使用感知器进行多分类#

import matplotlib.pyplot as plt
import numpy as np
import pickle
import os

def train(positive_examples, negative_examples, num_iterations = 100):
    num_dims = positive_examples.shape[1]
    weights = np.zeros((num_dims,1)) # initialize weights
    
    pos_count = positive_examples.shape[0]
    neg_count = negative_examples.shape[0]
    
    report_frequency = 10
    
    for i in range(num_iterations):
        pos = random.choice(positive_examples)
        neg = random.choice(negative_examples)

        z = np.dot(pos, weights)   
        if z < 0:
            weights = weights + pos.reshape(weights.shape)

        z  = np.dot(neg, weights)
        if z >= 0:
            weights = weights - neg.reshape(weights.shape)
            
        if i % report_frequency == 0:             
            pos_out = np.dot(positive_examples, weights)
            neg_out = np.dot(negative_examples, weights)        
            pos_correct = (pos_out >= 0).sum() / float(pos_count)
            neg_correct = (neg_out < 0).sum() / float(neg_count)
            print("Iteration={}, pos correct={}, neg correct={}".format(i,pos_correct,neg_correct))

    return weights

def accuracy(weights, test_x, test_labels):
    res = np.dot(np.c_[test_x,np.ones(len(test_x))],weights)
    return (res.reshape(test_labels.shape)*test_labels>=0).sum()/float(len(test_labels))

accuracy(wts, test_x, test_labels)

1.0

!rm *.pkl
https://github.com/mnielsen/neural-networks-and-deep-learning/blob/master/data/mnist.pkl.gz!gzip -d mnist.pkl.gz

with open('mnist.pkl', 'rb') as mnist_pickle:
    MNIST = pickle.load(mnist_pickle)

print(MNIST['Train']['Features'][0][130:180])
print(MNIST['Train']['Labels'][0])
features = MNIST['Train']['Features'].astype(np.float32) / 256.0
labels = MNIST['Train']['Labels']
fig = plt.figure(figsize=(10,5))
for i in range(10):
    ax = fig.add_subplot(1,10,i+1)
    plt.imshow(features[i].reshape(28,28))
plt.show()

[  0   0 188 255  94   0   0   0   0   0   0   0   0   0   0   0   0   0
   0   0   0   0   0   0   0   0   0   0   0 191 250 253  93   0   0   0
   0   0   0   0   0   0   0   0   0   0   0   0   0   0]
1

def set_mnist_pos_neg(positive_label, negative_label):
    positive_indices = [i for i, j in enumerate(MNIST['Train']['Labels']) 
                          if j == positive_label]
    negative_indices = [i for i, j in enumerate(MNIST['Train']['Labels']) 
                          if j == negative_label]

    positive_images = MNIST['Train']['Features'][positive_indices]
    negative_images = MNIST['Train']['Features'][negative_indices]

    return positive_images, negative_images