Delete DATA_FASHION_MNIST directory
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David Rotermund
GitHub
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2 changed files with 0 additions and 169 deletions
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# MIT License
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# Copyright 2022 University of Bremen
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#
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# Permission is hereby granted, free of charge, to any person obtaining
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# a copy of this software and associated documentation files (the "Software"),
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# to deal in the Software without restriction, including without limitation
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# the rights to use, copy, modify, merge, publish, distribute, sublicense,
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# and/or sell copies of the Software, and to permit persons to whom the
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# Software is furnished to do so, subject to the following conditions:
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#
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# The above copyright notice and this permission notice shall be included
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# in all copies or substantial portions of the Software.
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#
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
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# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
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# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
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# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
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# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
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# OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR
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# THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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#
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#
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# David Rotermund ( davrot@uni-bremen.de )
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#
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#
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# Release history:
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# ================
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# 1.0.0 -- 01.05.2022: first release
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#
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#
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import numpy as np
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# [offset] [type] [value] [description]
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# 0000 32 bit integer 0x00000801(2049) magic number (MSB first)
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# 0004 32 bit integer 60000 number of items
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# 0008 unsigned byte ?? label
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# 0009 unsigned byte ?? label
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# ........
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# xxxx unsigned byte ?? label
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# The labels values are 0 to 9.
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class ReadLabel:
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"""Class for reading the labels from an MNIST label file"""
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def __init__(self, filename):
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self.filename: str = filename
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self.data = self.read_from_file(filename)
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def read_from_file(self, filename):
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int32_data = np.dtype(np.uint32)
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int32_data = int32_data.newbyteorder(">")
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file = open(filename, "rb")
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magic_flag = np.frombuffer(file.read(4), int32_data)[0]
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if magic_flag != 2049:
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data = np.zeros(0)
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number_of_elements = 0
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else:
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number_of_elements = np.frombuffer(file.read(4), int32_data)[0]
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if number_of_elements < 1:
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data = np.zeros(0)
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else:
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data = np.frombuffer(file.read(number_of_elements), dtype=np.uint8)
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file.close()
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return data
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# [offset] [type] [value] [description]
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# 0000 32 bit integer 0x00000803(2051) magic number
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# 0004 32 bit integer 60000 number of images
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# 0008 32 bit integer 28 number of rows
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# 0012 32 bit integer 28 number of columns
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# 0016 unsigned byte ?? pixel
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# 0017 unsigned byte ?? pixel
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# ........
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# xxxx unsigned byte ?? pixel
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# Pixels are organized row-wise.
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# Pixel values are 0 to 255. 0 means background (white), 255 means foreground (black).
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class ReadPicture:
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"""Class for reading the images from an MNIST image file"""
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def __init__(self, filename):
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self.filename: str = filename
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self.data = self.read_from_file(filename)
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def read_from_file(self, filename):
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int32_data = np.dtype(np.uint32)
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int32_data = int32_data.newbyteorder(">")
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file = open(filename, "rb")
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magic_flag = np.frombuffer(file.read(4), int32_data)[0]
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if magic_flag != 2051:
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data = np.zeros(0)
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number_of_elements = 0
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else:
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number_of_elements = np.frombuffer(file.read(4), int32_data)[0]
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if number_of_elements < 1:
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data = np.zeros(0)
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number_of_rows = 0
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else:
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number_of_rows = np.frombuffer(file.read(4), int32_data)[0]
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if number_of_rows != 28:
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data = np.zeros(0)
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number_of_columns = 0
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else:
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number_of_columns = np.frombuffer(file.read(4), int32_data)[0]
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if number_of_columns != 28:
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data = np.zeros(0)
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else:
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data = np.frombuffer(
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file.read(number_of_elements * number_of_rows * number_of_columns),
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dtype=np.uint8,
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)
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data = data.reshape(number_of_elements, number_of_columns, number_of_rows)
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file.close()
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return data
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def proprocess_data_set(test_mode):
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if test_mode is True:
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filename_out_pattern: str = "TestPatternStorage.npy"
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filename_out_label: str = "TestLabelStorage.npy"
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filename_in_image: str = "t10k-images-idx3-ubyte"
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filename_in_label = "t10k-labels-idx1-ubyte"
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else:
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filename_out_pattern = "TrainPatternStorage.npy"
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filename_out_label = "TrainLabelStorage.npy"
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filename_in_image = "train-images-idx3-ubyte"
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filename_in_label = "train-labels-idx1-ubyte"
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pictures = ReadPicture(filename_in_image)
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labels = ReadLabel(filename_in_label)
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# Down to 0 ... 1.0
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max_value = np.max(pictures.data.astype(np.float32))
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d = np.float32(pictures.data.astype(np.float32) / max_value)
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label_storage = np.uint64(labels.data)
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pattern_storage = d.astype(np.float32)
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np.save(filename_out_pattern, pattern_storage)
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np.save(filename_out_label, label_storage)
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proprocess_data_set(True)
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proprocess_data_set(False)
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@ -1,8 +0,0 @@
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https://github.com/zalandoresearch/fashion-mnist
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We need:
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t10k-images-idx3-ubyte.gz t10k-labels-idx1-ubyte.gz train-images-idx3-ubyte.gz train-labels-idx1-ubyte.gz
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Then
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gzip -d *.gz
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python convert.py
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