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.
X) anumpyarray, when it expected something else (atensor?). You have thetensorflowdocumentation and code, I don't.