(x_train, y_train), (x_test, y_test) = mnist.load_data()
# 第一个维度是样本数目，第二维度是通道数表示颜色通道数，第三维度是高，第四个维度是宽
x_train = x_train.reshape(x_train.shape[0], 1, 28, 28)
x_test = x_test.reshape(x_test.shape[0], 1, 28, 28)
# 将x_train, x_test的数据格式转为float32
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
# 归一化，将值映射到 0到1区间
x_train /= 255
x_test /= 255
# 因为是10分类，所以将类别向量(从0到10的整数向量)映射为二值类别矩阵，相当于将向量用one-hot重新编码
y_train = np_utils.to_categorical(y_train, 10)
y_test = np_utils.to_categorical(y_test, 10)
# 网络配置文件
config = {
# 参数模型所在路径，不为空框架会加载模型，用于预测或继续训练
"load_model": "AA-1000.model"
}
# 定义模型，传入配置项
AA = AADeepLearning(config=config)
# 使用测试集预测，返回概率分布和准确率， score:样本在各个分类上的概率， accuracy:准确率
score, accuracy = AA.predict(x_test=x_test, y_test=y_test)
print("test set accuracy:", accuracy)
# 第二和第三个维度是高和宽，
# 最后一个维度是通道维，表示颜色通道数
# x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
# x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
# input_shape = (img_rows, img_cols, 1)
# 将x_train, x_test的数据格式转为float32
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
# 归一化，将值映射到 0到1区间
x_train /= 255
x_test /= 255
# 将类别向量(从0到nb_classes的整数向量)映射为二值类别矩阵，
# 相当于将向量用one-hot重新编码
y_train = np_utils.to_categorical(y_train, nb_classes)
y_test = np_utils.to_categorical(y_test, nb_classes)
# 打印出相关信息
print('x_train shape:', x_train.shape)
print('y_train shape:', y_train.shape)
print('x_test shape:', x_test.shape)
print('y_test shape:', y_test.shape)
# 网络配置文件
config = {
# 初始学习率
"learning_rate": 0.001,
# 学习率衰减: 通常设置为 0.99
"learning_rate_decay": 0.9999,
# 优化策略: sgd/momentum/rmsprop