你好，我正在尝试将一个旧模型从TF1转换到TF2，但遇到了几个问题。我使用Google Colab在TF1和TF2之间切换，TF1运行一切正常，但在TF2下却不行。我用下面的简短代码复制了这个问题。
from keras.layers import *from keras import Modelfrom keras.backend import squeezedef create_model(): inputA = Input(shape=(1,)) x = Dense(1)(inputA) x = Model(inputs=inputA, outputs=x) print(x.predict([0.1])) inputB = Input(shape=(1,)) y = Dense(1)(inputB) y = Model(inputs=inputB, outputs=y) print(y.predict([0.1])) combined = concatenate(inputs = [x.output,y.output]) model = Model(inputs=[x.input, y.input], outputs=combined) return modelif (__name__ == "__main__") : model = create_model() model.compile(loss='mse',optimizer='RMSprop') model.summary() print(model.predict([[0.1],[0.1]]))这是使用TF2时的错误信息：
AssertionError: in user code: /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:1462 predict_function * return step_function(self, iterator) /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:1452 step_function ** outputs = model.distribute_strategy.run(run_step, args=(data,)) /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:1211 run return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs) /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2585 call_for_each_replica return self._call_for_each_replica(fn, args, kwargs) /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2945 _call_for_each_replica return fn(*args, **kwargs) /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:1445 run_step ** outputs = model.predict_step(data) /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:1418 predict_step return self(x, training=False) /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py:985 __call__ outputs = call_fn(inputs, *args, **kwargs) /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/functional.py:386 call inputs, training=training, mask=mask) /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/functional.py:517 _run_internal_graph assert x_id in tensor_dict, 'Could not compute output ' + str(x) AssertionError: Could not compute output Tensor("concatenate/concat:0", shape=(None, 2), dtype=float32)任何帮助都将不胜感激。
谢谢，V_W
回答：
你可以像这样修改你的代码，
from tf.keras.layers import *from tf.keras import Modeldef create_model(): inputA = Input(shape=(1,)) x = Dense(1)(inputA) modelA = Model(inputs=inputA, outputs=x) print(modelA.predict([0.1])) inputB = Input(shape=(1,)) y = Dense(1)(inputB) modelB = Model(inputs=inputB, outputs=y) print(modelB.predict([0.1])) concat = Concatenate()( [ x , y ] ) model = Model(inputs=[ inputA, inputB ], outputs=concat ) return model