how to train convolutional neural network

Classification BPN (Back Propagation net) Here I will introduce the Back-Propagation Network BPN-the back-propagation error of the drive item. A back propagation neural network is a multi-layer network that trains weights for non-linear differential functions, and is a type of forward

(Self.hidden_n):For O in range (self.output_n):Change = output_deltas[o] * Self.hidden_cells[h]Self.output_weights[h][o] + = learn * change + correct * self.output_correction[h][o]#????????????????Self.output_correction[h][o] = Change# Update Input weightsFor I in Range (Self.input_n):For h in range (Self.hidden_n):Change = hidden_deltas[h] * Self.input_cells[i]SELF.INPUT_WEIGHTS[I][H] + = learn * change + correct * self.input_correction[i][h]SELF.INPUT_CORRECTION[I][H] = Change# Get global Err

?? The error inverse propagation algorithm is by far the most successful neural network learning algorithm, the use of neural networks in practical tasks, mostly using BP algorithm to train.?? Given training set\ (d={(x_1,y_1), (x_2,y_2),...... (x_m,y_m)},x_i \in r^d,y_i \in r^l\), that is, the input example is\ (d\)At

limits, saturation is also divided into left soft saturation and right soft saturation:Soft saturation on the left:650) this.width=650; "src=" Http://s4.51cto.com/wyfs02/M01/85/51/wKioL1egCPPyiLciAAAZyDsSrvA710.jpg "title=" 640. WEBP (2). jpg "alt=" wkiol1egcppyilciaaazydssrva710.jpg "/>Right soft saturation:650) this.width=650; "src=" Http://s2.51cto.com/wyfs02/M01/85/51/wKiom1egCQLS6Pi2AAAXnt-EnL0768.jpg "title=" 640. WEBP (3). jpg "alt=" wkiom1egcqls6pi2aaaxnt-enl0768.jpg "/>In contrast to s

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

output, if the network final output is a value, then a scalar, if the final output more than one value (the sample's label y is also a vector), then it is a vector. For: we have Use these formulas to convert the vectors into another formula (the activation function is also extended to be expressed as a vector (component), i.e.): This is what we define WIJ as the above element, so that it is directly based on the wij of the label to form a matrix

based on the model name to ensure that the test group remains the same when the model is re-processed.Maximum_input_attributes: Specifies the maximum number of input variables that the algorithm can handle. Setting this value to 0 disables the input variable.Maximum_output_attributes: Specifies the maximum number of output variables that the algorithm can handle. Setting this value to 0 disables the output variable.Maximum_states: Specifies the maximum number of variable value states supported

; otherwise, the error occurs. Note the following points: 5. Random initialization (random initialization) For the theta parameter initialization problem, the simplest idea is to assign a value of 0 first: However, this assignment makes no difference in hiding nodes at the beginning of the computation. Just like this, the calculation process and result of A1 and A2 are the same, which is equivalent to a single node, causing waste. To break this situation, you can perform random

Paste the Experiment Code firstThe target uses the Amore method of the neural network to train the data and then test the data Library (amore)X1 X2 X11 X12 x21 x22 Y1 Y2 P Q Target =y1 NET , Error.criterium = ' LMS ', Stao = Na,hidden.layer = "Tansig",Output.layer = ' Purelin ', method = "ADAPTGDWM")Result , n.shows = 5) zPlot (q[1:100,1],z, col= "Blue", pch= "+"

The contents of this article for I learn to understand, there is wrong place also please point out. The so-called BP neural Network (back propagation) is to use the known data set along the neural network forward to calculate the predicted value, so as to obtain the deviation between the predicted value and the actua

New neural network architectures are in place anytime, anywhere, dcign,iilstm,dcgan~1. Forward propagation Network (FF or FFNN)Very straightforward, they transfer information from the trip (input and output, respectively). Neural networks usually have many layers, including input layers, hidden layers, and output layer

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

(per 100) Based on this information, we want to train a neural network that can predict whether the GDP per capita are more than aver Age for the country (label 1 if it is, 0 if it's not).I ' ve separated the dataset for training (121 countries) and testing (+ countries). The values are been normalised, by subtracting the mean and dividing by the standard d

-- 7--input= $SEQUENCES _tfrecord 8--train_output= $TRAIN _ DATA 9--eval_output= $EVAL _data--eval_ratio= $EVAL _ratio--encoder= $ENCODEROK, here our data processing is finished, the training file generated in "/tmp/training_melodies.tfrecord"4. Training Neural Network modelAfter the training data is generated, the model can be trained, using the RNN model:1 #

I saw the time series prediction using dynamic neural networks on the matlat Chinese forum. Http://www.ilovem http: // A http: // tlab.cn/thread-113431-1.html (1) first basic knowledge needs to be known Training data) Validation Data) Test Data) However, I do not quite understand the three. Thank you for your explanation. The following is an explanation of a Website: Http://stackoverflow.com/questions/2976452/whats-the-diference-between-

() cost = GetAttr (sys.modules[__name__], data[" cost "]" NET = Network (data["sizes"], cost=cost) net.weights = [Np.array (w) for W in data["weights"]] net.biases = [Np.array (b) for B-data["biases"]] return net def sigmoid (z): "" "" "the sigmoid function." " Return 1.0/(1.0+np.exp (z)) def sigmoid_prime (z): "" "derivative of the sigmoid function." "" return sigmoid (z) * (1-sigmoid (z)) Call a neura