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import tensorflow as tf
help(tf.nn.depthwise_conv2d)
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#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Mon Sep 4 13:43:09 2017
Author: Peiyong Jiang : jiangpeiyong@impcas.ac.cn
Function:
"""
import tensorflow as tf
import numpy as np
#导入可视化需要的库
import PIL.Image
#from io import StringIO
from io import BytesIO as StringIO
from IPython.display import clear_output, Image, display
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
plt.close('all')
#然后，我们还需要一个用于表示池塘表面状态的函数。
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()
PIL.Image.fromarray(a).save(f, fmt)
display(Image(data=f.getvalue()))
#最后，为了方便演示，这里我们需要打开一个 TensorFlow 的交互会话（interactive session）。当然为了以后能方便调用，我们可以把相关代码写到一个可以执行的Python文件中。
sess = tf.InteractiveSession()
#定义计算函数
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)
#定义偏微分方程
#首先,我们需要创建一个完美的 500 × 500 的正方形池塘,就像是我们在现实中找到的一样。
N = 32
#然后，我们需要创建了一个池塘和几滴将要坠入池塘的雨滴。
# 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(4):
a,b = np.random.randint(0, N, 2)
u_init[a,b] = np.random.uniform()
print(u_init)
'''
disPart=np.random.multivariate_normal([0,0],[[1.,0.],[0.,3.]],1000)
'''
plt.figure(1)
plt.clf()
plt.plot(disPart[:,0],disPart[:,1],'.')
plt.show()
'''
print('_______________')
print(disPart)
allHistDisPart=np.histogram2d(disPart[:,0],disPart[:,1],[N,N])
histDisPart=allHistDisPart[0]
print('===============')
print(histDisPart)
print('_______________')
xEdge,yEdge=allHistDisPart[1],allHistDisPart[2]
xGrid,yGrid=np.meshgrid((xEdge[0:-1:]+xEdge[1::])/2.,(yEdge[0:-1:]+yEdge[1::])/2.)
ut_init=np.float32(histDisPart)
DisplayArray(u_init, rng=[-0.1, 0.1])
#jpeg
#现在，让我们来指定该微分方程的一些详细参数。
# 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)
U_ = Ut
Ut_ = laplace(U)
# Operation to update the state
step = tf.group(
U.assign(U_),
Ut.assign(Ut_))
#开始仿真
#为了能看清仿真效果，我们可以用一个简单的 for 循环来远行我们的仿真程序。
# Initialize state to initial conditions
tf.global_variables_initializer().run()
# Run 1000 steps of PDE
for i in range(100):
# 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])
#fig = plt.figure(1)
#plt.clf()
#ax = fig.add_subplot(111,projection='3d')
#ax.scatter(xGrid,yGrid,U.eval())
#plt.show()
uTemp=list(U.eval())
uArray=np.array(uTemp)
fig = plt.figure(1)
plt.clf()
ax = fig.add_subplot(111,projection='3d')
ax.plot_surface(xGrid,yGrid,uArray)
plt.show()
#print(np.shape(xGrid))
plt.pause(0.3)
print('END')
In [1]:
help(tf.nn)
In [2]:
import tensorflow as tf
help(tf.nn.depthwise_conv2d)
In [ ]: