project-bikesharing/my_answers.py
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 ####
        # DONE: Hidden layer - replace these values with the appropriate calculations.
        hidden_inputs = np.dot(features, self.weights_input_to_hidden)
        hidden_outputs = np.array([self.activation_function(i) for i in hidden_inputs])
        
        # DONE: Output layer - Replace these values with the appropriate calculations.
        final_inputs = np.dot(hidden_outputs, self.weights_hidden_to_output)
        final_outputs = final_inputs  #np.sum(final_inputs)

        return final_outputs
 Run a forward pass through the network with input features 
        
            Arguments
            ---------
            features: 1D array of feature values
        
Run a forward pass through the network with input features
Arguments

1D array of feature values
Run a forward pass through the network with input features Arguments 1D array of feature values
def run(self, features):
        hidden_inputs = np.dot(features, self.weights_input_to_hidden)
        hidden_outputs = np.array([self.activation_function(i) for i in hidden_inputs])
        final_inputs = np.dot(hidden_outputs, self.weights_hidden_to_output)
        final_outputs = final_inputs  
        return final_outputs
def run ( self , features ) : hidden_inputs = np . dot ( features , self . weights_input_to_hidden ) hidden_outputs = np . array ( [ self . activation_function ( i ) for i in hidden_inputs ] ) final_inputs = np . dot ( hidden_outputs , self . weights_hidden_to_output ) final_outputs = final_inputs return final_outputs
Run a forward pass through the network with input features
Arguments
Run a forward pass through the network with input features Arguments
''' Run a forward pass through the network with input features 
        
            Arguments
            ---------
            features: 1D array of feature values
        ''' #### Implement the forward pass here #### # DONE: Hidden layer - replace these values with the appropriate calculations. # DONE: Output layer - Replace these values with the appropriate calculations. #np.sum(final_inputs)
self
features