使用matplotlib中的一些函数将tensorflow中的数据可视化,更加便于分析
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import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
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
# Make up some 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(tf.float32, [ None , 1 ])
ys = tf.placeholder(tf.float32, [ None , 1 ])
# add hidden layer
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 prediction and real data
loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction), reduction_indices = [ 1 ]))
train_step = tf.train.GradientDescentOptimizer( 0.1 ).minimize(loss)
# important step
#initialize_all_variables已被弃用,使用tf.global_variables_initializer代替。
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
# plot the real data
fig = plt.figure()
ax = fig.add_subplot( 1 , 1 , 1 )
ax.scatter(x_data, y_data)
plt.ion() #使plt不会在show之后停止而是继续运行
plt.show()
for i in range ( 1000 ):
# training
sess.run(train_step, feed_dict = {xs: x_data, ys: y_data})
if i % 50 = = 0 :
# to visualize the result and improvement
try :
ax.lines.remove(lines[ 0 ]) #在每一次绘图之前先讲上一次绘图删除,使得画面更加清晰
except Exception:
pass
prediction_value = sess.run(prediction, feed_dict = {xs: x_data})
# plot the prediction
lines = ax.plot(x_data, prediction_value, 'r-' , lw = 5 ) #'r-'指绘制一个红色的线
plt.pause( 1 ) #指等待一秒钟
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运行结果如下:(实际效果应该是动态的,应当使用ipython运行,使用jupyter运行则图片不是动态的)
注意:initialize_all_variables已被弃用,使用tf.global_variables_initializer代替。
以上这篇通过python的matplotlib包将Tensorflow数据进行可视化的方法就是小编分享给大家的全部内容了,希望能给大家一个参考,也希望大家多多支持服务器之家。
原文链接:https://blog.csdn.net/qq_38542085/article/details/78483280