22ug2_0013lab_sheet_2.py

# -*- coding: utf-8 -*-
"""22UG2-0013Lab Sheet 2.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1L8wrp1mrowfWkhYCTQiIdO2NdkC9PPD7

Lab Sheet 2 – Introduction to Neural Networks

22UG2-0013

Single Neuron Calculation

Step 1: Import Libraries
"""

import math   # For the exponential function in sigmoid

"""Step 2: Define Sigmoid Function"""

def sigmoid(z):
    return 1 / (1 + math.exp(-z))

"""Step 3: Define Inputs, Weights, and Bias"""

# Inputs
x1 = 1
x2 = 2

# Weights
w1 = 0.5
w2 = 0.3

# Bias
b = 0.1

"""Step 4: Calculate Neuron Output"""

# Step 1: Weighted sum
z = x1 * w1 + x2 * w2 + b

# Step 2: Apply sigmoid activation
output = sigmoid(z)

print("Neuron output:", output)

"""Step 5: Make It a Function"""

def simple_neuron(x1, x2, w1, w2, b):
    z = x1 * w1 + x2 * w2 + b
    return sigmoid(z)

# Test
result = simple_neuron(1, 2, 0.5, 0.3, 0.1)
print("Neuron output:", result)

"""Simple Neural Network

Step 1: Import Libraries
"""

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
import numpy as np

"""Step 2: Define the Model"""

# Create a Sequential model
model = Sequential()

# Input layer + Hidden layer
model.add(Dense(units=3, input_dim=2, activation='sigmoid'))

# Output layer
model.add(Dense(units=1, activation='sigmoid'))

"""Step 3: View Model Summary"""

model.summary()

"""Step 4: Test Forward Pass"""

# Example input data (2 features)
X_test = np.array([[1, 2],
                   [2, 3],
                   [3, 5]])

# Predict (forward pass)
output = model.predict(X_test)

print("Network output:\n", output)

"""Step 5: Make It a Function"""

def simple_neuron(x1, x2, w1, w2, b):
    z = x1 * w1 + x2 * w2 + b
    return sigmoid(z)

# Test
result = simple_neuron(1, 2, 0.5, 0.3, 0.1)
print("Neuron output:", result)