UNKNOWN

This is an implementation of a fully connected neural network in NumPy. By using the matrix approach to neural networks, this NumPy implementation is able to harvest the power of the BLAS library and efficiently perform the required calculations. The network can be trained by a wide range of learning algorithms.

Visit the project page or Read the documentation.

*The code has been tested.*

- Vanilla Backpropagation
- Backpropagation with classical momentum
- Backpropagation with Nesterov momentum
- RMSprop
- Adagrad
- Adam
- Resilient Backpropagation
- Scaled Conjugate Gradient
- SciPy’s Optimize

```
pip install nimblenet
```

- Python
- NumPy
- Optionally: SciPy

This script has been written with PYPY in mind. Use their jit-compiler to run this code blazingly fast.

- Implemented with matrix operations to ensure high performance.
- Dropout regularization is available to reduce overfitting. Implemented as desribed here.
- Martin Møller's
*Scaled Conjugate Gradient for Fast Supervised Learning*as published here. - PYPY friendly (requires pypy-numpy).
- Features a selection of cost functions (error functions) and activation functions

```
from nimblenet.activation_functions import sigmoid_function
from nimblenet.cost_functions import cross_entropy_cost
from nimblenet.learning_algorithms import RMSprop
from nimblenet.data_structures import Instance
from nimblenet.neuralnet import NeuralNet
dataset = [
Instance( [0,0], [0] ), Instance( [1,0], [1] ), Instance( [0,1], [1] ), Instance( [1,1], [0] )
]
settings = {
"n_inputs" : 2,
"layers" : [ (2, sigmoid_function), (1, sigmoid_function) ]
}
network = NeuralNet( settings )
training_set = dataset
test_set = dataset
cost_function = cross_entropy_cost
RMSprop(
network, # the network to train
training_set, # specify the training set
test_set, # specify the test set
cost_function, # specify the cost function to calculate error
)
```

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