python - 类型错误：在 keras VQ-VAE 示例中，维度值必须是整数或无

Question

我在 colab 和我的环境中尝试了关于 VQ-VAE 的 keras 示例。在这两个中我都遇到了同样的错误：

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-16-a6e2591462e2> in <module>()
      9         # Feed the whole array and retrieving the pixel value probabilities for the next
     10         # pixel.
---> 11         probs = sampler.predict(priors)
     12         # Use the probabilities to pick pixel values and append the values to the priors.
     13         priors[:, row, col] = probs[:, row, col]

9 frames
/usr/local/lib/python3.7/dist-packages/keras/engine/training.py in predict(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)
   1749           for step in data_handler.steps():
   1750             callbacks.on_predict_batch_begin(step)
-> 1751             tmp_batch_outputs = self.predict_function(iterator)
   1752             if data_handler.should_sync:
   1753               context.async_wait()

/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
    883 
    884       with OptionalXlaContext(self._jit_compile):
--> 885         result = self._call(*args, **kwds)
    886 
    887       new_tracing_count = self.experimental_get_tracing_count()

/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
    931       # This is the first call of __call__, so we have to initialize.
    932       initializers = []
--> 933       self._initialize(args, kwds, add_initializers_to=initializers)
    934     finally:
    935       # At this point we know that the initialization is complete (or less

/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
    758     self._concrete_stateful_fn = (
    759         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access
--> 760             *args, **kwds))
    761 
    762     def invalid_creator_scope(*unused_args, **unused_kwds):

/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
   3064       args, kwargs = None, None
   3065     with self._lock:
-> 3066       graph_function, _ = self._maybe_define_function(args, kwargs)
   3067     return graph_function
   3068 

/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
   3461 
   3462           self._function_cache.missed.add(call_context_key)
-> 3463           graph_function = self._create_graph_function(args, kwargs)
   3464           self._function_cache.primary[cache_key] = graph_function
   3465 

/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
   3306             arg_names=arg_names,
   3307             override_flat_arg_shapes=override_flat_arg_shapes,
-> 3308             capture_by_value=self._capture_by_value),
   3309         self._function_attributes,
   3310         function_spec=self.function_spec,

/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes, acd_record_initial_resource_uses)
   1005         _, original_func = tf_decorator.unwrap(python_func)
   1006 
-> 1007       func_outputs = python_func(*func_args, **func_kwargs)
   1008 
   1009       # invariant: `func_outputs` contains only Tensors, CompositeTensors,

/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
    666         # the function a weak reference to itself to avoid a reference cycle.
    667         with OptionalXlaContext(compile_with_xla):
--> 668           out = weak_wrapped_fn().__wrapped__(*args, **kwds)
    669         return out
    670 

/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
    992           except Exception as e:  # pylint:disable=broad-except
    993             if hasattr(e, "ag_error_metadata"):
--> 994               raise e.ag_error_metadata.to_exception(e)
    995             else:
    996               raise

TypeError: in user code:

    /usr/local/lib/python3.7/dist-packages/keras/engine/training.py:1586 predict_function  *
        return step_function(self, iterator)
    /usr/local/lib/python3.7/dist-packages/keras/engine/training.py:1576 step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:1286 run
        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:2849 call_for_each_replica
        return self._call_for_each_replica(fn, args, kwargs)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:3632 _call_for_each_replica
        return fn(*args, **kwargs)
    /usr/local/lib/python3.7/dist-packages/keras/engine/training.py:1569 run_step  **
        outputs = model.predict_step(data)
    /usr/local/lib/python3.7/dist-packages/keras/engine/training.py:1537 predict_step
        return self(x, training=False)
    /usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py:1037 __call__
        outputs = call_fn(inputs, *args, **kwargs)
    /usr/local/lib/python3.7/dist-packages/keras/engine/functional.py:415 call
        inputs, training=training, mask=mask)
    /usr/local/lib/python3.7/dist-packages/keras/engine/functional.py:550 _run_internal_graph
        outputs = node.layer(*args, **kwargs)
    /usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py:1044 __call__
        self._set_save_spec(inputs, args, kwargs)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/training/tracking/base.py:530 _method_wrapper
        result = method(self, *args, **kwargs)
    /usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py:3049 _set_save_spec
        flat_specs = [tf_utils.get_tensor_spec(x) for x in flat_kwarg]
    /usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py:3049 <listcomp>
        flat_specs = [tf_utils.get_tensor_spec(x) for x in flat_kwarg]
    /usr/local/lib/python3.7/dist-packages/keras/utils/tf_utils.py:467 get_tensor_spec
        spec = tf.TensorSpec(shape=t.shape, dtype=t.dtype, name=name)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/tensor_spec.py:51 __init__
        self._shape = tensor_shape.TensorShape(shape)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/tensor_shape.py:784 __init__
        self._dims = [as_dimension(dims)]
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/tensor_shape.py:729 as_dimension
        return Dimension(value)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/tensor_shape.py:209 __init__
        .format(value, type(value))), None)
    <string>:3 raise_from
        

    TypeError: Dimension value must be integer or None or have an __index__ method, got value '<attribute 'shape' of 'numpy.generic' objects>' with type '<class 'getset_descriptor'>'

错误来自这部分代码：

# Create a mini sampler model.
inputs = layers.Input(shape=pixel_cnn.input_shape[1:])
x = pixel_cnn(inputs, training=False)
dist = tfp.distributions.Categorical(logits=x)
sampled = dist.sample()
sampler = keras.Model(inputs, sampled)

# Create an empty array of priors.
batch = 10
priors = np.zeros(shape=(batch,) + (pixel_cnn.input_shape)[1:])
batch, rows, cols = priors.shape

# Iterate over the priors because generation has to be done sequentially pixel by pixel.
for row in range(rows):
    for col in range(cols):
        # Feed the whole array and retrieving the pixel value probabilities for the next
        # pixel.
        probs = sampler.predict(priors)
        # Use the probabilities to pick pixel values and append the values to the priors.
        priors[:, row, col] = probs[:, row, col]

print(f"Prior shape: {priors.shape}")

据我了解，问题来自priors与 pixel_cnn 输入的数组维度不匹配。但是输入 isKerasTensor(type_spec=TensorSpec(shape=(None, 32, 32), dtype=tf.float32, name='input_2'), name='input_2', description="created by layer 'input_2'")和形状priorsis (10, 32, 32)，所以没有不匹配...

我发现了那些类似的问题，但我仍然很难理解发生了什么：

TypeError: Dimension value must be integer or None or have an index method, got TensorShape([None, 1])

TypeError：维度值必须是整数或无或具有索引方法，得到值'TensorShape（[None，16]）'

有人可以解释为什么存在这个错误以及如何解决它吗？

标签： pythonpython-3.xtensorflowkeras