Image Identification.
First, set up the environment
to achieve python-based TensorFlow
installation 1.anaconda of
reference: HTTPS: //jingyan.baidu.com/article/f0062228503d2afbd3f0c8fe.html
2. Visualization Tool: jupyter notebook literate programming is conducive to data analysis
Reference: https: // www.jianshu.com/p/97fa4ed3edbc
open: after installing anacoda input in cmd
jupyter notebook
In the browser pop-up interface, click to start writing new code.
Close ways: you can click on shutdown in the notebook. Shut down the entire service: In the original terminal press Control + c two
code units: Here is where you write the code by pressing Shift + Enter to run the code, the results displayed below this unit. The left-hand unit has In [1]: a sequence tag code is easy for people to see the execution order.
Markdown unit: here on the text editor, syntax specification uses markdown, you can format text, insert links, images and even mathematical formulas.
Shift + Enter run using the same markdown unit to display formatted text.
Linux-like Vim editor, the notebook also has two modes:
Edit mode: Edit text and code. Select unit and press Enter to enter the edit mode, the left unit case green vertical line.
Command Mode: keyboard shortcut command for executing input. By Esc to enter command mode, the left unit case blue vertical line.
If you want to use shortcut keys, first press Esc to enter command mode, then press the corresponding key to achieve the operation of the document.
Such as switching unit into a code (Y) or markdown unit (M), an increase or a unit (B) beneath the module. View all shortcut command can press H.
Second, the use of image recognition keras
official document: HTTPS: //keras.io/zh/
https://keras-cn.readthedocs.io/en/latest/other/optimizers/
1.python3.7 back to 3.6
Open anaconda prompt -> input the install Python = Conda 3.6 -> Y
2. Install tensorFlow2.0:
After downloading install the corresponding version of the anaconda, the command installation tensorflow2.0 (such as the need to replace the line after the official version):
pip install tensorflow==2.0.0-beta1
python virtual environment
Create a new virtual environment:
conda create -n tensorflow python=3.6
View existing virtual environment:
conda env list
Switched virtual environment (name when tensorflow create for yourself, here tensorflow):
activate tensorflow
In this open jupyter notebook
In this open jupyter notebook
to create a new file python3
import tensorflow as tf print(tf._version_)
#ttributeError: module 'tensorflow' has no attribute '_version_'
solutions do not?
# 2.0.0-beta1
(With detailed code) based on the structures and the visualized image classification keras CNN model
https://blog.csdn.net/u011268787/article/details/79891284