def load_datasets(show_examples=False): X_train = load_MNIST.load_train_images() y_train = load_MNIST.load_train_labels() X_test = load_MNIST.load_test_images() y_test = load_MNIST.load_test_labels() if show_examples is True: sample = X_train[1, :, :] plt.imshow(sample) plt.show() print('样例的矩阵形式为:\n {}'.format(sample)) return X_train, X_test, y_train, y_test
from sklearn import preprocessing from sklearn.neural_network import MLPClassifier import time import sys import os sys.path.append(os.path.join(os.getcwd(), 'Modules')) import load_MNIST # TODO: 1.载入数据 start = time.time() train_images = load_MNIST.load_train_images() train_labels = load_MNIST.load_train_labels() test_images = load_MNIST.load_test_images() test_labels = load_MNIST.load_test_labels() print("载入数据集共耗时: {:.3f}s".format(time.time() - start)) # TODO: 2.数据划分及预处理I # 标准调整形态的方法 # X_train = train_images.reshape(train_images.shape[0], train_images.shape[1]*train_images.shape[2])/255 # 此处,因为我们已经知道的样本的形态,所以可以直接书写值 X_train = train_images.reshape(60000, 28 * 28) / 255 y_train = train_labels X_test = test_images.reshape(10000, 28 * 28) / 255 y_test = test_labels # 为了提高训练速度,我们只提取10%的样本进行演示 X_train_lite = X_train[0:5999, :]