import paddle.v2 as paddle 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 def main(): paddle.init(use_gpu=False, trainer_count=1) # define network topology 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)) # Here we can build the prediction network in different ways. Please # choose one by uncomment corresponding line. predict = softmax_regression(images) #predict = multilayer_perceptron(images) #predict = convolutional_neural_network(images) cost = paddle.layer.classification_cost(input=predict, label=label) parameters = paddle.parameters.create(cost) optimizer = paddle.optimizer.Momentum( learning_rate=0.1 / 128.0, momentum=0.9, regularization=paddle.optimizer.L2Regularization(rate=0.0005 * 128)) trainer = paddle.trainer.SGD(cost=cost, parameters=parameters, update_equation=optimizer) lists = [] def event_handler(event): if isinstance(event, paddle.event.EndIteration): if event.batch_id % 100 == 0: print "Pass %d, Batch %d, Cost %f, %s" % ( event.pass_id, event.batch_id, event.cost, event.metrics) if isinstance(event, paddle.event.EndPass): result = trainer.test(reader=paddle.reader.batched( paddle.dataset.mnist.test(), batch_size=128)) print "Test with Pass %d, Cost %f, %s\n" % ( event.pass_id, result.cost, result.metrics) lists.append((event.pass_id, result.cost, result.metrics['classification_error_evaluator'])) trainer.train( reader=paddle.batch( paddle.reader.shuffle( paddle.dataset.mnist.train(), buf_size=8192), batch_size=128), event_handler=event_handler, num_passes=100) # find the best pass best = sorted(lists, key=lambda list: float(list[1]))[0] print 'Best pass is %s, testing Avgcost is %s' % (best[0], best[1]) print 'The classification accuracy is %.2f%%' % (100 - float(best[2]) * 100) # output is a softmax layer. It returns probabilities. # Shape should be (100, 10) probs = paddle.infer( output=predict, parameters=parameters, reader=paddle.batch( paddle.reader.firstn( paddle.reader.map_readers(lambda item: (item[0], ), paddle.dataset.mnist.test()), n=100), batch_size=32)) print probs.shape if __name__ == '__main__': main()