(self, x, y, steps)
return self.sess.run((self.x_reconstr), {self.z: all_z})
Interpolate between and x and y input vectors in latent space.
        x, y np.arrays of size (n_points, dim_embedding).
Interpolate between and x and y input vectors in latent space.
        x, y np.arrays of size (n_points, dim_embedding).
Interpolate between and x and y input vectors in latent space . x y np . arrays of size ( n_points dim_embedding ) .
def interpolate(self, x, y, steps):
        """ Interpolate between and x and y input vectors in latent space.
        x, y np.arrays of size (n_points, dim_embedding).
        """
        in_feed = np.vstack((x, y))
        z1, z2 = self.transform(in_feed.reshape([2] + self.n_input))
        all_z = np.zeros((steps + 2, len(z1)))

        for i, alpha in enumerate(np.linspace(0, 1, steps + 2)):
            all_z[i, :] = (alpha * z2) + ((1.0 - alpha) * z1)

        return self.sess.run((self.x_reconstr), {self.z: all_z})
def interpolate ( self , x , y , steps ) : in_feed = np . vstack ( ( x , y ) ) z1 , z2 = self . transform ( in_feed . reshape ( [ 2 ] + self . n_input ) ) all_z = np . zeros ( ( steps + 2 , len ( z1 ) ) ) for i , alpha in enumerate ( np . linspace ( 0 , 1 , steps + 2 ) ) : all_z [ i , : ] = ( alpha * z2 ) + ( ( 1.0 - alpha ) * z1 ) return self . sess . run ( ( self . x_reconstr ) , { self . z : all_z } )