ValueError：“顺序”层的输入 0 与该层不兼容：预期形状 =（无，32,32,3），找到形状 =（32,32,3）

原文标题 ：ValueError: Input 0 of layer “sequential” is incompatible with the layer: expected shape=(None, 32, 32, 3), found shape=(32, 32, 3)

这是我收到的错误，一些帮助会很好：我已经粘贴了错误和我认为可能有问题的部分代码。如果你能帮助我解决这个问题，那就太好了。ValueError：在用户中代码： – – – – – – – – – – – – – – – – – – – – – – – – —————————ValueError Traceback (最近一次调用最后一次) in 27 return plot28—> 29 display (plotImages(x_test, data_test_picture, y_test, n_images=10))

<command-3924510788207782> in plotImages(x_test, images_arr, labels_arr, n_images)
     16         ax.set_yticks(())
     17 
---> 18         predict_x=model2000.predict([[x_test[rand]]])
     19         label=categoriesList[predictions[0]]
     20 

/databricks/python/lib/python3.8/site-packages/keras/utils/traceback_utils.py in error_handler(*args, **kwargs)
     65     except Exception as e:  # pylint: disable=broad-except
     66       filtered_tb = _process_traceback_frames(e.__traceback__)
---> 67       raise e.with_traceback(filtered_tb) from None
     68     finally:
     69       del filtered_tb

/databricks/python/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in autograph_handler(*args, **kwargs)
   1127           except Exception as e:  # pylint:disable=broad-except
   1128             if hasattr(e, "ag_error_metadata"):
-> 1129               raise e.ag_error_metadata.to_exception(e)
   1130             else:
   1131               raise

ValueError: in user code:

    File "/databricks/python/lib/python3.8/site-packages/keras/engine/training.py", line 1621, in predict_function  *
        return step_function(self, iterator)
    File "/databricks/python/lib/python3.8/site-packages/keras/engine/training.py", line 1611, in step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    File "/databricks/python/lib/python3.8/site-packages/keras/engine/training.py", line 1604, in run_step  **
        outputs = model.predict_step(data)
    File "/databricks/python/lib/python3.8/site-packages/keras/engine/training.py", line 1572, in predict_step
        return self(x, training=False)
    File "/databricks/python/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 67, in error_handler
        raise e.with_traceback(filtered_tb) from None
    File "/databricks/python/lib/python3.8/site-packages/keras/engine/input_spec.py", line 263, in assert_input_compatibility
        raise ValueError(f'Input {input_index} of layer "{layer_name}" is '

    ValueError: Input 0 of layer "sequential" is incompatible with the layer: expected shape=(None, 32, 32, 3), found shape=(32, 32, 3)

以下是我认为可能有用的部分代码：

def initializeModel():
  model = Sequential()

  model.add(Conv2D(32, (3, 3), padding='same',
                   input_shape=x_train.shape[1:]))
  model.add(Activation('relu'))
  model.add(Conv2D(32, (3, 3)))
  model.add(Activation('relu'))
  model.add(MaxPooling2D(pool_size=(2, 2)))
  model.add(Dropout(0.25))

  model.add(Conv2D(64, (3, 3), padding='same'))
  model.add(Activation('relu'))
  model.add(Conv2D(64, (3, 3)))
  model.add(Activation('relu'))
  model.add(MaxPooling2D(pool_size=(2, 2)))
  model.add(Dropout(0.25))

  model.add(Flatten())
  model.add(Dense(512))
  model.add(Activation('relu'))
  model.add(Dropout(0.5))
  model.add(Dense(num_classes))
  model.add(Activation('softmax'))
  return model

categoriesList=["airplane","automobile","bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck"]

import matplotlib.pyplot as plt
import random
def plotImages(x_test, images_arr, labels_arr, n_images=8):
    fig, axes = plt.subplots(n_images, n_images, figsize=(9,9))
    axes = axes.flatten()
    
    for i in range(100):
        rand = random.randint(0, x_test.shape[0] -1)
        img = images_arr[rand]
        ax = axes[i]
    
        ax.imshow( img, cmap="Greys_r")
        ax.set_xticks(())
        ax.set_yticks(())
        
        predict_x=model2000.predict([[x_test[rand]]])
        label=categoriesList[predictions[0]]  
        
        if labels_arr[rand][predictions[0]] == 0:
            ax.set_title(label, fontsize=18 - n_images, color="red")
        else:
            ax.set_title(label, fontsize=18 - n_images) 
        
    plot = plt.tight_layout()
    return plot
  
display (plotImages(x_test, data_test_picture, y_test, n_images=10))