model.py
def generate(self, z=None, x_dim = 26, y_dim = 26, scale = 5.0):
    """ Generate data by sampling from latent space.

    If z is not None, data for this point in latent space is
    generated. Otherwise, z is drawn from prior in latent
    space.
    """
    if z is None:
        z = np.random.normal(size=self.z_dim).astype(np.float32)
    # Note: This maps to mean of distribution, we could alternatively
    # sample from Gaussian distribution

    z = np.reshape(z, (self.batch_size, self.z_dim))

    G = self.generator(gen_x_dim = x_dim, gen_y_dim = y_dim, reuse = True)
    gen_x_vec, gen_y_vec, gen_r_vec = self.coordinates(x_dim, y_dim, scale = scale)
    image = self.sess.run(G, feed_dict={self.z: z, self.x: gen_x_vec, self.y: gen_y_vec, self.r: gen_r_vec})
    return image
 Generate data by sampling from latent space.

    If z is not None, data for this point in latent space is
    generated. Otherwise, z is drawn from prior in latent
    space.
    
Generate data by sampling from latent space.
If z is not None, data for this point in latent space is
generated. Otherwise, z is drawn from prior in latent
space.
Generate data by sampling from latent space . If z is not None data for this point in latent space is generated . Otherwise z is drawn from prior in latent space .
def generate(self, z=None, x_dim = 26, y_dim = 26, scale = 5.0):
    if z is None:
        z = np.random.normal(size=self.z_dim).astype(np.float32)
    z = np.reshape(z, (self.batch_size, self.z_dim))
    G = self.generator(gen_x_dim = x_dim, gen_y_dim = y_dim, reuse = True)
    gen_x_vec, gen_y_vec, gen_r_vec = self.coordinates(x_dim, y_dim, scale = scale)
    image = self.sess.run(G, feed_dict={self.z: z, self.x: gen_x_vec, self.y: gen_y_vec, self.r: gen_r_vec})
    return image
def generate ( self , z = None , x_dim = 26 , y_dim = 26 , scale = 5.0 ) : if z is None : z = np . random . normal ( size = self . z_dim ) . astype ( np . float32 ) z = np . reshape ( z , ( self . batch_size , self . z_dim ) ) G = self . generator ( gen_x_dim = x_dim , gen_y_dim = y_dim , reuse = True ) gen_x_vec , gen_y_vec , gen_r_vec = self . coordinates ( x_dim , y_dim , scale = scale ) image = self . sess . run ( G , feed_dict = { self . z : z , self . x : gen_x_vec , self . y : gen_y_vec , self . r : gen_r_vec } ) return image
Generate data by sampling from latent space.
Generate data by sampling from latent space .
""" Generate data by sampling from latent space.

    If z is not None, data for this point in latent space is
    generated. Otherwise, z is drawn from prior in latent
    space.
    """ # Note: This maps to mean of distribution, we could alternatively # sample from Gaussian distribution
self
z
x_dim
y_dim
scale