type object ‘Image’ has no attribute ‘fromarray’

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Question:
When I compile my code, it request me the result like below:D:PythonAnaconda3envstensorflowpython.exe D:/Python/pycharm_project/test/mnist_chuji
2017-08-15 14:07:37.587932: W c:tf_jenkinshomeworkspacerelease-windevicecpuoswindowstensorflowcoreplatformcpu_feature_guard.cc:45] The TensorFlow library wasn’t compiled to use SSE instructions, but these are available on your machine and could speed up CPU computations.
2017-08-15 14:07:37.588611: W c:tf_jenkinshomeworkspacerelease-windevicecpuoswindowstensorflowcoreplatformcpu_feature_guard.cc:45] The TensorFlow library wasn’t compiled to use SSE2 instructions, but these are available on your machine and could speed up CPU computations.
2017-08-15 14:07:37.589142: W c:tf_jenkinshomeworkspacerelease-windevicecpuoswindowstensorflowcoreplatformcpu_feature_guard.cc:45] The TensorFlow library wasn’t compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations.
2017-08-15 14:07:37.589598: W c:tf_jenkinshomeworkspacerelease-windevicecpuoswindowstensorflowcoreplatformcpu_feature_guard.cc:45] The TensorFlow library wasn’t compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2017-08-15 14:07:37.590038: W c:tf_jenkinshomeworkspacerelease-windevicecpuoswindowstensorflowcoreplatformcpu_feature_guard.cc:45] The TensorFlow library wasn’t compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2017-08-15 14:07:37.590437: W c:tf_jenkinshomeworkspacerelease-windevicecpuoswindowstensorflowcoreplatformcpu_feature_guard.cc:45] The TensorFlow library wasn’t compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
Traceback (most recent call last):File "D:/Python/pycharm_project/test/mnist_chuji", line 52, in
DisplayArray(u_init, rng=[-0.1, 0.1])
File "D:/Python/pycharm_project/test/mnist_chuji", line 15, in DisplayArray
Image.fromarray(a).save(f, fmt)
AttributeError: type object ‘Image’ has no attribute ‘fromarray’

Process finished with exit code 1

And here is my code( I have marked the line numbers that happened in the error list):#导入模拟仿真需要的库
import tensorflow as tf
import numpy as np

#导入可视化需要的库
from PIL import Image
from io import StringIO #python3 使用了io代替了sStringIO
from IPython.display import clear_output, Image, display

def DisplayArray(a, fmt=’jpeg’, rng=[0,1]):"""Display an array as a picture."""
a = (a – rng[0])/float(rng[1] – rng[0])*255
a = np.uint8(np.clip(a, 0, 255))
f = StringIO()
Image.fromarray(a).save(f, fmt) #line 15
display(Image(data=f.getvalue()))

sess = tf.InteractiveSession()

def make_kernel(a):"""Transform a 2D array into a convolution kernel"""
a = np.asarray(a)
a = a.reshape(list(a.shape) + [1,1])
return tf.constant(a, dtype=1)

def simple_conv(x, k):"""A simplified 2D convolution operation"""
x = tf.expand_dims(tf.expand_dims(x, 0), -1)
y = tf.nn.depthwise_conv2d(x, k, [1, 1, 1, 1], padding=’SAME’)
return y[0, :, :, 0]

def laplace(x):"""Compute the 2D laplacian of an array"""
laplace_k = make_kernel([[0.5, 1.0, 0.5],
[1.0, -6., 1.0],
[0.5, 1.0, 0.5]])
return simple_conv(x, laplace_k)

N = 500

# Initial Conditions — some rain drops hit a pond

# Set everything to zero
u_init = np.zeros([N, N], dtype="float32")
ut_init = np.zeros([N, N], dtype="float32")

# Some rain drops hit a pond at random points
for n in range(40):a,b = np.random.randint(0, N, 2)
u_init[a,b] = np.random.uniform()

DisplayArray(u_init, rng=[-0.1, 0.1]) #line 52

# Parameters:# eps — time resolution
# damping — wave damping
eps = tf.placeholder(tf.float32, shape=())
damping = tf.placeholder(tf.float32, shape=())

# Create variables for simulation state
U = tf.Variable(u_init)
Ut = tf.Variable(ut_init)

# Discretized PDE update rules
U_ = U + eps * Ut
Ut_ = Ut + eps * (laplace(U) – damping * Ut)

# Operation to update the state
step = tf.group(
U.assign(U_),
Ut.assign(Ut_))

# Initialize state to initial conditions
tf.initialize_all_variables().run()

# Run 1000 steps of PDE
for i in range(1000):# Step simulation
step.run({eps: 0.03, damping: 0.04})
# Visualize every 50 steps
if i % 50 == 0:clear_output()
DisplayArray(U.eval(), rng=[-0.1, 0.1])

I don’t why my ‘Image’ has no attribute ‘fromarray’. I have installed the lib pillow.

At the beginning, I thought maybe because there are two versions of python(2.7 and 3.5) in my computer that makes this problem. then, I uninstall all my python environment and install py3.5 only again with installing the pillow. But there is no help…


Answer:
Try this
#导入模拟仿真需要的库
import tensorflow as tf
import numpy as np

#导入可视化需要的库
from PIL import Image
from io import StringIO #python3 使用了io代替了sStringIO
from IPython.display import clear_output, Image as displayImage, display

def DisplayArray(a, fmt=’jpeg’, rng=[0,1]):"""Display an array as a picture."""
a = (a – rng[0])/float(rng[1] – rng[0])*255
a = np.uint8(np.clip(a, 0, 255))
f = StringIO()
Image.fromarray(a).save(f, fmt) #line 15
display(displayImage(data=f.getvalue()))

sess = tf.InteractiveSession()

def make_kernel(a):"""Transform a 2D array into a convolution kernel"""
a = np.asarray(a)
a = a.reshape(list(a.shape) + [1,1])
return tf.constant(a, dtype=1)

def simple_conv(x, k):"""A simplified 2D convolution operation"""
x = tf.expand_dims(tf.expand_dims(x, 0), -1)
y = tf.nn.depthwise_conv2d(x, k, [1, 1, 1, 1], padding=’SAME’)
return y[0, :, :, 0]

def laplace(x):"""Compute the 2D laplacian of an array"""
laplace_k = make_kernel([[0.5, 1.0, 0.5],
[1.0, -6., 1.0],
[0.5, 1.0, 0.5]])
return simple_conv(x, laplace_k)

N = 500

# Initial Conditions — some rain drops hit a pond

# Set everything to zero
u_init = np.zeros([N, N], dtype="float32")
ut_init = np.zeros([N, N], dtype="float32")

# Some rain drops hit a pond at random points
for n in range(40):a,b = np.random.randint(0, N, 2)
u_init[a,b] = np.random.uniform()

DisplayArray(u_init, rng=[-0.1, 0.1]) #line 52

# Parameters:# eps — time resolution
# damping — wave damping
eps = tf.placeholder(tf.float32, shape=())
damping = tf.placeholder(tf.float32, shape=())

# Create variables for simulation state
U = tf.Variable(u_init)
Ut = tf.Variable(ut_init)

# Discretized PDE update rules
U_ = U + eps * Ut
Ut_ = Ut + eps * (laplace(U) – damping * Ut)

# Operation to update the state
step = tf.group(
U.assign(U_),
Ut.assign(Ut_))

# Initialize state to initial conditions
tf.initialize_all_variables().run()

# Run 1000 steps of PDE
for i in range(1000):# Step simulation
step.run({eps: 0.03, damping: 0.04})
# Visualize every 50 steps
if i % 50 == 0:clear_output()
DisplayArray(U.eval(), rng=[-0.1, 0.1])
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