AttributeError: 'numpy.ndarray' object has no attribute 'op'
AttributeError: 'numpy.ndarray' object has no attribute 'op'
我有一个时间序列数据，我正在尝试在其上构建和训练 LSTM 模型。我有 1 个输入和 1 个输出对应于我的模型。我正在尝试建立一个多对多模型，其中输入长度恰好等于输出长度。
我输入的形状是
print(np.shape(X))
(1700,70,401)
#(examples, Timestep, Features)
我输出的形状是
print(np.shape(Y_1))
(1700,70,3)
#(examples, Timestep, Features)
现在，当我尝试通过顺序 API 解决此问题时，一切正常 运行。
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)
但是当我从函数 API 方法接近它时，它显示错误
AttributeError: 'numpy.ndarray' 对象没有属性 '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)
我的问题是如何解决错误？
我不能使用顺序 API 来解决问题，因为我想使用多个输出来训练，即我有 2 个不同的输出要训练（但是对于这个问题的范围，让我们假设我有一组输入和一组输出）！！
我得到的整个错误是
---------------------------------------------------------------------------
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'
更新
我尝试按照评论中的建议将 X 和 Y_1 类型转换为张量对象。它在 Sequential API 的情况下完美工作，但在 Fnctional API.
的情况下失败
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)
错误
AttributeError: Tensor.op 启用急切执行时无意义。
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.
在功能 API 版本中执行模型部分时，我在代码本身中犯了一个错误。
model_lstm = Model(inputs=X, outputs = Y_1)
以上部分代码我只给出了变量！！而在这一部分中，我们只是定义我们的模型将是什么。因此，在这里我们将只写下需要考虑的输入层是什么以及我的输出层是什么！构建模型时的输入层在代码中为 input_layer，输出层为 output_1。因此代码应该是
model_lstm = Model(inputs=input_layer, outputs = output_1)
然后我们可以做
model_lstm.fit(X,Y_1)
现在可以正常工作了！！
我有一个时间序列数据，我正在尝试在其上构建和训练 LSTM 模型。我有 1 个输入和 1 个输出对应于我的模型。我正在尝试建立一个多对多模型，其中输入长度恰好等于输出长度。
我输入的形状是
print(np.shape(X))
(1700,70,401)
#(examples, Timestep, Features)
我输出的形状是
print(np.shape(Y_1))
(1700,70,3)
#(examples, Timestep, Features)
现在，当我尝试通过顺序 API 解决此问题时，一切正常 运行。
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)
但是当我从函数 API 方法接近它时，它显示错误
AttributeError: 'numpy.ndarray' 对象没有属性 '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)
我的问题是如何解决错误？
我不能使用顺序 API 来解决问题，因为我想使用多个输出来训练，即我有 2 个不同的输出要训练（但是对于这个问题的范围，让我们假设我有一组输入和一组输出）！！
我得到的整个错误是
---------------------------------------------------------------------------
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'
更新
我尝试按照评论中的建议将 X 和 Y_1 类型转换为张量对象。它在 Sequential API 的情况下完美工作，但在 Fnctional API.
的情况下失败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)
错误
AttributeError: Tensor.op 启用急切执行时无意义。
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.
在功能 API 版本中执行模型部分时，我在代码本身中犯了一个错误。
model_lstm = Model(inputs=X, outputs = Y_1)
以上部分代码我只给出了变量！！而在这一部分中，我们只是定义我们的模型将是什么。因此，在这里我们将只写下需要考虑的输入层是什么以及我的输出层是什么！构建模型时的输入层在代码中为 input_layer，输出层为 output_1。因此代码应该是
model_lstm = Model(inputs=input_layer, outputs = output_1)
然后我们可以做
model_lstm.fit(X,Y_1)
现在可以正常工作了！！