A `Concatenate` layer requires inputs with matching shapes except for the concatenation axis. Received: input_shape=[(None, 32, 19, 512), (None, 32, 18, 512)]
时间: 2023-07-02 10:15:55 浏览: 59
这个错误提示是因为在使用 `Concatenate` 层时,输入的张量形状不匹配。具体来说,除了连接轴以外的维度应该是相同的。
根据你的错误提示,你正在尝试连接两个形状分别为 `(None, 32, 19, 512)` 和 `(None, 32, 18, 512)` 的张量,但它们的第三个维度不同。这意味着它们无法沿着第三个维度进行连接。
要解决这个问题,你可以通过调整模型中的某些层的形状来使它们匹配,或者使用 `Reshape` 层来显式调整张量的形状以使它们可以连接。例如,你可以使用 `Reshape` 层将 `(None, 32, 18, 512)` 的张量重塑为 `(None, 32, 19, 512)` 的形状,以便它们可以连接在一起:
```python
from tensorflow.keras.layers import Concatenate, Reshape
input1 = ...
input2 = Reshape((32, 19, 512))(input2_resized)
concatenated = Concatenate(axis=3)([input1, input2])
```
这里假设你已经将第二个输入张量调整为 `(None, 32, 19, 512)` 的形状。然后使用 `Concatenate` 层将两个张量沿着第四个维度连接起来。
相关问题
A `Concatenate` layer requires inputs with matching shapes except for the concatenation axis. Received: input_shape=[(None, 5), (None, 5, 1)]
The `Concatenate` layer in Keras requires inputs with matching shapes along all axes except for the concatenation axis. In the received input shape [(None, 5), (None, 5, 1)], the first input has a shape of (None, 5) and the second input has a shape of (None, 5, 1).
To resolve this issue and make the shapes compatible for concatenation, you can use a `Reshape` layer to reshape the second input from (None, 5, 1) to (None, 5). Here's an example:
```python
from tensorflow.keras.layers import Concatenate, Reshape
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input
input1 = Input(shape=(5,))
input2 = Input(shape=(5, 1))
reshaped_input2 = Reshape((5,), name='reshape')(input2)
concatenated_output = Concatenate()([input1, reshaped_input2])
model = Model(inputs=[input1, input2], outputs=concatenated_output)
```
In this example, the `Reshape` layer is used to reshape the second input from (None, 5, 1) to (None, 5), and then the `Concatenate` layer is used to concatenate the reshaped input with the first input. Now the inputs have matching shapes along all axes, and you can proceed with your model.
Traceback (most recent call last): File "I:\bishe\pythonProject\seeable.py", line 67, in <module> model = UNet() File "I:\bishe\pythonProject\seeable.py", line 28, in UNet merge5 = concatenate([conv3, up5], axis=3) File "I:\bishe\pythonProject\venv\lib\site-packages\keras\layers\merging\concatenate.py", line 231, in concatenate return Concatenate(axis=axis, **kwargs)(inputs) File "I:\bishe\pythonProject\venv\lib\site-packages\keras\utils\traceback_utils.py", line 70, in error_handler raise e.with_traceback(filtered_tb) from None File "I:\bishe\pythonProject\venv\lib\site-packages\keras\layers\merging\concatenate.py", line 131, in build raise ValueError(err_msg) ValueError: A `Concatenate` layer requires inputs with matching shapes except for the concatenation axis. Received: input_shape=[(None, 125, 100, 256), (None, 250, 200, 256)] 报错,解释
这是一个 Python 代码的错误信息。这个错误的原因是 `Concatenate` 层需要具有相同形状的输入,除了在拼接轴上。但是,你的代码中,`Concatenate` 层的输入形状不匹配。具体地说,你传递了两个张量给 `Concatenate` 层,但它们的大小不匹配。第一个输入的形状是 `(None, 125, 100, 256)`,而第二个输入的形状是 `(None, 250, 200, 256)`。因此,`Concatenate` 层无法将它们沿着轴 3 进行拼接。为了解决这个问题,你需要确保将两个输入张量的大小调整为相同的大小。