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TensorFlow-Examples-master

于 2018-04-01 发布 文件大小:2814KB
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代码说明：

  基于Tensorflow的Unet实现，里面有详细的教程。(TensorFlow for Unet, in which there are detailed teaching lecture.)

文件列表：

TensorFlow-Examples-master\.gitignore, 118 , 2018-03-07
TensorFlow-Examples-master\examples\1_Introduction\basic_eager_api.py, 1937 , 2018-03-15
TensorFlow-Examples-master\examples\1_Introduction\basic_operations.py, 2355 , 2018-03-07
TensorFlow-Examples-master\examples\1_Introduction\helloworld.py, 483 , 2018-03-15
TensorFlow-Examples-master\examples\2_BasicModels\kmeans.py, 3159 , 2018-03-07
TensorFlow-Examples-master\examples\2_BasicModels\linear_regression.py, 2986 , 2018-03-07
TensorFlow-Examples-master\examples\2_BasicModels\linear_regression_eager_api.py, 2043 , 2018-03-07
TensorFlow-Examples-master\examples\2_BasicModels\logistic_regression.py, 2360 , 2018-03-07
TensorFlow-Examples-master\examples\2_BasicModels\logistic_regression_eager_api.py, 3155 , 2018-03-07
TensorFlow-Examples-master\examples\2_BasicModels\nearest_neighbor.py, 1735 , 2018-03-15
TensorFlow-Examples-master\examples\2_BasicModels\random_forest.py, 2753 , 2018-03-07
TensorFlow-Examples-master\examples\3_NeuralNetworks\autoencoder.py, 4755 , 2018-03-07
TensorFlow-Examples-master\examples\3_NeuralNetworks\bidirectional_rnn.py, 4571 , 2018-03-07
TensorFlow-Examples-master\examples\3_NeuralNetworks\convolutional_network.py, 4759 , 2018-03-07
TensorFlow-Examples-master\examples\3_NeuralNetworks\convolutional_network_raw.py, 4814 , 2018-03-07
TensorFlow-Examples-master\examples\3_NeuralNetworks\dcgan.py, 6243 , 2018-03-07
TensorFlow-Examples-master\examples\3_NeuralNetworks\dynamic_rnn.py, 7373 , 2018-03-07
TensorFlow-Examples-master\examples\3_NeuralNetworks\gan.py, 5655 , 2018-03-07
TensorFlow-Examples-master\examples\3_NeuralNetworks\multilayer_perceptron.py, 3562 , 2018-03-07
TensorFlow-Examples-master\examples\3_NeuralNetworks\neural_network.py, 3413 , 2018-03-07
TensorFlow-Examples-master\examples\3_NeuralNetworks\neural_network_eager_api.py, 4221 , 2018-03-07
TensorFlow-Examples-master\examples\3_NeuralNetworks\neural_network_raw.py, 3384 , 2018-03-07
TensorFlow-Examples-master\examples\3_NeuralNetworks\recurrent_network.py, 3989 , 2018-03-07
TensorFlow-Examples-master\examples\3_NeuralNetworks\variational_autoencoder.py, 5319 , 2018-03-07
TensorFlow-Examples-master\examples\4_Utils\save_restore_model.py, 4859 , 2018-03-07
TensorFlow-Examples-master\examples\4_Utils\tensorboard_advanced.py, 5152 , 2018-03-07
TensorFlow-Examples-master\examples\4_Utils\tensorboard_basic.py, 3346 , 2018-03-07
TensorFlow-Examples-master\examples\5_DataManagement\build_an_image_dataset.py, 7397 , 2018-03-07
TensorFlow-Examples-master\examples\5_DataManagement\tensorflow_dataset_api.py, 5345 , 2018-03-07
TensorFlow-Examples-master\examples\6_MultiGPU\multigpu_basics.py, 2356 , 2018-03-07
TensorFlow-Examples-master\examples\6_MultiGPU\multigpu_cnn.py, 7873 , 2018-03-07
TensorFlow-Examples-master\input_data.py, 5709 , 2018-03-07
TensorFlow-Examples-master\LICENSE, 1386 , 2018-03-07
TensorFlow-Examples-master\notebooks\0_Prerequisite\ml_introduction.ipynb, 1914 , 2018-03-07
TensorFlow-Examples-master\notebooks\0_Prerequisite\mnist_dataset_intro.ipynb, 2622 , 2018-03-07
TensorFlow-Examples-master\notebooks\1_Introduction\basic_operations.ipynb, 5297 , 2018-03-07
TensorFlow-Examples-master\notebooks\1_Introduction\helloworld.ipynb, 1592 , 2018-03-07
TensorFlow-Examples-master\notebooks\2_BasicModels\kmeans.ipynb, 6364 , 2018-03-07
TensorFlow-Examples-master\notebooks\2_BasicModels\linear_regression.ipynb, 62033 , 2018-03-07
TensorFlow-Examples-master\notebooks\2_BasicModels\logistic_regression.ipynb, 5001 , 2018-03-07
TensorFlow-Examples-master\notebooks\2_BasicModels\nearest_neighbor.ipynb, 13095 , 2018-03-07
TensorFlow-Examples-master\notebooks\2_BasicModels\random_forest.ipynb, 7735 , 2018-03-07
TensorFlow-Examples-master\notebooks\3_NeuralNetworks\autoencoder.ipynb, 47239 , 2018-03-07
TensorFlow-Examples-master\notebooks\3_NeuralNetworks\bidirectional_rnn.ipynb, 11956 , 2018-03-07
TensorFlow-Examples-master\notebooks\3_NeuralNetworks\convolutional_network.ipynb, 33214 , 2018-03-07
TensorFlow-Examples-master\notebooks\3_NeuralNetworks\convolutional_network_raw.ipynb, 12114 , 2018-03-07
TensorFlow-Examples-master\notebooks\3_NeuralNetworks\dcgan.ipynb, 50310 , 2018-03-07
TensorFlow-Examples-master\notebooks\3_NeuralNetworks\dynamic_rnn.ipynb, 15134 , 2018-03-07
TensorFlow-Examples-master\notebooks\3_NeuralNetworks\gan.ipynb, 46898 , 2018-03-07
TensorFlow-Examples-master\notebooks\3_NeuralNetworks\neural_network.ipynb, 30679 , 2018-03-07
TensorFlow-Examples-master\notebooks\3_NeuralNetworks\neural_network_raw.ipynb, 7146 , 2018-03-07
TensorFlow-Examples-master\notebooks\3_NeuralNetworks\recurrent_network.ipynb, 11292 , 2018-03-07
TensorFlow-Examples-master\notebooks\3_NeuralNetworks\variational_autoencoder.ipynb, 298726 , 2018-03-07
TensorFlow-Examples-master\notebooks\4_Utils\save_restore_model.ipynb, 8471 , 2018-03-07
TensorFlow-Examples-master\notebooks\4_Utils\tensorboard_advanced.ipynb, 10238 , 2018-03-07
TensorFlow-Examples-master\notebooks\4_Utils\tensorboard_basic.ipynb, 6959 , 2018-03-07
TensorFlow-Examples-master\notebooks\5_DataManagement\build_an_image_dataset.ipynb, 10524 , 2018-03-07
TensorFlow-Examples-master\notebooks\5_DataManagement\tensorflow_dataset_api.ipynb, 8841 , 2018-03-07
TensorFlow-Examples-master\notebooks\6_MultiGPU\multigpu_basics.ipynb, 4385 , 2018-03-07
TensorFlow-Examples-master\notebooks\6_MultiGPU\multigpu_cnn.ipynb, 14411 , 2018-03-07
TensorFlow-Examples-master\README.md, 11953 , 2018-03-07
TensorFlow-Examples-master\resources\img\tensorboard_advanced_1.png, 286897 , 2018-03-07
TensorFlow-Examples-master\resources\img\tensorboard_advanced_2.png, 329796 , 2018-03-07
TensorFlow-Examples-master\resources\img\tensorboard_advanced_3.png, 1011024 , 2018-03-07
TensorFlow-Examples-master\resources\img\tensorboard_advanced_4.png, 607057 , 2018-03-07
TensorFlow-Examples-master\resources\img\tensorboard_basic_1.png, 284683 , 2018-03-07
TensorFlow-Examples-master\resources\img\tensorboard_basic_2.png, 349097 , 2018-03-07
TensorFlow-Examples-master\examples\1_Introduction, 0 , 2018-03-15
TensorFlow-Examples-master\examples\2_BasicModels, 0 , 2018-03-15
TensorFlow-Examples-master\examples\3_NeuralNetworks, 0 , 2018-03-15
TensorFlow-Examples-master\examples\4_Utils, 0 , 2018-03-15
TensorFlow-Examples-master\examples\5_DataManagement, 0 , 2018-03-15
TensorFlow-Examples-master\examples\6_MultiGPU, 0 , 2018-03-15
TensorFlow-Examples-master\notebooks\0_Prerequisite, 0 , 2018-03-15
TensorFlow-Examples-master\notebooks\1_Introduction, 0 , 2018-03-15
TensorFlow-Examples-master\notebooks\2_BasicModels, 0 , 2018-03-15
TensorFlow-Examples-master\notebooks\3_NeuralNetworks, 0 , 2018-03-15
TensorFlow-Examples-master\notebooks\4_Utils, 0 , 2018-03-15
TensorFlow-Examples-master\notebooks\5_DataManagement, 0 , 2018-03-15
TensorFlow-Examples-master\notebooks\6_MultiGPU, 0 , 2018-03-15
TensorFlow-Examples-master\resources\img, 0 , 2018-03-15
TensorFlow-Examples-master\examples, 0 , 2018-03-15
TensorFlow-Examples-master\notebooks, 0 , 2018-03-15
TensorFlow-Examples-master\resources, 0 , 2018-03-15
TensorFlow-Examples-master, 0 , 2018-03-15

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  • Fasteners-transferred-Tools
    请将本压缩文件包下的所有文件放在AutoCAD的支 持目录之下，如 AutoCAD 的安装目录下；或者在下拉 菜单中的 工具-> 选项 －> 文件 -> 支持文件搜索路 径 -> 添加 本程序所在目录。调入应用程序jgj.vlx ， 可以直接将本程序拖入AutoCAD绘图区域，或者在下拉 菜单的 工具－>加载应用程序 中找到本程序后加载即 可! 成功加载后，在命令行内输入bzjgj即可运行。在 对话框中,可以选择螺栓螺母的类型以及其它平、弹垫、 螺纹的显示等，具体使用方法可参见本文件夹中的图片。 体验版只能绘制规格为M10之组件,如果您要注册本工 具,可在命令行内输入 zc ,即可获得相关注册信息。(Please support all the compressed files in AutoCAD package under the Held under the directory, such as AutoCAD installation directory or pull-down Menu Tools-> Options-> File-> Support file search path Path-> add the program directory. Transferred to the application jgj.vlx, This program can be directly dragged into the AutoCAD drawing area, or in the drop-down Menu Tools-> Load application found that after loading the program Can! After successfully loaded in the command line, type bzjgj to run. In Dialog box, you can select the type of bolts and nuts and other flat, spring washers, Threaded display, specific methods can be found in the folder of pictures. Trial version can only draw specifications for the components of the M10, if you want to register the workers Tools, you can enter in the command line zc, you can get the relevant registration information.)
    2014-05-01 11:44:31下载
    积分：1
  • L22
    说明：  基于MATLAB编程的菲涅尔全息图的再现(Reconstruction of Fresnel hologram based on MATLAB)
    2020-04-08 15:14:38下载
    积分：1
  • matlabgaosi
    说明：  使用MATLAB语言编写的非高斯噪声程序，通俗易懂，非常好用(Using the MATLAB language for non-Gaussian noise process, user-friendly, very easy to use)
    2020-11-13 08:09:43下载
    积分：1
  • extract
    说明：  有关连通域的提取，以肺部图片作为示例，使用bwlabel函数，非常方便(Connected domain on the extraction of the lung images as an example, the use of bwlabel function, very convenient)
    2011-03-31 21:18:19下载
    积分：1
  • main
    说明：  以稀疏基有离散余弦变换基（DCT）和快速傅立叶变换基（FFT）做为稀疏基，高斯随机矩阵、部分哈达玛矩阵为测量矩阵，L1范数、正交匹配追踪算法（OMP）为重建算法进行压缩感知算法实现。 以f = cos(2*pi/256*t) + sin(2*pi/128*t)做为原信号，取原信号f的20%做为输入进行压缩感知重建。(The sparse basis includes discrete cosine transform (DCT) and fast Fourier transform (FFT) as sparse basis, Gaussian random matrix and partial Hadamard matrix as measurement matrix, L1 norm and orthogonal matching pursuit algorithm (OMP) as reconstruction algorithm. In this paper, the reconstructed signal is reconstructed by using the original sensing signal (sinf * t) as the original input signal (i.e. sinf * t * 2) + 2 * s * t * as the input signal.)
    2020-12-17 22:00:23下载
    积分：1
  • DTPS
    对目前光电的三网融合，并结合公司具体的业务进行探讨。(The current photoelectric triple play, combined with the company to discuss specific business.)
    2013-09-20 14:12:45下载
    积分：1
  • inverse.tar
    通过对图像进行逆透视变化，得到图像的俯视图，即鸟瞰图(Through the inverse perspective change of the image, we can get the top view of the image, that is aerial view.)
    2018-05-23 16:01:03下载
    积分：1
  • 0460
    水平百叶窗显示图片，讲一张图像垂直分成若干份，bitblt一行一行的显示(The level of blinds show pictures, talk about an image vertically divided into several copies, bitblt display line by line)
    2009-12-23 12:20:12下载
    积分：1
  • StarGuide
    通过与导航星库中的参考星进行三角形匹配实现姿态确定(satellite attitude determination)
    2021-03-22 14:39:17下载
    积分：1
  • 07103119166505
    shixian 图形图像编码，图像压缩以及分析(shixian graphics image coding, image compression and analysis)
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