TypeError: string argument expected, got 'bytes'

代码：

# 导入仿真库
import tensorflow as tf
import numpy as np

# 导入可视化库
import PIL.Image
from cStringIO import StringIO
from IPython.display import clear_output, Image, display
import scipy.ndimage as nd

# 导入仿真库
import tensorflow as tf
import numpy as np

# 导入可视化库
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'):
"""显示迭代计算出的彩色分形图像。"""
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()

# 使用NumPy创建一个在[-2,2]x[-2,2]范围内的2维复数数组

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.initialize_all_variables().run()

# 计算一个新值z: z^2 + x
zs_ = zs*zs + xs

# 这个新值会发散吗？
not_diverged = tf.complex_abs(zs_) < 4

# 更新zs并且迭代计算。
#
# 说明：在这些值发散之后，我们仍然在计算zs，这个计算消耗特别大！
#      如果稍微简单点，这里有更好的方法来处理。
#
step = tf.group(
zs.assign(zs_),
ns.assign_add(tf.cast(not_diverged, "float32"))
)

for i in range(200): step.run()

DisplayFractal(ns.eval())

def DisplayFractal(a, fmt='jpeg'):
"""显示迭代计算出的彩色分形图像。"""
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()

# 使用NumPy创建一个在[-2,2]x[-2,2]范围内的2维复数数组

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.initialize_all_variables().run()

# 计算一个新值z: z^2 + x
zs_ = zs*zs + xs

# 这个新值会发散吗？
not_diverged = tf.complex_abs(zs_) < 4

# 更新zs并且迭代计算。
#
# 说明：在这些值发散之后，我们仍然在计算zs，这个计算消耗特别大！
#      如果稍微简单点，这里有更好的方法来处理。
#
step = tf.group(
zs.assign(zs_),
ns.assign_add(tf.cast(not_diverged, "float32"))
)

for i in range(200): step.run()

DisplayFractal(ns.eval())

报错：

ModuleNotFoundError: No module named 'cStringIO'
AttributeError: module 'tensorflow' has no attribute 'complex_abs'
TypeError: string argument expected, got 'bytes'

解决办法：

from io import BytesIO
f = BytesIO()
not_diverged = tf.abs(zs_) < 4