tensorflow for deep learning from linear regression to reinforcement learning

Deep Learning: 4 (Logistic Regression exercise)-tornadomeet-blog Deep Learning: 4 (Logistic regression exercises) Preface: This section to practice the logistic regression rel

Install the deep learning framework TensorFlow in Ubuntu I recently learned about TensorFlow, a new open-source deep learning framework for Google. It was found that python 2.7.x is needed when installing it; I have been using Cen

1. Generating random numbers of Gaussian distributionsImport the NumPy module and generate a set of equations within the NumPy module by means ofy = 2 * x + 3Random coordinates of small fluctuations around. The code is as follows:1 ImportNumPy as NP2 ImportMatplotlib.pyplot as Plot3 4 5 defgetrandompoints (count):6Xlist = []7Ylist = []8 forIinchRange (count):9x = np.random.normal (0, 0.5)Teny = 2 * x + 3 + np.random.normal (0, 0.3) One xlist.append (x) A ylist.append (y) - returnxlist,

Reproduced in the Daily Digest of deep learning, convlstm principle and its tensorflow realizationThis document references convolutional LSTM network:a machine learning approach forPrecipitation nowcasting Today introduced a very famous network structure--convlstm, it not only has the LSTM time series modelling ability

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

the node matrix or the number of input Samples # Fourth parameter: Fill method, ' same ' means full 0 padding, ' VALID ' means no padding TensorFlow to realize the forward propagation of the average pool layer Pool = Tf.nn.avg_pool (actived_conv,ksize[1,3,3,1],strides=[1,2,2,1],padding= ' same ') # first parameter: Current layer node Matrix # The second parameter: the size of the filter # gives a one-dimensional array of length 4,

parameter Tmphtest = Inpweight%*% TV. PBiasmatrixte Ncol = Ncol (TV. P), Byrow = F)Tmphtest = tmphtest + biasmatrixte ########3. High dimensional mapping, usually select SIG function if (Actfun = ="Sig")Htest =1/(1 +Exp (-1 * tmphtest)) else { if (Actfun = ="Sin")Htest =Sin (tmphtest) else { if (Actfun = ="Radbas")Htest =Exp (-1 * (tmphtest^2)) else { if (Actfun = ="Hardlim")Htest = Hardlim (tmphtest) else { if (Actfun = ="Hardlims")Htest = Hardlims (tmphtest) else { if (Actfun = ="Satlins")Hte

, 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

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

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

from the loss function in linear regression, which is defined as: If the Newton method is used to solve the parameters in the regression equation, the iterative formula of the parameters is: One of the first-order and Hessian matrix expressions is as follows: May have some friends, not very clear how the above two formulas are deduced, the first formula is re

First the PO on the main Python code (2.7), this code can be found on the deep learning. 1 # Allocate symbolic variables for the data 2 index = T.lscalar () # Index to a [mini]batch 3 x = T.matrix (' x ') # The data is presented as rasterized images 4 y = t.ivector (' y ') # The labels is presented as 1D vector of 5 # [INT] Labels 6 7 # Construct the logistic

What is migration learning In deep learning, the so-called migration learning is to adapt a model of problem A to a new problem B by simply adjusting it. In actual use, it is often completed problem a training model has more perfect data, and problem B's data volume is small. The adjustment process is based on the act

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