<reponame>kaito0223/shakyou<filename>deep_learning_zero/dataset/neuralnet_mnist.py
# -*- coding: utf-8 -*-
import numpy as np
from mnist import load_mnist
import pickle

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 = pickel.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']


# -*- coding: utf-8 -*-
import numpy as np
from mnist import load_mnist
import pickle

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 = pickel.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']
# -*- coding: utf-8 -*-