human
I am working on the file "DLND-your-first-network/dlnd-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.
The network has two layers, 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 $f(x)=x$. A function that takes the input signal and generates an output signal, but takes into account the threshold, is called an activation function. We work through each layer of our network calculating the outputs for each neuron. All of the outputs from one layer become inputs to the neurons on the next layer. This process is called forward propagation.
We use the weights to propagate signals forward from the input to the output layers in a neural network. We use the weights to also propagate error backwards from the output back into the network to update our weights. This is called backpropagation.

Hint: You'll need the derivative of the output activation function ($f(x) = x$) for the backpropagation implementation. If you aren't familiar with calculus, this function is equivalent to the equation $y = x$. What is the slope of that equation? That is the derivative of $f(x)$.

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 program for it?

Write me a the slope of that equation?
assistant
#Here is the slope of that equation.

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.hidden_nodes**-0.5, 
                                       (self.hidden_nodes, self.input_nodes))

        self.weights_hidden_to_output = np.random.normal(0.0, self.output_nodes**-0.5, 
                                       (self.output_nodes, self.hidden_nodes))
        self.lr = learning_rate
        
        #### Set this to your implemented sigmoid function ####
        # Activation function is the sigmoid function
        self.activation_function = lambda x: 1 / (1 + np.exp(-x))
    
    def train(self, inputs_list, targets_list):
        # Convert inputs list to 2d array
        inputs = np.array(inputs_list, ndmin=2).T
        targets = np.array(targets_list, ndmin=2).T
        
        #### Implement the forward pass here ####
        ### Forward pass ###
        # DONE: Hidden layer
        hidden_inputs = np.dot(self.weights_input_to_hidden, inputs)
        hidden_outputs = self.activation_function(hidden_inputs)
        
        # DONE: Output layer
        final_inputs = np.dot(self.weights_hidden_to_output, hidden_outputs)
        final_outputs = final_inputs
        
        #### Implement the backward pass here ####
        ### Backward pass ###
        
        # DONE: Output error  (output_error = output_grad as activation funct is f(x) = x)
        output_errors = targets - final_outputs
        
        # DONE: Backpropagated error
        hidden_errors = np.dot(self.weights_hidden_to_output.T, output_errors)
        hidden_grad = hidden_outputs * (1 - hidden_outputs)
        
        # DONE: Update the weights    
        self.weights_hidden_to_output += self.lr * np.dot(output_errors,...