功能API中的Keras展平层？

我想将顺序版本中的keras代码更改为与功能版本相同的代码，如下所示

model = Sequential()
model.add(Flatten(input_shape=(1,) + (52,)))
model.add(Dense(100))
model.add(Activation('relu'))
model.add(Dense(2))
model.add(Activation('linear'))
print(model.summary())

但它有错误

input = Input(shape=(1,) + (52,))
i = Flatten()(input)
h = Dense(100, activation='relu')(i)
o = Dense(2, activation='linear')(h)
model = Model(inputs=i, outputs=o)
model.summary()

---

您的模型定义不正确，模型的输入参数应转到您的输入层，如下所示：

  File "C:\Users\SDS\Anaconda3\lib\site-packages\keras\legacy\interfaces.py", line 91, in wrapper
    return func(*args, **kwargs)
  File "C:\Users\SDS\Anaconda3\lib\site-packages\keras\engine\network.py", line 93, in __init__
    self._init_graph_network(*args, **kwargs)
  File "C:\Users\SDS\Anaconda3\lib\site-packages\keras\engine\network.py", line 237, in _init_graph_network
    self.inputs, self.outputs)
  File "C:\Users\SDS\Anaconda3\lib\site-packages\keras\engine\network.py", line 1430, in _map_graph_network
    str(layers_with_complete_input))
ValueError: Graph disconnected: cannot obtain value for tensor Tensor("input_1:0", shape=(?, 1, 52), dtype=float32) at layer "input_1". The following previous layers were accessed without issue: []

我相信除了输入层，你不能把任何张量作为模型的输入

input = Input(shape=(1,) + (52,))
i = Flatten()(input)
h = Dense(100, activation='relu')(i)
o = Dense(2, activation='linear')(h)
model = Model(inputs=inputs, outputs=o)