote down vote up 
def setUp(self):
        self.data_loader = TextLoader("tests/test_data", batch_size=2, seq_length=5)
Example #3
Source File: train.py    From poem-bot with MIT License 5 votes vote down vote up 
def train(args):
    data_loader = TextLoader(args.data_dir, args.batch_size, args.seq_length)
    args.vocab_size = data_loader.vocab_size

    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, 'chars_vocab.pkl'), 'wb') as f:
        cPickle.dump((data_loader.chars, data_loader.vocab), f)

    model = Model(args)

    with tf.Session() as sess:
        tf.initialize_all_variables().run()
        saver = tf.train.Saver(tf.all_variables())
        for e in range(args.num_epochs):
            sess.run(tf.assign(model.lr, args.learning_rate * (args.decay_rate ** e)))
            data_loader.reset_batch_pointer()
            state = model.initial_state.eval()
            for b in range(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}
                train_loss, state, _ = sess.run([model.cost, model.final_state, model.train_op], feed)
                end = time.time()
                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, end - start))
                if (e * data_loader.num_batches + b) % args.save_every == 0:
                    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))
Example #4
Source File: train.py    From tensorflow-grid-lstm with Apache License 2.0 4 votes vote down vote up 
def train(args):
    data_loader = TextLoader(args.data_dir, args.batch_size, args.seq_length)
    args.vocab_size = data_loader.vocab_size

    with open(os.path.join(args.save_dir, 'config.pkl'), 'wb') as f:
        pickle.dump(args, f)
    with open(os.path.join(args.save_dir, 'chars_vocab.pkl'), 'wb') as f:
        pickle.dump((data_loader.chars, data_loader.vocab), f)

    model = Model(args)

    with tf.Session() as sess:
        tf.global_variables_initializer().run()
        saver = tf.train.Saver(tf.global_variables())
        train_loss_iterations = {'iteration': [], 'epoch': [], 'train_loss': [], 'val_loss': []}

        for e in range(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)
            for b in range(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}
                train_loss, state, _ = sess.run([model.cost, model.final_state, model.train_op], feed)
                end = time.time()
                batch_idx = e * data_loader.num_batches + b
                print("{}/{} (epoch {}), train_loss = {:.3f}, time/batch = {:.3f}" \
                      .format(batch_idx,
                              args.num_epochs * data_loader.num_batches,
                              e, train_loss, end - start))
                train_loss_iterations['iteration'].append(batch_idx)
                train_loss_iterations['epoch'].append(e)
                train_loss_iterations['train_loss'].append(train_loss)

                if batch_idx % args.save_every == 0:

                    # evaluate
                    state_val = sess.run(model.initial_state)
                    avg_val_loss = 0
                    for x_val, y_val in data_loader.val_batches:
                        feed_val = {model.input_data: x_val, model.targets: y_val, model.initial_state: state_val}
                        val_loss, state_val, _ = sess.run([model.cost, model.final_state, model.train_op], feed_val)
                        avg_val_loss += val_loss / len(list(data_loader.val_batches))
                    print('val_loss: {:.3f}'.format(avg_val_loss))
                    train_loss_iterations['val_loss'].append(avg_val_loss)

                    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))
                else:
                    train_loss_iterations['val_loss'].append(None)

            pd.DataFrame(data=train_loss_iterations,
                         columns=train_loss_iterations.keys()).to_csv(os.path.join(args.save_dir, 'log.csv'))