``` #Import libraries for simulation import tensorflow as tf import numpy as np #Imports for visualization import PIL.Image from cStringIO import StringIO from IPython.display import clear_output, Image, display import scipy.ndimage as nd ``` ``` def DisplayFractal(a, fmt='jpeg'): """Display an array of iteration counts as a colorful picture of a fractal.""" a_cyclic = (6.28*a/20.0).reshape(list(a.shape)+[1]) img = np.concatenate([10+20*np.cos(a_cyclic), 30+50*np.sin(a_cyclic), 155-80*np.cos(a_cyclic)], 2) img[a==a.max()] = 0 a = img a = np.uint8(np.clip(a, 0, 255)) f = StringIO() PIL.Image.fromarray(a).save(f, fmt) display(Image(data=f.getvalue())) ``` ``` sess = tf.InteractiveSession() ``` Exception AssertionError: AssertionError() in > ignored ``` # Use NumPy to create a 2D array of complex numbers on [-2,2]x[-2,2] Y, X = np.mgrid[-1.3:1.3:0.005, -2:1:0.005] Z = X+1j*Y ``` ``` xs = tf.constant(Z.astype("complex64")) zs = tf.Variable(xs) ns = tf.Variable(tf.zeros_like(xs, "float32")) ``` ``` tf.InitializeAllVariables().run() ``` ``` # Compute the new values of z: z^2 + x zs_ = zs*zs + xs # Have we diverged with this new value? not_diverged = tf.complex_abs(zs_) < 4 # Operation to update the zs and the iteration count. #t # Note: We keep computing zs after they diverge! This # is very wasteful! There are better, if a little # less simple, ways to do this. # step = tf.group( zs.assign(zs_), ns.assign_add(tf.cast(not_diverged, "float32")) ) ``` ``` for i in range(200): step.run() ``` ``` DisplayFractal(ns.eval()) ``` ![jpeg](output_8_0.jpe) ``` ```