cnn ireport

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

-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

([]); 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

there is a total of n neurons in the network, p=0.5, then the equivalent of 2n sub-network training at the same time, is a model averaging method to improve generalization performance.Network Structure AnalysisUsually after the convolution layer should be a pooled layer, but alexnet only in the first convolutional layer, the second convolutional layer and the last convolutional layer behind the maximum pooling, because in the lower layer of the network, the size of the feature map is generally

,accuracy=false)# Plot The resultPlt.figureplt.plot (result.epoch,result.history[' ACC '],label="ACC") Plt.plot (result.epoch,result.history[' VAL_ACC '],label="VAL_ACC") Plt.scatter (result.epoch,result.history[' ACC '],marker=' * ') Plt.scatter (result.epoch,result.history[' VAL_ACC ']) Plt.legend (loc=' under Right ') Plt.show () Plt.figureplt.plot (result.epoch,result.history[' loss '],label="Loss") Plt.plot (result.epoch,result.history[' Val_loss '],label="Val_loss") Plt.scatter (result.epo

This article address:http://blog.csdn.net/u011239443/article/details/72861591 We are going to implement the nonstandard Lenet

Select the component, properties, to display the Chinese language, and configureNote: After the configuration is complete, to add the following jar files to support the display of Chinese itext itext 1.3.1 huike iTextAsian 1.0.0ireport Chinese

Content from UFLDL, code reference from Tornadomeet CnnCost.m1.Forward propagationConvolvedfeatures = Cnnconvolve (Filterdim, numfilters, images, Wc, BC); %for the first arrow activationspooled= Cnnpool (Pooldim, convolvedfeatures);corresponds to a

To import the desired lib: Import NumPy as NP from keras.datasets import mnist to keras.utils import np_utils from keras.models Import Sequential from keras.optimizers import Adam from keras.layers import dense,activation,convolution2d,

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Now that the operation has been successful, the problem was not recorded in time, and now can only be written in memory of these problems Problem 1:matlab command ' MATLAB ' not found. Please add ' matlab ' to your PATH. Solution: This problem has