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…