python - type object 'Image' has no attribute 'fromarray' -

when compile code, request me result below:

d:\python\anaconda3\envs\tensorflow\python.exe d:/python/pycharm_project/test/mnist_chuji 2017-08-15 14:07:37.587932: w c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] tensorflow library wasn't compiled use sse instructions, these available on machine , speed cpu computations. 2017-08-15 14:07:37.588611: w c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] tensorflow library wasn't compiled use sse2 instructions, these available on machine , speed cpu computations. 2017-08-15 14:07:37.589142: w c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] tensorflow library wasn't compiled use sse3 instructions, these available on machine , speed cpu computations. 2017-08-15 14:07:37.589598: w c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] tensorflow library wasn't compiled use sse4.1 instructions, these available on machine , speed cpu computations. 2017-08-15 14:07:37.590038: w c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] tensorflow library wasn't compiled use sse4.2 instructions, these available on machine , speed cpu computations. 2017-08-15 14:07:37.590437: w c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] tensorflow library wasn't compiled use avx instructions, these available on machine , speed cpu computations. traceback (most recent call last):   file "d:/python/pycharm_project/test/mnist_chuji", line 52, in <module>     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 exit code 1 

and here code( have marked line numbers happened in error list):

#导入模拟仿真需要的库 import tensorflow tf import numpy np  #导入可视化需要的库 pil import image io import stringio #python3 使用了io代替了sstringio ipython.display import clear_output, image, display  def displayarray(a, fmt='jpeg', rng=[0,1]):   """display array picture."""   = (a - rng[0])/float(rng[1] - rng[0])*255   = 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 2d array convolution kernel"""   = np.asarray(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 2d laplacian of 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 -- rain drops hit pond  # set 0 u_init = np.zeros([n, n], dtype="float32") ut_init = np.zeros([n, n], dtype="float32")  # rain drops hit pond @ random points 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 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 update state step = tf.group(   u.assign(u_),   ut.assign(ut_))  # initialize state initial conditions tf.initialize_all_variables().run()  # run 1000 steps of pde in range(1000):   # step simulation   step.run({eps: 0.03, damping: 0.04})   # visualize every 50 steps   if % 50 == 0:     clear_output()     displayarray(u.eval(), rng=[-0.1, 0.1]) 

i don't why 'image' has no attribute 'fromarray'. have installed lib pillow.

at beginning, thought maybe because there 2 versions of python(2.7 , 3.5) in computer makes problem. then, uninstall python environment , install py3.5 again installing pillow. there no help...

try

    #导入模拟仿真需要的库 import tensorflow tf import numpy np  #导入可视化需要的库 pil import image io import stringio #python3 使用了io代替了sstringio ipython.display import clear_output, image displayimage, display  def displayarray(a, fmt='jpeg', rng=[0,1]):   """display array picture."""   = (a - rng[0])/float(rng[1] - rng[0])*255   = 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 2d array convolution kernel"""   = np.asarray(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 2d laplacian of 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 -- rain drops hit pond  # set 0 u_init = np.zeros([n, n], dtype="float32") ut_init = np.zeros([n, n], dtype="float32")  # rain drops hit pond @ random points 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 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 update state step = tf.group(   u.assign(u_),   ut.assign(ut_))  # initialize state initial conditions tf.initialize_all_variables().run()  # run 1000 steps of pde in range(1000):   # step simulation   step.run({eps: 0.03, damping: 0.04})   # visualize every 50 steps   if % 50 == 0:     clear_output()     displayarray(u.eval(), rng=[-0.1, 0.1])