#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import paddle.v2 as paddle
import gzip


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,
                                 is_local=False,
                                 pserver_spec="localhost:3000")

    lists = []

    def event_handler(event):
        if isinstance(event, paddle.event.EndIteration):
            if event.batch_id % 1000 == 0:
                print "Pass %d, Batch %d, Cost %f, %s" % (
                    event.pass_id, event.batch_id, event.cost, event.metrics)

        elif isinstance(event, paddle.event.EndPass):
            result = trainer.test(reader=paddle.batch(
                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)

    test_creator = paddle.dataset.mnist.test()
    test_data = []
    for item in test_creator():
        test_data.append((item[0], ))
        if len(test_data) == 100:
            break

    # output is a softmax layer. It returns probabilities.
    # Shape should be (100, 10)
    probs = paddle.infer(
        output_layer=predict, parameters=parameters, input=test_data)
    print probs.shape


if __name__ == '__main__':
    main()