Problem_27: Implement a Simple Neural Network from Scratch #ai #coding #ml #google

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ABOUT THIS PROBLEM:

Implement a Simple Neural Network from Scratch
Problem: Implement a simple feedforward neural network with one hidden layer from scratch using NumPy. Train this network on a small dataset.

Hint: Use the sigmoid activation function and backpropagation for training.

SOLUTION:
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Problem_27: Implement a Simple Neural Network from Scratch with one hidden layer #ai #coding #ml #google

class SimpleNN:
def __init__(self, input_size, hidden_size, output_size, learning_rate=0.01):

def sigmoid(self, z):

def sigmoid_derivative(self, z):
return z * (1 - z)

def forward(self, X):

def backward(self, X, y, output):
error = y - output

def train(self, X, y, epochs=1000):
for _ in range(epochs):

# Example usage:
# nn = SimpleNN(input_size=2, hidden_size=3, output_size=1)

#Sample_input :

import numpy as np

# Sample dataset
[0, 1],
[1, 0],
[1, 1]])

# Using the SimpleNN
nn = SimpleNN(input_size=2, hidden_size=3, output_size=1)

# Testing the trained network
print("Predicted Output:\n", output)

#Sample_output :

Predicted Output:
[[0.01]
[0.98]
[0.98]
[0.02]]

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