project-bikesharing/my_answers.py
def forward_pass_train(self, X):
        ''' Implement forward pass here 
         
            Arguments
            ---------
            X: features batch

        '''
        #### Implement the forward pass here ####
        ### Forward pass ###
        # DONE: Hidden layer - Replace these values with your calculations.
        hidden_inputs = np.matmul(X, self.weights_input_to_hidden)

        #hidden_outputs = None # signals from hidden layer
        hidden_outputs = self.activation_function(hidden_inputs)  # signals from hidden layer

        # DONE: Output layer - Replace these values with your calculations.
        final_inputs = np.matmul(hidden_outputs, self.weights_hidden_to_output)
        final_outputs = final_inputs   #do not use activation function for final output.  Do not use derivative for this during backprob.
        
        return final_outputs, hidden_outputs
 Implement forward pass here 
         
            Arguments
            ---------
            X: features batch

        
Implement forward pass here
Arguments

features batch
Implement forward pass here Arguments features batch
def forward_pass_train(self, X):
        hidden_inputs = np.matmul(X, self.weights_input_to_hidden)
        hidden_outputs = self.activation_function(hidden_inputs)  
        final_inputs = np.matmul(hidden_outputs, self.weights_hidden_to_output)
        final_outputs = final_inputs   
        return final_outputs, hidden_outputs
def forward_pass_train ( self , X ) : hidden_inputs = np . matmul ( X , self . weights_input_to_hidden ) hidden_outputs = self . activation_function ( hidden_inputs ) final_inputs = np . matmul ( hidden_outputs , self . weights_hidden_to_output ) final_outputs = final_inputs return final_outputs , hidden_outputs
Implement forward pass here
Arguments
Implement forward pass here Arguments
''' Implement forward pass here 
         
            Arguments
            ---------
            X: features batch

        ''' #### Implement the forward pass here #### ### Forward pass ### # DONE: Hidden layer - Replace these values with your calculations. #hidden_outputs = None # signals from hidden layer # signals from hidden layer # DONE: Output layer - Replace these values with your calculations. #do not use activation function for final output.  Do not use derivative for this during backprob.
self
X