Question

我是tensorflow的初学者，我试图通过这个链接https://pythonprogramming.net/rnn-tensorflow-python-machine-learning-tutorial/向神经网络的代码添加摘要我收到错误但我不知道出了什么问题？这是代码

  import tensorflow as tf
  from tensorflow.examples.tutorials.mnist import input_data
  mnist=input_data.read_data_sets("/tmp/data",one_hot=True)
  n_nodes_hl1=500
  n_nodes_hl2=500
  n_nodes_hl3=500

  n_classes=10
  batch_size=100

  x=tf.placeholder("float",[None,784])
  y=tf.placeholder("float")

 def neural_net(data):
     hidden_1_layer={"weight":tf.Variable(tf.random_normal([784,n_nodes_hl1])),"bias":tf.Variable(tf.random_normal([n_nodes_hl1]))}
     hidden_2_layer={"weight":tf.Variable(tf.random_normal([n_nodes_hl1,n_nodes_hl2])),"bias":tf.Variable(tf.random_normal([n_nodes_hl2]))}
     hidden_3_layer={"weight":tf.Variable(tf.random_normal([n_nodes_hl2,n_nodes_hl3])),"bias":tf.Variable(tf.random_normal([n_nodes_hl3]))}
     output_layer={"weight":tf.Variable(tf.random_normal([n_nodes_hl3,n_classes])),"bias":tf.Variable(tf.random_normal([n_classes]))}

     l1=tf.add(tf.matmul(data,hidden_1_layer["weight"]),hidden_1_layer["bias"])
     l1=tf.nn.relu(l1)
     l2 = tf.add(tf.matmul(l1,hidden_2_layer["weight"]), hidden_2_layer["bias"])
     l2 = tf.nn.relu(l2)
     l3 = tf.add(tf.matmul(l2,hidden_3_layer["weight"]), hidden_3_layer["bias"])
     l3 = tf.nn.relu(l3)
     output = tf.matmul(l3,output_layer["weight"])+ output_layer["bias"]

     return output

 def train_net(x):
     prediction=neural_net(x)
     cost=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(prediction,y),name='cost')
     optimizer=tf.train.AdamOptimizer().minimize(cost)

     hm_epoch=3

     for value in [x,y,prediction,cost]:
         tf.summary.scalar([value.op.name],value)

     summaries=tf.summary.merge_all()

     with tf.Session() as sess:
         sess.run(tf.global_variables_initializer())
         summarywriter=tf.summary.FileWriter("layers",sess.graph)
         for epoch in range(hm_epoch):
             epoch_loss=0
             for i in range(int(mnist.train.num_examples/batch_size)):
                 epoch_x,epoch_y=mnist.train.next_batch(batch_size)
                 summarywriter.add_summary(sess.run(summaries,feed_dict={x:epoch_x,y:epoch_y}),i)
                 epoch_loss+=c
             print('epoch ',epoch,' completed out of ',hm_epoch," loss ",epoch_loss)
         correct=tf.equal(tf.argmax(prediction,1),tf.argmax(y,1))
         accuracy=tf.reduce_mean(tf.cast(correct,'float'))
         print('accuracy ',accuracy.eval({x:mnist.test.images,y:mnist.test.labels}))

train_net(x)

这是错误

 File "C:/Users/PC-Sara/AppData/Local/Programs/Python/Python35/tf-layers.py", line 69, in <module>
    train_net(x)
 File "C:/Users/PC-Sara/AppData/Local/Programs/Python/Python35/tf-layers.py", line 46, in train_net
    tf.summary.scalar([value.op.name],value)
 File "C:\Users\PC-Sara\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\summary\summary.py", line 114, in scalar
    name = _clean_tag(name)
 File "C:\Users\PC-Sara\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\summary\summary.py", line 86, in _clean_tag
    new_name = _INVALID_TAG_CHARACTERS.sub('_', name)
 TypeError: expected string or bytes-like object

Answer 1

tf.summary.scalar期望名称作为第一个参数，而不是数组。这应该工作：

tf.summary.scalar(value.op.name, value)

Tensorflow：TypeError：期望的字符串或类字节对象

1 个答案: