├── .gitignore ├── CNN-Model.R ├── Image-Classifiaction.Rproj ├── Image-Classification ├── LICENSE └── README.md /.gitignore: -------------------------------------------------------------------------------- 1 | .Rproj.user 2 | .Rhistory 3 | .RData 4 | .Ruserdata 5 | -------------------------------------------------------------------------------- /CNN-Model.R: -------------------------------------------------------------------------------- 1 | #Image Classification on Cifar 10 2 | 3 | 4 | library(keras) 5 | 6 | cifar<-dataset_cifar10() 7 | 8 | 9 | #Training Data 10 | train_x<-cifar$train$x / 255 11 | train_y<-to_categorical(cifar$train$y,num_classes = 10) 12 | 13 | #Test Data 14 | test_x<-cifar$test$x/255 15 | test_y<-to_categorical(cifar$test$y,num_classes = 10) 16 | 17 | 18 | #checking the dimentions 19 | dim(train_x) 20 | 21 | cat("No of training samples\t--",dim(train_x)[[1]] , 22 | "\tNo of test samples\t--",dim(test_x)[[1]]) 23 | 24 | 25 | #Defining the Model 26 | 27 | model<-keras_model_sequential() 28 | 29 | 30 | #Configuring the Model 31 | model %>% 32 | layer_conv_2d(filter=48,kernel_size=c(3,3),padding="same", 33 | input_shape=c(32,32,3)) %>% 34 | layer_activation("relu") %>% 35 | layer_conv_2d(filter=48,kernel_size=c(3,3)) %>% 36 | layer_activation("relu") %>% 37 | layer_max_pooling_2d(pool_size=c(2,2)) %>% 38 | layer_dropout(0.25) %>% 39 | 40 | layer_conv_2d(filter=48 , kernel_size=c(3,3),padding="same") %>% 41 | layer_activation("relu") %>% 42 | layer_conv_2d(filter=48,kernel_size=c(3,3) ) %>% 43 | layer_activation("relu") %>% 44 | layer_max_pooling_2d(pool_size=c(2,2)) %>% 45 | layer_dropout(0.25) %>% 46 | 47 | #flatten the input 48 | layer_flatten() %>% 49 | layer_dense(512) %>% 50 | layer_activation("relu") %>% 51 | layer_dropout(0.5) %>% 52 | #output layer-10 classes-10 units 53 | layer_dense(10) %>% 54 | #applying softmax nonlinear activation function to the output layer to calculate 55 | #cross-entropy 56 | layer_activation("softmax") #for computing Probabilities of classes-"logit(log probabilities) 57 | 58 | 59 | #Optimizer -rmsProp to do parameter updates 60 | opt <- optimizer_rmsprop(lr = 0.0001, decay = 1e-6) 61 | 62 | #Compiling the Model 63 | model %>% compile( 64 | loss = "categorical_crossentropy", 65 | optimizer = opt, 66 | metrics = "accuracy" 67 | ) 68 | 69 | #Summary of the Model and its Architecture 70 | summary(model) 71 | 72 | 73 | 74 | #TRAINING PROCESS OF THE MODEL 75 | data_augmentation <- TRUE 76 | 77 | 78 | if(!data_augmentation) { 79 | model %>% fit( 80 | train_x,train_y ,batch_size=32,epochs=5, 81 | validation_data = list(test_x, test_y), 82 | shuffle=TRUE 83 | ) 84 | 85 | } else { 86 | #Generating images 87 | datagen <- image_data_generator( 88 | featurewise_center = TRUE, 89 | featurewise_std_normalization = TRUE, 90 | rotation_range = 20, 91 | width_shift_range = 0.30, 92 | height_shift_range = 0.30, 93 | horizontal_flip = TRUE 94 | ) 95 | #Fit image data generator internal statistics to some sample data 96 | 97 | datagen %>% fit_image_data_generator(train_x) 98 | #Generates batches of augmented/normalized data from image data and labels 99 | model %>% fit_generator( 100 | flow_images_from_data(train_x, train_y, datagen, batch_size = 32, 101 | save_to_dir="F:/PROJECTS/CNNcifarimages/"), 102 | steps_per_epoch=as.integer(50000/32), #no of training samples/batch size 103 | epochs = 5, 104 | validation_data = list(test_x, test_y) 105 | 106 | 107 | ) 108 | 109 | 110 | } 111 | 112 | #after training 113 | #loss: 1.5014 - acc: 0.4529 - val_loss: 2.7578 - val_acc: 0.1665 for 5 epochs( ie iterating 5 times over dataset) 114 | 115 | 116 | 117 | 118 | #Model to yaml 119 | 120 | yaml<-model_to_yaml(model) 121 | class(yaml) 122 | 123 | #saving to JSON 124 | json<-model_to_json(model) 125 | json 126 | 127 | #saving the Model's architecture 128 | save_model_hdf5(model,filepath = "F:/PROJECTS/Image-Classification", overwrite = TRUE, 129 | include_optimizer = TRUE) 130 | 131 | load_model_hdf5(filepath = "F:/PROJECTS/Image-Classification",compile = T) 132 | 133 | 134 | -------------------------------------------------------------------------------- /Image-Classifiaction.Rproj: -------------------------------------------------------------------------------- 1 | Version: 1.0 2 | 3 | RestoreWorkspace: Default 4 | SaveWorkspace: Default 5 | AlwaysSaveHistory: Default 6 | 7 | EnableCodeIndexing: Yes 8 | UseSpacesForTab: Yes 9 | NumSpacesForTab: 2 10 | Encoding: UTF-8 11 | 12 | RnwWeave: Sweave 13 | LaTeX: pdfLaTeX 14 | -------------------------------------------------------------------------------- /Image-Classification: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/anishsingh20/Image-Classifiaction-using-CNN-and-keras-in-R/d038cd8e459a82ac3a296beccecc171f4d722ed6/Image-Classification -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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For regularization, we also apply the usual weight decay losses to all learned variables. 8 | 9 | The softmax function is a generalization of __logistic(sigmoid)__ function which is used to compute the probabilites of the real valued predictions at the end of fully connected dense layer and then use those probability values to compute loss(error) values. Below is the softmax activation function. 10 | 11 |     ![github logo](https://wikimedia.org/api/rest_v1/media/math/render/svg/46c32a5089726d673c30a0abfda7b35ecf0fe3ca) 12 | 13 | The objective function for the model is the sum of the cross entropy loss and all these weight decay terms, as 14 | returned by the loss() function. 15 | 16 | ## Dependencies 17 | __devtools::install_github('rstudio/keras')__ 18 | 19 | 20 | __library(reticulate) #interface for Python in R__ 21 | 22 | __library(tensorflow) #For all computations running in the backend on CPU__ 23 | 24 | __library(keras)__ 25 | 26 | 27 | ## Model Architecture 28 | 29 | 30 | #### Overview 31 | *CIFAR-10* classification is a common benchmark problem in machine learning. The problem is to classify RGB 32x32 pixel images across 10 categories 32 | 33 | 34 | Model's Architecture-- 35 | 36 | 37 | 38 | Layer | Description 39 | ------------ | ------------- 40 | Conv2D-1 | A 2-D Convolution Layer with ReLu activation 41 | Conv2D-1 | A 2-D Convolution Layer with ReLu activation 42 | Pool-1 | Max pooling layer 43 | Conv2D-2 | A 2-D Convolution Layer with ReLu activation 44 | Conv2D-2 | A 2-D Convolution Layer with ReLu activation 45 | Pool-2 | Max pooling layer 46 | Local-1 | Fully Connected layer with ReLu activation and 512 units 47 | Output-1| Output layer with 10 Units(each for a class) 48 | Softmax_activation| Non-Linear transformation to the outputs to compute Probabilities 49 | 50 | 51 | 52 | 53 | # Model's Output 54 | 55 | ![GitHub Logo](https://thkimorgblog.files.wordpress.com/2016/03/e18489e185b3e1848fe185b3e18485e185b5e186abe18489e185a3e186ba-2016-03-12-e1848be185a9e1848ce185a5e186ab-1-02-16.png?w=764) 56 | 57 | 58 | 59 | 60 | 61 | ### Plot of Epochs(no of iterations over the Training dataset) vs Accuracy Of the Model 62 | 63 | 64 | ![GitHub Logo](http://imagine.enpc.fr/~zagoruys/cifar.png) 65 | 66 | --------------------------------------------------------------------------------