train_coversongs.py 文件源码

def dev_step(x_dev, y_dev, writer=None):
            '''
            Evaluates model on full dev set.
            --------------------------------
            Since full dev set likely won't fit into memory, this function
            splits the dev set into minibatches and returns the average 
            of loss and accuracy to cmd line and to summary writer
            '''
            dev_stats = StatisticsCollector()

            dev_batches = data_helpers.batch_iter(list(zip(x_dev, y_dev)), 
                                      FLAGS.batch_size, 1)
            for dev_batch in dev_batches:
                if len(dev_batch) > 0:
                    x_dev_batch, y_dev_batch = zip(*dev_batch)
                    feed_dict = {
                      cnn.input_song1: tuple(spect_dict[i[0]] for i in x_dev_batch),
                      cnn.input_song2: tuple(spect_dict[i[1]] for i in x_dev_batch),
                      cnn.input_y: y_dev_batch,
                      cnn.dropout_keep_prob: 1.0
                    }

                    step, loss, accuracy = sess.run(
                        [global_step, cnn.loss, cnn.accuracy],
                        feed_dict)
                    dev_stats.collect(accuracy, loss)

            time_str = datetime.datetime.now().isoformat()
            batch_accuracy, batch_loss, summaries = dev_stats.report()
            print("{}: step {}, loss {:g}, acc {:g}".format(time_str, step, batch_loss, batch_accuracy))
            if writer:
                writer.add_summary(summaries, step)

        # Generate training batches