Closed. This question needs to be more focused. It is not currently accepting answers. Want to improve this question? Update the question so it focuses on one problem only by editing this post. Closed 2 years ago. Improve this question I need to build a neural network accepting data from a particular .csv file where most columns’ type is object,
Tag: neural-network
How to add an attention layer to LSTM autoencoder built as sequential keras model in python?
So I want to build an autoencoder model for sequence data. I have started to build a sequential keras model in python and now I want to add an attention layer in the middle, but have no idea how to approach this. My model so far: So far I have tried to add an attention function copied from here and
Tensor Flow Conv1D for binary classification CNN
I’m creating a Conv1D layer in a CNN for binary classification, and I’m quite new to Machine Learning and I need some help to figure out the correct values for Conv1D: I would like to know if there is a way to determine the “Right” hyper-parameters that will fit my dataset best. Answer For the completion, here is the documentation
AttributeError: ‘numpy.ndarray’ object has no attribute ‘self’
I started implementing the backend of neural network but got stuck in a code of python. The below is the code for neural Network. While i was making use of the userdefined class in one of the application to be made, i got an error by name attributeError. Please help me out solving it. I tried all the indentation syntax
validation accuracy not improving
No matter how many epochs I use or change learning rate, my validation accuracy only remains in 50’s. Im using 1 dropout layer right now and if I use 2 dropout layers, my max train accuracy is 40% with 59% validation accuracy. And currently with 1 dropout layer, here’s my results: Again max, it can reach is 59%. Here’s the
In Keras, can I use an arbitrary algorithm as a loss function for a network?
I has been trying to understand this machine learning problem for many days now and it really confuses me, I need some help. I am trying to train a neural network whose input is an image, and which generates another image as output (it is not a very large image, it is 8×8 pixels). And I have an arbitrary fancy_algorithm()
PyTorch DataLoader shuffle
I did an experiment and I did not get the result I was expecting. For the first part, I am using I save trainloader.dataset.targets to the variable a, and trainloader.dataset.data to the variable b before training my model. Then, I train the model using trainloader. After the training is finished, I save trainloader.dataset.targets to the variable c, and trainloader.dataset.data to
Neural Network Results always the same
Edit: For anyone interested. I made it slight better. I used L2 regularizer=0.0001, I added two more dense layers with 3 and 5 nodes with no activation functions. Added doupout=0.1 for the 2nd and 3rd GRU layers.Reduced batch size to 1000 and also set loss function to mae Important note: I discovered that my TEST dataframe wwas extremely small compared
Denormalization of output from neural network
I have used the MinMax normalization in order to normalize my dataset, both features and label. My question is, it’s correct to normalize also the label? If yes, how can I denormalize the output of the neural network (the one that I predict with the test set that is normalized)? I can’t upload the dataset, but it is composed by
How to see the loss of the best epoch from early stopping in Keras?
I have managed to implement early stopping into my Keras model, but I am not sure how I can view the loss of the best epoch. The way I have defined the loss score, means that the returned score comes from the final epoch, not the best epoch. Example: So in this example, I would like to see the loss