sgd_hw/p4.py

from sklearn import datasets
import numpy as np
from layer import *
import os
import pickle
import matplotlib
import matplotlib.pyplot as plt
import random

matplotlib.use("TkAgg")

PICKLE_DATA_FILENAME = "mnist.pickle"
if not os.path.exists(PICKLE_DATA_FILENAME):
    X, y = datasets.fetch_openml('mnist_784', return_X_y=True, cache=True, as_frame= False)
    with open(PICKLE_DATA_FILENAME,"wb") as file:
        pickle.dump(X,file)
        pickle.dump(y,file)
else:
    with open(PICKLE_DATA_FILENAME,"rb") as file:
        X = pickle.load(file)
        y = pickle.load(file)

i = random.randint(0,len(X) - 1)
#plt.imshow(X[0].reshape(28,28),cmap='gray',interpolation='none')
#plt.show()

#simple normalize
X = X / 255

y = np.array([int(i) for i in y])
Y = np.eye(10)[y]

gen:np.random.Generator = np.random.default_rng()
eta = 0.01

MiniBatchN = 100

weight1 = Variable(gen.normal(0,1,size=(784,10)))
bias1 = Variable(gen.normal(0,1,size=(10)))

accuracy_list = []

for iteration in range(0,100):
    choiced_index = gen.choice(range(0,60000),MiniBatchN)
    input_var = Variable(X[choiced_index])
    U1 = (input_var @ weight1 + bias1)
    J = SoftmaxWithNegativeLogLikelihood(U1,Y[choiced_index])

    J.backprop(np.ones(()))
    #update variable
    weight1 = Variable(weight1.numpy() - (weight1.grad) * eta)
    bias1 = Variable(bias1.numpy() - (bias1.grad) * eta)
    if iteration % 5 == 0:
        print(iteration,'iteration : avg(J) == ',np.average(J.numpy()))
        s = J.softmax_numpy()
        #print(Y[0:1000].shape)
        s = np.round(s)
        confusion = (np.transpose(Y[choiced_index])@s)
        accuracy = np.trace(confusion).sum() / MiniBatchN
        print('accuracy : ',accuracy * 100,'%')
        accuracy_list.append(accuracy)

plt.title("accuracy")
plt.plot(np.linspace(0,len(accuracy_list),len(accuracy_list)),accuracy_list)
plt.show()
plt.title("confusion matrix")
plt.imshow(confusion,cmap='gray')
plt.show()