How many parameters does MNIST have?

(a) Model reduction in a linear model classifying MNIST data using a product of 10 sums of K matrices for various K. The reference model has a total of 7850 trainable parameters.

What is the size of MNIST dataset?

square 28×28 pixel
The MNIST dataset is an acronym that stands for the Modified National Institute of Standards and Technology dataset. It is a dataset of 60,000 small square 28×28 pixel grayscale images of handwritten single digits between 0 and 9.

What is a good accuracy for MNIST?

The MNIST Handwritten Digits dataset is considered as the “Hello World” of Computer Vision. Most standard implementations of neural networks achieve an accuracy of ~(98–99) percent in correctly classifying the handwritten digits.

How many features does MNIST have?

60,000
The MNIST dataset contains 60,000 training cases and 10,000 test cases of handwritten digits (0 to 9). Each digit is normalized and centered in a gray-scale (0 – 255) image with size 28 × 28. Each image consists of 784 pixels that represent the features of the digits.

How many parameters are in a convolutional layer?

In a CNN, each layer has two kinds of parameters : weights and biases.

How many total learn able parameters are present in the model?

Here, there are 15 parameters — 12 weights and 3 biases. There is 1 filter for each input feature map.

What is the pixel size of Cifar 10 image dataset?

60,000 32×32 pixel
CIFAR-10 Photo Classification Dataset The dataset is comprised of 60,000 32×32 pixel color photographs of objects from 10 classes, such as frogs, birds, cats, ships, etc.

What is LeNet model?

LeNet is a convolutional neural network structure proposed by Yann LeCun et al. Convolutional neural networks are a kind of feed-forward neural network whose artificial neurons can respond to a part of the surrounding cells in the coverage range and perform well in large-scale image processing.

What is the number of parameters?

Basically, the number of parameters in a given layer is the count of “learnable” (assuming such a word exists) elements for a filter aka parameters for the filter for that layer. Parameters in general are weights that are learnt during training.