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

Before Change

    nn.save_parameters(parameter_file)

    // 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",
             "data": ["x"],
             "output": ["y"]}]}
    save.save(os.path.join(args.model_save_path,
                           "{}_result.nnp".format(args.net)), runtime_contents)

After Change

    solver.set_parameters(nn.get_parameters())
    start_point = 0

    if args.checkpoint is not None:
        // load weights and solver state info from specified checkpoint file.
        start_point = load_checkpoint(args.checkpoint, solver)

    // Create monitor.
    from nnabla.monitor import Monitor, MonitorSeries, MonitorTimeElapsed
    monitor = Monitor(args.monitor_path)
    monitor_loss = MonitorSeries("Training loss", monitor, interval=10)
    monitor_err = MonitorSeries("Training error", monitor, interval=10)
    monitor_time = MonitorTimeElapsed("Training time", monitor, interval=100)
    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))
    vdata = data_iterator_mnist(args.batch_size, False)
    // Training loop.
    for i in range(start_point, args.max_iter):
        if i % args.val_interval == 0:
            // Validation
            ve = 0.0
            for j in range(args.val_iter):
                vimage.d, vlabel.d = vdata.next()
                vpred.forward(clear_buffer=True)
                vpred.data.cast(np.float32, ctx)
                ve += categorical_error(vpred.d, vlabel.d)
            monitor_verr.add(i, ve / args.val_iter)
        if i % args.model_save_interval == 0:
            // save checkpoint file
            save_checkpoint(args.model_save_path, i, solver)
        // Training forward
        image.d, label.d = data.next()
        solver.zero_grad()

Instances