1 关于tensorflow的安装 参看官方文档

#使用 [Homebrew](https://brew.sh/) 软件包管理器安装：
/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
export PATH="/usr/local/bin:/usr/local/sbin:$PATH"
brew update
brew install python  # Python 3
sudo pip3 install -U virtualenv  # system-wide install


#创建一个新的虚拟环境，方法是选择 Python 解释器并创建一个 ./venv 目录来存放它：
virtualenv --system-site-packages -p python3 ./venv
#使用特定于 shell 的命令激活该虚拟环境：
source ./venv/bin/activate  # sh, bash, ksh, or zsh

(venv)$  pip install --upgrade pip
#安装tensorflow
(venv)$  pip install --upgrade tensorflow

2.安装vs code

a. 安装插件 Python

b. (工作目录)/.vscode/settings.json 文件设置如下

{
    "python.pythonPath": "/Users/Apple/venv/bin/python3",
    "python.autoComplete.extraPaths": [
        "/Users/mirage/venv/lib/python3.7/site-packages/"
    ]
}

c. (工作目录)/.vscode/launch.json 文件设置如下

{
    // Use IntelliSense to learn about possible attributes.
    // Hover to view descriptions of existing attributes.
    // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
    "version": "0.2.0",
    "configurations": [
        {
            "name": "Python: Current File",
            "type": "python",
            "pythonPath": "${config:python.pythonPath}",
            "request": "launch",
            //"program": "${file}",
            "program": "${workspaceRoot}/chapter01.py",
            "console": "integratedTerminal"
        }
    ]
}

3. 示例代码

import tensorflow as tf
import numpy as np

#实例化一个Sequential，并添加一个一层的全连接神经网络
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(input_dim=1,units=1))
#编译神经网络模型 损失函数用mse，随机梯度下降为optimizer
model.compile(loss='mse', optimizer='sgd')

#初始化数据 
#生成10个数据 从1到10
X = np.linspace(1, 10, 10)
Y = 2*X

#训练数据 verbose=1 为显示进度信息 epochs=5 训练5期 validation_split表示分离20%的数据用来验证
model.fit(X, Y, verbose=1, epochs=5, validation_split=0.2)

#保存数据 以及加载数据
#filename = 'model.h5'
#model.save(filename)
#model = tf.keras.models.load_model(filename)

#验证数据
x = tf.constant([1, 2, 3, 4])
print(model.predict(x))
#输出：[[2.0426083]  [4.0222816]  [6.0019546]  [7.981628 ]]