def main()

in example/mnist_with_visdom.py [0:0]

• 61 lines of code
• 2 McCabe index (conditional complexity)

def main():
    params = {
        'conv0.weight': conv_init(1, 50, 5), 'conv0.bias': torch.zeros(50),
        'conv1.weight': conv_init(50, 50, 5), 'conv1.bias': torch.zeros(50),
        'linear2.weight': linear_init(800, 512), 'linear2.bias': torch.zeros(512),
        'linear3.weight': linear_init(512, 10), 'linear3.bias': torch.zeros(10),
    }
    params = {k: Variable(v, requires_grad=True) for k, v in params.items()}

    optimizer = torch.optim.SGD(
        params.values(), lr=0.01, momentum=0.9, weight_decay=0.0005)

    engine = Engine()
    meter_loss = tnt.meter.AverageValueMeter()
    classerr = tnt.meter.ClassErrorMeter(accuracy=True)
    confusion_meter = tnt.meter.ConfusionMeter(10, normalized=True)

    port = 8097
    train_loss_logger = VisdomPlotLogger(
        'line', port=port, opts={'title': 'Train Loss'})
    train_err_logger = VisdomPlotLogger(
        'line', port=port, opts={'title': 'Train Class Error'})
    test_loss_logger = VisdomPlotLogger(
        'line', port=port, opts={'title': 'Test Loss'})
    test_err_logger = VisdomPlotLogger(
        'line', port=port, opts={'title': 'Test Class Error'})
    confusion_logger = VisdomLogger('heatmap', port=port, opts={'title': 'Confusion matrix',
                                                                'columnnames': list(range(10)),
                                                                'rownames': list(range(10))})

    def h(sample):
        inputs = Variable(sample[0].float() / 255.0)
        targets = Variable(torch.LongTensor(sample[1]))
        o = f(params, inputs, sample[2])
        return F.cross_entropy(o, targets), o

    def reset_meters():
        classerr.reset()
        meter_loss.reset()
        confusion_meter.reset()

    def on_sample(state):
        state['sample'].append(state['train'])

    def on_forward(state):
        classerr.add(state['output'].data,
                     torch.LongTensor(state['sample'][1]))
        confusion_meter.add(state['output'].data,
                            torch.LongTensor(state['sample'][1]))
        meter_loss.add(state['loss'].data[0])

    def on_start_epoch(state):
        reset_meters()
        state['iterator'] = tqdm(state['iterator'])

    def on_end_epoch(state):
        print('Training loss: %.4f, accuracy: %.2f%%' % (meter_loss.value()[0], classerr.value()[0]))
        train_loss_logger.log(state['epoch'], meter_loss.value()[0])
        train_err_logger.log(state['epoch'], classerr.value()[0])

        # do validation at the end of each epoch
        reset_meters()
        engine.test(h, get_iterator(False))
        test_loss_logger.log(state['epoch'], meter_loss.value()[0])
        test_err_logger.log(state['epoch'], classerr.value()[0])
        confusion_logger.log(confusion_meter.value())
        print('Testing loss: %.4f, accuracy: %.2f%%' % (meter_loss.value()[0], classerr.value()[0]))

    engine.hooks['on_sample'] = on_sample
    engine.hooks['on_forward'] = on_forward
    engine.hooks['on_start_epoch'] = on_start_epoch
    engine.hooks['on_end_epoch'] = on_end_epoch
    engine.train(h, get_iterator(True), maxepoch=10, optimizer=optimizer)