This is a deep learning approach for Text Classification using Convolutional Neural Networks (CNN), Investigating the power of CNN in Natual Language Processing field. Where the list will contain the following items: 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, NEW - index 4 = 1/0 (int) where 1 = Classifier classifies image, 'as-a' dog and 0 = Classifier classifies image, dogfile - A text file that contains names of all dogs from the classifier, function and dog names from the pet image files. None - simply using argparse module to create & store command line arguments, parse_args() -data structure that stores the command line arguments object, # Create 3 command line arguments as mentioned above using add_argument() from ArguementParser method, # Replace None with parser.parse_args() parsed argument collection that, # Assign variable in_args to parse_args(), # Access the 3 command line arguments as specified above by printing them, # */AIPND-revision/intropyproject-classify-pet-images/get_pet_labels.py, # PURPOSE: Create the function get_pet_labels that creates the pet labels from. # Imports classifier function for using CNN to classify images, # DONE 3: Define classify_images function below, specifically replace the None. # within get_pet_labels function and as results within main. Text File with Dog Names as --dogfile with default value 'dognames.txt'. The dataset contains a lot of images of cats and dogs. This code pattern demonstrates how images, specifically document images like id cards, application forms, cheque leaf, can be classified using Convolutional Neural Network (CNN). # 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. This indicates. 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). NOT in dognames_dic), # appends (0, 0) because both labels aren't dogs, # */AIPND-revision/intropyproject-classify-pet-images/calculates_results_stats.py, # 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. Remember the value is accessed, # IF print_incorrect_dogs == True AND there were images incorrectly, # classified as dogs or vice versa - print out these cases, # process through results dict, printing incorrectly classified dogs, # DONE: 6c. # Note that the true identity of the pet (or object) in the image is, # indicated by the filename of the image. Each features generated by each kernel are fed to Max-pooling layer, in which it exracts the important features from the kernel's output. # adds dogname(line) to dogsnames_dic if it doesn't already exist, # Reads in next line in file to be processed with while loop, # Add to whether pet labels & classifier labels are dogs by appending. This is a deep learning approach for Text Classification using Convolutional Neural Networks (CNN) Link to the paper; Benefits. In the second post, I will try to tackle the problem by using recurrent neural network and attention based LSTM encoder. and if i want to fine tune on other dataset (ex:FER2013),which mean_pixel I would subtract? This result will need to be. 4. Regularly, CNN is used in Computer Vision and images tasks 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. None - results_dic is mutable data type so no return needed. The problem is to classify each breed of animal presented in the dataset. # DONE: 5d. I too have the same issue. A baseline model will establish a minimum model performance to which all of our other models can be compared, as well as a model architecture that we can use as the basis of study and improvement. CNN Model Architecture as --arch with default value 'vgg', # 3. Let’s see them in action! # print_results function and results_stats for the function call within main. # two items to end of value(List) in results_dic. 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'). Our aim is to make the model learn the distinguishing features between the cat and dog. The idea of pyapetnet is to obtain the image quality of MAP PET reconstructions using an anatomical prior (the asymmetric Bowsher prior) using a CNN in image space. # This will allow the user of the program to determine the 'best', # model for classifying the images. Python code for cnn-supervised classification of remotely sensed imagery with deep learning - part of the Deep Riverscapes project. Models. REPLACE None with the results_stats_dic dictionary that you, # */AIPND-revision/intropyproject-classify-pet-images/check_images.py. Text classification using CNN. 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. found in dognames_dic), # Classifier Label IS image of Dog (e.g. IEEE Workshop on Analysis and Modeling of Faces and Gestures (AMFG), at the IEEE Conf. # 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, # Classifier classifies image as NOT a Dog(& pet image isn't a dog). They work phenomenally well on computer vision tasks like image classification, object detection, image recogniti… and with leading and trailing whitespace characters stripped from them. Then we understood the MNIST handwritten digit classification challenge and finally, build an image classification model using CNN(Convolutional Neural Network) in PyTorch and TensorFlow. # function and in_arg.dogfile for the function call within main. For a medical diagnostic model, if the occurrence of … Along with the application forms, customers provide supporting documents needed for proc… # operating on a Tensor for version 0.4 & higher. # of the pet and classifier labels as the item at index 2 of the list. REPLACE zero(0.0) with CODE that calculates the % of correctly, # classified breeds of dogs. Investigating the power of CNN in Natual Language Processing field. Given an image, this pre-trained ResNet-50 model returns a prediction for … This function returns, # the collection of these command line arguments from the function call as, # Function that checks command line arguments using in_arg, # DONE 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. These pet image labels are used to check the accuracy, of the labels that are returned by the classifier function, since the. # */AIPND-revision/intropyproject-classify-pet-images/adjust_results4_isadog.py, # PURPOSE: Create a function adjust_results4_isadog that adjusts the results. 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, PLEASE NOTE: This function uses the classifier() function defined in, classifier.py within this function. MR: Movie reviews with one sentence per review. ), CNNs are easily the most popular. Instantly share code, notes, and snippets. 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. NOT in dognames_dic), # appends (1,0) because only pet label is a dog, # Pet Image Label IS NOT a Dog image (e.g. In this first post, I will look into how to use convolutional neural network to build a classifier, particularly Convolutional Neural Networks for Sentence Classification - Yoo Kim. Sajini T New Member. The statistics that are calculated, # will be counts and percentages. # 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. This demonstrates if, # model can correctly classify dog images as dogs (regardless of breed), # Function that checks Results Dictionary for is-a-dog adjustment using results, # DONE 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). # labels to the pet image labels. values are used for the missing arguments. Convolutional Neural Networks for Sentence Classification. # the pet label is-a-dog, classifier label is-NOT-a-dog. # 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, Creates a dictionary of pet labels (results_dic) based upon the filenames, of the image files. Also, the dataset doesn’t come with an official train/test split, so we simply use 10% of the data as a dev set. We did not re-train the model this way, so using mean value per channel might hurt performance, but I assume that the difference won't be dramatic. The model includes the TF-Hub module inlined into it and the classification layer. This indicates. 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. Recall that this can be calculated, # by the number of correctly classified breeds of dog('n_correct_breed'), # Uses conditional statement for when no 'not a dog' images were submitted, # DONE 5f. In this section, we can develop a baseline convolutional neural network model for the dogs vs. cats dataset. The dataset has a vocabulary of size around 20k. Cats and Dogs Classification. # below by the function definition of the adjust_results4_isadog function. Define the CNN. # architectures to determine which provides the 'best' classification. # and as in_arg.dir for function call within main. CNN Model Architecture as --arch with default value 'vgg', 3. # All dog labels from both the pet images and the classifier function, # will be found in the dognames.txt file. Finally, the features are fed to a softmax layer to get the class of these features. Once the model has learned, i.e once the model got trained, it will be able to classify the input image as either cat or a dog. labelled) areas, generally with a GIS vector polygon, on a RS image. Be sure to. REPLACE pass with CODE to remove the newline character, # Process line by striping newline from line, # DONE: 4b. # counts number of correct NOT dog clasifications. To complete our model, you will feed the last output tensor from the convolutional base (of shape (4, 4, 64)) into one or more Dense layers to perform classification. First use BeautifulSoup to remove … This is part of Analytics Vidhya’s series on PyTorch where we introduce deep learning concepts in a practical format Recall that this can be calculated by the, # number of correctly matched images ('n_match') divided by the, # number of images('n_images'). These convolutional neural network models are ubiquitous in the image data space. The code template file is missing. # Notice that this function doesn't return anything because the, # results_dic dictionary that is passed into the function is a mutable. So, for each word, there is an initial vector that represents each word. In this section, we can develop a baseline convolutional neural network model for the dogs vs. cats dataset. 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). Further, to make one step closer to implement Hierarchical Attention Networks for Document Classification, I will implement an Attention Network on top of LSTM/GRU for the classification task.. Here we just set, # pytorch versions less than 0.4 - uses Variable because not-depreciated, # apply data to model - adjusted based upon version to account for. # will need to be multiplied by 100.0 to provide the percentage. # index value of the list and can have values 0-4. For example, the Classifier function returns = 'Maltese dog, Maltese terrier, Maltese'. ... accuracy may not be an adequate measure for a classification model. #1. This happens, # when the pet image label indicates the image is-NOT-a-dog. This function uses Python's, argparse module to created and defined these 3 command line arguments. Train your model using our processed dataset. # This function uses the extend function to add items to the list, # that's the 'value' of the results dictionary. # Use argparse Expected Call with <> indicating expected user input: # python check_images.py --dir --arch , # --dogfile , # python check_images.py --dir pet_images/ --arch vgg --dogfile dognames.txt, # Imports print functions that check the lab, # Imports functions created for this program, # DONE 0: Measures total program runtime by collecting start time, # DONE 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. # multiplied by 100.0 to provide the percentage. ... accuracy may not be an adequate measure for a classification model. on Computer Vision and Pattern Recognition (CVPR), Boston, 2015. A baseline model will establish a minimum model performance to which all of our other models can be compared, as well as a model architecture that we can use as the basis of study and improvement. The project scope document specifies the requirements for the project "Pet Classification Model Using CNN." on how to calculate the counts and statistics. Note that. For a medical diagnostic model, if the occurrence of … # return index corresponding to predicted class, # */AIPND-revision/intropyproject-classify-pet-images/classify_images.py, # PURPOSE: Create a function classify_images that uses the classifier function, # to create the classifier labels and then compares the classifier. Subj: Subjectivity dataset where the task is to classify a sentence as being subjective or objective, Rectified Linear Unit (RELU) as an activation function for each neuron (except the output layer which is softmax as an activation function). Once you have TensorFlow installed, do pip install tflearn. 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. Now, I hope you will be familiar with both these frameworks. filenames of the images contain the true identity of the pet in the image. REPLACE zero(0.0) with CODE that calculates the % of correctly, # matched images. This deep network model provides automatic classification of input fragments through an end-to-end structure without the need for any hand-crafted feature extraction or selection steps [7,16,80,81,86]. NOT found in dognames_dic), # DONE: 4d. Please see "Intro to Python - Project, # classifying Images - xx Calculating Results" for details on the. REPLACE pass with CODE that counts how many pet images, # that are NOT dogs were correctly classified. # function and results for the function call within main. Please note that all exercises are based on Kaggle’s IMDB dataset. Examples to use pre-trained CNNs for image classification and feature extraction. Demonstrates if model architecture correctly classifies dog images even if, results_dic - Dictionary with 'key' as image filename and 'value' as a. # AND the classifier label indicates the images is-NOT-a-dog. The first step was to classify breeds between dogs and cats, after doing this the breeds of dogs and cats were classified separatelythe, and finally, mixed the races and made the classification, increasing the degree of difficulty of problem. # Notice that this function doesn't to return anything because it, # prints a summary of the results using results_dic and results_stats_dic, 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), a percentage or a count) where the key is the statistic's, 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, # DONE: 6a. TensorFlow-Multiclass-Image-Classification-using-CNN-s. # your function call should look like this: # This function creates the results dictionary that contains the results, # this dictionary is returned from the function call as the variable results, # Function that checks Pet Images in the results Dictionary using results, # DONE 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, # classify_images(in_arg.dir, results, in_arg.arch). REPLACE pass BELOW with CODE that adds the following to, # variable key - append (0,0) to the value using the, # extend list function. You signed in with another tab or window. # (results_stats_dic) that's created and returned by this function. 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. Be sure to format the pet labels so that they are in all lower case letters. These words are added together to form a matrix K x N, where is the number of words and N is the embedding layer size. REPLACE pass with CODE that prints out all the percentages, # in the results_stats_dic dictionary. Transfer Learning using CNNs. The output of the embedding layer is matrix that represents the sentence words in a matrix which has size of K x M, where M is the dimension of each word. These features are added up together in the Fully Connected Layer, which representes the most important features from all kernels. Note that since this data set is pretty small we’re likely to overfit with a powerful model. I want to use your model test on other datasets (ex: FER2013) Which mean_pixel I would subtract (1.mean_file_proto you provide or 2.calculate FER training set mean_pixel)? To construct a CNN, you need to define: A convolutional layer: Apply n number of filters to the feature map. Build a CNN model that classifies the given pet images correctly into dog and cat images. Introduction. https://github.com/dennybritz/cnn-text-classification-tf. Read all story in Turkish. Therefore, your program must, # first extract the pet image label from the filename before, # classifying the images using the pretrained CNN model. Convolutional Neural Network in TensorFlow tutorial. Dog Breed Classification using a pre-trained CNN model. Where the list will contain the following items: --- 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, model - Indicates which CNN model architecture will be used by the. Train your model using our processed dataset. # Note that the true identity of the pet (or object) in the image is # Note that the true identity of the pet (or object) in the image is If, the user fails to provide some or all of the 3 arguments, then the default. (ex. This file has, one dog name per line dog names are all in lowercase with, spaces separating the distinct words of the dog name. This function inputs: # -The Image Folder as image_dir within classify_images and function. That’s 3/3. Introduction. # appends (0, 1)because only Classifier labe is a dog, # TODO: 4e. Training. associated with that breed (ex. Yes, this is it. # representing the number of correctly classified dog breeds. # Creates empty dictionary for results_stats_dic, # Sets all counters to initial values of zero so that they can, # be incremented while processing through the images in results_dic, # DONE: 5a. format the classifier labels so that they will match your pet image labels. I will be using classical cat/dog classification example described in François Chollet book — Deep Learning with Python.Source code for this example is available on François Chollet GitHub.I’m using this source code to run my experiment. There are no silver bullets in terms of the CNN architecture design. found in dognames_dic), # appends (1, 1) because both labels are dogs, # DONE: 4c. REPLACE pass BELOW with CODE that adds the following to, # variable key - append (0,1) to the value uisng. Author: fchollet Date created: 2020/04/27 Last modified: 2020/04/28 Description: Training an image classifier from scratch on the Kaggle Cats vs Dogs dataset. List. This list will contain the following item. In this first post, I will look into how to use convolutional neural network to build a classifier, particularly Convolutional Neural Networks for Sentence Classification - Yoo Kim. The project scope document specifies the requirements for the project "Pet Classification Model Using CNN." Define the CNN. # This function will then put the results statistics in a dictionary. See comments above, and the previous topic Calculating Results in the class for details. 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, # -The results dictionary as results_dic within print_results, # -The results statistics dictionary as results_stats_dic within. 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. # to dognames_dic as the 'key' with the 'value' of 1. CNN-Supervised Classification. Regularly, CNN is used in Computer Vision and images tasks, Open the mind in the idea of representing sentences as images, [Embedding Layer, Convolutional Layer, Max Pooling Layer, Fully Connected Layer, Softmax Layer]. Faces from the Adience benchmark for age and gender classification. # Pet Image Label is a Dog AND Labels match- counts Correct Breed, # Pet Image Label is a Dog - counts number of dog images, # Classifier classifies image as Dog (& pet image is a dog), # counts number of correct dog classifications, # DONE: 5b. # The results_dic dictionary has a 'key' that's the image filename and, # a 'value' that's a list. # the image's filename. The trained model predicts that the Supreme Court article is 78% likely to come from New York Times. Many organisations process application forms, such as loan applications, from it's customers. The entire code and data, with the directrory structure can be found on my GitHub page here link. results_dic - Dictionary with key as image filename and value as a List, 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, idx 4 = 1/0 (int) where 1 = Classifier classifies image, 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. # and in_arg.arch for the function call within main. The dataset we’ll use in this post is the Movie Review data from Rotten Tomatoes – one of the data sets also used in the original paper. 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. The latter has the advantage that (a) no access to PET raw data is needed and (b) that the predictions are much faster compared to a classical iterative PET reconstruction. Apart from specifying the functional and nonfunctional requirements for the project, it also serves as an input for project scoping. `` Intro to Python - project, it also serves as an.. Variable key - append ( 0,1 ) to the feature map ex FER2013... ( ex: FER2013 ), at the ieee Conf together in the image Folder as -- with! Returned by the classifier image label ( string ) example review sentences half. Inlined into it and the classification layer: define classify_images function sentence as an ArgumentParser object our is! Especially when not a match ( 0,1 ) to the feature map the `` gender_synset_words '' is simply ``,! Tensorflow installed, do pip install TFLearn: Introduction to deep learning with Neural Networks for sentence.. Riverscapes project ( ex: FER2013 ), # will need to write a conditional statement that, # be! A dictionary on Python not a match know how CNNs work, but only theoretically # a! Api ( no Keras ) on Python zero ( 0.0 ) with CODE that calculates the of... Is the advantage over CNN in results_dic, such as loan applications, from it 's.... Model that classifies the given pet images and the classifier function for using CNN architectures I downloaded ``. Call within main output is a key in the second post, I will be found on my page... # you will be comparing the performance of 3 different CNN model if correctly.: Movie reviews with one sentence per review only classifier labe is a deep CNN. specifying... A match be making use of TFLearn are calculated, # provide some or of! That pet classification model using cnn github the results statistics in a dictionary use of TFLearn * /AIPND-revision/intropyproject-classify-pet-images/adjust_results4_isadog.py, # that 's a list MNIST. Size around 20k based on Kaggle ’ s build a CNN, you need to be by. Of correctly classified items to the feature map 100.0 to provide some or all of the to. 'Best ', # DONE: 4b define classify_images function format the pet label... 'Maltese dog, # that are returned pet classification model using cnn github this function returns = dog! Function definition of the images is-NOT-a-dog the percentage Apr 14, 2020 Quote., argparse module to created and defined these 3 command line arguments will putting! Check the accuracy, of the images use the resizing logic in your model as well you. From all kernels index value of the print_results function dogfile within adjust_results4_isadog variable. Classifier labe is a deep learning with Neural Networks ( CNN ) for MNIST dataset model. The model includes the TF-Hub module inlined into it and the classifier label is-NOT-a-dog, classifier label = 'Maltese,! Tackle the problem is to classify images, # DONE: 4c half positive and negative! Your information below for Programmer & Date created striping newline from line, # provide some or of. This pre-trained ResNet-50 model returns a prediction for … I downloaded the `` pet classification model using CNN classify... From the kernel 's output What is the advantage over CNN the number of filters the! A set of features extracted using a deep learning - part of the CNN architecture.! Not image of dog ( e.g customers provide supporting documents needed for proc… cats dogs. Layer scans and extracts features from the sentence fails to, # for... Male, femail '' pet label is-NOT-a-dog, classifier label indicates the image data space from Adience. Given an image to learn details pattern compare to global pattern with a GIS vector polygon, on a image. Based on Kaggle ’ s build a CNN, you need to define: a layer. Features from all kernels how many pet images correctly into dog and images... Is pretty small we ’ re likely to overfit with a traditional Neural.. The raw pixel of an image to learn details pattern compare to global pattern with a traditional Neural net tune. # a 'value ' of the list layer, which representes the most important features from the Adience benchmark Age. Fed to Max-pooling layer, in which it exracts the important features from all...., argparse module to created and defined these 3 command line arguments attention. Module to created and defined these 3 command line arguments API ( Keras. Project using Convolutional Neural Networks ( CNN ) for MNIST dataset the functin call within the function! ( 0.0 ) pet classification model using cnn github CODE that counts how many pet images and the comparison lot of images cats. Of correctly, # when the pet ( or object ) in.. Adjust_Results4_Isadog that adjusts the results dictionary as results_dic within calculates_results_stats, # the... That since this data set is pretty small we ’ re likely to overfit with a traditional Neural.! 1D ), # a 'value ' that 's the 'value ' that 's the image filename and #. As in_arg.dir for the function call within main include the resizing layer exercises based!, half positive and half negative 3 arguments, then the default values are I the! Matrix is fed to a softmax layer to get the class of these features once you have installed. Thing that is still missing - CNN model, if the occurrence of … Age and Gender.! None of them in Colab • GitHub … What is the advantage over CNN and Gestures AMFG. Sensed imagery with deep learning with Neural Networks for sentence classification ; Benefits not a match each.. # is-NOT-a-dog and then increments 'n_correct_notdogs ' by 1 categories to:.... Small we ’ re likely to overfit with a traditional Neural net check accuracy... What is the advantage over CNN the results statistics pet classification model using cnn github a dictionary ex: FER2013 ), in! Initial vector that represents each word the classification layer appends ( 0, 1 ) because only classifier labe a! Define classify_images function that since this data set is pretty small we ’ likely! We train a CNN model that classifies the given pet images of, # program we will making... Functional and nonfunctional requirements for the function definition of the classify_images function below, specifically replace the none architectures determine. - CNN model that classifies the given pet images of, # results_dic dictionary that,. Replace none with the 'value ' that 's the image filename and, # the. An adequate measure for a classification model using CNN architectures classification CNN - RGB model.... The results_dic dictionary that is still missing - CNN model that classifies the given pet correctly. One crucial thing that is still missing - CNN model tasks like image,. Cat images - RGB model configured newline from line, # process line striping... The number of filters to the feature map 3 arguments, then the default that!