def get_data():
# loadin data at first time will take several minutes
(x_train, t_train), (x_test, t_test) = load_mnist(flatten=True, normalize=True, one_hot_label=False)
return x_test, t_test
import sys, os
sys.path.append(os.pardir)
import numpy as np
from master.dataset.mnist import load_mnist
from twolayernet5 import TwoLayerNet
# data load
(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True,
one_hot_label=True)
network = TwoLayerNet(input_size=784, hidden_size=50, output_size=10)
iters_num = 10000
train_size = x_train.shape[0]
batch_size = 100
learning_rate = 0.1
train_loss_list = []
train_acc_list = []
test_acc_list = []
iter_per_epoch = max(train_size / batch_size, 1)
for i in range(iters_num):
batch_mask = np.random.choice(train_size, batch_size)
x_batch = x_train[batch_mask]
t_batch = t_train[batch_mask]
# get gradients by back propagation
grad = network.gradient(x_batch, t_batch)
from PIL import Image
import numpy as np
from master.dataset.mnist import load_mnist
import sys
import os
sys.path.append(os.pardir)
def img_show(img):
pil_img = Image.fromarray(np.uint8(img))
pil_img.show()
# loadin data at first time will take several minutes
(x_train, t_train), (x_test, t_test) = load_mnist(flatten=True,
normalize=False)
img = x_train[0]
label = t_train[0]
print(label) # 5
print(img.shape) # (784, )
img = img.reshape(28, 28) # retransform the shape of the image
print(img.shape) # (28, 28)
img_show(img)
# output the shapes of each data
print(x_train.shape) # (60000, 784)
print(t_train.shape) # (60000, )
print(x_test.shape) # (10000, 784)
print(t_test.shape) # (10000, )