将下载下来的MNIST手写数字数据集转化成为图片

解析源文件下载(总共包含60000个训练数据和10000个测试数据)

  1. 训练集解析
  2. 测试集解析

对于训练集的代码

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
import numpy as np
import struct

from PIL import Image
import os

data_file = 'MNIST_data\\train-images.idx3-ubyte' # 需要修改的路径
# It's 47040016B, but we should set to 47040000B
data_file_size = 47040016
data_file_size = str(data_file_size - 16) + 'B'

data_buf = open(data_file, 'rb').read()

magic, numImages, numRows, numColumns = struct.unpack_from(
'>IIII', data_buf, 0)
datas = struct.unpack_from(
'>' + data_file_size, data_buf, struct.calcsize('>IIII'))
datas = np.array(datas).astype(np.uint8).reshape(
numImages, 1, numRows, numColumns)

label_file = 'MNIST_data\\train-labels.idx1-ubyte' # 需要修改的路径

# It's 60008B, but we should set to 60000B
label_file_size = 60008
label_file_size = str(label_file_size - 8) + 'B'

label_buf = open(label_file, 'rb').read()

magic, numLabels = struct.unpack_from('>II', label_buf, 0)
labels = struct.unpack_from(
'>' + label_file_size, label_buf, struct.calcsize('>II'))
labels = np.array(labels).astype(np.int64)

datas_root = 'MNIST_data\\' # 需要修改的路径
if not os.path.exists(datas_root):
os.mkdir(datas_root)

for i in range(10):
file_name = datas_root + os.sep + str(i)
if not os.path.exists(file_name):
os.mkdir(file_name)

for ii in range(numLabels):
img = Image.fromarray(datas[ii, 0, 0:28, 0:28])
label = labels[ii]
file_name = datas_root + os.sep + str(label) + os.sep + \
'mnist_train_' + str(ii) + '.png'
img.save(file_name)

对于测试集的代码

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
import numpy as np
import struct

from PIL import Image
import os

data_file = 'MNIST_data\\t10k-images.idx3-ubyte' # 需要修改的路径

# It's 7840016B, but we should set to 7840000B
data_file_size = 7840016
data_file_size = str(data_file_size - 16) + 'B'

data_buf = open(data_file, 'rb').read()

magic, numImages, numRows, numColumns = struct.unpack_from(
'>IIII', data_buf, 0)
datas = struct.unpack_from(
'>' + data_file_size, data_buf, struct.calcsize('>IIII'))
datas = np.array(datas).astype(np.uint8).reshape(
numImages, 1, numRows, numColumns)

label_file = 'MNIST_data\\t10k-labels.idx1-ubyte' # 需要修改的路径

# It's 10008B, but we should set to 10000B
label_file_size = 10008
label_file_size = str(label_file_size - 8) + 'B'

label_buf = open(label_file, 'rb').read()

magic, numLabels = struct.unpack_from('>II', label_buf, 0)
labels = struct.unpack_from(
'>' + label_file_size, label_buf, struct.calcsize('>II'))
labels = np.array(labels).astype(np.int64)

datas_root = 'MNIST_data\\test_dataset' # 需要修改的路径

if not os.path.exists(datas_root):
os.mkdir(datas_root)

for i in range(10):
file_name = datas_root + os.sep + str(i)
if not os.path.exists(file_name):
os.mkdir(file_name)

for ii in range(numLabels):
img = Image.fromarray(datas[ii, 0, 0:28, 0:28])
label = labels[ii]
file_name = datas_root + os.sep + str(label) + os.sep + \
'mnist_test_' + str(ii) + '.png'
img.save(file_name)
打赏了解一下?
0%