7331bc49dab2d0078054b25500890f8d3eb7733e,mnist-collection/classification.py,,train,#,107

Before Change

    // append F.Softmax to the prediction graph so users see intuitive outputs
    runtime_contents = {
        "networks": [
            {"name": "Validation",
             "batch_size": args.batch_size,
             "outputs": {"y": F.softmax(vpred)},
             "names": {"x": vimage}}],
        "executors": [
            {"name": "Runtime",
             "network": "Validation",

After Change

    monitor_verr = MonitorSeries("Test error", monitor, interval=10)

    // save_nnp
    contents = save_nnp({"x": vimage}, {"y": vpred}, args.batch_size)
    save.save(os.path.join(args.model_save_path,
                           "{}_result_epoch0.nnp".format(args.net)), contents)

    // Initialize DataIterator for MNIST.
    from numpy.random import RandomState
    data = data_iterator_mnist(args.batch_size, True, rng=RandomState(1223))

Instances