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This repo use transfer learning to build an image classifier for flowers

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Name		Name	Last commit message	Last commit date
Latest commit History 2 Commits
__pycache__		__pycache__
flowers		flowers
README.md		README.md
cat_to_name.json		cat_to_name.json
model_utils.py		model_utils.py
predict.py		predict.py
predict_utils.py		predict_utils.py
test.py		test.py
train.py		train.py

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Motivation

Command line application to train & predict with a classifier for flowers species

Files

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

Predict flower name from an image with predict.py along with the probability of that name. That is, 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 KK most likely classes: python predict.py input checkpoint --top_3
- Use a mapping of categories to real names: python predict.py input checkpoint --category_names cat_to_nameson
- Use GPU for inference: python predict.py input checkpoint --gpu