simpleNN | simple package used for training Convolutional Neural | Machine Learning library
kandi X-RAY | simpleNN Summary
kandi X-RAY | simpleNN Summary
SimpleNN is a simple package used for training Convolutional Neural Network (CNN) with following supports:. Currently, both implementations support two optimization methods: Newton method and stochastic gradient method (SG). The implementation document of Newton method is available at
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Top functions reviewed by kandi - BETA
- Calculate all ops in a minibatch
- R Gauss - Newton V
- Rop op
- Inverse of tensors
- Calculate the norm of a tensor
- Vectorize tensors
- Create a newton model
- Perform a minibatch
- Runs a singleton function
- Gradient tracer
- Run prediction on a given network
- Creates a CNN
- Read image data
- Normalize images
- Grain function
- Perform prediction on a network
- Parse arguments
- Create a tf model
simpleNN Key Features
simpleNN Examples and Code Snippets
Community Discussions
Trending Discussions on simpleNN
QUESTION
I started using Ignite recently and i found it very interesting.
I would like to train a model using as an optimizer the LBFGS algorithm from the torch.optim
module.
This is my code:
...ANSWER
Answered 2019-Sep-23 at 09:50The way to do it is like this:
QUESTION
ANSWER
Answered 2018-Mar-17 at 11:19QUESTION
I have the following architecture of a Convolutional Neural Network in matconvnet which I use to train on my own data:
...ANSWER
Answered 2017-Jul-04 at 21:08In your MatConvNet version, you use SGD with momentum.
In Keras, you use rmsprop
With a different learning rule you should try different learning rates. Also sometimes momentum is helpful when training a CNN.
Could you try the SGD+momentum in Keras and let me know what happens?
Another thing that might be different is that the initialization. for example in MatConvNet you use gaussian initialization with f= 0.0125 as the standard deviation. In Keras I'm not sure about the default initialization.
In general, if you don't use batch normalization, the network is prone to many numerical issues. If you use batch normalization in both networks, I bet the results would be similar. Is there any reason you don't want to use batch normalization?
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install simpleNN
You can use simpleNN like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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