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Do both the kernel values and weights in FCC get optimized? Yes. Some of the designs for image processing neural networks prior to CNNs had separate filter processing states. For instance, Sobel filters were popular choices in earlier attempts at machine learning on images, and they can be thought of as fixed CNN-like layers. They may still have a role in ...


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There's a few reasons I can think of, though I have not read an explicit description of why it is done this way. It's likely that people just started doing it this way because it's most logical, and people who have attempted to try your method of having reduced connections have seen a performance hit and so no change was made. The first reason is that if you ...


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Not quite sure about RNN & LSTM (and it always depends on the task), but for CNN the answer is clearly no; CNN routinely include FC layers. Quoting from the highly popular (and recommended) Stanford course CS231n: Convolutional Neural Networks for Visual Recognition: ConvNet Architectures We have seen that Convolutional Networks are commonly made up of ...


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I'm sure the biases are initially initialized to zero but I don't know how the weights are handled. Looking at the Dense layer docs: by default Dense layers biases are initialized with zeros (bias_initializer='zeros') and weights are initialized with Glorot uniform (kernel_initializer='glorot_uniform'). ... "unusual" element to point here; I've ...


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two hidden layers each comprising two neurons From your description it looks like that you only have 6 parameters for your inner layer (2x2 weight matrix + 2 biases). The whole network should be easy to interpret: you've got two 13-dimensional weight vectors $\vec{w}_1,\vec{w}_2$ that are dot-multiplied with the inputs, plus two biases $b$ and activation $\...


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Bias is one of the hyperparameters in neural networks, which let you shift activation function. Disabling bias means setting bias to be zero. Even though, in many cases, bias is a big help for successful learning, in some cases, you may want to add an extra constraint to your neural network in finding the objective function. For example, in the paper below, ...


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