结果可视化

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

def add_layer(inputs, input_size, output_size, activation_function = None):
    Weights = tf.Variable(tf.random_normal([input_size, output_size]))
    biases = tf.Variable(tf.zeros([1, output_size]) + 0.1) #biases初始化为0.1的列向量
    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

#create_real data
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

#define placeholder for inputs to network
xs = tf.placeholder(dtype=tf.float32,shape=[None, 1])
ys = tf.placeholder(dtype=tf.float32,shape=[None, 1])

#add hiden layer 输入层输出层一个神经元（因为只有一个属性），隐层十个神经元
l1 = add_layer(xs, 1, 10, activation_function=tf.nn.relu)

#add output layer
prediction = add_layer(l1, 10, 1, activation_function=None)

#the error between predic  and real data
loss = tf.reduce_mean(tf.reduce_sum(tf.square(prediction - ys), reduction_indices=[1])) #相当于转化为横向量
train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss)

#initialize all variable
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)

fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.scatter(x_data, y_data)
plt.ion()

for i in range(1000):
    #training
    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, color = 'red')
       plt.pause(0.5)

while(True):
    plt.pause(1)

plt.ion进入交互模式，最后的while循环保证界面不会自动关闭

刚打算用qq截图，发现打开qq界面后，每隔一秒，该图界面会弹出挡住qq界面，也就是说使用plt,ion()，该图一直在刷新

猜你喜欢