TensorFlow Demo2

import tensorflow as tf
import numpy as np


def add_layer(inputs,in_size,out_size,activation_function=None):
    Weights = tf.Variable(tf.random_normal([in_size,out_size]))
    biases =  tf.Variable(tf.zeros([1,out_size])) + 0.1
    Wx_plus_b = tf.matmul(inputs,Weights) + biases
    if activation_function is None :
        outputs= Wx_plus_b
    else:
        outputs = activation_function(Wx_plus_b)
    return outputs

x_data = np.linspace(-1,1,300)[:,np.newaxis]
noise = np.random.normal(0,0.05,x_data.shape)
y_data = np.square(x_data) - 0.5 + noise

xs = tf.placeholder(tf.float32,[None,1])
ys = tf.placeholder(tf.float32,[None,1])

l1 = add_layer(xs,1,10,activation_function=tf.nn.relu)
prediction = add_layer(l1,10,1,activation_function=None)

loss =tf.reduce_mean(tf.reduce_sum( tf.square(ys-prediction),reduction_indices=[1]))

train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)

for i in range(1000):
    sess.run(train_step,feed_dict={xs:x_data,ys:y_data})
    if i % 50 == 0:
        print(sess.run(loss,feed_dict={xs:x_data,ys:y_data}))

 输出的结果逐渐接近于0，代表我们的学习训练起到了作用。

为了更好的看到学习的效果，可以通过matplotlib来做图片展示

首先在电脑上安装matplotlib库，在安装过程中又碰到之前安装Tensorflow时一样的错误，也用同样的方法解决掉：

pip install matplotlib --ignore-installed six

之后在代码开头部分引入matplotlib库：

import matplotlib.pyplot as plt

将最后部分的代码改成如下代码：

#以前的代码...........
sess.run(init)

fig = plt.figure()
ax = fig.add_subplot(1,1,1)
ax.scatter(x_data,y_data)
plt.ion()
plt.show()
for i in range(1000):
    sess.run(train_step,feed_dict={xs:x_data,ys:y_data})
    if i % 50 == 0:
        try:
            ax.lines.remove(lines[0])
        except Exception:
            pass
        prediction_value = sess.run(prediction,feed_dict={xs:x_data})
        lines = ax.plot(x_data,prediction_value,'r-',lw=2)
        plt.pause(1)

再次运行代码。我们会看到图片由这样的曲线

变成这样的曲线

这样我们就清晰的看到了学习进步的过程了。