api_train_v2.py

import math
import numpy as np
import gzip
import logging
import paddle.v2.dataset.conll05 as conll05
import paddle.v2.evaluator as evaluator
import paddle.v2 as paddle

word_dict, verb_dict, label_dict = conll05.get_dict()
word_dict_len = len(word_dict)
label_dict_len = len(label_dict)
pred_len = len(verb_dict)

mark_dict_len = 2
word_dim = 32
mark_dim = 5
hidden_dim = 512
depth = 8
default_std = 1 / math.sqrt(hidden_dim) / 3.0
mix_hidden_lr = 1e-3


def d_type(size):
    return paddle.data_type.integer_value_sequence(size)


def db_lstm():
    #8 features
    word = paddle.layer.data(name='word_data', type=d_type(word_dict_len))
    predicate = paddle.layer.data(name='verb_data', type=d_type(pred_len))

    ctx_n2 = paddle.layer.data(name='ctx_n2_data', type=d_type(word_dict_len))
    ctx_n1 = paddle.layer.data(name='ctx_n1_data', type=d_type(word_dict_len))
    ctx_0 = paddle.layer.data(name='ctx_0_data', type=d_type(word_dict_len))
    ctx_p1 = paddle.layer.data(name='ctx_p1_data', type=d_type(word_dict_len))
    ctx_p2 = paddle.layer.data(name='ctx_p2_data', type=d_type(word_dict_len))
    mark = paddle.layer.data(name='mark_data', type=d_type(mark_dict_len))

    emb_para = paddle.attr.Param(name='emb', initial_std=0., is_static=True)
    std_0 = paddle.attr.Param(initial_std=0.)
    std_default = paddle.attr.Param(initial_std=default_std)

    predicate_embedding = paddle.layer.embedding(
        size=word_dim,
        input=predicate,
        param_attr=paddle.attr.Param(
            name='vemb', initial_std=default_std))
    mark_embedding = paddle.layer.embedding(
        size=mark_dim, input=mark, param_attr=std_0)

    word_input = [word, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2]
    emb_layers = [
        paddle.layer.embedding(
            size=word_dim, input=x, param_attr=emb_para) for x in word_input
    ]
    emb_layers.append(predicate_embedding)
    emb_layers.append(mark_embedding)

    hidden_0 = paddle.layer.mixed(
        size=hidden_dim,
        bias_attr=std_default,
        input=[
            paddle.layer.full_matrix_projection(
                input=emb, param_attr=std_default) for emb in emb_layers
        ])

    lstm_para_attr = paddle.attr.Param(initial_std=0.0, learning_rate=1.0)
    hidden_para_attr = paddle.attr.Param(
        initial_std=default_std, learning_rate=mix_hidden_lr)

    lstm_0 = paddle.layer.lstmemory(
        input=hidden_0,
        act=paddle.activation.Relu(),
        gate_act=paddle.activation.Sigmoid(),
        state_act=paddle.activation.Sigmoid(),
        bias_attr=std_0,
        param_attr=lstm_para_attr)

    #stack L-LSTM and R-LSTM with direct edges
    input_tmp = [hidden_0, lstm_0]

    for i in range(1, depth):
        mix_hidden = paddle.layer.mixed(
            size=hidden_dim,
            bias_attr=std_default,
            input=[
                paddle.layer.full_matrix_projection(
                    input=input_tmp[0], param_attr=hidden_para_attr),
                paddle.layer.full_matrix_projection(
                    input=input_tmp[1], param_attr=lstm_para_attr)
            ])

        lstm = paddle.layer.lstmemory(
            input=mix_hidden,
            act=paddle.activation.Relu(),
            gate_act=paddle.activation.Sigmoid(),
            state_act=paddle.activation.Sigmoid(),
            reverse=((i % 2) == 1),
            bias_attr=std_0,
            param_attr=lstm_para_attr)

        input_tmp = [mix_hidden, lstm]

    feature_out = paddle.layer.mixed(
        size=label_dict_len,
        bias_attr=std_default,
        input=[
            paddle.layer.full_matrix_projection(
                input=input_tmp[0], param_attr=hidden_para_attr),
            paddle.layer.full_matrix_projection(
                input=input_tmp[1], param_attr=lstm_para_attr)
        ], )

    return feature_out


def load_parameter(file_name, h, w):
    with open(file_name, 'rb') as f:
        f.read(16)  # skip header.
        return np.fromfile(f, dtype=np.float32).reshape(h, w)


def test_a_batch(inferer, test_data, tag_dict):
    probs = inferer.infer(input=test_data, field='id')
    assert len(probs) == sum(len(x[0]) for x in test_data)
    for test_sample in test_data:
        start_id = 0
        pre_lab = [
            tag_dict[probs[start_id + i]] for i in xrange(len(test_sample[0]))
        ]
        print pre_lab
        start_id += len(test_sample[0])


def main(is_predict=False):
    paddle.init(use_gpu=False, trainer_count=1)

    # define network topology
    feature_out = db_lstm()
    target = paddle.layer.data(name='target', type=d_type(label_dict_len))
    crf_cost = paddle.layer.crf(size=label_dict_len,
                                input=feature_out,
                                label=target,
                                param_attr=paddle.attr.Param(
                                    name='crfw',
                                    initial_std=default_std,
                                    learning_rate=mix_hidden_lr))

    crf_dec = paddle.layer.crf_decoding(
        size=label_dict_len,
        input=feature_out,
        label=target,
        param_attr=paddle.attr.Param(name='crfw'))
    evaluator.sum(input=crf_dec)

    # create parameters
    parameters = paddle.parameters.create(crf_cost)
    parameters.set('emb', load_parameter(conll05.get_embedding(), 44068, 32))

    # create optimizer
    optimizer = paddle.optimizer.Momentum(
        momentum=0,
        learning_rate=2e-2,
        regularization=paddle.optimizer.L2Regularization(rate=8e-4),
        model_average=paddle.optimizer.ModelAverage(
            average_window=0.5, max_average_window=10000), )

    trainer = paddle.trainer.SGD(cost=crf_cost,
                                 parameters=parameters,
                                 update_equation=optimizer,
                                 extra_layers=crf_dec)

    reader = paddle.batch(
        paddle.reader.shuffle(
            conll05.test(), buf_size=8192), batch_size=10)

    feeding = {
        'word_data': 0,
        'ctx_n2_data': 1,
        'ctx_n1_data': 2,
        'ctx_0_data': 3,
        'ctx_p1_data': 4,
        'ctx_p2_data': 5,
        'verb_data': 6,
        'mark_data': 7,
        'target': 8
    }

    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 event.batch_id % 1000 == 0:
                result = trainer.test(reader=reader, feeding=feeding)
                print "\nTest with Pass %d, Batch %d, %s" % (
                    event.pass_id, event.batch_id, result.metrics)

        if isinstance(event, paddle.event.EndPass):
            # save parameters
            with gzip.open('params_pass_%d.tar.gz' % event.pass_id, 'w') as f:
                parameters.to_tar(f)

            result = trainer.test(reader=reader, feeding=feeding)
            print "\nTest with Pass %d, %s" % (event.pass_id, result.metrics)

    if not is_predict:
        trainer.train(
            reader=reader,
            event_handler=event_handler,
            num_passes=10,
            feeding=feeding)
    else:
        labels_reverse = {}
        for (k, v) in label_dict.items():
            labels_reverse[v] = k
        test_creator = paddle.dataset.conll05.test()

        predict = paddle.layer.crf_decoding(
            size=label_dict_len,
            input=feature_out,
            param_attr=paddle.attr.Param(name='crfw'))

        test_pass = 0
        with gzip.open('params_pass_%d.tar.gz' % (test_pass)) as f:
            parameters = paddle.parameters.Parameters.from_tar(f)
            inferer = paddle.inference.Inference(
                output_layer=predict, parameters=parameters)

            # prepare test data
            test_data = []
            test_batch_size = 50

            for idx, item in enumerate(test_creator()):
                test_data.append(item[0:8])

                if idx and (not idx % test_batch_size):
                    test_a_batch(inferer, test_data, labels_reverse)
                    test_data = []
            test_a_batch(inferer, test_data, labels_reverse)
            test_data = []


if __name__ == '__main__':
    main(is_predict=False)