What does model.eval() do in pytorch?
When should I use .eval()? I understand it is supposed to allow me to “evaluate my model”. How do I turn it back off for training?
machine-learning
Load S3 Data into AWS SageMaker Notebook
I’ve just started to experiment with AWS SageMaker and would like to load data from an S3 bucket into a pandas dataframe in my SageMaker python jupyter notebook for analysis.
Extract upper or lower triangular part of a numpy matrix
I have a matrix A and I want 2 matrices U and L such that U contains the upper triangular elements of A (all elements above and not including diagonal) and similarly for L(all elements below and not including diagonal). Is there a numpy method to do this?
Save and load model optimizer state
I have a set of fairly complicated models that I am training and I am looking for a way to save and load the model optimizer states. The “trainer models” consist of different combinations of several other “weight models”, of which some have shared weights, some have frozen weights depending on the trainer, etc. It is a bit too complicated of an example to share, but in short, I am not able to use model.save('model_file.h5') and keras.models.load_model('model_file.h5') when stopping and starting my training.
How does Pytorch’s “Fold” and “Unfold” work?
I’ve gone through the official doc. I’m having a hard time understanding what this function is used for and how it works. Can someone explain this in layman’s terms?
Does Any one got “AttributeError: ‘str’ object has no attribute ‘decode’ ” , while Loading a Keras Saved Model
After Training, I saved Both Keras whole Model and Only Weights using
Error in Python script “Expected 2D array, got 1D array instead:”?
I’m following this tutorial to make this ML prediction:
ValueError at /image/ Tensor Tensor(“activation_5/Softmax:0”, shape=(?, 4), dtype=float32) is not an element of this graph
I am building an image processing classifier and this code is an API to predict the image class of the image the whole code is running except this line (pred = model.predict_classes(test_image)) this API is made in Django framework and am using python 2.7
ValueError: Error when checking target: expected model_2 to have shape (None, 252, 252, 1) but got array with shape (300, 128, 128, 3)
hi I am building a image classifier for one-class classification in which i’ve used autoencoder while running this model I am getting this error by this line (autoencoder_model.fit) (ValueError: Error when checking target: expected model_2 to have shape (None, 252, 252, 1) but got array with shape (300, 128, 128, 3).)
Does the SVM in sklearn support incremental (online) learning?
I am currently in the process of designing a recommender system for text articles (a binary case of ‘interesting’ or ‘not interesting’). One of my specifications is that it should continuously update to changing trends.