I am trying to run TensorFlow Sequential model in Python to classify data recordings from accelerometers. I receive an error:

Layer "sequential" expects 1 input(s), but it received > 16200 input tensors. 16200 is the frames per file * number of files.

Truncated error:

(27, 600, 3, 100)
(27, 44)
Epoch 1/40
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_26740/926534238.py in <module>
----> 1 history = model.fit(x = X_train, y = y_train, epochs=40, batch_size = 8 , shuffle=False, validation_split=0.2, callbacks=callbacks)
      2 
      3 y_pred = np.argmax(y_pred, axis = 1)
      4 y_test = np.argmax(y_test, axis = 1)
      5 

~\AppData\Local\Programs\Python\Python39\lib\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

~\AppData\Local\Programs\Python\Python39\lib\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 "C:\Users\Dan\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\engine\training.py", line 878, in train_function  *
        return step_function(self, iterator)
    File "C:\Users\Dan\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\engine\training.py", line 867, in step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    File "C:\Users\Dan\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\engine\training.py", line 860, in run_step  **
        outputs = model.train_step(data)
    File "C:\Users\Dan\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\engine\training.py", line 808, in train_step
        y_pred = self(x, training=True)
    File "C:\Users\Dan\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\utils\traceback_utils.py", line 67, in error_handler
        raise e.with_traceback(filtered_tb) from None
    File "C:\Users\Dan\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\engine\input_spec.py", line 199, in assert_input_compatibility
        raise ValueError(f'Layer "{layer_name}" expects {len(input_spec)} input(s),'

    ValueError: Layer "sequential" expects 1 input(s), but it received 16200 input tensors. Inputs received: [<tf.Tensor 'IteratorGetNext:0' shape=(None, 100) dtype=float32>, <tf.Tensor 'IteratorGetNext:1' shape=(None, 100) dtype=float32>, <tf.Tensor 'IteratorGetNext:2' shape=(None, 100) dtype=float32>, <tf.Tensor 'IteratorGetNext:3' shape=(None, 100) dtype=float32>, <tf.Tensor 'IteratorGetNext:4' shape=(None, 100) dtype=float32>, <tf.Tensor 'IteratorGetNext:5' shape=(None, 100) dtype=float32>, <tf.Tensor

And the Code is:

print(np.shape(X_train))
print(np.shape(y_train))
model = Sequential()
model.add(ConvLSTM2D(filters = 64, 
            kernel_size = (3, 3), 
            return_sequences = False, 
            data_format = "channels_last", 
            input_shape = (numberFrames, img_height, img_width, 1)
            )
        )
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(256, activation="relu"))
model.add(Dropout(0.3))
model.add(Dense(np.shape(y_train)[1], activation = "softmax"))
 
model.summary()
 
opt = tf.keras.optimizers.SGD(learning_rate=0.001)
model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=["accuracy"])
 
earlystop = EarlyStopping(patience=7)   
callbacks = [earlystop]

history = model.fit(x = X_train, y = y_train, epochs=40, batch_size = 8 , shuffle=False, validation_split=0.2, callbacks=callbacks)