#Contributers Are Welcome ๐ฉโ๐ป๐จโ๐ป
A simple and flexible python library that allows you to build custom Neural Networks where you can easily tweak parameters to change how your network behaves
pip install flexible-neural-network
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First initialize a Neural Net object and pass number of inputs, outputs, and hidden layers
myNN = NeuralNet(number_of_inputs, number_of_outputs, number_of_hidden_layers)
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You can choose how what activation function to use from: "relu", "sigmoid, "tanh"
myNN = NeuralNet(number_of_inputs, number_of_outputs, number_of_hidden_layers, activation_func="sigmoid")
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You can choose modify the learning rate
myNN = NeuralNet(number_of_inputs, number_of_outputs, number_of_hidden_layers, learning_rate=0.1)
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You can choose tweak the number of nodes in each hidden layer
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by assigning an integer number such as 3: if there was 4 hidden layers then each layer will have 3 nodes => [3, 3, 3, 3]
myNN = NeuralNet(number_of_inputs, number_of_outputs, number_of_hidden_layers, nodes_in_each_layer=3)
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by assigning a list of integers number such as [3, 5, 2, 3] that has a length of number_of_hidden_layers: if there was 4 hidden layers then each layer will have different number of nodes nodes correspondingly => [3, 5, 2, 3]
myNN = NeuralNet(number_of_inputs, number_of_outputs, number_of_hidden_layers, nodes_in_each_layer=[3, 5, 2, 3])
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myNN = NeuralNet(2, 1, 2, nodes_in_each_layer=4, learning_rate=0.1, activation_func="sigmoid")
data = np.array([
[3, 1.5, 1],
[2, 1, 0],
[4, 1.5, 1],
[3, 1, 0],
[3.5, .5, 1],
[2, .5, 0],
[5.5, 1, 1],
[1, 1, 0]
])
mystery_data = [2, 1] # should be classified as 1
Here we specified the number of epochs to be 1
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Train single entries:
myNN.train(data[0, 0:2], data[0, 2], epochs=1)
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Train multiple entries
myNN.train_many(data[:, 0:2], data[:, 2], epochs=1)
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test single/multiple entries
output = myNN.test(mystery_flower)
where output is always an np.ndarray with size as the specfied in the object's constructor. for the current example it's = [1.45327823] -
Save NN for later
myNN.save("file_name")
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Load NN without the need for retraining
myNN = NeuralNet.load("file_name")
Obviously NNs do not give exact answers and its our job to determine which class is it belongs to and judging from the training data we only have class 0, or 1 and the output we got is nearer to 1 than 0 so we should classify it as 1