model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) # fit model model.fit(trainX, trainy, epochs=300, verbose=0) return model We can then call the get_data() function to prepare the dataset and the get_model() function to fit and return the model....
classCustomTrainer(Trainer):defcompute_loss(self,model,inputs,return_outputs=False):labels=inputs.get("labels")# forward passoutputs=model(**inputs)logits=outputs.get("logits")# compute custom loss (suppose one has 3 labels with different weights)loss_fct=nn.CrossEntropyLoss(weight=torch.tensor...
Example of the output: $ ./bin/Release/pcomet -n 4 -e dataset/football_true_community.groups dataset/football_detected_community.groups Executing in 4 threads... Entropy metric timings: 0.000577824; VI: 0.536747, NMI: 0.924195 Cluster metric timings: 0.000714213; F1-measure: 0.914482, NVD: 0.0...
In this tutorial, you will discover how to use the McNemar’s statistical hypothesis test to compare machine learning classifier models on a single test dataset. After completing this tutorial, you will know: The recommendation of the McNemar’s test for models that are expensive to train, which...
After constructing a 3D model of the urban street using Rhinoceros 8 software, we generate the shaded area of each street tree using Grasshopper Python. In parallel, we simulate the pedestrian flow by defining the properties of pedestrians, the walking area, and the walking rules and goals. ...
I noticed there is a lot of discussion out there on exactly what people mean when they talk about entropy. I agree with you in that "entropy" would use all the values for a dataset but I'm talking about the amount of information needed to reproduce a single string as a dataset...