python - 张量板错误 'Can not convert a AdamOptimizer into a Tensor or Operation.'

Question

我制作了一个 DNN 回归模型来预测数据表中没有的结果，但我无法制作 tensorboard。

此代码来自https://deeplearning4j.org/linear-regression.html 以及香港大学 Sunghun Kim 撰写的讲义。

    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

我在这里发现了一个错误optimizer = tf.train.AdamOptimizer(learning_rate=1e-5) 相反，它应该是 optimizer = tf.train.AdamOptimizer(learning_rate=1e-5).minimize(cost)

否则，您最终会评估优化器本身。

否则你应该更换附近的优化器

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

由

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