Here you'll learn how to build Tensorflow for the raspberry pi 3 with either the Python API or as a standalone shared library which can be interfaced from the C++ API and eventually as a library which can be used in other languages.
For the C++ library this tutorial will show you how extract tensorflow library and headers to use in any environment you want.
(This tutorial couldn't be possible without the help of the people from the References section)
| #!/usr/bin/env python3 | |
| # -*- coding: utf-8 -*- | |
| # Licensed under WTFPL or the Unlicense or CC0. | |
| # This uses Python 3, but it's easy to port to Python 2 by changing | |
| # strings to u'xx'. | |
| import itertools | |
| def num2chinese(num, big=False, simp=True, o=False, twoalt=False): |
| <?xml version="1.0"?> | |
| <!DOCTYPE fontconfig SYSTEM "fonts.dtd"> | |
| <fontconfig> | |
| <match target="pattern"> | |
| <test name="lang"> | |
| <string>zh-cn</string> | |
| </test> | |
| <test qual="any" name="family"> | |
| <string>serif</string> |
| from flask import Flask, Response | |
| app = Flask(__name__) | |
| @app.route("/wav") | |
| def streamwav(): | |
| def generate(): | |
| with open("signals/song.wav", "rb") as fwav: | |
| data = fwav.read(1024) |
Updated 4/11/2018
Here's my experience of installing the NVIDIA CUDA kit 9.0 on a fresh install of Ubuntu Desktop 16.04.4 LTS.
###1. 自定义激活函数
可以采用以下几种方法：
from keras import backend as K
from keras.layers.core import Lambda
from keras.engine import Layer
| '''This script goes along the blog post | |
| "Building powerful image classification models using very little data" | |
| from blog.keras.io. | |
| It uses data that can be downloaded at: | |
| https://www.kaggle.com/c/dogs-vs-cats/data | |
| In our setup, we: | |
| - created a data/ folder | |
| - created train/ and validation/ subfolders inside data/ | |
| - created cats/ and dogs/ subfolders inside train/ and validation/ | |
| - put the cat pictures index 0-999 in data/train/cats |
| '''This script goes along the blog post | |
| "Building powerful image classification models using very little data" | |
| from blog.keras.io. | |
| It uses data that can be downloaded at: | |
| https://www.kaggle.com/c/dogs-vs-cats/data | |
| In our setup, we: | |
| - created a data/ folder | |
| - created train/ and validation/ subfolders inside data/ | |
| - created cats/ and dogs/ subfolders inside train/ and validation/ | |
| - put the cat pictures index 0-999 in data/train/cats |
##VGG19 model for Keras
This is the Keras model of the 19-layer network used by the VGG team in the ILSVRC-2014 competition.
It has been obtained by directly converting the Caffe model provived by the authors.
Details about the network architecture can be found in the following arXiv paper:
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan, A. Zisserman