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Pytorch_ImageClassifier

This project builds a deep learning network to identify 102 different types of flowers. The dataset was obtained from the 102 category flowers dataset. While this specific example is used on this data, this model can be trained on any set of labeled images. Below are a few examples of the variability between classes and within the classes themselves.

Class variability between classes

The 3 images below are: (Spear Thistle) (Fire Lily) (Cantenbury Bells)

Class variability within classes

Each of the 3 images below is a Toad Lily

Architecture

1. Developing the application

This is a very abbreviated version of the sequene.

Load the data

data_dir = 'flowers'
train_dir = data_dir + '/train'
valid_dir = data_dir + '/valid'
test_dir = data_dir + '/test'

params_dict = {'train': {'dir': train_dir, 'batch': 64, 'shuffle': True},
               'validate':{'dir': valid_dir, 'batch': 64, 'shuffle': True},
               'test':{'dir': test_dir, 'batch': 64, 'shuffle': False}}

datasets, dataloaders = Data_Utilities.generate_datasets(params_dict, list(params_dict.keys()))

Build and train network

# check for gpu
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# network instance
neural_net = Net_Utilities.net_from_torchvision([1024,512], 102, 'relu', device, learn_rate = 0.001)
# train for 25 epochs
neural_net.train_network(dataloaders['train'], dataloaders['validate'], 5, plot = True)

Test the network

# validate
loss, acc = neural_net.validate_network(dataloaders['test'])
print('acc on test is {} % \nloss is {}'.format(acc, loss))

Output:
acc on test is 85.69240203270544% 
loss is 0.5390129307141671

Saving the network

# save the network checkpoint
neural_net.save_model_checkpoint('checkpoint_nn_module_1.pth', datasets['train'].class_to_idx)

Load the checkpoint

# load it
loaded_net = Net_Utilities.load_neural_net('checkpoint_nn_module_1.pth', 'train')

Class Prediction

results_dict = Net_Operations.predict(loaded_net, img_path, flowers_to_name)
for k,v in results_dict.items():
    print('{}:{}'.format(k, v))

Output:
predicted_idx:[9, 62, 45, 85, 64]
classes:['yellow iris', 'black-eyed susan', 'buttercup', 'columbine', 'californian poppy']
idx_to_class:['15', '63', '48', '84', '65']
probabilities:[0.9390610456466675, 0.03997050225734711, 0.006514083594083786, 0.00519171729683876, 0.0030165603384375572]

Check results

Display n number of images and their top k probabilities along with the actual image

2. Command line application specifications

The project submission must include at least two files train.py and predict.py. The first file, train.py, will train a new network on a dataset and save the model as a checkpoint. The second file, predict.py, uses a trained network to predict the class for an input image.

• 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, 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

Test command line application

predict.py train.py