Tensorflow——可视化训练过程

1.前言

本节将以图像化形式展示模型训练过程，观察模型是怎样一步步拟合成最终的曲线。

2.可视化训练过程

2.1.导入必要模块

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

2.2.定义添加层函数

def add_layer(inputs,in_size,out_size,activation_functional=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_functional is None:
        outputs = Wx_plus_b
    else:
        outputs = activation_functional(Wx_plus_b)
    return outputs

2.3.构造数据

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

plt.scatter(x_data,y_data)
plt.show()

2.4.训练

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

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

loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction),reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

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

2.5.可视化

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(line[0])
        except Exception:
            pass
        prediction_value = sess.run(prediction, feed_dict={xs:x_data})
        lines = ax.plot(x_data, prediction_value,'r-',lw=5)
        plt.pause(1)