cnn current

One of the key steps in the error back propagation of the CNN (Convolutional Neural network) is to pass the error of a convolution (convolve) layer to the pool layer on the previous layer, because it is 2D back in CNN, Unlike conventional neural networks where 1D is slightly different in detail, the following is a simple example of how to decompose this counter step in detail.Suppose that in a

Deep Learning: Running CNN on iOS1 Introduction As an iOS developer, when studying deep learning, I always thought that I would run deep learning on the iPhone, whether on a mobile phone or using trained data for testing.Because the iOS development environment supports C ++, as long as your code is C/C ++, you can basically run it on iOS.How can we run CNN on iOS faster and better?2 Method 1: Transcoding Us

7014Image 7044dtype: int64X.shape == (2140, 9216); X.min == 0.000; X.max == 1.000y.shape == (2140, 30); y.min == -0.920; y.max == 0.996This result tells us that the feature points of many graphs are incomplete, such as the right lip angle, only 2,267 samples. We dropped all the images with less than 15 feature points, and this line did it:DF = Df.dropna () # Drop all rows this has missing values in themTrain our network with the remaining 2140 pictures as a training se

more time. This time our network learned more general, theoretically speaking, learning more general law than to learn to fit is always more difficult.This network will take an hour of training time, and we want to make sure that the resulting model is saved after training. Then you can go to have a cup of tea or do housework, washing clothes is also a good choice.net3.fit(X, y)importas picklewith open(‘net3.pickle‘‘wb‘as f: pickle.dump(net3, f, -1)$ python kfkd.py...Epoch | Train Loss | V

/bottom[0]->num ();} Template__global__void Smoothl1backward (const int N, const dtype*inch, dtype*Out , Dtype sigma2) { F'(x) = Sigma * Sigma * x if |x| // =Sign (x) otherwise cuda_kernel_loop (index, N) {Dtype val=inch[index]; Dtype Abs_val=ABS (val);if(Abs_val sigma2) {Out[index]= Sigma2 *Val;} Else{Out[index]= (Dtype (0) Dtype (0)); } }} templatevoid Smoothl1losslayertop, const vectorbottom) { After forwards, Diff_ holds w_in * (B0-B1) int count=Diff_.count (); Smoothl1backward(Count, Diff_.

Call function print f (-2)Step 1 Define the input variablesA = Theano.tensor.scalar ()b =theano.tensor.matrix ()Simplified import theano.tensor as TStep 2 Define the relationship of the output variable to the input variableX1=t.matrix ()X2=t.matrix ()Y1=x1*x2Y2=t.dot (X1,X2) #矩阵乘法Step 3 declaring the functionF= theano.function ([x],y)The function input must be a list band []Example1 ImportTheano2 ImportTheano.tensor as T3 4A=T.matrix ()5b=T.matrix ()6c = A *b7D =T.dot (A, b)8f1=theano.

latest progress in deep learning--the anti-neural network. It mainly includes the idea of resisting the neural network and two specific Gan networks, the deep convolution countermeasure Generation Network (Dcgan) and the image translation (PIX2PIX) model. The knowledge points involved include generator G, discriminant D, deconvolution, u-net and so on. ... 10th Automatic Machine Learning Network-AUTOML This course provides an explanation of the latest advances in deep learning-automated machine

Note: My English proficiency is limited, translation is inappropriate, please the original English, do not like to spray, the other, the translation of this article is limited to academic exchanges, does not involve any copyright issues, if there is improper infringement or any other other than academic communication problems, please leave a message I, I immediately delete, thank you!!"Classification of benign and malignant breast tumors based on regional growth"SummaryBenign tumors are consider

See above -collect high-quality callout data-Input and output data are normalized to prevent numerical problems, and the method is the principal component analysis of what.-Initialization of parameters is important. Too small, the parameters are not moving at all. General weight parameter 0.01 mean variance, 0 mean value of Gaussian distribution is omnipotent, not to try to bigger. The deviation parameter is all 0.-with SGD, Minibatch size 128. or smaller size, but the throughput becomes small

1. Import various modulesThe basic form is:Import Module NameImport a module from a file2. Import data (take two types of classification issues as an example, Numclass = 2)Training Set DataAs you can see, data is a four-dimensional ndarrayTags for training sets3. Convert the imported data to the data format I keras acceptableThe label format required for Keras should be binary class matrices, so you need to convert the input label data to take advantage of the Keras enhanced to_categorical funct

A number of well-known websites such as CNN have encountered error 503 errors recently, according to foreign media reports. Foreign media said, according to users, affected by the social news site, including Reddit, The New York Times, CNN, BuzzFeed and other well-known sites, their network management system has a big problem.▲CNN and many other well-known sites

The mnist examples of convolutional neural networks and the neural network examples in the previous blog post are mostly the same. But CNN has more layers, and the network model needs to be built on its own.The procedure is more complicated, I will be divided into several parts to describe.First, download and load the data:Importimport= Input_data.read_data_sets ("mnist_data/" , One_hot=true) # Download and load mnist data x = Tf.placeholder (Tf.f

: Train multiple models, averaging the results when you test, and you can get a 2% boost. When training a single model, the results of checkpoints in the average different periods can also be improved. You can combine the parameters of the test with the parameters of the training when testing: 1. Whether CNN or Rnn,batch normalization useful, not necessarily result in a few points, convergence is much

because there is no GPU, so in the CPU training their own data, the middle encountered a variety of pits, fortunately did not give up, This process is documented in this article. 1, under the CPU configuration faster r-cnn, reference blog: http://blog.csdn.net/wjx2012yt/article/details/52197698#quote2, in the CPU training data set, need to py-faster-rcnn within the Roi_pooling_layer and Smooth_l1_loss_layer changed to the CPU version,and recompile. Th

-recognition-of-handwritten-digi Note: This code has an obvious bug when it comes to creating a CNN. If you see it, I'm going to look at the structural description of the simplified LeNet-5 and find out what the problem is. Literature: http://blog.csdn.net/celerychen2009/article/details/8973218http://www.so.com/s?ie=utf-8src= 360se7_addrq= convolutional Network http://www.baidu.com/s?ie=utf-8f=8rsv_bp=1rsv_idx=1tn=baiduwd= convolutional%20networksrsv

The receptive field is a kind of thing, from the angle of CNN visualization, is the output featuremap a node response to the input image of the area is to feel wild.For example, if our first layer is a 3*3 core, then each node in the Featuremap that we get through this convolution is derived from this 3*3 convolution core and the 3*3 region of the original image, then we call this Featuremap node to feel the wild size 3*3If you go through the pooling

the array must be 1. Padding= ' SAME ', which means fill 0, does not change the size of the image. Note that Tf.nn.bias_add (conv1,conv1_biases) does not add directly to CONV1 and conv1_biases. Layer2 With Tf.name_scope (' Layer2-pool1 '): pool1 = Tf.nn.max_pool (relu1,ksize=[1,2,2,1],strides=[1,2,2,1],padding= ' SAME ') The Max_pool representation is the pool layer that takes the maximum value.Let's take a look at the parameter ksize: Ksize:a List of ints that has length >= 4. The

([]); Ax.set_yticks ([]) -idx+=1 - plt.show () thePlot_image_labels_prediction_1 (X_img_test,y_label_test,prediction,0,Ten) thepredicted_probability=model.predict (x_img_test_normalize) the def show_predicted_probability (y,prediction,x_img_test,predicted_probability,i): thePrint'Label:', label_dict[y[i][0]],'Predict:', Label_dict[prediction[i]]) -Plt.figure (Figsize= (2,2)) thePlt.imshow (Np.reshape (X_img_test[i], ( +, +,3))) the plt.show () the forJinchRangeTen):94Print (label_dict[j]+

. Summarize the above experimental results:4. The following should be the principle of Li Feifei's Ted speech:5. Some recommendations for working with small datasets:V: Squeezing out of the last few Percent1. Using a small size filter is much better than using a large size filter, and a small size filter can increase the number of non-linearities and reduce the parameters that need to be trained (imagine a 7*7 patch with a 7 The filter convolution of the *7, and the filter convolution of the thr

ImageNet classification with deep convolutional neural Networks Alexnet is the model structure used by Hinton and his students Alex Krizhevsky in the 12 Imagenet Challenge, which refreshes the chance of image classification from the deep Learning in the image of this piece began again and again more than State-of-art, even to the point of defeating mankind, look at the process of this article, found a lot of previous fragmented to see some of the optimization techniquesReference: TensorFl