import math
import random


def logistic(x):
    return 1.0 / (1 + math.exp(-x))


def deriv_logistic(x):
    lgst = logistic(x)
    return (1 - lgst) * lgst


def hyperbolic_tangent(x):
    return math.tanh(x)


def deriv_hyperbolic_tangent(x):
    th = math.tanh(x)
    return 1 - th * th


def between(min, max):
    """
    Return a real random value between min and max.
    """
    return random.random() * (max - min) + min


def make_matrix(N, M):
    """
    Make an N rows by M columns matrix.
    """
    return [[0 for i in range(M)] for i in range(N)]

import math
import pickle
import random


def logistic(x):
    return 1.0 / (1 + math.exp(-x))


def deriv_logistic(x):
    lgst = logistic(x)
    return (1 - lgst) * lgst


def hyperbolic_tangent(x):
    return math.tanh(x)


def deriv_hyperbolic_tangent(x):
    th = math.tanh(x)
    return 1 - th * th


def between(min, max):
    """
    Return a real random value between min and max.
    """
    return random.random() * (max - min) + min


def make_matrix(N, M):
    """
    Make an N rows by M columns matrix.
    """
    return [[0 for i in range(M)] for i in range(N)]


def serialize(nn, fname):
    with open(fname, 'wb') as f:
        pickle.dump(nn, f)


def deserialize(fname):
    with open(fname, 'rb') as f:
        nn = pickle.load(f)
        return nn

Add pickle serialize and deserialize
Add pickle serialize and deserialize

  import math
+ import pickle
  import random
  
  
  def logistic(x):
      return 1.0 / (1 + math.exp(-x))
  
  
  def deriv_logistic(x):
      lgst = logistic(x)
      return (1 - lgst) * lgst
  
  
  def hyperbolic_tangent(x):
      return math.tanh(x)
  
  
  def deriv_hyperbolic_tangent(x):
      th = math.tanh(x)
      return 1 - th * th
  
  
  def between(min, max):
      """
      Return a real random value between min and max.
      """
      return random.random() * (max - min) + min
  
  
  def make_matrix(N, M):
      """
      Make an N rows by M columns matrix.
      """
      return [[0 for i in range(M)] for i in range(N)]
  
+ 
+ def serialize(nn, fname):
+     with open(fname, 'wb') as f:
+         pickle.dump(nn, f)
+ 
+ 
+ def deserialize(fname):
+     with open(fname, 'rb') as f:
+         nn = pickle.load(f)
+         return nn
+ 
Add pickle serialize and deserialize
## Code Before:
import math
import random


def logistic(x):
    return 1.0 / (1 + math.exp(-x))


def deriv_logistic(x):
    lgst = logistic(x)
    return (1 - lgst) * lgst


def hyperbolic_tangent(x):
    return math.tanh(x)


def deriv_hyperbolic_tangent(x):
    th = math.tanh(x)
    return 1 - th * th


def between(min, max):
    """
    Return a real random value between min and max.
    """
    return random.random() * (max - min) + min


def make_matrix(N, M):
    """
    Make an N rows by M columns matrix.
    """
    return [[0 for i in range(M)] for i in range(N)]

## Instruction:
Add pickle serialize and deserialize
## Code After:
import math
import pickle
import random


def logistic(x):
    return 1.0 / (1 + math.exp(-x))


def deriv_logistic(x):
    lgst = logistic(x)
    return (1 - lgst) * lgst


def hyperbolic_tangent(x):
    return math.tanh(x)


def deriv_hyperbolic_tangent(x):
    th = math.tanh(x)
    return 1 - th * th


def between(min, max):
    """
    Return a real random value between min and max.
    """
    return random.random() * (max - min) + min


def make_matrix(N, M):
    """
    Make an N rows by M columns matrix.
    """
    return [[0 for i in range(M)] for i in range(N)]


def serialize(nn, fname):
    with open(fname, 'wb') as f:
        pickle.dump(nn, f)


def deserialize(fname):
    with open(fname, 'rb') as f:
        nn = pickle.load(f)
        return nn
