| import numpy as np |
| from tqdm import tqdm |
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|
| class BackPropogation: |
| def __init__(self,learning_rate=0.01, epochs=100,activation_function='step'): |
| self.bias = 0 |
| self.learning_rate = learning_rate |
| self.max_epochs = epochs |
| self.activation_function = activation_function |
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|
| def activate(self, x): |
| if self.activation_function == 'step': |
| return 1 if x >= 0 else 0 |
| elif self.activation_function == 'sigmoid': |
| return 1 if (1 / (1 + np.exp(-x)))>=0.5 else 0 |
| elif self.activation_function == 'relu': |
| return 1 if max(0,x)>=0.5 else 0 |
|
|
| def fit(self, X, y): |
| error_sum=0 |
| n_features = X.shape[1] |
| self.weights = np.zeros((n_features)) |
| for epoch in tqdm(range(self.max_epochs)): |
| for i in range(len(X)): |
| inputs = X[i] |
| target = y[i] |
| weighted_sum = np.dot(inputs, self.weights) + self.bias |
| prediction = self.activate(weighted_sum) |
| |
| |
| error = target - prediction |
| self.weights += self.learning_rate * error * inputs |
| self.bias += self.learning_rate * error |
| |
| print(f"Updated Weights after epoch {epoch} with {self.weights}") |
| print("Training Completed") |
|
|
| def predict(self, X): |
| predictions = [] |
| for i in range(len(X)): |
| inputs = X[i] |
| weighted_sum = np.dot(inputs, self.weights) + self.bias |
| prediction = self.activate(weighted_sum) |
| predictions.append(prediction) |
| return predictions |
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