python - ValueError : A `Concatenate` layer requires inputs with matching shapes except for the concat axis. 得到输入形状 : [(None, 523、523、32) 等

Question

我正在尝试使用以下代码使用 tensorflow 连接图层，但出现意外错误。我是 tensorflow 的新手

inp = Input(shape=(1050,1050,3))
x1= layers.Conv2D(16 ,(3,3), activation='relu')(inp)
x1= layers.Conv2D(32,(3,3), activation='relu')(x1)
x1= layers.MaxPooling2D(2,2)(x1)
x2= layers.Conv2D(32,(3,3), activation='relu')(x1)
x2= layers.Conv2D(64,(3,3), activation='relu')(x2)
x2= layers.MaxPooling2D(3,3)(x2)
x3= layers.Conv2D(64,(3,3), activation='relu')(x2)
x3= layers.Conv2D(64,(2,2), activation='relu')(x3)
x3= layers.Conv2D(64,(3,3), activation='relu')(x3)
x3= layers.Dropout(0.2)(x3)
x3= layers.MaxPooling2D(2,2)(x3)
x4= layers.Conv2D(64,(3,3), activation='relu')(x3)
x4= layers.MaxPooling2D(2,2)(x4)
x = layers.Dropout(0.2)(x4)
o = layers.Concatenate(axis=3)([x1, x2, x3, x4, x])
y = layers.Flatten()(o)
y = layers.Dense(1024, activation='relu')(y)
y = layers.Dense(5, activation='softmax')(y) 

model = Model(inp, y)
model.summary()
model.compile(loss='sparse_categorical_crossentropy',optimizer=RMSprop(lr=0.001),metrics=['accuracy'])

主要错误可以在标题中看到
但我提供了回溯错误以供引用
错误是

ValueError                                Traceback (most recent call last)
<ipython-input-12-31a1fcec98a4> in <module>
     14 x4= layers.MaxPooling2D(2,2)(x4)
     15 x = layers.Dropout(0.2)(x4)
---> 16 o = layers.Concatenate(axis=3)([x1, x2, x3, x4, x])
     17 y = layers.Flatten()(o)
     18 y = layers.Dense(1024, activation='relu')(y)

/opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py in __call__(self, inputs, *args, **kwargs)
    589           # Build layer if applicable (if the `build` method has been
    590           # overridden).
--> 591           self._maybe_build(inputs)
    592 
    593           # Wrapping `call` function in autograph to allow for dynamic control

/opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py in _maybe_build(self, inputs)
   1879       # operations.
   1880       with tf_utils.maybe_init_scope(self):
-> 1881         self.build(input_shapes)
   1882     # We must set self.built since user defined build functions are not
   1883     # constrained to set self.built.

/opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/utils/tf_utils.py in wrapper(instance, input_shape)
    293     if input_shape is not None:
    294       input_shape = convert_shapes(input_shape, to_tuples=True)
--> 295     output_shape = fn(instance, input_shape)
    296     # Return shapes from `fn` as TensorShapes.
    297     if output_shape is not None:

/opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/layers/merge.py in build(self, input_shape)
    389                        'inputs with matching shapes '
    390                        'except for the concat axis. '
--> 391                        'Got inputs shapes: %s' % (input_shape))
    392 
    393   def _merge_function(self, inputs):

ValueError: A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got inputs shapes: [(None, 523, 523, 32), (None, 173, 173, 64), (None, 84, 84, 64), (None, 41, 41, 64), (None, 41, 41, 64)]

我已经使用 tensorflow.keras 导入了运行代码所需的所有必要文件

Answer 1

您不能对具有不同尺寸(即高度和宽度)的输入执行连接操作。在您的情况下，您正在尝试执行此操作 layers.Concatenate(axis=3)([x1, x2, x3, x4, x])在哪里

x1 has dimension = (None, 523, 523, 32)
x2 has dimension = (None, 173, 173, 64)
x3 has dimension = (None, 84, 84, 64)
x4 has dimension = (None, 41, 41, 64)
and x has dimension = (None, 41, 41, 64)

发生错误的原因是，所有输入维度，即要连接的高度和宽度都不同。要解决错误，您必须将所有输入都设置为相同的维度，即相同的高度和宽度，这可以通过将图层采样到固定维度来实现。根据您的用例，您可以下采样或上采样以达到所需的维度。

ValueError: A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got inputs shapes: [(None, 523, 523, 32), (None, 173, 173, 64), (None, 84, 84, 64), (None, 41, 41, 64), (None, 41, 41, 64)]

错误状态 layer requires inputs with matching shapes ，这只不过是输入的高度和宽度。