tensorboard keras

In order to amplify the data set, 2 ways are used to amplify the data. 1. Data enhancement processing using Keras 2. Data enhancement processing using Skimage Keras includes processing, there is featurewise visual image will be slightly dimmed, samplewise visual image will become class X-ray image form, ZCA processing visual image will become gray image, rotation range randomly rotated image, horizonta

This paper describes how to apply the deep learning-based target detection algorithm to the specific project development, which embodies the value of deep learning technology in actual production, and is considered as a landing realization of AI algorithm. The algorithms section of this article can be found in the previous blogs: [AI Development] Python+tensorflow to build its own computer Vision API Service [AI development] Video Multi-object tracking implementation based on deep learning [AI d

Wunda Automatic driving target detection data set: Automatic driving target detection autonomous Driving-car detection Welcome to your Week 3 programming assignment. You'll learn about object detection using the very powerful YOLO model. Many of the ideas in this notebook is described in the YOLO Papers:redmon et al., (https://arxiv.org/abs/1506.0 2640) and Redmon and Farhadi, (https://arxiv.org/abs/1612.08242). You'll learnto:-use object detection on a car detection dataset-Deal with bounding b

people that still seem t O think that's the import keras leap for every hurdle, and so they, in knowing it, with some tremendous advantage over their C Ompetition. It can be seen that deep learning spreads the fanatics are not popular. Even the experts, who stand on top of science, now lose a great deal of enthusiasm for using the term, with only a bit of frustration, preferring to downplay the power of modern neural networks and avoid lett

learning rate. Learning_rate_decay_factor = 0.1 # LEARNING RATE decay FACTOR. Initial_learning_rate = 0.1 # INITIAL LEARNING RATE. # If A model is trained with multiple GPUs, prefix all Op names with Tower_name # to differentiate the operations. Note that this prefix is removed from the # names to the summaries when visualizing a model. Tower_name = ' TOWER ' data_url = ' http://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz ' Create a summary function to record histogram and scalar, note

= Tf.train.GradientDescentOptimizer (0.01) train = Optimizer.minimize (loss) #开始训练模型 print (' ================start================= ') for I in Range (10000): _, Loss_value = Sess.run ((train, loss), feed_dict={x_inputs:x}) if I% 1000 = 0:print (Loss_value) #关闭可视化writer, the visual model can be loaded through Tensorboard--logdir/users/yourname/pythonworkspace/tmp/log writer . Close () #构建savedModel构建器 builder = Tf.saved_model.builder.SavedModelBuil

for each instance in the tutorial is controlled in about 30 lines, which is easy to understand and reads as follows: Pytorch Basics Linear regression Logistic regression Feedforward Neural Network convolutional Neural Network Deep Residual Network Recurrent neural Network Bidirectional Recurrent neural Network Language Model (RNN-LM) Generative adversarial network Image captioning (CNN-RNN) Deep convolutional GAN (Dcgan) Variational Auto-encoder Neural Style Transfer

in a virtualenv manner: Installing Pip and VirtualenvTake a look at your Python version first:python --versionThen install according to the version:sudo# for Python 2.7sudo# for Python 3.n Create a virtualenv for TensorFlow environmentvirtualenv# for Python 2.7virtualenv# for Python 3.nI put it in the user root directory.mkdir ～/tensorflow Activate the environment you just createdsource# bash, sh, ksh, or zshInto the environment prepared for the TensorFlow.PS: If you want to quit, usede

: /job:localhost/replica:0/task:0/gpu:0a: /job:localhost/replica:0/task:0/gpu:0MatMul: /job:localhost/replica:0/task:0/gpu:0[[ 22. 28.] [ 49. 64.]] (2). Example TestDownload the TensorFlow source on GitHub with a lot of sample codeRun Example:python mnist_with_summaries.py..............................The results just started to stall ...couldn‘t open CUDA library cupti64_80.dllCheck, this DLL in NVIDIA GPU Computing Toolkit\CUDA\v8.0\extras\CUPTI\libx64 , because this also did not add to

Python1. Theano is a Python class library that uses array vectors to define and calculate mathematical expressions. It makes it easy to write deep learning algorithms in a python environment. On top of it, many classes of libraries have been built.1.Keras is a compact, highly modular neural network library that is designed to reference torch, written in Python, to support the invocation of GPU and CPU-optimized Theano operations.2.PYLEARN2 is a librar

Mark, let's study for a moment.Original address: http://www.csdn.net/article/2015-09-15/2825714Python1. Theano is a Python class library that uses array vectors to define and calculate mathematical expressions. It makes it easy to write deep learning algorithms in a python environment. On top of it, many classes of libraries have been built.1.Keras is a compact, highly modular neural network library that is designed to reference torch, written in Pyth

ReferencesMask R-CNNMask R-CNN DetailedOpen Source code: tensorflow version code link ; keras and TensorFlow version code link ; mxnet version code link First, MASK-RCNNMask R-CNN is an instance segmentation (Instance segmentation) algorithm, which can accomplish various tasks, such as target classification, target detection, semantic segmentation, case segmentation, human posture recognition, etc. by adding diff

robust and generic model for your own projects:1. Tensorflow.js also released the Posenet, which may be an interesting way to use it. From the machine's point of view, tracking the position of the wrist, elbow, and shoulder in the picture should be enough to predict with most words. When you spell something, the position of the finger is often important.2. Using a CNN-based approach, such as the "Pac Man" example, can improve accuracy and make the model more resistant to translational invarianc

input and output streams, while bufferedwriter can add a layer of slow Data Writing.Are we still making real progress in the commercial aspect of Keras? ---- The reader and writer classes have an objective mark, which is intended to provide a standard method, use the characters used by the previous machine (not the Macintosh Greek, or the Windows Spanish) both can be converted to Unicode. This means that when we move data from one platform to another

, just three computers instead of 1000, could do that, and the secret was to use GPU technology. So, Caffe in the original design concept, is the GPU as the core computing, CPU for auxiliary control and I/O framework.The compiler macro functionality provided by C/+ + enables Caffe to create code with different platform requirements by simply adding a single macro to the flexible mix programming.The latest version of Caffe, on the CPU and GPU, the balance is very good. CPU multithread

250 CPU servers.NVIDIA Tesla? P100 Accelerator.First video card with Pascal architectureOwns 18 billion transistorsUsing NVIDIA Nvlink?Manufacturing process using 16nm FinFETThe Tesla P100 is not only the most powerful GPU accelerator today,It's also the most technologically advanced GPU chip.Distributed deep learning system for DatainsightBased on the TensorFlow distributed version of the scenario, the CPU and GPU of each server in the cluster can be utilized simultaneouslySpark-based distribu

Bengio, LeCun, Jordan, Hinton, Schmidhuber, Ng, de Freitas and OpenAI had done Reddit AMA's. These is nice places-to-start to get a zeitgeist of the field.Hinton and Ng lectures at Coursera, UFLDL, cs224d and cs231n at Stanford, the deep learning course at udacity, and the sum Mer School at IPAM has excellent tutorials, video lectures and programming exercises that should help you get STARTED.NB Sp The online book by Nielsen, notes for cs231n, and blogs by karpathy, Olah and Britz has clear expl

execution lets you interact like a pure Python programmer: Instant Writing and instant, progressive debugging, rather than holding your breath while building those huge charts. I am also a "scholar" (probably an alien) who is returning to normal, but I have fallen in love with TF's eager execution since it appeared. Strong Amway!#3: Build neural networks line by rowKeras + TensorFlow = easier neural network build!Keras is committed to user-friendline

/release/1.67.0/source/TAR-XVF Compressed Files CD Extract Directory sudo./bootstrap.sh \ Use sudo to get the relevant permissions. sudo./b2 sudo./b2 Install sudo ln-s/usr/local/lib/libboost_unit_test_framework.a/usr/lib64/libboost_unit_test_framework.a Yum-y Install Automake Libtool Flex Bison pkgconfig gcc-c++ boost-devel libevent-devel zlib-devel python-devel ruby-devel Openssl-devel Thrift 0.11.0Https://github.com/apache/thrift Http://ftp.wayne.edu/apache/thrift/0.11.0/thrift-0.11.0.tar.gzht

systems (but only because of policy barriers or industry inertia). 3. It 's too hard . This refers to C + + and Java, whose code is too low-level. The advantage is that the computation is fast and the downside is that it takes time to develop. In order to complete a data analysis, the operation speed can be properly sacrificed, giving way to development time. Alternatively, after the initial analysis and algorithm development, the algorithm is passed to the backend to be implemented in C + + or