srl_train.py

import math
import numpy as np
import gzip
import paddle.v2 as paddle
import paddle.v2.evaluator as evaluator 
import srl_data_feeder as srl_data
from paddle.trainer_config_helpers import *

import math


model_parameters='models/parameters/srl/srl_params_pass'


# word vector
word_vector_values = srl_data.get_embedding()



word_dict, verb_dict, label_dict = srl_data.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 = 50
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))
    # we are changing the pred_len to word_dict_len for testing purpose
    predicate = paddle.layer.data(name='verb_data', type=d_type(word_dict_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 main():
    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)
    
    # evaluator.chunk(
    #     input=crf_dec,
    #     label=target,
    #     chunk_scheme="IOB",
    #     num_chunk_types=label_dict_len / 2)

    # chunk_types=label_dict_len/2
    # chunk_evaluator(
    # name="chunk_f1",
    # input=crf_dec,
    # label=target,
    # chunk_scheme="IOB",
    # num_chunk_types=chunk_types)

    # create parameters
    parameters = paddle.parameters.create(crf_cost)
    print "total word vocab in word vector: " , len(word_vector_values)
    parameters.set('emb', word_vector_values)
    

    buf_size=10000
    batch_size=10
    learning_rate=1e-2/batch_size

    # create optimizer
    optimizer = paddle.optimizer.Momentum(
        momentum=0,
        #chang learning rate here. 
        #learning_rate=2e-2,
        learning_rate=learning_rate,
        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)

    train_reader = paddle.batch(
        srl_data.shuffle(srl_data.train(), buf_size=buf_size), batch_size=batch_size)

    test_reader = paddle.batch(
        srl_data.shuffle(srl_data.test(), buf_size=buf_size), batch_size=batch_size)

    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
    }
    lists = []
    def event_handler(event):
        if isinstance(event, paddle.event.EndIteration):
            if event.batch_id % 200 == 0:
                print "Pass %d, Batch %d, Cost %f, %s" % (
                    event.pass_id, event.batch_id, event.cost, event.metrics)

            if event.batch_id % 10000 == 0:
                result = trainer.test(reader=test_reader, feeding=feeding)
                print "evaluation  with Pass %d, Batch %d, %s" % (
                    event.pass_id, event.batch_id, result.metrics)

        if isinstance(event, paddle.event.EndPass):
            # save parameters
            if event.pass_id % 10 == 0:
                print 'Saving parameters..'
                path=model_parameters+'_%d.tar.gz' % event.pass_id
                with gzip.open(path, 'w') as f:
                    parameters.to_tar(f)

                result = trainer.test(reader=test_reader, feeding=feeding)
                print "\n\\Test result after end of Pass %d, %s" % (event.pass_id, result.metrics)
                lists.append((event.pass_id, result.cost,
                          result.cost))    

            

    trainer.train(
        reader=train_reader,
        event_handler=event_handler,
        num_passes=700,
        feeding=feeding)


	
    best = sorted(lists, key=lambda list: float(list[1]))[0]
    print best
    print 'Best pass is %s, testing Avgcost is %s' % (best[0], best[1])
    print 'The classification accuracy is %.2f%%' % (100 - float(best[2]) )

    test_creator = srl_data.test()
    test_data = []
    for item in test_creator():
        test_data.append(item[0:8])
        if len(test_data) == 1:
            break

    predict = paddle.layer.crf_decoding(
        size=label_dict_len,
        input=feature_out,
        param_attr=paddle.attr.Param(name='crfw'))
    probs = paddle.infer(
        output_layer=predict,
        parameters=parameters,
        input=test_data,
        field='id')
    assert len(probs) == len(test_data[0][0])
    labels_reverse = {}
    for (k, v) in label_dict.items():
        labels_reverse[v] = k
    pre_lab = [labels_reverse[i] for i in probs]
    print pre_lab
    
    for l in lists:
    	print l


if __name__ == '__main__':
    main()