* remove conflicts
* all tests passed

* add DataSet.get_field(), to fetch a FieldArray based on its name
* remove old tutorials & add new tutorials

* refine code style
* fix tests
* add a new tutorial

* add code comments
* merge *_saver.py & *_loader.py in io/
* (ancient codes) rename Loss into LossFromTorch

* clean up unused codes

* remove unused codes in metrics.py
* add tests for DataSet
* add tests for FieldArray
* add tests for metrics.py
* fix predictor, add tests for predictor
* fix bucket sampler, add tests for bucket sampler

# Conflicts:
#	test/core/test_trainer.py

2. loss中修复一个错误
3. metric中fast_param调整

* fix FieldArray type check bugs

2. trainer中update_every改为print_every, 因为update_every可能引起optimizer update的误解
3. fieldarray content支持使用np.ndarray初始化

# Conflicts:
#	fastNLP/core/dataset.py
#	fastNLP/core/trainer.py
#	test/core/test_trainer.py

Trainer support print_train and tqdm train.

* fix losses的_fast_param_map的bug
* Trainer添加sampelr初始化参数，并调整参数顺序
* refine codes

* DataSet修改__repr__，优化print(datset)的输出
* Instance修改__repr__，优化print的输出
* Optimizer优化传参提示
* Trainer去除kwargs参数
* losses.py加个参数
* 对应test code的修改

* 更新Loss的接口形参跟metric保持一致
* 添加对几种loss的测试
* embed_loader采用维度独立的方法采样
* 对应测试代码的修改

* 更新测试代码，跑通所有测试，覆盖率65%
* refine代码规范和某些注释
* fix tester self.use_cuda未赋值先使用的bug
* 添加tutorial样例数据——tutorial_sample_dataset.csv
* 【unsolved】embed_loader在计算np.cov时遇到segmentation fault

* dataset检查slice开始位置，确保结果不为空
* fieldarray检查content不为空
* optimizer接受的model params是一个generator，不能赋值
* code style refine

* improve Loss initialization interface
* improve test codes for trainer

* fix bug in DataSet.split
* fix bugs in FieldArray, to allow content as a list
* fix bug in losses check
* ...

更新Optimizer: 多种初始化方法
1. SGD()
2. SGD(0.01)
3. SGD(lr=0.01)
4. SGD(lr=0.01, momentum=0.9)
5. SGD(model.parameters(), lr=0.1, momentum=0.9)

optimizer.SGD(lr=xxx);如果没有传入parameters，则在trainer中帮他加入parameter

* 添加fast_load_embedding方法，用vocab的词索引pre-trained中的embedding
* 如果vocab有词没出现在pre-train中，从已有embedding中正态采样

Update embed_loader:
* add fast_load_embedding method, to index pre-trained embedding with words in Vocab
* If words in Vocab are not exist in pre-trained, sample them from normal distribution computed by current embeddings

* 增量添加单词到词典中
* lazy update: 当用到词典的时候才重新build
* 当新添加的词导致词典大小超出限制时，打印一个warning

Update Vocabulary:
* More words can be added after the building.
* Lazy update: rebuild automatically when vocab is used.
* print warning when max size is reached

* In init, detect content type to be Python int, float, or str.
* In append(), check type consistence.
* In init & append(), int will be cast into float if they occur together.
* Map Python type into numpy dtype
* Raise error if type detection fails.

* refine interface of set_target & set_input
* rename DataSet.Instance into DataSet.DataSetIter
* remove unused methods in DataSet.DataSetIter
* remove __setattr__ in DataSet; It is dangerous.
* comment adjustment