Python test.evaluate() Examples

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Example #1
Source File: train.py    From personalized-dialog with MIT License 5 votes vote down vote up 
def main(train_tensor, dev_tensor, candidates_tensor, model, config):
    logger.info('Run main with config {}'.format(config))

    epochs = config['epochs']
    batch_size = config['batch_size']
    negative_cand = config['negative_cand']
    save_dir = config['save_dir']

    # TODO: Add LR decay
    optimizer = tf.train.AdamOptimizer(config['lr']).minimize(model.loss)

    prev_best_accuracy = 0

    saver = tf.train.Saver()
    config = tf.ConfigProto()
    config.gpu_options.allow_growth = True
    with tf.Session(config=config) as sess:
        sess.run(tf.global_variables_initializer())

        for epoch in range(epochs):
            avg_loss = _train(train_tensor, batch_size, negative_cand, model, optimizer, sess)
            # TODO: Refine dev loss calculation
            avg_dev_loss = _forward_all(dev_tensor, model, sess)
            logger.info('Epoch: {}; Train loss: {}; Dev loss: {};'.format(epoch, avg_loss, avg_dev_loss))

            if epoch % 2 == 0:
                dev_eval = evaluate(dev_tensor, candidates_tensor, sess, model)
                logger.info('Evaluation: {}'.format(dev_eval))
                accuracy = dev_eval[2]
                if accuracy >= prev_best_accuracy:
                    logger.debug('Saving checkpoint')
                    prev_best_accuracy = accuracy
                    saver.save(sess, save_dir)