1.下载安装Python3.5

因为3.6在windows下暂时还没提供pip的接口……

确保环境添加好了环境变量（PowerShell不用重启）

2.安装Tensorflow

管理员打开PowerShell

 pip3 install --upgrade tensorflow

或者：

pip3 install tensorflow
pip3 install tensorflow-gpu
pip3 install tensorlayer //上面二选一,后安装tensorlayer,也可以不装

3.确保有VS2015

安安静静等一会儿…

测试一下：

线性回归的例子，先安装下画图需要的…

 pip3 install matplotlib

下面就是简单粗暴的代码

import tensorflow as tf  
import numpy  
import matplotlib.pyplot as plt  
rng = numpy.random  # Parameters  
learning_rate = 0.01  
training_epochs = 2000  
display_step = 50  # Training Data  
train_X = numpy.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,7.042,10.791,5.313,7.997,5.654,9.27,3.1])  
train_Y = numpy.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,2.827,3.465,1.65,2.904,2.42,2.94,1.3])  
n_samples = train_X.shape[0]  # tf Graph Input  
X = tf.placeholder("float")  
Y = tf.placeholder("float")  # Create Model  # Set model weights  
W = tf.Variable(rng.randn(), name="weight")  
b = tf.Variable(rng.randn(), name="bias")  # Construct a linear model  
activation = tf.add(tf.multiply(X, W), b)  # Minimize the squared errors  
cost = tf.reduce_sum(tf.pow(activation-Y, 2))/(2*n_samples) #L2 loss  
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) #Gradient descent  # Initializing the variables  
init = tf.initialize_all_variables()  # Launch the graph  
with tf.Session() as sess:  sess.run(init)  # Fit all training data  for epoch in range(training_epochs):  for (x, y) in zip(train_X, train_Y):  sess.run(optimizer, feed_dict={X: x, Y: y})  #Graphic display  plt.plot(train_X, train_Y, 'ro', label='Original data')  plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')  plt.legend()  plt.show()

结果如图：