94、tensorflow实现语音识别0,1,2,3,4,5,6,7,8,9
'''
Created on 2017年7月23日 @author: weizhen
'''
#导入库
from __future__ import division,print_function,absolute_import
import tflearn
import speech_data
import tensorflow as tf
#定义参数
#learning rate是在更新权重的时候用，太高可用很快
#但是loss大，太低较准但是很慢
learning_rate=0.0001
training_iters=300000#STEPS
batch_size=64 width=20 #mfcc features
height=80 #(max) length of utterance
classes = 10 #digits #用speech_data.mfcc_batch_generator获取语音数据并处理成批次，
#然后创建training和testing数据
batch=word_batch=speech_data.mfcc_batch_generator(batch_size)
X,Y=next(batch)
trainX,trainY=X,Y
testX,testY=X,Y #overfit for now #4.建立模型
#speech recognition 是个many to many的问题
#所以用Recurrent NN
#通常的RNN，它的输出结果是受整个网络的影响的
#而LSTM比RNN好的地方是，它能记住并且控制影响的点，
#所以这里我们用LSTM
#每一层到底需要多少个神经元是没有规定的，太少了的话预测效果不好
#太多了会overfitting,这里普遍取128
#为了减轻过拟合的影响，我们用dropout,它可以随机地关闭一些神经元，
#这样网络就被迫选择其他路径，进而生成想对generalized模型
#接下来建立一个fully connected的层
#它可以使前一层的所有节点都连接过来，输出10类
#因为数字是0-9，激活函数用softmax,它可以把数字变换成概率
#最后用个regression层来输出唯一的类别，用adam优化器来使
#cross entropy损失达到最小 #Network building
net=tflearn.input_data([None,width,height])
net=tflearn.lstm(net,128,dropout=0.8)
net=tflearn.fully_connected(net,classes,activation='softmax')
net=tflearn.regression(net,optimizer='adam',learning_rate=learning_rate,loss='categorical_crossentropy') #5.训练模型并预测
#然后用tflearn.DNN函数来初始化一下模型，接下来就可以训练并预测，最好再保存训练好的模型
#Traing
### add this "fix" for tensorflow version erros
col=tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
for x in col:
tf.add_to_collection(tf.GraphKeys.VARIABLES,x) model=tflearn.DNN(net,tensorboard_verbose=0) while 1: #training_iters
model.fit(trainX, trainY, n_epoch=10, validation_set=(testX,testY), show_metric=True, batch_size=batch_size)
_y=model.predict(X)
model.save("tflearn.lstm.model")
print(_y)
下面是训练的结果
Training Step: 3097 | total loss: [1m[32m1.51596[0m[0m | time: 1.059s
[2K
| Adam | epoch: 3097 | loss: 1.51596 - acc: 0.6324 | val_loss: 0.36655 - val_acc: 1.0000 -- iter: 64/64
--
Training Step: 3098 | total loss: [1m[32m1.64602[0m[0m | time: 1.050s
[2K
| Adam | epoch: 3098 | loss: 1.64602 - acc: 0.5801 | val_loss: 0.36642 - val_acc: 1.0000 -- iter: 64/64
--
Training Step: 3099 | total loss: [1m[32m1.54328[0m[0m | time: 1.052s
[2K
| Adam | epoch: 3099 | loss: 1.54328 - acc: 0.6206 | val_loss: 0.36673 - val_acc: 1.0000 -- iter: 64/64
--
Training Step: 3100 | total loss: [1m[32m1.65763[0m[0m | time: 1.044s
[2K
| Adam | epoch: 3100 | loss: 1.65763 - acc: 0.5741 | val_loss: 0.36645 - val_acc: 1.0000 -- iter: 64/64
--
---------------------------------
Run id: E1W1VX
Log directory: /tmp/tflearn_logs/
---------------------------------
Training samples: 64
Validation samples: 64
--
Training Step: 3101 | total loss: [1m[32m1.56009[0m[0m | time: 1.328s
[2K
| Adam | epoch: 3101 | loss: 1.56009 - acc: 0.6167 | val_loss: 0.36696 - val_acc: 1.0000 -- iter: 64/64
--
Training Step: 3102 | total loss: [1m[32m1.68916[0m[0m | time: 1.034s
[2K
| Adam | epoch: 3102 | loss: 1.68916 - acc: 0.5660 | val_loss: 0.36689 - val_acc: 1.0000 -- iter: 64/64
--
Training Step: 3103 | total loss: [1m[32m1.58796[0m[0m | time: 1.044s
[2K
| Adam | epoch: 3103 | loss: 1.58796 - acc: 0.6078 | val_loss: 0.36627 - val_acc: 1.0000 -- iter: 64/64
--
Training Step: 3104 | total loss: [1m[32m1.49236[0m[0m | time: 1.055s
[2K
| Adam | epoch: 3104 | loss: 1.49236 - acc: 0.6470 | val_loss: 0.36599 - val_acc: 1.0000 -- iter: 64/64
--
Training Step: 3105 | total loss: [1m[32m1.60916[0m[0m | time: 1.028s
[2K
| Adam | epoch: 3105 | loss: 1.60916 - acc: 0.5995 | val_loss: 0.36535 - val_acc: 1.0000 -- iter: 64/64
--
Training Step: 3106 | total loss: [1m[32m1.51083[0m[0m | time: 1.049s
[2K
| Adam | epoch: 3106 | loss: 1.51083 - acc: 0.6396 | val_loss: 0.36534 - val_acc: 1.0000 -- iter: 64/64
--
Training Step: 3107 | total loss: [1m[32m1.63413[0m[0m | time: 1.066s
[2K
| Adam | epoch: 3107 | loss: 1.63413 - acc: 0.5865 | val_loss: 0.36566 - val_acc: 1.0000 -- iter: 64/64
--
Training Step: 3108 | total loss: [1m[32m1.74167[0m[0m | time: 1.042s
[2K
| Adam | epoch: 3108 | loss: 1.74167 - acc: 0.5373 | val_loss: 0.36556 - val_acc: 1.0000 -- iter: 64/64
--
Training Step: 3109 | total loss: [1m[32m1.63324[0m[0m | time: 1.051s
[2K
| Adam | epoch: 3109 | loss: 1.63324 - acc: 0.5835 | val_loss: 0.36557 - val_acc: 1.0000 -- iter: 64/64
--
Training Step: 3110 | total loss: [1m[32m1.75479[0m[0m | time: 1.042s
[2K
| Adam | epoch: 3110 | loss: 1.75479 - acc: 0.5377 | val_loss: 0.36524 - val_acc: 1.0000 -- iter: 64/64
--
---------------------------------
Run id: 93CFSE
Log directory: /tmp/tflearn_logs/
---------------------------------
Training samples: 64
Validation samples: 64
--
Training Step: 3111 | total loss: [1m[32m1.64290[0m[0m | time: 1.320s
[2K
| Adam | epoch: 3111 | loss: 1.64290 - acc: 0.5839 | val_loss: 0.36560 - val_acc: 1.0000 -- iter: 64/64
--
Training Step: 3112 | total loss: [1m[32m1.76515[0m[0m | time: 1.029s
[2K
| Adam | epoch: 3112 | loss: 1.76515 - acc: 0.5349 | val_loss: 0.36552 - val_acc: 1.0000 -- iter: 64/64
--
Training Step: 3113 | total loss: [1m[32m1.65166[0m[0m | time: 1.050s
[2K
| Adam | epoch: 3113 | loss: 1.65166 - acc: 0.5814 | val_loss: 0.36609 - val_acc: 1.0000 -- iter: 64/64
--
Training Step: 3114 | total loss: [1m[32m1.76346[0m[0m | time: 1.062s
[2K
| Adam | epoch: 3114 | loss: 1.76346 - acc: 0.5342 | val_loss: 0.36636 - val_acc: 1.0000 -- iter: 64/64
--
Training Step: 3115 | total loss: [1m[32m1.65255[0m[0m | time: 1.042s
[2K
| Adam | epoch: 3115 | loss: 1.65255 - acc: 0.5808 | val_loss: 0.36636 - val_acc: 1.0000 -- iter: 64/64
--
Training Step: 3116 | total loss: [1m[32m1.55663[0m[0m | time: 1.042s
[2K
| Adam | epoch: 3116 | loss: 1.55663 - acc: 0.6227 | val_loss: 0.36689 - val_acc: 1.0000 -- iter: 64/64
--
Training Step: 3117 | total loss: [1m[32m1.67928[0m[0m | time: 1.051s
[2K
| Adam | epoch: 3117 | loss: 1.67928 - acc: 0.5729 | val_loss: 0.36726 - val_acc: 1.0000 -- iter: 64/64
--
Training Step: 3118 | total loss: [1m[32m1.78375[0m[0m | time: 1.043s
[2K
| Adam | epoch: 3118 | loss: 1.78375 - acc: 0.5266 | val_loss: 0.36714 - val_acc: 1.0000 -- iter: 64/64
--
Training Step: 3119 | total loss: [1m[32m1.67364[0m[0m | time: 1.041s
[2K
| Adam | epoch: 3119 | loss: 1.67364 - acc: 0.5724 | val_loss: 0.36725 - val_acc: 1.0000 -- iter: 64/64
--
Training Step: 3120 | total loss: [1m[32m1.79457[0m[0m | time: 1.044s
[2K
| Adam | epoch: 3120 | loss: 1.79457 - acc: 0.5276 | val_loss: 0.36694 - val_acc: 1.0000 -- iter: 64/64
--
---------------------------------
Run id: YE812Z
Log directory: /tmp/tflearn_logs/
---------------------------------
Training samples: 64
Validation samples: 64
--
Training Step: 3121 | total loss: [1m[32m1.68830[0m[0m | time: 1.351s
[2K
| Adam | epoch: 3121 | loss: 1.68830 - acc: 0.5686 | val_loss: 0.36691 - val_acc: 1.0000 -- iter: 64/64
--
Training Step: 3122 | total loss: [1m[32m1.79857[0m[0m | time: 1.022s
[2K
| Adam | epoch: 3122 | loss: 1.79857 - acc: 0.5227 | val_loss: 0.36642 - val_acc: 1.0000 -- iter: 64/64
--
Training Step: 3123 | total loss: [1m[32m1.68557[0m[0m | time: 1.071s
[2K
| Adam | epoch: 3123 | loss: 1.68557 - acc: 0.5673 | val_loss: 0.36519 - val_acc: 1.0000 -- iter: 64/64
--
Training Step: 3124 | total loss: [1m[32m1.58528[0m[0m | time: 1.042s
[2K
| Adam | epoch: 3124 | loss: 1.58528 - acc: 0.6106 | val_loss: 0.36366 - val_acc: 1.0000 -- iter: 64/64
--
Training Step: 3125 | total loss: [1m[32m1.49228[0m[0m | time: 1.042s
[2K
| Adam | epoch: 3125 | loss: 1.49228 - acc: 0.6495 | val_loss: 0.36180 - val_acc: 1.0000 -- iter: 64/64
--
Training Step: 3126 | total loss: [1m[32m1.41012[0m[0m | time: 1.052s
[2K
| Adam | epoch: 3126 | loss: 1.41012 - acc: 0.6846 | val_loss: 0.36061 - val_acc: 1.0000 -- iter: 64/64
--
Training Step: 3127 | total loss: [1m[32m1.55866[0m[0m | time: 1.023s
[2K
| Adam | epoch: 3127 | loss: 1.55866 - acc: 0.6286 | val_loss: 0.35908 - val_acc: 1.0000 -- iter: 64/64
--
Training Step: 3128 | total loss: [1m[32m1.46943[0m[0m | time: 1.044s
[2K
| Adam | epoch: 3128 | loss: 1.46943 - acc: 0.6657 | val_loss: 0.35735 - val_acc: 1.0000 -- iter: 64/64
--
Training Step: 3129 | total loss: [1m[32m1.39050[0m[0m | time: 1.042s
[2K
| Adam | epoch: 3129 | loss: 1.39050 - acc: 0.6992 | val_loss: 0.35632 - val_acc: 1.0000 -- iter: 64/64
--
Training Step: 3130 | total loss: [1m[32m1.54006[0m[0m | time: 1.043s
[2K
| Adam | epoch: 3130 | loss: 1.54006 - acc: 0.6371 | val_loss: 0.35513 - val_acc: 1.0000 -- iter: 64/64
--
---------------------------------
Run id: YGRXY5
Log directory: /tmp/tflearn_logs/
---------------------------------
Training samples: 64
Validation samples: 64
--
Training Step: 3131 | total loss: [1m[32m1.45402[0m[0m | time: 1.336s
[2K
| Adam | epoch: 3131 | loss: 1.45402 - acc: 0.6702 | val_loss: 0.35442 - val_acc: 1.0000 -- iter: 64/64
--
Training Step: 3132 | total loss: [1m[32m1.59202[0m[0m | time: 1.029s
[2K
| Adam | epoch: 3132 | loss: 1.59202 - acc: 0.6110 | val_loss: 0.35325 - val_acc: 1.0000 -- iter: 64/64
--
Training Step: 3133 | total loss: [1m[32m1.50035[0m[0m | time: 1.070s
[2K
| Adam | epoch: 3133 | loss: 1.50035 - acc: 0.6499 | val_loss: 0.35195 - val_acc: 1.0000 -- iter: 64/64
--
Training Step: 3134 | total loss: [1m[32m1.41417[0m[0m | time: 1.042s
[2K
| Adam | epoch: 3134 | loss: 1.41417 - acc: 0.6849 | val_loss: 0.35042 - val_acc: 1.0000 -- iter: 64/64
--
Training Step: 3135 | total loss: [1m[32m1.34060[0m[0m | time: 1.037s
[2K
| Adam | epoch: 3135 | loss: 1.34060 - acc: 0.7149 | val_loss: 0.34937 - val_acc: 1.0000 -- iter: 64/64
--
Training Step: 3136 | total loss: [1m[32m1.47476[0m[0m | time: 1.039s
[2K
| Adam | epoch: 3136 | loss: 1.47476 - acc: 0.6574 | val_loss: 0.34826 - val_acc: 1.0000 -- iter: 64/64
--
Training Step: 3137 | total loss: [1m[32m1.38535[0m[0m | time: 1.053s
[2K
| Adam | epoch: 3137 | loss: 1.38535 - acc: 0.6917 | val_loss: 0.34739 - val_acc: 1.0000 -- iter: 64/64
--
Training Step: 3138 | total loss: [1m[32m1.51673[0m[0m | time: 1.063s
[2K
| Adam | epoch: 3138 | loss: 1.51673 - acc: 0.6413 | val_loss: 0.34637 - val_acc: 1.0000 -- iter: 64/64
--
Training Step: 3139 | total loss: [1m[32m1.42892[0m[0m | time: 1.042s
[2K
| Adam | epoch: 3139 | loss: 1.42892 - acc: 0.6756 | val_loss: 0.34570 - val_acc: 1.0000 -- iter: 64/64
--
Training Step: 3140 | total loss: [1m[32m1.58217[0m[0m | time: 1.052s
[2K
| Adam | epoch: 3140 | loss: 1.58217 - acc: 0.6112 | val_loss: 0.34494 - val_acc: 1.0000 -- iter: 64/64
--
---------------------------------
这里边有一个死循环，具体怎么回事我也不太清楚。
下边是可视化训练，展示训练的图像
94、tensorflow实现语音识别0,1,2,3,4,5,6,7,8,9的更多相关文章
- Google Tensorflow 源码编译（三）：tensorflow<v0.5.0>
这几天终于把tensorflow安装上了,中间遇到过不少的问题,这里记录下来.供大家想源码安装的参考. 安装环境:POWER8处理器,Docker容器Ubuntu14.04镜像. Build Tens ...
- 【适合N卡独显电脑的环境配置】Tensorflow教程-Windows 10下安装tensorflow 1.5.0 GPU with Anaconda
注意: 1.目前Anaconda 更新原命令activate tensorflow 改为 conda activate tensorflow 2. 目前windows with anaconda 可以 ...
- TensorFlow 1.2.0新版本完美支持Python3.6，windows在cmd中输入pip install tensorflow就能下载应用最新tensorflow
TensorFlow 1.2.0新版本完美支持Python3.6,windows在cmd中输入pip install tensorflow就能下载应用最新tensorflow 只需在cmd中输入pip ...
- 【python】python安装tensorflow报错：python No matching distribution found for tensorflow==1.12.0
python安装tensorflow报错:python No matching distribution found for tensorflow==1.12.0 python版本是3.7.2 要安装 ...
- 使用anaconda 3安装tensorflow 1.15.0 （win10环境）
0.写在前面 之前其实安装过一次tensorflow,但是由于电脑中毒,重装了系统,把所有的环境全部删除了.之前在博客里转发了一篇别人在win10安装tensorflow的教程,但是版本比较旧了, ...
- tensorflow 1.12.0 gpu + python3.6.8 + win10 + GTX1060 + cuda9.0 + cudnn7.4 + vs2017）
在安装tensorflow-gpu时,也看过不少的博客,讲得乱七八糟的,也不能这样说,只是每个人安装的环境或需求不一样,因此没有找到一个适合自己的教程去安装tensorflow-gpu版本.当然,入手 ...
- windows7 64位安装tensorflow 1.4.0 CPU版本
机器学习和深度学习真是新生代的宠儿,我也被安排来搞这个了,这下是真的从0开始了.看了几天ppt,想跑跑代码试试,装个环境. 都说tensorflow很火很好用,反正我什么也不懂,准备把这些框架一个一个 ...
- TensorFlow基础笔记(0) tensorflow的基本数据类型操作
import numpy as np import tensorflow as tf #build a graph print("build a graph") #生产变量tens ...
- tensorflow CUDA 9.0安装成功
berli@berli-dev:~/tensorflow$ bazel-bin/tensorflow/examples/label_image/label_image 2017-12-18 00:04 ...
随机推荐
- English-Difference between original and source
最近跟网页翻译怼上了,在给翻译前的页面起名是纠结于使用 original page 还是 source page,就查了一下 original 和 source 的区别. original: n. 原 ...
- mockito测试入门学习
一.什么是mock测试,什么是mock对象? 先来看看下面这个示例: 从上图可以看出如果我们要对A进行测试,那么就要先把整个依赖树构建出来,也就是BCDE的实例. 一种替代方案就是使用mocks 从图 ...
- CodeForce-1196C-Robot Breakout
原题链接 原题大意: 每个机器人分散,可以上下左右移动,但是有些机器人不能向某些方向移动 给出每个机器人的坐标,和每个方向能否正常行进 思路: 用minx, maxx, miny, maxy 记录可以 ...
- upc组队赛14 Communication【并查集+floyd /Tarjan】
Communication 题目描述 The Ministry of Communication has an extremely wonderful message system, designed ...
- asp.net的处理机制(.ashx/.aspx)
浅谈自己对asp.net 处理机制的图解 图解的内容有点多(包含asp.net 的处理机制和页面生命周期的重要事件,建议小伙伴把图片下载查看可好?) asp.net处理机制解说 当浏览器发送一条请求给 ...
- git笔记十：本地仓库同步到gitlab
本地仓库同步到gitlab 帮助文档 git remote --help 操作场景: 本地创建git仓库(含有readme.md文件), commit了三次 gitlab网站创建了一个项目 添加了re ...
- LeetCode6 dp
120. Triangle 我的解法用了一个二维数组,这样比较浪费空间.O(n*n) 但是标准答案自底向上,一是不需要进行特别判断,二是可以覆盖数组,则只需要O(n)的空间大小. class Solu ...
- 如何判断索引是否生效--explain
explain 显示了MySql 如何使用索引来处理select语句以及连接表. 使用方式在select 前面加上 explain就可以了 示例:explain select id , name ta ...
- python面试如何以相反顺序展示一个文件的内容？
>>> for line in reversed(list(open('Today.txt'))): print(line.rstrip())containeritertools D ...
- redis 入门之集合
sadd 将一个或多个 member 元素加入到集合 key 当中,已经存在于集合的 member 元素将被忽略.假如 key 不存在,则创建一个只包含 member 元素作成员的集合.当 key 不 ...