AttributeError: 'numpy.ndarray' object has no attribute 'op' - numpy

Question

I am have a time series data and I am trying to build and train an LSTM model over it. I have 1 input and 1 Output corresponding to my model. I am trying to build a Many to Many model where Input length is exactly equal to output length.
The shape of my inputs are
print(np.shape(X))
(1700,70,401)
#(examples, Timestep, Features)
Shape of my output is
print(np.shape(Y_1))
(1700,70,3)
#(examples, Timestep, Features)
Now When I am trying to approach this problem via sequential API everything is running fine.
model = Sequential()
model.add(LSTM(32, input_shape=(70,401), return_sequences=True))
model.add(Dense(3,activation='softmax'))
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.01),loss=tf.keras.losses.CategoricalCrossentropy())
model.fit(X, Y_1, epochs=2,verbose=1)
But When I am approaching it from the functional API approach then it is showing the error
AttributeError: 'numpy.ndarray' object has no attribute 'op'
input_layer = Input(shape=(70,401))
hidden = LSTM(32,return_sequences=True)(input_layer)
output_1 = Dense(3, activation='softmax')(hidden)
# output_2 = Dense(np.shape(Y_2)[2], activation='softmax')(hidden)
model_lstm = Model(inputs=X, outputs = Y_1)
My question is How do I resolve the error?
I can not use the sequential API to solve the problem because I want to use Multiple Outputs to train i.e. I have 2 different outputs on which I want to train(But for the scope of this question let's just assume I have one set of input and one set of output)!!
The Entire error that I am getting is
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-66-df3a5a1656f0> in <module>
----> 1 model_lstm = Model(X, Y_1)
/root/anaconda3/envs/TensorPy36/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training.py in __init__(self, *args, **kwargs)
144
145 def __init__(self, *args, **kwargs):
--> 146 super(Model, self).__init__(*args, **kwargs)
147 _keras_api_gauge.get_cell('model').set(True)
148 # initializing _distribution_strategy here since it is possible to call
/root/anaconda3/envs/TensorPy36/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/network.py in __init__(self, *args, **kwargs)
165 'inputs' in kwargs and 'outputs' in kwargs):
166 # Graph network
--> 167 self._init_graph_network(*args, **kwargs)
168 else:
169 # Subclassed network
/root/anaconda3/envs/TensorPy36/lib/python3.7/site-packages/tensorflow_core/python/training/tracking/base.py in _method_wrapper(self, *args, **kwargs)
455 self._self_setattr_tracking = False # pylint: disable=protected-access
456 try:
--> 457 result = method(self, *args, **kwargs)
458 finally:
459 self._self_setattr_tracking = previous_value # pylint: disable=protected-access
/root/anaconda3/envs/TensorPy36/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/network.py in _init_graph_network(self, inputs, outputs, name, **kwargs)
268
269 if any(not hasattr(tensor, '_keras_history') for tensor in self.outputs):
--> 270 base_layer_utils.create_keras_history(self._nested_outputs)
271
272 self._base_init(name=name, **kwargs)
/root/anaconda3/envs/TensorPy36/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/base_layer_utils.py in create_keras_history(tensors)
182 keras_tensors: The Tensors found that came from a Keras Layer.
183 """
--> 184 _, created_layers = _create_keras_history_helper(tensors, set(), [])
185 return created_layers
186
/root/anaconda3/envs/TensorPy36/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/base_layer_utils.py in _create_keras_history_helper(tensors, processed_ops, created_layers)
208 if getattr(tensor, '_keras_history', None) is not None:
209 continue
--> 210 op = tensor.op # The Op that created this Tensor.
211 if op not in processed_ops:
212 # Recursively set `_keras_history`.
AttributeError: 'numpy.ndarray' object has no attribute 'op'
Update
I tried type cast X and Y_1 to the tensor objects as suggested in the comments. It is perfectly working in the case of Sequential API but failing for Fnctional API.
X_tensor = tf.convert_to_tensor(X, dtype=tf.float32)
y_tensor=tf.convert_to_tensor(Y_1, dtype=tf.int32)
model_lstm = Model(X_tensor, y_tensor)
Error
AttributeError: Tensor.op is meaningless when eager execution is enabled.
AttributeError Traceback (most recent call last)
<ipython-input-100-d090ea2b5a90> in <module>
----> 1 model_lstm = Model(X_tensor, y_tensor)
/root/anaconda3/envs/TensorPy36/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training.py in __init__(self, *args, **kwargs)
144
145 def __init__(self, *args, **kwargs):
--> 146 super(Model, self).__init__(*args, **kwargs)
147 _keras_api_gauge.get_cell('model').set(True)
148 # initializing _distribution_strategy here since it is possible to call
/root/anaconda3/envs/TensorPy36/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/network.py in __init__(self, *args, **kwargs)
165 'inputs' in kwargs and 'outputs' in kwargs):
166 # Graph network
--> 167 self._init_graph_network(*args, **kwargs)
168 else:
169 # Subclassed network
/root/anaconda3/envs/TensorPy36/lib/python3.7/site-packages/tensorflow_core/python/training/tracking/base.py in _method_wrapper(self, *args, **kwargs)
455 self._self_setattr_tracking = False # pylint: disable=protected-access
456 try:
--> 457 result = method(self, *args, **kwargs)
458 finally:
459 self._self_setattr_tracking = previous_value # pylint: disable=protected-access
/root/anaconda3/envs/TensorPy36/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/network.py in _init_graph_network(self, inputs, outputs, name, **kwargs)
268
269 if any(not hasattr(tensor, '_keras_history') for tensor in self.outputs):
--> 270 base_layer_utils.create_keras_history(self._nested_outputs)
271
272 self._base_init(name=name, **kwargs)
/root/anaconda3/envs/TensorPy36/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/base_layer_utils.py in create_keras_history(tensors)
182 keras_tensors: The Tensors found that came from a Keras Layer.
183 """
--> 184 _, created_layers = _create_keras_history_helper(tensors, set(), [])
185 return created_layers
186
/root/anaconda3/envs/TensorPy36/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/base_layer_utils.py in _create_keras_history_helper(tensors, processed_ops, created_layers)
208 if getattr(tensor, '_keras_history', None) is not None:
209 continue
--> 210 op = tensor.op # The Op that created this Tensor.
211 if op not in processed_ops:
212 # Recursively set `_keras_history`.
/root/anaconda3/envs/TensorPy36/lib/python3.7/site-packages/tensorflow_core/python/framework/ops.py in op(self)
1078 def op(self):
1079 raise AttributeError(
-> 1080 "Tensor.op is meaningless when eager execution is enabled.")
1081
1082 #property
AttributeError: Tensor.op is meaningless when eager execution is enabled.

Answer 1

I made a mistake in the code itself while executing the Model part of in the functional API version.
model_lstm = Model(inputs=X, outputs = Y_1)
I have given the variables inside the above part of the code only!! While in this part we just define what our model is going to be. Hence Here we will just write what is our input layer that needs to be considered and what will be my output layer here!! Input layer while constructing my model will be input_layer in the code and output layer will be output_1. Hence code should be
model_lstm = Model(inputs=input_layer, outputs = output_1)
and after that we can do
model_lstm.fit(X,Y_1)
This will work perfectly fine now!!