In this project, we'll train an image classifier to recognize different species of flowers. You can imagine using something like this in a phone app that tells you the name of the flower your camera is looking at. In practice you'd train this classifier, then export it for use in your application. We'll be using this dataset of 102 flower categories
The train.py file allows you to train a neural network on a dataset. Basic usage is with the following command:
python train.py [data_directory]
A data directory must be supplied and it must have three subdirectories test, valid, train housing your pictures.
--gpu-off
Gpu is set to true unless this tag is given
--save_dir
Choose save directory for model
--arch
Choose between vgg and densenet architectures.
--learning rate
Define learning rate.
--epochs
Define number of epochs.
--hidden_units
Define number of hidden units. Ex. --hidden_units 500,300,200
--dropout
Define dropout rate.
python train.py flowers --arch vgg --epochs 3 --dropout .3 --cat_to_name map.json
The predict.py file allows you to classify an image via the neural network trained with train.py. Basic usage is with the following command:
python predict.py [img_path] [checkpoint]
The image path points to the picture you'd like to classify. The checkpoint points to the .pth file created by predict.py that houses your model.
--gpu-off
Gpu is set to true unless this tag is given
--cat_to_name
Set category to name .json for model
--topk
define number of probable options returned
python predict.py flowers/ddl.jpg checkpoint.pth --topk 5
GPU? True
Loading checkpoint...
Done
Loaded checkpoint from checkpoint.pth
topk_prob_arr: [ 9.70751166e-01 2.91926507e-02 4.20815559e-05 6.94843493e-06 5.63376852e-06]
topk_class_arr: ['common dandelion', "colt's foot", 'english marigold', 'marigold', 'buttercup']
Prediction: common dandelion with 97.1% probability.
python predict.py flowers/ddl.jpg checkpoint.pth --topk 5 --gpu-off
GPU? False
Loading checkpoint...
Done
Loaded checkpoint from checkpoint.pth
topk_prob_arr: [ 9.70751584e-01 2.91927140e-02 4.20815559e-05 6.94841492e-06 5.63376352e-06]
topk_class_arr: ['common dandelion', "colt's foot", 'english marigold', 'marigold', 'buttercup']
Prediction: common dandelion with 97.1% probability.
python predict.py flowers/ddl.jpg checkpoint.pth
GPU? True
Loading checkpoint...
Done
Loaded checkpoint from checkpoint.pth
Prediction: common dandelion with 97.1% probability.
python train.py flowers --epochs 10 --hidden_units 500,400 --arch densenet
GPU? True
Training with Densenet201
/opt/conda/lib/python3.6/site-packages/torchvision-0.2.1-py3.6.egg/torchvision/models/densenet.py:212: UserWarning: nn.init.kaiming_normal is now deprecated in favor of nn.init.kaiming_normal_.
Downloading: "https://download.pytorch.org/models/densenet201-c1103571.pth" to /root/.torch/models/densenet201-c1103571.pth
100%|██████████████████████████████████████████████████████████████████████████████████████████████████| 81131730/81131730 [00:02<00:00, 28585382.36it/s]
Beginning deep learning...
Epoch: 1/10.. Training Loss: 4.516.. Validation Loss: 4.379.. Validation Accuracy: 5.3%
Epoch: 1/10.. Training Loss: 4.336.. Validation Loss: 4.042.. Validation Accuracy: 12.4%
Epoch: 1/10.. Training Loss: 3.955.. Validation Loss: 3.399.. Validation Accuracy: 28.4%
Epoch: 1/10.. Training Loss: 3.376.. Validation Loss: 2.725.. Validation Accuracy: 37.5%
Epoch: 2/10.. Training Loss: 2.477.. Validation Loss: 2.221.. Validation Accuracy: 51.3%
Epoch: 2/10.. Training Loss: 2.524.. Validation Loss: 1.733.. Validation Accuracy: 59.7%
Epoch: 2/10.. Training Loss: 2.192.. Validation Loss: 1.476.. Validation Accuracy: 64.8%
Epoch: 2/10.. Training Loss: 1.990.. Validation Loss: 1.226.. Validation Accuracy: 69.8%
Epoch: 3/10.. Training Loss: 1.310.. Validation Loss: 1.069.. Validation Accuracy: 73.7%
Epoch: 3/10.. Training Loss: 1.583.. Validation Loss: 0.920.. Validation Accuracy: 78.6%
Epoch: 3/10.. Training Loss: 1.425.. Validation Loss: 0.853.. Validation Accuracy: 78.9%
Epoch: 3/10.. Training Loss: 1.474.. Validation Loss: 0.744.. Validation Accuracy: 81.3%
Epoch: 4/10.. Training Loss: 0.786.. Validation Loss: 0.707.. Validation Accuracy: 82.2%
Epoch: 4/10.. Training Loss: 1.200.. Validation Loss: 0.590.. Validation Accuracy: 86.0%
Epoch: 4/10.. Training Loss: 1.175.. Validation Loss: 0.604.. Validation Accuracy: 84.8%
Epoch: 4/10.. Training Loss: 1.134.. Validation Loss: 0.514.. Validation Accuracy: 88.6%
Epoch: 5/10.. Training Loss: 0.541.. Validation Loss: 0.531.. Validation Accuracy: 88.4%
Epoch: 5/10.. Training Loss: 1.041.. Validation Loss: 0.458.. Validation Accuracy: 89.7%
Epoch: 5/10.. Training Loss: 0.985.. Validation Loss: 0.475.. Validation Accuracy: 88.8%
Epoch: 5/10.. Training Loss: 0.993.. Validation Loss: 0.414.. Validation Accuracy: 90.4%
Epoch: 6/10.. Training Loss: 0.371.. Validation Loss: 0.439.. Validation Accuracy: 89.3%
Epoch: 6/10.. Training Loss: 0.864.. Validation Loss: 0.376.. Validation Accuracy: 90.3%
Epoch: 6/10.. Training Loss: 0.829.. Validation Loss: 0.342.. Validation Accuracy: 91.0%
Epoch: 6/10.. Training Loss: 0.839.. Validation Loss: 0.371.. Validation Accuracy: 89.5%
Epoch: 7/10.. Training Loss: 0.242.. Validation Loss: 0.359.. Validation Accuracy: 90.5%
Epoch: 7/10.. Training Loss: 0.763.. Validation Loss: 0.303.. Validation Accuracy: 92.7%
Epoch: 7/10.. Training Loss: 0.723.. Validation Loss: 0.315.. Validation Accuracy: 92.3%
Epoch: 7/10.. Training Loss: 0.831.. Validation Loss: 0.326.. Validation Accuracy: 91.5%
Epoch: 8/10.. Training Loss: 0.113.. Validation Loss: 0.344.. Validation Accuracy: 92.4%
Epoch: 8/10.. Training Loss: 0.775.. Validation Loss: 0.310.. Validation Accuracy: 91.6%
Epoch: 8/10.. Training Loss: 0.707.. Validation Loss: 0.266.. Validation Accuracy: 93.1%
Epoch: 8/10.. Training Loss: 0.681.. Validation Loss: 0.317.. Validation Accuracy: 92.2%
Epoch: 9/10.. Training Loss: 0.026.. Validation Loss: 0.290.. Validation Accuracy: 92.8%
Epoch: 9/10.. Training Loss: 0.682.. Validation Loss: 0.281.. Validation Accuracy: 92.2%
Epoch: 9/10.. Training Loss: 0.672.. Validation Loss: 0.273.. Validation Accuracy: 92.8%
Epoch: 9/10.. Training Loss: 0.643.. Validation Loss: 0.266.. Validation Accuracy: 93.1%
Epoch: 9/10.. Training Loss: 0.631.. Validation Loss: 0.237.. Validation Accuracy: 94.0%
Epoch: 10/10.. Training Loss: 0.586.. Validation Loss: 0.252.. Validation Accuracy: 93.4%
Epoch: 10/10.. Training Loss: 0.634.. Validation Loss: 0.240.. Validation Accuracy: 94.0%
Epoch: 10/10.. Training Loss: 0.629.. Validation Loss: 0.245.. Validation Accuracy: 93.7%
Epoch: 10/10.. Training Loss: 0.634.. Validation Loss: 0.246.. Validation Accuracy: 92.9%
Train time: 34m 5s
Checking accuracy with test data...
Beginning accuracy test...
0/0
42/64
86/128
116/192
150/256
196/320
239/384
286/448
330/512
368/576
406/640
442/704
488/768
Accuracy of the network on test images: 62 %
Accuracy test time: 0m 17s
Saving checkpoint...
Saved!
python train.py flowers --epochs 10 --hidden_units 500,400 --arch vgg
GPU? True
Training with Vgg16
Downloading: "https://download.pytorch.org/models/vgg16-397923af.pth" to /root/.torch/models/vgg16-397923af.pth
100%|████████████████████████████████████████████████████████████████████████████████████████████████| 553433881/553433881 [00:18<00:00, 30353209.74it/s]
Beginning deep learning...
Epoch: 1/10.. Training Loss: 4.499.. Validation Loss: 3.717.. Validation Accuracy: 22.7%
Epoch: 1/10.. Training Loss: 3.807.. Validation Loss: 2.762.. Validation Accuracy: 40.5%
Epoch: 1/10.. Training Loss: 3.138.. Validation Loss: 2.044.. Validation Accuracy: 52.3%
Epoch: 1/10.. Training Loss: 2.713.. Validation Loss: 1.588.. Validation Accuracy: 60.6%
Epoch: 2/10.. Training Loss: 2.057.. Validation Loss: 1.330.. Validation Accuracy: 66.1%
Epoch: 2/10.. Training Loss: 2.015.. Validation Loss: 1.135.. Validation Accuracy: 69.5%
Epoch: 2/10.. Training Loss: 1.931.. Validation Loss: 0.934.. Validation Accuracy: 75.7%
Epoch: 2/10.. Training Loss: 1.880.. Validation Loss: 0.915.. Validation Accuracy: 76.3%
Epoch: 3/10.. Training Loss: 1.222.. Validation Loss: 0.798.. Validation Accuracy: 78.8%
Epoch: 3/10.. Training Loss: 1.641.. Validation Loss: 0.811.. Validation Accuracy: 79.7%
Epoch: 3/10.. Training Loss: 1.597.. Validation Loss: 0.726.. Validation Accuracy: 79.7%
Epoch: 3/10.. Training Loss: 1.495.. Validation Loss: 0.694.. Validation Accuracy: 82.9%
Epoch: 4/10.. Training Loss: 0.892.. Validation Loss: 0.615.. Validation Accuracy: 83.0%
Epoch: 4/10.. Training Loss: 1.369.. Validation Loss: 0.613.. Validation Accuracy: 83.6%
Epoch: 4/10.. Training Loss: 1.367.. Validation Loss: 0.616.. Validation Accuracy: 83.2%
Epoch: 4/10.. Training Loss: 1.325.. Validation Loss: 0.570.. Validation Accuracy: 84.2%
Epoch: 5/10.. Training Loss: 0.624.. Validation Loss: 0.533.. Validation Accuracy: 84.8%
Epoch: 5/10.. Training Loss: 1.204.. Validation Loss: 0.532.. Validation Accuracy: 84.8%
Epoch: 5/10.. Training Loss: 1.170.. Validation Loss: 0.513.. Validation Accuracy: 85.7%
Epoch: 5/10.. Training Loss: 1.186.. Validation Loss: 0.490.. Validation Accuracy: 86.4%
Epoch: 6/10.. Training Loss: 0.461.. Validation Loss: 0.502.. Validation Accuracy: 87.4%
Epoch: 6/10.. Training Loss: 1.085.. Validation Loss: 0.504.. Validation Accuracy: 86.6%
Epoch: 6/10.. Training Loss: 1.123.. Validation Loss: 0.458.. Validation Accuracy: 87.9%
Epoch: 6/10.. Training Loss: 1.120.. Validation Loss: 0.467.. Validation Accuracy: 88.2%
Epoch: 7/10.. Training Loss: 0.281.. Validation Loss: 0.471.. Validation Accuracy: 87.2%
Epoch: 7/10.. Training Loss: 1.117.. Validation Loss: 0.456.. Validation Accuracy: 89.0%
Epoch: 7/10.. Training Loss: 1.085.. Validation Loss: 0.476.. Validation Accuracy: 88.4%
Epoch: 7/10.. Training Loss: 1.071.. Validation Loss: 0.421.. Validation Accuracy: 89.8%
Epoch: 8/10.. Training Loss: 0.144.. Validation Loss: 0.392.. Validation Accuracy: 90.1%
Epoch: 8/10.. Training Loss: 0.984.. Validation Loss: 0.423.. Validation Accuracy: 88.7%
Epoch: 8/10.. Training Loss: 1.047.. Validation Loss: 0.386.. Validation Accuracy: 90.0%
Epoch: 8/10.. Training Loss: 0.991.. Validation Loss: 0.386.. Validation Accuracy: 89.7%
Epoch: 9/10.. Training Loss: 0.025.. Validation Loss: 0.391.. Validation Accuracy: 88.9%
Epoch: 9/10.. Training Loss: 0.968.. Validation Loss: 0.398.. Validation Accuracy: 88.9%
Epoch: 9/10.. Training Loss: 0.964.. Validation Loss: 0.417.. Validation Accuracy: 89.0%
Epoch: 9/10.. Training Loss: 0.985.. Validation Loss: 0.401.. Validation Accuracy: 90.5%
Epoch: 9/10.. Training Loss: 1.025.. Validation Loss: 0.361.. Validation Accuracy: 91.2%
Epoch: 10/10.. Training Loss: 0.886.. Validation Loss: 0.389.. Validation Accuracy: 90.9%
Epoch: 10/10.. Training Loss: 0.980.. Validation Loss: 0.363.. Validation Accuracy: 89.6%
Epoch: 10/10.. Training Loss: 0.946.. Validation Loss: 0.343.. Validation Accuracy: 91.5%
Epoch: 10/10.. Training Loss: 0.929.. Validation Loss: 0.369.. Validation Accuracy: 89.6%
Train time: 34m 57s
Checking accuracy with test data...
Beginning accuracy test...
0/0
46/64
98/128
149/192
193/256
247/320
292/384
351/448
400/512
459/576
518/640
561/704
613/768
Accuracy of the network on test images: 79 %
Accuracy test time: 0m 17s
Saving checkpoint...
Saved!