Poor performance help- muti-class classification by ANN
I'm implementing a 7-class classification task with normalised features and one-hot encoded labels. However, the training and validation accuracies have been extremely poor.
As shown, I normalised features from with StandardScaler() method and each feature vector turns out a 54-dim numpy array. Also, I one-encoded labels in the following manner.
As illustrated below, the labels are (num_y, 7) numpy arrays.
My network architecture:
It is shown here how I designed my model. And I'm wonder if the poor result has something to do with the selection of loss function (I've been using Categorical Cross-Entropy)
I appreciate any response from you. Thanks a lot!
The use of accuracy is obviously wrong. The code I refer to is not provided in your question, but I can speculate that you are comparing the true labels with your model outputs. Your model probably returns a vector of dimensionality 7 which constitutes a probability density function over the classes (due to the softmax activation in your final layer) like this:
model returns: (0.7 0 0.02 0.02 0.02 0.04 0.2) -- they sum to 1 because they represent probabilities
and then you are comparing these numbers with: (1 0 0 0 0 0 0)
what you have to do is translate the model output to the corresponding predicted label ((0.7 0 0.02 0.02 0.02 0.04 0.2) corresponds to (1 0 0 0 0 0 0) because the first output neuron has the larger value (0.7)). You may do that by applying a max function after your model outputs.
To make sure thats whats wrong with your problem formulation print the vector you are comparing with the true labels to get your accuracy and check if they are 7 numbers that sum up to 1.
- Training loss increases instead of decrease with epochs
- Soundfile imports audio in two different formats
- Error for neuralnet package in R
- tensorflow.keras only runs correctly once
- How do filters run across an RGB image, in first layer of a CNN?
- Why do neural networks work so well?
- Will larger batch size make computation time less in machine learning?
- Neural network learning to sum two numbers
- Why shouldn't we use multiple activation functions in the same layer?
- how to implement custom metric in keras?
- Change the threshold value of the keras RELU activation function
- Neuralnet RMSE is 10x bigger than linear model's RMSE on test data set
- Back Propagation in Convolutional Neural Networks and how to update filters
- Ordering of batch normalization and dropout?
- What is freezing/unfreezing a layer in neural networks?
- Loaded Keras Model Throws Error While Predicting (Likely Issues with Masking)
- Need some help debugging this java
- Linear vs nonlinear neural network?
- Can we use GNN on graphs with only edge features?
- How do I initialize weights in PyTorch?
- Does one convolutional filter always have different coefficients for each of the channels of the previous layer?
- How to use a Dense layer with an input that has a dynamically sized dimension?
- Should we do learning rate decay for adam optimizer
- InceptionResnetV2 STEM block keras implementation mismatch the one in the original paper?
- TypeError: '_IncompatibleKeys' object is not callable
- Debugging GAN covergence
- Loss is increasing from first epoch itself
- Handling Absence of Color Data in 3D Mesh Neural Network Input
- Can a neural network be trained while it changes in size?
- unexpected transformer's dataset structure after set_transform or with_transform