
'''simple docstring'''







import collections
import gzip
import os
import urllib

import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated

_lowercase :  List[Any]    							=       collections.namedtuple("_Datasets", ["train", "validation", "test"])

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
_lowercase :  Any    							=       """https://storage.googleapis.com/cvdf-datasets/mnist/"""
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Any							):



						"""simple docstring"""





						lowercase_	:     Optional[Any]													=       numpy.dtype(numpy.uintaa							).newbyteorder('''>'''							)
						return numpy.frombuffer(bytestream.read(4							)      ,      dtype=A__							)[0]
@deprecated(A__      ,      '''Please use tf.data to implement this functionality.'''							)
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: List[Any]							):



						"""simple docstring"""





						print('''Extracting'''      ,      f.name							)
						with gzip.GzipFile(fileobj=A__							) as bytestream:
												lowercase_	:     Dict													=       _readaa(A__							)
												if magic != 2051:
																		raise ValueError(
																		    '''Invalid magic number %d in MNIST image file: %s''' % (magic, f.name)							)
												lowercase_	:     Optional[Any]													=       _readaa(A__							)
												lowercase_	:     Union[str, Any]													=       _readaa(A__							)
												lowercase_	:     Tuple													=       _readaa(A__							)
												lowercase_	:     Any													=       bytestream.read(rows * cols * num_images							)
												lowercase_	:     List[Any]													=       numpy.frombuffer(A__      ,      dtype=numpy.uinta							)
												lowercase_	:     Any													=       data.reshape(A__      ,      A__      ,      A__      ,      1							)
												return data
@deprecated(A__      ,      '''Please use tf.one_hot on tensors.'''							)
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Union[str, Any]      ,      __SCREAMING_SNAKE_CASE					: int							):



						"""simple docstring"""





						lowercase_	:     str													=       labels_dense.shape[0]
						lowercase_	:     Union[str, Any]													=       numpy.arange(A__							) * num_classes
						lowercase_	:     Any													=       numpy.zeros((num_labels, num_classes)							)
						lowercase_	:     int													=       1
						return labels_one_hot
@deprecated(A__      ,      '''Please use tf.data to implement this functionality.'''							)
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Dict      ,      __SCREAMING_SNAKE_CASE					: Dict=False      ,      __SCREAMING_SNAKE_CASE					: Optional[int]=10							):



						"""simple docstring"""





						print('''Extracting'''      ,      f.name							)
						with gzip.GzipFile(fileobj=A__							) as bytestream:
												lowercase_	:     Optional[int]													=       _readaa(A__							)
												if magic != 2049:
																		raise ValueError(
																		    '''Invalid magic number %d in MNIST label file: %s''' % (magic, f.name)							)
												lowercase_	:     Optional[int]													=       _readaa(A__							)
												lowercase_	:     Any													=       bytestream.read(A__							)
												lowercase_	:     Tuple													=       numpy.frombuffer(A__      ,      dtype=numpy.uinta							)
												if one_hot:
																		return _dense_to_one_hot(A__      ,      A__							)
												return labels






class  lowerCAmelCase__   :






					@deprecated(
					    __SCREAMING_SNAKE_CASE				,							'''Please use alternatives such as official/mnist/_DataSet.py'''
					    ''' from tensorflow/models.'''				,							)
					def __init__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE=False				,							__SCREAMING_SNAKE_CASE=False				,							__SCREAMING_SNAKE_CASE=dtypes.floataa				,							__SCREAMING_SNAKE_CASE=True				,							__SCREAMING_SNAKE_CASE=None				,							):


											"""simple docstring"""





											lowercase_	:     int													=       random_seed.get_seed(__SCREAMING_SNAKE_CASE		)
											# If op level seed is not set, use whatever graph level seed is returned
											numpy.random.seed(seeda if seed is None else seeda		)
											lowercase_	:     Union[str, Any]													=       dtypes.as_dtype(__SCREAMING_SNAKE_CASE		).base_dtype
											if dtype not in (dtypes.uinta, dtypes.floataa):
																	raise TypeError('''Invalid image dtype %r, expected uint8 or float32''' % dtype		)
											if fake_data:
																	lowercase_	:     str													=       1_00_00
																	lowercase_	:     Optional[int]													=       one_hot
											else:
																	assert (
																	    images.shape[0] == labels.shape[0]
																	), F'''images.shape: {images.shape} labels.shape: {labels.shape}'''
																	lowercase_	:     List[str]													=       images.shape[0]

																	# Convert shape from [num examples, rows, columns, depth]
																	# to [num examples, rows*columns] (assuming depth == 1)
																	if reshape:
																							assert images.shape[3] == 1
																							lowercase_	:     str													=       images.reshape(
																							    images.shape[0]				,							images.shape[1] * images.shape[2]		)
																	if dtype == dtypes.floataa:
																							# Convert from [0, 255] -> [0.0, 1.0].
																							lowercase_	:     Tuple													=       images.astype(numpy.floataa		)
																							lowercase_	:     List[str]													=       numpy.multiply(__SCREAMING_SNAKE_CASE				,							1.0 / 2_55.0		)
											lowercase_	:     Optional[int]													=       images
											lowercase_	:     Tuple													=       labels
											lowercase_	:     Any													=       0
											lowercase_	:     Dict													=       0






					@property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											return self._images






					@property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											return self._labels






					@property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											return self._num_examples






					@property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											return self._epochs_completed






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE=False				,							__SCREAMING_SNAKE_CASE=True		):


											"""simple docstring"""





											if fake_data:
																	lowercase_	:     Dict													=       [1] * 7_84
																	lowercase_	:     List[Any]													=       [1] + [0] * 9 if self.one_hot else 0
																	return (
																	    [fake_image for _ in range(__SCREAMING_SNAKE_CASE		)],
																	    [fake_label for _ in range(__SCREAMING_SNAKE_CASE		)],
																	)
											lowercase_	:     Dict													=       self._index_in_epoch
											# Shuffle for the first epoch
											if self._epochs_completed == 0 and start == 0 and shuffle:
																	lowercase_	:     Optional[int]													=       numpy.arange(self._num_examples		)
																	numpy.random.shuffle(__SCREAMING_SNAKE_CASE		)
																	lowercase_	:     List[str]													=       self.images[perma]
																	lowercase_	:     str													=       self.labels[perma]
											# Go to the next epoch
											if start + batch_size > self._num_examples:
																	# Finished epoch
																	self._epochs_completed += 1
																	# Get the rest examples in this epoch
																	lowercase_	:     Union[str, Any]													=       self._num_examples - start
																	lowercase_	:     Optional[int]													=       self._images[start : self._num_examples]
																	lowercase_	:     Any													=       self._labels[start : self._num_examples]
																	# Shuffle the data
																	if shuffle:
																							lowercase_	:     List[str]													=       numpy.arange(self._num_examples		)
																							numpy.random.shuffle(__SCREAMING_SNAKE_CASE		)
																							lowercase_	:     List[str]													=       self.images[perm]
																							lowercase_	:     str													=       self.labels[perm]
																	# Start next epoch
																	lowercase_	:     List[Any]													=       0
																	lowercase_	:     Tuple													=       batch_size - rest_num_examples
																	lowercase_	:     str													=       self._index_in_epoch
																	lowercase_	:     Optional[Any]													=       self._images[start:end]
																	lowercase_	:     List[str]													=       self._labels[start:end]
																	return (
																	    numpy.concatenate((images_rest_part, images_new_part)				,							axis=0		),
																	    numpy.concatenate((labels_rest_part, labels_new_part)				,							axis=0		),
																	)
											else:
																	self._index_in_epoch += batch_size
																	lowercase_	:     str													=       self._index_in_epoch
																	return self._images[start:end], self._labels[start:end]







@deprecated(A__      ,      '''Please write your own downloading logic.'''							)
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: str      ,      __SCREAMING_SNAKE_CASE					: Any      ,      __SCREAMING_SNAKE_CASE					: List[str]							):



						"""simple docstring"""





						if not gfile.Exists(A__							):
												gfile.MakeDirs(A__							)
						lowercase_	:     Union[str, Any]													=       os.path.join(A__      ,      A__							)
						if not gfile.Exists(A__							):
												urllib.request.urlretrieve(A__      ,      A__							)  # noqa: S310
												with gfile.GFile(A__							) as f:
																		lowercase_	:     Optional[Any]													=       f.size()
												print('''Successfully downloaded'''      ,      A__      ,      A__      ,      '''bytes.'''							)
						return filepath




@deprecated(
    A__      ,      '''Please use alternatives such as:''' ''' tensorflow_datasets.load(\'mnist\')'''							)
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Any      ,      __SCREAMING_SNAKE_CASE					: Optional[int]=False      ,      __SCREAMING_SNAKE_CASE					: Optional[int]=False      ,      __SCREAMING_SNAKE_CASE					: Optional[int]=dtypes.floataa      ,      __SCREAMING_SNAKE_CASE					: int=True      ,      __SCREAMING_SNAKE_CASE					: Tuple=5000      ,      __SCREAMING_SNAKE_CASE					: Optional[int]=None      ,      __SCREAMING_SNAKE_CASE					: str=DEFAULT_SOURCE_URL      ,      ):



						"""simple docstring"""





						if fake_data:

												def fake():
																		return _DataSet(
																		    []      ,      []      ,      fake_data=A__      ,      one_hot=A__      ,      dtype=A__      ,      seed=A__							)

												lowercase_	:     List[str]													=       fake()
												lowercase_	:     int													=       fake()
												lowercase_	:     str													=       fake()
												return _Datasets(train=A__      ,      validation=A__      ,      test=A__							)

						if not source_url:  # empty string check
												lowercase_	:     List[str]													=       DEFAULT_SOURCE_URL

						lowercase_	:     Tuple													=       'train-images-idx3-ubyte.gz'
						lowercase_	:     str													=       'train-labels-idx1-ubyte.gz'
						lowercase_	:     str													=       't10k-images-idx3-ubyte.gz'
						lowercase_	:     List[Any]													=       't10k-labels-idx1-ubyte.gz'

						lowercase_	:     Dict													=       _maybe_download(
						    A__      ,      A__      ,      source_url + train_images_file							)
						with gfile.Open(A__      ,      '''rb'''							) as f:
												lowercase_	:     Union[str, Any]													=       _extract_images(A__							)

						lowercase_	:     Any													=       _maybe_download(
						    A__      ,      A__      ,      source_url + train_labels_file							)
						with gfile.Open(A__      ,      '''rb'''							) as f:
												lowercase_	:     List[Any]													=       _extract_labels(A__      ,      one_hot=A__							)

						lowercase_	:     Union[str, Any]													=       _maybe_download(
						    A__      ,      A__      ,      source_url + test_images_file							)
						with gfile.Open(A__      ,      '''rb'''							) as f:
												lowercase_	:     Optional[Any]													=       _extract_images(A__							)

						lowercase_	:     List[Any]													=       _maybe_download(
						    A__      ,      A__      ,      source_url + test_labels_file							)
						with gfile.Open(A__      ,      '''rb'''							) as f:
												lowercase_	:     List[str]													=       _extract_labels(A__      ,      one_hot=A__							)

						if not 0 <= validation_size <= len(A__							):
												lowercase_	:     str													=       (
												    'Validation size should be between 0 and '
												    F'''{len(A__							)}. Received: {validation_size}.'''
												)
												raise ValueError(A__							)

						lowercase_	:     Union[str, Any]													=       train_images[:validation_size]
						lowercase_	:     str													=       train_labels[:validation_size]
						lowercase_	:     Optional[int]													=       train_images[validation_size:]
						lowercase_	:     str													=       train_labels[validation_size:]

						lowercase_	:     Optional[int]													=       {'dtype': dtype, 'reshape': reshape, 'seed': seed}

						lowercase_	:     Dict													=       _DataSet(A__      ,      A__      ,      **A__							)
						lowercase_	:     Union[str, Any]													=       _DataSet(A__      ,      A__      ,      **A__							)
						lowercase_	:     Dict													=       _DataSet(A__      ,      A__      ,      **A__							)

						return _Datasets(train=A__      ,      validation=A__      ,      test=A__							)










import math
import random



def A_	(		A__		,      A__ = False				)  ->		float:
       if deriv:
              return value * (1 - value)
       return 1 / (1 + math.exp(-value				))


# Initial Value
lowercase      :       Optional[Any]									=  0.02



def A_	(		A__		,      A__				)  ->		float:
       a__						:	int =				float(2 * (random.randint(1		,      100				)) - 1				)

       for _ in range(A__				):
              # Forward propagation
              a__						:	Union[str, Any] =				sigmoid_function(INITIAL_VALUE * weight				)
              # How much did we miss?
              a__						:	Optional[Any] =				(expected / 100) - layer_a
              # Error delta
              a__						:	Optional[int] =				layer_1_error * sigmoid_function(A__		,      A__				)
              # Update weight
              weight += INITIAL_VALUE * layer_1_delta

       return layer_a * 100


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    lowercase      :       Dict									=  int(input("""Expected value: """))
    lowercase      :       Dict									=  int(input("""Number of propagations: """))
    print(forward_propagation(expected, number_propagations))


