Question

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


def add_layer(inputs,in_size,out_size,activation_function=None):
with tf.name_scope('layer'):
    with tf.name_scope('weight'):
     Weights = tf.Variable(tf.random_normal([in_size,out_size]),name='W')
    with tf.name_scope('biases'): 
     biases = tf.Variable(tf.zeros([1,out_size])+0.1,name='b')
    with tf.name_scope('Wx_plus_b'):
     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

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 the placeholder for input
with tf.name_scope('inputs'):
xs = tf.placeholder(tf.float32,[None,1],name='x_input')                        
ys = tf.placeholder(tf.float32,[None,1],name='y_input')

#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)


# error between prediction and real data
with tf.name_scope('loss'):
 loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction),
                 reduction_indices=[1]),name='loss')
with tf.name_scope('train'):
 train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
init = tf.global_variables_initializer()



sess = tf.Session()
writer = tf.train.SummaryWrite("C:\\Users\\duke\\Desktop\\tensorflow      example\\",sess.graph)
sess.run(init)
#fig = plt.figure()
#ax = fig.add_subplot(1,1,1)
#ax.scatter(x_data,y_data)
#plt.ion()
#plt.show()


#training step
for i in range (1000):
     sess.run(train_step, feed_dict={xs:x_data,ys:y_data})
      if i % 50 ==0:
     # print(sess.run(loss,feed_dict={xs:x_data,ys:y_data}))
      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,'r-',lw=5)
      plt.pause(0.1)



sess.close()

我目前使用tensorflow构建一个简单的3层NN，我想使用tensorboard来显示gragh。但是当我运行模块时，它显示：AttributeError：module＆＃39; tensorflow.python.training.training＆＃39;没有属性＆＃39; SummaryWrite＆＃39;。我真的很困惑....

Answer 1

正如@Neal 所评论的，SummaryWriter 不再导出到 train 模块中，因为它已被弃用。请参阅 here。

改为使用 tf.summary.FileWriter

AttributeError：module＆＃39; tensorflow.python.training.training＆＃39;没有属性＆＃39; SummaryWrite

1 个答案: