neural-network

问题 I need to train a neural network with 2-4 hidden layers, not sure yet on the structure of the actual net. I was thinking to train it using Hadoop map reduce (cluster of 12 pcs) or a gpu in order to get faster results. What do you think it would be better ? Also are there any available libraries that have these already implemented? Thanks 回答1: I've been luckily to work in a lab which has dabbled in both of these methods for training networks, and while both are useful in very computationally

问题 I can find a list of activation functions in math but not in code. So i guess this would be the right place for such a list in code if there ever should be one. starting with the translation of the algorithms in these 2 links: https://en.wikipedia.org/wiki/Activation_function https://stats.stackexchange.com/questions/115258/comprehensive-list-of-activation-functions-in-neural-networks-with-pros-cons the goal is to have an Activation class (with the functions and their derivative) with easy

问题 For my graduate research I am creating a neural network that trains to recognize images. I am going much more complex than just taking a grid of RGB values, downsampling, and and sending them to the input of the network, like many examples do. I actually use over 100 independently trained neural networks that detect features, such as lines, shading patterns, etc. Much more like the human eye, and it works really well so far! The problem is I have quite a bit of training data. I show it over

问题 If we have a neural network such as the multilayer perceptron back propagation neural network that uses sigmodial logistic activation functions is it possible to feed the network outputs and have it compute back a set of inputs? Since we can reverse the activation function by using the natural logarithm and inverse operations until we have a sum value that is made up of all the weights multiplied by their inputs i would think that it would be possible to at least get sets of possible inputs

问题 I read a few books and articles about Convolutional neural network, it seems I understand the concept but I don't know how to put it up like in image below: (source: what-when-how.com) from 28x28 normalized pixel INPUT we get 4 feature maps of size 24x24. but how to get them ? resizing the INPUT image ? or performing image transformations? but what kind of transformations? or cutting the input image into 4 pieces of size 24x24 by 4 corner? I don't understand the process, to me it seem they

问题 I have trouble getting the DeepBeliefTrainer to work on my data in PyBrain/Python. Since I can't find any examples other than unsupervised on how to use the deep learning in PyBrain, I hope that someone can give examples that would show a basic concept of usage. I have tried to initialize using: epochs = 100 layerDims = [768,100,100,1] net = buildNetwork(*layerDims) dataset = self.dataset trainer = DeepBeliefTrainer(net, dataset=dataSet) trainer.trainEpochs(epochs) I try to use a

问题 I am currently working on a neural network based approach to short document classification, and since the corpuses I am working with are usually around ten words, the standard statistical document classification methods are of limited use. Due to this fact I am attempting to implement some form of automated synonym detection for the matches provided in the training. My question more specifically is about resolving a situation as follows: Say I have classifications of "Involving Food", and one

问题 I want to have a for loop that the number of its iterations is depend on a tensor value. For example: for i in tf.range(input_placeholder[1,1]): # do something However I get the following error: "TypeError: 'Tensor' object is not iterable" What should I do? 回答1: To do this you will need to use the tensorflow while loop (tf.while_loop) as follows: i = tf.constant(0) while_condition = lambda i: tf.less(i, input_placeholder[1, 1]) def body(i): # do something here which you want to do in your

问题 What is the difference between categorical_accuracy and sparse_categorical_accuracy in Keras? There is no hint in the documentation for these metrics, and by asking Dr. Google, I did not find answers for that either. The source code can be found here: def categorical_accuracy(y_true, y_pred): return K.cast(K.equal(K.argmax(y_true, axis=-1), K.argmax(y_pred, axis=-1)), K.floatx()) def sparse_categorical_accuracy(y_true, y_pred): return K.cast(K.equal(K.max(y_true, axis=-1), K.cast(K.argmax(y

问题 I am a little new to neural networks and keras. I have some images with size 6*7 and the size of the filter is 15. I want to have several filters and train a convolutional layer separately on each and then combine them. I have looked at one example here: model = Sequential() model.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1], border_mode='valid', input_shape=input_shape)) model.add(Activation('relu')) model.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1])) model