neural-network

问题 I want to use caffe with a vector label, not integer. I have checked some answers, and it seems HDF5 is a better way. But then I'm stucked with error like: accuracy_layer.cpp:34] Check failed: outer_num_ * inner_num_ == bottom[1]->count() (50 vs. 200) Number of labels must match number of predictions; e.g., if label axis == 1 and prediction shape is (N, C, H, W), label count (number of labels) must be N*H*W , with integer values in {0, 1, ..., C-1}. with HDF5 created as: f = h5py.File('train

问题 I'm using Keras with Tensorflow as backend. I am trying to save a model in my main process and then load/run (i.e. call model.predict ) within another process. I'm currently just trying the naive approach from the docs to save/load the model: https://keras.io/getting-started/faq/#how-can-i-save-a-keras-model. So basically: model.save() in main process model = load_model() in child process model.predict() in child process However, it simply hangs on the load_model call. Searching around I've

问题 The increase in network size is not the cause(problem) here is my code for i in [32, 64, 128, 256, 512]: for j in [32, 64, 128, 256, 512]: for k in [32, 64, 128, 256, 512]: for l in [0.1, 0.2, 0.3, 0.4, 0.5]: model = Sequential() model.add(Dense(i)) model.add(Dropout(l)) model.add(Dense(j)) model.add(Dropout(l)) model.add(Dense(k)) model.add(Dropout(l)) model.compile(~) hist = model.fit(~) plt.savefig(str(count) + '.png') plt.clf() f = open(str(count) + '.csv', 'w') text = ~ f.write(text) f

问题 I have implemented the Self-Organizing Map(SOM) algorithm in MATLAB. Suppose each of the data points are represented in 2-dimensional space. The problem is that I want to visualize the movement of each of the data points in the training phase i.e. I want to see how the points are moving and eventually forming clusters as the algorithm is in progress say at every fix duration. I believe that this can be done through Simulation in MATLAB,but I don't know how to incorporate my MATLAB code for

问题 I have a problem with concept of Validation for NN. suppose I have 100 set of input variables (for example 8 input, X1,...,X8) and want to predict one Target(Y). now I have two ways to use NN: 1- use 70 set of data for training NN and then use trained NN to predict other 30 sets of Target for validation and then plot output VS Target for this 30 sets as validation plot. 2- use 100 sets of data for training NN and then divide all outputs to two part (70% and 30%). plot 70% of outputs VS

问题 I am trying neural network application on Windows Phone 8. And I am writing below code: ActivationNetwork network = null; //global variable network = new ActivationNetwork( new BipolarSigmoidFunction(2), //aktivation func. 9, //input count 20, //hidden layer count 1 //output count ); This code working on desktop project.(C#) But not working Windows Phone 8. This is Aforge Framework's function. I installed via nuget. When I clicked run I am getting error. My error code: An exception of type

问题 I am relatively new to tensorflow and I have attempted to adapt some code from a tutorial to process my own data. The data can be found here: https://github.com/z12332/tensorflow-test-1/blob/master/export.csv Keep in mind, that the dataset being fed consists of only columns 9 though 27 (with nan's converted to 0) and column 30 as the labels here is the link to the tutorial code: https://github.com/llSourcell/How_to_use_Tensorflow_for_classification-LIVE/blob/master/demo.ipynb I am able to get

问题 I am currently having a problem that I haven't been able to figure out for a couple of days with a self-implemented neural network in Java. My network has two input neurons corresponding to x and y coordinates of a pixel in a given greyscale image and one output representing the tone of the pixel. The learning algorithm I'm using is RPROP. The problem I am having is that after numerous iterations of feeding the network with the whole training data set, it converges to a point very far away

问题 I used Caffe GoogleNet model to train my own data (10k images, 2 classes). I stop it at 400000th iteration with an accuracy of ~80%. If I run the below command: ./build/examples/cpp_classification/classification.bin models/bvlc_googlenet/deploy.prototxt models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel data/ilsvrc12/imagenet_mean.binaryproto data/ilsvrc12/synset_words.txt 1.png it gives me a different -- apparently random -- result each time (i.e. if I run it n times, then I

问题 In what order do the commands in NEURON .MOD/NMODL file sections get executed? Specifically, within these blocks: DERIVATIVE, BREAKPOINT and NET_RECEIVE. 回答1: For every time-step, the order of execution is as follows: NET_RECEIVE : If there is net_send() an event that targets this mechanism, lines here are executed first. Skipped otherwise. Lines in BREAKPOINT : The SOLVE ... METHOD line is ignored. All lines after SOLVE are executed. With a printf() statement, you would see two calls.