Good morning everyone,

I’m currently working on implementing de QuickNAT architecture in Python. This model has a decoder path with un-pooling operations. I’m trying to replicate this using UpSampling2D from TensorFlow Keras, but I obtain this message: “A `Concatenate`

layer requires inputs with matching shapes except for the concatenation axis. Received: input_shape=[(None, 54, 34, 64), (None, 55, 35, 64)]” while trying to make the deep conections (concatenate).

My code for the dense blocks is:

conv1 = BatchNormalization()(inputs)

conv1 = Activation(‘relu’)(conv1)

conv1 = Conv2D(64, kernel_size=(5, 5), padding=‘same’)(conv1)

```
conv2 = BatchNormalization()(conv1)
conv2 = Activation('relu')(conv2)
conv2 = Conv2D(64, kernel_size=(5, 5), padding='same')(conv2)
conv3 = BatchNormalization()(conv2)
conv3 = Activation('relu')(conv3)
conv3 = Conv2D(64, kernel_size=(1, 1), padding='same')(conv3)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv3)
```

and, for the decoder part, the code is four times this:

#Decoder

up1 = UpSampling2D(size=(2, 2))(conv13)

merge1 = concatenate([up1, conv12], axis=3)

```
#DenseBlock 1
conv14 = BatchNormalization()(merge1)
conv14 = Activation('relu')(conv14)
conv14 = Conv2D(64, kernel_size=(5, 5), padding='same')(conv14)
conv15 = BatchNormalization()(conv14)
conv15 = Activation('relu')(conv15)
conv15 = Conv2D(64, kernel_size=(5, 5), padding='same')(conv15)
conv16 = BatchNormalization()(conv15)
conv16 = Activation('relu')(conv16)
conv16 = Conv2D(64, kernel_size=(1, 1), padding='same')(conv16)
pool5 = MaxPooling2D(pool_size=(2, 2))(conv16)
```

The error comes from concatenate, of course. However, I don’t know how to proceed. I apologize for any grammatical mistakes and thank you in advance.