tensorflow in production

Error valueMeasure the loss error between two tensor or one tensor and 0, which can be used in a regression task or for the purpose of regularization (weight decay).loss tf.nn.l2_loss(t, name=None) Explanation: This function uses the L2 norm to calculate the error value of the tensor, but does not prescribe and takes only half of the value of the L2 norm, as follows: output = sum(t ** 2) / 2 Input parameters: t: One Tensor . The data type must be one of the followi

Environment: virtualenv xxx_pyvirtualenv -p python3 xxx_pyEnter the environment:source xxx_py/bin/activateExit:deactivate Use Tsinghua Mirror Temporary usepip install -i https://pypi.tuna.tsinghua.edu.cn/simple some-package Set as Defaultpip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple Resources:Tsinghua PyPI Mirror Use HelpVIRTUALENV Introduction and basic useOne of the essential artifacts of Python development: virtualenvvirtualenv

TensorFlow let neural networks automatically create musicA few days ago to see an interesting share, the main idea is how to use TensorFlow teach neural network automatically create music. It sounds so fun, there's wood! As a Coldplay, the first idea was to automatically generate a music like the Coldplay genre, so I started to follow the tutorial on GitHub (project name: Projects Magenta) Step by step, get

Pointer-network is a branch of the recent seq2seq comparison fire, which is widely used in the system of reading comprehension based on deep learning. interested can read the original paper recommended readinghttps://medium.com/@devnag/pointer-networks-in-tensorflow-with-sample-code-14645063f264??This idea is also relatively simple is that the decoded predictions are confined to the input position . it works in a lot of places .For example, consider a

A detailed description of the TF data read queue mechanism TFR file multi-threaded queue read and write operations: Tfrecod File Write operation: Import TensorFlow as Tfdef _int64_feature (value): # value must be an iterator object # non-int data use bytes instead of Int64 to return Tf.train.Feature (Int64_list=tf.train.int64list (Value=[value])) Num_shards = 2instance_perpshard = 2for i in rang

. Shaoqing Ren, kaiming He, Ross Girshick, Jian Sun thesis Faster r-cnn:towards real-time Object Detection with region proposal NETW Orks "https://arxiv.org/abs/1506.01497.Add a new function module.FCN (deconvolution), Stnet, CNN and RNN/LSTM hybrid structures.MNIST alexnet implementation. Network structure diagram.1. Study the network paper carefully, understand each layer input, output value, network structure.2. Implement the network by loading data, defining the network model, training the m

This shows the process of compiling the TensorFlow from the source code.The original tutorial https://github.com/tensorflow/tensorflow/blob/master/tensorflow/g3doc/get_started/os_setup.md# on GitHub Installing-from-sourcesFirst, install the Build tool Bazel (Http://www.bazel.io/docs/install.html#install-on-ubuntu)Insta

1.ubuntu version of the choice: read a lot of blog, recommended the use of UBUNTU14, stable compatibility good.Installation of 2.tensorflow:Http://wiki.jikexueyuan.com/project/tensorflow-zh/get_started/introduction.htmlHere is a detailed description of how to install, I was the first method of choice, the successful installation completed.Later, try to install TensorFlow and Caffe under Docker.3. Test wheth

Operating system: UBUNTU-16.04.2-DESKTOP-AMD64TensorFlow version: 1.0.0Python version: 2.7.12To open SSH:sudo Install openssh-server sudossh startInstall PIP:sudo Install Python-pipInstall TensorFlow:GitHub Address: Https://github.com/tensorflow/tensorflowwget https://CI.TENSORFLOW.ORG/VIEW/NIGHTLY/JOB/NIGHTLY-MATRIX-CPU/TF_BUILD_IS_OPT=OPT,TF _build_is_pip=pip,tf_build_python_version=python2,label=cpu-slave/lastsuccessfulbuild/artifact/pip_test/w

1. The ' list ' object has no attribute ' lower ' has the following error Traceback (most recent): File "h:/fasionai/mynet/train.py", line Problem reason: Tf.nn.max_pool () instead of Tf.nn.pool () 2.tensorflow Version inconsistency 1) attributeerror:module ' TensorFlow ' has no attribute ' scalar_summary ' Tf.image_summary (' images ', images) instead: tf.summary.image (' images ', images) 2 attribut

Three months has not updated Ah, come back a more ~ ~ CSDN mainly on some coding process encountered in the functions, problems, solutions. Partial practice In addition, if you want to see some of the theoretical aspects, welcome to add me to know the homepage Csdn DMS Almost do not look, there are problems Exchange can send a mailbox: hzmaoxiaofeng@163.com or know DMS, see I will be the first time to reply to you In the previous article, someone and I asked the question of the location of the p

This paper first analyzes the structure of the LENET-5 model, and then based on the LENET-5 model to write the TensorFlow code to achieve mnist digital recognition, the code part of the detailed annotation, at present also in the learning phase, there are errors welcome to point out that we learn together. The LENET-5 model structure diagram is as follows: The LENET-5 model has a total of 7 layers. First layer: convolution layer The first layer of th

installation This uses the Pip to install the TensorFlow CPU version $ sudo pip install HTTPS://STORAGE.GOOGLEAPIS.COM/TENSORFLOW/LINUX/CPU/TENSORFLOW-0.5.0-CP27-NONE-LINUX_X86_64.WHL Check the success of the installation with handwriting recognition examples from the runtime when the installation is complete $ cd/usr/lib/python2.7/site-packages/

The LENET-5 model, presented by Professor Yann LeCun in his paper gradient-basedlearning applied to document recognition in 1998, was the first volume to be successfully applied to digital recognition issues. Accumulated neural network. On the Mnist dataset, the LENET-5 model can achieve a correct rate of approximately 99.2%. The LENET-5 model has a total of 7 layers, and Figure 7 shows the architecture of the LENET-5 model. The structure of each layer of the LENET-5 model is described in deta

1vmware install Ubuntu (oneself follow other tutorial easy to install Ubuntu, then install TensorFlow always error), follow this step follow TensorFlow step successful click Open Link 2 ubuntu install TensorFlow (CPU) Use PIP to click Open Link 3minist data set test, but will error, follow the online method changed after correct run click Open Link a softmax modi

In the process of learning tensorflow, we need to know what the value of a tensor is, which is important, especially at the time of Debug. Maybe you can say, this is easy, just print it. In fact, print only prints output shape information, and to print out the value of the tensor, you need to use class TF. Session, class TF. InteractiveSession. Because we are building graph, we only build tensor structure shape information, and do not perform data ope

Really every machine has his temper, follow the online tutorial constantly error ..... has been in the online tutorial, find solutions, finally, or installed 1 according to Anaconda3 here no longer write, mainly in accordance with TensorFlow 2 open the terminal, replace the source (faster) Input Conda config--add channels https:// Mirrors. Tuna. Tsinghua. edu. cn/anaconda/pkgs/main/Conda config--add channels https://mirrors. Tuna Tsinghua. edu. cn/ana

This article reproduced from: https://zhuanlan.zhihu.com/p/23361413, the original title: TensorFlow Serving Taste Fresh In the 2016, machine learning became more popular in the post-war era of Alpha go and Li Shishi. Google also launched the TensorFlow serving this year and added a fire.TensorFlow serving is a high-performance open Source library for machine learning model serving. It can deploy the traine