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278 lines
9.0 KiB
278 lines
9.0 KiB
import math
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import numpy as np
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import gzip
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import logging
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import paddle.v2.dataset.conll05 as conll05
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import paddle.v2.evaluator as evaluator
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import paddle.v2 as paddle
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logger = logging.getLogger('paddle')
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word_dict, verb_dict, label_dict = conll05.get_dict()
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word_dict_len = len(word_dict)
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label_dict_len = len(label_dict)
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pred_len = len(verb_dict)
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mark_dict_len = 2
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word_dim = 32
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mark_dim = 5
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hidden_dim = 512
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depth = 8
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default_std = 1 / math.sqrt(hidden_dim) / 3.0
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mix_hidden_lr = 1e-3
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def d_type(size):
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return paddle.data_type.integer_value_sequence(size)
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def db_lstm():
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#8 features
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word = paddle.layer.data(name='word_data', type=d_type(word_dict_len))
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predicate = paddle.layer.data(name='verb_data', type=d_type(pred_len))
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ctx_n2 = paddle.layer.data(name='ctx_n2_data', type=d_type(word_dict_len))
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ctx_n1 = paddle.layer.data(name='ctx_n1_data', type=d_type(word_dict_len))
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ctx_0 = paddle.layer.data(name='ctx_0_data', type=d_type(word_dict_len))
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ctx_p1 = paddle.layer.data(name='ctx_p1_data', type=d_type(word_dict_len))
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ctx_p2 = paddle.layer.data(name='ctx_p2_data', type=d_type(word_dict_len))
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mark = paddle.layer.data(name='mark_data', type=d_type(mark_dict_len))
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emb_para = paddle.attr.Param(name='emb', initial_std=0., is_static=True)
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std_0 = paddle.attr.Param(initial_std=0.)
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std_default = paddle.attr.Param(initial_std=default_std)
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predicate_embedding = paddle.layer.embedding(
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size=word_dim,
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input=predicate,
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param_attr=paddle.attr.Param(
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name='vemb', initial_std=default_std))
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mark_embedding = paddle.layer.embedding(
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size=mark_dim, input=mark, param_attr=std_0)
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word_input = [word, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2]
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emb_layers = [
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paddle.layer.embedding(
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size=word_dim, input=x, param_attr=emb_para) for x in word_input
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]
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emb_layers.append(predicate_embedding)
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emb_layers.append(mark_embedding)
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hidden_0 = paddle.layer.mixed(
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size=hidden_dim,
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bias_attr=std_default,
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input=[
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paddle.layer.full_matrix_projection(
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input=emb, param_attr=std_default) for emb in emb_layers
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])
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lstm_para_attr = paddle.attr.Param(initial_std=0.0, learning_rate=1.0)
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hidden_para_attr = paddle.attr.Param(
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initial_std=default_std, learning_rate=mix_hidden_lr)
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lstm_0 = paddle.layer.lstmemory(
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input=hidden_0,
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act=paddle.activation.Relu(),
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gate_act=paddle.activation.Sigmoid(),
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state_act=paddle.activation.Sigmoid(),
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bias_attr=std_0,
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param_attr=lstm_para_attr)
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#stack L-LSTM and R-LSTM with direct edges
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input_tmp = [hidden_0, lstm_0]
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for i in range(1, depth):
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mix_hidden = paddle.layer.mixed(
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size=hidden_dim,
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bias_attr=std_default,
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input=[
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paddle.layer.full_matrix_projection(
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input=input_tmp[0], param_attr=hidden_para_attr),
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paddle.layer.full_matrix_projection(
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input=input_tmp[1], param_attr=lstm_para_attr)
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])
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lstm = paddle.layer.lstmemory(
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input=mix_hidden,
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act=paddle.activation.Relu(),
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gate_act=paddle.activation.Sigmoid(),
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state_act=paddle.activation.Sigmoid(),
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reverse=((i % 2) == 1),
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bias_attr=std_0,
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param_attr=lstm_para_attr)
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input_tmp = [mix_hidden, lstm]
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feature_out = paddle.layer.mixed(
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size=label_dict_len,
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bias_attr=std_default,
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input=[
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paddle.layer.full_matrix_projection(
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input=input_tmp[0], param_attr=hidden_para_attr),
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paddle.layer.full_matrix_projection(
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input=input_tmp[1], param_attr=lstm_para_attr)
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], )
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return feature_out
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def load_parameter(file_name, h, w):
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with open(file_name, 'rb') as f:
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f.read(16) # skip header.
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return np.fromfile(f, dtype=np.float32).reshape(h, w)
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def train():
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paddle.init(use_gpu=False, trainer_count=1)
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# define network topology
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feature_out = db_lstm()
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target = paddle.layer.data(name='target', type=d_type(label_dict_len))
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crf_cost = paddle.layer.crf(size=label_dict_len,
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input=feature_out,
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label=target,
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param_attr=paddle.attr.Param(
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name='crfw',
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initial_std=default_std,
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learning_rate=mix_hidden_lr))
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crf_dec = paddle.layer.crf_decoding(
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size=label_dict_len,
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input=feature_out,
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label=target,
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param_attr=paddle.attr.Param(name='crfw'))
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evaluator.sum(input=crf_dec)
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# create parameters
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parameters = paddle.parameters.create(crf_cost)
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parameters.set('emb', load_parameter(conll05.get_embedding(), 44068, 32))
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# create optimizer
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optimizer = paddle.optimizer.Momentum(
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momentum=0,
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learning_rate=2e-2,
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regularization=paddle.optimizer.L2Regularization(rate=8e-4),
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model_average=paddle.optimizer.ModelAverage(
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average_window=0.5, max_average_window=10000), )
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trainer = paddle.trainer.SGD(cost=crf_cost,
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parameters=parameters,
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update_equation=optimizer,
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extra_layers=crf_dec)
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reader = paddle.batch(
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paddle.reader.shuffle(
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conll05.test(), buf_size=8192), batch_size=10)
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feeding = {
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'word_data': 0,
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'ctx_n2_data': 1,
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'ctx_n1_data': 2,
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'ctx_0_data': 3,
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'ctx_p1_data': 4,
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'ctx_p2_data': 5,
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'verb_data': 6,
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'mark_data': 7,
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'target': 8
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}
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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 % 100 == 0:
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logger.info("Pass %d, Batch %d, Cost %f, %s" % (
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event.pass_id, event.batch_id, event.cost, event.metrics))
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if event.batch_id and event.batch_id % 1000 == 0:
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result = trainer.test(reader=reader, feeding=feeding)
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logger.info("\nTest with Pass %d, Batch %d, %s" %
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(event.pass_id, event.batch_id, result.metrics))
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if isinstance(event, paddle.event.EndPass):
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# save parameters
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with gzip.open('params_pass_%d.tar.gz' % event.pass_id, 'w') as f:
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parameters.to_tar(f)
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result = trainer.test(reader=reader, feeding=feeding)
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logger.info("\nTest with Pass %d, %s" %
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(event.pass_id, result.metrics))
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trainer.train(
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reader=reader,
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event_handler=event_handler,
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num_passes=10,
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feeding=feeding)
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def infer_a_batch(inferer, test_data, word_dict, pred_dict, label_dict):
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probs = inferer.infer(input=test_data, field='id')
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assert len(probs) == sum(len(x[0]) for x in test_data)
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for idx, test_sample in enumerate(test_data):
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start_id = 0
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pred_str = "%s\t" % (pred_dict[test_sample[6][0]])
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for w, tag in zip(test_sample[0],
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probs[start_id:start_id + len(test_sample[0])]):
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pred_str += "%s[%s] " % (word_dict[w], label_dict[tag])
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print(pred_str.strip())
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start_id += len(test_sample[0])
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def infer():
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label_dict_reverse = dict((value, key)
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for key, value in label_dict.iteritems())
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word_dict_reverse = dict((value, key)
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for key, value in word_dict.iteritems())
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pred_dict_reverse = dict((value, key)
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for key, value in verb_dict.iteritems())
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test_creator = paddle.dataset.conll05.test()
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paddle.init(use_gpu=False, trainer_count=1)
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# define network topology
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feature_out = db_lstm()
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predict = paddle.layer.crf_decoding(
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size=label_dict_len,
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input=feature_out,
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param_attr=paddle.attr.Param(name='crfw'))
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test_pass = 0
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with gzip.open('params_pass_%d.tar.gz' % (test_pass)) as f:
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parameters = paddle.parameters.Parameters.from_tar(f)
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inferer = paddle.inference.Inference(
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output_layer=predict, parameters=parameters)
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# prepare test data
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test_data = []
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test_batch_size = 50
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for idx, item in enumerate(test_creator()):
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test_data.append(item[0:8])
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if idx and (not idx % test_batch_size):
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infer_a_batch(
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inferer,
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test_data,
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word_dict_reverse,
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pred_dict_reverse,
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label_dict_reverse, )
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test_data = []
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infer_a_batch(
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inferer,
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test_data,
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word_dict_reverse,
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pred_dict_reverse,
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label_dict_reverse, )
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test_data = []
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def main(is_inferring=False):
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if is_inferring:
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infer()
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else:
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train()
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if __name__ == '__main__':
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main(is_inferring=False)
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