线性回归的TensorBoard可视化

一实例

将模型的生成值加入到直方图数据中，将损失值写入到标量数据中

二代码

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
plotdata = { "batchsize":[], "loss":[] }
def moving_average(a, w=10):
    if len(a) < w:
        return a[:]    
    return [val if idx < w else sum(a[(idx-w):idx])/w for idx, val in enumerate(a)]
#生成模拟数据
train_X = np.linspace(-1, 1, 100)
train_Y = 2 * train_X + np.random.randn(*train_X.shape) * 0.3 # y=2x，但是加入了噪声
#图形显示
plt.plot(train_X, train_Y, 'ro', label='Original data')
plt.legend()
plt.show()
tf.reset_default_graph()
# 创建模型
# 占位符
X = tf.placeholder("float")
Y = tf.placeholder("float")
# 模型参数
W = tf.Variable(tf.random_normal([1]), name="weight")
b = tf.Variable(tf.zeros([1]), name="bias")
# 前向结构
z = tf.multiply(X, W)+ b
tf.summary.histogram('z',z)#将预测值以直方图显示
#反向优化
cost =tf.reduce_mean( tf.square(Y - z))
tf.summary.scalar('loss_function', cost)#将损失以标量显示
learning_rate = 0.01
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) #Gradient descent
# 初始化变量
init = tf.global_variables_initializer()
#参数设置
training_epochs = 20
display_step = 2
# 启动session
with tf.Session() as sess:
    sess.run(init)
    
    merged_summary_op = tf.summary.merge_all()#合并所有summary
    #创建summary_writer，用于写文件
    summary_writer = tf.summary.FileWriter('log/mnist_with_summaries',sess.graph)
    # 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})
            
            #生成summary
            summary_str = sess.run(merged_summary_op,feed_dict={X: x, Y: y});
            summary_writer.add_summary(summary_str, epoch);#将summary 写入文件
        #显示训练中的详细信息
        if epoch % display_step == 0:
            loss = sess.run(cost, feed_dict={X: train_X, Y:train_Y})
            print ("Epoch:", epoch+1, "cost=", loss,"W=", sess.run(W), "b=", sess.run(b))
            if not (loss == "NA" ):
                plotdata["batchsize"].append(epoch)
                plotdata["loss"].append(loss)
    print (" Finished!")
    print ("cost=", sess.run(cost, feed_dict={X: train_X, Y: train_Y}), "W=", sess.run(W), "b=", sess.run(b))
    #print ("cost:",cost.eval({X: train_X, Y: train_Y}))
    #图形显示
    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()
    
    plotdata["avgloss"] = moving_average(plotdata["loss"])
    plt.figure(1)
    plt.subplot(211)
    plt.plot(plotdata["batchsize"], plotdata["avgloss"], 'b--')
    plt.xlabel('Minibatch number')
    plt.ylabel('Loss')
    plt.title('Minibatch run vs. Training loss')
     
    plt.show()
    print ("x=0.2，z=", sess.run(z, feed_dict={X: 0.2}))

三运行结果

四在cmd中执行如下命令

注意路径写法

E:\AI\TensorFlow\code\code\log\mnist_with_summaries>tensorboard --logdir=.

另外一种写法

五可视化结果

六参考

https://blog.csdn.net/sinat_30651073/article/details/78747996

https://blog.csdn.net/silver_666/article/details/78563818

http://www.tensorfly.cn/

线性回归的TensorBoard可视化

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