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The line

d_conv3 = Conv2D(16, (3, 3), activation='relu')(up2)

miss the padding argument. Therefore instead of a 256 X 256 X 16 output you get a 254 X 254 X 16, which becomes a 508 X 508 X 16 after the upsampling, and finaly a 508 X 508 X 3 after the last Conv2D

MSE Error needs to compare two images of the same size, and the input is a 512 X 512 X 3. Juste add the padding argument as you do in the other Conv2D and it should work just fine

d_conv3 = Conv2D(16, (3, 3), activation='relu', padding='same')(up2)

Here's one approach:

You can create a boolean mask for the required condition. For pe1 that would be:

• value in lo1 is smaller or equal to pe1
• value in up1 is larger or equal to pe1
• value in lo1 is larger than 0

This would make this mask:

On state level, class String? addRole; and from Items[] remove ''.

On User Add

First, your code is a bit confusing because in the condition you compare to vec2[5] but in the message you say Vec1[5].

Second, I'm not really sure why do you use std::distance instead of just de-referencing the iterator:

As the error trace indicates, the issue you are facing in trying to work with this autoencoder is that the model is expecting the input shape to be (?, 800, 20, 1) but, the output shape is (?, 796, 20, 1). While it is getting each image input and output as

(800, 20, 1). (Check your model summary's input and output shape!)

1. I have fixed with padding = 'same' and re-adjusted the shapes so that the input tensor shape and the output are the same. Check out the kernel sizes that I have modified to get the output shape as needed.

2. On top of that, for a stacked conv encoder-decoder architecture (such as auto-encoder) it is recommended that you translate the spatial information into feature maps/filters/channels with subsequent layers. Right now you start with 8 filters then move to 4 in the encoder. It should be more like 4->8->16. Check this diagram for reference towards the intuition.

With the above recommendations, I have made the changes here.

There are two cases for your problem.

1. You want to run the second block only if the first if block is true.
2. You want to run the second block either the first block is true or false

First case

I found the answer, fortunately. I forgot to call the superclass of the tensorflow keras model; I just had to add super(MyModel, self).__init__() to my __init__() function.

So my model looks like this now:

It is beacause you used a while loop. Your window is waiting for it to end.

You should use intstead a window.after(time, target) :