human
I am working on the file "GAN/GAN 101.ipynb".
The first task is:
Define model
Remarks
Initialize the variables with caution
In the two loss function defined below, singulars will occur in case the output of the network contains $1$ or $0$, given that the loss function contains the term $\log(x)$ or $\log(1-x)$. In our case the output layer of both generator and discriminator network contains a simgoid function. In $y=sigmoid(wx+b)$, if the weights are too large then the result of this term quickly approaches $0$ or $1$ as $x$ moves away from $0$. Hence the mean and stddev of weight distribution at the output layer are set to $0$ a relatively small value, respectively, to opt out the occurence of singulars in loss functions.
Can you write Python program for it?
 Can you provide a the Python program you requested?
assistant
#Here is the Python program you requested.

class GAN:
    def __init__(self, noise_input_tensor, image_input_tensor, generator_hidden_dim, discriminator_hidden_dim):
        self._noise_input = noise_input_tensor
        self._image_input = image_input_tensor
        with tf.variable_scope("generator"):
            self._generator_output, self._generator_parameters = self._fnn(
                noise_input_tensor, generator_hidden_dim, image_input_tensor.shape[1], 
                activation=lambda x: 255 * tf.sigmoid(x)) # (0, 1) -> (0.0, 255.0)
        with tf.variable_scope("discriminator"):
            self._discriminator_output_for_real_data, self._discriminator_parameters = self._fnn(
                image_input_tensor, discriminator_hidden_dim, 1, activation=tf.sigmoid)
        with tf.variable_scope("discriminator", reuse=True):  # Share weights and biases
            d_o_fake = self._discriminator_output_for_synth, _ = self._fnn(self._generator_output, 
                                                                           discriminator_hidden_dim, 
                                                                           1, 
                                                                           activation=tf.sigmoid)
        
    def _fnn(self, input_tensor, hidden_dim, output_dim, activation=None):
        w_xh = tf.get_variable(initializer=tf.truncated_normal_initializer(mean=0.0, stddev=0.01),
                            shape=[input_tensor.shape[1], hidden_dim], name="W_xh")
        b_xh = tf.get_variable(initializer=tf.truncated_normal_initializer(mean=0.0, stddev=0.01),
                            shape=[hidden_dim], name="b_xh")
        hidden = tf.nn.relu(tf.add(tf.matmul(input_tensor, w_xh), b_xh))
        w_ho = tf.get_variable(initializer=tf.truncated_normal_initializer(mean=0.0, stddev=0.01), 
                            shape=[hidden_dim, output_dim], name="W_ho")
        b_ho = tf.get_variable(initializer=tf.truncated_normal_initializer(mean=0.0, stddev=0.01), 
                            shape=[output_dim],...