human
I am working on the file "01_first-neural-network/Your_first_neural_network.ipynb".
The first task is:
Time to build the network
Below you'll build your network. We've built out the structure and the backwards pass.
You'll implement the forward pass through the network. You'll also set the hyperparameters: the learning rate, the number of hidden units, and the number of training passes.
<img src="assets/neural_network.png" width=300px>

The network has three layers: an input layer, a hidden layer and an output layer.
The hidden layer will use the sigmoid function for activations.
The output layer has only one node and is used for the regression
The output of the node is the same as the input of the node. That is, the activation function is $f(x)=x$.

Below, you have these tasks:
1. Implement the sigmoid function to use as the activation function. Set self.activation_function in __init__ to your sigmoid function.
2. Implement the forward pass in the train method.
3. Implement the backpropagation algorithm in the train method, including calculating the output error.
4. Implement the forward pass in the run method.
Can you write Python code for it?
 ... Write me a the Python code you requested?
assistant
#Here is the Python code you requested.

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):
            ### Forward pass ###
            hidden_inputs = np.dot(self.weights_input_to_hidden.T, X)           
            hidden_outputs = self.activation_function(hidden_inputs)

            final_inputs = np.dot(self.weights_hidden_to_output.T, hidden_outputs)
            final_outputs = final_inputs

            
            ### Backward pass ###
            error = y - final_outputs
            output_error_term = error
            
            hidden_error = output_error_term * self.weights_hidden_to_output
            hidden_error_term = hidden_error.T * hidden_outputs * (1 - hidden_outputs)

            # Weight step (input to hidden)
           ...