neural-network

问题 I'm trying to understand RNNs (not a specific one) with the Reber Grammar inputs (not embedded for now). You can find the jupyter notebook on this link (please disregard markdowns because I failed on the first version with output and it's not up-to-date :) ) . For every timestep, I provide the input and expected output for the training (so it's a many-to-many model). Input/output are "OneHotEncoded" (based on the string "BTSXPVE") so for example B is [1, 0, 0, 0, 0, 0, 0] V is [0, 0, 0, 0, 0,

问题 I'm building a convolutional neural network using Tensorflow (I'm new with both), in order to recognize letters. I've got a very weird behavior with the dropout layer : if I don't put it (ie. keep_proba at 1), it performs quite well and learns (see Tensorboard screenshots of accuracy and loss below, with training in blue and testing in orange). However, when I put the dropout layer during the training phase (I tried at 0.8 and 0.5), the network learns nothing : loss falls quickly around 3 or

问题 I am new to caffe and Machine learning algorithms. Are there any tutorials to train and TEST LeNet on the MNIST data using caffe and python 2.7(preferably). Thanks 回答1: There's caffe's ipython notebook example Learning LeNet. The code is not limited to ipython notebook (although easier to navigate). You can copy them into the python interpreter and run them there. 来源： https://stackoverflow.com/questions/35151739/how-to-train-and-test-lenet-using-caffe-using-python

问题 when I run train_caffenet.sh , I get the following errors: I0906 10:56:42.327703 21556 solver.cpp:91] Creating training net from net file: /home/pris/caffe-master/examples/myself/train_val.prototxt [libprotobuf ERROR google/protobuf/text_format.cc:245] Error parsing text-format caffe.NetParameter: 26:12: Message type "caffe.ImageDataParameter" has no field named "backend". F0906 10:56:42.327837 21556 upgrade_proto.cpp:79] Check failed: ReadProtoFromTextFile(param_file, param) Failed to parse

问题 I am new to c++, when I was reading codes about caffe layers, I find functions like loss_weight_size() and add_loss_weight() . I searched the whole project and didn't find the definition of these functions. So what are these functions? template <typename Dtype> void LossLayer<Dtype>::LayerSetUp( const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) { // LossLayers have a non-zero (1) loss by default. if (this->layer_param_.loss_weight_size() == 0) { this->layer_param_.add_loss

问题 My data set is a HDF5 file consists of data with shape [129028,1,12,1024] and label of shape [129028,1,1,1] . But when I run solver.prototxt, I get the error message: I0413 08:54:34.689985 17769 hdf5.cpp:32] Datatype class: H5T_FLOAT F0413 08:54:40.661201 17769 hdf5_data_layer.cpp:53] Check failed: hdf_blobs_[i] ->shape(0) == num (1 vs. 1024) *** Check failure stack trace: *** 回答1: It looks like you saved your hdf5 from matlab, rather than python (judging by your previous question). When

问题 I am using Neural Network (NN) wizard in MATLAB for some implementations. Also i can use code-based version of NN in MATLAB which is available after construction NN by wizard (It is clear!). When we provide our NN with MATLAB, it is a fully connected input-hidden layer. For example, if you have 4 inputs in the input layer and 2 neurons in the hidden layer, we have fully connected relation between 4 inputs and 2 neurons in hidden layer. I am going to manipulate this connections. For example,

问题 My neural net is slightly modified version of model proposed on this paper: https://arxiv.org/pdf/1606.01781.pdf My goal is to classify text to 9 different categories. I'm using 29 convolutional layers and have set the max length of any text to 256 characters. Training data has 900k and validation data 35k samples. The data is quite imbalanced and therefore I have done some data augmentation to balance the training data (have not touched the validation data obviously) and then used class

问题 I am trying to prepare my data set for fully convolutional network. I've looked through some data sets and I'm having a really hard time figuring out how to format it. For instance, in the Kitti data set, there are these 2 images and this text file in the training folder : image 1 image 2 text P0: 7.215377000000e+02 0.000000000000e+00 6.095593000000e+02 0.000000000000e+00 0.000000000000e+00 7.215377000000e+02 1.728540000000e+02 0.000000000000e+00 0.000000000000e+00 0.000000000000e+00 1

问题 Here's question proposed at the end of the chapter in 70-774 exam reference book. If you connect a neural network with a Tune Model Hyperparameters module configured with Random Sweep and Maximum number of runs on random sweep = 1, how many neural networks are trained during the execution of the experiment? Why? If you connect a validation dataset to the third input of the Tune Model Hyperparameters module, how many neural networks are trained now? And the answer is : Without validation