api_train_v2.py 4.9 KB
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import paddle.v2 as paddle
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import cPickle
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def softmax_regression(img):
    predict = paddle.layer.fc(input=img,
                              size=10,
                              act=paddle.activation.Softmax())
    return predict


def multilayer_perceptron(img):
    # The first fully-connected layer
    hidden1 = paddle.layer.fc(input=img, size=128, act=paddle.activation.Relu())
    # The second fully-connected layer and the according activation function
    hidden2 = paddle.layer.fc(input=hidden1,
                              size=64,
                              act=paddle.activation.Relu())
    # The thrid fully-connected layer, note that the hidden size should be 10,
    # which is the number of unique digits
    predict = paddle.layer.fc(input=hidden2,
                              size=10,
                              act=paddle.activation.Softmax())
    return predict


def convolutional_neural_network(img):
    # first conv layer
    conv_pool_1 = paddle.networks.simple_img_conv_pool(
        input=img,
        filter_size=5,
        num_filters=20,
        num_channel=1,
        pool_size=2,
        pool_stride=2,
        act=paddle.activation.Tanh())
    # second conv layer
    conv_pool_2 = paddle.networks.simple_img_conv_pool(
        input=conv_pool_1,
        filter_size=5,
        num_filters=50,
        num_channel=20,
        pool_size=2,
        pool_stride=2,
        act=paddle.activation.Tanh())
    # The first fully-connected layer
    fc1 = paddle.layer.fc(input=conv_pool_2,
                          size=128,
                          act=paddle.activation.Tanh())
    # The softmax layer, note that the hidden size should be 10,
    # which is the number of unique digits
    predict = paddle.layer.fc(input=fc1,
                              size=10,
                              act=paddle.activation.Softmax())
    return predict


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def main():
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    paddle.init(use_gpu=False, trainer_count=1)
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    # define network topology
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    images = paddle.layer.data(
        name='pixel', type=paddle.data_type.dense_vector(784))
    label = paddle.layer.data(
        name='label', type=paddle.data_type.integer_value(10))
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    # Here we can build the prediction network in different ways. Please
    # choose one by uncomment corresponding line.
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    predict = softmax_regression(images)
    #predict = multilayer_perceptron(images)
    #predict = convolutional_neural_network(images)

    cost = paddle.layer.classification_cost(input=predict, label=label)
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    try:
        with open('params.pkl', 'r') as f:
            parameters = cPickle.load(f)
    except IOError:
        parameters = paddle.parameters.create(cost)
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    optimizer = paddle.optimizer.Momentum(
        learning_rate=0.1 / 128.0,
        momentum=0.9,
        regularization=paddle.optimizer.L2Regularization(rate=0.0005 * 128))
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    trainer = paddle.trainer.SGD(cost=cost,
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                                 parameters=parameters,
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                                 update_equation=optimizer)
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    lists = []
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    def event_handler(event):
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        if isinstance(event, paddle.event.EndIteration):
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            if event.batch_id % 1000 == 0:
                result = trainer.test(reader=paddle.reader.batched(
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                    paddle.dataset.mnist.test(), batch_size=256))
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                print "Pass %d, Batch %d, Cost %f, %s, Testing metrics %s" % (
                    event.pass_id, event.batch_id, event.cost, event.metrics,
                    result.metrics)

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                with open('params.pkl', 'w') as f:
                    cPickle.dump(
                        parameters, f, protocol=cPickle.HIGHEST_PROTOCOL)

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        elif isinstance(event, paddle.event.EndPass):
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            result = trainer.test(reader=paddle.reader.batched(
                paddle.dataset.mnist.test(), batch_size=128))
            print "Test with Pass %d, Cost %f, %s\n" % (
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                event.pass_id, result.cost, result.metrics)
            lists.append((event.pass_id, result.cost,
                          result.metrics['classification_error_evaluator']))
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    trainer.train(
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        reader=paddle.reader.batched(
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            paddle.reader.shuffle(
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                paddle.dataset.mnist.train(), buf_size=8192),
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            batch_size=128),
        event_handler=event_handler,
        num_passes=100)
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    # find the best pass
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    best = sorted(lists, key=lambda list: float(list[1]))[0]
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    print 'Best pass is %s, testing Avgcost is %s' % (best[0], best[1])
    print 'The classification accuracy is %.2f%%' % (100 - float(best[2]) * 100)
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    # output is a softmax layer. It returns probabilities.
    # Shape should be (100, 10)
    probs = paddle.infer(
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        output=predict,
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        parameters=parameters,
        reader=paddle.reader.batched(
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            paddle.reader.firstn(
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                paddle.reader.map_readers(lambda item: (item[0], ),
                                          paddle.dataset.mnist.test()),
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                n=100),
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            batch_size=32))
    print probs.shape

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if __name__ == '__main__':
    main()