transfer learning tensorflow

Range (len (P)): if p[index]! = 0 :print (index, P[index])The output is as follows:Here are 0 recommendations for users: 54 0.190727177 0.17746378828 0.171810251043 0.169892861113 0.174583264. SummaryThe above is to use TensorFlow to build the BPR algorithm model, and use this algorithm model to do Movielens 100K recommended process. In the actual product project, if you want to use the BPR algorithm, one is to pay attention to the hidden

In deep learning, regardless of the learning framework, we encounter an important problem, that is, after training, how to store the depth of the network parameters. How these network parameters are invoked at the time of the test. In response to these two questions, this blog post explores how TensorFlow solves them. This blog is divided into three parts, the fi

This book is published by only cloud technology Caicloud, the main content is familiar with the basic structure of TensorFlow framework and practical application in the field of depth learning.For specific code see:1. Official:Caicloud/tensorflow-tutorial:example tensorflow codes and Caicloud TensorFlow as a Service de

1. General machine learning predictive function coefficient (y=0.1x+0.3) #-*-CODING:GBK-*- import tensorflow as tf import numpy as NP #生成数据, y=0.1x+0.3 X_data=np.random.rand ( Astype (np.float32) y_data=x_data*0.1+0.3 # # #开始创建tensorflow结构 ### WEIGHT=TF. Variable (Tf.random_uniform ([1],-1.0,1.0)) BIASES=TF. Variable (Tf.zeros ([1])) y=weight*x_data+biases #

mainstream framework, of course, not to say that Keras and CNTK are not mainstream, the article does not have any interest related things, but the keras itself has a variety of frameworks as the back end, So there is no point in contrast to its back-end frame, Keras is undoubtedly the slowest. and CNTK because the author of Windows is not feeling so also not within the range of evaluation (CNTK is also a good framework, of course, also cross-platform, interested parties can go to trample on the

Learning notes TF055: TensorFlow neural network provides a simple one-dimensional quadratic function. tf055tensorflow TensorFlow running mode. Load data, define hyperparameters, build networks, train models, evaluate models, and predict. Construct raw data that satisfies the quadratic function y = ax ^ 2 + B, and construct the simplest neural network, including t

tags (space delimited): Wang Cao TensorFlow notes Note-taker: Wang GrassNote Finishing Time February 24, 2017TensorFlow official English document address: Https://www.tensorflow.org/get_started/mnist/beginnersOfficial documents When this article was compiled last updated: February 15, 2017 1. Case Background This article is followed by the second tutorial of the official TensorFlow document – Identifying ha

Current Computer Configuration: Ubuntu 16.04 + GTX1080 GraphicsConfiguring a deep learning environment, using Tsinghua Source to install a Miniconda environment is a very good choice. In particular, today found Conda install-c Menpo opencv3 A command can be smoothly installed on the OPENCV, before their own time also encountered a lot of errors. Conda installation of the TensorFlow and pytorch two kinds of

: 0300 cost = 0.134895071 W = 0.3842099 B =-0.16695316EPOCH: 0350 cost = 0.128200993 W = 0.37620357 B =-0.10935676EPOCH: 0400 cost = 0.122280121 W = 0.36867347 B =-0.055185713EPOCH: 0450 cost = 0.117043234 W = 0.36159125 B =-0.004236537EPOCH: 0500 cost = 0.112411365 W = 0.3549302 B = 0.04368245EPOCH: 0550 cost = 0.108314596 W = 0.34866524 B = 0.08875148EPOCH: 0600 cost = 0.104691163 W = 0.34277305 B = 0.13114017EPOCH: 0650 cost = 0.101486407 W = 0.33723122 B = 0.17100765EPOCH: 0700 cost = 0.0986

Python-dev If the previous command doesn't work, you can use the following command to resolveUsing the Aptitude tool sudo apt-get install aptitudesudo aptitude install Python-dev Install the Python-dev now to install the PYTHON-PIP. sudo apt-get install Python-pip Type PIP in the terminal and, if shown, the installation succeeds4. Installation ResultsThe packages used for numeric calculations and drawings are now installed with Pip, respectively, NumPy scipy mat

Amazon open machine learning system source code: Challenges Google TensorFlowAmazon took a bigger step in the open-source technology field and announced the opening of the company's machine learning software DSSTNE source code. This latest project will compete with Google's TensorFlow, which was open-source last year. Amazon said that DSSTNE has excellent perform

Last year in Beijing participated in a big data conference organized by O ' Reilly and Cloudera, Strata , and was fortunate to have the O ' Reilly published hands-on machine learning with Scikit-learn and TensorFlow English book, in general, this is a good technical book, a lot of people are also recommending this book. The author of the book passes specific examples, Few theories and two mature Python fra

the profile file ( Note: If you are not using version 8.0, you need to modify the version number ):→~ Export cuda_home=/usr/local/cuda-8.0→~ Export Path=/usr/local/cuda-8.0/bin${path:+:${path}}→~ Export Ld_library_path=/usr/local/cuda-8.0/lib64${ld_library_path:+:${ld_library_path}}After modification:→~ Source/etc/profileVerify that the configuration is successful:→~ nvcc-vThe following message appears to be successful: 4. Installing the CUDNN Acceleration LibraryThis article uses the CUDA8.0,

1. Installing the PYTHON3.0 Series version (Windows)1) Download: Install 3.5.0 in this website (: https://www.python.org/downloads/release/python-350/)Installation2) Add environment variables: Add python's installation location to "Path":Verify that Python is installed successfully and enter Python in cmd to verify:2. Installing TensorFlow1) First install PIP: Switch to the script directory under the newly installed Python directory:Easy_install.exe pipAdd the PIP to the environment variable (sa

, inception-resnet and the Impact of residual Connections on Learni Ng, the highlight of the paper is that: the googlenet Inception v4 network structure with better effect is proposed, and the structure of the network with residual error is more effective than V4 but the training speed is faster.googlenet Inception V4 Network Structuregooglenet Inception resnet Network Structure Code practices　　TensorFlow code in the Slim module has a complete implem

Session:with Tf.device ("/gpu:0"): Session.run (init) coord=tf.train.Coordinator () Threads= Tf.train.start_queue_runners (coord=coord) Max_iter=10000ITER=0ifOs.path.exists (Os.path.join ("Model",'model.ckpt')) isTrue:tf.train.Saver (Max_to_keep=none). Restore (Session, Os.path.join ("Model",'model.ckpt')) whileiterMax_iter:#Loss_np,_,label_np,image_np,inf_np=session.run ([Loss,opti,batch_image,batch_label,inf])B_batch_image,b_batch_label =Session.run ([Batch_image,batch_label]) l

First, Introduction In many machine learning and depth learning applications, we find that the most used optimizer is Adam, why? The following is the optimizer in TensorFlow: See also for details: Https://www.tensorflow.org/api_guides/python/train In the Keras also have Sgd,rmsprop,adagrad,adadelta,adam, details: https://keras.io/optimizers/ We can find that in a

problems, the best time is often not the training process, but the process of data tagging), so generally speaking, the amount of data in question B is less.So, the same model in the use of large samples is a good solution to the problem a, then there is reason to believe that the training in the model of the weight parameters can be able to do a good job of feature extraction task (at least the first few layers are so), so since already have such a model, then take it.Therefore, migration

Installation use Official Document Connection: Https://www.tensorflow.org/get_started/get_started_for_beginnersIn accordance with the text of the GitHub connection to download files directly GG, Hung ladder or clone do not move, helpless, had to go to that page to use the example of the py file copy came to the local, need to copy two files: https://github.com/tensorflow/models/tree/master/samples/core/get_started/iris_data.py https://github.com/

6th Chapter Image Recognition and convolution neural network 6.1 image recognition problems and the classic data set 6.2 convolution neural network introduction 6.3 convolutional neural network common structure 6.3.1 convolution layer 6.3.2 Pool Layer 6.4 Classic convolutional neural network model 6.4.1 LENET-5 model 6.4.2 in Ception Model 6.5 convolution neural network to realize migration learning 6.5.1 Migration