idleuncle

def softmax(x):
    e_x = np.exp(x - np.max(x, axis=1).reshape(-1, 1))
    return e_x / e_x.sum(axis=1).reshape(-1, 1)

idleuncle

#!/usr/bin/env python
# -*- coding: utf-8 -*-
import sys, time
import collections
import numpy as np


class Progbar(object):
    """Displays a progress bar.

idleuncle

def shuffle(train_data: np.array, seed: int = 8864):
    np.random.seed(seed)
    n = train_data.shape[0]
    indices = np.random.randint(0, n, n)
    train_data = [train_data[i] for i in indices]

idleuncle

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%  这是一份 beamer 文档. 本源文件仅供学习参考之用.                                         %%%
%%%  使用请注明出处. 本文作者拥有版权 (c)2017. 保留所有权利.                               %%%
%%%  若不能编译通过, 可能是您的 beamer 需要更新了.                                             %%%
%%%  更新的具体方法可参考: http://bbs.ctex.org/forums/index.php?showtopic=27695 %%%
%%%  温馨提示：文档基本对所都的宏包和命令都加了注释，一般情况下不要删除，会发生错误喔 %%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\documentclass[CJK,notheorems,compress,mathserif,table,11pt]{beamer} %更改全文字体大小，设置xxpt（如9pt，10pt,.....）%一定要定义documentclass[cjk]{beamer}，别忘了“cjk”，否则编译不通过
%\useoutertheme[height=0.1\textwidth,width=0.15\textwidth,hideothersubsections]{sidebar}%加上此命令会出现上部和左侧边框
\usetheme{Madrid}%主题AnnArbor Antibes Bergen Berkeley Berlin Boadilla boxes CambridgeUS Copenhagen Darmstadt

idleuncle

# https://github.com/ShahariarRabby/Mnist_cnn_Swish
from keras import backend as K
from keras.layers import Activationfrom 
keras.utils.generic_utils import get_custom_objects
def swish(x):
    return (K.sigmoid(x) * x)
get_custom_objects().update({'swish': swish})
#Now just add Swish as an activation

model.add(Conv2D(filters = 32, kernel_size = (5,5),padding = ‘Same’, 

idleuncle

# coding=utf-8

from keras import Input, Model
from keras import backend as K
from keras.layers import Embedding, Dense, SimpleRNN, Lambda, Concatenate, Conv1D, GlobalMaxPooling1D


class RCNN(object):
    def __init__(self, maxlen, max_features, embedding_dims,
                 class_num=1,

idleuncle

# coding=utf-8

from keras import Input, Model
from keras.layers import Embedding, Dense, Concatenate, Conv1D, Bidirectional, CuDNNLSTM, GlobalAveragePooling1D, GlobalMaxPooling1D


class RCNNVariant(object):
    """Variant of RCNN.
        Base on structure of RCNN, we do some improvement:
        1. Ignore the shift for left/right context.

idleuncle

# coding=utf-8

from keras import Input, Model
from keras.layers import Embedding, GlobalAveragePooling1D, Dense


class FastText(object):
    def __init__(self, maxlen, max_features, embedding_dims,
                 class_num=1,
                 last_activation='sigmoid'):

idleuncle

# coding=utf-8

from keras import Input, Model
from keras.layers import Embedding, Dense, Bidirectional, CuDNNLSTM, TimeDistributed

from attention import Attention


class HAN(object):
    def __init__(self, maxlen_sentence, maxlen_word, max_features, embedding_dims,

idleuncle

# coding=utf-8

from keras import Input, Model
from keras.layers import Embedding, Dense, Dropout, Bidirectional, CuDNNLSTM

from attention import Attention


class TextAttBiRNN(object):
    def __init__(self, maxlen, max_features, embedding_dims,