Open hflyzju opened 5 years ago
#coding:utf-8 from __future__ import print_function import paddle as paddle import paddle.fluid as fluid import six import numpy import math import sys EMBED_SIZE = 32 # embedding维度 HIDDEN_SIZE = 256 # 隐层大小 N = 5 # ngram大小，这里固定取5 BATCH_SIZE = 100 # batch大小 PASS_NUM = 100 # 训练轮数 use_cuda = False # 如果用GPU训练，则设置为True word_dict = paddle.dataset.imikolov.build_dict() dict_size = len(word_dict) def inference_program(words, is_sparse): embed_first = fluid.layers.embedding( input=words[0], size=[dict_size, EMBED_SIZE], dtype='float32', is_sparse=is_sparse, param_attr='shared_w') embed_second = fluid.layers.embedding( input=words[1], size=[dict_size, EMBED_SIZE], dtype='float32', is_sparse=is_sparse, param_attr='shared_w') embed_third = fluid.layers.embedding( input=words[2], size=[dict_size, EMBED_SIZE], dtype='float32', is_sparse=is_sparse, param_attr='shared_w') embed_fourth = fluid.layers.embedding( input=words[3], size=[dict_size, EMBED_SIZE], dtype='float32', is_sparse=is_sparse, param_attr='shared_w') concat_embed = fluid.layers.concat( input=[embed_first, embed_second, embed_third, embed_fourth], axis=1) hidden1 = fluid.layers.fc(input=concat_embed, size=HIDDEN_SIZE, act='sigmoid') predict_word = fluid.layers.fc(input=hidden1, size=dict_size, act='softmax') return predict_word,embed_first def train_program(predict_word): # 'next_word'的定义必须要在inference_program的声明之后， # 否则train program输入数据的顺序就变成了[next_word, firstw, secondw, # thirdw, fourthw], 这是不正确的. next_word = fluid.layers.data(name='nextw', shape=[1], dtype='int64') cost = fluid.layers.cross_entropy(input=predict_word, label=next_word) avg_cost = fluid.layers.mean(cost) return avg_cost def optimizer_func(): return fluid.optimizer.AdagradOptimizer( learning_rate=3e-3, regularization=fluid.regularizer.L2DecayRegularizer(8e-4)) def train(if_use_cuda, params_dirname, embedding_params_dirname, is_sparse=True): place = fluid.CUDAPlace(0) if if_use_cuda else fluid.CPUPlace() train_reader = paddle.batch( paddle.dataset.imikolov.train(word_dict, N), BATCH_SIZE) test_reader = paddle.batch( paddle.dataset.imikolov.test(word_dict, N), BATCH_SIZE) first_word = fluid.layers.data(name='firstw', shape=[1], dtype='int64') second_word = fluid.layers.data(name='secondw', shape=[1], dtype='int64') third_word = fluid.layers.data(name='thirdw', shape=[1], dtype='int64') forth_word = fluid.layers.data(name='fourthw', shape=[1], dtype='int64') next_word = fluid.layers.data(name='nextw', shape=[1], dtype='int64') word_list = [first_word, second_word, third_word, forth_word, next_word] feed_order = ['firstw', 'secondw', 'thirdw', 'fourthw', 'nextw'] main_program = fluid.default_main_program() star_program = fluid.default_startup_program() predict_word, embed_first = inference_program(word_list, is_sparse) avg_cost = train_program(predict_word) test_program = main_program.clone(for_test=True) sgd_optimizer = optimizer_func() sgd_optimizer.minimize(avg_cost) exe = fluid.Executor(place) def train_test(program, reader): count = 0 feed_var_list = [ program.global_block().var(var_name) for var_name in feed_order ] feeder_test = fluid.DataFeeder(feed_list=feed_var_list, place=place) test_exe = fluid.Executor(place) accumulated = len([avg_cost]) * [0] for test_data in reader(): avg_cost_np = test_exe.run( program=program, feed=feeder_test.feed(test_data), fetch_list=[avg_cost]) accumulated = [ x[0] + x[1][0] for x in zip(accumulated, avg_cost_np) ] count += 1 return [x / count for x in accumulated] def train_loop(): step = 0 feed_var_list_loop = [ main_program.global_block().var(var_name) for var_name in feed_order ] feeder = fluid.DataFeeder(feed_list=feed_var_list_loop, place=place) exe.run(star_program) for pass_id in range(PASS_NUM): for data in train_reader(): avg_cost_np = exe.run( main_program, feed=feeder.feed(data), fetch_list=[avg_cost]) if step % 10 == 0: outs = train_test(test_program, test_reader) print("Step %d: Average Cost %f" % (step, outs[0])) # 整个训练过程要花费几个小时，如果平均损失低于5.8， # 我们就认为模型已经达到很好的效果可以停止训练了。 # 注意5.8是一个相对较高的值，为了获取更好的模型，可以将 # 这里的阈值设为3.5，但训练时间也会更长。 if outs[0] < 5.8: if params_dirname is not None: fluid.io.save_inference_model(params_dirname, [ 'firstw', 'secondw', 'thirdw', 'fourthw' ], [predict_word], exe) # 保存embedding参数 if embedding_params_dirname is not None: fluid.io.save_inference_model(embedding_params_dirname, [ 'firstw', 'secondw', 'thirdw', 'fourthw' ], [embed_first], exe) return step += 1 if math.isnan(float(avg_cost_np[0])): sys.exit("got NaN loss, training failed.") raise AssertionError("Cost is too large {0:2.2}".format(avg_cost_np[0])) train_loop() def infer(use_cuda, params_dirname=None): place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() exe = fluid.Executor(place) inference_scope = fluid.core.Scope() with fluid.scope_guard(inference_scope): # 使用fluid.io.load_inference_model获取inference program， # feed变量的名称feed_target_names和从scope中fetch的对象fetch_targets [inferencer, feed_target_names, fetch_targets] = fluid.io.load_inference_model(params_dirname, exe) # 设置输入，用四个LoDTensor来表示4个词语。这里每个词都是一个id， # 用来查询embedding表获取对应的词向量，因此其形状大小是[1]。 # recursive_sequence_lengths设置的是基于长度的LoD，因此都应该设为[[1]] # 注意recursive_sequence_lengths是列表的列表 data1 = numpy.asarray([[211]], dtype=numpy.int64) # 'among' data2 = numpy.asarray([[6]], dtype=numpy.int64) # 'a' data3 = numpy.asarray([[96]], dtype=numpy.int64) # 'group' data4 = numpy.asarray([[4]], dtype=numpy.int64) # 'of' lod = numpy.asarray([[1]], dtype=numpy.int64) first_word = fluid.create_lod_tensor(data1, lod, place) second_word = fluid.create_lod_tensor(data2, lod, place) third_word = fluid.create_lod_tensor(data3, lod, place) fourth_word = fluid.create_lod_tensor(data4, lod, place) assert feed_target_names[0] == 'firstw' assert feed_target_names[1] == 'secondw' assert feed_target_names[2] == 'thirdw' assert feed_target_names[3] == 'fourthw' # 构造feed词典 {feed_target_name: feed_target_data} # 预测结果包含在results之中 results = exe.run( inferencer, feed={ feed_target_names[0]: first_word, feed_target_names[1]: second_word, feed_target_names[2]: third_word, feed_target_names[3]: fourth_word }, fetch_list=fetch_targets, return_numpy=False) print(numpy.array(results[0])) print('next word results[0]:',numpy.array(results[0])) print('next word results[0].shape:',numpy.array(results[0]).shape) most_possible_word_index = numpy.argmax(results[0]) print(most_possible_word_index) print([ key for key, value in six.iteritems(word_dict) if value == most_possible_word_index ][0]) def embedding_infer(use_cuda, embedding_params_dirname=None): place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() exe = fluid.Executor(place) inference_scope = fluid.core.Scope() with fluid.scope_guard(inference_scope): # 使用fluid.io.load_inference_model获取inference program， # feed变量的名称feed_target_names和从scope中fetch的对象fetch_targets [inferencer, feed_target_names, fetch_targets] = fluid.io.load_inference_model(embedding_params_dirname, exe) # 设置输入，用四个LoDTensor来表示4个词语。这里每个词都是一个id， # 用来查询embedding表获取对应的词向量，因此其形状大小是[1]。 # recursive_sequence_lengths设置的是基于长度的LoD，因此都应该设为[[1]] # 注意recursive_sequence_lengths是列表的列表 data1 = numpy.asarray([[211]], dtype=numpy.int64) # 'among' data2 = numpy.asarray([[6]], dtype=numpy.int64) # 'a' data3 = numpy.asarray([[96]], dtype=numpy.int64) # 'group' data4 = numpy.asarray([[4]], dtype=numpy.int64) # 'of' lod = numpy.asarray([[1]], dtype=numpy.int64) first_word = fluid.create_lod_tensor(data1, lod, place) second_word = fluid.create_lod_tensor(data2, lod, place) third_word = fluid.create_lod_tensor(data3, lod, place) fourth_word = fluid.create_lod_tensor(data4, lod, place) assert feed_target_names[0] == 'firstw' assert feed_target_names[1] == 'secondw' assert feed_target_names[2] == 'thirdw' assert feed_target_names[3] == 'fourthw' # 构造feed词典 {feed_target_name: feed_target_data} # 预测结果包含在results之中 print(feed_target_names) results = exe.run( inferencer, feed={ feed_target_names[0]: first_word, feed_target_names[1]: second_word, feed_target_names[2]: third_word, feed_target_names[3]: fourth_word }, # feed={ # feed_target_names[0]: data1, # feed_target_names[1]: data2, # feed_target_names[2]: data3, # feed_target_names[3]: data4 # }, fetch_list=fetch_targets, return_numpy=False) print( word embedding results[0]:',numpy.array(results[0])) print( word embedding results[0].shape:',numpy.array(results[0]).shape) def main(use_cuda, is_sparse): if use_cuda and not fluid.core.is_compiled_with_cuda(): return params_dirname = "word2vec.inference.model" embedding_params_dirname = "word2vec.embedding.inference.model" train( if_use_cuda=use_cuda, params_dirname=params_dirname, embedding_params_dirname=embedding_params_dirname, is_sparse=is_sparse) infer(use_cuda=use_cuda, params_dirname=params_dirname) embedding_infer(use_cuda=use_cuda,embedding_params_dirname=embedding_params_dirname) main(use_cuda=use_cuda, is_sparse=True)
output:
Step 0: Average Cost 7.377010 Step 10: Average Cost 6.168406 Step 20: Average Cost 5.790505 [[0.02989654 0.03431715 0.00012712 ... 0.00019843 0.00016911 0.02760296]] next word results[0]: [[0.02989654 0.03431715 0.00012712 ... 0.00019843 0.00016911 0.02760296]] next word results[0].shape: (1, 2073) 1 <e> [u'firstw', u'secondw', u'thirdw', u'fourthw'] word embedding results[0]: [[0.02989654 0.03431715 0.00012712 ... 0.00019843 0.00016911 0.02760296]] word embedding results[0].shape: (1, 2073)
上面这种方式出来结果有问题？bug？还是我提取的方式不对?
hflyzju
commented
5 years ago hflyzju
commented
5 years ago
output: