tensorflow模拟仿真 博客分类: tensorflow tensorflow
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2024-03-21 09:16:17
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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http://wiki.jikexueyuan.com/project/tensorflow-zh/tutorials/pdes.html
https://*.com/questions/28237210/image-does-not-display-in-ipython/42567537?utm_medium=organic&utm_source=google_rich_qa&utm_campaign=google_rich_qa
代码运行不了
修改如下
把display(Image(data=f.getvalue()))
改成
scipy.misc.imsave("testimg.jpg", a)
image = PIL.Image.open("testimg.jpg")
image.show()
python hello.py
完整版还不完美的图片闪现的版本
参考http://wiki.jikexueyuan.com/project/tensorflow-zh/tutorials/pdes.html
https://*.com/questions/28237210/image-does-not-display-in-ipython/42567537?utm_medium=organic&utm_source=google_rich_qa&utm_campaign=google_rich_qa
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
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])
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