This library is a modification of my previous one. Click Here to check my previous library.
$ [sudo] pip3 install nicenet$ git clone https://github.com/Subhash3/Neural_Net_Using_NumPy.git>>> from nicenet import NeuralNetworkinputs = 2
outputs = 1
network = NeuralNetwork(inputs, outputs, cost="mse")
# Add 2 hidden layers with 16 neurons each and activation function 'tanh'
network.addLayer(16, activation_function="tanh")
network.addLayer(16, activation_function="tanh")
# Finish the neural network by adding the output layer with sigmoid activation function.
network.compile(activation_function="sigmoid")The package contains a Dataset class to create a dataset.
>>> from nicenet import DatasetMake sure you have inputs and target values in seperate files in csv format.
input_file = "inputs.csv"
target_file = "targets.csv"
# Create a dataset object with the same inputs and outputs defined for the network.
datasetCreator = Dataset(inputs, outputs)
datasetCreator.makeDataset(input_file, target_file)
data, size = datasetCreator.getRawData()If you want to manually make a dataset, follow these rules:
- Dataset must be a list of data samples.
- A data sample is a tuple containing inputs and target values.
- Input and target values are column vector of size (inputs x 1) and (outputs x 1) respectively.
For eg, a typical XOR data set looks something like :
>>> XOR_data = [
(
np.array([[0], [0]]),
np.array([[0]])
),
(
np.array([[0], [1]]),
np.array([[1]])
),
(
np.array([[1], [0]]),
np.array([[1]])
),
(
np.array([[1], [1]]),
np.array([[0]])
)
]
>>> size = 4The library provides a Train function which accepts the dataset, dataset size, and two optional parameters epochs, and logging.
def Train(dataset, size, epochs=5000, logging=True) :
....
....For Eg: If you want to train your network for 1000 epochs.
>>> network.Train(data, size, epochs=1000)Notice that I didn't change the value of log_outputs as I want the output to printed for each epoch.
Plot a nice epoch vs error graph
>>> network.epoch_vs_error()Know how well the model performed.
>>> network.evaluate()To take a look at all the layers' info
>>> network.display()Sometimes, learning rate might have to be altered for better convergence.
>>> network.setLearningRate(0.1)You can export a trained model to a json file which can be loaded and used for predictions in the future.
filename = "model.json"
network.export_model(filename)To load a model from an exported model (json) file. load_model is a static function, so you must not call this on a NeuralNetwork object!.
filename = "model.json"
network = NeuralNetwork.load_model(filename)- [x] Generalize the gradient descent algorithm
- [x] Generalise the loss function => Write a separate class for it!
- [x] Implement Cross Entropy Loss
- [ ] Data scaling
- [x] Min Max scaler
- [ ] Data Standardization
- [x] Change the datasample type to a tuple instead of a list.
- [x] Show Progress bar if epoch_logging is False
- [x] Use a function as a parameter to Train method to compare predictions and actual targets.
- [ ] convert all camel-cased vars to snake-case.
- [ ] NeuralNetwork.py
- [ ] Layer.py
- [ ] Dataset.py
- [ ] ActivationFunction.py
- [ ] Utils.py
- [ ] LossFunctions.py
- [ ] API docs
- [x] Add doc strings to all functions.
- [x] Make the class/function declarations' docs collapsable.
- [ ] Merge API md files and embed them in Readme.
- [ ] Create a section, API, in README to provide documentation for all prototypes.
- [ ] Implement Batch Training
- [ ] Write a separate class for Scalers as the scaling methods increase.
- [ ] Linear and Relu activation functions
- [ ] Ability to perform regression
- [ ] Separate out outputlayer from other layers. => Create a separate class for output layer which inherits Layer.
- [ ] Convolution Nets
- [ ] Recurrent Nets
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