python神经网络案例——CNN卷积神经网络实现mnist手写体识别
转自：https://blog.csdn.net/luanpeng825485697/article/details/79088938
全栈工程师开发手册 （作者：栾鹏）
python教程全解
CNN卷积神经网络的理论教程参考
http://blog.csdn.net/luanpeng825485697/article/details/79009241
我们实现一个MNIST.py文件，专门用来读取手写体文件中的数据。
我们尝试运行一下。读取第0-28个样本的内容。
可以看到打印输出样式类似如下。
import numpy as np #注意python和numpy版本的对应，如果不对应提示出错。
class Loader(object):
def __init__(self, path, count):
self.path = path
self.count = count
def get_file_content(self):
print(self.path)
f = open(self.path, 'rb')
content = f.read()
f.close()
return content
# def to_int(self, byte):
# return struct.unpack('B', byte)[0]
class ImageLoader(Loader):
def get_picture(self, content, index):
start = index * 28 * 28 + 16
picture = []
for i in range(28):
picture.append([])
for j in range(28):
byte1 = content[start + i * 28 + j]
picture[i].append(byte1)
return picture
def get_one_sample(self, picture):
sample = []
for i in range(28):
for j in range(28):
sample.append(picture[i][j])
return sample
def load(self,onerow=False):
content = self.get_file_content()
data_set = []
for index in range(self.count):
onepic =self.get_picture(content, index)
if onerow: onepic = self.get_one_sample(onepic)
data_set.append(onepic)
return data_set
class LabelLoader(Loader):
def load(self):
content = self.get_file_content()
labels = []
for index in range(self.count):
onelabel = content[index + 8]
onelabelvec = self.norm(onelabel)
labels.append(onelabelvec)
return labels
def norm(self, label):
label_vec = []
# label_value = self.to_int(label)
label_value = label
for i in range(10):
if i == label_value:
label_vec.append(1)
else:
label_vec.append(0)
return label_vec
def get_training_data_set(num,onerow=False):
image_loader = ImageLoader('/home/gis/PycharmProjects/cnn/MNIST_data/train-images.idx3-ubyte', num)
label_loader = LabelLoader('/home/gis/PycharmProjects/cnn/MNIST_data/train-labels.idx1-ubyte', num)
return image_loader.load(onerow), label_loader.load()
def get_test_data_set(num,onerow=False):
image_loader = ImageLoader('/home/gis/PycharmProjects/cnn/MNIST_data/t10k-images.idx3-ubyte', num)
label_loader = LabelLoader('/home/gis/PycharmProjects/cnn/MNIST_data/t10k-labels.idx1-ubyte', num)
return image_loader.load(onerow), label_loader.load()
def printimg(onepic):
onepic=onepic.reshape(28,28)
for i in range(28):
for j in range(28):
if onepic[i, j] == 0:
print(' ', end='') # python3支持这种格式，如果是python2不支持print这种格式
else: # print "* "
print('* ', end='') # python3geshi
print('')
if __name__=="__main__":
train_data_set, train_labels = get_training_data_set(100)
train_data_set = np.array(train_data_set)
train_labels = np.array(train_labels)
for i in range(28): #实现输出前28个手写体数字。
onepic = train_data_set[i]
printimg(onepic)
print(train_labels[i].argmax())