Fassion_MNIST_Classification

This repository consists of different classification models trained on the Fassion Mnist data set.

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ABOUT THE DATA SET

The images and labels used to train all the models in the repositary was loaded directly from KERAS using the below command

# To load data
(x_train,y_train),(x_test,y_test) = tf.keras.datasets.fassion_mnist.load_data()

The train data has 60000 samples and the test data has 10000 samples which sums up to a total of 70000 samples. Each image has a resolution of 28X28 which gives us 784 pixels when flattened.

There are a total number of 10 classes of images and every image has its label.

CLASSIFICATION MODELS

Four machine_learning models and three deep_learning models were trained out of which the simple artificial nural network named ANN1 gave the best results. Below are the resuls of different models.

MODEL_NAME	ACCURACY	CODE	To view confusion matrix
ANN1	0.9758999943733215	🔗	-
CNN2	0.9008	🔗	-
CNN1	0.8882	🔗	-
Random_Forest	0.88	🔗	🔗
K_Nearest_Neighbor	0.86	🔗	🔗
Support_Vector_Machines	0.8402	🔗	🔗

ANN1 the model with the most accuracy

The ANN1 model is a simple ANN with only one hidden layer which uses RELU as its activation function and the hidden layer uses SOFTMAX as this is a classification problem. the model takes in a input of 784 pixels and gives a output of 10 probabilities threfore the hidden layer follows the rule {(2/3 * input) + output} which gives 532 outputs from the hidden layer. The model gives a surprisingly good accuracy of 97% in the test data.