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Could you post the pseudocode of your backpropagation algorithm? I recommend you start off as simple as possible (this includes your cost f(x), I would simply use Yexpected-Youtput) and see if it works and then continue adding things. If it's your first time with neural networks, I recommend you check this link out and you could also try practising the ...


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If you look at the words in your dictionary (vocab) before/after pruning, most likely you'd see there isn't a lot of difference, not so much to affect your model performance. In fact, creating a dictionary and model training are two more or less indpendent processes. To make your life easier, you could find the largest dev set you can find for building ...


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What you are trying to achieve, is a game that learns to play flappy bird. For doing this you need a neural network AND a genetic algorithm, those two things work together. About your concerns on the output, you don't have to know if the action will benefit or not, i will soon explain why. The neural network part So, what you need is to know how to build a ...


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I had recently used a slightly unorthodox method to process such images, which involved using RNNs. Assume the image dimensions to be (16000, 120, 16) = (width, height, channels), as in the question. Apply a 2D convolution (or multiple such convolutions) of shape(1, k, c), such that the output of the convolutions becomes (16000, 1, c). So if you only use a ...


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Firstly, the key of implementing a good genetic algorithm like the NEAT one is fitness. Now fitness is everything, it tells basically what your snakes will learn. If you have a bad fitness function, your AIs will target the wrong goal. You shouldn't give fitness when a snake is aligning with food, because that's not what you want. What you really want your ...


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Each node is a position in the arrays values = value of the node conn = indexes of connected nodes If its an undirected graph, each node must have all the nodes to which they are attached. Instead, in directed graphs, only the start node has the index. For your image: values = ['A','B','C','D','E'] conn = [[1,2,3],[0,4],[0,3,4],[0,2,3],[1,3]] Example = '...


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Look, your code says your network has many outputs. Look at the two lines below. This two lines says the output depends on the dimension of np.dot(w, inputs). In your case it's 4 diminutional vector. And in the last line you are assigning them as output. You can write self.output = sigmoid(np.dot(new_weihts, inputs)) instead of self.output = inputs. Must ...


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I don't think this classify as an NLP problem, there is almost no semantic analysis needed, it is more like a classification problem using categorical features. NLTK is surely valuable if you want to perform some text 'cleaning' or preprocessing before encoding the variables. The only NLP application that I think you could apply here is some sentiment ...


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