I’m trying to make a UNet based Autoencoder model to De-Blur images. I defined the model as follows:

```
def conv_operation(x, filters, kernel_size, strides=2):
x = Conv2D(filters=filters,
kernel_size=kernel_size,
strides=strides,
padding='same')(x)
x = BatchNormalization()(x)
x = ReLU()(x)
return x
def conv_transpose_operation(x, filters, kernel_size):
x = Conv2DTranspose(filters=filters,
kernel_size=kernel_size,
strides=2,
padding='same')(x)
x = BatchNormalization()(x)
x = ReLU()(x)
return x
def deblurring_autoencoder():
dae_inputs = Input(shape=(200,200, 3), name='dae_input')
conv_block1 = conv_operation(dae_inputs, 32, 3)
conv_block2 = conv_operation(conv_block1, 64, 3)
conv_block3 = conv_operation(conv_block2, 128, 3)
conv_block4 = conv_operation(conv_block3, 256, 3)
conv_block5 = conv_operation(conv_block4, 256, 3, 1)
deconv_block1 = conv_transpose_operation(conv_block5, 256,3)
merge1 = Concatenate()([conv_block3,deconv_block1])
deconv_block2 = conv_transpose_operation(merge1, 128, 2)
merge2 = Concatenate()([deconv_block2, conv_block2])
deconv_block3 = conv_transpose_operation(merge2, 64, 3)
merge3 = Concatenate()([deconv_block3, conv_block1])
deconv_block4 = conv_transpose_operation(merge3, 32, 3)
final_deconv = Conv2DTranspose(filters=3, kernel_size=3)(deconv_block4)
dae_outputs = Activation('sigmoid', name='dae_output')(final_deconv)
return Model(dae_inputs, dae_outputs, name='dae')
```

After these definitons, I try to make the model like follows:

```
model= deblurring_autoencoder()
```

When I run the above line, I get a long error that basically tells me that my code is breaking at the line:

```
merge1 = Concatenate()([conv_block3,deconv_block1])
```

due to a dimensionality error. The error says:

```
A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got inputs shapes: [(None, 25, 25, 128), (None, 26, 26, 256)]
```

I tried to manually check all the dimensions after each convolution and they seem to fit perfectly for me. One things I noticed is that whenever i take the input shape in the `Input()`

function as (32,32,3) or (64,64,3) or (128,128,3), etc, I get no errors.

How can I resolve this?

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Author: Aryan Sethi