An image classifier that identifies flower types in an image. I created this to learn numpy, matplotlib, jupyter notebooks, neural networks and machine learning with pytorch. It's the final project in the Udacity Nanodegree: AI Programming with Python.
- Python 3.5.2
- pytorch==0.4.0
- torchvision==0.2.1
- Download this dataset and unpack into the images directory using the torchvision datasets format. See images folder README for more details.
train.py is a script to train a neural network on the given data directory. Use command-line options to customize the
hyperparameters of the neural network.
Prints out training loss, validation loss, and validation accuracy as the network trains. Then saves a checkpoint.
To see command-line options, exec: python train.py --help.
Example execution and result:
$ python train.py images --gpu --epochs=1 --verbose
Input args: Namespace(arch=<NetworkArchitectures.VGG16: 'vgg16'>, data_dir='images', dropout_rate=0.2, epochs=1, gpu=True, hidden_units=12544, learning_rate=0.0001, save_path
='checkpoint.pth', verbose=True)
Training ...
Epoch: 1/1.. Training Loss: 3.334.. Test Loss: 1.711.. Test Accuracy: 0.584
Epoch: 1/1.. Training Loss: 1.725.. Test Loss: 0.926.. Test Accuracy: 0.747
Epoch: 1/1.. Training Loss: 1.258.. Test Loss: 0.650.. Test Accuracy: 0.843
Training complete.
Testing network ...
Test Loss: 0.748.. Test Accuracy: 0.818
Testing complete.
Saving checkpoint.
Checkpoint saved to: checkpoint.pth.
Loading network from checkpoint: checkpoint.pth.
Network loaded.
Testing network ...
Test Loss: 0.748.. Test Accuracy: 0.818
Testing complete.
predict.py is a script to predict the flower name from an image, along with the probability of that name.
Loads the network from the given checkpoint, then prints out the topk classes and probabilities for the given image.
To see command-line options, exec: python predict.py --help.
Example execution and result:
$ python predict.py images/valid/13/image_05749.jpg --verbose --gpu
Input args: Namespace(category_names='cat_to_name.json', checkpoint_path='checkpoint.pth', gpu=True, image_path='images/valid/13/image_05749.jpg', top_k=5, verbose=True)
Loading network from checkpoint: checkpoint.pth.
Network loaded.
Class: king protea, Probability: 0.9999961256980896
Class: artichoke, Probability: 3.1754266274219844e-06
Class: alpine sea holly, Probability: 2.5260263214477163e-07
Class: globe thistle, Probability: 1.8669176427010825e-07
Class: bee balm, Probability: 5.972618311034239e-08