'''导入模拟库'''
import tensorflow as tf
import numpy as np
'''导入可视化'''
from PIL.Image
from io import BytesIO
from IPython.display import Image, display
'''现在我们将定义一个函数来实际显示图像
迭代次数'''
def DisplayFractal(a, fmt='jpeg'):
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 = BytesIO()
PIL.Image.fromarray(a).save(f, fmt)
display(Image(data=f.getvalue()))
sess = tf.InteractiveSession()
# Use NumPy to create a 2D array of complex numbers
Y, X = np.mgrid[-1.3:1.3:0.005, -2:1:0.005]
Z = X+1j*Y
print(Z)
#Now we define and initialize TensorFlow tensors.
xs = tf.constant(Z.astype(np.complex64))
zs = tf.Variable(xs)
ns = tf.Variable(tf.zeros_like(xs, tf.float32))
tf.global_variables_initializer().run()
zs_ = zs*zs + xs
print(zs)
# Have we diverged with this new value?
not_diverged = tf.abs(zs_) < 4
'''
更新 zs 和迭代计数的操作。
注意：在它们发散后我们继续计算 zs！这
非常浪费！如果有一点的话，还有更好的
不太简单，方法来做到这一点。
'''
步骤 = tf.group(zs.assign(zs_), ns.assign_add(tf.cast(not_diverged,
tf.float32)))
for i in range(200): step.run()
DisplayFractal(ns.eval())
