Chiang97912

import torch 
import time
import os
import argparse
import shutil
import sys
 
def parse_args():
    parser = argparse.ArgumentParser(description='Matrix multiplication')
    parser.add_argument('--gpus', help='gpu amount', required=True, type=int)

Chiang97912

# -*- coding:utf-8 -*-


def MRR(ranked_list, ground_truth):
    """ 平均倒排名 """
    rr = 0.
    for i in range(len(ranked_list)):
        for j in range(len(ranked_list[i])):
            # if ground_truth[i][0] == ranked_list[i][j]:
            if ranked_list[i][j] in ground_truth[i]:

Chiang97912

set rtp+=~/.vim/bundle/Vundle.vim
call vundle#begin()
Plugin 'VundleVim/Vundle.vim'
Plugin 'scrooloose/nerdtree'
Plugin 'flazz/vim-colorschemes'
Plugin 'ervandew/supertab'
Plugin 'davidhalter/jedi-vim'
Plugin 'mattn/emmet-vim'
Plugin 'vim-syntastic/syntastic'
Plugin 'nvie/vim-flake8'

Chiang97912

def attention_3d_block(inputs):
    # inputs.shape = (batch_size, time_steps, input_dim)
    input_dim = int(inputs.shape[2])
    a = Permute((2, 1))(inputs)
    a = Reshape((input_dim, TIME_STEPS))(a) # this line is not useful. It's just to know which dimension is what.
    a = Dense(TIME_STEPS, activation='softmax')(a)
    if SINGLE_ATTENTION_VECTOR:
        a = Lambda(lambda x: K.mean(x, axis=1), name='dim_reduction')(a)
        a = RepeatVector(input_dim)(a)
    a_probs = Permute((2, 1), name='attention_vec')(a)