network

Engine/Neurons.py

@@ -8,6 +8,7 @@ import os
 import pandas as pd
 from PIL import Image
 from torchvision.io import read_image
+
 from torchvision.transforms import Resize, Lambda, transforms, ToTensor
 
 from Constants import *
@@ -25,7 +26,7 @@ class Neurons:
         #self.loadImages()
         self.train_dataloader = DataLoader(dataset, batch_size=64, shuffle=True)
 
-        input_dim = 28 * 28
+        input_dim = 100
         output_dim = 10
         hidden_dim = 10
         print("create model")
@@ -45,7 +46,7 @@ class Neurons:
         optimizer = optim.SGD(model.parameters(), lr=0.001)
 
         for epoch in range(n_iter):
-            for image, label in next(iter(self.train_dataloader)):
+            for image, label in self.train_dataloader:
                 optimizer.zero_grad()
 
                 output = model(image)
@@ -122,7 +123,7 @@ class CustomImageDataset(Dataset):
 
     def __getitem__(self, idx):
         img_path = os.path.join(self.img_dir, self.img_labels.iloc[idx, 0])
-        image = Image.open(img_path)
+        image = Image.open(img_path).convert("RGB")
         label = self.img_labels.iloc[idx, 1]
         if self.transform:
             image = self.transform(image)

venv/Lib/site-packages/mnist-0.2.2.dist-info/INSTALLER

@@ -0,0 +1 @@
+pip

venv/Lib/site-packages/mnist-0.2.2.dist-info/METADATA

@@ -0,0 +1,45 @@
+Metadata-Version: 2.1
+Name: mnist
+Version: 0.2.2
+Summary: Python utilities to download and parse the MNIST dataset
+Home-page: https://github.com/datapythonista/mnist
+Author: Marc Garcia
+Author-email: garcia.marc@gmail.com
+License: BSD
+Platform: UNKNOWN
+Classifier: Development Status :: 4 - Beta
+Classifier: Environment :: Console
+Classifier: Operating System :: OS Independent
+Classifier: Intended Audience :: Science/Research
+Classifier: Programming Language :: Python :: 2
+Classifier: Programming Language :: Python :: 2.7
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.5
+Classifier: Programming Language :: Python :: 3.6
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Topic :: Scientific/Engineering
+Requires-Dist: numpy
+Requires-Dist: mock; python_version=="2.7"
+
+
+The MNIST database is available at http://yann.lecun.com/exdb/mnist/
+
+The MNIST database is a dataset of handwritten digits. It has 60,000
+training samples, and 10,000 test samples. Each image is represented
+by 28x28 pixels, each containing a value 0 - 255 with its grayscale value.
+
+It is a subset of a larger set available from NIST. The digits have been
+size-normalized and centered in a fixed-size image.
+
+It is a good database for people who want to try learning techniques and
+pattern recognition methods on real-world data while spending minimal
+efforts on preprocessing and formatting.
+
+There are four files available, which contain separately train and test,
+and images and labels.
+
+Thanks to Yann LeCun, Corinna Cortes, Christopher J.C. Burges.
+
+mnist makes it easier to download and parse MNIST files.
+
+

venv/Lib/site-packages/mnist-0.2.2.dist-info/RECORD

@@ -0,0 +1,8 @@
+mnist-0.2.2.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4
+mnist-0.2.2.dist-info/METADATA,sha256=6EcFTjjdJvJY5OeJ0GK_83z2kM8-wJd2gt5qIYNwDag,1649
+mnist-0.2.2.dist-info/RECORD,,
+mnist-0.2.2.dist-info/REQUESTED,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+mnist-0.2.2.dist-info/WHEEL,sha256=J3CsTk7Mf2JNUyhImI-mjX-fmI4oDjyiXgWT4qgZiCE,110
+mnist-0.2.2.dist-info/top_level.txt,sha256=LZk4DcL6HJ7bDK2qWX3Jgg25BAR1cgCUo--jQ78YiHg,6
+mnist/__init__.py,sha256=FrH55GR3AqdLQ1Ddvl_e3x2QcaZQCxHQ6TSEtKiPElA,5992
+mnist/__pycache__/__init__.cpython-39.pyc,,

venv/Lib/site-packages/mnist-0.2.2.dist-info/WHEEL

@@ -0,0 +1,6 @@
+Wheel-Version: 1.0
+Generator: bdist_wheel (0.31.0)
+Root-Is-Purelib: true
+Tag: py2-none-any
+Tag: py3-none-any
+

venv/Lib/site-packages/mnist-0.2.2.dist-info/top_level.txt

@@ -0,0 +1 @@
+mnist

venv/Lib/site-packages/mnist/__init__.py

@@ -0,0 +1,4 @@
+from .loader import MNIST
+from .packer import label_packer, img_packer
+
+__all__ = [MNIST, label_packer, img_packer]

venv/Lib/site-packages/mnist/loader.py

@@ -0,0 +1,301 @@
+import gzip
+import os
+import struct
+from array import array
+import random
+
+_allowed_modes = (
+    # integer values in {0..255}
+    'vanilla',
+
+    # integer values in {0,1}
+    # values set at 1 (instead of 0) with probability p = orig/255
+    # as in Ruslan Salakhutdinov and Iain Murray's paper
+    # 'On The Quantitative Analysis of Deep Belief Network' (2008)
+    'randomly_binarized',
+
+    # integer values in {0,1}
+    # values set at 1 (instead of 0) if orig/255 > 0.5
+    'rounded_binarized',
+)
+
+_allowed_return_types = (
+    # default return type. Computationally more expensive.
+    # Useful if numpy is not installed.
+    'lists',
+
+    # Numpy module will be dynamically loaded on demand.
+    'numpy',
+)
+
+np = None
+def _import_numpy():
+    # will be called only when the numpy return type has been specifically
+    # requested via the 'return_type' parameter in MNIST class' constructor.
+    global np
+    if np is None: # import only once
+        try:
+            import numpy as _np
+        except ImportError as e:
+            raise MNISTException(
+                "need to have numpy installed to return numpy arrays."\
+                +" Otherwise, please set return_type='lists' in constructor."
+            )
+        np = _np
+    else:
+        pass # was already previously imported
+    return np
+
+class MNISTException(Exception):
+    pass
+
+class MNIST(object):
+    def __init__(self, path='.', mode='vanilla', return_type='lists', gz=False):
+        self.path = path
+
+        assert mode in _allowed_modes, \
+            "selected mode '{}' not in {}".format(mode,_allowed_modes)
+
+        self._mode = mode
+
+        assert return_type in _allowed_return_types, \
+            "selected return_type '{}' not in {}".format(
+                return_type,
+                _allowed_return_types
+            )
+
+        self._return_type = return_type
+
+        self.test_img_fname = 't10k-images-idx3-ubyte'
+        self.test_lbl_fname = 't10k-labels-idx1-ubyte'
+
+        self.train_img_fname = 'train-images-idx3-ubyte'
+        self.train_lbl_fname = 'train-labels-idx1-ubyte'
+
+        self.gz = gz
+        self.emnistRotate = False
+
+        self.test_images = []
+        self.test_labels = []
+
+        self.train_images = []
+        self.train_labels = []
+
+    def select_emnist(self, dataset='digits'):
+        '''
+        Select one of the EMNIST datasets
+
+        Available datasets:
+            - balanced
+            - byclass
+            - bymerge
+            - digits
+            - letters
+            - mnist
+        '''
+        template = 'emnist-{0}-{1}-{2}-idx{3}-ubyte'
+
+        self.gz = True
+        self.emnistRotate = True
+
+        self.test_img_fname = template.format(dataset, 'test', 'images', 3)
+        self.test_lbl_fname = template.format(dataset, 'test', 'labels', 1)
+
+        self.train_img_fname = template.format(dataset, 'train', 'images', 3)
+        self.train_lbl_fname = template.format(dataset, 'train', 'labels', 1)
+
+    @property # read only because set only once, via constructor
+    def mode(self):
+        return self._mode
+
+    @property # read only because set only once, via constructor
+    def return_type(self):
+        return self._return_type
+
+    def load_testing(self):
+        ims, labels = self.load(os.path.join(self.path, self.test_img_fname),
+                                os.path.join(self.path, self.test_lbl_fname))
+
+        self.test_images = self.process_images(ims)
+        self.test_labels = self.process_labels(labels)
+
+        return self.test_images, self.test_labels
+
+    def load_training(self):
+        ims, labels = self.load(os.path.join(self.path, self.train_img_fname),
+                                os.path.join(self.path, self.train_lbl_fname))
+
+        self.train_images = self.process_images(ims)
+        self.train_labels = self.process_labels(labels)
+
+        return self.train_images, self.train_labels
+
+    def load_training_in_batches(self, batch_size):
+        if type(batch_size) is not int:
+            raise ValueError('batch_size must be a int number')
+        batch_sp = 0
+        last = False
+        self._get_dataset_size(os.path.join(self.path, self.train_img_fname),
+                               os.path.join(self.path, self.train_lbl_fname))
+
+        while True:
+            ims, labels = self.load(
+                os.path.join(self.path, self.train_img_fname),
+                os.path.join(self.path, self.train_lbl_fname),
+                batch=[batch_sp, batch_size])
+
+            self.train_images = self.process_images(ims)
+            self.train_labels = self.process_labels(labels)
+
+            yield self.train_images, self.train_labels
+
+            if last:
+                break
+
+            batch_sp += batch_size
+            if batch_sp + batch_size > self.dataset_size:
+                last = True
+                batch_size = self.dataset_size - batch_sp
+
+    def _get_dataset_size(self, path_img, path_lbl):
+        with self.opener(path_lbl, 'rb') as file:
+            magic, lb_size = struct.unpack(">II", file.read(8))
+            if magic != 2049:
+                raise ValueError('Magic number mismatch, expected 2049,'
+                                 'got {}'.format(magic))
+
+        with self.opener(path_img, 'rb') as file:
+            magic, im_size = struct.unpack(">II", file.read(8))
+            if magic != 2051:
+                raise ValueError('Magic number mismatch, expected 2051,'
+                                 'got {}'.format(magic))
+
+        if lb_size != im_size:
+            raise ValueError('image size is not equal to label size')
+
+        self.dataset_size = lb_size
+
+    def process_images(self, images):
+        if self.return_type is 'lists':
+            return self.process_images_to_lists(images)
+        elif self.return_type is 'numpy':
+            return self.process_images_to_numpy(images)
+        else:
+            raise MNISTException("unknown return_type '{}'".format(self.return_type))
+
+    def process_labels(self, labels):
+        if self.return_type is 'lists':
+            return labels
+        elif self.return_type is 'numpy':
+            _np = _import_numpy()
+            return _np.array(labels)
+        else:
+            raise MNISTException("unknown return_type '{}'".format(self.return_type))
+
+    def process_images_to_numpy(self,images):
+        _np = _import_numpy()
+
+        images_np = _np.array(images)
+
+        if self.mode == 'vanilla':
+            pass # no processing, return them vanilla
+
+        elif self.mode == 'randomly_binarized':
+            r = _np.random.random(images_np.shape)
+            images_np = (r <= ( images_np / 255)).astype('int') # bool to 0/1
+
+        elif self.mode == 'rounded_binarized':
+            images_np = ((images_np / 255) > 0.5).astype('int') # bool to 0/1
+
+        else:
+            raise MNISTException("unknown mode '{}'".format(self.mode))
+
+        return images_np
+
+    def process_images_to_lists(self,images):
+        if self.mode == 'vanilla':
+            pass # no processing, return them vanilla
+
+        elif self.mode == 'randomly_binarized':
+            for i in range(len(images)):
+                for j in range(len(images[i])):
+                    pixel = images[i][j]
+                    images[i][j] = int(random.random() <= pixel/255) # bool to 0/1
+
+        elif self.mode == 'rounded_binarized':
+            for i in range(len(images)):
+                for j in range(len(images[i])):
+                    pixel = images[i][j]
+                    images[i][j] = int(pixel/255 > 0.5) # bool to 0/1
+        else:
+            raise MNISTException("unknown mode '{}'".format(self.mode))
+
+        return images
+
+    def opener(self, path_fn, *args, **kwargs):
+        if self.gz:
+            return gzip.open(path_fn + '.gz', *args, **kwargs)
+        else:
+            return open(path_fn, *args, **kwargs)
+
+    def load(self, path_img, path_lbl, batch=None):
+        if batch is not None:
+            if type(batch) is not list or len(batch) is not 2:
+                raise ValueError('batch should be a 1-D list'
+                                 '(start_point, batch_size)')
+
+        with self.opener(path_lbl, 'rb') as file:
+            magic, size = struct.unpack(">II", file.read(8))
+            if magic != 2049:
+                raise ValueError('Magic number mismatch, expected 2049,'
+                                 'got {}'.format(magic))
+
+            labels = array("B", file.read())
+
+        with self.opener(path_img, 'rb') as file:
+            magic, size, rows, cols = struct.unpack(">IIII", file.read(16))
+            if magic != 2051:
+                raise ValueError('Magic number mismatch, expected 2051,'
+                                 'got {}'.format(magic))
+
+            image_data = array("B", file.read())
+
+        if batch is not None:
+            image_data = image_data[batch[0] * rows * cols:\
+                                    (batch[0] + batch[1]) * rows * cols]
+            labels = labels[batch[0]: batch[0] + batch[1]]
+            size = batch[1]
+
+        images = []
+        for i in range(size):
+            images.append([0] * rows * cols)
+
+        for i in range(size):
+            images[i][:] = image_data[i * rows * cols:(i + 1) * rows * cols]
+
+            # for some reason EMNIST is mirrored and rotated
+            if self.emnistRotate:
+                x = image_data[i * rows * cols:(i + 1) * rows * cols]
+
+                subs = []
+                for r in range(rows):
+                    subs.append(x[(rows - r) * cols - cols:(rows - r)*cols])
+
+                l = list(zip(*reversed(subs)))
+                fixed = [item for sublist in l for item in sublist]
+
+                images[i][:] = fixed
+
+        return images, labels
+
+    @classmethod
+    def display(cls, img, width=28, threshold=200):
+        render = ''
+        for i in range(len(img)):
+            if i % width == 0:
+                render += '\n'
+            if img[i] > threshold:
+                render += '@'
+            else:
+                render += '.'
+        return render

venv/Lib/site-packages/mnist/packer.py

@@ -0,0 +1,62 @@
+import gzip
+import os
+import struct
+
+
+def _binary_writter(data, filepath):
+    with open(filepath, 'wb') as file:
+        file.write(data)
+
+
+def _gzip_writter(data, filepath):
+    with gzip.open(filepath, 'wb') as file:
+        file.write(data)
+
+
+def img_packer(path, filename, imgs, gzip=False,
+               magic=2051, rows=28, cols=28):
+    data = b''
+    data += struct.pack(">IIII", magic, len(imgs), rows, cols)
+
+    to_list = list()
+    if type(imgs).__name__ == 'array':
+        to_list = list(imgs)
+    elif type(imgs).__name__ == 'ndarray':
+        to_list = list(imgs)
+    elif type(imgs).__name__ == 'list':
+        to_list = imgs
+    else:
+        raise TypeError('Unsupported data type.')
+
+    for i in to_list:
+        pack_format = '>' + 'B' * len(i)
+        data += struct.pack(pack_format, *i)
+
+    if gzip:
+        _gzip_writter(data, os.path.join(path, filename))
+    else:
+        _binary_writter(data, os.path.join(path, filename))
+
+
+def label_packer(path, filename, label,
+                 gzip=False, magic=2049):
+    data = b''
+    data += struct.pack(">II", magic, len(label))
+
+    to_list = list()
+    if type(label).__name__ == 'array':
+        to_list = list(label)
+    elif type(label).__name__ == 'ndarray':
+        to_list = list(label)
+    elif type(label).__name__ == 'list':
+        to_list = label
+    else:
+        raise TypeError('Unsupported label type.')
+
+    pack_format = '>' + 'B' * len(to_list)
+    data += struct.pack(pack_format, *to_list)
+
+    if gzip:
+        _gzip_writter(data, os.path.join(path, filename))
+    else:
+        _binary_writter(data, os.path.join(path, filename))

venv/Lib/site-packages/python_mnist-0.7.dist-info/AUTHORS

@@ -0,0 +1 @@
+Richard Marko <srk(at)48(dot)io>

venv/Lib/site-packages/python_mnist-0.7.dist-info/INSTALLER

@@ -0,0 +1 @@
+pip

venv/Lib/site-packages/python_mnist-0.7.dist-info/LICENSE

@@ -0,0 +1,25 @@
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions
+are met:
+1. Redistributions of source code must retain the above copyright
+   notice, and the entire permission notice in its entirety,
+   including the disclaimer of warranties.
+2. Redistributions in binary form must reproduce the above copyright
+   notice, this list of conditions and the following disclaimer in the
+   documentation and/or other materials provided with the distribution.
+3. The name of the author may not be used to endorse or promote
+   products derived from this software without specific prior
+   written permission.
+
+THIS SOFTWARE IS PROVIDED ``AS IS'' AND ANY EXPRESS OR IMPLIED
+WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE, ALL OF
+WHICH ARE HEREBY DISCLAIMED.  IN NO EVENT SHALL THE AUTHOR BE
+LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT
+OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR
+BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
+LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE
+USE OF THIS SOFTWARE, EVEN IF NOT ADVISED OF THE POSSIBILITY OF SUCH
+DAMAGE.

venv/Lib/site-packages/python_mnist-0.7.dist-info/METADATA

@@ -0,0 +1,169 @@
+Metadata-Version: 2.1
+Name: python-mnist
+Version: 0.7
+Summary: Simple MNIST and EMNIST data parser written in pure Python
+Home-page: https://github.com/sorki/python-mnist
+Author: Richard Marko
+Author-email: srk@48.io
+License: BSD
+Platform: UNKNOWN
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: Operating System :: OS Independent
+Classifier: Programming Language :: Python
+
+python-mnist
+============
+
+Simple MNIST and EMNIST data parser written in pure Python.
+
+MNIST is a database of handwritten digits available on
+http://yann.lecun.com/exdb/mnist/. EMNIST is an extended MNIST database
+https://www.nist.gov/itl/iad/image-group/emnist-dataset.
+
+Requirements
+------------
+
+-  Python 2 or Python 3
+
+Usage
+-----
+
+-  ``git clone https://github.com/sorki/python-mnist``
+
+-  ``cd python-mnist``
+
+-  Get MNIST data:
+
+   ::
+
+      ./bin/mnist_get_data.sh
+
+-  Check preview with:
+
+   ::
+
+      PYTHONPATH=. ./bin/mnist_preview
+
+Installation
+------------
+
+Get the package from PyPi:
+
+::
+
+   pip install python-mnist
+
+or install with ``setup.py``:
+
+::
+
+   python setup.py install
+
+Code sample:
+
+::
+
+   from mnist import MNIST
+   mndata = MNIST('./dir_with_mnist_data_files')
+   images, labels = mndata.load_training()
+
+To enable loading of gzip-ed files use:
+
+::
+
+   mndata.gz = True
+
+Library tries to load files named t10k-images-idx3-ubyte
+train-labels-idx1-ubyte train-images-idx3-ubyte and
+t10k-labels-idx1-ubyte. If loading throws an exception check if these
+names match.
+
+EMNIST
+------
+
+-  Get EMNIST data:
+
+   ::
+
+      ./bin/emnist_get_data.sh
+
+-  Check preview with:
+
+   ::
+
+      PYTHONPATH=. ./bin/emnist_preview
+
+To use EMNIST datasets you need to call:
+
+::
+
+   mndata.select_emnist('digits')
+
+Where digits is one of the available EMNIST datasets. You can choose
+from
+
+   -  balanced
+   -  byclass
+   -  bymerge
+   -  digits
+   -  letters
+   -  mnist
+
+EMNIST loader uses gziped files by default, this can be disabled by by
+setting:
+
+::
+
+   mndata.gz = False
+
+You also need to unpack EMNIST files as bin/emnist_get_data.sh script
+won't do it for you. EMNIST loader also needs to mirror and rotate
+images so it is a bit slower (If this is an issue for you, you should
+repack the data to avoid mirroring and rotation on each load).
+
+Notes
+-----
+
+This package doesn't use numpy by design as when I've tried to find a
+working implementation all of them were based on some archaic version of
+numpy and none of them worked. This loads data files with struct.unpack
+instead.
+
+Example
+-------
+
+::
+
+   $ PYTHONPATH=. ./bin/mnist_preview
+   Showing num: 3
+
+   ............................
+   ............................
+   ............................
+   ............................
+   ............................
+   ............................
+   .............@@@@@..........
+   ..........@@@@@@@@@@........
+   .......@@@@@@......@@.......
+   .......@@@........@@@.......
+   .................@@.........
+   ................@@@.........
+   ...............@@@@@........
+   .............@@@............
+   .............@.......@......
+   .....................@......
+   .....................@@.....
+   ....................@@......
+   ...................@@@......
+   .................@@@@.......
+   ................@@@@........
+   ....@........@@@@@..........
+   ....@@@@@@@@@@@@............
+   ......@@@@@@................
+   ............................
+   ............................
+   ............................
+   ............................
+

venv/Lib/site-packages/python_mnist-0.7.dist-info/RECORD

@@ -0,0 +1,19 @@
+../../Scripts/emnist_get_data.sh,sha256=KcyB6aRMSJc1ttt0GeQHfNeOE1K0jWLD9Ou0lvwGauQ,337
+../../Scripts/emnist_preview,sha256=jrWtZ6MYGFq89clptlQcL7OA0z1ULVXfPHx_DoDdFkk,1223
+../../Scripts/emnist_repack,sha256=g1RocXo1ZODzpSl2_uSK0VDieABwwKWzD2uoDvKp7Vo,1191
+../../Scripts/mnist_get_data.sh,sha256=pV1P4dtLPGNPgfIn4zcyztlUmHL6nwlAONdIb1k83sc,284
+../../Scripts/mnist_preview,sha256=rp9IsSiCEsNus46JkW1a22vaGLQmgrrHb0FrxjxSM_M,851
+mnist/__init__.py,sha256=4Qo1t78Wb6oniBzjcqqrDyCXXN1qIqJeopC6CANibHM,116
+mnist/__pycache__/__init__.cpython-39.pyc,,
+mnist/__pycache__/loader.cpython-39.pyc,,
+mnist/__pycache__/packer.cpython-39.pyc,,
+mnist/loader.py,sha256=VVx63_2QInSvoQiJYGsUktysLThTSKzwv85MtPPACD0,10039
+mnist/packer.py,sha256=K3IpvL1pV4d0INYK-9aD3V201Qq8_QEVgi7rOSd6ubk,1630
+python_mnist-0.7.dist-info/AUTHORS,sha256=hIRY5VS6F-AnrVF6Zvgnw7hy0I4xvopBFafEMEzEKuo,33
+python_mnist-0.7.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4
+python_mnist-0.7.dist-info/LICENSE,sha256=m3GKlGD-1ZUkZkISNbx560nU6erMkg16ndYoWrj9bG0,1391
+python_mnist-0.7.dist-info/METADATA,sha256=u-BxMiB8Eo3W5XVFchd5he3UPgl3syNknSrZxBvyq5U,3511
+python_mnist-0.7.dist-info/RECORD,,
+python_mnist-0.7.dist-info/REQUESTED,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+python_mnist-0.7.dist-info/WHEEL,sha256=8zNYZbwQSXoB9IfXOjPfeNwvAsALAjffgk27FqvCWbo,110
+python_mnist-0.7.dist-info/top_level.txt,sha256=LZk4DcL6HJ7bDK2qWX3Jgg25BAR1cgCUo--jQ78YiHg,6

venv/Lib/site-packages/python_mnist-0.7.dist-info/WHEEL

@@ -0,0 +1,6 @@
+Wheel-Version: 1.0
+Generator: bdist_wheel (0.33.6)
+Root-Is-Purelib: true
+Tag: py2-none-any
+Tag: py3-none-any
+

venv/Lib/site-packages/python_mnist-0.7.dist-info/top_level.txt

@@ -0,0 +1 @@
+mnist

venv/Scripts/emnist_get_data.sh

@@ -0,0 +1,16 @@
+#!/usr/bin/env bash
+
+if [ -d emnist_data ]; then
+    echo "emnist_data directory already present, exiting"
+    exit 1
+fi
+
+mkdir emnist_data
+pushd emnist_data
+#wget http://biometrics.nist.gov/cs_links/EMNIST/gzip.zip
+wget http://www.itl.nist.gov/iaui/vip/cs_links/EMNIST/gzip.zip
+unzip gzip.zip
+rm -f gzip.zip
+mv gzip/* .
+rmdir gzip
+popd

venv/Scripts/emnist_preview

@@ -0,0 +1,44 @@
+#!c:\users\kratu\pycharmprojects\projekt_ai-automatyczny_saper\venv\scripts\python.exe
+
+import random
+import argparse
+from mnist import MNIST
+
+
+if __name__ == "__main__":
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--id", default=None, type=int,
+                        help="ID (position) of the letter to show")
+    parser.add_argument("--training", action="store_true",
+                        help="Use training set instead of testing set")
+    parser.add_argument("--dataset", default="digits",
+                        help="EMNIST dataset to load")
+    parser.add_argument("--data", default="./emnist_data",
+                        help="Path to MNIST data dir")
+
+    args = parser.parse_args()
+
+    mn = MNIST(args.data)
+    mn.select_emnist(args.dataset)
+
+    if args.training:
+        img, label = mn.load_training()
+    else:
+        img, label = mn.load_testing()
+
+    if args.id:
+        which = args.id
+    else:
+        which = random.randrange(0, len(label))
+
+    print('Showing id {}, num: {}'.format(which, label[which]))
+
+    # letters dataset uses A=1 B=2 ...
+    if args.dataset == 'letters':
+        print('Letter "{}"'.format(chr(label[which] + ord('a') - 1)))
+
+    print(mn.display(img[which]))
+    wat = img[which]
+    #import IPython
+    #IPython.embed()

venv/Scripts/emnist_repack

@@ -0,0 +1,43 @@
+#!c:\users\kratu\pycharmprojects\projekt_ai-automatyczny_saper\venv\scripts\python.exe
+
+import argparse
+import os.path
+from mnist import MNIST
+from mnist import img_packer
+
+if __name__ == '__main__':
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--data", default="./emnist_data",
+                        help="Path to MNIST data dir")
+    parser.add_argument("--output", default=None,
+                        help="Where to save result")
+
+    args = parser.parse_args()
+
+    DATASETS = ["balanced", "byclass", "bymerge",
+                "digits", "letters", "mnist"]
+
+    mn = MNIST(args.data)
+
+    if not args.output:
+        dest = args.data
+        train_img_fname = 'rf_' + mn.train_img_fname
+        test_img_fname = 'rf_' + mn.test_img_fname
+    else:
+        dest = args.output
+        train_img_fname = mn.train_img_fname
+        test_img_fname = mn.test_img_fname
+
+    for dt_name in DATASETS:
+        mn.select_emnist(dt_name)
+
+        print("========procesing {} dataset========".format(dt_name))
+
+        tra_img, _ = mn.load_training()
+        img_packer(dest, train_img_fname,
+                   tra_img, gzip=True)
+
+        tes_img, _ = mn.load_testing()
+        img_packer(dest, test_img_fname,
+                   tes_img, gzip=True)

venv/Scripts/mnist_get_data.sh

@@ -0,0 +1,13 @@
+#!/usr/bin/env bash
+
+if [ -d data ]; then
+    echo "data directory already present, exiting"
+    exit 1
+fi
+
+mkdir data
+wget --recursive --level=1 --cut-dirs=3 --no-host-directories \
+  --directory-prefix=data --accept '*.gz' http://yann.lecun.com/exdb/mnist/
+pushd data
+gunzip *
+popd

venv/Scripts/mnist_preview

@@ -0,0 +1,34 @@
+#!c:\users\kratu\pycharmprojects\projekt_ai-automatyczny_saper\venv\scripts\python.exe
+
+import random
+import argparse
+from mnist import MNIST
+
+
+if __name__ == "__main__":
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--id", default=None, type=int,
+                        help="ID (position) of the letter to show")
+    parser.add_argument("--training", action="store_true",
+                        help="Use training set instead of testing set")
+
+    parser.add_argument("--data", default="./data",
+                        help="Path to MNIST data dir")
+
+    args = parser.parse_args()
+
+    mn = MNIST(args.data)
+
+    if args.training:
+        img, label = mn.load_training()
+    else:
+        img, label = mn.load_testing()
+
+    if args.id:
+        which = args.id
+    else:
+        which = random.randrange(0, len(label))
+
+    print('Showing num: {}'.format(label[which]))
+    print(mn.display(img[which]))