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 ]]
