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The first formula you quote is for an image with one input channel and one output channel, it just focuses on height and width. In this case, if we consider a 5x5 convolution, the Kernel will just have size 5x5, $m$ and $n$ and going from -2 to +2. Now if our input has 3 channels (RGB, but could be feature maps). we need to use each channel as an input, and ...

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No, your understanding is not correct. Each of the 64 filters of the second layer will be applied to each of the 32 channels from the output of the first layer, resulting in 64 channels in the output of the second layer. When the input of a convolutional layer has multiple channels, the convolution filter itself has the same number of channels. In your ...

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About Pre-trained model : This is a very common practise (especially in image recognition) and here is how we use it. Let's imagine you want to recognize different types of food (beef, pork, vegetables, ...). You know some networks already exist that recognize all types of objects (boats, cars, food, sofas, ...). This objective of transfer learning is to use ...

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You may try Keras DepthwiseConv2D layer Depthwise Separable convolutions consist of performing just the first step in a depthwise spatial convolution (which acts on each input channel separately). The depth_multiplier argument controls how many output channels are generated per input channel in the depthwise step. It will convolute each Channel separately. ...

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For padding in CNN's there is a useful answer in this link:https://stats.stackexchange.com/questions/246512/convolutional-layers-to-pad-or-not-to-pad In Q-learning an action is taken in current state, and a next state is obtained. Then state-action value of the current state-action pair is updated by using best state-action value of obtained next state. If ...

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Transpose in mathematics means to change the order of matrix in an opposite way, the same notion carries here but not the exact sense, you are talking about. The same problem exists with the word 'convolution', it means something else in mathematics. What is done in deep learning in name of convolution is cross-correlation in mathematics.

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One way is to pad the images while training. That is to say, while training, Keras will expect all tensors in a batch to be of the same size. However, while inference, if you use only a single image, it can be of any size. So what you can do while training is to pad your 100 x 100 images so that their new dimension after padding becomes 240 x 360. You can ...

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