Problem in creating a Numpy Dataset for Classification problem

I am trying to train a classification Model to classify Images, but my images here are Numpy arrays of 1’s and 0’s as shown in the Image, For now, I have tried, x1_data=[] def create_array(columns=5,rows=5,randomness=.3): board = np.zeros([rows,columns],dtype=’int64′) for i in range(rows): for j in range(columns): if np.random.random() <= randomness: board[i,j] = 1 return board…

How to compute number of weights of CNN?

How can we compute number of weights considering a convolutional neural network that is used to classify images into two classes : INPUT: 100×100 gray-scale images. LAYER 1: Convolutional layer with 60 7×7 convolutional filters (stride=1, valid padding). LAYER 2: Convolutional layer with 100 5×5 convolutional filters (stride=1, valid padding). LAYER 3: A max pooling…

How can we prove that an autoassociator network will continue to perform if we zero the diagonal elements of a weight matrix?

How can I we prove that an autoassociator network will continue to perform if we zero the diagonal elements of a weight matrix that has been determined by the Hebb rule. In other words, suppose that the weight matrix is determined from: $W = PP^T- QI$ where $Q$ is the number of prototype vectors. Hint…

Why cannot an AI agent adjust the reward function directly?

In standard Reinforcement Learning the reward function is specified by an AI designer and is external to the AI agent. The agent attempts to find a behaviour that collects higher cumulative discounted reward. In Evolutionary Reinforcement Learning the reward function is specified by the agent’s genetic code and evolved in simulated Darwinian evolution over multiple…