tensorflow reinforcement learning

Learning Google's deep learning finally a little bit of the prospect, to share my tensorflow learning process. TensorFlow's official Chinese document is jerky, and the dataset has been used in the Mnist binary dataset. And not much about how to build their own picture dataset Tfrecords. First paste my conversion code t

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

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

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

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/

-learning (it is a simplification of reinforcement learning). We build an environmental return system matrix R Now we put a matrix-like q into the brain of the robot, Q will save the environment information that the robot obtains by walking. The wardrobe of the matrix Q represents the state of the robot, and the column header of Q represents the next transition

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

TensorFlow deep learning convolutional neural network CNN, tensorflowcnn I. Convolutional Neural Network Overview ConvolutionalNeural Network (CNN) was originally designed to solve image recognition and other problems. CNN's current applications are not limited to images and videos, but can also be used for time series signals, for example, audio signal and text data. CNN, as a deep

#Save to fileImportTensorFlow as TFImportNumPy as NP## (1) Save to file stores related variables in Files#Remember to define the same dtype and shape when restoreW = tf. Variable ([[1,2,3],[3,4,5]],dtype=tf.float32,name='Weights') b= TF. Variable ([[1,2,3]],dtype=tf.float32,name='biases') Init=tf.initialize_all_variables () Saver=Tf.train.Saver () with TF. Session () as Sess:sess.run (init) Save_path= Saver.save (Sess,"my_net/save_net.ckpt") Print("Save to Path:", Save_path)## (2) Restore var

1. Download Anaconda (preferred website, but very slow) anaconda2-4.0.0-linux-x86_64.sh The Anaconda installation package can also be downloaded to https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/. 2. Configure some sources, otherwise too slow.= = = Already successful, run the conda install numpy test.However, it is also convenient to build a virtual environment.Create a virtual Environment Conda create-n ' environment name xxx ' python= ' version number 'conda config --a

Introduction of Style migration Style Transfer is one of the most interesting applications of deep learning, as shown in this way, we can use this method to "migrate" the style of a picture to another picture: However, the speed of the original style migration (click to view the paper) is very slow. On the GPU, it takes about 10 minutes to generate a picture, and it may take several hours if you use only the CPU without using the GPU to run the progr

Objective Because of the problem of image Learning machine learning, choose TensorFlow, but seems to go directly from the example of imagenet, but found how to find the end (Python will not, machine learning also do not understand), but according to my past experience, in this situation, and no discerning to the road,

Model optimization is important for both traditional machine learning and deep learning, especially in deep learning, and it is likely that more difficult challenges will need to be addressed during training. At present, the popular and widely used optimization algorithm has a random gradient descent, with the momentum of the random gradient descent, Rmsprop algo

installation was successful, import the NumPy with Python, as follows to complete the installation4. Installing TensorFlow1.> download the corresponding version of the TensorFlow, must be corresponding to the Python version, the latest is the support python3.6 version, for: https://pypi.org/project/tensorflow-gpu/#files, Because my Python version is 3.6, so download TENSORFLOW_GPU-1.8.0-CP36-CP36M-WIN_AMD6

respectively, their input is real data x and random variable Z. G (z) is a sample of the Pdata P_{data} generated by G to conform as much as possible to the real data distribution. If the input from the discriminant is from the real data, the callout is 1. If the input sample is g (z), the callout is 0.The goal of D here is to achieve a two classification of the data source: True (from the distribution of real data x) or pseudo (the pseudo data G (z) from the generator), the objective of G is t

format (299,299,3), we gave (224,224,3), this time the error is still "in the Ckpt file found no weight", really very pit, looked for a long time to find the problem; 3) The corresponding weight of the file may really not have this weight, this time we are going to download a standard ckpt file, make sure to include the ownership value, the bottom to a can detect ckpt file in the name of the value of the code: 4) You may also encounter Invalidargumenterror (see above for traceback): Assign