NNModel.py

import numpy as np


class NeuralNetwork(object):
    def __init__(self, input_nodes, hidden_nodes, output_nodes, learning_rate):
        # Set number of nodes in input, hidden and output layers.
        self.input_nodes = input_nodes
        self.hidden_nodes = hidden_nodes
        self.output_nodes = output_nodes

        # Initialize weights
        self.weights_input_to_hidden = np.random.normal(0.0, self.input_nodes ** -0.5,
                                                        (self.input_nodes, self.hidden_nodes))

        self.weights_hidden_to_output = np.random.normal(0.0, self.hidden_nodes ** -0.5,
                                                         (self.hidden_nodes, self.output_nodes))
        self.lr = learning_rate

        self.activation_function = lambda x : 1/(1+np.exp(-x))

    def train(self, features, targets):
        ''' Train the network on batch of features and targets.
            Arguments
            ---------
            features: 2D array, each row is one data record, each column is a feature
            targets: 1D array of target values
        '''
        n_records = features.shape[0]
        delta_weights_i_h = np.zeros(self.weights_input_to_hidden.shape)
        delta_weights_h_o = np.zeros(self.weights_hidden_to_output.shape)
        for X, y in zip(features, targets):
            final_outputs, hidden_outputs = self.forward_pass_train(X)
            delta_weights_i_h, delta_weights_h_o = self.backpropagation(final_outputs, hidden_outputs, X, y, delta_weights_i_h, delta_weights_h_o)


            self.update_weights(delta_weights_i_h, delta_weights_h_o, n_records)

    def forward_pass_train(self, X):
        ''' Implement forward pass here
            Arguments
            ---------
            X: features batch
        '''
        hidden_inputs = np.dot(X, self.weights_input_to_hidden)
        hidden_outputs = self.activation_function(hidden_inputs)
        final_inputs = np.dot(hidden_outputs, self.weights_hidden_to_output)  # signals into final output layer
        final_outputs = final_inputs * 1

        return final_outputs, hidden_outputs
    
    def sigmoid_derivate(self, out):
        return  out * (1.0 - out)

    def backpropagation(self, final_outputs, hidden_outputs, X, y, delta_weights_i_h, delta_weights_h_o):
        ''' Implement backpropagation
            Arguments
            ---------
            final_outputs: output from forward pass
            y: target (i.e. label) batch
            delta_weights_i_h: change in weights from input to hidden layers
            delta_weights_h_o: change in weights from hidden to output layers
        '''

        error = y - final_outputs
        output_error_term = error
        hidden_error = np.dot(self.weights_hidden_to_output, output_error_term)

        hidden_error_term = hidden_error * self.sigmoid_derivate(hidden_outputs)

        delta_weights_i_h += hidden_error_term * X[:, None]
        delta_weights_h_o += output_error_term * hidden_outputs[:, None]
        
        return delta_weights_i_h, delta_weights_h_o


    def update_weights(self, delta_weights_i_h, delta_weights_h_o, n_records):
        ''' Update weights on gradient descent step
            Arguments
            ---------
            delta_weights_i_h: change in weights from input to hidden layers
            delta_weights_h_o: change in weights from hidden to output layers
            n_records: number of records
        '''
        self.weights_hidden_to_output +=  self.lr * delta_weights_h_o / n_records  # update hidden-to-output weights with gradient descent step
        self.weights_input_to_hidden += self.lr * delta_weights_i_h / n_records

    def run(self, features):
        ''' Run a forward pass through the network with input features
            Arguments
            ---------
            features: 1D array of feature values
        '''

        #### Implement the forward pass here ####
        # TODO: Hidden layer - replace these values with the appropriate calculations.
        hidden_inputs = np.dot(features, self.weights_input_to_hidden)  # signals into hidden layer
        hidden_outputs = self.activation_function(hidden_inputs)

        # TODO: Output layer - Replace these values with the appropriate calculations.
        final_inputs = np.dot(hidden_outputs, self.weights_hidden_to_output)  # signals into final output layer
        final_outputs = final_inputs  # signals from final output layer

        return final_outputs


#########################################################
# Set your hyperparameters here
##########################################################
iterations = 1500
learning_rate = 0.1
hidden_nodes = 5
output_nodes = 1