Implementation of training and testing process of VGG16 in TensorFlow
- TensorFlow
- Python 2.7.x
- Pre-trained VGG16 model parameters
VGG16_modelParams.tensorflow - Training dataset CIFAR-10 dataset
The testing code testing.py test on a image of weasel using the pre-traind VGG16 model parameters provided by Davi Frossard. We saved the model parameters as a tensorflow readable file VGG16_modelParams.tensorflow.
Download the parameter file to the same folder and run
$ python testing.py
Testing result
Restoring VGG16 model parameters ...
Classification Result:
Category Name: weasel
Probability: 88.05%
Category Name: polecat, fitch, foulmart, foumart, Mustela putorius
Probability: 6.82%
Category Name: mink
Probability: 4.79%
The training code training.py shows a demo of the training process in TensorFlow. Download the CIFAR-10 dataset to the same folder and run
$ python training.py
The print information during traing will be like the follow. Of course, the results may vary with the initalization. The epoch number is set to 10 to get a quick result. For the same reason, we trained on a small model (3 layers) rather than on VGG16 model. Although the accuracy is not good enough, it roughly shows the trend of lower loss and higher accuracy with more epoch.
Loading dataset ...
Color center of images: [ 0.59276742 0.57485735 0.44807526]
Training ...
...
(GPU Info)
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Epoch 1/10:
Train Loss = 2.33 Accuracy = 18.00%
Valid Loss = 2.13 Accuracy = 25.00%
Epoch 2/10:
Train Loss = 2.08 Accuracy = 20.00%
Valid Loss = 1.95 Accuracy = 33.30%
Epoch 3/10:
Train Loss = 1.99 Accuracy = 26.00%
Valid Loss = 1.85 Accuracy = 34.70%
Epoch 4/10:
Train Loss = 1.80 Accuracy = 40.00%
Valid Loss = 1.79 Accuracy = 37.20%
Epoch 5/10:
Train Loss = 1.47 Accuracy = 44.00%
Valid Loss = 1.66 Accuracy = 41.10%
Epoch 6/10:
Train Loss = 1.25 Accuracy = 54.00%
Valid Loss = 1.61 Accuracy = 43.60%
Epoch 7/10:
Train Loss = 1.24 Accuracy = 60.00%
Valid Loss = 1.68 Accuracy = 43.20%
Epoch 8/10:
Train Loss = 1.06 Accuracy = 64.00%
Valid Loss = 1.62 Accuracy = 46.00%
Epoch 9/10:
Train Loss = 0.98 Accuracy = 72.00%
Valid Loss = 1.55 Accuracy = 47.00%
Epoch 10/10:
Train Loss = 0.90 Accuracy = 76.00%
Valid Loss = 1.60 Accuracy = 45.40%
layerConstructor.pyprovides higher level functions to build basic convolution, pooling, and fully connection layers.VGG16_model.pybuild the VGG16 model, respectively, usinglayerConstructor.pyimagenet_classes.pystores the class names of the ImageNet dataset, cited from Davi Frossardweasel.pngis an example image for testingtraining.pyandtesting.pyare demos of traing and testing