I have a question regarding CNN. I do understand how they work on the surface. Very simply put, they are D-NN for images.
I'll use this example as a reference for this question. In the example, they are not initializing weights and biases anywhere. They are using
tf.layers.conv2d() function in the example.
Let's focus only on 2 of the function's arguments:
filters: specify the number of filters to apply
kernel_size: specifies the size of each filter
We are not defining these filters. Does tensorflow define them for us or in general if I were to use this for a different set of images how does this work?
Does this filter include both weights and biases? If not, then what exactly does the
GradientDescentOptimizer()defined in the above example update after each training step?
I understood the code and understand how the entire process works. I also understand convolution and how it works. But, I'm trying to apply these concepts and implement my own code using tensorflow and CNN and I'm kind of stuck here.