关于TensorFlow、Keras、Python版本匹配一览表
作者：许野平
这篇文章主要介绍了关于TensorFlow、Keras、Python版本匹配一览表,具有很好的参考价值,希望对大家有所帮助,如有错误或未考虑完全的地方,望不吝赐教
TensorFlow、Keras、Python 版本匹配一览表
兴冲冲装完软件，发现运行不了，查了下资料，发现是TensorFlow、Keras、Python 版本匹配问题。
这里提供一个版本匹配清单，需要严格按此标准安装。
版本匹配清单
| Framework | Env name | Description |
|---|---|---|
| TensorFlow 2.2 | tensorflow-2.2 | TensorFlow 2.2.0 + Keras 2.3.1 on Python 3.7. |
| TensorFlow 2.1 | tensorflow-2.1 | TensorFlow 2.1.0 + Keras 2.3.1 on Python 3.6. |
| TensorFlow 2.0 | tensorflow-2.0 | TensorFlow 2.0.0 + Keras 2.3.1 on Python 3.6. |
| TensorFlow 1.15 | tensorflow-1.15 | TensorFlow 1.15.0 + Keras 2.3.1 on Python 3.6. |
| TensorFlow 1.14 | tensorflow-1.14 | TensorFlow 1.14.0 + Keras 2.2.5 on Python 3.6. |
| TensorFlow 1.13 | tensorflow-1.13 | TensorFlow 1.13.0 + Keras 2.2.4 on Python 3.6. |
| TensorFlow 1.12 | tensorflow-1.12 | TensorFlow 1.12.0 + Keras 2.2.4 on Python 3.6. |
| tensorflow-1.12:py2 | TensorFlow 1.12.0 + Keras 2.2.4 on Python 2. | |
| TensorFlow 1.11 | tensorflow-1.11 | TensorFlow 1.11.0 + Keras 2.2.4 on Python 3.6. |
| tensorflow-1.11:py2 | TensorFlow 1.11.0 + Keras 2.2.4 on Python 2. | |
| TensorFlow 1.10 | tensorflow-1.10 | TensorFlow 1.10.0 + Keras 2.2.0 on Python 3.6. |
| tensorflow-1.10:py2 | TensorFlow 1.10.0 + Keras 2.2.0 on Python 2. | |
| TensorFlow 1.9 | tensorflow-1.9 | TensorFlow 1.9.0 + Keras 2.2.0 on Python 3.6. |
| tensorflow-1.9:py2 | TensorFlow 1.9.0 + Keras 2.2.0 on Python 2. | |
| TensorFlow 1.8 | tensorflow-1.8 | TensorFlow 1.8.0 + Keras 2.1.6 on Python 3.6. |
| tensorflow-1.8:py2 | TensorFlow 1.8.0 + Keras 2.1.6 on Python 2. | |
| TensorFlow 1.7 | tensorflow-1.7 | TensorFlow 1.7.0 + Keras 2.1.6 on Python 3.6. |
| tensorflow-1.7:py2 | TensorFlow 1.7.0 + Keras 2.1.6 on Python 2. | |
| TensorFlow 1.5 | tensorflow-1.5 | TensorFlow 1.5.0 + Keras 2.1.6 on Python 3.6. |
| tensorflow-1.5:py2 | TensorFlow 1.5.0 + Keras 2.0.8 on Python 2. | |
| TensorFlow 1.4 | tensorflow-1.4 | TensorFlow 1.4.0 + Keras 2.0.8 on Python 3.6. |
| tensorflow-1.4:py2 | TensorFlow 1.4.0 + Keras 2.0.8 on Python 2. | |
| TensorFlow 1.3 | tensorflow-1.3 | TensorFlow 1.3.0 + Keras 2.0.6 on Python 3.6. |
| tensorflow-1.3:py2 | TensorFlow 1.3.0 + Keras 2.0.6 on Python 2. |
附上一段测试程序（鸢尾花分类简化版）
这一段代码不需要准备数据文件，可直接验证是否可以训练模型。
#ex7-2.py
#导入库包
import numpy as np
import keras
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import Dense
#读入数据
train_x = np.array([[1.4, 0.2],
[1.7, 0.4],
[1.5, 0.4],
[2.3, 0.7],
[2.7, 1.1],
[2.6, 0.9],
[4.6, 1.3],
[3.5, 1.0],
[3.9, 1.2]])
train_y = np.array([[1, 0, 0],
[1, 0, 0],
[1, 0, 0],
[0, 1, 0],
[0, 1, 0],
[0, 1, 0],
[0, 0, 1],
[0, 0, 1],
[0, 0, 1]])
#搭建模型
model = Sequential()
model.add(Dense(units = 2, input_dim = 2))
#model.add(Dense(units = 2, input_dim = 2, activation = 'sigmoid'))
model.add(Dense(units = 3, activation = 'softmax'))
#编译模型
model.compile(optimizer = 'adam', loss = 'mse')
#训练模型
model.fit(x = train_x, y = train_y, epochs = 10000)
#保存模型
keras.models.save_model(model, 'iris2.model')
总结
以上为个人经验，希望能给大家一个参考，也希望大家多多支持脚本之家。