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mlp_momentum.py
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mlp_momentum.py
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import numpy as np
import matplotlib.pyplot as plt
import time
class MLP:
def __init__(self, train_data, target, num_epochs=100, num_input=2, num_hidden=2, num_output=1, lr=0.1, momentum=0.9):
self.train_data = train_data
self.target = target
self.num_epochs = num_epochs
self.lr = lr
self.momentum = momentum
# Initialize weights randomly
self.input_hidden_weights = np.random.uniform(size=(num_input, num_hidden))
self.hidden_output_weights = np.random.uniform(size=(num_hidden, num_output))
# Initialize biases
self.hidden_bias = np.random.uniform(size=(1,num_hidden))
self.output_bias = np.random.uniform(size=(1,num_output))
# Initialize plot lists
self.mse = []
self.hidden_mse = []
self.classification_errors = []
self.input_hidden_weights_history = []
self.hidden_output_weights_history = []
def update_weights(self):
# Calculate MSE on the hidden layer
hidden_prediction_error = (self.target - self.output_final) @ self.hidden_output_weights.T
hidden_mse = np.mean(np.square(hidden_prediction_error))
self.hidden_mse.append(hidden_mse)
prediction_error = self.target - self.output_final
# Calculate the gradients of the weights connecting the input layer to the hidden layer
input_hidden_weight_gradients = self.train_data.T @ (((prediction_error * self.sigmoid_der(self.output_final)) * self.hidden_output_weights.T) * self.sigmoid_der(self.hidden_output))
# Calculate the gradients of the weights connecting the hidden layer to the output layer
hidden_output_weight_gradients = self.hidden_output.T @ (prediction_error * self.sigmoid_der(self.output_final))
# Update the weights with momentum
self.input_hidden_weights_delta = self.lr * input_hidden_weight_gradients + self.momentum * getattr(self, 'input_hidden_weights_delta', 0)
self.input_hidden_weights += self.input_hidden_weights_delta
self.hidden_output_weights_delta = self.lr * hidden_output_weight_gradients + self.momentum * getattr(self, 'hidden_output_weights_delta', 0)
self.hidden_output_weights += self.hidden_output_weights_delta
# Update the biases of the neurons in the hidden layer
self.hidden_bias += np.sum(self.lr * ((prediction_error * self.sigmoid_der(self.output_final)) * self.hidden_output_weights.T) * self.sigmoid_der(self.hidden_output), axis=0)
# Update the biases of the neurons in the output layer
self.output_bias += np.sum(self.lr * prediction_error * self.sigmoid_der(self.output_final), axis=0)
# Append current weights to history
self.input_hidden_weights_history.append(self.input_hidden_weights.copy())
self.hidden_output_weights_history.append(self.hidden_output_weights.copy())
def sigmoid(self, x):
return 1 / (1 + np.exp(-x))
def sigmoid_der(self, x):
return x * (1 - x)
def forward(self, input_data):
# Calculate the input to the hidden layer
self.hidden_input = input_data @ self.input_hidden_weights + self.hidden_bias
# Apply the sigmoid activation function to the hidden layer input
self.hidden_output = self.sigmoid(self.hidden_input)
# Calculate the input to the output layer
self.output_input = self.hidden_output @ self.hidden_output_weights + self.output_bias
# Apply the sigmoid activation function to the output layer input
self.output_final = self.sigmoid(self.output_input)
return self.output_final
def classify(self, datapoint):
datapoint = np.transpose(datapoint)
output = self.forward(datapoint)
return 1 if output >= 0.5 else 0
def train(self, min_mse=0):
for _ in range(self.num_epochs):
self.forward(self.train_data)
# Calculate MSE
current_mse = np.mean(np.square(self.target - self.output_final))
self.mse.append(current_mse)
self.update_weights()
errors = 0
for i in range(len(self.train_data)):
if self.classify(self.train_data[i]) != self.target[i]:
errors += 1
self.classification_errors.append(errors)
if current_mse <= min_mse:
break
def test(self, X_test):
y_pred = self.forward(X_test)
print("Input | Output")
for i in range(len(X_test)):
print(f"{X_test[i].tolist()} | {y_pred[i].item()}")
def plot_mse(self):
plt.plot(range(len(self.mse)), self.mse)
plt.title('Mean Squared Error (MSE) Over Epochs')
plt.xlabel('Epochs')
plt.ylabel('MSE')
plt.show()
def plot_all_mse(self):
plt.figure(figsize=(15, 5))
plt.subplot(1, 2, 1)
plt.plot(range(len(self.mse)), self.mse)
plt.title('Mean Squared Error (MSE) Over Epochs - Output')
plt.xlabel('Epochs')
plt.ylabel('MSE')
plt.grid()
plt.subplot(1, 2, 2)
plt.plot(range(len(self.hidden_mse)), self.hidden_mse)
plt.title('Mean Squared Error (MSE) Over Epochs - Hidden Layer')
plt.xlabel('Epochs')
plt.ylabel('MSE')
plt.grid()
plt.tight_layout()
plt.show()
def plot_classification_errors(self):
plt.plot(range(len(self.classification_errors)), self.classification_errors)
plt.title('Classification Errors Over Epochs')
plt.xlabel('Epochs')
plt.ylabel('Misclassified Points')
plt.grid()
plt.show()
def plot_all(self):
plt.figure(figsize=(15, 5))
plt.subplot(1, 2, 1)
plt.plot(range(len(self.mse)), self.mse)
plt.title('Mean Squared Error (MSE) Over Epochs')
plt.xlabel('Epochs')
plt.ylabel('MSE')
plt.grid()
plt.subplot(1, 2, 2)
plt.plot(range(len(self.classification_errors)), self.classification_errors)
plt.title('Classification Errors Over Epochs')
plt.xlabel('Epochs')
plt.ylabel('Misclassified Points')
plt.grid()
plt.show()
def plot_decision_boundary(self):
# Define the range for the meshgrid
x_min, x_max = -0.5, 1.5
y_min, y_max = -0.5, 1.5
h = 0.01 # step size in the mesh
# Create a meshgrid of points
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
# Predict the class for each point in the meshgrid
Z = np.array([self.classify(np.array([xx.ravel()[i], yy.ravel()[i]])) for i in range(len(xx.ravel()))])
Z = Z.reshape(xx.shape)
# Plot the decision boundary
plt.contourf(xx, yy, Z, cmap=plt.cm.RdBu, alpha=0.5)
# Plot the training points
colors = ['red' if target == 0 else 'green' for target in self.target.flatten()]
plt.scatter(self.train_data[:, 0], self.train_data[:, 1], c=colors, edgecolors='k', s=150)
plt.xlabel('Input 1')
plt.ylabel('Input 2')
plt.title('Decision Boundary')
plt.show()
def plot_weights(self):
plt.figure(figsize=(10, 5))
plt.subplot(1, 2, 1)
plt.imshow(self.input_hidden_weights, cmap='viridis', aspect='auto')
plt.title('Input-Hidden Weights')
plt.xlabel('Hidden Neurons')
plt.ylabel('Input Neurons')
plt.grid()
plt.colorbar()
plt.subplot(1, 2, 2)
plt.imshow(self.hidden_output_weights, cmap='viridis', aspect='auto')
plt.title('Hidden-Output Weights')
plt.xlabel('Output Neurons')
plt.ylabel('Hidden Neurons')
plt.grid()
plt.colorbar()
plt.tight_layout()
plt.show()
def plot_weights(self):
plt.figure(figsize=(10, 5))
plt.subplot(1, 2, 1)
for i in range(len(self.input_hidden_weights_history[0])):
for j in range(len(self.input_hidden_weights_history[0][0])):
weights = [epoch[i][j] for epoch in self.input_hidden_weights_history]
plt.plot(range(len(weights)), weights, label=f'Input {i+1} to Hidden {j+1}')
plt.title('Input-Hidden Weights History')
plt.xlabel('Epochs')
plt.ylabel('Weights')
plt.grid()
plt.legend()
plt.subplot(1, 2, 2)
for i in range(len(self.hidden_output_weights_history[0])):
for j in range(len(self.hidden_output_weights_history[0][0])):
weights = [epoch[i][j] for epoch in self.hidden_output_weights_history]
plt.plot(range(len(weights)), weights, label=f'Hidden {i+1} to Output {j+1}')
plt.title('Hidden-Output Weights History')
plt.xlabel('Epochs')
plt.ylabel('Weights')
plt.grid()
plt.legend()
plt.tight_layout()
plt.show()
''' Main function '''
def main():
# XOR dataset
X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
y = np.array([[0], [1], [1], [0]])
start_time = time.time()
mlp = MLP(train_data=X, target=y, num_epochs=2000, num_input=2, num_hidden=2, num_output=1, lr=0.1, momentum=0.9)
mlp.train()
end_time = time.time()
print(f'Elapsed time: {end_time - start_time}s')
print(f"Last MSE value: {mlp.mse[-1]}")
# XOR test
X_test = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
mlp.test(X_test)
# Plotting
#mlp.plot_mse()
#mlp.plot_all_mse()
#mlp.plot_classification_errors()
#mlp.plot_all()
#mlp.plot_decision_boundary()
mlp.plot_weights()
if __name__ == "__main__":
main()