# 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 paddle.fluid as fluid import numpy import sys from functools import partial import math import os EMBED_SIZE = 32 HIDDEN_SIZE = 256 N = 5 BATCH_SIZE = 100 use_cuda = False # set to True if training with GPU word_dict = paddle.dataset.imikolov.build_dict() dict_size = len(word_dict) def inference_program(is_sparse): 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') fourth_word = fluid.layers.data(name='fourthw', shape=[1], dtype='int64') embed_first = fluid.layers.embedding( input=first_word, size=[dict_size, EMBED_SIZE], dtype='float32', is_sparse=is_sparse, param_attr='shared_w') embed_second = fluid.layers.embedding( input=second_word, size=[dict_size, EMBED_SIZE], dtype='float32', is_sparse=is_sparse, param_attr='shared_w') embed_third = fluid.layers.embedding( input=third_word, size=[dict_size, EMBED_SIZE], dtype='float32', is_sparse=is_sparse, param_attr='shared_w') embed_fourth = fluid.layers.embedding( input=fourth_word, 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 def train_program(is_sparse): # The declaration of 'next_word' must be after the invoking of inference_program, # or the data input order of train program would be [next_word, firstw, secondw, # thirdw, fourthw], which is not correct. predict_word = inference_program(is_sparse) 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(use_cuda, train_program, params_dirname): 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) place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() def event_handler(event): if isinstance(event, fluid.EndStepEvent): outs = trainer.test( reader=test_reader, feed_order=['firstw', 'secondw', 'thirdw', 'fourthw', 'nextw']) avg_cost = outs[0] if event.step % 10 == 0: print "Step %d: Average Cost %f" % (event.step, avg_cost) # If average cost is lower than 5.8, we consider the model good enough to stop. # Note 5.8 is a relatively high value. In order to get a better model, one should # aim for avg_cost lower than 3.5. But the training could take longer time. if avg_cost < 5.8: trainer.save_params(params_dirname) trainer.stop() if math.isnan(avg_cost): sys.exit("got NaN loss, training failed.") trainer = fluid.Trainer( train_func=train_program, # optimizer=fluid.optimizer.SGD(learning_rate=0.001), optimizer_func=optimizer_func, place=place) trainer.train( reader=train_reader, num_epochs=1, event_handler=event_handler, feed_order=['firstw', 'secondw', 'thirdw', 'fourthw', 'nextw']) def infer(use_cuda, inference_program, params_dirname=None): place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() inferencer = fluid.Inferencer( infer_func=inference_program, param_path=params_dirname, place=place) # Setup inputs by creating 4 LoDTensors representing 4 words. Here each word # is simply an index to look up for the corresponding word vector and hence # the shape of word (base_shape) should be [1]. The length-based level of # detail (lod) info of each LoDtensor should be [[1]] meaning there is only # one lod_level and there is only one sequence of one word on this level. # Note that lod info should be a list of lists. data1 = [[211]] # 'among' data2 = [[6]] # 'a' data3 = [[96]] # 'group' data4 = [[4]] # 'of' lod = [[1]] 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) result = inferencer.infer( { 'firstw': first_word, 'secondw': second_word, 'thirdw': third_word, 'fourthw': fourth_word }, return_numpy=False) print(numpy.array(result[0])) most_possible_word_index = numpy.argmax(result[0]) print(most_possible_word_index) print([ key for key, value in word_dict.iteritems() if value == most_possible_word_index ][0]) def main(use_cuda, is_sparse): if use_cuda and not fluid.core.is_compiled_with_cuda(): return params_dirname = "word2vec.inference.model" train( use_cuda=use_cuda, train_program=partial(train_program, is_sparse), params_dirname=params_dirname) infer( use_cuda=use_cuda, inference_program=partial(inference_program, is_sparse), params_dirname=params_dirname) if __name__ == '__main__': main(use_cuda=use_cuda, is_sparse=True)