Machine Learning using Keras as a front-end and Tensorflow (GPU) as backend
A simple Convolutional Neural Network with 2 sets of Convolution/Maxpool layers, a flatten layer and two fully-connected layers ending in a softmax. Several lines are commented out; they will be restored if they appreciably impact the model accuracy. My test system is:
- Dell Inspiron 15 7000
- Intel Core i7 6700HQ CPU @ 2.60GHz
- 8 GB of DDR3 SDRAM
- Nvidia GTX 960M GPU with 4GB GDDR5 VRAM
Using the CPU verison of Tensorflow (backend) epochs routinely took over 530 seconds to complete; however GPU acceleration now yields epochs that take ~ 55 seconds to complete - a ~10x drop. My CUDA information is:
- CUDA Toolkit 8.0
- cuDNN v5.1
- tensorflow-gpu 1.1.0
Here are the last few epochs of training on a dataset from Wikimedia Commons used in a Google Developers Codelab for Machine Learning. With the last epoch, the model attained an accuracy of 87.25%.
Now, with the weights of the model saved as a .h5 file, I use the prediction module to predict m.jpg, an image from the test set:
and the validation result is a follows:
