diff --git a/0_python/02 Print Statement.py b/0_python/02 Print Statement.py deleted file mode 100644 index 487ed44..0000000 --- a/0_python/02 Print Statement.py +++ /dev/null @@ -1,134 +0,0 @@ -# --- -# jupyter: -# jupytext_format_version: '1.2' -# kernelspec: -# display_name: Python 3 -# language: python -# name: python3 -# language_info: -# codemirror_mode: -# name: ipython -# version: 3 -# file_extension: .py -# mimetype: text/x-python -# name: python -# nbconvert_exporter: python -# pygments_lexer: ipython3 -# version: 3.5.2 -# --- - -# All the IPython Notebooks in this lecture series are available at https://github.com/rajathkumarmp/Python-Lectures - -# # Print Statement - -# The **print** statement can be used in the following different ways : -# -# - print("Hello World") -# - print("Hello", ) -# - print("Hello" + ) -# - print("Hello %s" % ) - -print("Hello World") - -# In Python, single, double and triple quotes are used to denote a string. -# Most use single quotes when declaring a single character. -# Double quotes when declaring a line and triple quotes when declaring a paragraph/multiple lines. - -print('Hey') - -print("""My name is Rajath Kumar M.P. - -I love Python.""") - -# Strings can be assigned to variable say _string1_ and _string2_ which can called when using the print statement. - -# + {"scrolled": true} -string1 = 'World' -print('Hello', string1) - -string2 = '!' -print('Hello', string1, string2) -# - - -# String concatenation is the "addition" of two strings. Observe that while concatenating there will be no space between the strings. - -print('Hello' + string1 + string2) - -# **%s** is used to refer to a variable which contains a string. - -print("Hello %s" % string1) - -# Similarly, when using other data types -# -# - %s -> string -# - %d -> Integer -# - %f -> Float -# - %o -> Octal -# - %x -> Hexadecimal -# - %e -> exponential -# -# This can be used for conversions inside the print statement itself. - -print("Actual Number = %d" % 18) -print("Float of the number = %f" % 18) -print("Octal equivalent of the number = %o" % 18) -print("Hexadecimal equivalent of the number = %x" % 18) -print("Exponential equivalent of the number = %e" % 18) - -# When referring to multiple variables parenthesis is used. - -print "Hello %s %s" %(string1,string2) - -# ## Other Examples - -# The following are other different ways the print statement can be put to use. - -print("I want %%d to be printed %s" %'here') - -print('_A'*10) - -print("Jan\nFeb\nMar\nApr\nMay\nJun\nJul\nAug") - -print("\n".join("Jan Feb Mar Apr May Jun Jul Aug".split(" "))) - -print("I want \\n to be printed.") - -print """ -Routine: -\t- Eat -\t- Sleep\n\t- Repeat -""" - -# # PrecisionWidth and FieldWidth - -# Fieldwidth is the width of the entire number and precision is the width towards the right. One can alter these widths based on the requirements. -# -# The default Precision Width is set to 6. - -"%f" % 3.121312312312 - -# Notice upto 6 decimal points are returned. To specify the number of decimal points, '%(fieldwidth).(precisionwidth)f' is used. - -"%.5f" % 3.121312312312 - -# If the field width is set more than the necessary than the data right aligns itself to adjust to the specified values. - -"%9.5f" % 3.121312312312 - -# Zero padding is done by adding a 0 at the start of fieldwidth. - -"%020.5f" % 3.121312312312 - -# For proper alignment, a space can be left blank in the field width so that when a negative number is used, proper alignment is maintained. - -print "% 9f" % 3.121312312312 -print "% 9f" % -3.121312312312 - -# '+' sign can be returned at the beginning of a positive number by adding a + sign at the beginning of the field width. - -print "%+9f" % 3.121312312312 -print "% 9f" % -3.121312312312 - -# As mentioned above, the data right aligns itself when the field width mentioned is larger than the actualy field width. But left alignment can be done by specifying a negative symbol in the field width. - -"%-9.3f" % 3.121312312312 diff --git a/0_python/03 Data Structure.py b/0_python/03 Data Structure.py deleted file mode 100644 index 38775c7..0000000 --- a/0_python/03 Data Structure.py +++ /dev/null @@ -1,386 +0,0 @@ -# --- -# jupyter: -# jupytext_format_version: '1.2' -# kernelspec: -# display_name: Python 3 -# language: python -# name: python3 -# language_info: -# codemirror_mode: -# name: ipython -# version: 3 -# file_extension: .py -# mimetype: text/x-python -# name: python -# nbconvert_exporter: python -# pygments_lexer: ipython3 -# version: 3.5.2 -# --- - -# All the IPython Notebooks in this lecture series are available at https://github.com/rajathkumarmp/Python-Lectures - -# # Data Structures - -# In simple terms, It is the the collection or group of data in a particular structure. - -# ## Lists - -# Lists are the most commonly used data structure. Think of it as a sequence of data that is enclosed in square brackets and data are separated by a comma. Each of these data can be accessed by calling it's index value. -# -# Lists are declared by just equating a variable to '[ ]' or list. - -a = [] - -print(type(a)) - -# One can directly assign the sequence of data to a list x as shown. - -x = ['apple', 'orange', 'peach'] - -# ### Indexing - -# In python, Indexing starts from 0. Thus now the list x, which has two elements will have apple at 0 index and orange at 1 index. - -x[0] - -# Indexing can also be done in reverse order. That is the last element can be accessed first. Here, indexing starts from -1. Thus index value -1 will be orange and index -2 will be apple. - -x[-1] - -# As you might have already guessed, x[0] = x[-2], x[1] = x[-1]. This concept can be extended towards lists with more many elements. - -y = ['carrot','potato'] - -# Here we have declared two lists x and y each containing its own data. Now, these two lists can again be put into another list say z which will have it's data as two lists. This list inside a list is called as nested lists and is how an array would be declared which we will see later. - -z = [x,y] -print(z) - -# Indexing in nested lists can be quite confusing if you do not understand how indexing works in python. So let us break it down and then arrive at a conclusion. -# -# Let us access the data 'apple' in the above nested list. -# First, at index 0 there is a list ['apple','orange'] and at index 1 there is another list ['carrot','potato']. Hence z[0] should give us the first list which contains 'apple'. - -z1 = z[0] -print(z1) - -# Now observe that z1 is not at all a nested list thus to access 'apple', z1 should be indexed at 0. - -z1[0] - -# Instead of doing the above, In python, you can access 'apple' by just writing the index values each time side by side. - -z[0][0] - -# If there was a list inside a list inside a list then you can access the innermost value by executing z[ ][ ][ ]. - -# ### Slicing - -# Indexing was only limited to accessing a single element, Slicing on the other hand is accessing a sequence of data inside the list. In other words "slicing" the list. -# -# Slicing is done by defining the index values of the first element and the last element from the parent list that is required in the sliced list. It is written as parentlist[ a : b ] where a,b are the index values from the parent list. If a or b is not defined then the index value is considered to be the first value for a if a is not defined and the last value for b when b is not defined. - -num = [0,1,2,3,4,5,6,7,8,9] - -print(num[0:4]) -print(num[4:]) - -# You can also slice a parent list with a fixed length or step length. - -num[:9:3] - -# ### Built in List Functions - -# To find the length of the list or the number of elements in a list, **len( )** is used. - -len(num) - -# If the list consists of all integer elements then **min( )** and **max( )** gives the minimum and maximum value in the list. - -min(num) - -max(num) - -# Lists can be concatenated by adding, '+' them. The resultant list will contain all the elements of the lists that were added. The resultant list will not be a nested list. - -[1,2,3] + [5,4,7] - -# There might arise a requirement where you might need to check if a particular element is there in a predefined list. Consider the below list. - -names = ['Earth','Air','Fire','Water'] - -# To check if 'Fire' and 'Rajath' is present in the list names. A conventional approach would be to use a for loop and iterate over the list and use the if condition. But in python you can use 'a in b' concept which would return 'True' if a is present in b and 'False' if not. - -'Fire' in names - -'Rajath' in names - -# In a list with elements as string, **max( )** and **min( )** is applicable. **max( )** would return a string element whose ASCII value is the highest and the lowest when **min( )** is used. Note that only the first index of each element is considered each time and if they value is the same then second index considered so on and so forth. - -mlist = ['bzaa','ds','nc','az','z','klm'] - -print(max(mlist)) -print(min(mlist)) - -# Here the first index of each element is considered and thus z has the highest ASCII value thus it is returned and minimum ASCII is a. But what if numbers are declared as strings? - -nlist = ['1','94','93','1000'] - -print(max(nlist)) -print(min(nlist)) - -# Even if the numbers are declared in a string the first index of each element is considered and the maximum and minimum values are returned accordingly. - -# But if you want to find the **max( )** string element based on the length of the string then another parameter 'key=len' is declared inside the **max( )** and **min( )** function. - -print(max(names, key=len)) -print(min(names, key=len)) - -# But even 'Water' has length 5. **max()** or **min()** function returns the first element when there are two or more elements with the same length. -# -# Any other built in function can be used or lambda function (will be discussed later) in place of len. -# -# A string can be converted into a list by using the **list()** function. - -list('hello') - -# **append( )** is used to add a element at the end of the list. - -lst = [1,1,4,8,7] - -lst.append(1) -print(lst) - -# **count( )** is used to count the number of a particular element that is present in the list. - -lst.count(1) - -# **append( )** function can also be used to add a entire list at the end. Observe that the resultant list becomes a nested list. - -lst1 = [5,4,2,8] - -lst.append(lst1) -print(lst) - -# But if nested list is not what is desired then **extend( )** function can be used. - -lst.extend(lst1) -print(lst) - -# **index( )** is used to find the index value of a particular element. Note that if there are multiple elements of the same value then the first index value of that element is returned. - -lst.index(1) - -# **insert(x,y)** is used to insert a element y at a specified index value x. **append( )** function made it only possible to insert at the end. - -lst.insert(5, 'name') -print(lst) - -# **insert(x,y)** inserts but does not replace element. If you want to replace the element with another element you simply assign the value to that particular index. - -lst[5] = 'Python' -print(lst) - -# **pop( )** function return the last element in the list. This is similar to the operation of a stack. Hence it wouldn't be wrong to tell that lists can be used as a stack. - -lst.pop() - -# Index value can be specified to pop a ceratin element corresponding to that index value. - -lst.pop(0) - -# **pop( )** is used to remove element based on it's index value which can be assigned to a variable. One can also remove element by specifying the element itself using the **remove( )** function. - -lst.remove('Python') -print(lst) - -# Alternative to **remove** function but with using index value is **del** - -del lst[1] -print(lst) - -# The entire elements present in the list can be reversed by using the **reverse()** function. - -lst.reverse() -print(lst) - -# Note that the nested list [5,4,2,8] is treated as a single element of the parent list lst. Thus the elements inside the nested list is not reversed. -# -# Python offers built in operation **sort( )** to arrange the elements in ascending order. - -lst.sort() -print(lst) - -# For descending order, By default the reverse condition will be False for reverse. Hence changing it to True would arrange the elements in descending order. - -lst.sort(reverse=True) -print(lst) - -# Similarly for lists containing string elements, **sort( )** would sort the elements based on it's ASCII value in ascending and by specifying reverse=True in descending. - -names.sort() -print(names) -names.sort(reverse=True) -print(names) - -# To sort based on length key=len should be specified as shown. - -names.sort(key=len) -print(names) -names.sort(key=len,reverse=True) -print(names) - -# ### Copying a list - -# Most of the new python programmers commit this mistake. Consider the following, - -lista= [2,1,4,3] - -listb = lista -print(listb) - -# Here, We have declared a list, lista = [2,1,4,3]. This list is copied to listb by assigning it's value and it get's copied as seen. Now we perform some random operations on lista. - -lista.pop() -print(lista) -lista.append(9) -print(lista) - -print listb - -# listb has also changed though no operation has been performed on it. This is because you have assigned the same memory space of lista to listb. So how do fix this? -# -# If you recall, in slicing we had seen that parentlist[a:b] returns a list from parent list with start index a and end index b and if a and b is not mentioned then by default it considers the first and last element. We use the same concept here. By doing so, we are assigning the data of lista to listb as a variable. - -lista = [2,1,4,3] - -listb = lista[:] -print(listb) - -lista.pop() -print(lista) -lista.append(9) -print(lista) - -print(listb) - -# ## Tuples - -# Tuples are similar to lists but only big difference is the elements inside a list can be changed but in tuple it cannot be changed. Think of tuples as something which has to be True for a particular something and cannot be True for no other values. For better understanding, Recall **divmod()** function. - -xyz = divmod(10,3) -print(xyz) -print(type(xyz)) - -# Here the quotient has to be 3 and the remainder has to be 1. These values cannot be changed whatsoever when 10 is divided by 3. Hence divmod returns these values in a tuple. - -# To define a tuple, A variable is assigned to paranthesis ( ) or tuple( ). - -tup = () -tup2 = tuple() - -# If you want to directly declare a tuple it can be done by using a comma at the end of the data. - -27, - -# 27 when multiplied by 2 yields 54, But when multiplied with a tuple the data is repeated twice. - -2*(27,) - -# Values can be assigned while declaring a tuple. It takes a list as input and converts it into a tuple or it takes a string and converts it into a tuple. - -# + {"scrolled": true} -tup3 = tuple([1,2,3]) -print(tup3) -tup4 = tuple('Hello') -print(tup4) -# - - -# It follows the same indexing and slicing as Lists. - -print(tup3[1]) -tup5 = tup4[:3] -print(tup5) - -# ### Mapping one tuple to another - -(a,b,c)= ('alpha','beta','gamma') - -print(a,b,c) - -d = tuple('RajathKumarMP') -print(d) - -# ### Built In Tuple functions - -# **count()** function counts the number of specified element that is present in the tuple. - -d.count('a') - -# **index()** function returns the index of the specified element. If the elements are more than one then the index of the first element of that specified element is returned - -d.index('a') - -# ## Sets - -# Sets are mainly used to eliminate repeated numbers in a sequence/list. It is also used to perform some standard set operations. -# -# Sets are declared as set() which will initialize a empty set. Also set([sequence]) can be executed to declare a set with elements - -set1 = set() -print(type(set1)) - -set0 = set([1,2,2,3,3,4]) -print(set0) - -# elements 2,3 which are repeated twice are seen only once. Thus in a set each element is distinct. - -# ### Built-in Functions - -set1 = set([1,2,3]) - -set2 = set([2,3,4,5]) - -# **union( )** function returns a set which contains all the elements of both the sets without repition. - -set1.union(set2) - -# **add( )** will add a particular element into the set. Note that the index of the newly added element is arbitrary and can be placed anywhere not neccessarily in the end. - -set1.add(0) -set1 - -# **intersection( )** function outputs a set which contains all the elements that are in both sets. - -set1.intersection(set2) - -# **difference( )** function ouptuts a set which contains elements that are in set1 and not in set2. - -set1.difference(set2) - -# **symmetric_difference( )** function ouputs a function which contains elements that are in one of the sets. - -set2.symmetric_difference(set1) - -# **issubset( ), isdisjoint( ), issuperset( )** is used to check if the set1/set2 is a subset, disjoint or superset of set2/set1 respectively. - -set1.issubset(set2) - -set2.isdisjoint(set1) - -set2.issuperset(set1) - -# **pop( )** is used to remove an arbitrary element in the set - -set1.pop() -print(set1) - -# **remove( )** function deletes the specified element from the set. - -set1.remove(2) -set1 - -# **clear( )** is used to clear all the elements and make that set an empty set. - -set1.clear() -set1 diff --git a/0_python/05 Control Flow.py b/0_python/05 Control Flow.py deleted file mode 100644 index 17588c4..0000000 --- a/0_python/05 Control Flow.py +++ /dev/null @@ -1,175 +0,0 @@ -# --- -# jupyter: -# jupytext_format_version: '1.2' -# kernelspec: -# display_name: Python 3 -# language: python -# name: python3 -# language_info: -# codemirror_mode: -# name: ipython -# version: 3 -# file_extension: .py -# mimetype: text/x-python -# name: python -# nbconvert_exporter: python -# pygments_lexer: ipython3 -# version: 3.5.2 -# --- - -# All the IPython Notebooks in this lecture series are available at https://github.com/rajathkumarmp/Python-Lectures - -# # Control Flow Statements - -# ## If - -# if some_condition: -# -# algorithm - -x = 12 -if x >10: - print("Hello") - -# ## If-else - -# if some_condition: -# -# algorithm -# -# else: -# -# algorithm - -x = 12 -if x > 10: - print("hello") -else: - print("world") - -# ## if-elif - -# if some_condition: -# -# algorithm -# -# elif some_condition: -# -# algorithm -# -# else: -# -# algorithm - -x = 10 -y = 12 -if x > y: - print("x>y") -elif x < y: - print("x y: - print("x>y") -elif x < y: - print("x=7: - break - -# ## Continue - -# This continues the rest of the loop. Sometimes when a condition is satisfied there are chances of the loop getting terminated. This can be avoided using continue statement. - -for i in range(10): - if i>4: - print("The end.") - continue - elif i<7: - print(i) - -# ## List Comprehensions - -# Python makes it simple to generate a required list with a single line of code using list comprehensions. For example If i need to generate multiples of say 27 I write the code using for loop as, - -res = [] -for i in range(1,11): - x = 27*i - res.append(x) -print res - -# Since you are generating another list altogether and that is what is required, List comprehensions is a more efficient way to solve this problem. - -[27*x for x in range(1,11)] - -# That's it!. Only remember to enclose it in square brackets - -# Understanding the code, The first bit of the code is always the algorithm and then leave a space and then write the necessary loop. But you might be wondering can nested loops be extended to list comprehensions? Yes you can. - -[27*x for x in range(1,20) if x<=10] - -# Let me add one more loop to make you understand better, - -[27*z for i in range(50) if i==27 for z in range(1,11)] diff --git a/0_python/07 Class.py b/0_python/07 Class.py deleted file mode 100644 index 81f6e37..0000000 --- a/0_python/07 Class.py +++ /dev/null @@ -1,298 +0,0 @@ -# --- -# jupyter: -# jupytext_format_version: '1.2' -# kernelspec: -# display_name: Python 3 -# language: python -# name: python3 -# language_info: -# codemirror_mode: -# name: ipython -# version: 3 -# file_extension: .py -# mimetype: text/x-python -# name: python -# nbconvert_exporter: python -# pygments_lexer: ipython3 -# version: 3.5.2 -# --- - -# All the IPython Notebooks in this lecture series are available at https://github.com/rajathkumarmp/Python-Lectures - -# # Classes - -# Variables, Lists, Dictionaries etc in python is a object. Without getting into the theory part of Object Oriented Programming, explanation of the concepts will be done along this tutorial. - -# A class is declared as follows - -# class class_name: -# -# Functions - -class FirstClass: - pass - -# **pass** in python means do nothing. - -# Above, a class object named "FirstClass" is declared now consider a "egclass" which has all the characteristics of "FirstClass". So all you have to do is, equate the "egclass" to "FirstClass". In python jargon this is called as creating an instance. "egclass" is the instance of "FirstClass" - -egclass = FirstClass() - -type(egclass) - -type(FirstClass) - -# Now let us add some "functionality" to the class. So that our "FirstClass" is defined in a better way. A function inside a class is called as a "Method" of that class - -# Most of the classes will have a function named "\_\_init\_\_". These are called as magic methods. In this method you basically initialize the variables of that class or any other initial algorithms which is applicable to all methods is specified in this method. A variable inside a class is called an attribute. - -# These helps simplify the process of initializing a instance. For example, -# -# Without the use of magic method or \_\_init\_\_ which is otherwise called as constructors. One had to define a **init( )** method and call the **init( )** function. - -eg0 = FirstClass() -eg0.init() - -# But when the constructor is defined the \_\_init\_\_ is called thus intializing the instance created. - -# We will make our "FirstClass" to accept two variables name and symbol. -# -# I will be explaining about the "self" in a while. - -class FirstClass: - def __init__(self,name,symbol): - self.name = name - self.symbol = symbol - -# Now that we have defined a function and added the \_\_init\_\_ method. We can create a instance of FirstClass which now accepts two arguments. - -eg1 = FirstClass('one',1) -eg2 = FirstClass('two',2) - -print(eg1.name, eg1.symbol) -print(eg2.name, eg2.symbol) - -# **dir( )** function comes very handy in looking into what the class contains and what all method it offers - -dir(FirstClass) - -# **dir( )** of an instance also shows it's defined attributes. - -dir(eg1) - -# Changing the FirstClass function a bit, - -class FirstClass: - def __init__(self,name,symbol): - self.n = name - self.s = symbol - -# Changing self.name and self.symbol to self.n and self.s respectively will yield, - -eg1 = FirstClass('one',1) -eg2 = FirstClass('two',2) - -print(eg1.name, eg1.symbol) -print(eg2.name, eg2.symbol) - -# AttributeError, Remember variables are nothing but attributes inside a class? So this means we have not given the correct attribute for the instance. - -dir(eg1) - -print(eg1.n, eg1.s) -print(eg2.n, eg2.s) - -# So now we have solved the error. Now let us compare the two examples that we saw. -# -# When I declared self.name and self.symbol, there was no attribute error for eg1.name and eg1.symbol and when I declared self.n and self.s, there was no attribute error for eg1.n and eg1.s -# -# From the above we can conclude that self is nothing but the instance itself. -# -# Remember, self is not predefined it is userdefined. You can make use of anything you are comfortable with. But it has become a common practice to use self. - -class FirstClass: - def __init__(asdf1234,name,symbol): - asdf1234.n = name - asdf1234.s = symbol - -eg1 = FirstClass('one',1) -eg2 = FirstClass('two',2) - -print(eg1.n, eg1.s) -print(eg2.n, eg2.s) - -# Since eg1 and eg2 are instances of FirstClass it need not necessarily be limited to FirstClass itself. It might extend itself by declaring other attributes without having the attribute to be declared inside the FirstClass. - -eg1.cube = 1 -eg2.cube = 8 - -dir(eg1) - -# Just like global and local variables as we saw earlier, even classes have it's own types of variables. -# -# Class Attribute : attributes defined outside the method and is applicable to all the instances. -# -# Instance Attribute : attributes defined inside a method and is applicable to only that method and is unique to each instance. - -class FirstClass: - test = 'test' - def __init__(self,name,symbol): - self.name = name - self.symbol = symbol - -# Here test is a class attribute and name is a instance attribute. - -eg3 = FirstClass('Three',3) - -print(eg3.test, eg3.name) - -# Let us add some more methods to FirstClass. - -class FirstClass: - def __init__(self,name,symbol): - self.name = name - self.symbol = symbol - def square(self): - return self.symbol * self.symbol - def cube(self): - return self.symbol * self.symbol * self.symbol - def multiply(self, x): - return self.symbol * x - -eg4 = FirstClass('Five',5) - -print eg4.square() -print eg4.cube() - -eg4.multiply(2) - -# The above can also be written as, - -FirstClass.multiply(eg4,2) - -# ## Inheritance - -# There might be cases where a new class would have all the previous characteristics of an already defined class. So the new class can "inherit" the previous class and add it's own methods to it. This is called as inheritance. - -# Consider class SoftwareEngineer which has a method salary. - -class SoftwareEngineer: - def __init__(self,name,age): - self.name = name - self.age = age - def salary(self, value): - self.money = value - print(self.name,"earns",self.money) - -a = SoftwareEngineer('Kartik',26) - -a.salary(40000) - -dir(SoftwareEngineer) - -# Now consider another class Artist which tells us about the amount of money an artist earns and his artform. - -class Artist: - def __init__(self,name,age): - self.name = name - self.age = age - def money(self,value): - self.money = value - print(self.name,"earns",self.money) - def artform(self, job): - self.job = job - print(self.name,"is a", self.job) - -b = Artist('Nitin',20) - -b.money(50000) -b.artform('Musician') - -dir(Artist) - -# money method and salary method are the same. So we can generalize the method to salary and inherit the SoftwareEngineer class to Artist class. Now the artist class becomes, - -class Artist(SoftwareEngineer): - def artform(self, job): - self.job = job - print(self.name,"is a", self.job) - -c = Artist('Nishanth',21) - -dir(Artist) - -c.salary(60000) -c.artform('Dancer') - -# Suppose say while inheriting a particular method is not suitable for the new class. One can override this method by defining again that method with the same name inside the new class. - -class Artist(SoftwareEngineer): - def artform(self, job): - self.job = job - print(self.name,"is a", self.job) - def salary(self, value): - self.money = value - print(self.name,"earns",self.money) - print("I am overriding the SoftwareEngineer class's salary method") - -c = Artist('Nishanth',21) - -c.salary(60000) -c.artform('Dancer') - -# If not sure how many times methods will be called it will become difficult to declare so many variables to carry each result hence it is better to declare a list and append the result. - -class emptylist: - def __init__(self): - self.data = [] - def one(self,x): - self.data.append(x) - def two(self, x ): - self.data.append(x**2) - def three(self, x): - self.data.append(x**3) - -xc = emptylist() - -xc.one(1) -print xc.data - -# Since xc.data is a list direct list operations can also be performed. - -xc.data.append(8) -print xc.data - -xc.two(3) -print xc.data - -# If the number of input arguments varies from instance to instance asterisk can be used as shown. - -class NotSure: - def __init__(self, *args): - self.data = ''.join(list(args)) - -yz = NotSure('I', 'Do' , 'Not', 'Know', 'What', 'To','Type') - -yz.data - -# # Where to go from here? - -# Practice alone can help you get the hang of python. Give your self problem statements and solve them. You can also sign up to any competitive coding platform for problem statements. The more you code the more you discover and the more you start appreciating the language. -# -# -# Now that you have been introduced to python, You can try out the different python libraries in the field of your interest. I highly recommend you to check out this curated list of Python frameworks, libraries and software http://awesome-python.com -# -# -# The official python documentation : https://docs.python.org/2/ -# -# -# You can also check out Python practice programs written by my friend, Kartik Kannapur. Github Repo : https://github.com/rajathkumarmp/Python-Lectures -# -# -# Enjoy solving problem statements because life is short, you need python! -# -# -# Peace. -# -# -# Rajath Kumar M.P ( rajathkumar dot exe at gmail dot com) diff --git a/0_python/00 Introduction.ipynb b/0_python/0_Introduction.ipynb similarity index 100% rename from 0_python/00 Introduction.ipynb rename to 0_python/0_Introduction.ipynb diff --git a/0_python/01 Basics.ipynb b/0_python/1_Basics.ipynb similarity index 100% rename from 0_python/01 Basics.ipynb rename to 0_python/1_Basics.ipynb diff --git a/0_python/02 Print Statement.ipynb b/0_python/2_Print_Statement.ipynb similarity index 100% rename from 0_python/02 Print Statement.ipynb rename to 0_python/2_Print_Statement.ipynb diff --git a/0_python/03 Data Structure.ipynb b/0_python/3_Data_Structure_1.ipynb similarity index 100% rename from 0_python/03 Data Structure.ipynb rename to 0_python/3_Data_Structure_1.ipynb diff --git a/0_python/04 Data Structure 2.ipynb b/0_python/4_Data_Structure_2.ipynb similarity index 100% rename from 0_python/04 Data Structure 2.ipynb rename to 0_python/4_Data_Structure_2.ipynb diff --git a/0_python/05 Control Flow.ipynb b/0_python/5_Control_Flow.ipynb similarity index 100% rename from 0_python/05 Control Flow.ipynb rename to 0_python/5_Control_Flow.ipynb diff --git a/0_python/06 Function.ipynb b/0_python/6_Function.ipynb similarity index 100% rename from 0_python/06 Function.ipynb rename to 0_python/6_Function.ipynb diff --git a/0_python/07 Class.ipynb b/0_python/7_Class.ipynb similarity index 100% rename from 0_python/07 Class.ipynb rename to 0_python/7_Class.ipynb diff --git a/1_logistic_regression/Least_squares.ipynb b/1_logistic_regression/Least_squares.ipynb index 10cd6c9..b5e6431 100644 --- a/1_logistic_regression/Least_squares.ipynb +++ b/1_logistic_regression/Least_squares.ipynb @@ -18,12 +18,12 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -106,7 +106,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -118,7 +118,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -212,7 +212,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -969,13 +969,7 @@ "epoch 747: loss = 1765080.409388, a = 736.413451, b = 152.655201\n", "epoch 748: loss = 1764899.846541, a = 736.837393, b = 152.654930\n", "epoch 749: loss = 1764720.003201, a = 737.260487, b = 152.654660\n", - "epoch 750: loss = 1764540.876496, a = 737.682735, b = 152.654390\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "epoch 750: loss = 1764540.876496, a = 737.682735, b = 152.654390\n", "epoch 751: loss = 1764362.463568, a = 738.104139, b = 152.654121\n", "epoch 752: loss = 1764184.761567, a = 738.524700, b = 152.653853\n", "epoch 753: loss = 1764007.767659, a = 738.944420, b = 152.653585\n", @@ -1176,7 +1170,13 @@ "epoch 948: loss = 1740073.049267, a = 806.639154, b = 152.610348\n", "epoch 949: loss = 1739992.084021, a = 806.922668, b = 152.610167\n", "epoch 950: loss = 1739911.440986, a = 807.205615, b = 152.609986\n", - "epoch 951: loss = 1739831.118877, a = 807.487996, b = 152.609806\n", + "epoch 951: loss = 1739831.118877, a = 807.487996, b = 152.609806\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "epoch 952: loss = 1739751.116414, a = 807.769812, b = 152.609626\n", "epoch 953: loss = 1739671.432323, a = 808.051065, b = 152.609446\n", "epoch 954: loss = 1739592.065335, a = 808.331755, b = 152.609267\n", @@ -1500,13 +1500,7 @@ "epoch 1272: loss = 1725310.896600, a = 874.295618, b = 152.567136\n", "epoch 1273: loss = 1725288.607410, a = 874.443841, b = 152.567042\n", "epoch 1274: loss = 1725266.406608, a = 874.591768, b = 152.566947\n", - "epoch 1275: loss = 1725244.293841, a = 874.739399, b = 152.566853\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "epoch 1275: loss = 1725244.293841, a = 874.739399, b = 152.566853\n", "epoch 1276: loss = 1725222.268760, a = 874.886735, b = 152.566759\n", "epoch 1277: loss = 1725200.331014, a = 875.033776, b = 152.566665\n", "epoch 1278: loss = 1725178.480257, a = 875.180523, b = 152.566571\n", @@ -1633,7 +1627,13 @@ "epoch 1399: loss = 1723083.256642, a = 890.934407, b = 152.556509\n", "epoch 1400: loss = 1723069.792927, a = 891.049358, b = 152.556436\n", "epoch 1401: loss = 1723056.382489, a = 891.164079, b = 152.556363\n", - "epoch 1402: loss = 1723043.025115, a = 891.278571, b = 152.556289\n", + "epoch 1402: loss = 1723043.025115, a = 891.278571, b = 152.556289\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "epoch 1403: loss = 1723029.720594, a = 891.392834, b = 152.556216\n", "epoch 1404: loss = 1723016.468716, a = 891.506868, b = 152.556144\n", "epoch 1405: loss = 1723003.269272, a = 891.620675, b = 152.556071\n", @@ -1931,7 +1931,13 @@ "epoch 1697: loss = 1720722.510311, a = 916.760321, b = 152.540014\n", "epoch 1698: loss = 1720718.362630, a = 916.823629, b = 152.539974\n", "epoch 1699: loss = 1720714.231237, a = 916.886810, b = 152.539934\n", - "epoch 1700: loss = 1720710.116066, a = 916.949865, b = 152.539893\n", + "epoch 1700: loss = 1720710.116066, a = 916.949865, b = 152.539893\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "epoch 1701: loss = 1720706.017053, a = 917.012794, b = 152.539853\n", "epoch 1702: loss = 1720701.934135, a = 917.075597, b = 152.539813\n", "epoch 1703: loss = 1720697.867247, a = 917.138274, b = 152.539773\n", @@ -2273,13 +2279,7 @@ "epoch 2039: loss = 1719939.679051, a = 932.453563, b = 152.529991\n", "epoch 2040: loss = 1719938.588590, a = 932.485490, b = 152.529971\n", "epoch 2041: loss = 1719937.502342, a = 932.517352, b = 152.529950\n", - "epoch 2042: loss = 1719936.420291, a = 932.549151, b = 152.529930\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "epoch 2042: loss = 1719936.420291, a = 932.549151, b = 152.529930\n", "epoch 2043: loss = 1719935.342420, a = 932.580887, b = 152.529910\n", "epoch 2044: loss = 1719934.268714, a = 932.612559, b = 152.529890\n", "epoch 2045: loss = 1719933.199154, a = 932.644167, b = 152.529869\n", @@ -2686,7 +2686,13 @@ "epoch 2446: loss = 1719714.693893, a = 941.350072, b = 152.524309\n", "epoch 2447: loss = 1719714.462381, a = 941.364208, b = 152.524300\n", "epoch 2448: loss = 1719714.231731, a = 941.378316, b = 152.524291\n", - "epoch 2449: loss = 1719714.001940, a = 941.392396, b = 152.524282\n", + "epoch 2449: loss = 1719714.001940, a = 941.392396, b = 152.524282\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "epoch 2450: loss = 1719713.773003, a = 941.406448, b = 152.524273\n", "epoch 2451: loss = 1719713.544917, a = 941.420472, b = 152.524264\n", "epoch 2452: loss = 1719713.317680, a = 941.434467, b = 152.524255\n", @@ -2694,13 +2700,7 @@ "epoch 2454: loss = 1719712.865738, a = 941.462375, b = 152.524237\n", "epoch 2455: loss = 1719712.641027, a = 941.476287, b = 152.524228\n", "epoch 2456: loss = 1719712.417151, a = 941.490171, b = 152.524219\n", - "epoch 2457: loss = 1719712.194107, a = 941.504027, b = 152.524211\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "epoch 2457: loss = 1719712.194107, a = 941.504027, b = 152.524211\n", "epoch 2458: loss = 1719711.971892, a = 941.517856, b = 152.524202\n", "epoch 2459: loss = 1719711.750502, a = 941.531657, b = 152.524193\n", "epoch 2460: loss = 1719711.529935, a = 941.545430, b = 152.524184\n", @@ -3192,7 +3192,13 @@ "epoch 2946: loss = 1719661.403852, a = 945.820957, b = 152.521453\n", "epoch 2947: loss = 1719661.365180, a = 945.826153, b = 152.521450\n", "epoch 2948: loss = 1719661.326639, a = 945.831339, b = 152.521447\n", - "epoch 2949: loss = 1719661.288228, a = 945.836514, b = 152.521443\n", + "epoch 2949: loss = 1719661.288228, a = 945.836514, b = 152.521443\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "epoch 2950: loss = 1719661.249948, a = 945.841679, b = 152.521440\n", "epoch 2951: loss = 1719661.211798, a = 945.846834, b = 152.521437\n", "epoch 2952: loss = 1719661.173777, a = 945.851978, b = 152.521434\n", @@ -3247,7 +3253,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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+8fD+jwMHhrf/FRgTfLM16cKPh5moVxrkpGIq0uwGaZzTPaHbIPnwQ2MV66BBRkz+2Wfhk0/gzDMzJmVB0LhR3ayUUhZIKbtJKY+VUt4e3v6VlPIEKeURUsrfSil3h7fvCv9/RPj1r5LdCU3q8GPEEjV8QaowUqFECdI45+WGLLfr/O8++PxzKCyEPn1g7VpDC796NQwdmvELnhKlwayM1aQGP0YsCMMX1KRiKpQoQaltipaXsWNXddz2VK6ubRB8++1eLfy++xoTriNHGn83ErSh13jCjxFLt8wwlmQrUYJS21gtuALYp2kTraRxw08/GXnhH3rICMmMHGnkpwkX3WlMaEOv8YSTEbOSAqZrpWO6Et0FNWpQjXi2V9oXpGn07NgBM2YYWvgdO2DYMJg4sd5r4RMhY7NXatKDG+Oo2scqW2JOKJspQ7oCyQuXWLUHULYlE7xhN/c547M4BkRgD+Q9e+DRR43QzA8/GPH4yZOhS5fgG50huM1eqQ29pg47Q+3mi5cOw6Rqc7MmWZRbeL7JaItXQ+X2Pif6ftQHAuljba2RpuDWW400Bf36GWmDTzopSa3OHNwa+oY91azxRKLqmHRIAVVttjLyyWiLH+lokFWs/LQ3kyo3JfSZM/PC9+gBl15qJB177TV4551GYeS9oGP0mjoSNdSJTLqqwi9OnrJXwx30BLCf9Axu7nPs/Zh+cfeEvfhMrNzk+zO3dKmRF/6//4WOHY3VrZdc0uBlkn7Rhl5TR6LqGL+TrlYGaPTcFSCpU52ojJKqzS1yQ+yqqk36BLAXQ2Uab1Ww1LzPKoNc/M1WFqzcxLYKY7SSlxNi4uAuro2024dSKiexPX/mPv/cyCBZVAS/+hU8+CD8/vf1Ji98utCPP00diS4m8htqsDJAVTUyTloYO6QvWl5GxZ54nXlOKJsJ53ZJSapbtyttY9MiWLU5chRjZZBnL/22zsgDlFdWMfrFFa7DL24eSuOLPmXUnNKU5dZx/Zn79lu46iro2hXeesuYcP3yS7j+em3kXaA9ek0dQcgC/WjUvYRfzH1V9VBjvdygDXust9u/cyvmlZQ5yk1VBh6Mh1DkffZyP7wUMnHynouWl/HM0m/jRhxeMoV6xfEzt2XLXi28lPCXvxiVnhqhFj4RtKHXRJGOtLYqA6TaF9T1UPdp5m0xkZcwhVVIZV5JGRf0zGfxms2u5KaxCIhTAXm5H+D+weAUWrMLK9nNHyQa2rH8zO3cuTcv/I4dcMUVMGlSo9bCJ4I29Jq0Y2WAQtkiKkYPe41S0fIypSH04g17nZxUhVQWr9lsKdl0U5z7gJz4XDZW90OgLjfrdg7FyXu2u3dO8weR50+IPXvgscfg9tsNLfx558EddzRoLXwq0IZek3ZUBki1zTQsVnhR1XhVzHhViLh56OzcU03R8rKo61ndj/6dWzHn4w1U1USb+1CWt7w3diM21UhCgOP8QcKhndpamDMHxo/fq4V/+WUtkwwIbeg1GYHKAFmtFlV5yaFsQf/Oreg+6Y06HX2L3BATzrVWpng10F4VIm5CMFU11jF2q/vR67CWTHpllW/VjROqkcSlvds5ev2+1ydICQsXGjloSkuhWzdYsADOOqvBpgxOB9rQa+oVdgalplby7NJvqY3Ytq2iypBqEv/Q8Gq4vcpH7UoYRuKlUlYQWnq7kVNebohmTbLYXlllGX8PNEFdpBa+QweYPbtRpAxOB9rQawIl2RpsOy/ZItEjoPaavRpur6qk2P3BOs5uFadPBm7WK2yrqCInlK1coBVIgrrVqw3ljKmF/8c/YMQILZNMItrQawIjFSsv3XrJsVh5zX7kpF696sj9C25/I0oHb5LMCEXkgzdLCGpiclvFxvzBPuaekAR3wwYji+STT8I++xgTrqNGNaq88OlCG3pNYDhN1BUtL2Pi/FWu4ucqzH1vfGFFnNGyQxVaSCQc4nX0Um5h5K22BzUqin3werlfduEk1T1TtnvLFpgyxVjFamrhx46FVq0c25+ONNMNEW3oNQkR+WW002AXLS9j9IsrouSSdvFzO8x9R84pdbV/Mioy+Rm9uIlv+x0VWRlFN/JOFX6qYcW2+/bnP+aoxx7gmNn/3KuFnzgRDjvM1/nSnZenPqNnPTS+ic3cqKJ1Xo6yWpIZP/dKYUE+eYrYdmQopEVuiGkXHhe4cfCTddHNcn8/51Vl0HSz6CqULQhlRceO/FbDMtvdpKaayz5ZwMKHruSYh+82CnGvXAlPPOHKyMeez8RvgXWN9ug1CeDGYzSNxigb79uvNG/i4C5py9fuR2boJr7t57wqo5htEZMHyBaCWik9ZQl14rvySoSs5dzV7/HX92bTvnwTy9p04Q+F45g3+2+ezmWez8t2jT3a0KeYZMQd/Z4z0bbYfekERJ3TLt+L39TBqSj0rcKvzNBpTkB13iwh6DBmgaeHQ42U5ISyXT0IE7pnUlL442dc/dqjHPvDl6xu1Z7hF07gnY69yG+R6+uUmVZnuL7jGLoRQrQVQiwWQnwuhFglhPhLePtEIUSZEKI0/HN2xDFjhRDrhBBrhRADk9mB+oSfIhXJOmcQbVF96fLzcvh66iCWjBlQZ0BGD+wUFyKAxOPnhQX5LBkzIO56ySbRTJ9ezguG0Va9T3bvQ9IzeC5bBgMGMP2JMeTt2sHIc27k7Csf4J3DjyenaRPf9yNZ97ex4iZGXw3cKKU8BugNXC+EOCb82nQpZffwz2sA4dcuAboAZwIPCyHiP7mNkGTEHf2eM4i2ePkyFhbkM+23x0XF1c34OZBRVY/ckIzqT1bnzbbQXsa+T3bvQ9IehKtXw5Ah0Ls3rFoFDzxAyRsf8nHfs0FkJXw/knV/GyuOoRsp5SZgU/jvX4QQqwG7u30e8LyUcjfwtRBiHXAC8GEA7a3XJCPu6PecQbQlNnRyQE4IIWDUnFKmLVwbF2KwClskoq4IMgzm51zJyvQZed4OYxZY7hP5PqU0hLVhg5FF8oknDC38pEmGFn6//TgPOO+EDoFdKh2ZVBsqnmL0Qoj2QAGwDOgL/EkIcQVQjOH1b8N4CCyNOGwjFg8GIcQIYARAu0aSejQZcUe/5wyqLeaX0YvBdlrE4yZJltX1Rs4pZeL8VZ7zv1ida9ScUkbOKY3LFR8EXh4qbt+npBvFLVuMgtv/+Iehhb/hBmN1q4MWXpMZuJZXCiH2BeYBI6WUPwOPAIcD3TE8/nu9XFhKOVNK2UtK2atVI/mwJCPu6PecQbfFbSgodm5AtYjHaWShUvyUV1bZzjVYFce2OpfZqqArLHmdG0l7rHrnTqPwR8eOcO+9Rl3WtWuNXPGN5HvbEHBl6IUQIQwj/4yU8iUAKeUPUsoaKWUt8ChGeAagDGgbcXib8LZGTzLijn7PGXRb3IaC3C7iUY0sTENtpxFXzTX41ZsHqd9WPRAnzl9luX+6YtX//mg90wpH8uPBbWHcODYV9Da08E8+Ce3bJ/Xa9R0rZyLdOIZuhBACeBxYLaW8L2L7oeH4PcD5wGfhv+cDzwoh7gNaA0cCHwXa6npMMobYfs/peSm7DW5DDG7mAFQeq5uKTXbX8ao3dzqfH1QPlfLKqri89CYpjVXX1vLx3f+k+92TOW/bJj5qcwzXFY7l8w5dmVLVgsLUtKLekqkret149H2By4EBMVLKu4UQnwohVgL9gVEAUspVwAvA58B/gOullP7WYWtSjl/ZpdsQg8pTzxbC0WP1sqTf6jpOenOv5/ODlYrGJK2rPs288L16cfzY66lo0owrL5zARb+7i5I2x+hVqS7J1BW9blQ372Osf4nlNZtj7gDuSKBdmjTht4KQW+WHKs2tm3CEW69aNSJQjTrMCVdzUVds2b4gY+J2I4e0rfpctsxIMrZ4MbRvz6hzbuTfR/ejNiv64eenfY0tMVmmrujVK2M1USTyQXUTYkhECuimYpMALuhp3Q7VQ6Z/51Z17ckPl+2zKvYdBPk2fUj5qs81a2DcOHjpJWNi9f774dpr+Wj6EmoDUGRlahgjmWTqil5t6DVRpOKD6uaBYHqCZeWVdTH0vJwQoWxhmUPdRAKL12xWXhfia7HOKymLMkbzSsqSNuE5emAnRs9dkXDt14TYuNHIIvnEE5Cba/z917/CfvvVtTHh4iIksb5sBhPUvQsabeg1UYwe2CkunXBKjRDqPOrllVWEsgQtckOUV1QpM2aWlVcq88LEPmSsatDaGaNEQxHmvkHVfvXUnq1b92rha2vhz382PPoYmWRQC7BSFcbIpPBQOvMv2aENvSae2BmZFNdotpt0raqV5DZtwvLbzrCVWUZOJIM6VODFGAUVighKReO6PTt3wgMPwF13wc8/w+WXGytabWSSQbQxFaPDTAwPZeKKXp2PXhPFtIVr48IKXnPG2+mI3WiM3aZvUCUAi8RJ8aAyOlbb3SgqUqmhdmxPVRX8859wxBHGKtZ+/Qwt/KxZKdHCp2KxV6aqXDIN7dFrokh0uG3nYQGuvC+nSVfTCMcOk+0qXKnwElN1ujep9i5V7dm0bSfMmQPjx8O6ddC3L7z4Ipx8cuBtsCMVYYxMVblkGtrQa6JIdLjt5GG5iYfbFQCPNcKRw2RVKMeu7V6MkdO9SfXkY1x7pOSU9csZ9/7TcPcXcOyx8MorMGhQciuQ25DsMEamqlwyDR260USR6HDbzsNy631FLvuHvYuMzMVUYJ3W2KrtoWzBzt3VtqEUt6l8ne5Nqr3LyPYc991ann1+HE+/cBvtZCU89RSUlsI556TNyKeCtOcCqidoj14TRaLDbScPy633ZZeewSk8YrY9LzfEjl3VlFdWKff1gtO9SbV3WViQz77rvyT7tvH0/+w9tu2Tx8rRk+j295uhWbOkXDPTyFSVS6YhpEOOj1TQq1cvWVxcnO5maALAKh+NufIV8L0q1kQVnsnPy2HJmAGu9o2tmZqoUYjU/Futqk2KJn/jxr154XNy4G9/i9LCaxoHQogSKWUvp/20R6+xJBFtcvNQVp0xt9KIJ1LfVjVJaxUescttA2oP30vfYx9sEuqMfSK57CPbYBZ0Ka+oolPTKv7xzUKOfOFJqKmB6683tPC/+pXna2gaD9rQa+Lwqx6x8uZ3V9dG7eNncs5N1kqr8IiblAmxk6Ve+67KZW81wnBLbBvKK6toXrWL64rn84dl89h3dwXfDhpCuwfv0SmDNa7Qhl4Th5NyRuXtqo678YUVjJpT6jtU4pS1UjX5ZqfeiSTS8/eqnAlCjhp7PyPb0KSmmotXvsENHzzPwTu2sujw45l26jB2HHk0S7SR17hEG3pNHCojZXq3Km/Xb6jEb3vAPjwSO1FnVbYQokcDXg23nwlYVUw/8v4KWcvZa5Zw43tP03Hbd3ycfwzXn3czxW26ACC0TlzjAW3oAySTcm4k0h6V8coWwtbb9RMqcYNdemGn8EhkqEg1URw5GvBquL0msbKK6UdSWVVDv/Wl/O2/T9Lt+3WsOegwrr7gVt46/IQomaTWiWu8oHX0AeG3YEcmtqd/Z+taoE71Xd2kJIjc3y1W5xUYffKSZsBNWT6vumyvpf7swlDdNv2P2c+P46k54zmw8mf+OmgUZ1/5AG8dcWKUkdc6cY1XtEcfEJmWkjWR9qjS/KpK7qlSErgJlaiIHY1c0DOfxWs2K8Mdkde3w2ky2I8u28sEs9VDruOWjdz43tMMWruELTn7M+OcP9Jx/I0sW7weWV5JXoTqJhNGipr6hzb0AZFpOTfs4ux9p75ta8ScSu7ZhSm8hkqssFK+mDnirSSWfh6odmGtZC7bjwwNHfzLT/xlyXNctPJNdoWaMaPvUGb3uYDxQ3szuCCfwSd2TEobNI0PbegDIp05N6yMlqo9ZsgDvCcViy25Z8bsTTVOrHH0u2rRbjQSxAM1naltRw/sxJRnPuDK9+cwvOQVsmprebrHIB486WKa5x/KeO2ta5KANvQBka7KMiqjdUHP/KjKSUDcqk1wn1TM7Iu5n1tD6cc7tjPmQTxQ0xZmq6igcOHTnD1zKk12/EzRMafx7NlXc9nQ0yjRxl2TRPRkbEB4nZQLCpXRWrxmc1x73KbxdepLsnOA2+WIDyKJVcrDbFVV8H//Z+SFHzuWpv1OJqu0lCGTD6dBAAAgAElEQVSfvc3cuy/VHrwm6Th69EKItsBTwMEYDuFMKeX9QoiWwBygPbAeuEhKuU0IIYD7gbOBCmC4lPKT5DQ/s0hHZRk7o2VVNi/RpGJgnZgsti2JSE3djCj8nrtoeVmgk8S2166thblzjbzwX3wBffoYeeJPOcVVWzWaoHATuqkGbpRSfiKE2A8oEUK8CQwH3pJSThVCjAHGADcDZwFHhn9OBB4J/9Z4xI1RUYUysoSgaHmZY0gmMo2vG6NZtLzMMgRktsXcx21ox66PVtsTeYCY7bIy8n4niZWx/UWLYMwYKCmBLl1g/vyMTBmcaWs/NMnB0dBLKTcBm8J//yKEWA3kA+cBp4V3mwW8g2HozwOekkZazKVCiDwhxKHh82hc4taoqJb510gZt3+sAc1tms3OPTWe0vhOW7hWGQIy9fduY+BOfbR6KCQyiWqnYVdNKkcaQquRQFy/iosNA//WW9CuHTz5JFx2GWQ7ry9INZlYb1WTHDzF6IUQ7YECYBlwcITx/h4jtAPGQ2BDxGEbw9s0HnAbBzfj6dkWnqJq/yVjBjD94u5U7Ik3ek6xdrs49nPLNlC0vEwZ2ond7jXWr9p/0iurXNVpdYrBxy4qi110Zrtg7H//g9/+Fo4/HlasgOnTjW3DhmWkkQddb7Ux4Vp1I4TYF5gHjJRS/iwiDIuUUgohPCW2F0KMAEYAtGvXzsuhjQIvE4aFBfmMmlPq6Tx2nrmdQbRLc2COIoQAK5uYHQ4n+a3xqtq+raKKbRXxoxKIDv/k5Ybq9lMR6aE7JVMDQws/tvhFuOc/0Lw53HYb3Hgj7L+/7XGZQKat/dAkD1eGXggRwjDyz0gpXwpv/sEMyQghDgV+DG8vA9pGHN4mvC0KKeVMYCYYhUd8tr/B4lVG6HV/J2OuYvTAToyaU6o00naG0XwQOBlPr320asPE+avYXV0bFZYIZQlC2YKqGvuPm3lv7O7R/rt2cN3SuVxZMp+mSPjjH41J13qUF17XW208OIZuwiqax4HVUsr7Il6aDwwL/z0M+HfE9iuEQW9gu47Pe8erjNDr/qovswifS0VhQT6X9m6HnylFq6RosXjto4ryyqq4a1XVSvZp2qRONmoV7oK998bqHjWv2sUfls7lvX9ezbXL5rH5jHPIWrsGHnggysgXLS9zFU5KJ7reauPBTYy+L3A5MEAIURr+ORuYCvxGCPEF8Ovw/wCvAV8B64BHgT8G3+yGj1ddvlVBbTMMYWVkVInCLu3dznEibnJhVy7trQ635eWELA2IKsZtki0EF/RU14o1QymRxcLzckK254xle2VVXSHwey86ztbQRd6jJjXVDC39D+/MHMGY/z5JSf7RnHPl/bRdMA86RqcqyLQEdyrStfZDk3rcqG7eB6UDd7rF/hK4PsF2NVoSkbt5WbWaiB69aHkZ80qsjVZOKJuJg7tYntuuFCAYoZ15JWX0OqylrTrHzLljGmQrzX3zUJZlPD7SS3e6B4UF+SAli/7+EKPefZrDt5ZR0rozNwy+iY/aHlv3UI0l0xLc2ZGOtR+a1KNTIGQQQcjdvBgZv19y1SRlthBRHqHVuZ1i9FZtteuTmY8+1lhbXcsqLGF7D956i8IxYygsLuaLVodxzZBbWXSEkRfeLsShJzk1mYY29BmEF/25ygv1UjzbL6pz1UppG3YxC103D2VRXlHlWnXjZDjtjLWv0VFJiaGFX7SoTgu/qkt/Vi9ah3BxLpW6Jy/XCDPpRUqaVKMNfQbhxhO08/rBOnEZBKuk8KLWsCp0nRPKZvrF3ZWhnNjz+FWHeBmxFC0v4/nZi7h8wWMMWvs+u/Na0Gz6dPjDH6B5cwqBwl7uZMCqqQgp9SIlTXrQSc3SgEqRYScrNLHz+lXaeCcljVdUao3+nVvF9cuuvSoVzc7d1VETl37UIV5UL/95o4TdV/+e2dOv4rSvirm/zyX0/f2jFJ36W0Mb75HtldZa/e2VVXqRkiYtCOmghEgFvXr1ksXFxeluRkqwKsYRyhbs07QJ5ZVVcR55Tig7Ku7dYcwCpTEHa28eYP3UQQG0fi/jiz7luWUbqJGSbCHo3bEFn3y7PS4mrorHC+DrqYMoWl7GpFdWxYU6YvvtJdyhKngSpyjZtg3uuotd980gq6aGZ7ufyYN9LuanfVoAhnpon2ZNPIdYVMnj8vNylAvFzPuRCDok1PgQQpRIKXs57adDNynGyqOrqpF1+WYke8Mv+RZfVqcwhsrAQHCGwFTdmHLJGilZ8uXWuP1MKaRT+cFpC9fGGfrYuQkvYRjHuY7KSvjHP2DKFNi+nf8cfSr3nnIZG/IOiTqmvLLKUx4gE7vsm27DVV7RISGNHdrQpxg3k6KmkV8yZkBdCMI0zv07t2LOxxuiVneGsoWj0iRIQ+AmNYCJm/KDiahUIh9eebkhpKTOOMfyw9Yd8OijMHEifPcdnH023Hkn017f4nrFrRuJpJ1ss/ibrTyz9Nu4UVuiobX6JOnUpB5t6FOM22X835VXWhrnOR9tiPeQw//aGZi+U98OzBB4UfBElh9UjST8TrbG3h9lHhspOWvtEsYumQ0/bYSTToLnnoN+/QAYXRsf6lHhtu+q7JvzSsqijLwA5SIxL2hJp8YObehTjCqtcCyt83Kswzy18WGQqlpZZ7BVIY4gDYHbh5U5CewUdlHdk4o91XE59SNxM7Los76Um/87i+O+/4KfOx4FjxXB4MFReeGtHpAVe6otHxwH5ITqYvBmWMoqxOa2vRJYvGaz7XFu0HlrNHZoQ59iYo1KXm6IHbuqowy4OZRXZaS0ws5gJ1pVKRanxGYmEndhIXOfifNXRYVdtlVU2RYssXvYHPv9Om767yz6rV/O9wf8ik8m3keP8TdQtPJ7pt21OG50Efswspw0zxLs3FNd10bzfqrCYLFzIslc45CumsWa+oE29GnAyqhYhTacUgZEojLYiVRVUrVLFWuORZUiwAqzv7HxdbuCJVZ02FrGje/N5pw177E1Z38eGHQdN8y9j0OaN/c0T+HFy7dqp9W1krnGIdESi5qGjTb0AeNH2aIKbViW/ssSIIiajLUz2HbpCi7oaRjXUXNK49rqZBQnF3al12Et6x5GVrJQr96k2/CSVZ9+9csW/vLBc1y84g12N2nK/X0uYXafCxn3u94QNvI3vrDCuUJUBLHvS4cxC1y3XxWmCeI+qdB5azQqtKEPkKAlbiovzWqbKvWAXZGQeSVlyrZ6UXEIqFO8bK+s8u1Nuo0zRxrU/Xft4A/L5nJl8Ss0qa1mbq9B3HviRTTNb824iDqzqlFN7PnsHtROcxOR7VQ9tExFlfa6NalEG/oASYbETeWlOZ3PLrwB1rnhI9vqVA7QSvFipjbw21e3cebWeTn8tLmc4Z+8wnVL55K3awdFx5zKM4Ou4cW7L+OSmPM6Tdo6FTUv/mYri9dstg2/xLZT9VAwZbMaTSrRhj5AMkniZmfc7Fasmm1VLXQyc8Hb1W9NNM2y7fHV1TxYUcKhj97FIb9sYXHHnkzrN4yv2xzJlCFdbftkRaSBVvUpci4iMvxipboxRwRBhbM0miDQhj5AMkniZmfcpgzpqpzoPSBcyEMV5jC3e63f6sXYW+4rJbz0EowbR8HatWzt2oPre4/jtZZH0Tovhyk2DxTV+xKbVtku3BL7v5VnHjsiiDzOrQRTo0kGOqlZgLhJvpWqEnOqh0t+Xg6FBfmMHtjJmNiNYWdYu65SzJjb3T68AknY9fbbcOKJcOGFkJUFL79MyxXFPDRzFF9PHcSSMQMcdfpW78u9Fx0Xt3DLLVYPBdUoKnI9gUaTDrShDxCn0mypLDGnKhVYVl5J36lvA7Bv8/gBXVWNVGaWjEynsHN3teu2+A5dffIJDBwIp58O338P//oXfPopFBZGLXhywul9MVHdMyusHgp2IwKdnVKTTnToJgHsdOZWpDIfSWS8OzZebD5g7OL0dooft+kCTDyHrr74Am69FebMgZYt4d574Y9/9JUy2MSN9NCqz/07t4pSJ4E61p7sRVEajV+0ofeJHymlk5IlaEzjZpU21y6zZJYQdakHYvtilTPHDk8TkJs2we23w2OPQdOmMH48/O1vcMABrq+XKFZ9NtcLOE0w260Y1qkINOlEG3qf+PHOnZQsycJOS2+lwKmRUvnQ8uKZZgksQyRxlJfD3XfDjBlQVQXXXmsY+UMOsT8uycSO2Jyko8nMTqnRJIKO0ftEZfDKyiuVE61OSpZkULS8TBlnNmPVVg8a1SSqF8/U8QFWWQnTpkHHjkZu+MJCWLMGHnwwI4y8n/mUyYVdmX5xd8f5AI0mlTh69EKIfwHnAD9KKY8Nb5sI/B4w0+7dIqV8LfzaWOBqoAa4QUq5MAntTjt28dhIwwB7veJ8m0U0XnGbakFVXhCM7JB2ycnMh1lsce9QtohLwQCSyqraqOMjs2pGUV0NTz5p5IUvK4MzzzQMfffunvuXLBKZT3EzH5Du/mkaF248+ieBMy22T5dSdg//mEb+GOASoEv4mIeFEPFFQRsAqnqnkcR6xX5qn1rhxdu0C7Vsq6iyTUqWlxui+6Q3GDmntO5a5ZVVIKFFbijKY90VY+Qtr29q4bt2hd//Htq0gcWL4fXX44z86Lkrovo3eu6KpKiTVHLXZC5+S6X6SqMBF4ZeSvkuEF8nzprzgOellLullF8D64ATEmhfxhIr2VMRaRjcyvyc8FJgOpFJwG0VVZbVmqpqJblNm0Rp2B0Lmy9eDL17wwUXGNLIl1+GDz+E006LO2bSK6uiRgxgyD4nvbLKd1+ssDO4bgq1+0UXCNekmkQmY/8khLgCKAZulFJuA/KBpRH7bAxva5BEDtFVBaFjDUMQGQbt5gciyw6OHtjJdaGTRNugylMzud0eQwv/xhuGB//443DFFdBE/dFTpQJWVpDyiZ3BHT2wE6NfXBFVJyCUJQKZVM2kVBmaxoHfydhHgMOB7sAm4F6vJxBCjBBCFAshijdvTrzCTroJKizjBpVXaS6Iip0jiB1F5IXTHATZhtjRygnVW3nzo0fof+lZUFwM99xj6OOvusrWyCcLqxCNo8GNHaoFJI5K5mhBo7FCSBeKDyFEe+BVczJW9Vp4IhYp5ZTwawuBiVLKD+3O36tXL1lcXOy17RmH1wk2vxNyVtWPVFkV3eRk8UpOKFsdcorVwo8aBaNHK7XwVvcgttJUJJf1bsfkQuvkZSqs+psTyqZZkyzL65iT426zT/p5363ao9U5Gq8IIUqklL0c9/Nj6IUQh0opN4X/HgWcKKW8RAjRBXgWIy7fGngLOFJKaWtR6ouhD1IpkeiX3W2ZOgF8PXWQ8nivi7Va5IaYcG6X+DaWlxtSyRkzYM8eGDECxo+n6PvauJWmi9dsti2jeEHPfOZ8tMGyPi5AbiiLO4d0c33vVWG1FrkhdlXVWr4HdmokAbYrhc0+mP20+qx4+SxphY5GRWCGXgjxHHAacBDwAzAh/H93DCdyPXBthOEfB1wFVAMjpZSvOzWiPhj6oL0wlfHxm69cdb5sIeKSd7k5Lpa8nBATB1sY+MpKeOghQx65dSsMHWp49Ecc4XvkYGZ6HGlTM9fLve8wZoGl0RbA9Iu7WxpRN/clJ5RN81CW5dyBVYpiP5+VZHv/+iFSvwnUo0829cHQB22Y7YyP6YF79fpURtXOMKjaYbZFed3qapg1y9DCb9xoaOHvvBMKCup2cfsQsWL91EG0dyjdly0EtVI63hs/712i4S0r/HxWgv7cRaJDSPUft4Zep0BQ4DY04lcp4ZS73msuHXOb17qonishSWlII8eNM1axnnACPP20pUzSr5EXGP1XpYwwMV9zujduK1dFEpsULgj8fFaSqdBJZZI9TXrRKRAssNJXe0lX64b+nVvFndNNtSMnrbWbuqiReFILRWrhwVj8tHSppZEH/zl8zLS+Q09s6/oYu3vjd/2CmbffbmFcXk7IdWrjA3JCnmsRJFOho2WejQft0VtgZWTd1Al1S9HyMuaVlEWdUwAX9Nyrsff6JXSqEasyDK7K9y1fDmPHwsKFjlr4yJFQIkHB78or69Q1zyz7FjcRRjsD5Xf9glNJxomDu9TtZ5faOJQl2Lmnuk7l47b6lp/RiFsyqSKaJrloQ2+BncEwJ9kSKQ2nepAsXrN3PYHXL6GTQXIKU1j2Y906Iy/888/zc85+PHjaVbw54EL+UtCNQoWRdxPXjoyt79xdbSlxNPs5ubArkwu7Rj1AshQhnWQYKKeSjOZ9c0ptXLGnOm7StrKqhhtfWGF5vImrB7FPkvkQ0WQW2tBb4JSwLNGJMDfeutcvoVuD5ETR8jIen/shF73+BENXLKQ2FOJffS/m4V7n83PzfWFnraUnWrS8zHJ+IJbYyT7VmoD+nVtFHRf5MFJNIiZrcZpqDsMpZXHk6x0UE8t2KaEjzwV7jb0ZokrU2CfzIaLJLHSM3gKnuGyiMUynuKvpvZrFQcA5rtw8ZP1WOhmkSF59dzXf//lvzJl2GZesWMhzxw3k5GtmMvXkyw0jHyY2Hm4aXjsjryqtqBrdzCspU8awCwvyuaBnft29yRYiKuwVJKrPws7d1Z6SkNmNNpzmXnQSNE2iaI/eAjsFCyQeIrDz1mO9VbM4iJ2ndemjH8alCAbjKe7Ky921Cx56iFNuncQBlb8w/+h+3HvKZXzTorXykMiHnV3YCPytzrULa5hzHOZ7UyMl80rK6HVYy6SVZJz0yqqo0Et5ZZWrGLuJU84hO+chWeoYP1XSNPUT7dErKCzI596LjktK/ho7FYhXtU3R8jKWfGmdXNQ6cXAE1dVGwe0jj4S//Y3SQ45k0LAZ3DD4JlsjD9EPO6c5jdgwDDg/HGBvWCPWc0119sfCgnxym8b7RF6uab7nKiWSnfOQLHWMzqLZeNAevQ3JjGGqJkC9fqknzrdP3WvpoUkJRUWGFn71akML/9RT3LJMWsajrfLomKELM0Wx3ZyGlbft1khZea7pkAUGcU2zD17nF5KljtHyysaD9ugdKCzIZ8mYAVG515OJF9100fIyZfIvkzgP7Z134KSTYMgQqK2FefMMLXz//kpN/aW929EiNzrjpRm6KFpe5jinYRXTz/KgsY81PG7ukaqgiGq7E0Hp2f1o+pOVGVVn0Ww8aEOfYXj5UrsdYpeVVxpa+LPOgv79jZQFjz0Gn31mGPyw0VUZocmFXW1DF5HHqYgsS+g0cRtLrOFxukfjiz5lVERVLDP2PL7oU9+TmkEaWyfnIfZhBPGppoNIU5DK1Nqa9KJDNxmGl3CRmyF2u22buPG92XDXf6FFCyPD5PXXQ456AZWfkJJ5nFMBFlVsPlsIhp7YNm6hkZXhsbtHRcvLeGbpt3GhpsqqGp5btsFTegi31wwS1QTplCFdE85tE4uWVzYetKHPQNyu4rSLjbfasY0/f/A8Q1f8h+qsJjx+ysW80P93/O+nbFrf/2FUqgU3X3K3cWIn/b/qgVErJZMLu8YtNIo04LHbrQyfXTF0r+khYgmiOpgTqc4/k4o+adKPNvQKgkjfmuwUsFZGdb/dOxmx7CWuLi6iaXUVzx83kPv7DmXzvi1ht7GPWWwbSV3O96ASgxUW5FP8zdY679nUuIORiVFlhM0HhpXh8SIDtDPaqiRpmRST1hOkmmSgDb0FQeiLg9Io2z0sIofeP/20nT+tXsjli58lr/IXXul8CveechnrW1pfK7b4Nth7jm6H+VYa9zkfbWDOxxssrwnOcWEvXq5q5CHAdWgonej8M5pkoPPRWxBEDvAgzuEqX3h1NTz9NBW3jCf3++94t30Bd586jM8OOcLVNWJRVaRyi9cc9G5yBtnlzM/Py4l68EC8fFEAl4ZLEGZ6oQ2dI17jBZ2PPgGCGD4neg5V7pg6T7Z7a/j3v+GWW2D1ar5o3Ympl9zBh4cdB0QnX1MlDrOidV5OQsbQa4jB7qFntsOuMIr5UImctJwypKvtKCiTDaaeINUkA23oLQhi+OymsIjqy+wkQWy78iPoM87Qv3fqxLhLJ/BMfq86mSREJ1+z8hJD2SIqRg+G4Wx/YE5CISe7CeJYIleJxt4Pq1S/kVgt4jIfgqlY75AoVv21qzGr0SSC1tFbEIS+2O4cTkmqVBLEY374iidfmMDzz42FDRvg0Ufhs894ts3xUUbeJFL6GJsE7OLj23LxCW2jimRIYMmXWxNaFu+0eCqSGikpWl5G90lvMDJG9/7M0m+VRj4/L0fp5deHSUur93/20m910jJN0tCG3gK/FYncnsMpx0issWq3bRP3z5/Ga0/eQPdNa/nsL+Pgiy/gmmugSRNX2TCtkoAtWLnJdXEQLxLEyH7bVZlqkRti7EufWoaV7MI1S8YMUC7Oqg+Tlm7y/OicM5ogaRShGz8x5yBiuX4XH5nhj4N2Glr435UaWviHT7qIw6ZOYFC/Y6KOc8qGqYr1eyl8LYH2YxbULWwyqz9ZEdlvVR52MFLueC2+bRry+lw0w+1Dsz6MTjT1gwZv6DMxFatT/H5sn0PZMO7vXLHsZZpV7+H54wbyyMlDqWh1MOWvfc24xRsRAsorquoeXFOGdGXi/FV13nHzUBbF32yN8uSDoEZKZi/9FsDW2Dv1NS8nxHaHCeLYOHykIU/1pGWQah238xj1YXSiqR84yiuFEP8CzgF+lFIeG97WEpgDtAfWAxdJKbcJIQRwP3A2UAEMl1J+4tSIZMor7WSOowd2ivvyQuqXuYNhxO4adCSDP/g33HknbNnCom6ncceJQ9mW356de6ptdegX9MyPm7y0mrCMJC8nxO7qWs9eNRghmS+nnO24n51ccNrCtUqDZ/YpEyYog5Y8uim5qCWVGje4lVe6MfT9gB3AUxGG/m5gq5RyqhBiDNBCSnmzEOJs4M8Yhv5E4H4p5YlOjUimobfTYOeEsuMKOCOiFxMl+oVTeYKR29vs35T793xKj3/NMCZZf/MbmDIFevYE3GnTVas+VQhg+sXdgb0PtrzcEDt2VUcpcexYH6G3d1IRqe6BlcFrkRtiwrldMsbIqe5/ZO1brw8hrbrRBEFgOnop5btCiPYxm88DTgv/PQt4B7g5vP0paTw9lgoh8oQQh0opN7lverCohsnZQsQZGCsDl0ieEaewUZ0WftxN8PnncPzx8MQTcPrpUedxE6v1Gp4x91aNaNw8WEwc+6mYq6gvmnHV/TfvuZ9wYKbr+TUNC1crY8OG/tUIj75cSpkX/lsA26SUeUKIV4GpUsr3w6+9BdwspbR115Pp0auG3V7CFW5XixYtL4sqOacKneTn5bCkTxMYMwY+/BCOOgruuAMuuKBOJhnp8WW58NazBLh0xAHrsI05egEYOafU9vjcUBZDerZh8ZrNyodCokXUrUjHyla3q32T0V+Nxo6UrYyVUkohhOfZPiHECGAEQLt27RJthi3NmmTVGTQzLODGazVxMylWtLyM0XNXRIV9rG7K0T9+xU0vzoKxJdC6NcycCVdeCU2aRJ0rtm6sI9JYBBV5/VCWICtLsLs6uqhgTigbIeIVL5VVNUycvypufysqqmrrJmVVeEmF4IZ0Taw71Xs1SUQlk+mpGTT1G786+h+EEIcChH//GN5eBrSN2K9NeFscUsqZUspeUsperVrF1xQNAtMwROq0d4WLaFst7AllCWPFaARuJXvTFq5VTpYCtC3/numv3MOCJ/5Cz+/Wwl13wbp18PvfRxl581xeJ0hrMeYWzJBKXk4IBHFGOy8nxJQhXSmvsFa8lFdW+ZqctUJAoIt+0lXj1O3aAL8qGacFdBpNovj16OcDw4Cp4d//jtj+JyHE8xiTsdvTGZ+3MwzmEDso1Y3Kmzto5zb+9MEcflf6H2qysnmsz2/Jv/M2Bp3axfO53FAjZZ3HbvXg+WVXNeAtVYFfJHDjCyuAYDzudKbwjYypq8KBbhwCK8891TnoNY0PR0MvhHgOY+L1ICHERmAChoF/QQhxNfANcFF499cwFDfrMOSVVyahza6xMwxu0v96IdZw7ru7gt9/9BLXfFxEs+o9zDnuDB7s+ztO/3V3RtgYeatzecVuMVSNlIx96VNLOWYyMK8HiRl7s85sMvPJuw2f+J1EVoWeVO+BXjClCYoGnaZYNYlmNxGZiIxy9NwVZO3ezWXLX+P6D1+gZeXPvBrOC/91OC98KEuwb/MmUYudnAptJAMhoE/Hliz5cmvSrhFJXk6I0gln+DrW7n4EpTdPRXpgO5mm1QNMT+5qnHA7Gdugc92oEoupJiITifUWdjuEOU3X8s5jf+DWtx/js4MPZ/Cw6fzpvJvrjDwYEs5tFVXKWKzpVVZW1djmiXEiLydkm1xMSgI38tlZ6vaWV1b5jjnb1ZkNyhCnIv5vJ9PURbo1yaRBG3pVYjHVRGRZeSUdxiyg79S33RslKQ0tfLdu9Jj4Vw49sh0sWkS/rz/h00OOdDw80phETsqBtQFwgwAmDu5SV8LPD+Y9c0uL3BD3/vY424dTZD/7Tn3b9b22qzMblLetukZZOMwXBKoQk/m5TCSJnkZjR4PPdWO1MMVOWhnpaZvHK3n33Wgt/IsvUtSxN9Pe+B/fvbnAlf4d9j5grPY3PXur81iFoMxqSoUF+Ux6ZZXjtVWYKzXdzBXkhLKjVrKqNPjm3IhXiWSyy+vZxf+BwCSc/Tu3spSk9u/cSi+g0iSVBu3Rq3CTM9122L5yJQwaBKeeCt98Y2jhV62i6PCTGPvyZ3UyOS+rVe32Vw3tJw7uEucJTr+4e13JvG2KkYsb5pWU0b9zK5cjCsnIOaUcPvY1Rs4pRRXBaZ2X4ytEEkR9ABVORV7ctM8ti9ds9rRdowmKBu/RWxGrmnBdxOKrr+C22+DZZ+GAA2DqVPjznyE3t+58yZhAVSVgs1MJJWqYKqtqWLxmMxf0zOe5ZRscDKGh1Tf3sVqhaxrmUTbevopkpkpw+54FoZcO0gEAAA/KSURBVIBJpzxU07hplIYeokM6KjVEXWjghx9g8mT4v/+D7Gy46Sa4+WZo0aJu36LlZbZhDq9pFyKPM42alWFTSQKdjEeL3JCjx19WXplQmmOrpF+qsJlTGCZZoQ23RjaIMJGbEJReIatJBo0ydBOLKjQwtm9rmDABDj8cHnkErryS/xS9R9+8M+hw1wd1E4lFy8sY/eIK5fmtJttmXNxdWSUpW4i6/S7oaRhHq4lLuxWVdoZpxsXdWX7bGcrrm2RZqJO8UCslX08dFFXDNZlhGD+4MeBBtc+p73qFrCZZNGgdvRciPanD9s3mH+XL6DrrQfjpJ/jtb2HyZIp27mOptQZZF76IxU6LbaXdNhOh5SsKZEfmabdLJmaGSZRJ1RRFw01i8+b4QaUDzySv1bJwuou1DolcT9V3u9oJWk+vsSKwfPSpIBMMPQA1NTB7thGH//ZbI13w1KnQy7iPbrMYRjLj4u62RsL84peVV8Zlu1Rlv3QqKGJm22yvKONnvl60vCyqKpWZATM/L4edu6sta7nC3pCMXf56N4uNMsXgZ0o7VLUT3GZP1TQ+Upa9skEgJbzyCj//9Sb2/3ItKw85gsevvpv+1/8u6gvvZ9IstsiI1URqYUG+5UNEZcydHs1mOCLfJiZs5ck2a7LXONvVer33ouPiioqUlVfWyUDzXRjLTCrxmCnSxmTLSDWNF23o33vP0MJ/8AFbW+Yz5rwxvN6pD1Jk8UaM4fGag6ZFbigufXFZeSWj58Yn+gpKeREZ87UroK2SOY6cU0rxN1tta71GttuvkczERF7p9uzrc8FzTWbTeA39ypVwyy2wYAEceih3nT+KRw8/lersvbck1vC4zUsORox7wrldmPTKqrhYd1WNZNQLpYyaU1pnUILIJikEUeGSSFmi6XGbfbK71uyl39L38JZs3bknzuhMHGyfkM3EyWgmKjUM2ignY4ThtY31peKWpv7R+FQ3X38Nl18O3bvDkiVGDH7dOv551OlRRt4k0vDEplSwY9qFRnhDJWGUMnoVrvvFSWqkjDdKhQX5dWqPyNJ3Tiz9apvvZflu1COqcIQEuk96w1Zpkgx1StC5bvy2sbAgnyVjBsSplTSaRGg8hv7HH+GGG6BTJ5g719DCf/WVoYfPzVUantjtkV9ElTzR3N536tuummYuTnJT3MKJWPll36lvM3JOqWeZZE04j4wfo+PGaNqtTi6vrGL0iyuURjEZCciCXsyUriIpGo0VjcPQP/ggdOwIDz8Mw4cblZ2mTo1a8ORH3606pn/nVlHJydzwXXlllGG996LjXB8biek1xiZI80oimTPdGE1zdKS6TlWtVBrFZKwwdfugd4teBavJJBqHod9/fzjrLFi1yshLk29dTMJPqKJ5aO8tNMv0LV6z2bMHfUBOKK49LXJDir3VmF5joukYends4byTAruwTOSir8KCfGpt5L0qoxi0UYbgF3Ilo40ajV8ax2TsFVcYPw54UZBYyRPN+qx+vLbyyirGF33K5MKuddsmnNvFVwGSILzG9VuczxGrwzcLr48e2InRL66w1NfHTnLaTUKrjGIy1ClBT4RqBY0mk2gchj4J2MkT/fLM0m/pdVhLS9WMXfK1WEwDaRe2aZEb4ufKamUeG6eHhZn2IdKYb6uo4sYXVzD0hLbYzVZHqpnaH2ht6LMESqOYLHVKkHp6raDRZBJ6ZawHIuVyybprdsvd3azMNVekAo7l94q/2WqZH92pHU5tMVfY2iGA6Rd3V6ZpSKT0oEbTWNArYwMkNkThlbycEDv3VLvKHWPnSVuFA0LZgn2aNmF7pXVelljNfLYQXNBzr+f6zNJv4wxtKFs4hhjs2ulk5GFvbnrVrtt93uuGSLoXcmnqP9rQO5BooW4BlE44w/XDwm6yzk84oGJPddT/NVIyr6SMXoe1VBrafZo2cTQkiSzwcspNb55fk1mpIjT1F23oHUhUvZIlBB3GLKB1Xk7dqlLVylQ3k3VOceTxRZ+6KBRSU/ewsCLWm7byKEcP7GQ7H6FK0BaZB0d1HwTq+LwXgvCE0+1NZ2KqCE39IyF5pRBivRDiUyFEqRCiOLytpRDiTSHEF+Hf/nV6KURVsDpRBUuNlHF1aJeMGcD6qYPqctIHVRB6fNGnzF76ratCIabhsiJLiLr+q1Z4AlzWu53y/JK987FmicP1UwfV5dnpMGYBFXuqCcXUHYyseZsIQayezYT88FqPrwmCIDz6/lLKnyL+HwO8JaWcKoQYE/7/5gCukzTshsdOIQqnlMGRxHpiQWdNfEYxsWqF6Z1ahaVqpKxLvGbnUS4ZM6AuBGR1j0wP3pzUjVXqbKuoIksYcxiqOQa/BOEJZ4I3rTNaaoIgGQumzgNmhf+eBRQm4RqBYveFdiokbmXk7fb36ompRhpW+3lRApkGdcqQrpbFvKtqjILfqoec2Q9zNa9KTRnZ34nzV8Vp62tlciZeg/CEM8GbzrSKXJr6SaKGXgJvCCFKhBAjwtsOllJuCv/9PXBwgtdIOnZf6MKCfC7ome+YxMzEXB2ryoPjxRPzEjrwkkMlMtWwsTrV9aF1xPbDzUpQ1US0hMBDI0GsTM2E1a1+V2xrNJEkauhPllL2AM4CrhdC9It8URoifUszIoQYIYQoFkIUb968OcFmJIbTF3rxms2uveV9mjWpyxgZG38OZTnLFiPxkhjLrZfpJdWw3Tli+2HneZqjEjcElfgrCE84U7xpndFSkygJxeillGXh3z8KIV4GTgB+EEIcKqXcJIQ4FPhRcexMYCYYC6YSaUeiOC1X9z3cjx0GxPyfSM722GPzckOWKZGbNckiW0BFuKbtruoair/ZGnWdvJyQqzUCApRxdJX0E9QLt1QEERoJYmWqXt2qaSj4XhkrhNgHyJJS/hL++03gduB0YEvEZGxLKeVNdufKhJWxfoo2W2FOPjoVerbS58fWWVWdo0VuiF1VtXEFrRFELcrKCWXTo90BLPlya9w5Luvdri6vjlU6A1W7veKnzq4uhq3RuMPtythEQjcHA+8LIVYAHwELpJT/AaYCvxFCfAH8Ovx/xmM3PHaakDVxMwowt096ZZWvnO05oWykJO7YqlpJVY2sS/trxnKXfrXNsh3PLdtQ93dhQT7Tfntc3bxC7EDETbjCjzy1RW4oLrylJxo1muDxHbqRUn4FxCVNl1JuwfDqGwx2YQnVKMBOFle0vExZeSo2Z7vVNexWlNZIWWcsCwvylYuaYrX2kVJPr4uE/MhTI0c2OjSi0SQXndQsSYwv+jQuj4wZmrGr2eombOEmHGKe5/Cxr1kuoMoWgi+nnO3YDzfYhalU8x9aOaLRJE4qQjcaBUXLy5hXUhaXAsBMJuaUuMwJN6Ek8xpDT2xr+bpqux+c5KlaHqjRpBed6yYJWMkiJYZME9ThjEh9ux2RIR2noh3mhKuZ/yZbCIae2DaqwEmiOK3eDHoFsEaj8YY29EnAaSJWFc7wom83jadKvRM5Mphc2DVQwx6LVUUpr2sGNBpN8tCGPgm48XAhGH12xmi9HdYMaDSa9KEnY5OAG418Q8JpzYBGo0kOusJUGskYLztFZELyL41Go0Yb+iTRGCYgTQ28akyoU+lqNJmBNvQaXziVWNQrXDWazEEbeo0v7Eos5jfwUJVGU9/Qhl7jC1X8XYCegNVoMgy9Mlbji0woyqHRaNyhDb3GF5lSlEOj0TijQzcaXzQ2CalGU5/Rhl7jm8YgIdVoGgI6dKPRaDQNHG3oNRqNpoGjDb1Go9E0cLSh12g0mgaONvQajUbTwMmINMVCiM3ANz4PPwj4KcDmZAoNtV/QcPvWUPsFum+ZymFSylZOO2WEoU8EIUSxm3zM9Y2G2i9ouH1rqP0C3bf6jg7daDQaTQNHG3qNRqNp4DQEQz8z3Q1IEg21X9Bw+9ZQ+wW6b/Waeh+j12g0Go09DcGj12g0Go0NGW/ohRAthRBvCiG+CP9uodjvP0KIciHEqzHbOwghlgkh1gkh5gghmqam5c546Nuw8D5fCCGGRWx/RwixVghRGv75Vepab9nOM8PtWSeEGGPxerPwe7Au/J60j3htbHj7WiHEwFS22w1++yaEaC+EqIx4j/6Z6rY74aJv/YQQnwghqoUQF8a8ZvnZzAQS7FdNxHs2P3WtThJSyoz+Ae4GxoT/HgPcpdjvdOBc4NWY7S8Al4T//idwXbr75KVvQEvgq/DvFuG/W4Rfewfole5+hNuSDXwJdASaAiuAY2L2+SPwz/DflwBzwn8fE96/GdAhfJ7sdPcpoL61Bz5Ldx8S7Ft7oBvwFHChm89mun8S6Vf4tR3p7kOQPxnv0QPnAbPCf88CCq12klK+BfwSuU0IIYABwFyn49OEm74NBN6UUm6VUm4D3gTOTFH7vHACsE5K+ZWUcg/wPEb/Ions71zg9PB7dB7wvJRyt5Tya2Bd+HyZQiJ9y3Qc+yalXC+lXAnUxhybyZ/NRPrV4KgPhv5gKeWm8N/fAwd7OPZAoFxKWR3+fyOQSQnU3fQtH9gQ8X9sH54IDy9vTbNhcWpn1D7h92Q7xnvk5th0kkjfADoIIZYLIf4rhDgl2Y31SCL3PpPft0Tb1lwIUSyEWCqEyCTn0BcZUXhECLEIOMTipXGR/0gppRCiXsmEkty3S6WUZUKI/YB5wOUYw1BN5rAJaCel3CKE6AkUCSG6SCl/TnfDNLYcFv5udQTeFkJ8KqX8Mt2N8ktGGHop5a9VrwkhfhBCHCql3CSEOBT40cOptwB5QogmYS+rDVCWYHM9EUDfyoDTIv5vgxGbR0pZFv79ixDiWYzharoMfRnQNuJ/q3tt7rNRCNEEOADjPXJzbDrx3TdpBHx3A0gpS4QQXwJHAcVJb7U7Ern3ys9mBpDQZyriu/WVEOIdoAAj5l8vqQ+hm/mAOZs/DPi32wPDX7LFgDmj7un4FOCmbwuBM4QQLcKqnDOAhUKIJkKIgwCEECHgHOCzFLRZxcfAkWGVU1OMCclYtUJkfy8E3g6/R/OBS8LKlQ7AkcBHKWq3G3z3TQjRSgiRDRD2Do/EmLTMFNz0TYXlZzNJ7fSK736F+9Ms/PdBQF/g86S1NBWkezbY6QcjzvkW8AWwCGgZ3t4LeCxiv/eAzUAlRjxuYHh7RwyjsQ54EWiW7j756NtV4favA64Mb9sHKAFWAquA+0mzUgU4G/gfhuczLrztdmBw+O/m4fdgXfg96Rhx7LjwcWuBs9L93gTVN+CC8PtTCnwCnJvuvvjo2/Hh79ROjBHYKrvPZqb8+O0X0Af4FEOp8ylwdbr7kuiPXhmr0Wg0DZz6ELrRaDQaTQJoQ6/RaDQNHG3oNRqNpoGjDb1Go9E0cLSh12g0mgaONvQajUbTwNGGXqPRaBo42tBrNBpNA+f/ATews6G10pN3AAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] @@ -3293,7 +3299,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -3853,7 +3859,7 @@ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", + "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", @@ -4079,7 +4085,7 @@ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -4149,7 +4155,10 @@ "y = at^2 + bt + c\n", "$$\n", "The we need at least three data to compute the parameters $a, b, c$.\n", - "\n" + "\n", + "$$\n", + "L = \\sum_{i=1}^N (y_i - at^2 - bt - c)^2\n", + "$$\n" ] }, { @@ -4189,6 +4198,23 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "### How to get the update items?\n", + "\n", + "$$\n", + "L = \\sum_{i=1}^N (y_i - at^2 - bt - c)^2\n", + "$$\n", + "\n", + "\\begin{eqnarray}\n", + "\\frac{\\partial L}{\\partial a} & = & - 2\\sum_{i=1}^N (y_i - at^2 - bt -c) t^2 \\\\\n", + "\\frac{\\partial L}{\\partial b} & = & - 2\\sum_{i=1}^N (y_i - at^2 - bt -c) t \\\\\n", + "\\frac{\\partial L}{\\partial c} & = & - 2\\sum_{i=1}^N (y_i - at^2 - bt -c)\n", + "\\end{eqnarray}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "## How to use sklearn to solve linear problem?\n", "\n" ] diff --git a/1_logistic_regression/Least_squares.py b/1_logistic_regression/Least_squares.py index 8e0e7e3..313f820 100644 --- a/1_logistic_regression/Least_squares.py +++ b/1_logistic_regression/Least_squares.py @@ -239,6 +239,9 @@ plt.show() # $$ # The we need at least three data to compute the parameters $a, b, c$. # +# $$ +# L = \sum_{i=1}^N (y_i - at^2 - bt - c)^2 +# $$ # # + @@ -256,6 +259,18 @@ plt.scatter(t, y) plt.show() # - +# ### How to get the update items? +# +# $$ +# L = \sum_{i=1}^N (y_i - at^2 - bt - c)^2 +# $$ +# +# \begin{eqnarray} +# \frac{\partial L}{\partial a} & = & - 2\sum_{i=1}^N (y_i - at^2 - bt -c) t^2 \\ +# \frac{\partial L}{\partial b} & = & - 2\sum_{i=1}^N (y_i - at^2 - bt -c) t \\ +# \frac{\partial L}{\partial c} & = & - 2\sum_{i=1}^N (y_i - at^2 - bt -c) +# \end{eqnarray} + # ## How to use sklearn to solve linear problem? # # diff --git a/1_logistic_regression/Logistic_regression.ipynb b/1_logistic_regression/Logistic_regression.ipynb index 5dbcea2..1b0d7d0 100644 --- a/1_logistic_regression/Logistic_regression.ipynb +++ b/1_logistic_regression/Logistic_regression.ipynb @@ -20,6 +20,7 @@ "\n", "逻辑回归就是一种减小预测范围，将预测值限定为$[0,1]$间的一种回归模型，其回归方程与回归曲线如图2所示。逻辑曲线在$z=0$时，十分敏感，在$z>>0$或$z<<0$处，都不敏感，将预测值限定为$(0,1)$。\n", "\n", + "FIXME: this figure is wrong\n", "![LogisticFunction](images/fig2.gif)\n", "\n" ] @@ -158,7 +159,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -174,7 +175,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -197,16 +198,16 @@ { "data": { "text/plain": [ - "Text(0.5,1,'Original Data')" + "Text(0.5, 1.0, 'Original Data')" ] }, - "execution_count": 6, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -230,7 +231,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -258,7 +259,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -302,17 +303,19 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], diff --git a/1_logistic_regression/Logistic_regression.py b/1_logistic_regression/Logistic_regression.py index 50ab97f..9c60c39 100644 --- a/1_logistic_regression/Logistic_regression.py +++ b/1_logistic_regression/Logistic_regression.py @@ -34,6 +34,7 @@ # # 逻辑回归就是一种减小预测范围，将预测值限定为$[0,1]$间的一种回归模型，其回归方程与回归曲线如图2所示。逻辑曲线在$z=0$时，十分敏感，在$z>>0$或$z<<0$处，都不敏感，将预测值限定为$(0,1)$。 # +# FIXME: this figure is wrong # ![LogisticFunction](images/fig2.gif) # # diff --git a/1_nn/mlp_bp.ipynb b/1_nn/mlp_bp.ipynb index d5776a4..3c70b7e 100644 --- a/1_nn/mlp_bp.ipynb +++ b/1_nn/mlp_bp.ipynb @@ -4750,7 +4750,7 @@ "1. 我们希望得到的每个类别的概率\n", "2. 如何做多分类问题？\n", "3. 如何能让神经网络更快的训练好？\n", - "4. 如何抽象，让神经网络的类支持更多的类型的层" + "4. 如何更好的构建网络的类定义，从而让神经网络的类支持更多的类型的处理层？" ] }, { diff --git a/1_nn/mlp_bp.py b/1_nn/mlp_bp.py index 957fdc9..6dede82 100644 --- a/1_nn/mlp_bp.py +++ b/1_nn/mlp_bp.py @@ -546,7 +546,7 @@ print(y_res[1:10, :]) # 1. 我们希望得到的每个类别的概率 # 2. 如何做多分类问题？ # 3. 如何能让神经网络更快的训练好？ -# 4. 如何抽象，让神经网络的类支持更多的类型的层 +# 4. 如何更好的构建网络的类定义，从而让神经网络的类支持更多的类型的处理层？ # ## References # * 反向传播算法 diff --git a/1_nn/softmax_ce.ipynb b/1_nn/softmax_ce.ipynb index cdbd162..fc635f1 100644 --- a/1_nn/softmax_ce.ipynb +++ b/1_nn/softmax_ce.ipynb @@ -135,7 +135,7 @@ "metadata": {}, "source": [ "## 问题\n", - "如何将本节所讲的softmax，交叉熵代价函数应用到上节所讲的方法中？" + "如何将本节所讲的softmax，交叉熵代价函数应用到上节所讲的BP方法中？" ] }, { @@ -168,8 +168,7 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" - }, - "main_language": "python" + } }, "nbformat": 4, "nbformat_minor": 2 diff --git a/1_nn/softmax_ce.py b/1_nn/softmax_ce.py index 4baf0d2..5a49d45 100644 --- a/1_nn/softmax_ce.py +++ b/1_nn/softmax_ce.py @@ -136,7 +136,7 @@ # \end{eqnarray} # ## 问题 -# 如何将本节所讲的softmax，交叉熵代价函数应用到上节所讲的方法中？ +# 如何将本节所讲的softmax，交叉熵代价函数应用到上节所讲的BP方法中？ # ## References # diff --git a/2_pytorch/1_NN/logistic-regression/data.txt b/2_pytorch/1_NN/data.txt similarity index 100% rename from 2_pytorch/1_NN/logistic-regression/data.txt rename to 2_pytorch/1_NN/data.txt diff --git a/2_pytorch/1_NN/logistic-regression/logistic-regression.ipynb b/2_pytorch/1_NN/logistic-regression.ipynb similarity index 100% rename from 2_pytorch/1_NN/logistic-regression/logistic-regression.ipynb rename to 2_pytorch/1_NN/logistic-regression.ipynb diff --git a/2_pytorch/1_NN/logistic-regression/logistic-regression.py b/2_pytorch/1_NN/logistic-regression.py similarity index 100% rename from 2_pytorch/1_NN/logistic-regression/logistic-regression.py rename to 2_pytorch/1_NN/logistic-regression.py diff --git a/2_pytorch/1_NN/nn_intro.ipynb b/2_pytorch/1_NN/nn_summary.ipynb similarity index 100% rename from 2_pytorch/1_NN/nn_intro.ipynb rename to 2_pytorch/1_NN/nn_summary.ipynb diff --git a/2_pytorch/2_CNN/googlenet.ipynb b/2_pytorch/2_CNN/googlenet.ipynb index a203563..54f3b0f 100644 --- a/2_pytorch/2_CNN/googlenet.ipynb +++ b/2_pytorch/2_CNN/googlenet.ipynb @@ -377,7 +377,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.3" + "version": "3.5.2" } }, "nbformat": 4, diff --git a/2_pytorch/2_CNN/googlenet.py b/2_pytorch/2_CNN/googlenet.py new file mode 100644 index 0000000..5885233 --- /dev/null +++ b/2_pytorch/2_CNN/googlenet.py @@ -0,0 +1,206 @@ +# -*- coding: utf-8 -*- +# --- +# jupyter: +# jupytext_format_version: '1.2' +# kernelspec: +# display_name: Python 3 +# language: python +# name: python3 +# language_info: +# codemirror_mode: +# name: ipython +# version: 3 +# file_extension: .py +# mimetype: text/x-python +# name: python +# nbconvert_exporter: python +# pygments_lexer: ipython3 +# version: 3.5.2 +# --- + +# # GoogLeNet +# 前面我们讲的 VGG 是 2014 年 ImageNet 比赛的亚军，那么冠军是谁呢？就是我们马上要讲的 GoogLeNet，这是 Google 的研究人员提出的网络结构，在当时取得了非常大的影响，因为网络的结构变得前所未有，它颠覆了大家对卷积网络的串联的印象和固定做法，采用了一种非常有效的 inception 模块，得到了比 VGG 更深的网络结构，但是却比 VGG 的参数更少，因为其去掉了后面的全连接层，所以参数大大减少，同时有了很高的计算效率。 +# +# ![](https://ws2.sinaimg.cn/large/006tNc79ly1fmprhdocouj30qb08vac3.jpg) +# +# 这是 googlenet 的网络示意图，下面我们介绍一下其作为创新的 inception 模块。 + +# ## Inception 模块 +# 在上面的网络中，我们看到了多个四个并行卷积的层，这些四个卷积并行的层就是 inception 模块，可视化如下 +# +# ![](https://ws4.sinaimg.cn/large/006tNc79gy1fmprivb2hxj30dn09dwef.jpg) +# + +# 一个 inception 模块的四个并行线路如下： +# 1.一个 1 x 1 的卷积，一个小的感受野进行卷积提取特征 +# 2.一个 1 x 1 的卷积加上一个 3 x 3 的卷积，1 x 1 的卷积降低输入的特征通道，减少参数计算量，然后接一个 3 x 3 的卷积做一个较大感受野的卷积 +# 3.一个 1 x 1 的卷积加上一个 5 x 5 的卷积，作用和第二个一样 +# 4.一个 3 x 3 的最大池化加上 1 x 1 的卷积，最大池化改变输入的特征排列，1 x 1 的卷积进行特征提取 +# +# 最后将四个并行线路得到的特征在通道这个维度上拼接在一起，下面我们可以实现一下 + +# + {"ExecuteTime": {"end_time": "2017-12-22T12:51:05.427292Z", "start_time": "2017-12-22T12:51:04.924747Z"}} +import sys +sys.path.append('..') + +import numpy as np +import torch +from torch import nn +from torch.autograd import Variable +from torchvision.datasets import CIFAR10 + +# + {"ExecuteTime": {"end_time": "2017-12-22T12:51:08.890890Z", "start_time": "2017-12-22T12:51:08.876313Z"}} +# 定义一个卷积加一个 relu 激活函数和一个 batchnorm 作为一个基本的层结构 +def conv_relu(in_channel, out_channel, kernel, stride=1, padding=0): + layer = nn.Sequential( + nn.Conv2d(in_channel, out_channel, kernel, stride, padding), + nn.BatchNorm2d(out_channel, eps=1e-3), + nn.ReLU(True) + ) + return layer + +# + {"ExecuteTime": {"end_time": "2017-12-22T12:51:09.671474Z", "start_time": "2017-12-22T12:51:09.587337Z"}} +class inception(nn.Module): + def __init__(self, in_channel, out1_1, out2_1, out2_3, out3_1, out3_5, out4_1): + super(inception, self).__init__() + # 第一条线路 + self.branch1x1 = conv_relu(in_channel, out1_1, 1) + + # 第二条线路 + self.branch3x3 = nn.Sequential( + conv_relu(in_channel, out2_1, 1), + conv_relu(out2_1, out2_3, 3, padding=1) + ) + + # 第三条线路 + self.branch5x5 = nn.Sequential( + conv_relu(in_channel, out3_1, 1), + conv_relu(out3_1, out3_5, 5, padding=2) + ) + + # 第四条线路 + self.branch_pool = nn.Sequential( + nn.MaxPool2d(3, stride=1, padding=1), + conv_relu(in_channel, out4_1, 1) + ) + + def forward(self, x): + f1 = self.branch1x1(x) + f2 = self.branch3x3(x) + f3 = self.branch5x5(x) + f4 = self.branch_pool(x) + output = torch.cat((f1, f2, f3, f4), dim=1) + return output + +# + {"ExecuteTime": {"end_time": "2017-12-22T12:51:10.948630Z", "start_time": "2017-12-22T12:51:10.757903Z"}} +test_net = inception(3, 64, 48, 64, 64, 96, 32) +test_x = Variable(torch.zeros(1, 3, 96, 96)) +print('input shape: {} x {} x {}'.format(test_x.shape[1], test_x.shape[2], test_x.shape[3])) +test_y = test_net(test_x) +print('output shape: {} x {} x {}'.format(test_y.shape[1], test_y.shape[2], test_y.shape[3])) +# - + +# 可以看到输入经过了 inception 模块之后，大小没有变化，通道的维度变多了 + +# 下面我们定义 GoogLeNet，GoogLeNet 可以看作是很多个 inception 模块的串联，注意，原论文中使用了多个输出来解决梯度消失的问题，这里我们只定义一个简单版本的 GoogLeNet，简化为一个输出 + +# + {"ExecuteTime": {"end_time": "2017-12-22T12:51:13.149380Z", "start_time": "2017-12-22T12:51:12.934110Z"}} +class googlenet(nn.Module): + def __init__(self, in_channel, num_classes, verbose=False): + super(googlenet, self).__init__() + self.verbose = verbose + + self.block1 = nn.Sequential( + conv_relu(in_channel, out_channel=64, kernel=7, stride=2, padding=3), + nn.MaxPool2d(3, 2) + ) + + self.block2 = nn.Sequential( + conv_relu(64, 64, kernel=1), + conv_relu(64, 192, kernel=3, padding=1), + nn.MaxPool2d(3, 2) + ) + + self.block3 = nn.Sequential( + inception(192, 64, 96, 128, 16, 32, 32), + inception(256, 128, 128, 192, 32, 96, 64), + nn.MaxPool2d(3, 2) + ) + + self.block4 = nn.Sequential( + inception(480, 192, 96, 208, 16, 48, 64), + inception(512, 160, 112, 224, 24, 64, 64), + inception(512, 128, 128, 256, 24, 64, 64), + inception(512, 112, 144, 288, 32, 64, 64), + inception(528, 256, 160, 320, 32, 128, 128), + nn.MaxPool2d(3, 2) + ) + + self.block5 = nn.Sequential( + inception(832, 256, 160, 320, 32, 128, 128), + inception(832, 384, 182, 384, 48, 128, 128), + nn.AvgPool2d(2) + ) + + self.classifier = nn.Linear(1024, num_classes) + + def forward(self, x): + x = self.block1(x) + if self.verbose: + print('block 1 output: {}'.format(x.shape)) + x = self.block2(x) + if self.verbose: + print('block 2 output: {}'.format(x.shape)) + x = self.block3(x) + if self.verbose: + print('block 3 output: {}'.format(x.shape)) + x = self.block4(x) + if self.verbose: + print('block 4 output: {}'.format(x.shape)) + x = self.block5(x) + if self.verbose: + print('block 5 output: {}'.format(x.shape)) + x = x.view(x.shape[0], -1) + x = self.classifier(x) + return x + +# + {"ExecuteTime": {"end_time": "2017-12-22T12:51:13.614936Z", "start_time": "2017-12-22T12:51:13.428383Z"}} +test_net = googlenet(3, 10, True) +test_x = Variable(torch.zeros(1, 3, 96, 96)) +test_y = test_net(test_x) +print('output: {}'.format(test_y.shape)) +# - + +# 可以看到输入的尺寸不断减小，通道的维度不断增加 + +# + {"ExecuteTime": {"end_time": "2017-12-22T12:51:16.387778Z", "start_time": "2017-12-22T12:51:15.121350Z"}} +from utils import train + +def data_tf(x): + x = x.resize((96, 96), 2) # 将图片放大到 96 x 96 + x = np.array(x, dtype='float32') / 255 + x = (x - 0.5) / 0.5 # 标准化，这个技巧之后会讲到 + x = x.transpose((2, 0, 1)) # 将 channel 放到第一维，只是 pytorch 要求的输入方式 + x = torch.from_numpy(x) + return x + +train_set = CIFAR10('./data', train=True, transform=data_tf) +train_data = torch.utils.data.DataLoader(train_set, batch_size=64, shuffle=True) +test_set = CIFAR10('./data', train=False, transform=data_tf) +test_data = torch.utils.data.DataLoader(test_set, batch_size=128, shuffle=False) + +net = googlenet(3, 10) +optimizer = torch.optim.SGD(net.parameters(), lr=0.01) +criterion = nn.CrossEntropyLoss() + +# + {"ExecuteTime": {"end_time": "2017-12-22T13:17:25.310685Z", "start_time": "2017-12-22T12:51:16.389607Z"}} +train(net, train_data, test_data, 20, optimizer, criterion) +# - + +# GoogLeNet 加入了更加结构化的 Inception 块使得我们能够使用更大的通道，更多的层，同时也控制了计算量。 +# +# **小练习：GoogLeNet 有很多后续的版本，尝试看看论文，看看有什么不同，实现一下： +# v1：最早的版本 +# v2：加入 batch normalization 加快训练 +# v3：对 inception 模块做了调整 +# v4：基于 ResNet 加入了 残差连接 ** diff --git a/2_pytorch/2_CNN/resnet.ipynb b/2_pytorch/2_CNN/resnet.ipynb index a954f56..60bf725 100644 --- a/2_pytorch/2_CNN/resnet.ipynb +++ b/2_pytorch/2_CNN/resnet.ipynb @@ -377,7 +377,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.3" + "version": "3.5.2" } }, "nbformat": 4, diff --git a/2_pytorch/2_CNN/resnet.py b/2_pytorch/2_CNN/resnet.py new file mode 100644 index 0000000..ac24ae4 --- /dev/null +++ b/2_pytorch/2_CNN/resnet.py @@ -0,0 +1,191 @@ +# -*- coding: utf-8 -*- +# --- +# jupyter: +# jupytext_format_version: '1.2' +# kernelspec: +# display_name: Python 3 +# language: python +# name: python3 +# language_info: +# codemirror_mode: +# name: ipython +# version: 3 +# file_extension: .py +# mimetype: text/x-python +# name: python +# nbconvert_exporter: python +# pygments_lexer: ipython3 +# version: 3.5.2 +# --- + +# # ResNet +# 当大家还在惊叹 GoogLeNet 的 inception 结构的时候，微软亚洲研究院的研究员已经在设计更深但结构更加简单的网络 ResNet，并且凭借这个网络子在 2015 年 ImageNet 比赛上大获全胜。 +# +# ResNet 有效地解决了深度神经网络难以训练的问题，可以训练高达 1000 层的卷积网络。网络之所以难以训练，是因为存在着梯度消失的问题，离 loss 函数越远的层，在反向传播的时候，梯度越小，就越难以更新，随着层数的增加，这个现象越严重。之前有两种常见的方案来解决这个问题： +# +# 1.按层训练，先训练比较浅的层，然后在不断增加层数，但是这种方法效果不是特别好，而且比较麻烦 +# +# 2.使用更宽的层，或者增加输出通道，而不加深网络的层数，这种结构往往得到的效果又不好 +# +# ResNet 通过引入了跨层链接解决了梯度回传消失的问题。 +# +# ![](https://ws1.sinaimg.cn/large/006tNc79ly1fmptq2snv9j30j808t74a.jpg) + +# 这就普通的网络连接跟跨层残差连接的对比图，使用普通的连接，上层的梯度必须要一层一层传回来，而是用残差连接，相当于中间有了一条更短的路，梯度能够从这条更短的路传回来，避免了梯度过小的情况。 +# +# 假设某层的输入是 x，期望输出是 H(x)， 如果我们直接把输入 x 传到输出作为初始结果，这就是一个更浅层的网络，更容易训练，而这个网络没有学会的部分，我们可以使用更深的网络 F(x) 去训练它，使得训练更加容易，最后希望拟合的结果就是 F(x) = H(x) - x，这就是一个残差的结构 +# +# 残差网络的结构就是上面这种残差块的堆叠，下面让我们来实现一个 residual block + +# + {"ExecuteTime": {"end_time": "2017-12-22T12:56:06.772059Z", "start_time": "2017-12-22T12:56:06.766027Z"}} +import sys +sys.path.append('..') + +import numpy as np +import torch +from torch import nn +import torch.nn.functional as F +from torch.autograd import Variable +from torchvision.datasets import CIFAR10 + +# + {"ExecuteTime": {"end_time": "2017-12-22T12:47:49.222432Z", "start_time": "2017-12-22T12:47:49.217940Z"}} +def conv3x3(in_channel, out_channel, stride=1): + return nn.Conv2d(in_channel, out_channel, 3, stride=stride, padding=1, bias=False) + +# + {"ExecuteTime": {"end_time": "2017-12-22T13:14:02.429145Z", "start_time": "2017-12-22T13:14:02.383322Z"}} +class residual_block(nn.Module): + def __init__(self, in_channel, out_channel, same_shape=True): + super(residual_block, self).__init__() + self.same_shape = same_shape + stride=1 if self.same_shape else 2 + + self.conv1 = conv3x3(in_channel, out_channel, stride=stride) + self.bn1 = nn.BatchNorm2d(out_channel) + + self.conv2 = conv3x3(out_channel, out_channel) + self.bn2 = nn.BatchNorm2d(out_channel) + if not self.same_shape: + self.conv3 = nn.Conv2d(in_channel, out_channel, 1, stride=stride) + + def forward(self, x): + out = self.conv1(x) + out = F.relu(self.bn1(out), True) + out = self.conv2(out) + out = F.relu(self.bn2(out), True) + + if not self.same_shape: + x = self.conv3(x) + return F.relu(x+out, True) +# - + +# 我们测试一下一个 residual block 的输入和输出 + +# + {"ExecuteTime": {"end_time": "2017-12-22T13:14:05.793185Z", "start_time": "2017-12-22T13:14:05.763382Z"}} +# 输入输出形状相同 +test_net = residual_block(32, 32) +test_x = Variable(torch.zeros(1, 32, 96, 96)) +print('input: {}'.format(test_x.shape)) +test_y = test_net(test_x) +print('output: {}'.format(test_y.shape)) + +# + {"ExecuteTime": {"end_time": "2017-12-22T13:14:11.929120Z", "start_time": "2017-12-22T13:14:11.914604Z"}} +# 输入输出形状不同 +test_net = residual_block(3, 32, False) +test_x = Variable(torch.zeros(1, 3, 96, 96)) +print('input: {}'.format(test_x.shape)) +test_y = test_net(test_x) +print('output: {}'.format(test_y.shape)) +# - + +# 下面我们尝试实现一个 ResNet，它就是 residual block 模块的堆叠 + +# + {"ExecuteTime": {"end_time": "2017-12-22T13:27:46.099404Z", "start_time": "2017-12-22T13:27:45.986235Z"}} +class resnet(nn.Module): + def __init__(self, in_channel, num_classes, verbose=False): + super(resnet, self).__init__() + self.verbose = verbose + + self.block1 = nn.Conv2d(in_channel, 64, 7, 2) + + self.block2 = nn.Sequential( + nn.MaxPool2d(3, 2), + residual_block(64, 64), + residual_block(64, 64) + ) + + self.block3 = nn.Sequential( + residual_block(64, 128, False), + residual_block(128, 128) + ) + + self.block4 = nn.Sequential( + residual_block(128, 256, False), + residual_block(256, 256) + ) + + self.block5 = nn.Sequential( + residual_block(256, 512, False), + residual_block(512, 512), + nn.AvgPool2d(3) + ) + + self.classifier = nn.Linear(512, num_classes) + + def forward(self, x): + x = self.block1(x) + if self.verbose: + print('block 1 output: {}'.format(x.shape)) + x = self.block2(x) + if self.verbose: + print('block 2 output: {}'.format(x.shape)) + x = self.block3(x) + if self.verbose: + print('block 3 output: {}'.format(x.shape)) + x = self.block4(x) + if self.verbose: + print('block 4 output: {}'.format(x.shape)) + x = self.block5(x) + if self.verbose: + print('block 5 output: {}'.format(x.shape)) + x = x.view(x.shape[0], -1) + x = self.classifier(x) + return x +# - + +# 输出一下每个 block 之后的大小 + +# + {"ExecuteTime": {"end_time": "2017-12-22T13:28:00.597030Z", "start_time": "2017-12-22T13:28:00.417746Z"}} +test_net = resnet(3, 10, True) +test_x = Variable(torch.zeros(1, 3, 96, 96)) +test_y = test_net(test_x) +print('output: {}'.format(test_y.shape)) + +# + {"ExecuteTime": {"end_time": "2017-12-22T13:29:01.484172Z", "start_time": "2017-12-22T13:29:00.095952Z"}} +from utils import train + +def data_tf(x): + x = x.resize((96, 96), 2) # 将图片放大到 96 x 96 + x = np.array(x, dtype='float32') / 255 + x = (x - 0.5) / 0.5 # 标准化，这个技巧之后会讲到 + x = x.transpose((2, 0, 1)) # 将 channel 放到第一维，只是 pytorch 要求的输入方式 + x = torch.from_numpy(x) + return x + +train_set = CIFAR10('./data', train=True, transform=data_tf) +train_data = torch.utils.data.DataLoader(train_set, batch_size=64, shuffle=True) +test_set = CIFAR10('./data', train=False, transform=data_tf) +test_data = torch.utils.data.DataLoader(test_set, batch_size=128, shuffle=False) + +net = resnet(3, 10) +optimizer = torch.optim.SGD(net.parameters(), lr=0.01) +criterion = nn.CrossEntropyLoss() + +# + {"ExecuteTime": {"end_time": "2017-12-22T13:45:00.783186Z", "start_time": "2017-12-22T13:29:09.214453Z"}} +train(net, train_data, test_data, 20, optimizer, criterion) +# - + +# ResNet 使用跨层通道使得训练非常深的卷积神经网络成为可能。同样它使用很简单的卷积层配置，使得其拓展更加简单。 +# +# **小练习： +# 1.尝试一下论文中提出的 bottleneck 的结构 +# 2.尝试改变 conv -> bn -> relu 的顺序为 bn -> relu -> conv，看看精度会不会提高** diff --git a/2_pytorch/imgs/Ipython-auto.png b/2_pytorch/imgs/Ipython-auto.png new file mode 100644 index 0000000..c6740d1 Binary files /dev/null and b/2_pytorch/imgs/Ipython-auto.png differ diff --git a/2_pytorch/imgs/Ipython-help.png b/2_pytorch/imgs/Ipython-help.png new file mode 100644 index 0000000..99ebae0 Binary files /dev/null and b/2_pytorch/imgs/Ipython-help.png differ diff --git a/2_pytorch/imgs/Jupyter主页面.png b/2_pytorch/imgs/Jupyter主页面.png new file mode 100644 index 0000000..0f76215 Binary files /dev/null and b/2_pytorch/imgs/Jupyter主页面.png differ diff --git a/2_pytorch/imgs/Notebook主界面.png b/2_pytorch/imgs/Notebook主界面.png new file mode 100644 index 0000000..321af1f Binary files /dev/null and b/2_pytorch/imgs/Notebook主界面.png differ diff --git a/2_pytorch/imgs/autograd_Variable.png b/2_pytorch/imgs/autograd_Variable.png new file mode 100644 index 0000000..6576cc8 Binary files /dev/null and b/2_pytorch/imgs/autograd_Variable.png differ diff --git a/2_pytorch/imgs/autograd_Variable.svg b/2_pytorch/imgs/autograd_Variable.svg new file mode 100644 index 0000000..6164d89 --- /dev/null +++ b/2_pytorch/imgs/autograd_Variable.svg @@ -0,0 +1,2 @@ + +
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[Python基础](0_python/) +1. [Python](0_python/) + - [Introduction](0_python/0_Introduction.ipynb) + - [Python Basics](0_python/1_Basics.ipynb) + - [Print Statement](0_python/2_Print_Statement.ipynb) + - [Data Structure 1](0_python/3_Data_Structure_1.ipynb) + - [Data Structure 2](0_python/4_Data_Structure_2.ipynb) + - [Control Flow](0_python/5_Control_Flow.ipynb) + - [Function](0_python/6_Function.ipynb) + - [Class](0_python/7_Class.ipynb) 2. [numpy & matplotlib](0_numpy_matplotlib_scipy_sympy/) -3. [kMenas](1_kmeans/) -4. [knn](1_knn/) + - [numpy](0_numpy_matplotlib_scipy_sympy/numpy_tutorial.ipynb) + - [matplotlib](0_numpy_matplotlib_scipy_sympy/matplotlib_simple_tutorial.ipynb) +3. [knn](1_knn/knn_classification.ipynb) +4. [kMenas](1_kmeans/knn_classification.ipynb) 5. [Logistic Regression](1_logistic_regression/) -6. [Neural Network](nn/) -7. CNN -8. PyTorch + - [Least squares](1_logistic_regression/Least_squares.ipynb) + - [Logistic regression](1_logistic_regression/Logistic_regression.ipynb) +6. [Neural Network](1_nn/) + - [Perceptron](1_nn/Perceptron.ipynb) + - [Multi-layer Perceptron & BP](1_nn/mlp_bp.ipynb) + - [Softmax & cross-entroy](1_nn/softmax_ce.ipynb) +7. [PyTorch](2_pytorch/) + - [short tutorial](PyTorch快速入门.ipynb) + - [basic/Tensor-and-Variable](2_pytorch/0_basic/Tensor-and-Variable.ipynb) + - [basic/autograd](2_pytorch/0_basic/autograd.ipynb) + - [basic/dynamic-graph](2_pytorch/0_basic/dynamic-graph.ipynb) + - [nn/linear-regression-gradient-descend](2_pytorch/1_NN/linear-regression-gradient-descend.ipynb) + - [nn/logistic-regression](2_pytorch/1_NN/logistic-regression.ipynb) + - [nn/nn-sequential-module](2_pytorch/1_NN/nn-sequential-module.ipynb) + - [nn/bp](2_pytorch/1_NN/bp.ipynb) + - [nn/deep-nn](2_pytorch/1_NN/deep-nn.ipynb) + - [nn/param_initialize](2_pytorch/1_NN/param_initialize.ipynb) + - [optim/sgd](2_pytorch/1_NN/optimizer/sgd.ipynb) + - [optim/adam](2_pytorch/1_NN/optimizer/adam.ipynb) + - [optim/adam](2_pytorch/1_NN/optimizer/adam.ipynb) + - [cnn/basic_conv](2_pytorch/2_CNN/basic_conv.ipynb) + - [cnn/batch-normalization](2_pytorch/2_CNN/batch-normalization.ipynb) + - [cnn/regularization](2_pytorch/2_CNN/regularization.ipynb) + - [cnn/lr-decay](2_pytorch/2_CNN/lr-decay.ipynb) + - [cnn/vgg](2_pytorch/2_CNN/vgg.ipynb) + - [cnn/googlenet](2_pytorch/2_CNN/googlenet.ipynb) + - [cnn/densenet](2_pytorch/2_CNN/densenet.ipynb) + - [cnn/resnet](2_pytorch/2_CNN/resnet.ipynb) + - [rnn/pytorch-rnn](2_pytorch/3_RNN/pytorch-rnn.ipynb) + - [rnn/rnn-for-image](2_pytorch/3_RNN/rnn-for-image.ipynb) + - [rnn/lstm-time-series](2_pytorch/3_RNN/time-series/lstm-time-series.ipynb) + - [gan/autoencoder](2_pytorch/4_GNN/autoencoder.ipynb) + - [gan/vae](2_pytorch/4_GNN/vae.ipynb) + - [gan/gan](2_pytorch/4_GNN/gan.ipynb) + + ## 其他参考 * [学习参考资料等](References.md) diff --git a/References.md b/References.md index 281cf52..6a7aabe 100644 --- a/References.md +++ b/References.md @@ -1,3 +1,22 @@ +--- +jupyter: + jupytext_format_version: '1.0' + kernelspec: + display_name: Python 3 + language: python + name: python3 + language_info: + codemirror_mode: + name: ipython + version: 3 + file_extension: .py + mimetype: text/x-python + name: python + nbconvert_exporter: python + pygments_lexer: ipython3 + version: 3.5.2 +--- + # References 可以自行在下属列表找找到适合自己的学习资料，虽然罗列的比较多，但是个人最好选择一个深入阅读、练习。当练习到一定程度，可以再看看其他的资料，这样弥补单一学习资料可能存在的欠缺。 diff --git a/dataset_circle.csv b/dataset_circle.csv deleted file mode 100644 index c7d9f8f..0000000 --- a/dataset_circle.csv +++ /dev/null @@ -1,400 +0,0 @@ --4.998874451622919324e+00,4.727671430051504586e+00,0.000000000000000000e+00 -3.280980164418858092e+00,1.135719744099867690e+01,0.000000000000000000e+00 --3.989307577792735593e+00,-7.472125124436091781e+00,0.000000000000000000e+00 --2.845588117474840750e+00,-1.110207598677712149e+01,0.000000000000000000e+00 --4.736524057786282604e+00,-9.232347813516641466e+00,0.000000000000000000e+00 --2.997596049424991360e+00,1.045323111011670036e+01,0.000000000000000000e+00 --7.808569372236178197e+00,6.841137640119772101e+00,0.000000000000000000e+00 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