{
	"name": "NotImplementedError",
	"message": "Iterating over a symbolic KerasTensor is not supported.",
	"stack": "---------------------------------------------------------------------------
NotImplementedError                       Traceback (most recent call last)
Cell In[33], line 5
      2 batch_size = 128
      4 # Instantiate the model
----> 5 model = build_model()
      7 model.summary()

Cell In[32], line 10, in build_model()
      6 input_shape = (6427,)
      8 inputs = Input(shape=input_shape) 
---> 10 model = Sequential([
     11 Dense(6427, activation='relu', input_shape = inputs),
     12 Dropout(0.25),
     13 Dense(3200, activation='relu'),
     14 Dropout(0.20),
     15 Dense(1800, activation='relu'),
     16 Dropout(0.15),
     17 Dense(1024, activation='relu'),
     18 Dropout(0.10),
     19 Dense(512, activation='relu'),
     20 Dropout(0.05),
     21 Dense(256, activation='relu'),
     22 Dense(128, activation='relu'),
     23 Dense(64, activation='relu'),
     24 Dense(32, activation='relu') 
     25 ])
     27 model.compile(optimizer='adam',
     28               loss='mse',  # Use Mean Squared Error for regression
     29               metrics=['mae'])  # Mean Absolute Error as an additional metric
     30 return model

File c:\\Users\\arisa\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\keras\\src\\models\\sequential.py:73, in Sequential.__init__(self, layers, trainable, name)
     71 if layers:
     72     for layer in layers:
---> 73         self.add(layer, rebuild=False)
     74     self._maybe_rebuild()

File c:\\Users\\arisa\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\keras\\src\\models\\sequential.py:86, in Sequential.add(self, layer, rebuild)
     84 if not self._layers:
     85     if getattr(layer, \"_input_shape_arg\", None) is not None:
---> 86         self.add(InputLayer(shape=layer._input_shape_arg))
     88 # If we are passed a Keras tensor created by keras.Input(), we
     89 # extract the input layer from its keras history and use that.
     90 if hasattr(layer, \"_keras_history\"):

File c:\\Users\\arisa\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\keras\\src\\layers\\core\\input_layer.py:46, in InputLayer.__init__(self, shape, batch_size, dtype, sparse, batch_shape, input_tensor, name, **kwargs)
     43     raise ValueError(\"You must pass a `shape` argument.\")
     45 if shape is not None:
---> 46     shape = backend.standardize_shape(shape)
     47     batch_shape = (batch_size,) + shape
     48 self.batch_shape = tuple(batch_shape)

File c:\\Users\\arisa\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\keras\\src\\backend\\common\\variables.py:510, in standardize_shape(shape)
    506         if isinstance(shape, tf.TensorShape):
    507             # `tf.TensorShape` may contain `Dimension` objects.
    508             # We need to convert the items in it to either int or `None`
    509             shape = shape.as_list()
--> 510     shape = tuple(shape)
    512 if config.backend() == \"torch\":
    513     # `shape` might be `torch.Size`. We need to convert the items in it to
    514     # either int or `None`
    515     shape = tuple(map(lambda x: int(x) if x is not None else None, shape))

File c:\\Users\\arisa\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\keras\\src\\backend\\common\\keras_tensor.py:120, in KerasTensor.__iter__(self)
    119 def __iter__(self):
--> 120     raise NotImplementedError(
    121         \"Iterating over a symbolic KerasTensor is not supported.\"
    122     )

NotImplementedError: Iterating over a symbolic KerasTensor is not supported."
}