Tensorboard錯誤'無法將AdamOptimizer轉換爲張量或操作。'

Question

我做了一個DNN迴歸模型來預測我們在數據表中沒有的結果，但是我無法生成張量板。

此代碼從https://deeplearning4j.org/linear-regression.html 以及香港大學金炯勳撰寫的講稿。

import tensorflow as tf 
    import numpy as np 
    tf.set_random_seed(777) #for reproducibility 

    # data Import 
    xy = np.loadtxt('Training_Data.csv', delimiter=',', dtype=np.float32) 
    x_data = xy[:,0:-1] 
    y_data = xy[:,[-1]] 

    # Make sure the shape and data are OK 
    print(x_data.shape, x_data) 
    print(y_data.shape, y_data) 

    # input place holders 
    X = tf.placeholder(tf.float32, shape=[None, 2]) 
    Y = tf.placeholder(tf.float32, shape=[None, 1]) 

    # weight & bias for nn Layers 

    W1 = tf.get_variable("W1", shape=[2, 512],initializer=tf.contrib.layers.xavier_initializer()) 
    b1 = tf.Variable(tf.random_normal([512])) 
    L1 = tf.nn.relu(tf.matmul(X, W1) + b1) 

    W2 = tf.get_variable("W2", shape=[512, 512], initializer=tf.contrib.layers.xavier_initializer()) 
    b2 = tf.Variable(tf.random_normal([512])) 
    L2= tf.nn.relu(tf.matmul(L1, W2) + b2) 

    W3 = tf.get_variable("W3", shape=[512, 1], initializer=tf.contrib.layers.xavier_initializer()) 
    b3 = tf.Variable(tf.random_normal([1])) 
    hypothesis = tf.matmul(L2, W3) + b3 

    # cost/loss function 
    cost = tf.reduce_mean(tf.square(hypothesis - Y)) 

    # Minimize/Optimizer 
    optimizer = tf.train.AdamOptimizer(learning_rate=1e-5) 
    train = optimizer.minimize(cost) 

    # Launch the graph in a session. 
    sess = tf.Session() 

    # Initializes global variables in the graph. 
    sess.run(tf.global_variables_initializer()) 

    # Fit the Line with new training data 
    for step in range(2001): 
     cost_val, hy_val, _ = sess.run([cost, hypothesis, train], feed_dict={X: x_data, Y: y_data}) 
     if step % 100 == 0: 
      print(step, "Cost: ", cost_val, "/n Prediction: /n", hy_val) 

    # Command What value you want 
    print("wing loadings will be ", sess.run(hypothesis, 
        feed_dict={X: [[0.0531, 0.05]]})) 

    w2_hist=tf.summary.histogram("weight2",W2) 
    cost_summ=tf.summary.scalar("cost",cost) 

    summary=tf.summary.merge_all() 

    #Create Summary writer 
    writer=tf.summary.FileWriter('C:\\Users\\jh902\\Documents\\.logs') 
    writer.add_graph(sess.graph) 

    s,_= sess.run([summary, optimizer], feed_dict={X: x_data, Y: y_data}) 
    writer.add_summary(s, global_step=2001) 

TypeError         Traceback (most recent call last) 
    C:\Users\jh902\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in __init__(self, fetches, contraction_fn) 
     266   self._unique_fetches.append(ops.get_default_graph().as_graph_element(
    --> 267    fetch, allow_tensor=True, allow_operation=True)) 
     268  except TypeError as e: 

    C:\Users\jh902\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py in as_graph_element(self, obj, allow_tensor, allow_operation) 
     2469  with self._lock: 
    -> 2470  return self._as_graph_element_locked(obj, allow_tensor, allow_operation) 
     2471 

    C:\Users\jh902\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py in _as_graph_element_locked(self, obj, allow_tensor, allow_operation) 
     2558  raise TypeError("Can not convert a %s into a %s." 
    -> 2559      % (type(obj).__name__, types_str)) 
     2560 

    TypeError: Can not convert a AdamOptimizer into a Tensor or Operation. 

    During handling of the above exception, another exception occurred: 

    TypeError         Traceback (most recent call last) 
    <ipython-input-20-b8394996caf6> in <module>() 
    ----> 1 s,_= sess.run([summary, optimizer], feed_dict={X: x_data, Y: y_data}) 
      2 writer.add_summary(s, global_step=2001) 

    C:\Users\jh902\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in run(self, fetches, feed_dict, options, run_metadata) 
     765  try: 
     766  result = self._run(None, fetches, feed_dict, options_ptr, 
    --> 767       run_metadata_ptr) 
     768  if run_metadata: 
     769   proto_data = tf_session.TF_GetBuffer(run_metadata_ptr) 

    C:\Users\jh902\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in _run(self, handle, fetches, feed_dict, options, run_metadata) 
     950 
     951  # Create a fetch handler to take care of the structure of fetches. 
    --> 952  fetch_handler = _FetchHandler(self._graph, fetches, feed_dict_string) 
     953 
     954  # Run request and get response. 

    C:\Users\jh902\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in __init__(self, graph, fetches, feeds) 
     406  """ 
     407  with graph.as_default(): 
    --> 408  self._fetch_mapper = _FetchMapper.for_fetch(fetches) 
     409  self._fetches = [] 
     410  self._targets = [] 

    C:\Users\jh902\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in for_fetch(fetch) 
     228  elif isinstance(fetch, (list, tuple)): 
     229  # NOTE(touts): This is also the code path for namedtuples. 
    --> 230  return _ListFetchMapper(fetch) 
     231  elif isinstance(fetch, dict): 
     232  return _DictFetchMapper(fetch) 

    C:\Users\jh902\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in __init__(self, fetches) 
     335  """ 
     336  self._fetch_type = type(fetches) 
    --> 337  self._mappers = [_FetchMapper.for_fetch(fetch) for fetch in fetches] 
     338  self._unique_fetches, self._value_indices = _uniquify_fetches(self._mappers) 
     339 

    C:\Users\jh902\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in <listcomp>(.0) 
     335  """ 
     336  self._fetch_type = type(fetches) 
    --> 337  self._mappers = [_FetchMapper.for_fetch(fetch) for fetch in fetches] 
     338  self._unique_fetches, self._value_indices = _uniquify_fetches(self._mappers) 
     339 

    C:\Users\jh902\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in for_fetch(fetch) 
     236   if isinstance(fetch, tensor_type): 
     237   fetches, contraction_fn = fetch_fn(fetch) 
    --> 238   return _ElementFetchMapper(fetches, contraction_fn) 
     239  # Did not find anything. 
     240  raise TypeError('Fetch argument %r has invalid type %r' % 

    C:\Users\jh902\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in __init__(self, fetches, contraction_fn) 
     269   raise TypeError('Fetch argument %r has invalid type %r, ' 
     270       'must be a string or Tensor. (%s)' 
    --> 271       % (fetch, type(fetch), str(e))) 
     272  except ValueError as e: 
     273   raise ValueError('Fetch argument %r cannot be interpreted as a ' 

    TypeError: Fetch argument <tensorflow.python.training.adam.AdamOptimizer object at 0x000001E08E7E1CF8> has invalid type <class 'tensorflow.python.training.adam.AdamOptimizer'>, must be a string or Tensor. (Can not convert a AdamOptimizer into a Tensor or Operation.) 

    tensorboard --logdir=.logs 
     File "<ipython-input-83-e4b16f0da480>", line 1 
     tensorboard --logdir=.logs 
          ^
    SyntaxError: invalid syntax 

Answer 1

s,_= sess.run([summary, optimizer], feed_dict={X: x_data, Y: y_data}) 

這就是問題所在。您正在嘗試評估優化器。您可以評估火車運行和獲得成本操作，但優化程序本身不能被忽略。如果您沒有獲取優化器，則應該解決問題。

Answer 2

我都看上了這裏的錯誤optimizer = tf.train.AdamOptimizer(learning_rate=1e-5) 相反，它應該已經optimizer = tf.train.AdamOptimizer(learning_rate=1e-5).minimize(cost)

否則，你最終會評估優化本身。