In this project, I build a Python application that can train an image classifier on a dataset, then predict new images using the trained model.
Prints out training loss, validation loss, and validation accuracy as the network trains
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Basic usage:
python train.py data_directory -
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, you'll pass in a single image /path/to/image and return the flower name and class probability.
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Basic usage:
python predict.py /path/to/image checkpoint -
Return top KK 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