cnn environment

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

://vision.stanford.edu/teaching/cs231n/syllabus.htmlnotes:http://cs231n.github.io/ Cloud : HTTP://PAN.BAIDU.COM/S/1PKSTIVP from Love Coco-love life3. Recent developments and practical tips for CNN (i)Ph. D., CAs Melody, who has just uploaded a talk slides in VALSE2016, on recent developments and practical tips on CNN, on CNN's Progress and Caffe's common skills [for a tutorial on Caffe use], see links http:

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

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The biggest problem with full-attached neural networks (Fully connected neural network) is that there are too many parameters for the full-connection layer. In addition to slowing down the calculation, it is easy to cause overfitting problems. Therefore, a more reasonable neural network structure is needed to effectively reduce the number of parameters in the neural network. convolutional Neural Networks (convolutional neural network,cnn) can do that.

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

is Faster r-cnn Doing well for pedestrian Detection?ECCV Liliang Zhang kaiming He　　Original link: http://arxiv.org/pdf/1607.07032v2.pdf　　Abstract: Pedestrian detection is argue said to be a specific subject, rather than general object detection. Although recent depth object detection methods such as: Fast/faster RCNN in general object detection, show a strong performance, but for pedestrian detection is not very successful. This paper studies the pro

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

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The CNN Formula derivation 1 prefaceBefore looking at this blog, please make sure that you have read my top two blog "Deep learning note 1 (convolutional neural Network)" and "BP algorithm and Formula derivation". and has read the paper "Notes on convolutional neural Networks" in the literature [1]. Because this is the interpretation of the literature [1] The derivation process of the formula in the first part of the thesis 2

The CNN Formula derivation 1 prefaceBefore looking at this blog, please make sure that you have read my top two blog "Deep learning note 1 (convolutional neural Network)" and "BP algorithm and Formula derivation". and has read the paper "Notes on convolutional neural Networks" in the literature [1]. Because this is the interpretation of the literature [1] The derivation process of the formula in the first part of the thesis Here is a hypothesis, perh

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

([]); 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

discriminant of a logistic regression, and the parameters of each intermediate node are recorded. So, for the Cbow model, there are:Then, the target function is:Then the parameters θ and x of the target function are updated by the random gradient descent method, so that the value of the objective function can be maximized.Similar to the Cbow model, Skip-gram is solved by optimizing the following objective functions.whichSo, the target function of Skip-gram is:The parameters θ and V (w) of the t

of pre-training network:Ultimately, this solution is 2.13 RMSE on the leaderboard.Part 11 conclusionsNow maybe you have a dozen ideas to try and you can find the source code of the tutorial final program and start your attempt. The code also includes generating the commit file, running Python kfkd.py to find out how the command is exercised with this script.There's a whole bunch of obvious improvements you can make: try to optimize each ad hoc network, and observe 6 networks, and you can see th

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

,In the above formula, the * number is the convolution operation, the kernel function k is rotated 180 degrees and then the error term is related to the operation, and then summed.Finally, we study how to calculate the partial derivative of the kernel function connected with the convolution layer after obtaining the error terms of each layer, and the formula is as follows.The partial derivative of the kernel function can be obtained when the error item of the convolution layer is rotated 180 deg