Introduction
- HighWay Networks implement in pytorch，implement the paper that Highway Networks and Training very deep networks.
Requirement
- python 3
- pytorch > 0.1
- torchtext > 0.1
- numpy
Summary of paper
- This is summary of paper by me, the blog explains
Highway Networksin detail: Highway Networks
Result
- The test result in my demo.
How to use the folder or file
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the file of hyperparams.py contains all hyperparams that need to modify, based on yours nedds, select neural networks what you want and config the hyperparams.
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the file of main-hyperparams.py is the main function,run the command ("python main_hyperparams.py") to execute the demo.
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the folder of models contains all neural networks models.
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the file of train_ALL_CNN.py is the train function about CNN
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the file of train_ALL_LSTM.py is the train function about LSTM
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the folder of loaddata contains some file of load dataset
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the folder of word2vec is the file of word embedding that you want to use
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the folder of data contains the dataset file,contains train data,dev data,test data.
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the file of Parameters.txt is being used to save all parameters values.
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the file of Test_Result.txt is being used to save the result of test,in the demo,save a model and test a model immediately,and int the end of training, will calculate the best result value.
How to use the Word Embedding in demo?
- the word embedding file saved in the folder of word2vec, but now is empty, because of it is to big,so if you want to use word embedding,you can to download word2vec or glove file, then saved in the folder of word2vec,and make the option of word_Embedding to True and modifiy the value of word_Embedding_Path in the hyperparams.py file.
Highway Networks and Highway Networks Variant
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model-BiLSTM-1.py is a simple bidirection LSTM neural networks model.
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model-BiLSTM-List.py is a simple bidirection LSTM neural networks model.
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model-BiLSTM-Cat.py is a simple bidirection LSTM variant neural networks model.
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model-CNN.py is a simple CNN neural networks model.
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model-HBiLSTM.py is a simple HIghway BiLstm neural networks model.
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model-HBiLSTM-CAT.py is a simple HIghway BiLstm variant neural networks model.
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model-HCNN.py is a simple HIghway CNN neural networks model.
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model-HighWay.py is a simple Highway networks model.
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model-HighWayBiLSTM.py is a simple Highway BiLstm variant networks model.
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model-HighWayCNN.py is a simple Highway CNN variant networks model.
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model-HighWay-BiLSTM.py is a HighWay NetWorks variant model with use in the BiLSTM model.
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model-HighWay-CNN.py is a HighWay NetWorks model variant with use in the CNN model.
How to config hyperparams in the file of hyperparams.py
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learning_rate: initial learning rate.
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epochs:number of epochs for train
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batch_size：batch size for training
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log_interval：how many steps to wait before logging training status
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test_interval：how many steps to wait before testing
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save_interval：how many steps to wait before saving
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save_dir：where to save the snapshot
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datafile_path：datafile path
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name_trainfile：name of the train file
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name_devfile：name of the dev file
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name_testfile: name of the test file
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char_data: whether to use the strategy of char-level data
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shuffle:whether to shuffle the dataset when load dataset
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epochs_shuffle:whether to shuffle the dataset when train in every epoch
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TWO-CLASS-TASK:execute two-classification-task
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dropout:the probability for dropout
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max_norm:l2 constraint of parameters
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clip-max-norm:the values of prevent the explosion and Vanishing in Gradient
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kernel_sizes:comma-separated kernel size to use for convolution
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kernel_num:number of each kind of kernel
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static:whether to update the gradient during train
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Adam:select the optimizer of adam
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SGD：select the optimizer of SGD
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Adadelta:select the optimizer of Adadelta
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optim-momentum-value:the parameter in the optimizer
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wide_conv:whether to use wide convcolution True : wide False : narrow
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min_freq:min freq to include during built the vocab when use torchtext, default is 1
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word_Embedding: use word embedding
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embed_dim:number of embedding dimension
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word-Embedding-Path:the path of word embedding file
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lstm-hidden-dim:the hidden dim with lstm model
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lstm-num-layers:the num of hidden layers with lstm
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no_cuda: use cuda
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num_threads:set the value of threads when run the demo
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init_weight:whether to init weight
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init-weight-value:the value of init weight
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weight-decay:L2 weight_decay,default value is zero in optimizer
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seed_num:set the num of random seed
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rm_model:whether to delete the model after test acc so that to save space