In my previous article, I had given the implementation of a Simple Linear Regression in both TensorFlow and PyTorch frameworks and compared their results. In this article, we shall go through the application of a Convolutional Neural Network (CNN) on a very famous Fashion MNIST dataset using both the frameworks and compare the results.

Origin —

Let us get a brief idea on both the frameworks and their history. Firstly, PyTorch is an open source machine learning library based on the Torch library. PyTorch was primarily developed by _Facebook’_s AI Research lab (FAIR). It is a free and open-source software.

On the other hand, TensorFlow was developed by the Google Brain team for internal Google research purpose. It is widely used for machine learning applications such as neural networks. It is also free and open-source software.

In order to see which framework is more efficient and simpler to use, we shall build a ConvNet using both the frameworks and compare them. For the TensorFlow, we shall be using the _Keras _library.

Problem —

For comparison of both the frameworks, we will use the famous Fashion-MNIST dataset. It is a datasetof Zalando’s article images consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28×28 grayscale image, associated with a label from 10 classes such as shirt, bag, sandal, coat, sneaker etc. We will build a CNN network with a common architecture of LeNet with both the frameworks and compare their results.

#mls #artificial-intelligence #machine-learning #deep-learning