human
I am working on the file "Day-5/Day 5 - Vanilla GAN Solved.ipynb".
The first task is:
5.3 The generator network
Can you write Python code for it?
assistant
#Here is the Python code you requested.

class Generator:
    """The generator network
    
    the generator network takes as input a vector z of dimension input_dim, and transforms it 
    to a vector of size output_dim. The network has one hidden layer of size hidden_dim.
    
    We will define the following methods: 
    
    __init__: initializes all variables by using tf.get_variable(...) 
                and stores them to the class, as well a list in self.theta
    forward: defines the forward pass of the network - how do the variables
                interact with respect to the inputs
    """
    
    def __init__(self, input_dim, hidden_dim, output_dim):
        """Constructor for the generator network. In the constructor, we will
        just initialize all the variables in the network.
        
        Args:
            input_dim: The dimension of the input data vector (z).
            hidden_dim: The dimension of the hidden layer of the neural network (h)
            output_dim: The dimension of the output layer (equivalent to the size of the image)            
            
        """
        
        with tf.variable_scope("generator"):
            self.W1 = tf.get_variable(name="W1", 
                                      shape=[input_dim, hidden_dim], 
                                      initializer=tf.contrib.layers.xavier_initializer())
            self.b1 = tf.get_variable(name="b1", 
                                      shape=[hidden_dim], 
                                      initializer=tf.zeros_initializer())
            
            self.W2 = tf.get_variable(name="W2", 
                                      shape=[hidden_dim, output_dim], 
                                      initializer=tf.contrib.layers.xavier_initializer())
            self.b2 = tf.get_variable(name="b2", 
                                      shape=[output_dim], 
                                      initializer=tf.zeros_initializer())

            self.theta = [self.W1, self.W2, self.b1, self.b2]
    
    def forward(self, z):
        """The...