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class Biaffine(nn.Module):
    def __init__(self, in1_features, in2_features, out_features,
                 bias=(True, True)):
        super(Biaffine, self).__init__()
        self.in1_features = in1_features
        self.in2_features = in2_features
        self.out_features = out_features
        self.bias = bias
        self.linear_input_size = in1_features + int(bias[0])

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class GCN(nn.Module):
    """ A GCN/Contextualized GCN module operated on dependency graphs. """

    def __init__(self, in_dim, mem_dim, num_layers, in_drop=0.5, out_drop=0.5, batch=True):
        super(GCN, self).__init__()
        self.layers = num_layers
        self.mem_dim = mem_dim
        self.in_dim = in_dim

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class GAT(nn.Module):
    def __init__(self, nfeat, nhid, nclass, dropout, alpha, nheads):
        """Dense version of GAT."""
        super(GAT, self).__init__()
        self.dropout = dropout

        self.attentions = [GraphAttentionLayer(nfeat, nhid, dropout=dropout, alpha=alpha, concat=True) for _ in range(nheads)]
        for i, attention in enumerate(self.attentions):
            self.add_module('attention_{}'.format(i), attention)

liuyijiang1994

from ekphrasis.classes.preprocessor import TextPreProcessor
from ekphrasis.classes.tokenizer import SocialTokenizer
from ekphrasis.dicts.emoticons import emoticons
import numpy as np

import re
import io

label2emotion = {0: "others", 1: "happy", 2: "sad", 3: "angry"}
emotion2label = {"others": 0, "happy": 1, "sad": 2, "angry": 3}

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#!/usr/bin/env python
# -*- coding:UTF-8 -*-

import torch
import torch.nn as nn
import torch.nn.init as init


def weight_init(m):
    '''

liuyijiang1994

package com.cyhone;

import java.text.SimpleDateFormat;
import java.util.Calendar;
import java.util.Date;
import java.util.concurrent.TimeUnit;

/**
 * @author cyhone
 * @date 2017/3/28