Tensorflowセッションは、単純な問題のためにナン

Question

import numpy as np 
import tensorflow as tf 

x_input=np.linspace(0,20,100) 
y_input = 4*x_input+6 


W=tf.Variable(0.0, name="weight") 
b= tf.Variable(0.0, name="bias") 

X=tf.placeholder(tf.float32,name='InputX') 
Y=tf.placeholder(tf.float32, name='InputY') 

Y_pred = X*W+b 

loss = tf.reduce_mean(tf.square(Y_pred-Y)) 
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01).minimize(loss) 

with tf.Session() as sess: 
    sess.run(tf.global_variables_initializer()) 
    for step in range(50): 
     total_loss = 0 
     for x, y in zip(x_input,y_input): 
      print (x, y) 
      sess.run([optimizer,loss], feed_dict={X:x,Y:y}) 
    w, b = sess.run([W, b]) 

    print("Model parameters: ",w,b) 
    # '''Above prints nan nan.''' 

Answer 1

を返します。あなたはより低い必要がありますlearning_rate;学習率の高いモデルパラメータの値が複数回更新されているように見えます。

# setting learning_rate 0.001, gives proper value 
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001).minimize(loss) 

... 
print(w, b) 
# results 4.05073 4.98799