我试图做一个文本分类使用ANN.我用keras Python，我从互联网上得到解决它.我的数据字数是1148，但我四舍五入到1200为input_shape.代码如下：

#Arsitektur ANN
model = Sequential()
model.add(Dense(units = 100, activation = 'sigmoid',  input_shape=(32, 1200)))
model.add(Dense(units = 2, activation = 'sigmoid'))
opt = Adam (learning_rate=0.001)
model.compile(loss = 'binary_crossentropy', optimizer = opt, 
              metrics = ['accuracy'])

print(model.summary())

接下来，我用下面的代码包含了超参数：

# Hyperparameter
epochs= 100
batch_size= 32
es = EarlyStopping(monitor="val_loss",mode='min',patience=10)
model_prediction = model.fit(arr_Train_X_Tfidf, Train_Y, epochs=epochs, 
                             batch_size=batch_size, verbose=1, 
                             validation_split=0.1, callbacks =[es])

但得到以下错误：

/usr/local/lib/python3.8/dist-packages/keras/engine/training.py in tf__train_function(iterator)
     13                 try:
     14                     do_return = True
---> 15                     retval_ = ag__.converted_call(ag__.ld(step_function), (ag__.ld(self), ag__.ld(iterator)), None, fscope)
     16                 except:
     17                     do_return = False

ValueError: in user code:

    File "/usr/local/lib/python3.8/dist-packages/keras/engine/training.py", line 1051, in train_function  *
        return step_function(self, iterator)
    File "/usr/local/lib/python3.8/dist-packages/keras/engine/training.py", line 1040, in step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    File "/usr/local/lib/python3.8/dist-packages/keras/engine/training.py", line 1030, in run_step  **
        outputs = model.train_step(data)
    File "/usr/local/lib/python3.8/dist-packages/keras/engine/training.py", line 889, in train_step
        y_pred = self(x, training=True)
    File "/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py", line 67, in error_handler
        raise e.with_traceback(filtered_tb) from None
    File "/usr/local/lib/python3.8/dist-packages/keras/engine/input_spec.py", line 264, in assert_input_compatibility
        raise ValueError(f'Input {input_index} of layer "{layer_name}" is '

    ValueError: Input 0 of layer "sequential_2" is incompatible with the layer: expected shape=(None, 32, 1200), found shape=(None, 1148)

有人知道问题出在哪里吗？以及如何解决？谢谢