“TypeError：'张量'对象是不可迭代的'与张量流量的错误估计器

我有一个程序性生成（无限）数据源，并试图使用它作为高级别Tensorflow Estimator的输入来训练基于图像的3D物体检测器。“TypeError：'张量'对象是不可迭代的'与张量流量的错误估计器

我设置的数据集，就像在Tensorflor估计Quickstart，我dataset_input_fn收益特征的元组和标签Tensor的，就像Estimator.train函数指定，这该怎么tutorial shows，但我得到一个错误时，试图调用列车功能：

TypeError: 'Tensor' object is not iterable.

我在做什么错？

def data_generator(): 
     """ 
     Generator for image (features) and ground truth object positions (labels) 

     Sample an image and object positions from a procedurally generated data source 
     """ 
     while True: 
      source.step() # generate next data point 

      object_ground_truth = source.get_ground_truth() # list of 9 floats 
      cam_img = source.get_cam_frame() # image (224, 224, 3) 
      yield (cam_img, object_ground_truth) 

    def dataset_input_fn(): 
     """ 
     Tensorflow `Dataset` object from generator 
     """ 

     dataset = tf.data.Dataset.from_generator(data_generator, (tf.uint8, tf.float32), \ 
      (tf.TensorShape([224, 224, 3]), tf.TensorShape([9]))) 
     dataset = dataset.batch(16) 

     iterator = dataset.make_one_shot_iterator() 

     features, labels = iterator.get_next() 
     return features, labels 

    def main(): 
     """ 
     Estimator [from Keras model](https://www.tensorflow.org/programmers_guide/estimators#creating_estimators_from_keras_models) 

     Try to call `est_vgg.train()` leads to the error 
     """ 
     .... 
     est_vgg16 = tf.keras.estimator.model_to_estimator(keras_model=keras_vgg16) 
     est_vgg16.train(input_fn=dataset_input_fn, steps=10) 
     ....

这里是full code

（注：事情是从这个问题不同的名称）

这里是堆栈跟踪：

Traceback (most recent call last): 
    File "./rock_detector.py", line 155, in <module> 
    main() 
    File "./rock_detector.py", line 117, in main 
    est_vgg16.train(input_fn=dataset_input_fn, steps=10) 
    File "/usr/local/lib/python3.6/site-packages/tensorflow/python/estimator/estimator.py", line 302, in train 
    loss = self._train_model(input_fn, hooks, saving_listeners) 
    File "/usr/local/lib/python3.6/site-packages/tensorflow/python/estimator/estimator.py", line 711, in _train_model 
    features, labels, model_fn_lib.ModeKeys.TRAIN, self.config) 
    File "/usr/local/lib/python3.6/site-packages/tensorflow/python/estimator/estimator.py", line 694, in _call_model_fn 
    model_fn_results = self._model_fn(features=features, **kwargs) 
    File "/usr/local/lib/python3.6/site-packages/tensorflow/python/keras/_impl/keras/estimator.py", line 145, in model_fn 
    labels) 
    File "/usr/local/lib/python3.6/site-packages/tensorflow/python/keras/_impl/keras/estimator.py", line 92, in _clone_and_build_model 
    keras_model, features) 
    File "/usr/local/lib/python3.6/site-packages/tensorflow/python/keras/_impl/keras/estimator.py", line 58, in _create_ordered_io 
    for key in estimator_io_dict: 
    File "/usr/local/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 505, in __iter__ 
    raise TypeError("'Tensor' object is not iterable.") 
TypeError: 'Tensor' object is not iterable.

来源

2017-11-11 matwilso

我想你只是想：'get_next = iterator.get_next（）; est_vgg16.train（input_fn = get_next，steps = 10'，但我没有使用keras，所以我不完全熟悉那里使用的'.train'函数。 –

您可以分享错误的完整堆栈跟踪？ – mrry

使用堆栈跟踪进行了更新后，很难理解高级api背后发生了什么，我通过切换到tf的更低级别的接口来实现尽可能多的工作，只是用发电机手动“喂食”，然而关于高级别api的好处是，它可以处理所有的训练和细节，并且可以优化处理。 – matwilso

让您的输入功能返回这样的功能词典：

def dataset_input_fn(): 
    ... 
    features, labels = iterator.get_next() 
    return {'image': features}, labels

来源

2017-11-14 15:45:31 Maxim

解决了这个问题，谢谢。必须在'dataset_input_fn'中将'tf.uint8'更改为'tf.float32'。 – matwilso

“TypeError：'张量'对象是不可迭代的'与张量流量的错误估计器

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