Linked Questions

I am training a model (Recurrent Neural Network) to classify 4 types of sequences. As I run my training I see the training loss going down until the point where I correctly classify over 90% of the ...

Is the following hypothesis true ? If a simple neural network cannot overfit a single training sample, there is something wrong with its architecture or its implementation. To give you more ...

I have some troubles trying to set up a multilayer perceptron for binary classification using tensorflow. I have a very large dataset (about 1,5*10^6 examples) each with a binary (0/1) label and 100 ...

I'm attempting to use a sequence of numbers (of fixed length) in order to predict a binary output (either 1 or 0) using Keras and a recurrent neural network. Each training example/sequence has 10 ...

I am making a CNN with 6 classes. The 8400 training samples are batched into 84 batches of size 100. I run the model and print out the loss after every batch, the loss is always either 0.0 or some ...

I am trying to train a small MLP in Pytorch. Here is the code for the net: ...

I've trained fitnet network for prediction steel's yield stress with MATLAB ann toolbox. The neural network should predict yield stress. I have about 250 vector ...

I am trying to train a simple neural network for regression, where the underlying function is a quadratic. Training data is generated by this underlying function, and I am just trying to get a network ...

I'm trying to learn how to use PyTorch and to do so I pulled some Forex + COVID data I've used with other models in the past to predict the next-day exchange rate. The data has some COVID infection ...

I am a beginner to CNN and using tensorflow in general. I have been referring to this image classification guide to train and classify my own dataset. I have 84310 images in 42 classes for the train ...

I'm struggling with my model (below), since despite some hyperparameters tuning i always end with a sudden rise of the loss function and then a 'infinite" plateau. My hypothesis were: -learning ...

I am training a network to solve a regression problem using Keras. During training, the loss of my model goes directly from 7 to more than 300000 dramatically. Here is the training output: Here is the ...

I'm trying to find patterns in a large dataset using the neuralnet package. My data file looks something like this (30,204,447 rows) : ...

I have been designing a neural network to perform predictions on construction item costs. I've developed a core set of predictors that seem to describe the problem space well - they appear to be ...

I'm using Keras to build and train a recurrent neural network. ...

I'm using this code to train a neural netowrk in MATLAB: ...

I have a CNN with 3 convolutional layers, 1 max-pooling layer and 2 fully-connected layers before applying softmax classification. The CNN is trained with Adagrad and I achieve a quite good ...

I have run into some problems when trying to train a network that fits some multivariate quadratic function, or the Euclidean distance between 2 points in a 3-dimensional space, where they are 'pretty ...

I'm working on making my own neural network using the NEAT algorithm. I have programmed the algorithm from scratch because I can't seem to get any of the libraries online working, but I'm 90% sure the ...

I am training a simple neural network in keras to fit my non-linear thermodynamic equation of state. I use backpropagation and stochastic gradient. The network approximates the equation of state but ...

I have a dataset set with ~40 features onto which I'm applying a multi-layer perceptron for regression purposes. The train, validation, and test sets are made up of 3M, 800K, and 800K examples each, ...

I am training a very simple 2D dataset with 2 features. Its tabular data and contains only numeric information. I tried using keras to train a neural network but the performance does not bulge. I ...

I am currently trying to automate some identification process of characteristic noise sounds. For acoustic feature, I calculate MFCC. I have downloaded a free MATLAB toolbox from Dan Ellis'es website. ...

I am currently implementing a simple neural network and the backprop algorithm in Python with numpy. I have already tested my backprop method using central differences and the resulting gradient is ...

I'm training an LSTM (using the Keras python library) to generate sequences. My X training data is a list of sequences, and the Y training data is a list of the final values of those sequences. The ...

I used MatConvNet to build a CNN model for regression. The input size is 20×20×1×32, the output size is 4×1×32, the convolutional filter size is 3×3×1. Now I found after training the training error ...

I'm using Matlab Neural Network Toolbox. I want learn feed forward net for my classification problem. But network doesn't learn anything useful, so I start checking network setting. I choose xor ...

I am trying to use LSTM to predict a time series data as you can see in the following image, the predicted graphs is very noisy: The original data is looking like this: That I normalized it like this ...

I designed my own neural network for solving the problem of text summarization. The number of documents in my training dataset is big (more than 100,000 documents) so it is hard to check it on the ...

I am currently exploring the training of Neural Networks. I have some toy data and I've trained a NN with 2 hidden layers on it and I get 99 % accuracy on the test set. But the problem is that if I ...

In my scenario, I use deep reinforcement learning to fix a problem that is related to transportation. During training, I plot the gradient and loss, I find that the gradient converges and then ...

I'm currently trying to get the basics of Pytorch, playing around with simple networks topologies for the fashion-MNIST dataset. However, when I record the loss of those models after each epochs, it ...

I have built a regular ANN–BP setup with one unit on input and output layer and 4 nodes in hidden with sigmoid. Giving it a simple task to approximate linear ...

I'm implementing a typical neural network with 1 hidden layer. The network does well with the logic XOR and other simple problems, but fails miserably when encountering a (16-input, 20~30 hidden, 3 ...

I am trying to learn a very simple sequence using an RNN (implemented in Keras) The input sequence is randomly generated integers between 0 to 100: x=np.random.randint(0,100, size=2000) while the ...

I'm having a similar problem to the following post (Feed-Forward) Neural Networks keep converging to mean. The model is built with Deep Neural Network library in Matlab by Masayuki Tanaka. The ...

I am trying to train a basic Neural Network to predict Football final scores based on: i) Time in the match ii) Current Score iii) Parameters representing strength of home and away team. In order ...

I'm replicating this paper for my PhD, which says that they are using deep learning to predict stock returns. So the inputs are (mostly) continuous variables that can be negative and positive. Outputs ...

I am fairly new to neural networks. I am trying to empirically show that a neural network can work better than logistic regression when the underlying function is non-linear. In my simulation study, ...

I have a dataset of energy measurements taken every minute from the energy footprint of home appliances. Based on that I am trying to detect human presence in the house. Since the data is sequential, ...

I am training a 4-class neural network classifier. The details of my data are: featurelength = 280 ...

I am training YOLO network consisting of resnet50 architecture.This problem is to find different text labels on the image and predict bounding boxes During training, I am seeing very less change in ...

I am trying to design a neural network for time series forecasting using LSTM neurons. I am stuck because the many different configurations that I tried so far are not performing well (actually they ...

I have hourly data for 365 days, and I would like to train a neural network model for 7 days and predict 8th day hourly data. It is a time series 24-h ahead regression problem. I am also applying such ...

I am trying to train a Deep Q Network (https://deepmind.com/research/dqn/) for a simple control task. The agent starts in the middle of a 1-dimensional line, at state 0.5. On each step, the agent can ...

I'm working on a neural network with one hidden layer. So far I've implemented the algorithm, and I've been able to numerically verify the partial derivatives I get from back propagation. My problem ...

I'm using the Deeplearning.net DBN tutorial to train my data set. I normalize the feature set to zero-mean-unit-variance. However, I can only get the network to predict 2 out 5 classes even though the ...

So in the code below, which is pretty standard LSTM training for the IMDB dataset, I have run extensive experiments where I changed the drop-out value from 0.5 all the way up to 1, and the accuracy on ...

I should develop a network that can read the result of throwing a dice. I have a dataset which consists on a synthetic collection of such images, together with the corresponding target values. Each ...

Hi I am trying to simulate the flow of water through a porous medium using ANNs. I have managed to get good result when the porous medium is homogeneous, however when it isn't the network seems to ...