def test10RandomImgs():
# 读取测试集结果
dl = DataLoader('MNIST', './datasets/MNIST/')
test_images, test_labels = dl.Load('test')
# 生成10个连续的随机数
idx = np.random.randint(0, test_images.shape[0] - 10)
random_image = test_images[idx:idx + 10, :]
# testlabel = nn.test(random_image.T)
MNIST_test = NN()
MNIST_test.loadParams()
out = MNIST_test.forward(random_image.T - 33.318421, 'test')
exp_out = np.exp(out)
softmax_out = exp_out / np.sum(exp_out, axis=0)
# 找到最大值索引，作为分类结果
cls_res = np.argmax(softmax_out, axis=0)
# 显示图像
for i in range(0, 10):
plt.subplot(2, 5, i + 1)
plt.title('分类: %d' % cls_res[i])
# 关闭坐标刻度
plt.xticks([])
plt.yticks([])
plt.imshow(np.reshape(random_image[i], [28, 28]), cmap='gray')
plt.show()
def test(mean=33.318421):
# 读取测试数据集，注意：测试时的均值仍然要与训练时均值相同
dl = DataLoader('MNIST', './datasets/MNIST/')
test_images, test_labels = dl.Load('test')
# 预处理 (0 均值化)
test_images -= mean
print('测试数据集数量: %d' % len(test_labels))
# 测试
MNIST_Net = NN()
MNIST_Net.loadParams()
MNIST_Net.valid(test_images.T, test_labels, 100, mode='test')
def train():
# 读取训练数据集
dl = DataLoader('MNIST', './datasets/MNIST/')
train_images, train_labels = dl.Load('train')
# 预处理 (0 均值化)
mean = np.mean(train_images)
print('数据集读取结束，训练数据均值: %f' % mean)
train_images -= mean
print('训练数据集数量: %d' % len(train_labels))
# 训练
startTime = time.time()
# 初始化网络
MNIST_Net = NN()
MNIST_Net.train(momentum=0.9,
learning_rate=0.01,
batchsize=100,
data_loader=dl,
valid_rate=10,
epoch=10,
alpha=0)
endTime = time.time()
print('训练时间：%d' % (endTime - startTime))
