“从本地文件中学习。有4760个训练数据和240个测试数据。我得到这些错误，有什么问题？”

train_dir=''
    train_csv = pd.read_csv(train_dir+'train.csv')
    train_images=[]
    train_labels=[]
    
    for file in train_csv['data']:
        image=np.array(Image.open(train_dir+'train/'+file))
        train_images.append(image)
    for label in train_csv['label']:
        train_labels.append(label)
    
    train_images=np.array(train_images)
    train_labels=np.array(train_labels)
    
    test_dir=''
    test_csv = pd.read_csv(test_dir+'test.csv')
    
    test_images=[]
    test_labels=[]
    
    for file in test_csv['data']:
        image=np.array(Image.open(test_dir+'test/'+file))
        test_images.append(image)
    for label in test_csv['label']:
        test_labels.append(label)
    
    test_images=np.array(test_images)
    test_labels=np.array(test_labels)
    
    import numpy as np
    import tensorflow as tf
    from tensorflow.keras.datasets import fashion_mnist
    
    from tensorflow.keras.models import Sequential
    from tensorflow.keras.layers import Dense
    from tensorflow.keras.optimizers import SGD,Adam,Adagrad,RMSprop
    
    
    print("train_images",train_images.shape)
    print("train_labels",train_labels.shape)
    
    
    print("test_images",test_images.shape)
    print("test_labels",test_labels.shape)
    
    train_images=train_images.reshape(4760,4096,3)
    test_images=test_images.reshape(240,4096,3)
    
    
    x_train=train_images.astype(np.float32)/255.0
    x_test=test_images.astype(np.float32)/255.0
    
    
    
    
    y_train=tf.keras.utils.to_categorical(train_labels,5)
    y_test=tf.keras.utils.to_categorical(test_labels,5)
    
    
    print("x_train",x_train.shape)
    print("y_train",y_train.shape)
    
    
    print("x_test",x_test.shape)
    print("y_test",y_test.shape)
    
    n_input=4096
    n_hidden1=5000
    n_hidden2=2500
    n_hidden3=2500
    n_hidden4=2500
    n_output=5
    
    batch_siz=256
    n_epoch=5
    
    
    def build_model():
        model=Sequential()
        model.add(Dense(units=n_hidden1,activation='relu',input_shape=(n_input,)))
        model.add(Dense(units=n_hidden2,activation='relu'))
        model.add(Dense(units=n_hidden3,activation='relu'))
        model.add(Dense(units=n_hidden4,activation='relu'))
        model.add(Dense(units=n_output,activation='softmax'))
        return model
    
    
    
    dmlp_adam=build_model()
    dmlp_adam.compile(loss='categorical_crossentropy',optimizer=Adam(),metrics=['accuracy'])
    hist_adam=dmlp_adam.fit(x_train,y_train,batch_size=batch_siz,epochs=n_epoch,validation_data=(x_test,y_test),verbose=2)

error:WARNING:tensorflow:Model was constructed with shape (None, 4096) for input KerasTensor(type_spec=TensorSpec(shape=(None, 4096), dtype=tf.float32, name='dense_input'), name='dense_input', description="created by layer 'dense_input'"), but it was called on an input with incompatible shape (None, 4096, 3).

ValueError: Exception encountered when calling layer "sequential" (type Sequential).
    
    Input 0 of layer "dense" is incompatible with the layer: expected axis -1 of input shape to have value 4096, but received input with shape (None, 4096, 3)
    
    Call arguments received by layer "sequential" (type Sequential):
      • inputs=tf.Tensor(shape=(None, 4096, 3), dtype=float32)
      • training=True
      • mask=None