Deep Learning Project to classify Images using PyTorch, Neural nets, GD, and transfer learning with GPU
Contents:
- Introduce, load and preprocess image dataset
- Building and training the classifier
- Function to build a Neural Network and use pretrained model (e.g. vgg16, densenet121).
- Batch Gradient Descent to optimize classifier using GPU Power
- Testing the trained network, saving and loading checkpoints
- Inference for classification
- Function to predict class of an Image
- Sanity checking
- Develop an AI application which can be used from the command line
Function 1: train.py
- Trains a neural network classifier.
Function 2: predict.py
- Predicts likelihood that Image belongs to a certain category trained with train.py
- tm_fun.py: some helper functions for part 2
- cat_to_name.json: Consists of the mapping of categories to flower names
- workspace-utils.py: Keeps workspace awake if GPU takes longer...