예측 + 미니배치 + 오차

가중치는 책에 있는거 가져와서 씀(chap03)

 

 

 

# coding: utf-8
import sys, os
sys.path.append(os.pardir)  # 부모 디렉터리의 파일을 가져올 수 있도록 설정
import numpy as np
import pickle
from dataset.mnist import load_mnist
from common.functions import sigmoid, softmax

np.max
def get_data():
    (x_train, t_train), (x_test, t_test) = load_mnist(normalize=True, flatten=True, one_hot_label=True)
    #print(len(t_train))
    #print(len(t_test))
    return x_train, t_train


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

def cross_entropy_error(y,t):
    if y.ndim==1: # 인덱스로 뽑았을땐 [[]]꼴이 되지만 for 문으로 돌렸다 치면 []
        t=t.reshape(1,t.size)
        y=y.reshape(1,y.size)
    # 다른 이유가 아니라 / y.shape[0]부분 때문
    # 10으로 나눠주게 되니까

    delta = 1e-7
    print(y.shape)
    print(y.ndim)
    #aa=np.array([1,2])
    print(y)
    print(t)
    return -np.sum(t*np.log(y+delta)) / y.shape[0]

x, t = get_data()
network = init_network()
accuracy_cnt = 0

train_size = x.shape[0] # 6만이겠지
print(train_size)
batch_size = 1
batch_mask = np.random.choice(train_size,batch_size)

x_batch = x[batch_mask]
t_batch = t[batch_mask]

y = predict(network,x_batch)
#print(len(y))
print(batch_mask[0])
print(y[0])#예측한거
print(t[batch_mask[0]])#답

result = cross_entropy_error(y,t_batch)
result = cross_entropy_error(np.array([  1.00422942e-03,   1.34813308e-04,   1.90760253e-03,   9.74361539e-01,
    9.08520860e-07,   1.45274522e-02,   9.53066490e-07,   3.48790345e-04,
    7.43788807e-03,   2.75873637e-04]),np.array([ 0,  0,  0,  1,  0,  0,  0,  0,  0,  0]))
print(result) # 0.0259727314115

aa = np.array([1,2,3,4])
print(aa)
aa = aa.reshape(1,aa.size)
print(aa)
aa = aa.reshape(2,2)
print(aa)