Flower-image_Classifier

Project code for Udacity's AI Programming with Python Nanodegree program. In this project, first develop code for an image classifier built with PyTorch, then convert it into a command line application. This project was established by Udacity and performed within Udacity's GPU enabled workspace.

Project Breakdown

Part1

In this first part of the project, implement an image classifier with PyTorch, train it to recognize different species of flowers.

The project is broken down into multiple steps:

• Load and preprocess the image dataset
  • import the data while applying proper transforms and segmenting them into respective training, validation, and testing datasets
• Building and training the classifier
  • Load a pre-trained network
  • Define a new, untrained feed-forward network as a classifier, using ReLU activations and dropout
  • Train the classifier layers using backpropagation using the pre-trained network to get the features
  • Track the loss and accuracy on the validation set to determine the best hyperparameters
• Use the trained classifier to predict image content
• Save Checkpoint

Part2

convert image classifier into a command line application.

1. Train: Train a new network on a data set with train.py
   • Basic usage: python train.py data_directory
   • Prints out training loss, validation loss, and validation accuracy as the network trains
   • Options:
     • Set directory to save checkpoints: python train.py data_dir --save_dir save_directory
     • Choose architecture: python train.py data_dir --arch "vgg13"
     • Set hyperparameters: python train.py data_dir --learning_rate 0.01 --hidden_units 512 --epochs 20
     • Use GPU for training: python train.py data_dir --gpu True
2. Predict: Predict flower name from an image with predict.py along with the probability of that name. That is, you'll pass in a single image /path/to/image and return the flower name and class probability.
   • Basic usage: python predict.py /path/to/image checkpoint
   • Options:
     • Return top K most likely classes: python predict.py input checkpoint --top_k 3
     • Use a mapping of categories to real names: python predict.py input checkpoint --category_names cat_to_name.json
     • Use GPU for inference: python predict.py input checkpoint --gpu True