Deep Learning with Tensorflow
- This repo implements each step in building deep learning model from scratch using python & Tensorflow.
- Most tutorials/blogs/implementations import datasets from APIs like tensorflow/keras etc. We won't, instead we will make our data loader.
- We will not use any high level APIs like keras or tf.keras etc. We will stick to basic tensorflow
- Fashion-MNIST is a dataset of 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 28x28 grayscale image, associated with a label from 10 classes.
- Fashion-MNIST is a direct drop-in replacement for the original MNIST digit dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits.
- MNIST is too easy
- MNIST is overused
- MNIST can not represent modern CV tasks (like Batchnorm)
Deep Learning heros like Ian Goodfellow & Francois Chollet have advised serious researchers to stay away from digit recognition MNIST.
I have downloaded the .gz files of train and test data along with labels from https://github.com/zalandoresearch/fashion-mnist#get-the-data
Each training and test example is assigned to one of the following labels:
Label Description
- 0 T-shirt/top
- 1 Trouser
- 2 Pullover
- 3 Dress
- 4 Coat
- 5 Sandal
- 6 Shirt
- 7 Sneaker
- 8 Bag
- 9 Ankle boot
You can see the sample images in Fashion_MNIST_samples.png.
I have written couple of blog-posts illustrating this repository explaining tensorflow and neural network models.