Keras – Dense and Conv2D values
Images that I would like to use to train the network are about the size of 4000px*3000px and about 40k of them, sorted in 250 classes.
I have made a CNN shown below:
machine-learning
Tensorflow classes: which class is which
I have a CNN model and using it to predict the class of an image:
How to extract the decision rules from scikit-learn decision-tree?
Can I extract the underlying decision-rules (or ‘decision paths’) from a trained tree in a decision tree as a textual list?
What are logits? What is the difference between softmax and softmax_cross_entropy_with_logits?
In the tensorflow API docs they use a keyword called logits. What is it? A lot of methods are written like:
Keras Dense layer’s input is not flattened
This is my test code:
Convert array of indices to 1-hot encoded numpy array
Let’s say I have a 1d numpy array
Make a custom loss function in keras
Hi I have been trying to make a custom loss function in keras for dice_error_coefficient. It has its implementations in tensorboard and I tried using the same function in keras with tensorflow but it keeps returning a NoneType when I used model.train_on_batch or model.fit where as it gives proper values when used in metrics in the model. Can please someone help me out with what should i do? I have tried following libraries like Keras-FCN by ahundt where he has used custom loss functions but none of it seems to work. The target and output in the code are y_true and y_pred respectively as used in the losses.py file in keras.
Keras not training on entire dataset
So I’ve been following Google’s official tensorflow guide and trying to build a simple neural network using Keras. But when it comes to training the model, it does not use the entire dataset (with 60000 entries) and instead uses only 1875 entries for training. Any possible fix?
Save classifier to disk in scikit-learn
How do I save a trained Naive Bayes classifier to disk and use it to predict data?