convolutional neural network tensorflow

Civilization number" and the Central State organ "youth civilization" title.Smart Apps Intelligent processing is the core problem 20w Human brain Power consumption Multilayer large-scale neural network ≈ convolutional Neural Network + LRM (different feature

, Q2 is closer to P, and its cross-entropy is smaller.In addition, the cross-entropy has another form of expression, or the use of the above hypothetical conditions: The result is: All of the above instructions are for a single sample case, and in the actual use of the training process, the data is often combined into a batch to use, so the output of the neural network used should be a m*n two-dimensional

NIPS 2016 article: Intel China Research Institute on Neural Network compression algorithm of the latest achievementsHttp://www.leiphone.com/news/201609/OzDFhW8CX4YWt369.htmlIntel China Research Institute's latest achievement in the field of deep learning--"dynamic surgery" algorithm 2016-09-05 11:33 reproduced pink Bear 0 reviewsLei Feng Net press: This article is the latest research results of Intel China

minimize the cost function to obtain parameters, in the neural network gradient descent algorithm has a special name called the inverse propagation algorithm. in the sample diagram of the neural network above, the input is directly connected to the hidden layer (hiddenlayer), and the output is called the output layer

the hidden state, and relies on gates to control. Gates ' control basis: The three gates used in the lstm described above are based on wxt+wht−1 W x t + W h t−1, but can be increased by connection to the memory cell or by deleting a gate's wxt W x t or wht−1 W H t−1 to reduce the control basis. For example, remove the ht−1 H t−1 in Zt=sigmoid (wz⋅[ht−1,xt]) Z t = S i g M o i d (W z⋅[h t−1, X T]) in the above image to Zt=sigmoid (wz⋅ht−1) Z t = s i g M o i d (W z⋅h t−1) After the introduction o

Next: convolutional neural network for image classification-medium9 ReLU (rectified Linear Units) LayersAfter each convolutional layer, an excitation layer is immediately entered, and an excitation function is called to add the nonlinear factor, and the problem of linear irreducible is rejected. Here we choose the meth

structure (1). Intuition of CNNIn deep learning book, author gives a very interesting insight. He consider convolution and pooling as a infinite strong prior distribution. The distribution indicates, all hidden units share the same weight, derived from certain amount of the input and has Parallel invariant feature.Under Bayesian statistics, prior distribuion is a subjective preference of the model based on experience. and the stronger the prior distribution is, the higher impact it'll has on th

Recently, the journal Platform Distill published an article by Google researchers, introducing a powerful tool for neural network visualization and style migration: micro-image parameterization. This article describes the tool in several ways. Image Classification Neural network has excellent image generation capa

implication of this is that the statistical characteristics of the part of the image are the same as the rest. This also means that the features we learn in this section can also be used in other parts, so we can use the same learning features for all the locations on this image. More intuitively, when a small piece is randomly selected from a large image, such as 8x8 as a sample, and some features are learned from this small sample, we can apply the feature learned from this 8x8 sample as a de

Deep Learning Neural Network pure C language basic Edition Today, Deep Learning has become a field of fire, and the performance of Deep Learning Neural Networks (DNN) in the field of computer vision is remarkable. Of course, convolutional neural networks are used in engineer

single unit with a complex memory unit .??TensorFlow examples of LSTMHttps://github.com/jikexueyuanwiki/tensorflow-zh/blob/master/SOURCE/tutorials/recurrent/index.mdhttp://colah.github.io/posts/2015-08-Understanding-LSTMs/It is mentioned herethat RNN can learn historical information when the distance is short, but RNN is powerless when the distance is longer . example of a short distance, predicting skylo

At present, there are neural networks in all aspects of engineering application, and younger brother is now learning neural network, a little conjecture.Most of the current neural network is to adjust their own weights, so as to learn. Under the structure of a certain

Constructing neural network with Keras Keras is one of the most popular depth learning libraries, making great contributions to the commercialization of artificial intelligence. It's very simple to use, allowing you to build a powerful neural network with a few lines of code. In this article, you will learn how to bui