FashionMNIST

Using two Deep learning models to Classify the image from Fashion-MNIST dataset

• Basic Model (fully connected layer model)

• Convolutional neural networks (ConvNets)

All model will use the Keras framework with R implementation

Fashion-MNIST Dataset

60000 images for training and 10000 images for testing Each example is a 28x28 gray-scale image, associated with a label from 10 classes 0 T-shirt/top,1 Trouser, 2 Pullover, 3 Dress, 4 Coat, 5 Sandal,6 Shirt, 7 Sneaker, 8 Bag ,9 Ankle boot 

The dataset is CSV format. The detailed format is label, pixel1, pixel2, pixel3, ... pixel784. Each image is 28 pixels in height and 28 pixels in width, for a total of 784 pixels in total

Define the Problem and Assembling Data Set

Problem:

• Train the 60000 images and test 10000 images to classify the image’s label

Problem Type:

• Multi-Class and single-label classification

Model Configuration:

• the softmax as the last-layer’s activation and the loss function will use the categorical_crossentropy. Hyper-parameters setting : use the dropout and L2 regularization to reduce over-fitting effects.

Assembling Data Set

Using the dataset_fashion_mnist() function from Keras to download the dataset (simple model) Downloaded from the Kaggle.com, then use the read_csv() to manipulate the data (ConvNets Model)

Simple NN Model

Result: Simple deep learning model achieves an accuracy of 88.11% and loss of 34.15%.

Convolutional Neural Networks Model

Data Preparation

In the ConvNets model, we will use the original CSV data and prepare the 4D Tensors image data format. Data downloaded from https://www.kaggle.com/zalando-research/fashionmnist

TrainSetData <- read_csv("fashion-mnist_train.csv”, col_types = cols(.default = "i"))�TestSetData <- read_csv("fashion-mnist_test.csv”, col_types = cols(.default = "i"))

• Unflattening, Reshaping and Normalization the data
• Plot the images as examples
• Function to plot image from a matrix x

plot_image <- function(x, title = "", title.color = "black") 
{dim(x) <- c(ImgRows, ImgCols) 
image(rotate(rotate(x)), axes = FALSE, col = grey(seq(0, 1, length = 256)),
main = list(title, col = title.color))}

Model

• ConvNets Model the stack of layer_conv_2d and layer_max_pooling_2d layers.
  A convent takes as input tensors of (image_height, image_width, image_channel). In this case, the input for the first layer’s size is input_shape = (28,28,1) Batch size is 256, epochs is 40, kernel size first is (5,5) then (3,3), dropout rate is 0.25/0.25/0.4/0.3

model <- keras_model_sequential()�model %>%�  layer_conv_2d(filters = 32, kernel_size = c(5,5), activation = 'relu',             input_shape = input_shape) %>% layer_max_pooling_2d(pool_size = c(2, 2)) %>% layer_dropout(rate = 0.25) %>% layer_conv_2d(filters = 64, kernel_size = c(3,3), activation = 'relu') %>% layer_max_pooling_2d(pool_size = c(2, 2)) %>% layer_dropout(rate = 0.25) %>% layer_conv_2d(filters = 128, kernel_size = c(3,3), activation = 'relu') %>% layer_dropout(rate = 0.4) %>%layer_flatten() %>% layer_dense(units = 128, activation = 'relu') %>% layer_dropout(rate = 0.3) %>% layer_dense(units = num_classes, activation = 'softmax')�

ConvNets model achieves an accuracy of 91.85% and loss is 20%, up from the previous model’s accuracy of 88.11% and loss of 34.15%.

Visualizing the Model Predictions

Lower layer acts as a collection of various edge detectors Higher layer carry increasingly less information about the visual contents of the image, and increasingly more information related to the class of the image

Visualizing the Model Predictions

for (i in 1:32) {n_row <- i * 10�  T_Tensor <- Train_X[n_row, , , 1] dim(T_Tensor) <- c(1, ImgRows, ImgCols, 1)�  pred <- model %>% predict(T_Tensor) plot_image(Train_X[n_row, , , 1],        Fashion_Labels[which.max(pred)],           "red")}

Summary

Our ConvNets model achieved an accuracy of 91.85%. It turns out our classifier does better than the Kaggle’s best baseline reported here, which is an SVM classifier with mean accuracy of 89.7%. Comparing the simple model, ConvNets is the best model for the attacking the image classification problems.
Tuning the model and hyper-parameters is very important to improve the accuracy.