# -*- coding: utf-8 -*-
class Parameters(object):

    embedding_dim = 100      #dimension of word embedding
    vocab_size = 10000       #number of vocabulary
    pre_trianing = None      #use vector_char trained by word2vec

    seq_length = 300          #max length of sentence
    num_classes = 10          #number of labels
    hidden_dim = 128        #the number of hidden units
    filters_size = [2, 3, 4]
    num_filters = 128

    keep_prob = 0.5         #droppout
    learning_rate = 1e-3    #learning rate
    lr_decay = 0.9          #learning rate decay
    clip = 5.0              #gradient clipping threshold

    num_epochs = 3          #epochs
    batch_size = 64         #batch_size

    train_filename = './data/cnews.train.txt'  #train data
    test_filename = './data/cnews.test.txt'    #test data
    val_filename = './data/cnews.val.txt'      #validation data
    vocab_filename = './data/vocab_word.txt'        #vocabulary
    vector_word_filename = './data/vector_word.txt'  #vector_word trained by word2vec
    vector_word_npz = './data/vector_word.npz'   # save vector_word to numpy file