custom-neural-net-creator 1.0

A Neural Network Module to create Custom Dense Neural Networks

Custom Neural Net Creator

This module allows users to simply create neural networks by adding the types of layers needed.

Example

This is an example of how to use this module on a the classic XOR Problem

import numpy as np

from custom_neural_net_creator.model import Model
from custom_neural_net_creator.dense import Dense
from custom_neural_net_creator.activation_layer import ActivationLayer
from custom_neural_net_creator.activation_functions import relu, relu_derivative, sigmoid, sigmoid_derivative, tanh, tanh_prime
from custom_neural_net_creator.loss_functions import mean_squared_error, mean_squared_error_derivative

#Input data for XOR
x = np.array([[[0,0]], [[0,1]], [[1,0]], [[1,1]]])
y = np.array([[[0]], [[1]], [[1]], [[0]]])

model = Model()

model.add(Dense(2, 10)) #Input takes in two inputs
model.add(ActivationLayer(relu, relu_derivative)) #First hidden layer has 10 neurons and uses RELU
model.add(Dense(10, 10))
model.add(ActivationLayer(relu, relu_derivative)) #Second hidden layer has 10 neurons and uses RELU
model.add(Dense(10,1))
model.add(ActivationLayer(sigmoid, sigmoid_derivative)) #Output layer is one neuron with Sigmoid as activation

#Train on training data
model.fit(x,y,mean_squared_error,mean_squared_error_derivative,epochs=1000,learning_rate=0.1,verbosity=3)
#Loss of Epoch #1000: 0.0002757698731393589

#Test model
predictions = model.predict(x[0:3])

print("Predicted: ")
print(predictions) #Predicted: [array([[0.02610931]]), array([[0.98778214]]), array([[0.9873547]])]

print("Actual:")
print(y[0:3])
# Actual:
# [[[0]]

# [[1]]

# [[1]]]