tensorflow模拟仿真

TensorFlow 不仅仅是用来机器学习，它更可以用来模拟仿真。在这里，我们将通过模拟仿真几滴落入一块方形水池的雨点的例子，来引导您如何使用 TensorFlow 中的偏微分方程来模拟仿真的基本使用方法。

参考[url]http://wiki.jikexueyuan.com/project/tensorflow-zh/tutorials/pdes.html[/url]

[url]https://stackoverflow.com/questions/28237210/image-does-not-display-in-ipython/42567537?utm_medium=organic&utm_source=google_rich_qa&utm_campaign=google_rich_qa[/url]
代码运行不了
修改如下

yum install libpng-devel freetype-devel   -y
yum install tcl tk tix-devel -y
yum install tk-devel tcl-devel -y
yum -y install tkinter

virtualenv pianweifen

pip install --upgrade pip
#pip-10.0.1
pip install tensorflow
pip install matplotlib
pip install pillow

pip install ipython
pip install scipy

把display(Image(data=f.getvalue()))
改成
scipy.misc.imsave("testimg.jpg", a)
image = PIL.Image.open("testimg.jpg")
image.show()

# encoding: utf-8
#导入模拟仿真需要的库
import tensorflow as tf
import numpy as np
import  matplotlib.pyplot as plt
#导入可视化需要的库
import PIL.Image
from cStringIO import StringIO
#from io import BytesIO
import scipy.misc
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()
  # f = BytesIO()
  PIL.Image.fromarray(a).save(f, fmt)
 # display(Image(data=f.getvalue()))
  scipy.misc.imsave("testimg.jpg", a)
  image = PIL.Image.open("testimg.jpg")
  image.show()
#def DisplayArray(a, rng=[0 ,1]):
#    plt.ion()
#    a = (a - rng[0])/float(rng[1]-rng[0])*255
#    a = np.uint8(np.clip(a, 0, 255))
#    print(a)
#    plt.imshow(a, cmap='gray')
#    plt.pause(1)
#    plt.close()
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])

]
python hello.py
[img]http://dl2.iteye.com/upload/attachment/0130/0049/ecfe97d0-e884-32f2-af79-ffc3e7cd1cce.png[/img]


完整版还不完美的图片闪现的版本

# encoding: utf-8
#导入模拟仿真需要的库
import tensorflow as tf
import numpy as np
import  matplotlib.pyplot as plt
#导入可视化需要的库
import PIL.Image
from cStringIO import StringIO
#from io import BytesIO
import scipy.misc
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()
    # f = BytesIO()
    PIL.Image.fromarray(a).save(f, fmt)
    #display(Image(data=f.getvalue()))
    scipy.misc.imsave("testimg.jpg", a)
    image = PIL.Image.open("testimg.jpg")
    image.show()
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])


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])