keras convolutional neural network

kernel and step operation, There may be the wrong dimension (analogy 2x3 matrix can not be multiplied by the 2x4 matrix, you need to replace the 2x4 matrix into a 3x4 matrix, here is the matrix of the 2x4 to add a row of 0 elements, so that it becomes the matrix of 3x4), the default is 0, preferably set to (kW-1)/ 2, which is the width of the convolution core 1 and then divided by 2. The padh default is PADW, preferably set to (kH-1)/2, which is the high-1 convolution core and then divided by 2

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

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

convolutional neural Networks:step by step Welcome to Course 4 ' s-A-assignment! In this assignment, you'll implement Convolutional (CONV) and pooling (POOL) layers in NumPy, including both forward pro Pagation and (optionally) backward propagation. notation: We assume that you are already familiar with numpy and/or have completed the previous courses. Let ' s g

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

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

Understanding deep learning requires familiarity with some simple mathematical concepts: tensors (tensor), Tensor operations tensor manipulation, differentiation differentiation, gradient descent gradient descent, and more."Hello World"----MNIST handwritten digit recognition#coding: Utf8import kerasfrom keras.datasets import mnistfrom keras import modelsfrom keras import Layersfrom keras.utils i Mport to_ca

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

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

, upload to the second cabinet, the machine identified some characteristics of the dog, very vague, continue to upload to the third cabinet, the other part of the dog features identified, the image is gradually clear up, so continue, like "winding" (convolution) action, has been "winding" to the tenth cabinet, the dog's face revealed the "truth", recognition task completed. Ah, it turns out to be the most popular image and speech recognition technology in the world today:

Code address for this section Https://github.com/vic-w/torch-practice/tree/master/rnn-timer RNN full name Recurrent neural network (convolutional neural Networks), which is a memory function by adding loops to the network. The natural language processing, image recognit

efficiency. The number of neurons that are linearly increased can be expressed in a number of different concepts that increase exponentially.Another advantage of distributed characterization is that the expression of information is not fundamentally compromised, even in the event of a local hardware failure.This idea let Geoffrey Hinton Epiphany, so that he has been in the field of neural network research

)}} {\partial h^{(t)}} \frac{\partial h^{(t)}}{\partial U} = \sum\limits_{t=1}^{\tau}diag (n (h^{(t)}) ^2) \delta^{(t)} (x^{ (t)}) ^t$$In addition to the gradient expression, RNN's inverse propagation algorithm and DNN are not very different, so here is no longer repeated summary.5. RNN SummaryThe general RNN model and forward backward propagation algorithm are summarized. Of course, some of the RNN models will be somewhat different, the natural forward-to-back propagation of the formula will be

information transfer rates (network throughput) Low-cost, small-scale construction of a particular structure network How to add a priori information to a neural network: There is no effective rule to achieve A special process can be implemented: Restricting th

the face have moved to another corner of the image, as shown in Fig. 3:The same number of activations occurs in this example, however they occur in a different region of the green and yellow VO Lumes. Therefore, any activation in the first slice of the yellow volume means that a-face is detected, independently of T He face location. Then the fully-connected layer was responsible to ' translate ' a face and a human body. In both examples, an activation is received at one of the fully-connected n

/1406.2661.gan first Paper:lan Goodfellow generative adversarial Networks 5. Algorithm: Using random gradient descent method to train d,g. Specifically also in the above article. 6.DCGAN Principle Introduction: The best model for image processing applications in deep learning is CNN, how CNN and Gan combine. The answer is Dcgan. The principle is the same as Gan. Just replaced the above G and D with two convolutional

. The great God has given us some advice [2][3] If you have a small amount of data input, choose an adaptive learning rate method. This way you don't have to tune the learning rate, because your data is small, and NN learning is a little time-consuming. In this case you should be more concerned about the accuracy of network classification. Rmsprop, Adadelta, are very similar to Adam and perform well in the same situation. Bias checking makes Adam's ef

applicationsThe blogger made an open source project and collected paper and papers related to the network.Welcome to star and contribution.Https://github.com/zhangqianhui/AdversarialNetsPapersApplication to combat NN. These apps can all be found in my open source project .(1) The paper [2] uses CNN for image generation, where D is used for classification and has a good effect.(2) the thesis [3] uses the prediction of the video frame against NN, which solves the problem that other algorithms can