From fddb4016b0c6091f2d7cd714c02017013d947832 Mon Sep 17 00:00:00 2001 From: David Rotermund <54365609+davrot@users.noreply.github.com> Date: Thu, 5 Jan 2023 13:21:52 +0100 Subject: [PATCH] Delete DATA_MNIST directory --- .../PyTorch_Non_Spike_Network/Dataset.py | 422 ------------------ .../PyTorch_Non_Spike_Network/Error.png | Bin 20714 -> 0 bytes .../Model_MNIST_A_49.pt.gz | Bin 218416 -> 0 bytes .../PyTorch_Non_Spike_Network/Parameter.py | 164 ------- ...ts.out.tfevents.1651325827.fedora.115860.0 | Bin 8184 -> 0 bytes DATA_MNIST/PyTorch_Non_Spike_Network/info.md | 1 - DATA_MNIST/PyTorch_Non_Spike_Network/plot.py | 31 -- DATA_MNIST/PyTorch_Non_Spike_Network/run.py | 180 -------- DATA_MNIST/convert.py | 161 ------- DATA_MNIST/data_url.txt | 8 - 10 files changed, 967 deletions(-) delete mode 100644 DATA_MNIST/PyTorch_Non_Spike_Network/Dataset.py delete mode 100644 DATA_MNIST/PyTorch_Non_Spike_Network/Error.png delete mode 100644 DATA_MNIST/PyTorch_Non_Spike_Network/Model_MNIST_A_49.pt.gz delete mode 100644 DATA_MNIST/PyTorch_Non_Spike_Network/Parameter.py delete mode 100644 DATA_MNIST/PyTorch_Non_Spike_Network/events.out.tfevents.1651325827.fedora.115860.0 delete mode 100644 DATA_MNIST/PyTorch_Non_Spike_Network/info.md delete mode 100644 DATA_MNIST/PyTorch_Non_Spike_Network/plot.py delete mode 100644 DATA_MNIST/PyTorch_Non_Spike_Network/run.py delete mode 100644 DATA_MNIST/convert.py delete mode 100644 DATA_MNIST/data_url.txt diff --git a/DATA_MNIST/PyTorch_Non_Spike_Network/Dataset.py b/DATA_MNIST/PyTorch_Non_Spike_Network/Dataset.py deleted file mode 100644 index 11f9854..0000000 --- a/DATA_MNIST/PyTorch_Non_Spike_Network/Dataset.py +++ /dev/null @@ -1,422 +0,0 @@ -# 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 -# -# - -from abc import ABC, abstractmethod -import torch -import numpy as np -import torchvision as tv # type: ignore -from Parameter import Config - - -class DatasetMaster(torch.utils.data.Dataset, ABC): - - path_label: str - label_storage: np.ndarray - pattern_storage: np.ndarray - number_of_pattern: int - mean: list[float] - - # Initialize - def __init__( - self, - train: bool = False, - path_pattern: str = "./", - path_label: str = "./", - ) -> None: - super().__init__() - - if train is True: - self.label_storage = np.load(path_label + "/TrainLabelStorage.npy") - else: - self.label_storage = np.load(path_label + "/TestLabelStorage.npy") - - if train is True: - self.pattern_storage = np.load(path_pattern + "/TrainPatternStorage.npy") - else: - self.pattern_storage = np.load(path_pattern + "/TestPatternStorage.npy") - - self.number_of_pattern = self.label_storage.shape[0] - - self.mean = [] - - def __len__(self) -> int: - return self.number_of_pattern - - # Get one pattern at position index - @abstractmethod - def __getitem__(self, index: int) -> tuple[torch.Tensor, int]: - pass - - @abstractmethod - def pattern_filter_test(self, pattern: torch.Tensor, cfg: Config) -> torch.Tensor: - pass - - @abstractmethod - def pattern_filter_train(self, pattern: torch.Tensor, cfg: Config) -> torch.Tensor: - pass - - -class DatasetMNIST(DatasetMaster): - """Contstructor""" - - # Initialize - def __init__( - self, - train: bool = False, - path_pattern: str = "./", - path_label: str = "./", - ) -> None: - super().__init__(train, path_pattern, path_label) - - self.pattern_storage = np.ascontiguousarray( - self.pattern_storage[:, np.newaxis, :, :].astype(dtype=np.float32) - ) - - self.pattern_storage /= np.max(self.pattern_storage) - - mean = self.pattern_storage.mean(3).mean(2).mean(0) - self.mean = [*mean] - - def __getitem__(self, index: int) -> tuple[torch.Tensor, int]: - - image = self.pattern_storage[index, 0:1, :, :] - target = int(self.label_storage[index]) - return torch.tensor(image), target - - def pattern_filter_test(self, pattern: torch.Tensor, cfg: Config) -> torch.Tensor: - """0. The test image comes in - 1. is center cropped - 2. on/off filteres - 3. returned. - - This is a 1 channel version (e.g. one gray channel). - """ - - assert len(cfg.image_statistics.mean) == 1 - assert len(cfg.image_statistics.the_size) == 2 - assert cfg.image_statistics.the_size[0] > 0 - assert cfg.image_statistics.the_size[1] > 0 - - # Transformation chain - my_transforms: torch.nn.Sequential = torch.nn.Sequential( - tv.transforms.CenterCrop(size=cfg.image_statistics.the_size), - ) - scripted_transforms = torch.jit.script(my_transforms) - - # Preprocess the input data - pattern = scripted_transforms(pattern) - - # => On/Off - my_on_off_filter: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[0]) - gray: torch.Tensor = my_on_off_filter( - pattern[:, 0:1, :, :], - ) - - return gray - - def pattern_filter_train(self, pattern: torch.Tensor, cfg: Config) -> torch.Tensor: - """0. The training image comes in - 1. is cropped from a random position - 2. on/off filteres - 3. returned. - - This is a 1 channel version (e.g. one gray channel). - """ - - assert len(cfg.image_statistics.mean) == 1 - assert len(cfg.image_statistics.the_size) == 2 - assert cfg.image_statistics.the_size[0] > 0 - assert cfg.image_statistics.the_size[1] > 0 - - # Transformation chain - my_transforms: torch.nn.Sequential = torch.nn.Sequential( - tv.transforms.RandomCrop(size=cfg.image_statistics.the_size), - ) - scripted_transforms = torch.jit.script(my_transforms) - - # Preprocess the input data - pattern = scripted_transforms(pattern) - - # => On/Off - my_on_off_filter: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[0]) - gray: torch.Tensor = my_on_off_filter( - pattern[:, 0:1, :, :], - ) - - return gray - - -class DatasetFashionMNIST(DatasetMaster): - """Contstructor""" - - # Initialize - def __init__( - self, - train: bool = False, - path_pattern: str = "./", - path_label: str = "./", - ) -> None: - super().__init__(train, path_pattern, path_label) - - self.pattern_storage = np.ascontiguousarray( - self.pattern_storage[:, np.newaxis, :, :].astype(dtype=np.float32) - ) - - self.pattern_storage /= np.max(self.pattern_storage) - - mean = self.pattern_storage.mean(3).mean(2).mean(0) - self.mean = [*mean] - - def __getitem__(self, index: int) -> tuple[torch.Tensor, int]: - - image = self.pattern_storage[index, 0:1, :, :] - target = int(self.label_storage[index]) - return torch.tensor(image), target - - def pattern_filter_test(self, pattern: torch.Tensor, cfg: Config) -> torch.Tensor: - """0. The test image comes in - 1. is center cropped - 2. on/off filteres - 3. returned. - - This is a 1 channel version (e.g. one gray channel). - """ - - assert len(cfg.image_statistics.mean) == 1 - assert len(cfg.image_statistics.the_size) == 2 - assert cfg.image_statistics.the_size[0] > 0 - assert cfg.image_statistics.the_size[1] > 0 - - # Transformation chain - my_transforms: torch.nn.Sequential = torch.nn.Sequential( - tv.transforms.CenterCrop(size=cfg.image_statistics.the_size), - ) - scripted_transforms = torch.jit.script(my_transforms) - - # Preprocess the input data - pattern = scripted_transforms(pattern) - - # => On/Off - my_on_off_filter: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[0]) - gray: torch.Tensor = my_on_off_filter( - pattern[:, 0:1, :, :], - ) - - return gray - - def pattern_filter_train(self, pattern: torch.Tensor, cfg: Config) -> torch.Tensor: - """0. The training image comes in - 1. is cropped from a random position - 2. on/off filteres - 3. returned. - - This is a 1 channel version (e.g. one gray channel). - """ - - assert len(cfg.image_statistics.mean) == 1 - assert len(cfg.image_statistics.the_size) == 2 - assert cfg.image_statistics.the_size[0] > 0 - assert cfg.image_statistics.the_size[1] > 0 - - # Transformation chain - my_transforms: torch.nn.Sequential = torch.nn.Sequential( - tv.transforms.RandomCrop(size=cfg.image_statistics.the_size), - tv.transforms.RandomHorizontalFlip(p=cfg.augmentation.flip_p), - tv.transforms.ColorJitter( - brightness=cfg.augmentation.jitter_brightness, - contrast=cfg.augmentation.jitter_contrast, - saturation=cfg.augmentation.jitter_saturation, - hue=cfg.augmentation.jitter_hue, - ), - ) - scripted_transforms = torch.jit.script(my_transforms) - - # Preprocess the input data - pattern = scripted_transforms(pattern) - - # => On/Off - my_on_off_filter: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[0]) - gray: torch.Tensor = my_on_off_filter( - pattern[:, 0:1, :, :], - ) - - return gray - - -class DatasetCIFAR(DatasetMaster): - """Contstructor""" - - # Initialize - def __init__( - self, - train: bool = False, - path_pattern: str = "./", - path_label: str = "./", - ) -> None: - super().__init__(train, path_pattern, path_label) - - self.pattern_storage = np.ascontiguousarray( - np.moveaxis(self.pattern_storage.astype(dtype=np.float32), 3, 1) - ) - self.pattern_storage /= np.max(self.pattern_storage) - - mean = self.pattern_storage.mean(3).mean(2).mean(0) - self.mean = [*mean] - - def __getitem__(self, index: int) -> tuple[torch.Tensor, int]: - - image = self.pattern_storage[index, :, :, :] - target = int(self.label_storage[index]) - return torch.tensor(image), target - - def pattern_filter_test(self, pattern: torch.Tensor, cfg: Config) -> torch.Tensor: - """0. The test image comes in - 1. is center cropped - 2. on/off filteres - 3. returned. - - This is a 3 channel version (e.g. r,g,b channels). - """ - - assert len(cfg.image_statistics.mean) == 3 - assert len(cfg.image_statistics.the_size) == 2 - assert cfg.image_statistics.the_size[0] > 0 - assert cfg.image_statistics.the_size[1] > 0 - - # Transformation chain - my_transforms: torch.nn.Sequential = torch.nn.Sequential( - tv.transforms.CenterCrop(size=cfg.image_statistics.the_size), - ) - scripted_transforms = torch.jit.script(my_transforms) - - # Preprocess the input data - pattern = scripted_transforms(pattern) - - # => On/Off - - my_on_off_filter_r: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[0]) - my_on_off_filter_g: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[1]) - my_on_off_filter_b: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[2]) - r: torch.Tensor = my_on_off_filter_r( - pattern[:, 0:1, :, :], - ) - g: torch.Tensor = my_on_off_filter_g( - pattern[:, 1:2, :, :], - ) - b: torch.Tensor = my_on_off_filter_b( - pattern[:, 2:3, :, :], - ) - - new_tensor: torch.Tensor = torch.cat((r, g, b), dim=1) - return new_tensor - - def pattern_filter_train(self, pattern: torch.Tensor, cfg: Config) -> torch.Tensor: - """0. The training image comes in - 1. is cropped from a random position - 2. is randomly horizontally flipped - 3. is randomly color jitteres - 4. on/off filteres - 5. returned. - - This is a 3 channel version (e.g. r,g,b channels). - """ - assert len(cfg.image_statistics.mean) == 3 - assert len(cfg.image_statistics.the_size) == 2 - assert cfg.image_statistics.the_size[0] > 0 - assert cfg.image_statistics.the_size[1] > 0 - - # Transformation chain - my_transforms: torch.nn.Sequential = torch.nn.Sequential( - tv.transforms.RandomCrop(size=cfg.image_statistics.the_size), - tv.transforms.RandomHorizontalFlip(p=cfg.augmentation.flip_p), - tv.transforms.ColorJitter( - brightness=cfg.augmentation.jitter_brightness, - contrast=cfg.augmentation.jitter_contrast, - saturation=cfg.augmentation.jitter_saturation, - hue=cfg.augmentation.jitter_hue, - ), - ) - scripted_transforms = torch.jit.script(my_transforms) - - # Preprocess the input data - pattern = scripted_transforms(pattern) - - # => On/Off - my_on_off_filter_r: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[0]) - my_on_off_filter_g: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[1]) - my_on_off_filter_b: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[2]) - r: torch.Tensor = my_on_off_filter_r( - pattern[:, 0:1, :, :], - ) - g: torch.Tensor = my_on_off_filter_g( - pattern[:, 1:2, :, :], - ) - b: torch.Tensor = my_on_off_filter_b( - pattern[:, 2:3, :, :], - ) - - new_tensor: torch.Tensor = torch.cat((r, g, b), dim=1) - return new_tensor - - -class OnOffFilter(torch.nn.Module): - def __init__(self, p: float = 0.5) -> None: - super(OnOffFilter, self).__init__() - self.p: float = p - - def forward(self, tensor: torch.Tensor) -> torch.Tensor: - - assert tensor.shape[1] == 1 - - tensor_clone = 2.0 * (tensor - self.p) - - temp_0: torch.Tensor = torch.where( - tensor_clone < 0.0, - -tensor_clone, - tensor_clone.new_zeros(tensor_clone.shape, dtype=tensor_clone.dtype), - ) - - temp_1: torch.Tensor = torch.where( 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zlAy&W4GJo=IK0qqq^7(d-*7yFojb$PdCx**abBIQZ1W-R^JkFQs8RezJBc_h6DRp4 zIwY|!oWxG!LVJS)-My%rpPik>jE45(>4jnFgYt8&~}`ARE6;vo~kjXY_=xadMa=UXY(`vqcxcxc6C zeH^s(Jgxa@8!_o+@bVd#_>V<(IJypN(ZE|n#{R=2G*Fz*d0I=ER9`2Yaa0fg)^`@E zN-d{e-P5SG(S1g;D-_jux^l*RvTSDdHro8OD+wLZ31n!jf?!~eEodxRL&OzQz{5Hn zik#Wdp85;DI=75=tdIEUzy0*6MVg48h{$C8AO6u6lm5{X3Zp2tmtw)O=@7y;=ah6b zqEcNGL$5r*g7ncJ7=e4L+ diff --git a/DATA_MNIST/PyTorch_Non_Spike_Network/Parameter.py b/DATA_MNIST/PyTorch_Non_Spike_Network/Parameter.py deleted file mode 100644 index 92fe247..0000000 --- a/DATA_MNIST/PyTorch_Non_Spike_Network/Parameter.py +++ /dev/null @@ -1,164 +0,0 @@ -# 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 -# -# - -# %% -from dataclasses import dataclass, field -import numpy as np -import torch -import os - - -@dataclass -class Network: - """Parameters of the network. The details about - its layers and the number of output neurons.""" - - number_of_output_neurons: int = field(default=0) - forward_kernel_size: list[list[int]] = field(default_factory=list) - forward_neuron_numbers: list[list[int]] = field(default_factory=list) - strides: list[list[int]] = field(default_factory=list) - dilation: list[list[int]] = field(default_factory=list) - padding: list[list[int]] = field(default_factory=list) - is_pooling_layer: list[bool] = field(default_factory=list) - w_trainable: list[bool] = field(default_factory=list) - eps_xy_trainable: list[bool] = field(default_factory=list) - eps_xy_mean: list[bool] = field(default_factory=list) - - -@dataclass -class LearningParameters: - """Parameter required for training""" - - loss_coeffs_mse: float = field(default=0.5) - loss_coeffs_kldiv: float = field(default=1.0) - learning_rate_gamma_w: float = field(default=-1.0) - learning_rate_gamma_eps_xy: float = field(default=-1.0) - learning_rate_threshold_w: float = field(default=0.00001) - learning_rate_threshold_eps_xy: float = field(default=0.00001) - learning_active: bool = field(default=True) - weight_noise_amplitude: float = field(default=0.01) - eps_xy_intitial: float = field(default=0.1) - test_every_x_learning_steps: int = field(default=50) - test_during_learning: bool = field(default=True) - lr_scheduler_factor: float = field(default=0.75) - lr_scheduler_patience: int = field(default=10) - optimizer_name: str = field(default="Adam") - lr_schedule_name: str = field(default="ReduceLROnPlateau") - number_of_batches_for_one_update: int = field(default=1) - alpha_number_of_iterations: int = field(default=0) - overload_path: str = field(default="./Previous") - - -@dataclass -class Augmentation: - """Parameters used for data augmentation.""" - - crop_width_in_pixel: int = field(default=2) - flip_p: float = field(default=0.5) - jitter_brightness: float = field(default=0.5) - jitter_contrast: float = field(default=0.1) - jitter_saturation: float = field(default=0.1) - jitter_hue: float = field(default=0.15) - - -@dataclass -class ImageStatistics: - """(Statistical) information about the input. i.e. - mean values and the x and y size of the input""" - - mean: list[float] = field(default_factory=list) - the_size: list[int] = field(default_factory=list) - - -@dataclass -class Config: - """Master config class.""" - - # Sub classes - network_structure: Network = field(default_factory=Network) - learning_parameters: LearningParameters = field(default_factory=LearningParameters) - augmentation: Augmentation = field(default_factory=Augmentation) - image_statistics: ImageStatistics = field(default_factory=ImageStatistics) - - batch_size: int = field(default=500) - data_mode: str = field(default="") - - learning_step: int = field(default=0) - learning_step_max: int = field(default=10000) - - number_of_cpu_processes: int = field(default=-1) - - number_of_spikes: int = field(default=0) - cooldown_after_number_of_spikes: int = field(default=0) - - weight_path: str = field(default="./Weights/") - eps_xy_path: str = field(default="./EpsXY/") - data_path: str = field(default="./") - - reduction_cooldown: float = field(default=25.0) - epsilon_0: float = field(default=1.0) - - update_after_x_batch: float = field(default=1.0) - - def __post_init__(self) -> None: - """Post init determines the number of cores. - Creates the required directory and gives us an optimized - (for the amount of cores) batch size.""" - number_of_cpu_processes_temp = os.cpu_count() - - if self.number_of_cpu_processes < 1: - if number_of_cpu_processes_temp is None: - self.number_of_cpu_processes = 1 - else: - self.number_of_cpu_processes = number_of_cpu_processes_temp - - os.makedirs(self.weight_path, exist_ok=True) - os.makedirs(self.eps_xy_path, exist_ok=True) - os.makedirs(self.data_path, exist_ok=True) - - self.batch_size = ( - self.batch_size // self.number_of_cpu_processes - ) * self.number_of_cpu_processes - - self.batch_size = np.max((self.batch_size, self.number_of_cpu_processes)) - self.batch_size = int(self.batch_size) - - def get_epsilon_t(self): - """Generates the time series of the basic epsilon.""" - np_epsilon_t: np.ndarray = np.ones((self.number_of_spikes), dtype=np.float32) - np_epsilon_t[ - self.cooldown_after_number_of_spikes : self.number_of_spikes - ] /= self.reduction_cooldown - return torch.tensor(np_epsilon_t) - - def get_update_after_x_pattern(self): - """Tells us after how many pattern we need to update the weights.""" - return self.batch_size * self.update_after_x_batch diff --git a/DATA_MNIST/PyTorch_Non_Spike_Network/events.out.tfevents.1651325827.fedora.115860.0 b/DATA_MNIST/PyTorch_Non_Spike_Network/events.out.tfevents.1651325827.fedora.115860.0 deleted file mode 100644 index af6401f0d3dc7fd3947205cc237fe85930dc443e..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 8184 zcmaLbdst2B9tZGBshvtnm%VqYNiL;yb28X7iPd`58OgZBSX=3~m&=GTy&qeRcJ9nQerDq@^ZT&n#h09?t$7b~ z^YLG@uHPjlCE3mLOk+-6ws7y?neLclA0|s|=}h-BSDN?yD;=KlV@$l(b!4Je8y}l& zXVBz>if;M6>(|*kTXlq$HT|#WWUer`9Uq^R?E3$;Mj88CN5p?zJ8K^G`4KXcrKtJ~ z&f1XREeM^%9Pn6P>e-nuSgZRW^IC@U6kzu-WD)87Pmj*#;EpH**?xUfn%E{|f2m#A z4G<@c>hek?8WF^MrEyHjr@KLbxFHnhDMS^AuHMRy1Vo!K+>|CBm$L!4>|6oSWN2ZH zNbEuoofk(lz0cZ|1LDYGI8Py7k3r6!@w))=U;3sr(OAK5A2?tgAl@&WUMmuf31Y*( zC?;jH(J(+XI)n2R;=NepQW^dSATHH6rHLv9yCrqNLqP2M%CSx)nh?a1j!{fwL3S`8 z`qkh(g}5^g#i*uu1L9$QQ<|8eV9T$aISYv8P1oLt#I6K!M)p)DWbyPIKvcZPc?vNj z9<54p=?aM3^i64Eo`N;lV)7CYqtX{Nh(uF@n6z>#6FBDR89>Zjf%6oiHXcIz&g zmrrhbFA~iOV#akfQ*|S|8W4R8ah^h4lz@Vh&2)fRq;EuVs`1s4W|G4j`vcb=6z3H0MT^|Zb}oMDcCC?tpfqEKC0rgNVFn|Jy|uQ3tV>_5S^ysJcSsfLGqk& z4S={!-;^dcD%kOT$5aC1rn~FgL}GV>s0vdvPyL#20HV=-oTm_dG|1EB=2k$=)i`4&Ax~Q2);X1f4CN|+b zg{ae@tLYZA0P&5!DNS@$vayv}hJbkTsLWI(+7ZNMr5sau(6AerV*N3krx43DD9t8t zG9dnV95qXB^UOt}13`>_$uTFdB&-I+!~&eB5U*=c%-9HXKtzXdQ<^9&7q{l~ zIzTL1lVTwf9SP!;TO4z`Mt&F&8!~a8LafrD;CtIs0deM9+>|B?hp|Ym>jOZ%w?4*7 zB+3b5ZZXHKpS58RAa-QoJcZbyLGyQ{+W=y!4mYKVPD*wVQw2X#gZ6q@i$n!M{N*9X zB#-?z0uXh1I8Pz|u0i2Z%_=~2%EwJ1g1;lAD za8sHn?2BjB6GH&8y>^I$NOUHMNf$Zh=2Gu{fEZAR^Auvc20hr?Jpd3->6_9-VTyJ- zT|6Kzw=GdXzM5lnnFn71;!%5?rw|d3M$WcN07M@L+>|D|E7>+z=Q2RFyjrCY ziLM0EX)niQ-hDL}5Yw;WJcZboM`pPtHvlp0I&Ml6g(=3gJr4jxtHgXKk@yusbkE|L zC1Fk~Kn$9L^Aut*j}GiNsRzVM`ld9|TFLHua=Zi(cgj~di$phqcuB`G6aSV9&x;Qg zI8PxC=27tU!+!!|q!Kr!iNdMdV1$<$Ao9hDt|GBFLF~RjSkW#+Y`_$EUBP(@QO%=I zhp!z6#6DMXQ<^Be0KH7S5&-e!od`FP=uQyJQaC1MtN8>#th za8sHXtz>(4om>luMbA9lMPeUo0+AQq(IJcXFWqe-e|7XdMEDQ-#=g?+KE@G|^JJ$LE3he+&C z5S7AzQN4Rb6d=C5jPn#?43BH#9rgCM?8bIems z{2M@A^#SK8#C1H{Sa%D4yO_4%rZiDFjQudHawd4tZbzSZi$qU?cy1=g`1kQ01&Hcn zI8Pxa^Qbf^)E7MHC;Fx|QP>y9Y2D!E-5l*5ACWkKAkLV;F{2M9{0xY{CgD7VxRFOY z8cPQQqGK{{N)v?_phrLDW-vux|3`iz(TgBD#Boeac{g}V8xer>6yh=->E?AQ0mKr0 zQ<^9&myoQ*y8*Gg%yN)O^d^Yggei__jrkT#aj-egQ;0b{vV8m9H9$O}Z%Pw||KeKH zUbg|!+Q%$VBn~8qLBSl8QPms1qo?}fJcW3gM;C@>?Eu6IeN&pKQnJPttwn%XB5xZk z5`75b*O44EY|!`(V2XxHoTm_leKG3U@Q;9)rf*6U9hK~eDY~tI`1VA{5RvFh5D!ih zZX=wcHGt@G3g;=rl{`wl9a{>BdHSX_Q8") - 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) diff --git a/DATA_MNIST/data_url.txt b/DATA_MNIST/data_url.txt deleted file mode 100644 index 7a6b872..0000000 --- a/DATA_MNIST/data_url.txt +++ /dev/null @@ -1,8 +0,0 @@ -http://yann.lecun.com/exdb/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