People also ask
How long does it take to train a neural network?
This is typically reached within 3-5 epochs with average final accuracy of 99.07%, cutting training time to around a third of the original at 57.4s 6.85s. Next we employ the trick of reducing both network size and regularization to speed up convergence.
How can I reduce the training time of CNN?
There are several ways to reduce the training time of CNN. At first you need to know about the configuration of your computer. Do you have GPU. You can add it with your computer. If you have that GPU, then concentrate on your feature maps. I think you have chosen your feature maps more than u need, most of the designer do this wrong.
How to have less time during the training phase?
You have three parameters that must adjust in order to have less time during the training phase: The size of the images or the input data. The number of layers (convolutional layers and max-pooling layers) of CNN. The use of GPUs instead of CPUs in the calculations.
How to reduce the computational expense while training a neural network?
 Other than that to reduce the computational expense while training your Neural Network, you can use Stochastic Gradient Descent, rather than conventional use of Gradient Descent approach, that would reduce the size of dataset required for training in each iteration.