tensorflow neural network

Deep Learning paper notes (IV.) The derivation and implementation of CNN convolution neural network[Email protected]Http://blog.csdn.net/zouxy09 I usually read some papers, but the old feeling after reading will slowly fade, a day to pick up when it seems to have not seen the same. So want to get used to some of the feeling useful papers in the knowledge points summarized, on the one hand in the process of

First, IntroductionIn machine learning and combinatorial optimization problems, the most common method is gradient descent method. For example, BP Neural network, the more neurons (units) of multilayer perceptron, the larger the corresponding weight matrix, each right can be regarded as one degree of freedom or variable. We know that the higher the freedom, the more variables, the more complex the model, th

In the previous article, we saw how neural networks use gradient descent algorithms to learn their weights and biases. However, we still have some explanations: we did not discuss how to calculate the gradient of the loss function. This article will explain the well-known BP algorithm, which is a fast algorithm for calculating gradients.The inverse propagation algorithm (backpropagation ALGORITHM,BP) was presented at 1970s, but its importance was not

catalog view Summary view Subscription [Top] "convolutional neural network-evolutionary history" from Lenet to AlexnetTags: CNN convolutional neural Network Deep learningMay 17, 2016 23:20:3046038 people read Comments (4) favorite reports Classification:"Machine Learning Deep Learning" (a)Copyright NO

tensorflow as Tfinput_data = tf. Variable ([[0.2, 0.1, 0.9]], Dtype = tf.float32) output = Tf.nn.softmax_cross_entropy_with_logits (Input_data, [[1,0,0]]) With TF. Session () as sess: init = tf.initialize_all_variables () sess.run (init) print Sess.run (input_data) Print Sess.run (output) print Sess.run (tf.shape (output))Sparse_softmax_cross_entropy_with_logits tf.nn.sparse_softmax_cross_entropy_with_logits(logits, labels, na

TravelseaLinks: https://zhuanlan.zhihu.com/p/22045213Source: KnowCopyright belongs to the author. Commercial reprint please contact the author for authorization, non-commercial reprint please specify the source.In recent years, the Deep convolutional Neural Network (DCNN) has been significantly improved in image classification and recognition. Looking back from 2014 to 2016 of these two years more time, has

BP (back propagation) network is the 1986 by the Rumelhart and McCelland, led by the team of scientists, is an error inverse propagation algorithm training Multilayer Feedforward Network, is currently the most widely used neural network model. BP network can learn and store

Turn from: Http://matlabbyexamples.blogspot.com/2011/03/starting-with-neural-network-in-matlab.htmlThe Neural Networks is A-to-model any-input to output relations based-some input output data when nothing was known about the model. This example shows your a very simple example and its modelling through neural

The neural network is used to deal with the nonlinear relationship, the relationship between input and output can be determined (there is a nonlinear relationship), can take advantage of the neural network self-learning (need to train the data set with explicit input and output), training after the weight value determi

BP (backward propogation) neural networkSimple to understand, neural network is a high-end fitting technology. There are a lot of tutorials, but in fact, I think it is enough to look at Stanford's relevant learning materials, and there are better translations at home: Introduction to Artificial neural

features, for each feature has 255 values;For such an image, if the use of two characteristics, there are about 3 million features, if it is also a logical return, the calculation of the cost is quite largeThis time we need to use the neural network.2. Neural network Model Representation 1The basic structure of the

The principle of RBF neural networks has been introduced in my blog, "RBF Neural Network for machine learning", which is not repeated here. Today is to introduce the common RBF neural Network learning Algorithm and RBF neural

BP (Back Propagation) network is a multi-layer feed-forward Network trained by the error inverse propagation algorithm, which was proposed by a team of scientists led by Rumelhart and mccelland in 1986, it is one of the most widely used neural networks. The BP network can learn and store a large number of input-output