Contents
Is the Keras model always predict the same class?
But keras model almost always predicts same class for all validation and test examples and the accuracy is stuck at ~50%. I have tried with a lot of different hidden layer sizes, activation functions, loss functions and optimizers but it was of no help.
How to get the same result in CNN?
This is my first time training a model in cnn and predicting results but I am getting same value for images I have input. Here is my code
When do all predictions give the same value?
When all the predictions are giving exact the same value you know that your model is not learning thus something is wrong! In your case the problem is having the last dense layer with the softmax AND the sigmoid activation.
When to use less hidden layers in keras?
The Keras issues section is not the right place to discuss all the architectures choices you got wrong in your NNs, so I will be closing this issue. if you deep net is not working, then use less hidden layers, until it works. don’t use tanh as an activation. It’s not the 90s anymore.
How many samples can be generated in a batch in keras?
Number of samples per batch. If unspecified, batch_size will default to 32. Do not specify the batch_size if your data is in the form of dataset, generators, or keras.utils.Sequence instances (since they generate batches).
How does training and evaluation work in keras?
In general, whether you are using built-in loops or writing your own, model training & evaluation works strictly in the same way across every kind of Keras model — Sequential models, models built with the Functional API, and models written from scratch via model subclassing.
When to use custom loss instance in keras?
If a custom Loss instance is used and reduction is set to NONE, return value has the shape [batch_size, d0, .. dN-1] ie. per-sample or per-timestep loss values; otherwise, it is a scalar. If the model has multiple outputs, you can use a different loss on each output by passing a dictionary or a list of losses.
Is there a way to predict more than one class?
Changed the final dense layer from 1 to 2 so this will return scores/probabilities for both classes. So when you use argmax, it will return the index position of the top score indicating which class it has predicted. I have solved this. I am currently working on the accuracy but the main problem is now solved.
How to use argmax to predict two classes?
1.Changed the class_mode to ‘categorical’ for the train and test generators 2.Changed the final dense layer from 1 to 2 so this will return scores/probabilities for both classes. So when you use argmax, it will return the index position of the top score indicating which class it has predicted.