代码收藏家技术教程 2022-08-05

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')

来源：许野平