Cours

public class LectureImage {
    public static void main(String[] args) {
            String path="/Users/allauzen/cours/l2/VA/tps/mnist/";
            String labelDB=path+"train-labels-idx1-ubyte";
            String imageDB=path+"train-images-idx3-ubyte";
            // Database creation
            MnistReader db = new MnistReader(labelDB, imageDB);
            // Acces to the first image 
            int idx = 1; // one variable to store the image index
                        // Warning: index starts at 1. 
            int [][] image = db.getImage(idx);
            // Acces to the corresponding label 
            int label = db.getLabel(idx);
            // Display the label
            System.out.println("Le label est "+ label);
            // Print the total number of images.
            System.out.println("Le total est "+ db.getTotalImages());       
            /*  Here you can write your own code*/
        }

public static float[] image2Array(int[][] image){
        float[] x  = new float[image.length*image[0].length+1];
        x[0] = 1;
        for (int i = 0; i < image.length; i++) {
            for (int j = 0; j < image[0].length; j++) {
                int k = image.length*i+j+1;
                x[k]=image[i][j];
            }
        }
        return x;
    }

import cPickle, gzip, numpy
# Load the dataset
f = gzip.open('mnist.pkl.gz', 'rb') 
train_set, valid_set, test_set = cPickle.load(f) 
f.close()

import pickle, gzip, numpy
# Load the dataset
f = gzip.open('./mnist.pkl.gz', 'rb')
u = pickle._Unpickler(f)
u.encoding = 'latin1'
p = u.load()
train_set, valid_set, test_set = p

im = train_set[0][0]      # the first image 
im = numpy.append([1.],im) # add one component 
label = train_set[1][0]  # its label 
numpy.dot(w,im)            # the dot product of the image with a 
                           #vector  of the same size.

import matplotlib.pyplot as plt 
im = train_set[0][0].reshape(28,28) 
plt.show()
plt.imshow(im.reshape(28,28) , plt.cm.gray)