Suppose that we train a neural network to classify images. The inputs are 3-by-3 grayscale images (ie. there are 9 pixels in each image), and the labels contain 10 classes. Connected with the input...

Machine learning


Extracted text: Suppose that we train a neural network to classify images. The inputs are 3-by-3 grayscale images (ie. there are 9 pixels in each image), and the labels contain 10 classes. Connected with the input layer (with the image as a flattened 1-dimensional vector), there are 5 nodes in the fırst hidden layer, 4 in the second hidden layer, and then followed with the softmax output layer. Each node is given an activation function of ReLU(x). How many trainable parameters are there in this neural network?