DL之DNN：自定义2层神经网络TwoLayerNet模型(计算梯度两种方法)利用MNIST数据集进行训练、预测
导读
利用python的numpy计算库，进行自定义搭建2层神经网络TwoLayerNet模型。分别利用两种计算梯度两种方法，数值微分计算法和反向传播算法，对MNIST数据集进行训练，输出loss变化曲线，并输出训练集、测试集的预测准确度。经过对比，发现，反向传播算法速度比微分求值算法快的多的多。
目录
输出结果
设计思路
核心代码
相关文章
DL之DNN：自定义2层神经网络TwoLayerNet模型(计算梯度两种方法)利用MNIST数据集进行训练、预测
输出结果
T1、因为采用T1法，半个多小时还没有出现下一个结果，博主果断放弃！采用了T2
T2、
设计思路
核心代码
load_mnist数据集代码
# coding: utf-8try:import urllib.requestexcept ImportError:raise ImportError('You should use Python 3.x')import os.pathimport gzipimport pickleimport osimport numpy as npurl_base = '/exdb/mnist/'key_file = {'train_img':'train-images-idx3-ubyte.gz','train_label':'train-labels-idx1-ubyte.gz','test_img':'t10k-images-idx3-ubyte.gz','test_label':'t10k-labels-idx1-ubyte.gz'}dataset_dir = os.path.dirname(os.path.abspath(__file__))save_file = dataset_dir + "/mnist.pkl"train_num = 60000test_num = 10000img_dim = (1, 28, 28)img_size = 784def _download(file_name):file_path = dataset_dir + "/" + file_nameif os.path.exists(file_path):returnprint("Downloading " + file_name + " ... ")urllib.request.urlretrieve(url_base + file_name, file_path)print("Done")def download_mnist():for v in key_file.values():_download(v)def _load_label(file_name):file_path = dataset_dir + "/" + file_nameprint("Converting " + file_name + " to NumPy Array ...")with gzip.open(file_path, 'rb') as f:labels = np.frombuffer(f.read(), np.uint8, offset=8)print("Done")return labelsdef _load_img(file_name):file_path = dataset_dir + "/" + file_nameprint("Converting " + file_name + " to NumPy Array ...") with gzip.open(file_path, 'rb') as f:data = np.frombuffer(f.read(), np.uint8, offset=16)data = data.reshape(-1, img_size)print("Done")return datadef _convert_numpy():dataset = {}dataset['train_img'] = _load_img(key_file['train_img'])dataset['train_label'] = _load_label(key_file['train_label']) dataset['test_img'] = _load_img(key_file['test_img'])dataset['test_label'] = _load_label(key_file['test_label'])return datasetdef init_mnist():download_mnist()dataset = _convert_numpy()print("Creating pickle file ...")with open(save_file, 'wb') as f:pickle.dump(dataset, f, -1)print("Done!")def _change_one_hot_label(X):T = np.zeros((X.size, 10))for idx, row in enumerate(T):row[X[idx]] = 1return Tdef load_mnist(normalize=True, flatten=True, one_hot_label=False):"""读入MNIST数据集Parameters----------normalize : 将图像的像素值正规化为0.0~1.0one_hot_label : one_hot_label为True的情况下，标签作为one-hot数组返回one-hot数组是指[0,0,1,0,0,0,0,0,0,0]这样的数组flatten : 是否将图像展开为一维数组Returns-------(训练图像, 训练标签), (测试图像, 测试标签)"""if not os.path.exists(save_file):init_mnist()with open(save_file, 'rb') as f:dataset = pickle.load(f)if normalize:for key in ('train_img', 'test_img'):dataset[key] = dataset[key].astype(np.float32)dataset[key] /= 255.0if one_hot_label:dataset['train_label'] = _change_one_hot_label(dataset['train_label'])dataset['test_label'] = _change_one_hot_label(dataset['test_label'])if not flatten:for key in ('train_img', 'test_img'):dataset[key] = dataset[key].reshape(-1, 1, 28, 28)return (dataset['train_img'], dataset['train_label']), (dataset['test_img'], dataset['test_label']) if __name__ == '__main__':init_mnist()
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