Question

根据Keras中的shared layers documentation，可以创建一个共享层并使用不同的输入形状实例化它。它给出了Conv2D共享层的示例，例如：

a = Input(shape=(32, 32, 3))
b = Input(shape=(64, 64, 3))

conv = Conv2D(16, (3, 3), padding='same')
conved_a = conv(a)

# Only one input so far, the following will work:
assert conv.input_shape == (None, 32, 32, 3)

conved_b = conv(b)
# now the `.input_shape` property wouldn't work, but this does:
assert conv.get_input_shape_at(0) == (None, 32, 32, 3)
assert conv.get_input_shape_at(1) == (None, 64, 64, 3)

我正在使用Dense层尝试相同的操作，但是似乎不起作用。这是我尝试过的方法，但由于输入形状不匹配，似乎出现了错误。我想念什么吗？

tf.keras.backend.clear_session()
dense = Dense(100)
i1 = Input(shape=(10,))
i2 = Input(shape=(200,))
d1 = dense(i1)
d2 = dense(i1)
d3 = dense(i2)

以下是堆栈跟踪：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-40-d3fc6212c6ef> in <module>()
      5 d1 = dense(i1)
      6 d2 = dense(i1)
----> 7 d3 = dense(i2)

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py in __call__(self, inputs, *args, **kwargs)
    751       # Check input assumptions set after layer building, e.g. input shape.
    752       if build_graph or in_deferred_mode:
--> 753         self._assert_input_compatibility(inputs)
    754 
    755       if not in_deferred_mode:

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py in _assert_input_compatibility(self, inputs)
   1511                   ' incompatible with the layer: expected axis ' + str(axis) +
   1512                   ' of input shape to have value ' + str(value) +
-> 1513                   ' but received input with shape ' + str(shape))
   1514       # Check shape.
   1515       if spec.shape is not None:

ValueError: Input 0 of layer dense is incompatible with the layer: expected axis -1 of input shape to have value 10 but received input with shape [None, 200]

Answer 1

在此行的dense上应用i1层时：

d1 = dense(i1)

将构造此密集层的权重，因此在将来，它将期望输入的形状与其权重兼容。这就是为什么在dense上应用i2层后看到以下错误的原因：

expected axis -1 of input shape to have value 10

i1的形状为(10,)，因此dense层将期望形状为(10,)的样本。但是i2的形状为(200,)，因此与dense层的输入不兼容。

将卷积层应用于不同宽度和高度（但通道数相同）的输入的原因仅仅是因为其权重的形状（即卷积核或滤波器）不取决于空间尺寸的输入（但是，这取决于输入中的通道数，这就是在示例中您同时提供a和b都具有3个通道的原因）。

Keras共享层具有不同的输入大小

1 个答案: