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ai-programming-with-python/C02P8-pre-trained-image-classifier/.python-version

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+3.12

ai-programming-with-python/C02P8-pre-trained-image-classifier/adjust_results4_isadog.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+# */AIPND-revision/intropyproject-classify-pet-images/adjust_results4_isadog.py
+#                                                                             
+# PROGRAMMER: 
+# DATE CREATED:                                 
+# REVISED DATE: 
+# PURPOSE: Create a function adjust_results4_isadog that adjusts the results 
+#          dictionary to indicate whether or not the pet image label is of-a-dog, 
+#          and to indicate whether or not the classifier image label is of-a-dog.
+#          All dog labels from both the pet images and the classifier function
+#          will be found in the dognames.txt file. We recommend reading all the
+#          dog names in dognames.txt into a dictionary where the 'key' is the 
+#          dog name (from dognames.txt) and the 'value' is one. If a label is 
+#          found to exist within this dictionary of dog names then the label 
+#          is of-a-dog, otherwise the label isn't of a dog. Alternatively one 
+#          could also read all the dog names into a list and then if the label
+#          is found to exist within this list - the label is of-a-dog, otherwise
+#          the label isn't of a dog. 
+#         This function inputs:
+#            -The results dictionary as results_dic within adjust_results4_isadog 
+#             function and results for the function call within main.
+#            -The text file with dog names as dogfile within adjust_results4_isadog
+#             function and in_arg.dogfile for the function call within main. 
+#           This function uses the extend function to add items to the list 
+#           that's the 'value' of the results dictionary. You will be adding the
+#           whether or not the pet image label is of-a-dog as the item at index
+#           3 of the list and whether or not the classifier label is of-a-dog as
+#           the item at index 4 of the list. Note we recommend setting the values
+#           at indices 3 & 4 to 1 when the label is of-a-dog and to 0 when the 
+#           label isn't a dog.
+#
+##
+# TODO 4: Define adjust_results4_isadog function below, specifically replace the None
+#       below by the function definition of the adjust_results4_isadog function. 
+#       Notice that this function doesn't return anything because the 
+#       results_dic dictionary that is passed into the function is a mutable 
+#       data type so no return is needed.
+# 
+def adjust_results4_isadog(results_dic, dogfile):
+    """
+    Adjusts the results dictionary to determine if classifier correctly 
+    classified images 'as a dog' or 'not a dog' especially when not a match. 
+    Demonstrates if model architecture correctly classifies dog images even if
+    it gets dog breed wrong (not a match).
+    Parameters:
+      results_dic - Dictionary with 'key' as image filename and 'value' as a 
+                    List. Where the list will contain the following items: 
+                  index 0 = pet image label (string)
+                  index 1 = classifier label (string)
+                  index 2 = 1/0 (int)  where 1 = match between pet image
+                    and classifer labels and 0 = no match between labels
+                ------ where index 3 & index 4 are added by this function -----
+                 NEW - index 3 = 1/0 (int)  where 1 = pet image 'is-a' dog and 
+                            0 = pet Image 'is-NOT-a' dog. 
+                 NEW - index 4 = 1/0 (int)  where 1 = Classifier classifies image 
+                            'as-a' dog and 0 = Classifier classifies image  
+                            'as-NOT-a' dog.
+     dogfile - A text file that contains names of all dogs from the classifier
+               function and dog names from the pet image files. This file has 
+               one dog name per line dog names are all in lowercase with 
+               spaces separating the distinct words of the dog name. Dog names
+               from the classifier function can be a string of dog names separated
+               by commas when a particular breed of dog has multiple dog names 
+               associated with that breed (ex. maltese dog, maltese terrier, 
+               maltese) (string - indicates text file's filename)
+    Returns:
+           None - results_dic is mutable data type so no return needed.
+    """           
+    None

ai-programming-with-python/C02P8-pre-trained-image-classifier/adjust_results4_isadog_hints.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+# */AIPND-revision/intropyproject-classify-pet-images/adjust_results4_isadog_hints.py
+#                                                                             
+# PROGRAMMER: 
+# DATE CREATED:                                 
+# REVISED DATE: 
+# PURPOSE: This is a *hints* file to help guide students in creating the 
+#          function adjust_results4_isadog that adjusts the results dictionary
+#          to indicate whether or not the pet image label is of-a-dog, 
+#          and to indicate whether or not the classifier image label is of-a-dog.
+#          All dog labels from both the pet images and the classifier function
+#          will be found in the dognames.txt file. We recommend reading all the
+#          dog names in dognames.txt into a dictionary where the 'key' is the 
+#          dog name (from dognames.txt) and the 'value' is one. If a label is 
+#          found to exist within this dictionary of dog names then the label 
+#          is of-a-dog, otherwise the label isn't of a dog. Alternatively one 
+#          could also read all the dog names into a list and then if the label
+#          is found to exist within this list - the label is of-a-dog, otherwise
+#          the label isn't of a dog. 
+#         This function inputs:
+#            -The results dictionary as results_dic within adjust_results4_isadog 
+#             function and results for the function call within main.
+#            -The text file with dog names as dogfile within adjust_results4_isadog
+#             function and in_arg.dogfile for the function call within main. 
+#           This function uses the extend function to add items to the list 
+#           that's the 'value' of the results dictionary. You will be adding the
+#           whether or not the pet image label is of-a-dog as the item at index
+#           3 of the list and whether or not the classifier label is of-a-dog as
+#           the item at index 4 of the list. Note we recommend setting the values
+#           at indices 3 & 4 to 1 when the label is of-a-dog and to 0 when the 
+#           label isn't a dog.
+#
+##
+# TODO 4: EDIT and ADD code BELOW to do the following that's stated in the 
+#       comments below that start with "TODO: 4" for the adjust_results4_isadog 
+#       function. Specifically EDIT and ADD code to define the 
+#       adjust_results4_isadog function. Notice that this function doesn't return
+#       anything because the results_dic dictionary that is passed into the 
+#       function is a mutable data type so no return is needed.
+# 
+def adjust_results4_isadog(results_dic, dogfile):
+    """
+    Adjusts the results dictionary to determine if classifier correctly 
+    classified images 'as a dog' or 'not a dog' especially when not a match. 
+    Demonstrates if model architecture correctly classifies dog images even if
+    it gets dog breed wrong (not a match).
+    Parameters:
+      results_dic - Dictionary with 'key' as image filename and 'value' as a 
+                    List. Where the list will contain the following items: 
+                  index 0 = pet image label (string)
+                  index 1 = classifier label (string)
+                  index 2 = 1/0 (int)  where 1 = match between pet image
+                    and classifer labels and 0 = no match between labels
+                ------ where index 3 & index 4 are added by this function -----
+                 NEW - index 3 = 1/0 (int)  where 1 = pet image 'is-a' dog and 
+                            0 = pet Image 'is-NOT-a' dog. 
+                 NEW - index 4 = 1/0 (int)  where 1 = Classifier classifies image 
+                            'as-a' dog and 0 = Classifier classifies image  
+                            'as-NOT-a' dog.
+     dogfile - A text file that contains names of all dogs from the classifier
+               function and dog names from the pet image files. This file has 
+               one dog name per line dog names are all in lowercase with 
+               spaces separating the distinct words of the dog name. Dog names
+               from the classifier function can be a string of dog names separated
+               by commas when a particular breed of dog has multiple dog names 
+               associated with that breed (ex. maltese dog, maltese terrier, 
+               maltese) (string - indicates text file's filename)
+    Returns:
+           None - results_dic is mutable data type so no return needed.
+    """           
+    # Creates dognames dictionary for quick matching to results_dic labels from
+    # real answer & classifier's answer
+    dognames_dic = dict()
+
+    # Reads in dognames from file, 1 name per line & automatically closes file
+    with open(dogfile, "r") as infile:
+        # Reads in dognames from first line in file
+        line = infile.readline()
+
+        # Processes each line in file until reaching EOF (end-of-file) by 
+        # processing line and adding dognames to dognames_dic with while loop
+        while line != "":
+
+            # TODO: 4a. REPLACE pass with CODE to remove the newline character
+            #           from the variable line  
+            #
+            # Process line by striping newline from line
+            pass
+
+            # TODO: 4b. REPLACE pass with CODE to check if the dogname(line) 
+            #          exists within dognames_dic, then if the dogname(line) 
+            #          doesn't exist within dognames_dic then add the dogname(line) 
+            #          to dognames_dic as the 'key' with the 'value' of 1. 
+            #
+            # adds dogname(line) to dogsnames_dic if it doesn't already exist 
+            # in the dogsnames_dic dictionary
+            pass 
+
+            # Reads in next line in file to be processed with while loop
+            # if this line isn't empty (EOF)
+            line = infile.readline()
+
+                
+    # Add to whether pet labels & classifier labels are dogs by appending
+    # two items to end of value(List) in results_dic. 
+    # List Index 3 = whether(1) or not(0) Pet Image Label is a dog AND 
+    # List Index 4 = whether(1) or not(0) Classifier Label is a dog
+    # How - iterate through results_dic if labels are found in dognames_dic
+    # then label "is a dog" index3/4=1 otherwise index3/4=0 "not a dog"
+    for key in results_dic:
+
+        # Pet Image Label IS of Dog (e.g. found in dognames_dic)
+        if results_dic[key][0] in dognames_dic:
+            
+            # Classifier Label IS image of Dog (e.g. found in dognames_dic)
+            # appends (1, 1) because both labels are dogs
+            if results_dic[key][1] in dognames_dic:
+                results_dic[key].extend((1, 1))
+
+            # TODO: 4c. REPLACE pass BELOW with CODE that adds the following to
+            #           results_dic dictionary for the key indicated by the 
+            #           variable key - append (1,0) to the value using 
+            #           the extend list function. This indicates
+            #           the pet label is-a-dog, classifier label is-NOT-a-dog. 
+            #                              
+            # Classifier Label IS NOT image of dog (e.g. NOT in dognames_dic)
+            # appends (1,0) because only pet label is a dog
+            else:
+                pass
+
+        # Pet Image Label IS NOT a Dog image (e.g. NOT found in dognames_dic)
+        else:
+            # TODO: 4d. REPLACE pass BELOW with CODE that adds the following to
+            #           results_dic dictionary for the key indicated by the 
+            #           variable key - append (0,1) to the value uisng
+            #           the extend list function. This indicates
+            #           the pet label is-NOT-a-dog, classifier label is-a-dog. 
+            #                              
+            # Classifier Label IS image of Dog (e.g. found in dognames_dic)
+            # appends (0, 1)because only Classifier labe is a dog
+            if results_dic[key][1] in dognames_dic:
+                pass
+
+            # TODO: 4e. REPLACE pass BELOW with CODE that adds the following to
+            #           results_dic dictionary for the key indicated by the 
+            #           variable key - append (0,0) to the value using the 
+            #           extend list function. This indicates
+            #           the pet label is-NOT-a-dog, classifier label is-NOT-a-dog. 
+            #                                              
+            # Classifier Label IS NOT image of Dog (e.g. NOT in dognames_dic)
+            # appends (0, 0) because both labels aren't dogs
+            else:
+                pass
+    

ai-programming-with-python/C02P8-pre-trained-image-classifier/calculates_results_stats.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+# */AIPND-revision/intropyproject-classify-pet-images/calculates_results_stats.py
+#                                                                             
+# PROGRAMMER:
+# DATE CREATED:                                  
+# REVISED DATE: 
+# PURPOSE: Create a function calculates_results_stats that calculates the 
+#          statistics of the results of the programrun using the classifier's model 
+#          architecture to classify the images. This function will use the 
+#          results in the results dictionary to calculate these statistics. 
+#          This function will then put the results statistics in a dictionary
+#          (results_stats_dic) that's created and returned by this function.
+#          This will allow the user of the program to determine the 'best' 
+#          model for classifying the images. The statistics that are calculated
+#          will be counts and percentages. Please see "Intro to Python - Project
+#          classifying Images - xx Calculating Results" for details on the 
+#          how to calculate the counts and percentages for this function.    
+#         This function inputs:
+#            -The results dictionary as results_dic within calculates_results_stats 
+#             function and results for the function call within main.
+#         This function creates and returns the Results Statistics Dictionary -
+#          results_stats_dic. This dictionary contains the results statistics 
+#          (either a percentage or a count) where the key is the statistic's 
+#           name (starting with 'pct' for percentage or 'n' for count) and value 
+#          is the statistic's value.  This dictionary should contain the 
+#          following keys:
+#            n_images - number of images
+#            n_dogs_img - number of dog images
+#            n_notdogs_img - number of NON-dog images
+#            n_match - number of matches between pet & classifier labels
+#            n_correct_dogs - number of correctly classified dog images
+#            n_correct_notdogs - number of correctly classified NON-dog images
+#            n_correct_breed - number of correctly classified dog breeds
+#            pct_match - percentage of correct matches
+#            pct_correct_dogs - percentage of correctly classified dogs
+#            pct_correct_breed - percentage of correctly classified dog breeds
+#            pct_correct_notdogs - percentage of correctly classified NON-dogs
+#
+##
+# TODO 5: Define calculates_results_stats function below, please be certain to replace None
+#       in the return statement with the results_stats_dic dictionary that you create 
+#       with this function
+# 
+def calculates_results_stats(results_dic):
+    """
+    Calculates statistics of the results of the program run using classifier's model 
+    architecture to classifying pet images. Then puts the results statistics in a 
+    dictionary (results_stats_dic) so that it's returned for printing as to help
+    the user to determine the 'best' model for classifying images. Note that 
+    the statistics calculated as the results are either percentages or counts.
+    Parameters:
+      results_dic - Dictionary with key as image filename and value as a List 
+             (index)idx 0 = pet image label (string)
+                    idx 1 = classifier label (string)
+                    idx 2 = 1/0 (int)  where 1 = match between pet image and 
+                            classifer labels and 0 = no match between labels
+                    idx 3 = 1/0 (int)  where 1 = pet image 'is-a' dog and 
+                            0 = pet Image 'is-NOT-a' dog. 
+                    idx 4 = 1/0 (int)  where 1 = Classifier classifies image 
+                            'as-a' dog and 0 = Classifier classifies image  
+                            'as-NOT-a' dog.
+    Returns:
+     results_stats_dic - Dictionary that contains the results statistics (either
+                    a percentage or a count) where the key is the statistic's 
+                     name (starting with 'pct' for percentage or 'n' for count)
+                     and the value is the statistic's value. See comments above
+                     and the previous topic Calculating Results in the class for details
+                     on how to calculate the counts and statistics.
+    """        
+    # Replace None with the results_stats_dic dictionary that you created with 
+    # this function 
+    return None

ai-programming-with-python/C02P8-pre-trained-image-classifier/calculates_results_stats_hints.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+# */AIPND-revision/intropyproject-classify-pet-images/calculates_results_stats_hints.py
+#                                                                             
+# PROGRAMMER:
+# DATE CREATED:                                  
+# REVISED DATE: 
+# PURPOSE: This is a *hints* file to help guide students in creating the 
+#          function calculates_results_stats that calculates the statistics
+#          of the results of the programrun using the classifier's model 
+#          architecture to classify the images. This function will use the 
+#          results in the results dictionary to calculate these statistics. 
+#          This function will then put the results statistics in a dictionary
+#          (results_stats_dic) that's created and returned by this function.
+#          This will allow the user of the program to determine the 'best' 
+#          model for classifying the images. The statistics that are calculated
+#          will be counts and percentages. Please see "Intro to Python - Project
+#          classifying Images - xx Calculating Results" for details on the 
+#          how to calculate the counts and percentages for this function.    
+#         This function inputs:
+#            -The results dictionary as results_dic within calculates_results_stats 
+#             function and results for the function call within main.
+#         This function creates and returns the Results Statistics Dictionary -
+#          results_stats_dic. This dictionary contains the results statistics 
+#          (either a percentage or a count) where the key is the statistic's 
+#           name (starting with 'pct' for percentage or 'n' for count) and value 
+#          is the statistic's value.  This dictionary should contain the 
+#          following keys:
+#            n_images - number of images
+#            n_dogs_img - number of dog images
+#            n_notdogs_img - number of NON-dog images
+#            n_match - number of matches between pet & classifier labels
+#            n_correct_dogs - number of correctly classified dog images
+#            n_correct_notdogs - number of correctly classified NON-dog images
+#            n_correct_breed - number of correctly classified dog breeds
+#            pct_match - percentage of correct matches
+#            pct_correct_dogs - percentage of correctly classified dogs
+#            pct_correct_breed - percentage of correctly classified dog breeds
+#            pct_correct_notdogs - percentage of correctly classified NON-dogs
+#
+##
+# TODO 5: EDIT and ADD code BELOW to do the following that's stated in the 
+#       comments below that start with "TODO: 5" for the calculates_results_stats 
+#       function. Please be certain to replace None in the return statement with
+#       the results_stats_dic dictionary that you create with this function
+# 
+def calculates_results_stats(results_dic):
+    """
+    Calculates statistics of the results of the program run using classifier's model 
+    architecture to classifying pet images. Then puts the results statistics in a 
+    dictionary (results_stats_dic) so that it's returned for printing as to help
+    the user to determine the 'best' model for classifying images. Note that 
+    the statistics calculated as the results are either percentages or counts.
+    Parameters:
+      results_dic - Dictionary with key as image filename and value as a List 
+             (index)idx 0 = pet image label (string)
+                    idx 1 = classifier label (string)
+                    idx 2 = 1/0 (int)  where 1 = match between pet image and 
+                            classifer labels and 0 = no match between labels
+                    idx 3 = 1/0 (int)  where 1 = pet image 'is-a' dog and 
+                            0 = pet Image 'is-NOT-a' dog. 
+                    idx 4 = 1/0 (int)  where 1 = Classifier classifies image 
+                            'as-a' dog and 0 = Classifier classifies image  
+                            'as-NOT-a' dog.
+    Returns:
+     results_stats_dic - Dictionary that contains the results statistics (either
+                    a percentage or a count) where the key is the statistic's 
+                     name (starting with 'pct' for percentage or 'n' for count)
+                     and the value is the statistic's value. See comments above
+                     and the classroom Item XX Calculating Results for details
+                     on how to calculate the counts and statistics.
+    """        
+    # Creates empty dictionary for results_stats_dic
+    results_stats_dic = dict()
+    
+    # Sets all counters to initial values of zero so that they can 
+    # be incremented while processing through the images in results_dic 
+    results_stats_dic['n_dogs_img'] = 0
+    results_stats_dic['n_match'] = 0
+    results_stats_dic['n_correct_dogs'] = 0
+    results_stats_dic['n_correct_notdogs'] = 0
+    results_stats_dic['n_correct_breed'] = 0       
+    
+    # process through the results dictionary
+    for key in results_dic:
+         
+        # Labels Match Exactly
+        if results_dic[key][2] == 1:
+            results_stats_dic['n_match'] += 1
+
+        # TODO: 5a. REPLACE pass with CODE that counts how many pet images of
+        #           dogs had their breed correctly classified. This happens 
+        #           when the pet image label indicates the image is-a-dog AND 
+        #           the pet image label and the classifier label match. You 
+        #           will need to write a conditional statement that determines
+        #           when the dog breed is correctly classified and then 
+        #           increments 'n_correct_breed' by 1. Recall 'n_correct_breed' 
+        #           is a key in the results_stats_dic dictionary with it's value 
+        #           representing the number of correctly classified dog breeds.
+        #           
+        # Pet Image Label is a Dog AND Labels match- counts Correct Breed
+        pass
+        
+        # Pet Image Label is a Dog - counts number of dog images
+        if results_dic[key][3] == 1:
+            results_stats_dic['n_dogs_img'] += 1
+            
+            # Classifier classifies image as Dog (& pet image is a dog)
+            # counts number of correct dog classifications
+            if results_dic[key][4] == 1:
+                results_stats_dic['n_correct_dogs'] += 1
+
+        # TODO: 5b. REPLACE pass with CODE that counts how many pet images 
+        #           that are NOT dogs were correctly classified. This happens 
+        #           when the pet image label indicates the image is-NOT-a-dog 
+        #           AND the classifier label indicates the images is-NOT-a-dog.
+        #           You will need to write a conditional statement that 
+        #           determines when the classifier label indicates the image 
+        #           is-NOT-a-dog and then increments 'n_correct_notdogs' by 1. 
+        #           Recall the 'else:' above 'pass' already indicates that the 
+        #           pet image label indicates the image is-NOT-a-dog and 
+        #          'n_correct_notdogs' is a key in the results_stats_dic dictionary 
+        #           with it's value representing the number of correctly 
+        #           classified NOT-a-dog images.
+        #           
+        # Pet Image Label is NOT a Dog
+        else:
+            # Classifier classifies image as NOT a Dog(& pet image isn't a dog)
+            # counts number of correct NOT dog clasifications.
+            pass
+
+
+    # Calculates run statistics (counts & percentages) below that are calculated
+    # using the counters from above.
+    
+    # calculates number of total images
+    results_stats_dic['n_images'] = len(results_dic)
+
+    # calculates number of not-a-dog images using - images & dog images counts
+    results_stats_dic['n_notdogs_img'] = (results_stats_dic['n_images'] - 
+                                      results_stats_dic['n_dogs_img']) 
+
+    # TODO: 5c. REPLACE zero(0.0) with CODE that calculates the % of correctly
+    #           matched images. Recall that this can be calculated by the
+    #           number of correctly matched images ('n_match') divided by the 
+    #           number of images('n_images'). This result will need to be 
+    #           multiplied by 100.0 to provide the percentage.
+    #    
+    # Calculates % correct for matches
+    results_stats_dic['pct_match'] = 0.0
+
+    # TODO: 5d. REPLACE zero(0.0) with CODE that calculates the % of correctly
+    #           classified dog images. Recall that this can be calculated by 
+    #           the number of correctly classified dog images('n_correct_dogs')
+    #           divided by the number of dog images('n_dogs_img'). This result 
+    #           will need to be multiplied by 100.0 to provide the percentage.
+    #    
+    # Calculates % correct dogs
+    results_stats_dic['pct_correct_dogs'] = 0.0
+
+    # TODO: 5e. REPLACE zero(0.0) with CODE that calculates the % of correctly
+    #           classified breeds of dogs. Recall that this can be calculated 
+    #           by the number of correctly classified breeds of dog('n_correct_breed') 
+    #           divided by the number of dog images('n_dogs_img'). This result 
+    #           will need to be multiplied by 100.0 to provide the percentage.
+    #    
+    # Calculates % correct breed of dog
+    results_stats_dic['pct_correct_breed'] = 0.0
+
+    # Calculates % correct not-a-dog images
+    # Uses conditional statement for when no 'not a dog' images were submitted 
+    if results_stats_dic['n_notdogs_img'] > 0:
+        results_stats_dic['pct_correct_notdogs'] = (results_stats_dic['n_correct_notdogs'] /
+                                                results_stats_dic['n_notdogs_img'])*100.0
+    else:
+        results_stats_dic['pct_correct_notdogs'] = 0.0
+
+        
+    # TODO 5f. REPLACE None with the results_stats_dic dictionary that you 
+    # created with this function 
+    return None

ai-programming-with-python/C02P8-pre-trained-image-classifier/check_images.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+# */AIPND-revision/intropyproject-classify-pet-images/check_images.py
+#
+# TODO 0: Add your information below for Programmer & Date Created.
+# PROGRAMMER: Alexander Hinrichs
+# DATE CREATED: 07.12.2025
+# REVISED DATE:
+# PURPOSE: Classifies pet images using a pretrained CNN model, compares these
+#          classifications to the true identity of the pets in the images, and
+#          summarizes how well the CNN performed on the image classification task.
+#          Note that the true identity of the pet (or object) in the image is
+#          indicated by the filename of the image. Therefore, your program must
+#          first extract the pet image label from the filename before
+#          classifying the images using the pretrained CNN model. With this
+#          program we will be comparing the performance of 3 different CNN model
+#          architectures to determine which provides the 'best' classification.
+#
+# Use argparse Expected Call with <> indicating expected user input:
+#      python check_images.py --dir <directory with images> --arch <model>
+#             --dogfile <file that contains dognames>
+#   Example call:
+#    python check_images.py --dir pet_images/ --arch vgg --dogfile dognames.txt
+##
+
+# Imports python modules
+from time import time, sleep
+
+# Imports print functions that check the lab
+from print_functions_for_lab_checks import *
+
+# Imports functions created for this program
+from get_input_args import get_input_args
+from get_pet_labels import get_pet_labels
+from classify_images import classify_images
+from adjust_results4_isadog import adjust_results4_isadog
+from calculates_results_stats import calculates_results_stats
+from print_results import print_results
+
+
+# Main program function defined below
+def main():
+    # TODO 0: Measures total program runtime by collecting start time
+    start_time = time()
+
+    # TODO 1: Define get_input_args function within the file get_input_args.py
+    # This function retrieves 3 Command Line Arugments from user as input from
+    # the user running the program from a terminal window. This function returns
+    # the collection of these command line arguments from the function call as
+    # the variable in_arg
+    in_arg = get_input_args()
+
+    # Function that checks command line arguments using in_arg
+    check_command_line_arguments(in_arg)
+
+    # TODO 2: Define get_pet_labels function within the file get_pet_labels.py
+    # Once the get_pet_labels function has been defined replace 'None'
+    # in the function call with in_arg.dir  Once you have done the replacements
+    # your function call should look like this:
+    #             get_pet_labels(in_arg.dir)
+    # This function creates the results dictionary that contains the results,
+    # this dictionary is returned from the function call as the variable results
+    results = get_pet_labels(None)
+
+    # Function that checks Pet Images in the results Dictionary using results
+    check_creating_pet_image_labels(results)
+
+    # TODO 3: Define classify_images function within the file classiy_images.py
+    # Once the classify_images function has been defined replace first 'None'
+    # in the function call with in_arg.dir and replace the last 'None' in the
+    # function call with in_arg.arch  Once you have done the replacements your
+    # function call should look like this:
+    #             classify_images(in_arg.dir, results, in_arg.arch)
+    # Creates Classifier Labels with classifier function, Compares Labels,
+    # and adds these results to the results dictionary - results
+    classify_images(None, results, None)
+
+    # Function that checks Results Dictionary using results
+    check_classifying_images(results)
+
+    # TODO 4: Define adjust_results4_isadog function within the file adjust_results4_isadog.py
+    # Once the adjust_results4_isadog function has been defined replace 'None'
+    # in the function call with in_arg.dogfile  Once you have done the
+    # replacements your function call should look like this:
+    #          adjust_results4_isadog(results, in_arg.dogfile)
+    # Adjusts the results dictionary to determine if classifier correctly
+    # classified images as 'a dog' or 'not a dog'. This demonstrates if
+    # model can correctly classify dog images as dogs (regardless of breed)
+    adjust_results4_isadog(results, None)
+
+    # Function that checks Results Dictionary for is-a-dog adjustment using results
+    check_classifying_labels_as_dogs(results)
+
+    # TODO 5: Define calculates_results_stats function within the file calculates_results_stats.py
+    # This function creates the results statistics dictionary that contains a
+    # summary of the results statistics (this includes counts & percentages). This
+    # dictionary is returned from the function call as the variable results_stats
+    # Calculates results of run and puts statistics in the Results Statistics
+    # Dictionary - called results_stats
+    results_stats = calculates_results_stats(results)
+
+    # Function that checks Results Statistics Dictionary using results_stats
+    check_calculating_results(results, results_stats)
+
+    # TODO 6: Define print_results function within the file print_results.py
+    # Once the print_results function has been defined replace 'None'
+    # in the function call with in_arg.arch  Once you have done the
+    # replacements your function call should look like this:
+    #      print_results(results, results_stats, in_arg.arch, True, True)
+    # Prints summary results, incorrect classifications of dogs (if requested)
+    # and incorrectly classified breeds (if requested)
+    print_results(results, results_stats, None, True, True)
+
+    # TODO 0: Measure total program runtime by collecting end time
+    end_time = time()
+
+    # TODO 0: Computes overall runtime in seconds & prints it in hh:mm:ss format
+    tot_time = (
+        end_time - start_time
+    )  # calculate difference between end time and start time
+    print(
+        "\n** Total Elapsed Runtime:",
+        str(int((tot_time / 3600)))
+        + ":"
+        + str(int((tot_time % 3600) / 60))
+        + ":"
+        + str(int((tot_time % 3600) % 60)),
+    )
+
+
+# Call to main function to run the program
+if __name__ == "__main__":
+    main()

ai-programming-with-python/C02P8-pre-trained-image-classifier/check_images.txt

@@ -0,0 +1,20 @@
+Questions regarding Uploaded Image Classification:
+
+1. Did the three model architectures classify the breed of dog in Dog_01.jpg to be the same breed? If not, report the differences in the classifications.
+
+Answer: 
+
+
+2. Did each of the three model architectures classify the breed of dog in Dog_01.jpg to be the same breed of dog as that model architecture classified Dog_02.jpg? If not, report the differences in the classifications.
+
+Answer: 
+
+
+3. Did the three model architectures correctly classify Animal_Name_01.jpg and Object_Name_01.jpg to not be dogs? If not, report the misclassifications.
+
+Answer: 
+
+
+4. Based upon your answers for questions 1. - 3. above, select the model architecture that you feel did the best at classifying the four uploaded images. Describe why you selected that model architecture as the best on uploaded image classification.
+
+Answer:

ai-programming-with-python/C02P8-pre-trained-image-classifier/classifier.py

@@ -0,0 +1,74 @@
+import ast
+from PIL import Image
+import torchvision.transforms as transforms
+from torch.autograd import Variable
+import torchvision.models as models
+from torch import __version__
+
+resnet18 = models.resnet18(pretrained=True)
+alexnet = models.alexnet(pretrained=True)
+vgg16 = models.vgg16(pretrained=True)
+
+models = {'resnet': resnet18, 'alexnet': alexnet, 'vgg': vgg16}
+
+# obtain ImageNet labels
+with open('imagenet1000_clsid_to_human.txt') as imagenet_classes_file:
+    imagenet_classes_dict = ast.literal_eval(imagenet_classes_file.read())
+
+def classifier(img_path, model_name):
+    # load the image
+    img_pil = Image.open(img_path)
+
+    # define transforms
+    preprocess = transforms.Compose([
+        transforms.Resize(256),
+        transforms.CenterCrop(224),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+    ])
+    
+    # preprocess the image
+    img_tensor = preprocess(img_pil)
+    
+    # resize the tensor (add dimension for batch)
+    img_tensor.unsqueeze_(0)
+    
+    # wrap input in variable, wrap input in variable - no longer needed for
+    # v 0.4 & higher code changed 04/26/2018 by Jennifer S. to handle PyTorch upgrade
+    pytorch_ver = __version__.split('.')
+    
+    # pytorch versions 0.4 & hihger - Variable depreciated so that it returns
+    # a tensor. So to address tensor as output (not wrapper) and to mimic the 
+    # affect of setting volatile = True (because we are using pretrained models
+    # for inference) we can set requires_gradient to False. Here we just set 
+    # requires_grad_ to False on our tensor 
+    if int(pytorch_ver[0]) > 0 or int(pytorch_ver[1]) >= 4:
+        img_tensor.requires_grad_(False)
+    
+    # pytorch versions less than 0.4 - uses Variable because not-depreciated
+    else:
+        # apply model to input
+        # wrap input in variable
+        data = Variable(img_tensor, volatile = True) 
+
+    # apply model to input
+    model = models[model_name]
+
+    # puts model in evaluation mode
+    # instead of (default)training mode
+    model = model.eval()
+    
+    # apply data to model - adjusted based upon version to account for 
+    # operating on a Tensor for version 0.4 & higher.
+    if int(pytorch_ver[0]) > 0 or int(pytorch_ver[1]) >= 4:
+        output = model(img_tensor)
+
+    # pytorch versions less than 0.4
+    else:
+        # apply data to model
+        output = model(data)
+
+    # return index corresponding to predicted class
+    pred_idx = output.data.numpy().argmax()
+
+    return imagenet_classes_dict[pred_idx]

ai-programming-with-python/C02P8-pre-trained-image-classifier/classify_images.py

@@ -0,0 +1,68 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+# */AIPND-revision/intropyproject-classify-pet-images/classify_images.py
+#                                                                             
+# PROGRAMMER: 
+# DATE CREATED:                                 
+# REVISED DATE: 
+# PURPOSE: Create a function classify_images that uses the classifier function 
+#          to create the classifier labels and then compares the classifier 
+#          labels to the pet image labels. This function inputs:
+#            -The Image Folder as image_dir within classify_images and function 
+#             and as in_arg.dir for function call within main. 
+#            -The results dictionary as results_dic within classify_images 
+#             function and results for the functin call within main.
+#            -The CNN model architecture as model wihtin classify_images function
+#             and in_arg.arch for the function call within main. 
+#           This function uses the extend function to add items to the list 
+#           that's the 'value' of the results dictionary. You will be adding the
+#           classifier label as the item at index 1 of the list and the comparison 
+#           of the pet and classifier labels as the item at index 2 of the list.
+#
+##
+# Imports classifier function for using CNN to classify images 
+from classifier import classifier 
+
+# TODO 3: Define classify_images function below, specifically replace the None
+#       below by the function definition of the classify_images function. 
+#       Notice that this function doesn't return anything because the 
+#       results_dic dictionary that is passed into the function is a mutable 
+#       data type so no return is needed.
+# 
+def classify_images(images_dir, results_dic, model):
+    """
+    Creates classifier labels with classifier function, compares pet labels to 
+    the classifier labels, and adds the classifier label and the comparison of 
+    the labels to the results dictionary using the extend function. Be sure to
+    format the classifier labels so that they will match your pet image labels.
+    The format will include putting the classifier labels in all lower case 
+    letters and strip the leading and trailing whitespace characters from them.
+    For example, the Classifier function returns = 'Maltese dog, Maltese terrier, Maltese' 
+    so the classifier label = 'maltese dog, maltese terrier, maltese'.
+    Recall that dog names from the classifier function can be a string of dog 
+    names separated by commas when a particular breed of dog has multiple dog 
+    names associated with that breed. For example, you will find pet images of
+    a 'dalmatian'(pet label) and it will match to the classifier label 
+    'dalmatian, coach dog, carriage dog' if the classifier function correctly 
+    classified the pet images of dalmatians.
+     PLEASE NOTE: This function uses the classifier() function defined in 
+     classifier.py within this function. The proper use of this function is
+     in test_classifier.py Please refer to this program prior to using the 
+     classifier() function to classify images within this function 
+     Parameters: 
+      images_dir - The (full) path to the folder of images that are to be
+                   classified by the classifier function (string)
+      results_dic - Results Dictionary with 'key' as image filename and 'value'
+                    as a List. Where the list will contain the following items: 
+                  index 0 = pet image label (string)
+                --- where index 1 & index 2 are added by this function ---
+                  NEW - index 1 = classifier label (string)
+                  NEW - index 2 = 1/0 (int)  where 1 = match between pet image
+                    and classifer labels and 0 = no match between labels
+      model - Indicates which CNN model architecture will be used by the 
+              classifier function to classify the pet images,
+              values must be either: resnet alexnet vgg (string)
+     Returns:
+           None - results_dic is mutable data type so no return needed.         
+    """
+    None 

ai-programming-with-python/C02P8-pre-trained-image-classifier/classify_images_hints.py

@@ -0,0 +1,118 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+# */AIPND-revision/intropyproject-classify-pet-images/classify_images_hints.py
+#                                                                             
+# PROGRAMMER: 
+# DATE CREATED:                                 
+# REVISED DATE: 
+# PURPOSE: This is a *hints* file to help guide students in creating the 
+#          function classify_images that uses the classifier function 
+#          to create the classifier labels and then compares the classifier 
+#          labels to the pet image labels. This function inputs:
+#            -The Image Folder as image_dir within classify_images and function 
+#             and as in_arg.dir for function call within main. 
+#            -The results dictionary as results_dic within classify_images 
+#             function and results for the functin call within main.
+#            -The CNN model architecture as model wihtin classify_images function
+#             and in_arg.arch for the function call within main. 
+#           This function uses the extend function to add items to the list 
+#           that's the 'value' of the results dictionary. You will be adding the
+#           classifier label as the item at index 1 of the list and the comparison 
+#           of the pet and classifier labels as the item at index 2 of the list.
+#
+##
+# Imports classifier function for using CNN to classify images 
+from classifier import classifier 
+
+# TODO 3: EDIT and ADD code BELOW to do the following that's stated in the 
+#       comments below that start with "TODO: 3" for the classify_images function 
+#       Specifically EDIT and ADD code to define the classify_images function. 
+#       Notice that this function doesn't return anything because the 
+#       results_dic dictionary that is passed into the function is a mutable 
+#       data type so no return is needed.
+# 
+def classify_images(images_dir, results_dic, model):
+    """
+    Creates classifier labels with classifier function, compares pet labels to 
+    the classifier labels, and adds the classifier label and the comparison of 
+    the labels to the results dictionary using the extend function. Be sure to
+    format the classifier labels so that they will match your pet image labels.
+    The format will include putting the classifier labels in all lower case 
+    letters and strip the leading and trailing whitespace characters from them.
+    For example, the Classifier function returns = 'Maltese dog, Maltese terrier, Maltese' 
+    so the classifier label = 'maltese dog, maltese terrier, maltese'.
+    Recall that dog names from the classifier function can be a string of dog 
+    names separated by commas when a particular breed of dog has multiple dog 
+    names associated with that breed. For example, you will find pet images of
+    a 'dalmatian'(pet label) and it will match to the classifier label 
+    'dalmatian, coach dog, carriage dog' if the classifier function correctly 
+    classified the pet images of dalmatians.
+     PLEASE NOTE: This function uses the classifier() function defined in 
+     classifier.py within this function. The proper use of this function is
+     in test_classifier.py Please refer to this program prior to using the 
+     classifier() function to classify images within this function 
+     Parameters: 
+      images_dir - The (full) path to the folder of images that are to be
+                   classified by the classifier function (string)
+      results_dic - Results Dictionary with 'key' as image filename and 'value'
+                    as a List. Where the list will contain the following items: 
+                  index 0 = pet image label (string)
+                --- where index 1 & index 2 are added by this function ---
+                  NEW - index 1 = classifier label (string)
+                  NEW - index 2 = 1/0 (int)  where 1 = match between pet image
+                    and classifer labels and 0 = no match between labels
+      model - Indicates which CNN model architecture will be used by the 
+              classifier function to classify the pet images,
+              values must be either: resnet alexnet vgg (string)
+     Returns:
+           None - results_dic is mutable data type so no return needed.         
+    """
+    # Process all files in the results_dic - use images_dir to give fullpath
+    # that indicates the folder and the filename (key) to be used in the 
+    # classifier function
+    for key in results_dic:
+       
+       # TODO: 3a. Set the string variable model_label to be the string that's 
+       #           returned from using the classifier function instead of the   
+       #           empty string below.
+       #
+       #  Runs classifier function to classify the images classifier function 
+       # inputs: path + filename  and  model, returns model_label 
+       # as classifier label
+       model_label = ""
+
+       # TODO: 3b. BELOW REPLACE pass with CODE to process the model_label to 
+       #           convert all characters within model_label to lowercase 
+       #           letters and then remove whitespace characters from the ends
+       #           of model_label. Be certain the resulting processed string 
+       #           is named model_label.
+       #
+       # Processes the results so they can be compared with pet image labels
+       # set labels to lowercase (lower) and stripping off whitespace(strip)
+       pass
+              
+       # defines truth as pet image label 
+       truth = results_dic[key][0]
+
+       # TODO: 3c. REPLACE pass BELOW with CODE that uses the extend list function
+       #           to add the classifier label (model_label) and the value of
+       #           1 (where the value of 1 indicates a match between pet image 
+       #           label and the classifier label) to the results_dic dictionary
+       #           for the key indicated by the variable key 
+       #
+       # If the pet image label is found within the classifier label list of terms 
+       # as an exact match to on of the terms in the list - then they are added to 
+       # results_dic as an exact match(1) using extend list function
+       if truth in model_label:
+           pass
+
+       # TODO: 3d. REPLACE pass BELOW with CODE that uses the extend list function
+       #           to add the classifier label (model_label) and the value of
+       #           0 (where the value of 0 indicates NOT a match between the pet 
+       #           image label and the classifier label) to the results_dic 
+       #           dictionary for the key indicated by the variable key
+       #                   
+       # if not found then added to results dictionary as NOT a match(0) using
+       # the extend function 
+       else:
+           pass

ai-programming-with-python/C02P8-pre-trained-image-classifier/dognames.txt

@@ -0,0 +1,224 @@
+chihuahua
+japanese spaniel
+maltese dog, maltese terrier, maltese
+pekinese, pekingese, peke
+shih-tzu
+blenheim spaniel
+papillon
+toy terrier
+rhodesian ridgeback
+afghan hound, afghan
+basset, basset hound
+beagle
+bloodhound, sleuthhound
+bluetick
+black-and-tan coonhound
+walker hound, walker foxhound
+english foxhound
+redbone
+borzoi, russian wolfhound
+irish wolfhound
+italian greyhound
+whippet
+ibizan hound, ibizan podenco
+norwegian elkhound, elkhound
+otterhound, otter hound
+saluki, gazelle hound
+scottish deerhound, deerhound
+weimaraner
+staffordshire bullterrier, staffordshire bull terrier
+american staffordshire terrier, staffordshire terrier, american pit bull terrier, pit bull terrier
+bedlington terrier
+border terrier
+kerry blue terrier
+irish terrier
+norfolk terrier
+norwich terrier
+yorkshire terrier
+wire-haired fox terrier
+lakeland terrier
+sealyham terrier, sealyham
+airedale, airedale terrier
+cairn, cairn terrier
+australian terrier
+dandie dinmont, dandie dinmont terrier
+boston bull, boston terrier
+miniature schnauzer
+giant schnauzer
+standard schnauzer, schnauzer
+scotch terrier, scottish terrier, scottie
+tibetan terrier, chrysanthemum dog
+silky terrier, sydney silky
+soft-coated wheaten terrier
+west highland white terrier
+lhasa, lhasa apso
+flat-coated retriever
+curly-coated retriever
+golden retriever
+labrador retriever
+chesapeake bay retriever
+german shorthaired pointer
+vizsla, hungarian pointer
+english setter
+irish setter, red setter
+gordon setter
+brittany spaniel
+clumber, clumber spaniel
+english springer, english springer spaniel
+welsh springer spaniel
+cocker spaniel, english cocker spaniel, cocker
+sussex spaniel
+irish water spaniel
+kuvasz
+schipperke
+groenendael
+malinois
+briard
+kelpie
+komondor
+old english sheepdog, bobtail
+shetland sheepdog, shetland sheep dog, shetland
+collie
+border collie
+bouvier des flandres, bouviers des flandres
+rottweiler
+german shepherd, german shepherd dog, german police dog, alsatian
+doberman, doberman pinscher
+miniature pinscher
+greater swiss mountain dog
+bernese mountain dog
+appenzeller
+entlebucher
+boxer
+bull mastiff
+tibetan mastiff
+french bulldog
+great dane
+saint bernard, st bernard
+eskimo dog, husky
+malamute, malemute, alaskan malamute
+siberian husky
+dalmatian, coach dog, carriage dog
+affenpinscher, monkey pinscher, monkey dog
+basenji
+pug, pug-dog
+leonberg
+newfoundland, newfoundland dog
+great pyrenees
+samoyed, samoyede
+pomeranian
+chow, chow chow
+keeshond
+brabancon griffon
+pembroke, pembroke welsh corgi, corgi
+cardigan, cardigan welsh corgi, corgi
+toy poodle
+miniature poodle
+standard poodle, poodle
+mexican hairless
+affenpinscher
+afghan hound
+airedale terrier
+akita
+alaskan malamute
+american eskimo dog
+american foxhound
+american staffordshire terrier
+american water spaniel
+anatolian shepherd dog
+australian cattle dog
+australian shepherd
+basset hound
+bearded collie
+beauceron
+belgian malinois
+belgian sheepdog
+belgian tervuren
+bichon frise
+black and tan coonhound
+black russian terrier
+bloodhound
+bluetick coonhound
+borzoi
+boston terrier
+bouvier des flandres
+boykin spaniel
+brittany
+brussels griffon
+bull terrier
+bulldog
+bullmastiff
+cairn terrier
+canaan dog
+cane corso
+cardigan welsh corgi
+cavalier king charles spaniel
+chinese crested
+chinese shar-pei
+chow chow
+clumber spaniel
+cocker spaniel
+corgi
+dachshund
+dalmatian
+dandie dinmont terrier
+deerhound
+doberman pinscher
+dogue de bordeaux
+english cocker spaniel
+english springer spaniel
+english toy spaniel
+entlebucher mountain dog
+field spaniel
+finnish spitz
+german pinscher
+german shepherd dog
+german wirehaired pointer
+glen of imaal terrier
+greyhound
+havanese
+ibizan hound
+icelandic sheepdog
+irish red and white setter
+irish setter
+japanese chin
+leonberger
+lhasa apso
+lowchen
+maltese
+manchester terrier
+mastiff
+neapolitan mastiff
+newfoundland
+norwegian buhund
+norwegian elkhound
+norwegian lundehund
+nova scotia duck tolling retriever
+old english sheepdog
+otterhound
+parson russell terrier
+pekingese
+pembroke welsh corgi
+petit basset griffon vendeen
+pharaoh hound
+plott
+pointer
+poodle
+portuguese water dog
+pug
+saint bernard
+saluki
+samoyed
+schnauzer
+scottish terrier
+sealyham terrier
+shetland sheepdog
+silky terrier
+smooth fox terrier
+staffordshire bull terrier
+tibetan terrier
+vizsla
+walker hound
+wirehaired pointing griffon
+xoloitzcuintli
+dog

ai-programming-with-python/C02P8-pre-trained-image-classifier/get_input_args.py

@@ -0,0 +1,48 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+# */AIPND-revision/intropyproject-classify-pet-images/get_input_args.py
+#                                                                             
+# PROGRAMMER: 
+# DATE CREATED:                                   
+# REVISED DATE: 
+# PURPOSE: Create a function that retrieves the following 3 command line inputs 
+#          from the user using the Argparse Python module. If the user fails to 
+#          provide some or all of the 3 inputs, then the default values are
+#          used for the missing inputs. Command Line Arguments:
+#     1. Image Folder as --dir with default value 'pet_images'
+#     2. CNN Model Architecture as --arch with default value 'vgg'
+#     3. Text File with Dog Names as --dogfile with default value 'dognames.txt'
+#
+##
+# Imports python modules
+import argparse
+
+# TODO 1: Define get_input_args function below please be certain to replace None
+#       in the return statement with parser.parse_args() parsed argument 
+#       collection that you created with this function
+# 
+def get_input_args():
+    """
+    Retrieves and parses the 3 command line arguments provided by the user when
+    they run the program from a terminal window. This function uses Python's 
+    argparse module to created and defined these 3 command line arguments. If 
+    the user fails to provide some or all of the 3 arguments, then the default 
+    values are used for the missing arguments. 
+    Command Line Arguments:
+      1. Image Folder as --dir with default value 'pet_images'
+      2. CNN Model Architecture as --arch with default value 'vgg'
+      3. Text File with Dog Names as --dogfile with default value 'dognames.txt'
+    This function returns these arguments as an ArgumentParser object.
+    Parameters:
+     None - simply using argparse module to create & store command line arguments
+    Returns:
+     parse_args() -data structure that stores the command line arguments object  
+    """
+    # Create Parse using ArgumentParser
+    
+    # Create 3 command line arguments as mentioned above using add_argument() from ArguementParser method
+    
+    
+    # Replace None with parser.parse_args() parsed argument collection that 
+    # you created with this function 
+    return None

ai-programming-with-python/C02P8-pre-trained-image-classifier/get_input_args_hints.py

@@ -0,0 +1,60 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+# */AIPND-revision/intropyproject-classify-pet-images/get_input_args_hints.py
+#                                                                             
+# PROGRAMMER: 
+# DATE CREATED:                                   
+# REVISED DATE: 
+# PURPOSE: This is a *hints* file to help guide students in creating the 
+#          function that retrieves the following 3 command line inputs from
+#          the user using the Argparse Python module. If the user fails to 
+#          provide some or all of the 3 inputs, then the default values are
+#          used for the missing inputs. Command Line Arguments:
+#     1. Image Folder as --dir with default value 'pet_images'
+#     2. CNN Model Architecture as --arch with default value 'vgg'
+#     3. Text File with Dog Names as --dogfile with default value 'dognames.txt'
+#
+##
+# Imports python modules
+import argparse
+
+# TODO 1: EDIT and ADD code BELOW to do the following that's stated in the 
+#       comments below that start with "TODO: 1" for the get_input_args function
+#       Please be certain to replace None in the return statement with 
+#       parser.parse_args() parsed argument collection that you created with 
+#       this function
+# 
+def get_input_args():
+    """
+    Retrieves and parses the 3 command line arguments provided by the user when
+    they run the program from a terminal window. This function uses Python's 
+    argparse module to created and defined these 3 command line arguments. If 
+    the user fails to provide some or all of the 3 arguments, then the default 
+    values are used for the missing arguments. 
+    Command Line Arguments:
+      1. Image Folder as --dir with default value 'pet_images'
+      2. CNN Model Architecture as --arch with default value 'vgg'
+      3. Text File with Dog Names as --dogfile with default value 'dognames.txt'
+    This function returns these arguments as an ArgumentParser object.
+    Parameters:
+     None - simply using argparse module to create & store command line arguments
+    Returns:
+     parse_args() -data structure that stores the command line arguments object  
+    """
+    # Creates parse 
+    parser = argparse.ArgumentParser()
+
+    # Creates 3 command line arguments args.dir for path to images files,
+    # args.arch which CNN model to use for classification, args.labels path to
+    # text file with names of dogs.
+    parser.add_argument('--dir', type=str, default='pet_images/', 
+                        help='path to folder of images')
+    # TODO: 1a. EDIT parse.add_argument statements BELOW to add type & help for:
+    #          --arch - the CNN model architecture
+    #          --dogfile - text file of names of dog breeds
+    parser.add_argument('--arch', default = 'vgg' )
+    parser.add_argument('--dogfile', default = 'dognames.txt' )
+
+    # TODO: 1b. Replace None with parser.parse_args() parsed argument 
+    # collection that you created with this function 
+    return None

ai-programming-with-python/C02P8-pre-trained-image-classifier/get_pet_labels.py

@@ -0,0 +1,45 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+# */AIPND-revision/intropyproject-classify-pet-images/get_pet_labels.py
+#                                                                             
+# PROGRAMMER: 
+# DATE CREATED:                                  
+# REVISED DATE: 
+# PURPOSE: Create the function get_pet_labels that creates the pet labels from 
+#          the image's filename. This function inputs: 
+#           - The Image Folder as image_dir within get_pet_labels function and 
+#             as in_arg.dir for the function call within the main function. 
+#          This function creates and returns the results dictionary as results_dic
+#          within get_pet_labels function and as results within main. 
+#          The results_dic dictionary has a 'key' that's the image filename and
+#          a 'value' that's a list. This list will contain the following item
+#          at index 0 : pet image label (string).
+#
+##
+# Imports python modules
+from os import listdir
+
+# TODO 2: Define get_pet_labels function below please be certain to replace None
+#       in the return statement with results_dic dictionary that you create 
+#       with this function
+# 
+def get_pet_labels(image_dir):
+    """
+    Creates a dictionary of pet labels (results_dic) based upon the filenames 
+    of the image files. These pet image labels are used to check the accuracy 
+    of the labels that are returned by the classifier function, since the 
+    filenames of the images contain the true identity of the pet in the image.
+    Be sure to format the pet labels so that they are in all lower case letters
+    and with leading and trailing whitespace characters stripped from them.
+    (ex. filename = 'Boston_terrier_02259.jpg' Pet label = 'boston terrier')
+    Parameters:
+     image_dir - The (full) path to the folder of images that are to be
+                 classified by the classifier function (string)
+    Returns:
+      results_dic - Dictionary with 'key' as image filename and 'value' as a 
+      List. The list contains for following item:
+         index 0 = pet image label (string)
+    """
+    # Replace None with the results_dic dictionary that you created with this
+    # function
+    return None

ai-programming-with-python/C02P8-pre-trained-image-classifier/get_pet_labels_hints.py

@@ -0,0 +1,85 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+# */AIPND-revision/intropyproject-classify-pet-images/get_pet_labels_hints.py
+#                                                                             
+# PROGRAMMER: 
+# DATE CREATED:                                  
+# REVISED DATE: 
+# PURPOSE: This is a *hints* file to help guide students in creating the 
+#          function get_pet_labels that creates the pet labels from the image's
+#          filename. This function inputs: 
+#           - The Image Folder as image_dir within get_pet_labels function and 
+#             as in_arg.dir for the function call within the main function. 
+#          This function creates and returns the results dictionary as results_dic
+#          within get_pet_labels function and as results within main. 
+#          The results_dic dictionary has a 'key' that's the image filename and
+#          a 'value' that's a list. This list will contain the following item
+#          at index 0 : pet image label (string).
+#
+##
+# Imports python modules
+from os import listdir
+
+# TODO 2: EDIT and ADD code BELOW to do the following that's stated in the 
+#       comments below that start with "TODO: 2" for the get_pet_labels function 
+#       Please be certain to replace None in the return statement with 
+#       results_dic dictionary that you create with this function
+# 
+def get_pet_labels(image_dir):
+    """
+    Creates a dictionary of pet labels (results_dic) based upon the filenames 
+    of the image files. These pet image labels are used to check the accuracy 
+    of the labels that are returned by the classifier function, since the 
+    filenames of the images contain the true identity of the pet in the image.
+    Be sure to format the pet labels so that they are in all lower case letters
+    and with leading and trailing whitespace characters stripped from them.
+    (ex. filename = 'Boston_terrier_02259.jpg' Pet label = 'boston terrier')
+    Parameters:
+     image_dir - The (full) path to the folder of images that are to be
+                 classified by the classifier function (string)
+    Returns:
+      results_dic - Dictionary with 'key' as image filename and 'value' as a 
+      List. The list contains for following item:
+         index 0 = pet image label (string)
+    """
+    # Creates list of files in directory
+    in_files = listdir(image_dir)
+    
+    # Processes each of the files to create a dictionary where the key
+    # is the filename and the value is the picture label (below).
+ 
+    # Creates empty dictionary for the results (pet labels, etc.)
+    results_dic = dict()
+   
+    # Processes through each file in the directory, extracting only the words
+    # of the file that contain the pet image label
+    for idx in range(0, len(in_files), 1):
+       
+       # Skips file if starts with . (like .DS_Store of Mac OSX) because it 
+       # isn't an pet image file
+       if in_files[idx][0] != ".":
+           
+           # Creates temporary label variable to hold pet label name extracted 
+           pet_label = ""
+
+           # TODO: 2a. BELOW REPLACE pass with CODE that will process each 
+           #          filename in the in_files list to extract the dog breed 
+           #          name from the filename. Recall that each filename can be
+           #          accessed by in_files[idx]. Be certain to place the 
+           #          extracted dog breed name in the variable pet_label 
+           #          that's created as an empty string ABOVE
+           pass
+
+           # If filename doesn't already exist in dictionary add it and it's
+           # pet label - otherwise print an error message because indicates 
+           # duplicate files (filenames)
+           if in_files[idx] not in results_dic:
+              results_dic[in_files[idx]] = [pet_label]
+              
+           else:
+               print("** Warning: Duplicate files exist in directory:", 
+                     in_files[idx])
+ 
+    # TODO 2b. Replace None with the results_dic dictionary that you created
+    # with this function
+    return None

ai-programming-with-python/C02P8-pre-trained-image-classifier/main.py

@@ -0,0 +1,6 @@
+def main():
+    print("Hello from 08-pre-trained-image-classification!")
+
+
+if __name__ == "__main__":
+    main()

ai-programming-with-python/C02P8-pre-trained-image-classifier/print_functions_for_lab_checks.py

@@ -0,0 +1,310 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+# */AIPND/intropylab-classifying-images/print_functions_for_lab_checks.py
+#                                                                             
+# PROGRAMMER: Jennifer S.                                                    
+# DATE CREATED: 05/14/2018                                  
+# REVISED DATE:             <=(Date Revised - if any)                         
+# PURPOSE:  This set of functions can be used to check your code after programming 
+#           each function. The top section of each part of the lab contains
+#           the section labeled 'Checking your code'. When directed within this
+#           section of the lab one can use these functions to more easily check 
+#           your code. See the docstrings below each function for details on how
+#           to use the function within your code.
+#
+##
+
+# Functions below defined to help with "Checking your code", specifically
+# running these functions with the appropriate input arguments within the
+# main() funtion will print out what's needed for "Checking your code"
+#
+def check_command_line_arguments(in_arg):
+    """
+    For Lab: Classifying Images - 7. Command Line Arguments
+    Prints each of the command line arguments passed in as parameter in_arg, 
+    assumes you defined all three command line arguments as outlined in 
+    '7. Command Line Arguments'
+    Parameters:
+     in_arg -data structure that stores the command line arguments object
+    Returns:
+     Nothing - just prints to console  
+    """
+    if in_arg is None:
+        print("* Doesn't Check the Command Line Arguments because 'get_input_args' hasn't been defined.")
+    else:
+        # prints command line agrs
+        print("Command Line Arguments:\n     dir =", in_arg.dir, 
+              "\n    arch =", in_arg.arch, "\n dogfile =", in_arg.dogfile)
+
+def check_creating_pet_image_labels(results_dic):
+    """    For Lab: Classifying Images - 9/10. Creating Pet Image Labels
+    Prints first 10 key-value pairs and makes sure there are 40 key-value 
+    pairs in your results_dic dictionary. Assumes you defined the results_dic
+    dictionary as was outlined in 
+    '9/10. Creating Pet Image Labels'
+    Parameters:
+      results_dic - Dictionary with key as image filename and value as a List 
+             (index)idx 0 = pet image label (string)
+    Returns:
+     Nothing - just prints to console  
+    """
+    if results_dic is None:
+        print("* Doesn't Check the Results Dictionary because 'get_pet_labels' hasn't been defined.")
+    else:
+        # Code to print 10 key-value pairs (or fewer if less than 10 images)
+        # & makes sure there are 40 pairs, one for each file in pet_images/
+        stop_point = len(results_dic)
+        if stop_point > 10:
+            stop_point = 10
+        print("\nPet Image Label Dictionary has", len(results_dic),
+              "key-value pairs.\nBelow are", stop_point, "of them:")
+    
+        # counter - to count how many labels have been printed
+        n = 0
+    
+        # for loop to iterate through the dictionary
+        for key in results_dic:
+ 
+            # prints only first 10 labels
+            if n < stop_point:
+                print("{:2d} key: {:>30}  label: {:>26}".format(n+1, key,
+                      results_dic[key][0]) )
+
+                # Increments counter
+                n += 1
+            
+            # If past first 10 (or fewer) labels the breaks out of loop
+            else:
+                break
+
+
+def check_classifying_images(results_dic):
+    """    For Lab: Classifying Images - 11/12. Classifying Images
+    Prints Pet Image Label and Classifier Label for ALL Matches followed by ALL 
+    NOT matches. Next prints out the total number of images followed by how 
+    many were matches and how many were not-matches to check all 40 images are
+    processed. Assumes you defined the results_dic dictionary as was 
+    outlined in '11/12. Classifying Images'
+    Parameters:
+      results_dic - Dictionary with key as image filename and value as a List 
+             (index)idx 0 = pet image label (string)
+                    idx 1 = classifier label (string)
+                    idx 2 = 1/0 (int)   where 1 = match between pet image and 
+                    classifer labels and 0 = no match between labels
+    Returns:
+     Nothing - just prints to console  
+
+    """
+    if results_dic is None:
+        print("* Doesn't Check the Results Dictionary because 'classify_images' hasn't been defined.")
+    elif len(results_dic[next(iter(results_dic))]) < 2:
+        print("* Doesn't Check the Results Dictionary because 'classify_images' hasn't been defined.")
+    else:
+        # Code for checking classify_images -
+        # Checks matches and not matches are classified correctly
+        # Checks that all 40 images are classified as a Match or Not-a Match
+    
+        # Sets counters for matches & NOT-matches
+        n_match = 0
+        n_notmatch = 0
+    
+        # Prints all Matches first
+        print("\n     MATCH:")
+        for key in results_dic:
+
+            # Prints only if a Match Index 2 == 1
+            if results_dic[key][2] == 1:
+
+                # Increments Match counter
+                n_match += 1
+                print("\n{:>30}: \nReal: {:>26}   Classifier: {:>30}".format(key, 
+                      results_dic[key][0], results_dic[key][1]))
+
+        # Prints all NOT-Matches next
+        print("\n NOT A MATCH:")
+        for key in results_dic:
+        
+            # Prints only if NOT-a-Match Index 2 == 0 
+            if results_dic[key][2] == 0:
+ 
+                # Increments Not-a-Match counter
+                n_notmatch += 1
+                print("\n{:>30}: \nReal: {:>26}   Classifier: {:>30}".format(key,
+                      results_dic[key][0], results_dic[key][1]))
+
+        # Prints Total Number of Images - expects 40 from pet_images folder
+        print("\n# Total Images",n_match + n_notmatch, "# Matches:",n_match ,
+              "# NOT Matches:",n_notmatch)
+
+ 
+def check_classifying_labels_as_dogs(results_dic):
+    """    For Lab: Classifying Images - 13. Classifying Labels as Dogs
+    Prints Pet Image Label, Classifier Label, whether Pet Label is-a-dog(1=Yes,
+    0=No), and whether Classifier Label is-a-dog(1=Yes, 0=No) for ALL Matches 
+    followed by ALL NOT matches. Next prints out the total number of images 
+    followed by how many were matches and how many were not-matches to check 
+    all 40 images are processed. Assumes you defined the results_dic dictionary
+    as was outlined in '13. Classifying Labels as Dogs'
+    Parameters:
+      results_dic - Dictionary with key as image filename and value as a List 
+             (index)idx 0 = pet image label (string)
+                    idx 1 = classifier label (string)
+                    idx 2 = 1/0 (int)   where 1 = match between pet image and 
+                    classifer labels and 0 = no match between labels
+                    idx 3 = 1/0 (int)  where 1 = pet image 'is-a' dog and 
+                            0 = pet Image 'is-NOT-a' dog. 
+                    idx 4 = 1/0 (int)  where 1 = Classifier classifies image 
+                            'as-a' dog and 0 = Classifier classifies image  
+                            'as-NOT-a' dog.
+    Returns:
+     Nothing - just prints to console  
+
+    """
+    if results_dic is None:
+        print("* Doesn't Check the Results Dictionary because 'adjust_results4_isadog' hasn't been defined.")
+    elif len(results_dic[next(iter(results_dic))]) < 4 :
+        print("* Doesn't Check the Results Dictionary because 'adjust_results4_isadog' hasn't been defined.")
+
+    else:
+        # Code for checking adjust_results4_isadog -
+        # Checks matches and not matches are classified correctly as "dogs" and
+        # "not-dogs" Checks that all 40 images are classified as a Match or Not-a 
+        # Match
+    
+        # Sets counters for matches & NOT-matches
+        n_match = 0
+        n_notmatch = 0
+    
+        # Prints all Matches first
+        print("\n     MATCH:")
+        for key in results_dic:
+
+            # Prints only if a Match Index 2 == 1
+            if results_dic[key][2] == 1:
+
+                # Increments Match counter
+                n_match += 1
+                print("\n{:>30}: \nReal: {:>26}   Classifier: {:>30}  \nPetLabelDog: {:1d}  ClassLabelDog: {:1d}".format(key,
+                      results_dic[key][0], results_dic[key][1], results_dic[key][3], 
+                      results_dic[key][4]))
+
+        # Prints all NOT-Matches next
+        print("\n NOT A MATCH:")
+        for key in results_dic:
+        
+            # Prints only if NOT-a-Match Index 2 == 0 
+            if results_dic[key][2] == 0:
+ 
+                # Increments Not-a-Match counter
+                n_notmatch += 1
+                print("\n{:>30}: \nReal: {:>26}   Classifier: {:>30}  \nPetLabelDog: {:1d}  ClassLabelDog: {:1d}".format(key,
+                      results_dic[key][0], results_dic[key][1], results_dic[key][3], 
+                      results_dic[key][4]))
+
+        # Prints Total Number of Images - expects 40 from pet_images folder
+        print("\n# Total Images",n_match + n_notmatch, "# Matches:",n_match ,
+              "# NOT Matches:",n_notmatch)
+
+
+
+def check_calculating_results(results_dic, results_stats_dic):
+    """    For Lab: Classifying Images - 14. Calculating Results
+    Prints First statistics from the results stats dictionary (that was created
+    by the calculates_results_stats() function), then prints the same statistics
+    that were calculated in this function using the results dictionary.
+    Assumes you defined the results_stats dictionary and the statistics 
+    as was outlined in '14. Calculating Results '
+    Parameters:
+      results_dic - Dictionary with key as image filename and value as a List 
+             (index)idx 0 = pet image label (string)
+                    idx 1 = classifier label (string)
+                    idx 2 = 1/0 (int)   where 1 = match between pet image and 
+                    classifer labels and 0 = no match between labels
+                    idx 3 = 1/0 (int)  where 1 = pet image 'is-a' dog and 
+                            0 = pet Image 'is-NOT-a' dog. 
+                    idx 4 = 1/0 (int)  where 1 = Classifier classifies image 
+                            'as-a' dog and 0 = Classifier classifies image  
+                            'as-NOT-a' dog.
+     results_stats_dic - Dictionary that contains the results statistics (either
+                     a percentage or a count) where the key is the statistic's 
+                     name (starting with 'pct' for percentage or 'n' for count)
+                     and the value is the statistic's value 
+    Returns:
+     Nothing - just prints to console  
+
+    """
+    if results_stats_dic is None:
+        print("* Doesn't Check the Results Dictionary because 'calculates_results_stats' hasn't been defined.")
+    else:
+        # Code for checking results_stats_dic -
+        # Checks calculations of counts & percentages BY using results_dic
+        # to re-calculate the values and then compare to the values
+        # in results_stats_dic
+    
+        # Initialize counters to zero and number of images total
+        n_images = len(results_dic)
+        n_pet_dog = 0
+        n_class_cdog = 0
+        n_class_cnotd = 0
+        n_match_breed = 0
+    
+        # Interates through results_dic dictionary to recompute the statistics
+        # outside of the calculates_results_stats() function
+        for key in results_dic:
+
+            # match (if dog then breed match)
+            if results_dic[key][2] == 1:
+
+                # isa dog (pet label) & breed match
+                if results_dic[key][3] == 1:
+                    n_pet_dog += 1
+
+                    # isa dog (classifier label) & breed match
+                    if results_dic[key][4] == 1:
+                        n_class_cdog += 1
+                        n_match_breed += 1
+
+                # NOT dog (pet_label)
+                else:
+
+                    # NOT dog (classifier label)
+                    if results_dic[key][4] == 0:
+                        n_class_cnotd += 1
+
+            # NOT - match (not a breed match if a dog)
+            else:
+ 
+                # NOT - match
+                # isa dog (pet label) 
+                if results_dic[key][3] == 1:
+                    n_pet_dog += 1
+
+                    # isa dog (classifier label)
+                    if results_dic[key][4] == 1:
+                        n_class_cdog += 1
+
+                # NOT dog (pet_label)
+                else:
+
+                    # NOT dog (classifier label)
+                    if results_dic[key][4] == 0:
+                        n_class_cnotd += 1
+
+                    
+        # calculates statistics based upon counters from above
+        n_pet_notd = n_images - n_pet_dog
+        pct_corr_dog = ( n_class_cdog / n_pet_dog )*100
+        pct_corr_notdog = ( n_class_cnotd / n_pet_notd )*100
+        pct_corr_breed = ( n_match_breed / n_pet_dog )*100
+    
+        # prints calculated statistics
+        print("\n ** Statistics from calculates_results_stats() function:")
+        print("N Images: {:2d}  N Dog Images: {:2d}  N NotDog Images: {:2d} \nPct Corr dog: {:5.1f} Pct Corr NOTdog: {:5.1f}  Pct Corr Breed: {:5.1f}".format(
+              results_stats_dic['n_images'], results_stats_dic['n_dogs_img'],
+              results_stats_dic['n_notdogs_img'], results_stats_dic['pct_correct_dogs'],
+              results_stats_dic['pct_correct_notdogs'],
+              results_stats_dic['pct_correct_breed']))
+        print("\n ** Check Statistics - calculated from this function as a check:")
+        print("N Images: {:2d}  N Dog Images: {:2d}  N NotDog Images: {:2d} \nPct Corr dog: {:5.1f} Pct Corr NOTdog: {:5.1f}  Pct Corr Breed: {:5.1f}".format(
+              n_images, n_pet_dog, n_pet_notd, pct_corr_dog, pct_corr_notdog,
+              pct_corr_breed))

ai-programming-with-python/C02P8-pre-trained-image-classifier/print_results.py

@@ -0,0 +1,66 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+# */AIPND-revision/intropyproject-classify-pet-images/print_results.py
+#                                                                             
+# PROGRAMMER: 
+# DATE CREATED:
+# REVISED DATE: 
+# PURPOSE: Create a function print_results that prints the results statistics
+#          from the results statistics dictionary (results_stats_dic). It 
+#          should also allow the user to be able to print out cases of misclassified
+#          dogs and cases of misclassified breeds of dog using the Results 
+#          dictionary (results_dic).  
+#         This function inputs:
+#            -The results dictionary as results_dic within print_results 
+#             function and results for the function call within main.
+#            -The results statistics dictionary as results_stats_dic within 
+#             print_results function and results_stats for the function call within main.
+#            -The CNN model architecture as model wihtin print_results function
+#             and in_arg.arch for the function call within main. 
+#            -Prints Incorrectly Classified Dogs as print_incorrect_dogs within
+#             print_results function and set as either boolean value True or 
+#             False in the function call within main (defaults to False)
+#            -Prints Incorrectly Classified Breeds as print_incorrect_breed within
+#             print_results function and set as either boolean value True or 
+#             False in the function call within main (defaults to False)
+#         This function does not output anything other than printing a summary
+#         of the final results.
+##
+# TODO 6: Define print_results function below, specifically replace the None
+#       below by the function definition of the print_results function. 
+#       Notice that this function doesn't to return anything because it  
+#       prints a summary of the results using results_dic and results_stats_dic
+# 
+def print_results(results_dic, results_stats_dic, model, 
+                  print_incorrect_dogs = False, print_incorrect_breed = False):
+    """
+    Prints summary results on the classification and then prints incorrectly 
+    classified dogs and incorrectly classified dog breeds if user indicates 
+    they want those printouts (use non-default values)
+    Parameters:
+      results_dic - Dictionary with key as image filename and value as a List 
+             (index)idx 0 = pet image label (string)
+                    idx 1 = classifier label (string)
+                    idx 2 = 1/0 (int)  where 1 = match between pet image and 
+                            classifer labels and 0 = no match between labels
+                    idx 3 = 1/0 (int)  where 1 = pet image 'is-a' dog and 
+                            0 = pet Image 'is-NOT-a' dog. 
+                    idx 4 = 1/0 (int)  where 1 = Classifier classifies image 
+                            'as-a' dog and 0 = Classifier classifies image  
+                            'as-NOT-a' dog.
+      results_stats_dic - Dictionary that contains the results statistics (either
+                   a  percentage or a count) where the key is the statistic's 
+                     name (starting with 'pct' for percentage or 'n' for count)
+                     and the value is the statistic's value 
+      model - Indicates which CNN model architecture will be used by the 
+              classifier function to classify the pet images,
+              values must be either: resnet alexnet vgg (string)
+      print_incorrect_dogs - True prints incorrectly classified dog images and 
+                             False doesn't print anything(default) (bool)  
+      print_incorrect_breed - True prints incorrectly classified dog breeds and 
+                              False doesn't print anything(default) (bool) 
+    Returns:
+           None - simply printing results.
+    """    
+    None
+                

ai-programming-with-python/C02P8-pre-trained-image-classifier/print_results_hints.py

@@ -0,0 +1,141 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+# */AIPND-revision/intropyproject-classify-pet-images/print_results_hints.py
+#                                                                             
+# PROGRAMMER: 
+# DATE CREATED:
+# REVISED DATE: 
+# PURPOSE: This is a *hints* file to help guide students in creating the 
+#          function print_results that prints the results statistics from the
+#          results statistics dictionary (results_stats_dic). It should also
+#          allow the user to be able to print out cases of misclassified
+#          dogs and cases of misclassified breeds of dog using the Results 
+#          dictionary (results_dic).  
+#         This function inputs:
+#            -The results dictionary as results_dic within print_results 
+#             function and results for the function call within main.
+#            -The results statistics dictionary as results_stats_dic within 
+#             print_results function and results_stats for the function call within main.
+#            -The CNN model architecture as model wihtin print_results function
+#             and in_arg.arch for the function call within main. 
+#            -Prints Incorrectly Classified Dogs as print_incorrect_dogs within
+#             print_results function and set as either boolean value True or 
+#             False in the function call within main (defaults to False)
+#            -Prints Incorrectly Classified Breeds as print_incorrect_breed within
+#             print_results function and set as either boolean value True or 
+#             False in the function call within main (defaults to False)
+#         This function does not output anything other than printing a summary
+#         of the final results.
+##
+# TODO 6: EDIT and ADD code BELOW to do the following that's stated in the 
+#       comments below that start with "TODO: 6" for the print_results function.
+#       Specifically edit and add code below within the the print_results function. 
+#       Notice that this function doesn't return anything because it prints 
+#       a summary of the results using results_dic and results_stats_dic
+# 
+def print_results(results_dic, results_stats_dic, model, 
+                  print_incorrect_dogs = False, print_incorrect_breed = False):
+    """
+    Prints summary results on the classification and then prints incorrectly 
+    classified dogs and incorrectly classified dog breeds if user indicates 
+    they want those printouts (use non-default values)
+    Parameters:
+      results_dic - Dictionary with key as image filename and value as a List 
+             (index)idx 0 = pet image label (string)
+                    idx 1 = classifier label (string)
+                    idx 2 = 1/0 (int)  where 1 = match between pet image and 
+                            classifer labels and 0 = no match between labels
+                    idx 3 = 1/0 (int)  where 1 = pet image 'is-a' dog and 
+                            0 = pet Image 'is-NOT-a' dog. 
+                    idx 4 = 1/0 (int)  where 1 = Classifier classifies image 
+                            'as-a' dog and 0 = Classifier classifies image  
+                            'as-NOT-a' dog.
+      results_stats_dic - Dictionary that contains the results statistics (either
+                   a  percentage or a count) where the key is the statistic's 
+                     name (starting with 'pct' for percentage or 'n' for count)
+                     and the value is the statistic's value 
+      model - Indicates which CNN model architecture will be used by the 
+              classifier function to classify the pet images,
+              values must be either: resnet alexnet vgg (string)
+      print_incorrect_dogs - True prints incorrectly classified dog images and 
+                             False doesn't print anything(default) (bool)  
+      print_incorrect_breed - True prints incorrectly classified dog breeds and 
+                              False doesn't print anything(default) (bool) 
+    Returns:
+           None - simply printing results.
+    """   
+    # Prints summary statistics over the run
+    print("\n\n*** Results Summary for CNN Model Architecture",model.upper(), 
+          "***")
+    print("{:20}: {:3d}".format('N Images', results_stats_dic['n_images']))
+    print("{:20}: {:3d}".format('N Dog Images', results_stats_dic['n_dogs_img']))
+
+    # TODO: 6a. REPLACE print("") with CODE that prints the text string 
+    #          'N Not-Dog Images' and then the number of NOT-dog images 
+    #          that's accessed by key 'n_notdogs_img' using dictionary 
+    #          results_stats_dic
+    #
+    print("")
+
+
+    # Prints summary statistics (percentages) on Model Run
+    print(" ")
+    for key in results_stats_dic:
+        # TODO: 6b. REPLACE pass with CODE that prints out all the percentages 
+        #           in the results_stats_dic dictionary. Recall that all 
+        #           percentages in results_stats_dic have 'keys' that start with 
+        #           the letter p. You will need to write a conditional 
+        #           statement that determines if the key starts with the letter
+        #           'p' and then you want to use a print statement to print 
+        #           both the key and the value. Remember the value is accessed 
+        #           by results_stats_dic[key]
+        #
+        pass 
+
+
+    # IF print_incorrect_dogs == True AND there were images incorrectly 
+    # classified as dogs or vice versa - print out these cases
+    if (print_incorrect_dogs and 
+        ( (results_stats_dic['n_correct_dogs'] + results_stats_dic['n_correct_notdogs'])
+          != results_stats_dic['n_images'] ) 
+       ):
+        print("\nINCORRECT Dog/NOT Dog Assignments:")
+
+        # process through results dict, printing incorrectly classified dogs
+        for key in results_dic:
+
+            # TODO: 6c. REPLACE pass with CODE that prints out the pet label 
+            #           and the classifier label from results_dic dictionary    
+            #           ONLY when the classifier function (classifier label) 
+            #           misclassified dogs specifically: 
+            #             pet label is-a-dog and classifier label is-NOT-a-dog 
+            #               -OR- 
+            #             pet label is-NOT-a-dog and classifier label is-a-dog 
+            #          You will need to write a conditional statement that 
+            #          determines if the classifier function misclassified dogs
+            #          See 'Adjusting Results Dictionary' section in 
+            #         'Classifying Labels as Dogs' for details on the 
+            #          format of the results_dic dictionary. Remember the value
+            #          is accessed by results_dic[key] and the value is a list
+            #          so results_dic[key][idx] - where idx represents the 
+            #          index value of the list and can have values 0-4.
+            #
+            # Pet Image Label is a Dog - Classified as NOT-A-DOG -OR- 
+            # Pet Image Label is NOT-a-Dog - Classified as a-DOG
+            pass
+
+    # IF print_incorrect_breed == True AND there were dogs whose breeds 
+    # were incorrectly classified - print out these cases                    
+    if (print_incorrect_breed and 
+        (results_stats_dic['n_correct_dogs'] != results_stats_dic['n_correct_breed']) 
+       ):
+        print("\nINCORRECT Dog Breed Assignment:")
+
+        # process through results dict, printing incorrectly classified breeds
+        for key in results_dic:
+
+            # Pet Image Label is-a-Dog, classified as-a-dog but is WRONG breed
+            if ( sum(results_dic[key][3:]) == 2 and
+                results_dic[key][2] == 0 ):
+                print("Real: {:>26}   Classifier: {:>30}".format(results_dic[key][0],
+                                                          results_dic[key][1]))

ai-programming-with-python/C02P8-pre-trained-image-classifier/pyproject.toml

@@ -0,0 +1,10 @@
+[project]
+name = "08-pre-trained-image-classification"
+version = "0.1.0"
+description = "Add your description here"
+requires-python = ">=3.12"
+dependencies = [
+    "pillow>=12.0.0",
+    "torch>=2.9.1",
+    "torchvision>=0.24.1",
+]

ai-programming-with-python/C02P8-pre-trained-image-classifier/run_models_batch.sh

@@ -0,0 +1,14 @@
+#!/bin/sh
+# */AIPND-revision/intropyproject-classify-pet-images/run_models_batch.sh
+#                                                                             
+# PROGRAMMER: Jennifer S.
+# DATE CREATED: 02/08/2018                                  
+# REVISED DATE: 02/27/2018  - 
+# PURPOSE: Runs all three models to test which provides 'best' solution.
+#          Please note output from each run has been piped into a text file.
+#
+# Usage: sh run_models_batch.sh    -- will run program from commandline within Project Workspace
+#  
+python check_images.py --dir pet_images/ --arch resnet  --dogfile dognames.txt > resnet_pet-images.txt
+python check_images.py --dir pet_images/ --arch alexnet --dogfile dognames.txt > alexnet_pet-images.txt
+python check_images.py --dir pet_images/ --arch vgg  --dogfile dognames.txt > vgg_pet-images.txt

ai-programming-with-python/C02P8-pre-trained-image-classifier/run_models_batch_uploaded.sh

@@ -0,0 +1,14 @@
+#!/bin/sh
+# */AIPND-revision/intropyproject-classify-pet-images/run_models_batch_uploaded.sh
+#                                                                             
+# PROGRAMMER: Jennifer S.
+# DATE CREATED: 02/08/2018                                  
+# REVISED DATE: 02/27/2018  - 
+# PURPOSE: Runs all three models to test which provides 'best' solution on the Uploaded Images.
+#          Please note output from each run has been piped into a text file.
+#
+# Usage: sh run_models_batch_uploaded.sh    -- will run program from commandline within Project Workspace
+#  
+python check_images.py --dir uploaded_images/ --arch resnet  --dogfile dognames.txt > resnet_uploaded-images.txt
+python check_images.py --dir uploaded_images/ --arch alexnet --dogfile dognames.txt > alexnet_uploaded-images.txt
+python check_images.py --dir uploaded_images/ --arch vgg  --dogfile dognames.txt > vgg_uploaded-images.txt

ai-programming-with-python/C02P8-pre-trained-image-classifier/test_classifier.py

@@ -0,0 +1,36 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+# */AIPND/intropylab-classifying-images/test_classifier.py
+#                                                                             
+# PROGRAMMER: Jennifer S.                                                    
+# DATE CREATED: 01/30/2018                                  
+# REVISED DATE:             <=(Date Revised - if any)                         
+# PURPOSE: To demonstrate the proper usage of the classifier() function that 
+#          is defined in classifier.py This function uses CNN model 
+#          architecture that has been pretrained on the ImageNet data to 
+#          classify images. The only model architectures that this function 
+#          will accept are: 'resnet', 'alexnet', and 'vgg'. See the example
+#          usage below.
+#
+# Usage: python test_classifier.py    -- will run program from commandline
+
+# Imports classifier function for using pretrained CNN to classify images 
+from classifier import classifier 
+
+# Defines a dog test image from pet_images folder
+test_image="pet_images/Collie_03797.jpg"
+
+# Defines a model architecture to be used for classification
+# NOTE: this function only works for model architectures: 
+#      'vgg', 'alexnet', 'resnet'  
+model = "vgg"
+
+# Demonstrates classifier() functions usage
+# NOTE: image_classication is a text string - It contains mixed case(both lower
+# and upper case letter) image labels that can be separated by commas when a 
+# label has more than one word that can describe it.
+image_classification = classifier(test_image, model)
+
+# prints result from running classifier() function
+print("\nResults from test_classifier.py\nImage:", test_image, "using model:",
+      model, "was classified as a:", image_classification)

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