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161
DATA_FASHION_MNIST/convert.py
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# MIT License
# Copyright 2022 University of Bremen
#
# Permission is hereby granted, free of charge, to any person obtaining
# a copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included
# in all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
# OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR
# THE USE OR OTHER DEALINGS IN THE SOFTWARE.
#
#
# David Rotermund ( davrot@uni-bremen.de )
#
#
# Release history:
# ================
# 1.0.0 -- 01.05.2022: first release
#
#
import numpy as np
# [offset] [type] [value] [description]
# 0000 32 bit integer 0x00000801(2049) magic number (MSB first)
# 0004 32 bit integer 60000 number of items
# 0008 unsigned byte ?? label
# 0009 unsigned byte ?? label
# ........
# xxxx unsigned byte ?? label
# The labels values are 0 to 9.
class ReadLabel:
"""Class for reading the labels from an MNIST label file"""
def __init__(self, filename):
self.filename: str = filename
self.data = self.read_from_file(filename)
def read_from_file(self, filename):
int32_data = np.dtype(np.uint32)
int32_data = int32_data.newbyteorder(">")
file = open(filename, "rb")
magic_flag = np.frombuffer(file.read(4), int32_data)[0]
if magic_flag != 2049:
data = np.zeros(0)
number_of_elements = 0
else:
number_of_elements = np.frombuffer(file.read(4), int32_data)[0]
if number_of_elements < 1:
data = np.zeros(0)
else:
data = np.frombuffer(file.read(number_of_elements), dtype=np.uint8)
file.close()
return data
# [offset] [type] [value] [description]
# 0000 32 bit integer 0x00000803(2051) magic number
# 0004 32 bit integer 60000 number of images
# 0008 32 bit integer 28 number of rows
# 0012 32 bit integer 28 number of columns
# 0016 unsigned byte ?? pixel
# 0017 unsigned byte ?? pixel
# ........
# xxxx unsigned byte ?? pixel
# Pixels are organized row-wise.
# Pixel values are 0 to 255. 0 means background (white), 255 means foreground (black).
class ReadPicture:
"""Class for reading the images from an MNIST image file"""
def __init__(self, filename):
self.filename: str = filename
self.data = self.read_from_file(filename)
def read_from_file(self, filename):
int32_data = np.dtype(np.uint32)
int32_data = int32_data.newbyteorder(">")
file = open(filename, "rb")
magic_flag = np.frombuffer(file.read(4), int32_data)[0]
if magic_flag != 2051:
data = np.zeros(0)
number_of_elements = 0
else:
number_of_elements = np.frombuffer(file.read(4), int32_data)[0]
if number_of_elements < 1:
data = np.zeros(0)
number_of_rows = 0
else:
number_of_rows = np.frombuffer(file.read(4), int32_data)[0]
if number_of_rows != 28:
data = np.zeros(0)
number_of_columns = 0
else:
number_of_columns = np.frombuffer(file.read(4), int32_data)[0]
if number_of_columns != 28:
data = np.zeros(0)
else:
data = np.frombuffer(
file.read(number_of_elements * number_of_rows * number_of_columns),
dtype=np.uint8,
)
data = data.reshape(number_of_elements, number_of_columns, number_of_rows)
file.close()
return data
def proprocess_data_set(test_mode):
if test_mode is True:
filename_out_pattern: str = "TestPatternStorage.npy"
filename_out_label: str = "TestLabelStorage.npy"
filename_in_image: str = "t10k-images-idx3-ubyte"
filename_in_label = "t10k-labels-idx1-ubyte"
else:
filename_out_pattern = "TrainPatternStorage.npy"
filename_out_label = "TrainLabelStorage.npy"
filename_in_image = "train-images-idx3-ubyte"
filename_in_label = "train-labels-idx1-ubyte"
pictures = ReadPicture(filename_in_image)
labels = ReadLabel(filename_in_label)
# Down to 0 ... 1.0
max_value = np.max(pictures.data.astype(np.float32))
d = np.float32(pictures.data.astype(np.float32) / max_value)
label_storage = np.uint64(labels.data)
pattern_storage = d.astype(np.float32)
np.save(filename_out_pattern, pattern_storage)
np.save(filename_out_label, label_storage)
proprocess_data_set(True)
proprocess_data_set(False)

8
DATA_FASHION_MNIST/data_url.txt
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https://github.com/zalandoresearch/fashion-mnist
We need:
t10k-images-idx3-ubyte.gz t10k-labels-idx1-ubyte.gz train-images-idx3-ubyte.gz train-labels-idx1-ubyte.gz
Then
gzip -d *.gz
python convert.py