GitHub Action formats our code with psf/black (#1569)

* GitHub Action formats our code with psf/black

@poyea Your review please.

* fixup! Format Python code with psf/black push

.github/workflows/autoblack.yml

@@ -1,34 +1,24 @@
-# GitHub Action that uses Black to reformat the Python code in an incoming pull request.
-# If all Python code in the pull request is complient with Black then this Action does nothing.
-# Othewrwise, Black is run and its changes are committed back to the incoming pull request.
+# GitHub Action that uses Black to reformat Python code (if needed) when doing a git push.
+# If all Python code in the repo is complient with Black then this Action does nothing.
+# Otherwise, Black is run and its changes are committed to the repo.
 # https://github.com/cclauss/autoblack
 
-name: autoblack
-on: [pull_request]
+name: autoblack_push
+on: [push]
 jobs:
   build:
     runs-on: ubuntu-latest
-    strategy:
-      max-parallel: 1
-      matrix:
-        python-version: [3.7]
     steps:
       - uses: actions/checkout@v1
-      - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v1
-        with:
-          python-version: ${{ matrix.python-version }}
-      - name: Install psf/black
-        run: pip install black
-      - name: Run black --check .
-        run: black --check .
-      - name: If needed, commit black changes to the pull request
+      - uses: actions/setup-python@v1
+      - run: pip install black
+      - run: black --check .
+      - name: If needed, commit black changes to a new pull request
         if: failure()
         run: |
           black .
-          git config --global user.name 'autoblack'
-          git config --global user.email 'cclauss@users.noreply.github.com'
+          git config --global user.name github-actions
+          git config --global user.email '${GITHUB_ACTOR}@users.noreply.github.com'          
           git remote set-url origin https://x-access-token:${{ secrets.GITHUB_TOKEN }}@github.com/$GITHUB_REPOSITORY
-          git checkout $GITHUB_HEAD_REF
-          git commit -am "fixup: Format Python code with psf/black"
-          git push
+          git commit -am "fixup! Format Python code with psf/black push"
+          git push --force origin HEAD:$GITHUB_REF

ciphers/deterministic_miller_rabin.py

@@ -41,19 +41,21 @@ def miller_rabin(n, allow_probable=False):
             "A return value of True indicates a probable prime."
         )
     # array bounds provided by analysis
-    bounds = [2_047,
-              1_373_653,
-              25_326_001,
-              3_215_031_751,
-              2_152_302_898_747,
-              3_474_749_660_383,
-              341_550_071_728_321,
-              1,
-              3_825_123_056_546_413_051,
-              1,
-              1,
-              318_665_857_834_031_151_167_461,
-              3_317_044_064_679_887_385_961_981]
+    bounds = [
+        2_047,
+        1_373_653,
+        25_326_001,
+        3_215_031_751,
+        2_152_302_898_747,
+        3_474_749_660_383,
+        341_550_071_728_321,
+        1,
+        3_825_123_056_546_413_051,
+        1,
+        1,
+        318_665_857_834_031_151_167_461,
+        3_317_044_064_679_887_385_961_981,
+    ]
 
     primes = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41]
     for idx, _p in enumerate(bounds, 1):
@@ -131,5 +133,5 @@ def test_miller_rabin():
     # upper limit for probabilistic test
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     test_miller_rabin()

ciphers/diffie.py

@@ -1,8 +1,8 @@
 def find_primitive(n):
     for r in range(1, n):
         li = []
-        for x in range(n-1):
-            val = pow(r,x,n)
+        for x in range(n - 1):
+            val = pow(r, x, n)
             if val in li:
                 break
             li.append(val)
@@ -11,16 +11,15 @@ def find_primitive(n):
 
 
 if __name__ == "__main__":
-    q = int(input('Enter a prime number q: '))
+    q = int(input("Enter a prime number q: "))
     a = find_primitive(q)
-    a_private = int(input('Enter private key of A: '))
+    a_private = int(input("Enter private key of A: "))
     a_public = pow(a, a_private, q)
-    b_private = int(input('Enter private key of B: '))
+    b_private = int(input("Enter private key of B: "))
     b_public = pow(a, b_private, q)
 
     a_secret = pow(b_public, a_private, q)
     b_secret = pow(a_public, b_private, q)
 
-    print('The key value generated by A is: ', a_secret)
-    print('The key value generated by B is: ', b_secret)
-    
+    print("The key value generated by A is: ", a_secret)
+    print("The key value generated by B is: ", b_secret)

data_structures/binary_tree/basic_binary_tree.py

@@ -22,7 +22,7 @@ def display(tree):  # In Order traversal of the tree
 
 
 def depth_of_tree(
-    tree
+    tree,
 ):  # This is the recursive function to find the depth of binary tree.
     if tree is None:
         return 0
@@ -36,7 +36,7 @@ def depth_of_tree(
 
 
 def is_full_binary_tree(
-    tree
+    tree,
 ):  # This functions returns that is it full binary tree or not?
     if tree is None:
         return True

data_structures/binary_tree/treap.py

@@ -172,7 +172,6 @@ def main():
         args = input()
 
     print("good by!")
-    
 
 
 if __name__ == "__main__":

data_structures/heap/min_heap.py

@@ -77,9 +77,10 @@ class MinHeap:
 
             if smallest != idx:
                 array[idx], array[smallest] = array[smallest], array[idx]
-                self.idx_of_element[array[idx]], self.idx_of_element[
-                    array[smallest]
-                ] = (
+                (
+                    self.idx_of_element[array[idx]],
+                    self.idx_of_element[array[smallest]],
+                ) = (
                     self.idx_of_element[array[smallest]],
                     self.idx_of_element[array[idx]],
                 )

data_structures/linked_list/doubly_linked_list.py

@@ -23,9 +23,7 @@ class LinkedList:  # making main class named linked list
     def deleteHead(self):
         temp = self.head
         self.head = self.head.next  # oldHead <--> 2ndElement(head)
-        self.head.previous = (
-            None
-        )  # oldHead --> 2ndElement(head) nothing pointing at it so the old head will be removed
+        self.head.previous = None  # oldHead --> 2ndElement(head) nothing pointing at it so the old head will be removed
         if self.head is None:
             self.tail = None  # if empty linked list
         return temp

data_structures/linked_list/from_sequence.py

@@ -1,6 +1,7 @@
 # Recursive Prorgam to create a Linked List from a sequence and
 # print a string representation of it.
 
+
 class Node:
     def __init__(self, data=None):
         self.data = data
@@ -17,7 +18,6 @@ class Node:
         return string_rep
 
 
-
 def make_linked_list(elements_list):
     """Creates a Linked List from the elements of the given sequence
     (list/tuple) and returns the head of the Linked List."""
@@ -36,8 +36,7 @@ def make_linked_list(elements_list):
     return head
 
 
-
-list_data = [1,3,5,32,44,12,43]
+list_data = [1, 3, 5, 32, 44, 12, 43]
 print(f"List: {list_data}")
 print("Creating Linked List from List.")
 linked_list = make_linked_list(list_data)

data_structures/linked_list/print_reverse.py

@@ -1,5 +1,6 @@
 # Program to print the elements of a linked list in reverse
 
+
 class Node:
     def __init__(self, data=None):
         self.data = data
@@ -16,7 +17,6 @@ class Node:
         return string_rep
 
 
-
 def make_linked_list(elements_list):
     """Creates a Linked List from the elements of the given sequence
     (list/tuple) and returns the head of the Linked List."""
@@ -34,6 +34,7 @@ def make_linked_list(elements_list):
         current = current.next
     return head
 
+
 def print_reverse(head_node):
     """Prints the elements of the given Linked List in reverse order"""
 
@@ -46,8 +47,7 @@ def print_reverse(head_node):
         print(head_node.data)
 
 
-
-list_data = [14,52,14,12,43]
+list_data = [14, 52, 14, 12, 43]
 linked_list = make_linked_list(list_data)
 print("Linked List:")
 print(linked_list)

dynamic_programming/longest_increasing_subsequence.py

@@ -48,8 +48,9 @@ def longest_subsequence(array: List[int]) -> List[int]:  # This function is recu
         return temp_array
     else:
         return longest_subseq
-    
+
 
 if __name__ == "__main__":
     import doctest
+
     doctest.testmod()

dynamic_programming/longest_increasing_subsequence_o(nlogn).py

@@ -6,6 +6,7 @@
 #############################
 from typing import List
 
+
 def CeilIndex(v, l, r, key):
     while r - l > 1:
         m = (l + r) // 2
@@ -49,4 +50,5 @@ def LongestIncreasingSubsequenceLength(v: List[int]) -> int:
 
 if __name__ == "__main__":
     import doctest
+
     doctest.testmod()

dynamic_programming/max_sub_array.py

@@ -75,6 +75,7 @@ if __name__ == "__main__":
     import time
     import matplotlib.pyplot as plt
     from random import randint
+
     inputs = [10, 100, 1000, 10000, 50000, 100000, 200000, 300000, 400000, 500000]
     tim = []
     for i in inputs:

file_transfer/send_file.py

@@ -2,8 +2,8 @@ if __name__ == "__main__":
     import socket  # Import socket module
 
     ONE_CONNECTION_ONLY = (
-        True
-    )  # Set this to False if you wish to continuously accept connections
+        True  # Set this to False if you wish to continuously accept connections
+    )
 
     filename = "mytext.txt"
     port = 12312  # Reserve a port for your service.

maths/ceil.py

@@ -9,10 +9,12 @@ def ceil(x) -> int:
     >>> all(ceil(n) == math.ceil(n) for n in (1, -1, 0, -0, 1.1, -1.1, 1.0, -1.0, 1_000_000_000))
     True
     """
-    return x if isinstance(x, int) or x - int(x) == 0 else int(x + 1) if x > 0 else int(x)
+    return (
+        x if isinstance(x, int) or x - int(x) == 0 else int(x + 1) if x > 0 else int(x)
+    )
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     import doctest
 
     doctest.testmod()

maths/factorial_python.py

@@ -28,7 +28,7 @@ def factorial(input_number: int) -> int:
     return result
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     import doctest
 
     doctest.testmod()

maths/factorial_recursive.py

@@ -24,7 +24,7 @@ def factorial(n: int) -> int:
     return 1 if n == 0 or n == 1 else n * factorial(n - 1)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     import doctest
 
     doctest.testmod()

maths/floor.py

@@ -9,10 +9,12 @@ def floor(x) -> int:
     >>> all(floor(n) == math.floor(n) for n in (1, -1, 0, -0, 1.1, -1.1, 1.0, -1.0, 1_000_000_000))
     True
     """
-    return x if isinstance(x, int) or x - int(x) == 0 else int(x) if x > 0 else int(x - 1)
+    return (
+        x if isinstance(x, int) or x - int(x) == 0 else int(x) if x > 0 else int(x - 1)
+    )
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     import doctest
 
     doctest.testmod()

maths/gaussian.py

@@ -50,7 +50,7 @@ def gaussian(x, mu: float = 0.0, sigma: float = 1.0) -> int:
     >>> gaussian(2523, mu=234234, sigma=3425)
     0.0
   """
-    return 1 / sqrt(2 * pi * sigma ** 2) * exp(-(x - mu) ** 2 / 2 * sigma ** 2)
+    return 1 / sqrt(2 * pi * sigma ** 2) * exp(-((x - mu) ** 2) / 2 * sigma ** 2)
 
 
 if __name__ == "__main__":

maths/perfect_square.py

@@ -21,7 +21,7 @@ def perfect_square(num: int) -> bool:
     return math.sqrt(num) * math.sqrt(num) == num
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     import doctest
 
     doctest.testmod()

neural_network/gan.py

@@ -7,28 +7,42 @@ import input_data
 random_numer = 42
 
 np.random.seed(random_numer)
+
+
 def ReLu(x):
-    mask = (x>0) * 1.0
-    return mask *x
+    mask = (x > 0) * 1.0
+    return mask * x
+
+
 def d_ReLu(x):
-    mask = (x>0) * 1.0
+    mask = (x > 0) * 1.0
     return mask
 
+
 def arctan(x):
     return np.arctan(x)
+
+
 def d_arctan(x):
     return 1 / (1 + x ** 2)
 
+
 def log(x):
-    return 1 / ( 1+ np.exp(-1*x))
+    return 1 / (1 + np.exp(-1 * x))
+
+
 def d_log(x):
     return log(x) * (1 - log(x))
 
+
 def tanh(x):
     return np.tanh(x)
+
+
 def d_tanh(x):
     return 1 - np.tanh(x) ** 2
 
+
 def plot(samples):
     fig = plt.figure(figsize=(4, 4))
     gs = gridspec.GridSpec(4, 4)
@@ -36,104 +50,140 @@ def plot(samples):
 
     for i, sample in enumerate(samples):
         ax = plt.subplot(gs[i])
-        plt.axis('off')
+        plt.axis("off")
         ax.set_xticklabels([])
         ax.set_yticklabels([])
-        ax.set_aspect('equal')
-        plt.imshow(sample.reshape(28, 28), cmap='Greys_r')
+        ax.set_aspect("equal")
+        plt.imshow(sample.reshape(28, 28), cmap="Greys_r")
 
     return fig
 
 
-
 # 1. Load Data and declare hyper
-print('--------- Load Data ----------')
-mnist = input_data.read_data_sets('MNIST_data', one_hot=False)
+print("--------- Load Data ----------")
+mnist = input_data.read_data_sets("MNIST_data", one_hot=False)
 temp = mnist.test
 images, labels = temp.images, temp.labels
-images, labels = shuffle(np.asarray(images),np.asarray(labels))
+images, labels = shuffle(np.asarray(images), np.asarray(labels))
 num_epoch = 10
 learing_rate = 0.00009
 G_input = 100
-hidden_input,hidden_input2,hidden_input3 = 128,256,346
-hidden_input4,hidden_input5,hidden_input6 = 480,560,686
+hidden_input, hidden_input2, hidden_input3 = 128, 256, 346
+hidden_input4, hidden_input5, hidden_input6 = 480, 560, 686
 
 
-
-print('--------- Declare Hyper Parameters ----------')
+print("--------- Declare Hyper Parameters ----------")
 # 2. Declare Weights
-D_W1 = np.random.normal(size=(784,hidden_input),scale=(1. / np.sqrt(784 / 2.)))   *0.002
+D_W1 = (
+    np.random.normal(size=(784, hidden_input), scale=(1.0 / np.sqrt(784 / 2.0))) * 0.002
+)
 # D_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.)))       *0.002
 D_b1 = np.zeros(hidden_input)
 
-D_W2 = np.random.normal(size=(hidden_input,1),scale=(1. / np.sqrt(hidden_input / 2.)))     *0.002
+D_W2 = (
+    np.random.normal(size=(hidden_input, 1), scale=(1.0 / np.sqrt(hidden_input / 2.0)))
+    * 0.002
+)
 # D_b2 = np.random.normal(size=(1),scale=(1. / np.sqrt(1 / 2.)))           *0.002
 D_b2 = np.zeros(1)
 
 
-G_W1 = np.random.normal(size=(G_input,hidden_input),scale=(1. / np.sqrt(G_input / 2.)))   *0.002
+G_W1 = (
+    np.random.normal(size=(G_input, hidden_input), scale=(1.0 / np.sqrt(G_input / 2.0)))
+    * 0.002
+)
 # G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.)))      *0.002
 G_b1 = np.zeros(hidden_input)
 
-G_W2 = np.random.normal(size=(hidden_input,hidden_input2),scale=(1. / np.sqrt(hidden_input / 2.)))   *0.002
+G_W2 = (
+    np.random.normal(
+        size=(hidden_input, hidden_input2), scale=(1.0 / np.sqrt(hidden_input / 2.0))
+    )
+    * 0.002
+)
 # G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.)))      *0.002
 G_b2 = np.zeros(hidden_input2)
 
-G_W3 = np.random.normal(size=(hidden_input2,hidden_input3),scale=(1. / np.sqrt(hidden_input2 / 2.)))   *0.002
+G_W3 = (
+    np.random.normal(
+        size=(hidden_input2, hidden_input3), scale=(1.0 / np.sqrt(hidden_input2 / 2.0))
+    )
+    * 0.002
+)
 # G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.)))      *0.002
 G_b3 = np.zeros(hidden_input3)
 
-G_W4 = np.random.normal(size=(hidden_input3,hidden_input4),scale=(1. / np.sqrt(hidden_input3 / 2.)))   *0.002
+G_W4 = (
+    np.random.normal(
+        size=(hidden_input3, hidden_input4), scale=(1.0 / np.sqrt(hidden_input3 / 2.0))
+    )
+    * 0.002
+)
 # G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.)))      *0.002
 G_b4 = np.zeros(hidden_input4)
 
-G_W5 = np.random.normal(size=(hidden_input4,hidden_input5),scale=(1. / np.sqrt(hidden_input4 / 2.)))   *0.002
+G_W5 = (
+    np.random.normal(
+        size=(hidden_input4, hidden_input5), scale=(1.0 / np.sqrt(hidden_input4 / 2.0))
+    )
+    * 0.002
+)
 # G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.)))      *0.002
 G_b5 = np.zeros(hidden_input5)
 
-G_W6 = np.random.normal(size=(hidden_input5,hidden_input6),scale=(1. / np.sqrt(hidden_input5 / 2.)))   *0.002
+G_W6 = (
+    np.random.normal(
+        size=(hidden_input5, hidden_input6), scale=(1.0 / np.sqrt(hidden_input5 / 2.0))
+    )
+    * 0.002
+)
 # G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.)))      *0.002
 G_b6 = np.zeros(hidden_input6)
 
-G_W7 = np.random.normal(size=(hidden_input6,784),scale=(1. / np.sqrt(hidden_input6 / 2.)))  *0.002
+G_W7 = (
+    np.random.normal(
+        size=(hidden_input6, 784), scale=(1.0 / np.sqrt(hidden_input6 / 2.0))
+    )
+    * 0.002
+)
 # G_b2 = np.random.normal(size=(784),scale=(1. / np.sqrt(784 / 2.)))      *0.002
 G_b7 = np.zeros(784)
 
 # 3. For Adam Optimzier
-v1,m1 = 0,0
-v2,m2 = 0,0
-v3,m3 = 0,0
-v4,m4 = 0,0
+v1, m1 = 0, 0
+v2, m2 = 0, 0
+v3, m3 = 0, 0
+v4, m4 = 0, 0
 
-v5,m5 = 0,0
-v6,m6 = 0,0
-v7,m7 = 0,0
-v8,m8 = 0,0
-v9,m9 = 0,0
-v10,m10 = 0,0
-v11,m11 = 0,0
-v12,m12 = 0,0
+v5, m5 = 0, 0
+v6, m6 = 0, 0
+v7, m7 = 0, 0
+v8, m8 = 0, 0
+v9, m9 = 0, 0
+v10, m10 = 0, 0
+v11, m11 = 0, 0
+v12, m12 = 0, 0
 
-v13,m13 = 0,0
-v14,m14 = 0,0
+v13, m13 = 0, 0
+v14, m14 = 0, 0
 
-v15,m15 = 0,0
-v16,m16 = 0,0
+v15, m15 = 0, 0
+v16, m16 = 0, 0
 
-v17,m17 = 0,0
-v18,m18 = 0,0
+v17, m17 = 0, 0
+v18, m18 = 0, 0
 
 
-beta_1,beta_2,eps = 0.9,0.999,0.00000001
+beta_1, beta_2, eps = 0.9, 0.999, 0.00000001
 
-print('--------- Started Training ----------')
+print("--------- Started Training ----------")
 for iter in range(num_epoch):
 
     random_int = np.random.randint(len(images) - 5)
-    current_image = np.expand_dims(images[random_int],axis=0)
+    current_image = np.expand_dims(images[random_int], axis=0)
 
     # Func: Generate The first Fake Data
-    Z = np.random.uniform(-1., 1., size=[1, G_input])
+    Z = np.random.uniform(-1.0, 1.0, size=[1, G_input])
     Gl1 = Z.dot(G_W1) + G_b1
     Gl1A = arctan(Gl1)
     Gl2 = Gl1A.dot(G_W2) + G_b2
@@ -164,38 +214,38 @@ for iter in range(num_epoch):
     Dl2_fA = log(Dl2_f)
 
     # Func: Cost D
-    D_cost = -np.log(Dl2_rA) + np.log(1.0- Dl2_fA)
+    D_cost = -np.log(Dl2_rA) + np.log(1.0 - Dl2_fA)
 
     # Func: Gradient
-    grad_f_w2_part_1 =  1/(1.0- Dl2_fA)
-    grad_f_w2_part_2 =  d_log(Dl2_f)
-    grad_f_w2_part_3 =   Dl1_fA
-    grad_f_w2 =       grad_f_w2_part_3.T.dot(grad_f_w2_part_1 * grad_f_w2_part_2)
+    grad_f_w2_part_1 = 1 / (1.0 - Dl2_fA)
+    grad_f_w2_part_2 = d_log(Dl2_f)
+    grad_f_w2_part_3 = Dl1_fA
+    grad_f_w2 = grad_f_w2_part_3.T.dot(grad_f_w2_part_1 * grad_f_w2_part_2)
     grad_f_b2 = grad_f_w2_part_1 * grad_f_w2_part_2
 
-    grad_f_w1_part_1 =  (grad_f_w2_part_1 * grad_f_w2_part_2).dot(D_W2.T)
-    grad_f_w1_part_2 =  d_ReLu(Dl1_f)
-    grad_f_w1_part_3 =   current_fake_data
-    grad_f_w1 =       grad_f_w1_part_3.T.dot(grad_f_w1_part_1 * grad_f_w1_part_2)
-    grad_f_b1 =      grad_f_w1_part_1 * grad_f_w1_part_2
+    grad_f_w1_part_1 = (grad_f_w2_part_1 * grad_f_w2_part_2).dot(D_W2.T)
+    grad_f_w1_part_2 = d_ReLu(Dl1_f)
+    grad_f_w1_part_3 = current_fake_data
+    grad_f_w1 = grad_f_w1_part_3.T.dot(grad_f_w1_part_1 * grad_f_w1_part_2)
+    grad_f_b1 = grad_f_w1_part_1 * grad_f_w1_part_2
 
-    grad_r_w2_part_1 =  - 1/Dl2_rA
-    grad_r_w2_part_2 =  d_log(Dl2_r)
-    grad_r_w2_part_3 =   Dl1_rA
-    grad_r_w2 =       grad_r_w2_part_3.T.dot(grad_r_w2_part_1 * grad_r_w2_part_2)
-    grad_r_b2 =       grad_r_w2_part_1 * grad_r_w2_part_2
+    grad_r_w2_part_1 = -1 / Dl2_rA
+    grad_r_w2_part_2 = d_log(Dl2_r)
+    grad_r_w2_part_3 = Dl1_rA
+    grad_r_w2 = grad_r_w2_part_3.T.dot(grad_r_w2_part_1 * grad_r_w2_part_2)
+    grad_r_b2 = grad_r_w2_part_1 * grad_r_w2_part_2
 
-    grad_r_w1_part_1 =  (grad_r_w2_part_1 * grad_r_w2_part_2).dot(D_W2.T)
-    grad_r_w1_part_2 =  d_ReLu(Dl1_r)
-    grad_r_w1_part_3 =   current_image
-    grad_r_w1 =       grad_r_w1_part_3.T.dot(grad_r_w1_part_1 * grad_r_w1_part_2)
-    grad_r_b1 =       grad_r_w1_part_1 * grad_r_w1_part_2
+    grad_r_w1_part_1 = (grad_r_w2_part_1 * grad_r_w2_part_2).dot(D_W2.T)
+    grad_r_w1_part_2 = d_ReLu(Dl1_r)
+    grad_r_w1_part_3 = current_image
+    grad_r_w1 = grad_r_w1_part_3.T.dot(grad_r_w1_part_1 * grad_r_w1_part_2)
+    grad_r_b1 = grad_r_w1_part_1 * grad_r_w1_part_2
 
-    grad_w1 =grad_f_w1 + grad_r_w1
-    grad_b1 =grad_f_b1 + grad_r_b1
+    grad_w1 = grad_f_w1 + grad_r_w1
+    grad_b1 = grad_f_b1 + grad_r_b1
 
-    grad_w2 =grad_f_w2 + grad_r_w2
-    grad_b2 =grad_f_b2 + grad_r_b2
+    grad_w2 = grad_f_w2 + grad_r_w2
+    grad_b2 = grad_f_b2 + grad_r_b2
 
     # ---- Update Gradient ----
     m1 = beta_1 * m1 + (1 - beta_1) * grad_w1
@@ -210,14 +260,22 @@ for iter in range(num_epoch):
     m4 = beta_1 * m4 + (1 - beta_1) * grad_b2
     v4 = beta_2 * v4 + (1 - beta_2) * grad_b2 ** 2
 
-    D_W1 = D_W1 - (learing_rate / (np.sqrt(v1 /(1-beta_2) ) + eps)) * (m1/(1-beta_1))
-    D_b1 = D_b1 - (learing_rate / (np.sqrt(v2 /(1-beta_2) ) + eps)) * (m2/(1-beta_1))
+    D_W1 = D_W1 - (learing_rate / (np.sqrt(v1 / (1 - beta_2)) + eps)) * (
+        m1 / (1 - beta_1)
+    )
+    D_b1 = D_b1 - (learing_rate / (np.sqrt(v2 / (1 - beta_2)) + eps)) * (
+        m2 / (1 - beta_1)
+    )
 
-    D_W2 = D_W2 - (learing_rate / (np.sqrt(v3 /(1-beta_2) ) + eps)) * (m3/(1-beta_1))
-    D_b2 = D_b2 - (learing_rate / (np.sqrt(v4 /(1-beta_2) ) + eps)) * (m4/(1-beta_1))
+    D_W2 = D_W2 - (learing_rate / (np.sqrt(v3 / (1 - beta_2)) + eps)) * (
+        m3 / (1 - beta_1)
+    )
+    D_b2 = D_b2 - (learing_rate / (np.sqrt(v4 / (1 - beta_2)) + eps)) * (
+        m4 / (1 - beta_1)
+    )
 
     # Func: Forward Feed for G
-    Z = np.random.uniform(-1., 1., size=[1, G_input])
+    Z = np.random.uniform(-1.0, 1.0, size=[1, G_input])
     Gl1 = Z.dot(G_W1) + G_b1
     Gl1A = arctan(Gl1)
     Gl2 = Gl1A.dot(G_W2) + G_b2
@@ -244,7 +302,9 @@ for iter in range(num_epoch):
     G_cost = -np.log(Dl2_A)
 
     # Func: Gradient
-    grad_G_w7_part_1 = ((-1/Dl2_A) * d_log(Dl2).dot(D_W2.T) * (d_ReLu(Dl1))).dot(D_W1.T)
+    grad_G_w7_part_1 = ((-1 / Dl2_A) * d_log(Dl2).dot(D_W2.T) * (d_ReLu(Dl1))).dot(
+        D_W1.T
+    )
     grad_G_w7_part_2 = d_log(Gl7)
     grad_G_w7_part_3 = Gl6A
     grad_G_w7 = grad_G_w7_part_3.T.dot(grad_G_w7_part_1 * grad_G_w7_part_1)
@@ -254,31 +314,31 @@ for iter in range(num_epoch):
     grad_G_w6_part_2 = d_ReLu(Gl6)
     grad_G_w6_part_3 = Gl5A
     grad_G_w6 = grad_G_w6_part_3.T.dot(grad_G_w6_part_1 * grad_G_w6_part_2)
-    grad_G_b6 = (grad_G_w6_part_1 * grad_G_w6_part_2)
+    grad_G_b6 = grad_G_w6_part_1 * grad_G_w6_part_2
 
     grad_G_w5_part_1 = (grad_G_w6_part_1 * grad_G_w6_part_2).dot(G_W6.T)
     grad_G_w5_part_2 = d_tanh(Gl5)
     grad_G_w5_part_3 = Gl4A
     grad_G_w5 = grad_G_w5_part_3.T.dot(grad_G_w5_part_1 * grad_G_w5_part_2)
-    grad_G_b5 = (grad_G_w5_part_1 * grad_G_w5_part_2)
+    grad_G_b5 = grad_G_w5_part_1 * grad_G_w5_part_2
 
     grad_G_w4_part_1 = (grad_G_w5_part_1 * grad_G_w5_part_2).dot(G_W5.T)
     grad_G_w4_part_2 = d_ReLu(Gl4)
     grad_G_w4_part_3 = Gl3A
     grad_G_w4 = grad_G_w4_part_3.T.dot(grad_G_w4_part_1 * grad_G_w4_part_2)
-    grad_G_b4 = (grad_G_w4_part_1 * grad_G_w4_part_2)
+    grad_G_b4 = grad_G_w4_part_1 * grad_G_w4_part_2
 
     grad_G_w3_part_1 = (grad_G_w4_part_1 * grad_G_w4_part_2).dot(G_W4.T)
     grad_G_w3_part_2 = d_arctan(Gl3)
     grad_G_w3_part_3 = Gl2A
     grad_G_w3 = grad_G_w3_part_3.T.dot(grad_G_w3_part_1 * grad_G_w3_part_2)
-    grad_G_b3 = (grad_G_w3_part_1 * grad_G_w3_part_2)
+    grad_G_b3 = grad_G_w3_part_1 * grad_G_w3_part_2
 
     grad_G_w2_part_1 = (grad_G_w3_part_1 * grad_G_w3_part_2).dot(G_W3.T)
     grad_G_w2_part_2 = d_ReLu(Gl2)
     grad_G_w2_part_3 = Gl1A
     grad_G_w2 = grad_G_w2_part_3.T.dot(grad_G_w2_part_1 * grad_G_w2_part_2)
-    grad_G_b2 = (grad_G_w2_part_1 * grad_G_w2_part_2)
+    grad_G_b2 = grad_G_w2_part_1 * grad_G_w2_part_2
 
     grad_G_w1_part_1 = (grad_G_w2_part_1 * grad_G_w2_part_2).dot(G_W2.T)
     grad_G_w1_part_2 = d_arctan(Gl1)
@@ -329,29 +389,57 @@ for iter in range(num_epoch):
     m18 = beta_1 * m18 + (1 - beta_1) * grad_G_b7
     v18 = beta_2 * v18 + (1 - beta_2) * grad_G_b7 ** 2
 
-    G_W1 = G_W1 - (learing_rate / (np.sqrt(v5 /(1-beta_2) ) + eps)) * (m5/(1-beta_1))
-    G_b1 = G_b1 - (learing_rate / (np.sqrt(v6 /(1-beta_2) ) + eps)) * (m6/(1-beta_1))
+    G_W1 = G_W1 - (learing_rate / (np.sqrt(v5 / (1 - beta_2)) + eps)) * (
+        m5 / (1 - beta_1)
+    )
+    G_b1 = G_b1 - (learing_rate / (np.sqrt(v6 / (1 - beta_2)) + eps)) * (
+        m6 / (1 - beta_1)
+    )
 
-    G_W2 = G_W2 - (learing_rate / (np.sqrt(v7 /(1-beta_2) ) + eps)) * (m7/(1-beta_1))
-    G_b2 = G_b2 - (learing_rate / (np.sqrt(v8 /(1-beta_2) ) + eps)) * (m8/(1-beta_1))
+    G_W2 = G_W2 - (learing_rate / (np.sqrt(v7 / (1 - beta_2)) + eps)) * (
+        m7 / (1 - beta_1)
+    )
+    G_b2 = G_b2 - (learing_rate / (np.sqrt(v8 / (1 - beta_2)) + eps)) * (
+        m8 / (1 - beta_1)
+    )
 
-    G_W3 = G_W3 - (learing_rate / (np.sqrt(v9 /(1-beta_2) ) + eps)) * (m9/(1-beta_1))
-    G_b3 = G_b3 - (learing_rate / (np.sqrt(v10 /(1-beta_2) ) + eps)) * (m10/(1-beta_1))
+    G_W3 = G_W3 - (learing_rate / (np.sqrt(v9 / (1 - beta_2)) + eps)) * (
+        m9 / (1 - beta_1)
+    )
+    G_b3 = G_b3 - (learing_rate / (np.sqrt(v10 / (1 - beta_2)) + eps)) * (
+        m10 / (1 - beta_1)
+    )
 
-    G_W4 = G_W4 - (learing_rate / (np.sqrt(v11 /(1-beta_2) ) + eps)) * (m11/(1-beta_1))
-    G_b4 = G_b4 - (learing_rate / (np.sqrt(v12 /(1-beta_2) ) + eps)) * (m12/(1-beta_1))
+    G_W4 = G_W4 - (learing_rate / (np.sqrt(v11 / (1 - beta_2)) + eps)) * (
+        m11 / (1 - beta_1)
+    )
+    G_b4 = G_b4 - (learing_rate / (np.sqrt(v12 / (1 - beta_2)) + eps)) * (
+        m12 / (1 - beta_1)
+    )
 
-    G_W5 = G_W5 - (learing_rate / (np.sqrt(v13 /(1-beta_2) ) + eps)) * (m13/(1-beta_1))
-    G_b5 = G_b5 - (learing_rate / (np.sqrt(v14 /(1-beta_2) ) + eps)) * (m14/(1-beta_1))
+    G_W5 = G_W5 - (learing_rate / (np.sqrt(v13 / (1 - beta_2)) + eps)) * (
+        m13 / (1 - beta_1)
+    )
+    G_b5 = G_b5 - (learing_rate / (np.sqrt(v14 / (1 - beta_2)) + eps)) * (
+        m14 / (1 - beta_1)
+    )
 
-    G_W6 = G_W6 - (learing_rate / (np.sqrt(v15 /(1-beta_2) ) + eps)) * (m15/(1-beta_1))
-    G_b6 = G_b6 - (learing_rate / (np.sqrt(v16 /(1-beta_2) ) + eps)) * (m16/(1-beta_1))
+    G_W6 = G_W6 - (learing_rate / (np.sqrt(v15 / (1 - beta_2)) + eps)) * (
+        m15 / (1 - beta_1)
+    )
+    G_b6 = G_b6 - (learing_rate / (np.sqrt(v16 / (1 - beta_2)) + eps)) * (
+        m16 / (1 - beta_1)
+    )
 
-    G_W7 = G_W7 - (learing_rate / (np.sqrt(v17 /(1-beta_2) ) + eps)) * (m17/(1-beta_1))
-    G_b7 = G_b7 - (learing_rate / (np.sqrt(v18 /(1-beta_2) ) + eps)) * (m18/(1-beta_1))
+    G_W7 = G_W7 - (learing_rate / (np.sqrt(v17 / (1 - beta_2)) + eps)) * (
+        m17 / (1 - beta_1)
+    )
+    G_b7 = G_b7 - (learing_rate / (np.sqrt(v18 / (1 - beta_2)) + eps)) * (
+        m18 / (1 - beta_1)
+    )
 
     # --- Print Error ----
-    #print("Current Iter: ",iter, " Current D cost:",D_cost, " Current G cost: ", G_cost,end='\r')
+    # print("Current Iter: ",iter, " Current D cost:",D_cost, " Current G cost: ", G_cost,end='\r')
 
     if iter == 0:
         learing_rate = learing_rate * 0.01
@@ -359,12 +447,20 @@ for iter in range(num_epoch):
         learing_rate = learing_rate * 0.01
 
     # ---- Print to Out put ----
-    if iter%10 == 0:
+    if iter % 10 == 0:
 
-        print("Current Iter: ",iter, " Current D cost:",D_cost, " Current G cost: ", G_cost,end='\r')
-        print('--------- Show Example Result See Tab Above ----------')
-        print('--------- Wait for the image to load ---------')
-        Z = np.random.uniform(-1., 1., size=[16, G_input])
+        print(
+            "Current Iter: ",
+            iter,
+            " Current D cost:",
+            D_cost,
+            " Current G cost: ",
+            G_cost,
+            end="\r",
+        )
+        print("--------- Show Example Result See Tab Above ----------")
+        print("--------- Wait for the image to load ---------")
+        Z = np.random.uniform(-1.0, 1.0, size=[16, G_input])
 
         Gl1 = Z.dot(G_W1) + G_b1
         Gl1A = arctan(Gl1)
@@ -384,8 +480,19 @@ for iter in range(num_epoch):
         current_fake_data = log(Gl7)
 
         fig = plot(current_fake_data)
-        fig.savefig('Click_Me_{}.png'.format(str(iter).zfill(3)+"_Ginput_"+str(G_input)+ \
-        "_hiddenone"+str(hidden_input) + "_hiddentwo"+str(hidden_input2) + "_LR_" + str(learing_rate)
-        ), bbox_inches='tight')
-#for complete explanation visit https://towardsdatascience.com/only-numpy-implementing-gan-general-adversarial-networks-and-adam-optimizer-using-numpy-with-2a7e4e032021
+        fig.savefig(
+            "Click_Me_{}.png".format(
+                str(iter).zfill(3)
+                + "_Ginput_"
+                + str(G_input)
+                + "_hiddenone"
+                + str(hidden_input)
+                + "_hiddentwo"
+                + str(hidden_input2)
+                + "_LR_"
+                + str(learing_rate)
+            ),
+            bbox_inches="tight",
+        )
+# for complete explanation visit https://towardsdatascience.com/only-numpy-implementing-gan-general-adversarial-networks-and-adam-optimizer-using-numpy-with-2a7e4e032021
 # -- end code --

neural_network/input_data.py

@@ -34,20 +34,20 @@ from tensorflow.python.framework import random_seed
 from tensorflow.python.platform import gfile
 from tensorflow.python.util.deprecation import deprecated
 
-_Datasets = collections.namedtuple('_Datasets', ['train', 'validation', 'test'])
+_Datasets = collections.namedtuple("_Datasets", ["train", "validation", "test"])
 
 # CVDF mirror of http://yann.lecun.com/exdb/mnist/
-DEFAULT_SOURCE_URL = 'https://storage.googleapis.com/cvdf-datasets/mnist/'
+DEFAULT_SOURCE_URL = "https://storage.googleapis.com/cvdf-datasets/mnist/"
 
 
 def _read32(bytestream):
-  dt = numpy.dtype(numpy.uint32).newbyteorder('>')
-  return numpy.frombuffer(bytestream.read(4), dtype=dt)[0]
+    dt = numpy.dtype(numpy.uint32).newbyteorder(">")
+    return numpy.frombuffer(bytestream.read(4), dtype=dt)[0]
 
 
-@deprecated(None, 'Please use tf.data to implement this functionality.')
+@deprecated(None, "Please use tf.data to implement this functionality.")
 def _extract_images(f):
-  """Extract the images into a 4D uint8 numpy array [index, y, x, depth].
+    """Extract the images into a 4D uint8 numpy array [index, y, x, depth].
 
   Args:
     f: A file object that can be passed into a gzip reader.
@@ -59,34 +59,35 @@ def _extract_images(f):
     ValueError: If the bytestream does not start with 2051.
 
   """
-  print('Extracting', f.name)
-  with gzip.GzipFile(fileobj=f) as bytestream:
-    magic = _read32(bytestream)
-    if magic != 2051:
-      raise ValueError('Invalid magic number %d in MNIST image file: %s' %
-                       (magic, f.name))
-    num_images = _read32(bytestream)
-    rows = _read32(bytestream)
-    cols = _read32(bytestream)
-    buf = bytestream.read(rows * cols * num_images)
-    data = numpy.frombuffer(buf, dtype=numpy.uint8)
-    data = data.reshape(num_images, rows, cols, 1)
-    return data
+    print("Extracting", f.name)
+    with gzip.GzipFile(fileobj=f) as bytestream:
+        magic = _read32(bytestream)
+        if magic != 2051:
+            raise ValueError(
+                "Invalid magic number %d in MNIST image file: %s" % (magic, f.name)
+            )
+        num_images = _read32(bytestream)
+        rows = _read32(bytestream)
+        cols = _read32(bytestream)
+        buf = bytestream.read(rows * cols * num_images)
+        data = numpy.frombuffer(buf, dtype=numpy.uint8)
+        data = data.reshape(num_images, rows, cols, 1)
+        return data
 
 
-@deprecated(None, 'Please use tf.one_hot on tensors.')
+@deprecated(None, "Please use tf.one_hot on tensors.")
 def _dense_to_one_hot(labels_dense, num_classes):
-  """Convert class labels from scalars to one-hot vectors."""
-  num_labels = labels_dense.shape[0]
-  index_offset = numpy.arange(num_labels) * num_classes
-  labels_one_hot = numpy.zeros((num_labels, num_classes))
-  labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1
-  return labels_one_hot
+    """Convert class labels from scalars to one-hot vectors."""
+    num_labels = labels_dense.shape[0]
+    index_offset = numpy.arange(num_labels) * num_classes
+    labels_one_hot = numpy.zeros((num_labels, num_classes))
+    labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1
+    return labels_one_hot
 
 
-@deprecated(None, 'Please use tf.data to implement this functionality.')
+@deprecated(None, "Please use tf.data to implement this functionality.")
 def _extract_labels(f, one_hot=False, num_classes=10):
-  """Extract the labels into a 1D uint8 numpy array [index].
+    """Extract the labels into a 1D uint8 numpy array [index].
 
   Args:
     f: A file object that can be passed into a gzip reader.
@@ -99,37 +100,43 @@ def _extract_labels(f, one_hot=False, num_classes=10):
   Raises:
     ValueError: If the bystream doesn't start with 2049.
   """
-  print('Extracting', f.name)
-  with gzip.GzipFile(fileobj=f) as bytestream:
-    magic = _read32(bytestream)
-    if magic != 2049:
-      raise ValueError('Invalid magic number %d in MNIST label file: %s' %
-                       (magic, f.name))
-    num_items = _read32(bytestream)
-    buf = bytestream.read(num_items)
-    labels = numpy.frombuffer(buf, dtype=numpy.uint8)
-    if one_hot:
-      return _dense_to_one_hot(labels, num_classes)
-    return labels
+    print("Extracting", f.name)
+    with gzip.GzipFile(fileobj=f) as bytestream:
+        magic = _read32(bytestream)
+        if magic != 2049:
+            raise ValueError(
+                "Invalid magic number %d in MNIST label file: %s" % (magic, f.name)
+            )
+        num_items = _read32(bytestream)
+        buf = bytestream.read(num_items)
+        labels = numpy.frombuffer(buf, dtype=numpy.uint8)
+        if one_hot:
+            return _dense_to_one_hot(labels, num_classes)
+        return labels
 
 
 class _DataSet(object):
-  """Container class for a _DataSet (deprecated).
+    """Container class for a _DataSet (deprecated).
 
   THIS CLASS IS DEPRECATED.
   """
 
-  @deprecated(None, 'Please use alternatives such as official/mnist/_DataSet.py'
-              ' from tensorflow/models.')
-  def __init__(self,
-               images,
-               labels,
-               fake_data=False,
-               one_hot=False,
-               dtype=dtypes.float32,
-               reshape=True,
-               seed=None):
-    """Construct a _DataSet.
+    @deprecated(
+        None,
+        "Please use alternatives such as official/mnist/_DataSet.py"
+        " from tensorflow/models.",
+    )
+    def __init__(
+        self,
+        images,
+        labels,
+        fake_data=False,
+        one_hot=False,
+        dtype=dtypes.float32,
+        reshape=True,
+        seed=None,
+    ):
+        """Construct a _DataSet.
 
     one_hot arg is used only if fake_data is true.  `dtype` can be either
     `uint8` to leave the input as `[0, 255]`, or `float32` to rescale into
@@ -146,101 +153,105 @@ class _DataSet(object):
       reshape: Bool. If True returned images are returned flattened to vectors.
       seed: The random seed to use.
     """
-    seed1, seed2 = random_seed.get_seed(seed)
-    # If op level seed is not set, use whatever graph level seed is returned
-    numpy.random.seed(seed1 if seed is None else seed2)
-    dtype = dtypes.as_dtype(dtype).base_dtype
-    if dtype not in (dtypes.uint8, dtypes.float32):
-      raise TypeError('Invalid image dtype %r, expected uint8 or float32' %
-                      dtype)
-    if fake_data:
-      self._num_examples = 10000
-      self.one_hot = one_hot
-    else:
-      assert images.shape[0] == labels.shape[0], (
-          'images.shape: %s labels.shape: %s' % (images.shape, labels.shape))
-      self._num_examples = images.shape[0]
+        seed1, seed2 = random_seed.get_seed(seed)
+        # If op level seed is not set, use whatever graph level seed is returned
+        numpy.random.seed(seed1 if seed is None else seed2)
+        dtype = dtypes.as_dtype(dtype).base_dtype
+        if dtype not in (dtypes.uint8, dtypes.float32):
+            raise TypeError("Invalid image dtype %r, expected uint8 or float32" % dtype)
+        if fake_data:
+            self._num_examples = 10000
+            self.one_hot = one_hot
+        else:
+            assert (
+                images.shape[0] == labels.shape[0]
+            ), "images.shape: %s labels.shape: %s" % (images.shape, labels.shape)
+            self._num_examples = 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
-        images = images.reshape(images.shape[0],
-                                images.shape[1] * images.shape[2])
-      if dtype == dtypes.float32:
-        # Convert from [0, 255] -> [0.0, 1.0].
-        images = images.astype(numpy.float32)
-        images = numpy.multiply(images, 1.0 / 255.0)
-    self._images = images
-    self._labels = labels
-    self._epochs_completed = 0
-    self._index_in_epoch = 0
+            # Convert shape from [num examples, rows, columns, depth]
+            # to [num examples, rows*columns] (assuming depth == 1)
+            if reshape:
+                assert images.shape[3] == 1
+                images = images.reshape(
+                    images.shape[0], images.shape[1] * images.shape[2]
+                )
+            if dtype == dtypes.float32:
+                # Convert from [0, 255] -> [0.0, 1.0].
+                images = images.astype(numpy.float32)
+                images = numpy.multiply(images, 1.0 / 255.0)
+        self._images = images
+        self._labels = labels
+        self._epochs_completed = 0
+        self._index_in_epoch = 0
 
-  @property
-  def images(self):
-    return self._images
+    @property
+    def images(self):
+        return self._images
 
-  @property
-  def labels(self):
-    return self._labels
+    @property
+    def labels(self):
+        return self._labels
 
-  @property
-  def num_examples(self):
-    return self._num_examples
+    @property
+    def num_examples(self):
+        return self._num_examples
 
-  @property
-  def epochs_completed(self):
-    return self._epochs_completed
+    @property
+    def epochs_completed(self):
+        return self._epochs_completed
 
-  def next_batch(self, batch_size, fake_data=False, shuffle=True):
-    """Return the next `batch_size` examples from this data set."""
-    if fake_data:
-      fake_image = [1] * 784
-      if self.one_hot:
-        fake_label = [1] + [0] * 9
-      else:
-        fake_label = 0
-      return [fake_image for _ in xrange(batch_size)
-             ], [fake_label for _ in xrange(batch_size)]
-    start = self._index_in_epoch
-    # Shuffle for the first epoch
-    if self._epochs_completed == 0 and start == 0 and shuffle:
-      perm0 = numpy.arange(self._num_examples)
-      numpy.random.shuffle(perm0)
-      self._images = self.images[perm0]
-      self._labels = self.labels[perm0]
-    # 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
-      rest_num_examples = self._num_examples - start
-      images_rest_part = self._images[start:self._num_examples]
-      labels_rest_part = self._labels[start:self._num_examples]
-      # Shuffle the data
-      if shuffle:
-        perm = numpy.arange(self._num_examples)
-        numpy.random.shuffle(perm)
-        self._images = self.images[perm]
-        self._labels = self.labels[perm]
-      # Start next epoch
-      start = 0
-      self._index_in_epoch = batch_size - rest_num_examples
-      end = self._index_in_epoch
-      images_new_part = self._images[start:end]
-      labels_new_part = 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
-      end = self._index_in_epoch
-      return self._images[start:end], self._labels[start:end]
+    def next_batch(self, batch_size, fake_data=False, shuffle=True):
+        """Return the next `batch_size` examples from this data set."""
+        if fake_data:
+            fake_image = [1] * 784
+            if self.one_hot:
+                fake_label = [1] + [0] * 9
+            else:
+                fake_label = 0
+            return (
+                [fake_image for _ in xrange(batch_size)],
+                [fake_label for _ in xrange(batch_size)],
+            )
+        start = self._index_in_epoch
+        # Shuffle for the first epoch
+        if self._epochs_completed == 0 and start == 0 and shuffle:
+            perm0 = numpy.arange(self._num_examples)
+            numpy.random.shuffle(perm0)
+            self._images = self.images[perm0]
+            self._labels = self.labels[perm0]
+        # 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
+            rest_num_examples = self._num_examples - start
+            images_rest_part = self._images[start : self._num_examples]
+            labels_rest_part = self._labels[start : self._num_examples]
+            # Shuffle the data
+            if shuffle:
+                perm = numpy.arange(self._num_examples)
+                numpy.random.shuffle(perm)
+                self._images = self.images[perm]
+                self._labels = self.labels[perm]
+            # Start next epoch
+            start = 0
+            self._index_in_epoch = batch_size - rest_num_examples
+            end = self._index_in_epoch
+            images_new_part = self._images[start:end]
+            labels_new_part = 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
+            end = self._index_in_epoch
+            return self._images[start:end], self._labels[start:end]
 
 
-@deprecated(None, 'Please write your own downloading logic.')
+@deprecated(None, "Please write your own downloading logic.")
 def _maybe_download(filename, work_directory, source_url):
-  """Download the data from source url, unless it's already here.
+    """Download the data from source url, unless it's already here.
 
   Args:
       filename: string, name of the file in the directory.
@@ -250,83 +261,90 @@ def _maybe_download(filename, work_directory, source_url):
   Returns:
       Path to resulting file.
   """
-  if not gfile.Exists(work_directory):
-    gfile.MakeDirs(work_directory)
-  filepath = os.path.join(work_directory, filename)
-  if not gfile.Exists(filepath):
-    urllib.request.urlretrieve(source_url, filepath)
-    with gfile.GFile(filepath) as f:
-      size = f.size()
-    print('Successfully downloaded', filename, size, 'bytes.')
-  return filepath
+    if not gfile.Exists(work_directory):
+        gfile.MakeDirs(work_directory)
+    filepath = os.path.join(work_directory, filename)
+    if not gfile.Exists(filepath):
+        urllib.request.urlretrieve(source_url, filepath)
+        with gfile.GFile(filepath) as f:
+            size = f.size()
+        print("Successfully downloaded", filename, size, "bytes.")
+    return filepath
 
 
-@deprecated(None, 'Please use alternatives such as:'
-            ' tensorflow_datasets.load(\'mnist\')')
-def read_data_sets(train_dir,
-                   fake_data=False,
-                   one_hot=False,
-                   dtype=dtypes.float32,
-                   reshape=True,
-                   validation_size=5000,
-                   seed=None,
-                   source_url=DEFAULT_SOURCE_URL):
-  if fake_data:
+@deprecated(
+    None, "Please use alternatives such as:" " tensorflow_datasets.load('mnist')"
+)
+def read_data_sets(
+    train_dir,
+    fake_data=False,
+    one_hot=False,
+    dtype=dtypes.float32,
+    reshape=True,
+    validation_size=5000,
+    seed=None,
+    source_url=DEFAULT_SOURCE_URL,
+):
+    if fake_data:
 
-    def fake():
-      return _DataSet([], [],
-                      fake_data=True,
-                      one_hot=one_hot,
-                      dtype=dtype,
-                      seed=seed)
+        def fake():
+            return _DataSet(
+                [], [], fake_data=True, one_hot=one_hot, dtype=dtype, seed=seed
+            )
+
+        train = fake()
+        validation = fake()
+        test = fake()
+        return _Datasets(train=train, validation=validation, test=test)
+
+    if not source_url:  # empty string check
+        source_url = DEFAULT_SOURCE_URL
+
+    train_images_file = "train-images-idx3-ubyte.gz"
+    train_labels_file = "train-labels-idx1-ubyte.gz"
+    test_images_file = "t10k-images-idx3-ubyte.gz"
+    test_labels_file = "t10k-labels-idx1-ubyte.gz"
+
+    local_file = _maybe_download(
+        train_images_file, train_dir, source_url + train_images_file
+    )
+    with gfile.Open(local_file, "rb") as f:
+        train_images = _extract_images(f)
+
+    local_file = _maybe_download(
+        train_labels_file, train_dir, source_url + train_labels_file
+    )
+    with gfile.Open(local_file, "rb") as f:
+        train_labels = _extract_labels(f, one_hot=one_hot)
+
+    local_file = _maybe_download(
+        test_images_file, train_dir, source_url + test_images_file
+    )
+    with gfile.Open(local_file, "rb") as f:
+        test_images = _extract_images(f)
+
+    local_file = _maybe_download(
+        test_labels_file, train_dir, source_url + test_labels_file
+    )
+    with gfile.Open(local_file, "rb") as f:
+        test_labels = _extract_labels(f, one_hot=one_hot)
+
+    if not 0 <= validation_size <= len(train_images):
+        raise ValueError(
+            "Validation size should be between 0 and {}. Received: {}.".format(
+                len(train_images), validation_size
+            )
+        )
+
+    validation_images = train_images[:validation_size]
+    validation_labels = train_labels[:validation_size]
+    train_images = train_images[validation_size:]
+    train_labels = train_labels[validation_size:]
+
+    options = dict(dtype=dtype, reshape=reshape, seed=seed)
+
+    train = _DataSet(train_images, train_labels, **options)
+    validation = _DataSet(validation_images, validation_labels, **options)
+    test = _DataSet(test_images, test_labels, **options)
 
-    train = fake()
-    validation = fake()
-    test = fake()
     return _Datasets(train=train, validation=validation, test=test)
-
-  if not source_url:  # empty string check
-    source_url = DEFAULT_SOURCE_URL
-
-  train_images_file = 'train-images-idx3-ubyte.gz'
-  train_labels_file = 'train-labels-idx1-ubyte.gz'
-  test_images_file = 't10k-images-idx3-ubyte.gz'
-  test_labels_file = 't10k-labels-idx1-ubyte.gz'
-
-  local_file = _maybe_download(train_images_file, train_dir,
-                               source_url + train_images_file)
-  with gfile.Open(local_file, 'rb') as f:
-    train_images = _extract_images(f)
-
-  local_file = _maybe_download(train_labels_file, train_dir,
-                               source_url + train_labels_file)
-  with gfile.Open(local_file, 'rb') as f:
-    train_labels = _extract_labels(f, one_hot=one_hot)
-
-  local_file = _maybe_download(test_images_file, train_dir,
-                               source_url + test_images_file)
-  with gfile.Open(local_file, 'rb') as f:
-    test_images = _extract_images(f)
-
-  local_file = _maybe_download(test_labels_file, train_dir,
-                               source_url + test_labels_file)
-  with gfile.Open(local_file, 'rb') as f:
-    test_labels = _extract_labels(f, one_hot=one_hot)
-
-  if not 0 <= validation_size <= len(train_images):
-    raise ValueError(
-        'Validation size should be between 0 and {}. Received: {}.'.format(
-            len(train_images), validation_size))
-
-  validation_images = train_images[:validation_size]
-  validation_labels = train_labels[:validation_size]
-  train_images = train_images[validation_size:]
-  train_labels = train_labels[validation_size:]
-
-  options = dict(dtype=dtype, reshape=reshape, seed=seed)
-
-  train = _DataSet(train_images, train_labels, **options)
-  validation = _DataSet(validation_images, validation_labels, **options)
-  test = _DataSet(test_images, test_labels, **options)
-
-  return _Datasets(train=train, validation=validation, test=test)

other/least_recently_used.py

@@ -2,12 +2,13 @@ from abc import abstractmethod
 import sys
 from collections import deque
 
+
 class LRUCache:
     """ Page Replacement Algorithm, Least Recently Used (LRU) Caching."""
 
-    dq_store = object() # Cache store of keys
-    key_reference_map = object() # References of the keys in cache
-    _MAX_CAPACITY: int = 10 # Maximum capacity of cache
+    dq_store = object()  # Cache store of keys
+    key_reference_map = object()  # References of the keys in cache
+    _MAX_CAPACITY: int = 10  # Maximum capacity of cache
 
     @abstractmethod
     def __init__(self, n: int):
@@ -19,7 +20,7 @@ class LRUCache:
         if not n:
             LRUCache._MAX_CAPACITY = sys.maxsize
         elif n < 0:
-            raise ValueError('n should be an integer greater than 0.')
+            raise ValueError("n should be an integer greater than 0.")
         else:
             LRUCache._MAX_CAPACITY = n
 
@@ -51,6 +52,7 @@ class LRUCache:
         for k in self.dq_store:
             print(k)
 
+
 if __name__ == "__main__":
     lru_cache = LRUCache(4)
     lru_cache.refer(1)

project_euler/problem_20/sol4.py

@@ -27,7 +27,7 @@ def solution(n):
     """
     fact = 1
     result = 0
-    for i in range(1,n + 1):
+    for i in range(1, n + 1):
         fact *= i
 
     for j in str(fact):

project_euler/problem_99/sol1.py

@@ -14,15 +14,13 @@ import os
 from math import log10
 
 
-def find_largest(data_file: str="base_exp.txt") -> int:
+def find_largest(data_file: str = "base_exp.txt") -> int:
     """
     >>> find_largest()
     709
     """
     largest = [0, 0]
-    for i, line in enumerate(
-        open(os.path.join(os.path.dirname(__file__), data_file))
-    ):
+    for i, line in enumerate(open(os.path.join(os.path.dirname(__file__), data_file))):
         a, x = list(map(int, line.split(",")))
         if x * log10(a) > largest[0]:
             largest = [x * log10(a), i + 1]

web_programming/get_imdbtop.py

@@ -3,8 +3,10 @@ import requests
 
 
 def imdb_top(imdb_top_n):
-    base_url = (f"https://www.imdb.com/search/title?title_type="
-                f"feature&sort=num_votes,desc&count={imdb_top_n}")
+    base_url = (
+        f"https://www.imdb.com/search/title?title_type="
+        f"feature&sort=num_votes,desc&count={imdb_top_n}"
+    )
     source = BeautifulSoup(requests.get(base_url).content, "html.parser")
     for m in source.findAll("div", class_="lister-item mode-advanced"):
         print("\n" + m.h3.a.text)  # movie's name