Python Keras安装和数据发生器返回“；检查输入时出错：预期输入“1有3个维度，但得到了形状为（无，1）的数组”；

我正在尝试使用pickle中存储的数据帧创建一个生成器。目标为3列，其名称以“target”开头。这些特征存储在“输入”列中。“输入”的每个单元格都是np数组。其尺寸为51x7（7为通道数）。 （我现在只想把重点放在数据生成器上）

当我尝试运行此代码时，出现以下错误：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-7-6dde9c267440> in <module>
      7 optimizer = Adam(lr=0.0001)
      8 model.compile(optimizer=optimizer, loss=RMSE)
----> 9 model.fit(train_generator, epochs=100, verbose=1, use_multiprocessing=False, workers=1)#, validation_data=validation_generator)

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_core\python\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
    817         max_queue_size=max_queue_size,
    818         workers=workers,
--> 819         use_multiprocessing=use_multiprocessing)
    820 
    821   def evaluate(self,

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in fit(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
    233           max_queue_size=max_queue_size,
    234           workers=workers,
--> 235           use_multiprocessing=use_multiprocessing)
    236 
    237       total_samples = _get_total_number_of_samples(training_data_adapter)

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in _process_training_inputs(model, x, y, batch_size, epochs, sample_weights, class_weights, steps_per_epoch, validation_split, validation_data, validation_steps, shuffle, distribution_strategy, max_queue_size, workers, use_multiprocessing)
    591         max_queue_size=max_queue_size,
    592         workers=workers,
--> 593         use_multiprocessing=use_multiprocessing)
    594     val_adapter = None
    595     if validation_data:

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in _process_inputs(model, mode, x, y, batch_size, epochs, sample_weights, class_weights, shuffle, steps, distribution_strategy, max_queue_size, workers, use_multiprocessing)
    704       max_queue_size=max_queue_size,
    705       workers=workers,
--> 706       use_multiprocessing=use_multiprocessing)
    707 
    708   return adapter

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_core\python\keras\engine\data_adapter.py in __init__(self, x, y, sample_weights, standardize_function, shuffle, workers, use_multiprocessing, max_queue_size, **kwargs)
    950         use_multiprocessing=use_multiprocessing,
    951         max_queue_size=max_queue_size,
--> 952         **kwargs)
    953 
    954   @staticmethod

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_core\python\keras\engine\data_adapter.py in __init__(self, x, y, sample_weights, standardize_function, workers, use_multiprocessing, max_queue_size, **kwargs)
    765 
    766     if standardize_function is not None:
--> 767       dataset = standardize_function(dataset)
    768 
    769     if kwargs.get("shuffle", False) and self.get_size() is not None:

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in standardize_function(dataset)
    682           return x, y
    683         return x, y, sample_weights
--> 684       return dataset.map(map_fn, num_parallel_calls=dataset_ops.AUTOTUNE)
    685 
    686   if mode == ModeKeys.PREDICT:

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_core\python\data\ops\dataset_ops.py in map(self, map_func, num_parallel_calls)
   1589     else:
   1590       return ParallelMapDataset(
-> 1591           self, map_func, num_parallel_calls, preserve_cardinality=True)
   1592 
   1593   def flat_map(self, map_func):

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_core\python\data\ops\dataset_ops.py in __init__(self, input_dataset, map_func, num_parallel_calls, use_inter_op_parallelism, preserve_cardinality, use_legacy_function)
   3924         self._transformation_name(),
   3925         dataset=input_dataset,
-> 3926         use_legacy_function=use_legacy_function)
   3927     self._num_parallel_calls = ops.convert_to_tensor(
   3928         num_parallel_calls, dtype=dtypes.int32, name="num_parallel_calls")

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_core\python\data\ops\dataset_ops.py in __init__(self, func, transformation_name, dataset, input_classes, input_shapes, input_types, input_structure, add_to_graph, use_legacy_function, defun_kwargs)
   3145       with tracking.resource_tracker_scope(resource_tracker):
   3146         # TODO(b/141462134): Switch to using garbage collection.
-> 3147         self._function = wrapper_fn._get_concrete_function_internal()
   3148 
   3149         if add_to_graph:

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_core\python\eager\function.py in _get_concrete_function_internal(self, *args, **kwargs)
   2393     """Bypasses error checking when getting a graph function."""
   2394     graph_function = self._get_concrete_function_internal_garbage_collected(
-> 2395         *args, **kwargs)
   2396     # We're returning this concrete function to someone, and they may keep a
   2397     # reference to the FuncGraph without keeping a reference to the

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_core\python\eager\function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
   2387       args, kwargs = None, None
   2388     with self._lock:
-> 2389       graph_function, _, _ = self._maybe_define_function(args, kwargs)
   2390     return graph_function
   2391 

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_core\python\eager\function.py in _maybe_define_function(self, args, kwargs)
   2701 
   2702       self._function_cache.missed.add(call_context_key)
-> 2703       graph_function = self._create_graph_function(args, kwargs)
   2704       self._function_cache.primary[cache_key] = graph_function
   2705       return graph_function, args, kwargs

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_core\python\eager\function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
   2591             arg_names=arg_names,
   2592             override_flat_arg_shapes=override_flat_arg_shapes,
-> 2593             capture_by_value=self._capture_by_value),
   2594         self._function_attributes,
   2595         # Tell the ConcreteFunction to clean up its graph once it goes out of

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_core\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)
    976                                           converted_func)
    977 
--> 978       func_outputs = python_func(*func_args, **func_kwargs)
    979 
    980       # invariant: `func_outputs` contains only Tensors, CompositeTensors,

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_core\python\data\ops\dataset_ops.py in wrapper_fn(*args)
   3138           attributes=defun_kwargs)
   3139       def wrapper_fn(*args):  # pylint: disable=missing-docstring
-> 3140         ret = _wrapper_helper(*args)
   3141         ret = structure.to_tensor_list(self._output_structure, ret)
   3142         return [ops.convert_to_tensor(t) for t in ret]

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_core\python\data\ops\dataset_ops.py in _wrapper_helper(*args)
   3080         nested_args = (nested_args,)
   3081 
-> 3082       ret = autograph.tf_convert(func, ag_ctx)(*nested_args)
   3083       # If `func` returns a list of tensors, `nest.flatten()` and
   3084       # `ops.convert_to_tensor()` would conspire to attempt to stack

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_core\python\autograph\impl\api.py in wrapper(*args, **kwargs)
    235       except Exception as e:  # pylint:disable=broad-except
    236         if hasattr(e, 'ag_error_metadata'):
--> 237           raise e.ag_error_metadata.to_exception(e)
    238         else:
    239           raise

ValueError: in converted code:

    C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py:677 map_fn
        batch_size=None)
    C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_core\python\keras\engine\training.py:2410 _standardize_tensors
        exception_prefix='input')
    C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_core\python\keras\engine\training_utils.py:573 standardize_input_data
        'with shape ' + str(data_shape))

    ValueError: Error when checking input: expected input_1 to have 3 dimensions, but got array with shape (None, 1)

它返回：

array（[（51，7）]，dtype=object）

---

我不确定它返回“object”作为列类型是否正常

您的

dataframe

一定有问题，这意味着存储的文件中存在一些问题，导致加载时形状不匹配

为了验证这就是问题所在，我让您的模型创建和拟合代码完全相同，但编写的测试生成器也几乎与您的原始代码相同，只是数据是动态生成的，这可以纠正形状

class DataGenerator(Sequence):
    def __init__(self, path_experiment, batch_size, mode='train', validation_ratio=0.1):
        self.batch_size = batch_size
        self.mode = mode
        self.indexes = None

        self.inputs =  np.random.random([batch_size,51,7] )
        self.targets = np.random.random([batch_size,3] )


    def __len__(self):
        return int((51*7) / float(self.batch_size))

    def get_batch_labels(self, idx):
        # Fetch a batch of labels
        return self.targets[idx * self.batch_size: (idx + 1) * self.batch_size]

    def get_batch_features(self, idx):
        # Fetch a batch of inputs
        return self.inputs[idx * self.batch_size: (idx + 1) * self.batch_size]

    def __getitem__(self, idx):
        batch_x = self.get_batch_features(idx)
        batch_y = self.get_batch_labels(idx)
        # print(batch_x[0].shape)  # Display (51,7)
        # print(batch_x[0].shape)  # Display (3,)
        return batch_x, batch_y

path=''
train_generator = DataGenerator(path, batch_size=32, mode='train')
validation_generator = DataGenerator(path, batch_size=32, mode='validation')

在这里，我确保输入是

[batch，51,7]

的形状。运行您的模型创建代码并进行调整，它可以正常工作

Epoch 1/100
11/11 [==============================] - 0s 1ms/step - loss: 0.3640
Epoch 2/100
11/11 [==============================] - 0s 884us/step - loss: 0.3508
Epoch 3/100
11/11 [==============================] - 0s 1ms/step - loss: 0.3350

---

问题必须出现在

self.dataframe['input'].values

中。添加

print（self.dataframe['input'].values.shape）

查看它是什么

解决方案在此线程中：（有关详细信息）

原来我最初想用dataframe做的事情不太可能。相反，我使用了：

---

self.inputs=np.stack（self.dataframe['input'].to_numpy（））

print（self.inputs.shape）

和

print（self.targets.shape）

分别给出（1101，）和（1101,3）。您知道如何修复self.inputs的形状吗？问题仍然存在于输入中，您可以打印（self.dataframe.columns）和打印（len（list（self.dataframe.columns））？问题已经解决，我将能够在几个小时内关闭它。如果你感兴趣，你可以看到另一个答案。dataframe有34列，其中3列是目标（以目标开始）

class DataGenerator(Sequence):
    def __init__(self, path_experiment, batch_size, mode='train', validation_ratio=0.1):
        self.batch_size = batch_size
        self.mode = mode
        self.indexes = None

        self.inputs =  np.random.random([batch_size,51,7] )
        self.targets = np.random.random([batch_size,3] )


    def __len__(self):
        return int((51*7) / float(self.batch_size))

    def get_batch_labels(self, idx):
        # Fetch a batch of labels
        return self.targets[idx * self.batch_size: (idx + 1) * self.batch_size]

    def get_batch_features(self, idx):
        # Fetch a batch of inputs
        return self.inputs[idx * self.batch_size: (idx + 1) * self.batch_size]

    def __getitem__(self, idx):
        batch_x = self.get_batch_features(idx)
        batch_y = self.get_batch_labels(idx)
        # print(batch_x[0].shape)  # Display (51,7)
        # print(batch_x[0].shape)  # Display (3,)
        return batch_x, batch_y

path=''
train_generator = DataGenerator(path, batch_size=32, mode='train')
validation_generator = DataGenerator(path, batch_size=32, mode='validation')

Epoch 1/100
11/11 [==============================] - 0s 1ms/step - loss: 0.3640
Epoch 2/100
11/11 [==============================] - 0s 884us/step - loss: 0.3508
Epoch 3/100
11/11 [==============================] - 0s 1ms/step - loss: 0.3350