tensorflow reinforcement learning

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

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

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

= Mnist.train.next_batch (batch_size)Sess.run (Optimizer, feed_dict={x:batch_x, y:batch_y, keep_prob:dropout})If step% Display_step = = 0:# Calculate loss value and accuracy, outputLoss, acc = Sess.run ([cost, accuracy], feed_dict={x:batch_x, Y:batch_y, Keep_prob:1.})Print "Iter" + str (step*batch_size) + ", Minibatch loss=" + "{:. 6f}". Format (Loss) + ", Training accuracy=" + "{:. 5f}". f Ormat (ACC)Step + = 1Print "Optimization finished!"# Calculate Test AccuracyPrint "Testing accuracy:", se

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

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