Question: 1 . Complete the implementation of the Neural network,implement train and test method,test the network on regression and classific Neural Network Class class NeuralNetwork: def

1. Complete the implementation of the Neural network,implement train and test method,test the network on regression and classific
Neural Network Class
class NeuralNetwork:
def __init__(self, input_size, hidden_size, output_size, activation='relu', learning_rate=0.01, regression=False):
# Initialize network parameters
self.input_size = input_size
self.hidden_size = hidden_size
self.output_size = output_size
self.activation = activation
self.learning_rate = learning_rate
self.regression = regression
# Initialize weights and biases
self.weights_input_hidden = np.random.randn(input_size, hidden_size)
self.biases_hidden = np.zeros((1, hidden_size))
self.weights_hidden_output = np.random.randn(hidden_size, output_size)
self.biases_output = np.zeros((1, output_size))
# Complete activation functions and their derivatives
def relu(self, x):
return #put your code here
def relu_derivative(self, x):
return #put your code here
def sigmoid(self, x):
return #put your code here
def sigmoid_derivative(self, x):
return #put your code here
def tanh(self, x):
return #put your code here
def tanh_derivative(self, x):
return #put your code here
def identity(self, x):
return #put your code here
def identity_derivative(self, x):
return #put your code here
def leaky_relu(self, x, alpha=0.01):
return #put your code here
def leaky_relu_derivative(self, x, alpha=0.01):
return #put your code here
def softmax(self, x):
exp_values = #put your code here
return #put your code here
# Complete the forward method
def forward(self, inputs):
# Forward pass through the network
self.hidden_layer_inputs = np.dot(inputs, self.weights_input_hidden)+ self.biases_hidden
if self.activation == 'relu':
self.hidden_layer_outputs = #put your code here
elif self.activation == 'sigmoid':
self.hidden_layer_outputs = #put your code here
elif self.activation == 'tanh':
self.hidden_layer_outputs = #put your code here
elif self.activation == 'leaky_relu':
self.hidden_layer_outputs = #put your code here
self.output_layer_inputs = np.dot(self.hidden_layer_outputs, self.weights_hidden_output)+ self.biases_output
if self.problem_type == 'classification':
self.output_layer_outputs = #put your code here
elif self.problem_type == 'regression':
self.output_layer_outputs = #put your code here
return self.output_layer_outputs
# Complete the backward method
def backward(self, inputs, targets):
# Backward pass through the network
# Compute error
output_error = #put your code here
output_delta = output_error
if self.activation == 'relu':
hidden_error = #put your code here
hidden_delta = #put your code here
elif self.activation == 'sigmoid':
hidden_error = #put your code here
hidden_delta = #put your code here
elif self.activation == 'tanh':
hidden_error = #put your code here
hidden_delta = #put your code here
elif self.activation == 'leaky_relu':
hidden_error = #put your code here
hidden_delta = #put your code here
# Update weights and biases
self.weights_hidden_output = #put your code here
self.biases_output = #put your code here
self.weights_input_hidden = #put your code here
self.biases_hidden = #put your code here
# Complete the train method
def train(self, inputs, targets, epochs):
# Train the network
for epoch in range(epochs):
# Forward propagation
predictions = #put your code here
# Backward propagation
#put your code here
# Compute loss
if self.problem_type == 'classification':
loss = #put your code here
elif self.problem_type == 'regression':
loss = #put your code here
if epoch %10==0:
print(f'Epoch {epoch}, Loss: {loss}')
# Complete the test method
def test(self, inputs, targets):
# Test the network on a separate dataset
predictions = #put your code here
if self.problem_type == 'classification':
predicted_classes = np.argmax(predictions, axis=1)
true_classes = np.argmax(targets, axis=1)
accuracy = np.mean(predicted_classes == true_classes)
print(f'Test Accuracy: {accuracy *100}%')
elif self.problem_type == 'regression':
mse = #put your code here
print(f'Mean Squared Error: {mse}')
return accuracy if self.problem_type == 'classification' else mse
(Remaining code is in image)
1 . Complete the implementation of the Neural

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