In [4]:

    

import sys, os
sys.path.append(os.pardir)
from dataset.mnist import load_mnist

(x_train, t_train), (x_test, t_test) = load_mnist(flatten=True, normalize=True)

print(x_train.shape)
print(t_train.shape)
print(x_test.shape)
print(t_test.shape)


    

    




(60000, 784)
(60000,)
(10000, 784)
(10000,)

In [18]:

    

import sys, os
sys.path.append(os.pardir)
import numpy as np
from dataset.mnist import load_mnist
from PIL import Image

def img_show(img):
    pil_img = Image.fromarray(np.uint8(img))
    pil_img.show()
    
(x_train, t_train), (x_test, t_test) = load_mnist(flatten=True, normalize=False)

img = x_train[0]
label = t_train[0]
print(label)

print(img.shape)
img = img.reshape(28, 28)
print(img.shape)

img_show(img)


    

    




5
(784,)
(28, 28)

In [5]:

    

import sys, os
sys.path.append(os.pardir)
import numpy as np
from dataset.mnist import load_mnist
from PIL import Image
import pickle

def sigmoid(x):
    return 1/ (1 + np.exp(-x))

def softmax(a):
    c = np.max(a)
    exp_a = np.exp(a - c)
    sum_exp_a = np.sum(exp_a)
    y = exp_a / sum_exp_a
    return y

def get_data():
    (x_train, t_train), (x_test, t_test) = load_mnist(flatten=True, normalize=True, one_hot_label=False)
    return x_test, t_test

def init_network():
    with open("sample_weight.pkl", "rb") as f:
        network = pickle.load(f)
    return network

def predict(network, x):
    W1, W2, W3 = network['W1'], network['W2'], network['W3']
    b1, b2, b3 = network['b1'], network['b2'], network['b3']
    
    a1 = np.dot(x, W1) + b1
    z1 = sigmoid(a1)
    a2 = np.dot(z1, W2) + b2
    z2 = sigmoid(a2)
    a3 = np.dot(z2, W3) + b3
    y = softmax(a3)
    
    return y

x, t = get_data()
network = init_network()

accuracy_cnt = 0
for i in range(len(x)):
    y = predict(network, x[i])
    p = np.argmax(y) # 最も確率の高い要素のインデックスを取得
    if p == t[i]:
        accuracy_cnt += 1

print("Accurancy:" + str(float(accuracy_cnt) / len(x)))


    

    




Accurancy:0.9352

In [7]:

    

x, _ = get_data()
network = init_network()
W1, W2, W3 = network['W1'], network['W2'], network['W3']

print(x.shape)
print(x[0].shape)
print(W1.shape)
print(W2.shape)
print(W3.shape)


    

    




(10000, 784)
(784,)
(784, 50)
(50, 100)
(100, 10)

In [8]:

    

# batch
x, t = get_data()
network = init_network()

batch_size = 100
accuracy_cnt = 0

for i in range(0, len(x), batch_size):
    x_batch = x[i:i+batch_size]
    y_batch = predict(network, x_batch)
    p = np.argmax(y_batch, axis=1)
    accuracy_cnt += np.sum(p == t[i:i+batch_size])
    
print("Accurancy:" + str(float(accuracy_cnt) / len(x)))


    

    




Accurancy:0.9352