Question

我尝试使用以下数据集：http://deeplearning.net/data/mnist/mnist.pkl.gz使python中的脚本能够识别手写数字。

有关此问题和我要实现的算法的更多信息，可以在以下链接中找到：http://neuralnetworksanddeeplearning.com/chap1.html

我已经使用感知器为每个数字实现了分类算法。

import cPickle, gzip
import numpy as np

f = gzip.open('mnist.pkl.gz', 'rb')
train_set, valid_set, test_set = cPickle.load(f)
f.close()

def activation(x):
    if x > 0:
        return 1
    return 0

bias = 0.5
learningRate = 0.01

images = train_set[0]
targets = train_set[1]

weights = np.random.uniform(0,1,(10,784))
for nr in range(0,10):
    for i in range(0,49999):
        x = images[i]
        t = targets[i]
        z = np.dot(weights[nr],x) + bias
        output = activation(z)
        weights[nr] = weights[nr] + (t - output) * x * learningRate
        bias = bias + (t - output) * learningRate

images = test_set[0]
targets = test_set[1]

OK = 0

for i in range range(0, 10000):
    vec = []
    for j in range(0,10):
        vec.append(np.dot(weights[j],images[i]))
    if np.argmax(vec) == targets[i]:
        OK = OK + 1

print("The network recognized " + str(OK) +'/'+ "10000")

我通常会识别出10％的数字，这意味着我的算法无能为力，与随机算法相同。

即使我知道这个问题很普遍，我也可以在网上轻松找到另一种解决方案，但我仍在要求您帮助我确定代码中的错误。

也许我错误地初始化了learningRate，bias和weights的值。

Answer 1

thanks to @Kevinj22 and the other ones, I was able to solve this problem in the end.

import cPickle, gzip
import numpy as np

f = gzip.open('mnist.pkl.gz', 'rb')
train_set, valid_set, test_set = cPickle.load(f)
f.close()

def activation(x):
    if x > 0:
        return 1
    return 0

learningRate = 0.01

images = train_set[0]
targets = train_set[1]

weights = np.random.uniform(0,1,(10,784))

for nr in range(0,10):
    for i in range(0,50000):
        x = images[i]
        t = targets[i]
        z = np.dot(weights[nr],x)
        output = activation(z)
        if nr == t:
            target = 1
        else:
            target = 0
        adjust = np.multiply((target - output) * learningRate, x)
        weights[nr] = np.add(weights[nr], adjust)

images = test_set[0]
targets = test_set[1]

OK = 0

for i in range(0, 10000):
    vec = []
    for j in range(0,10):
        vec.append(np.dot(weights[j],images[i]))
    if np.argmax(vec) == targets[i]:
        OK = OK + 1

print("The network recognized " + str(OK) +'/'+ "10000")

here is my updated code. I didn't introduce loss computation in my first attempt. I also get rid of bias because I didn't find it useful in my implementation.

I run this piece of code 10 times, with an average accuracy of 88%

MNIST手写数字

1 个答案: