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I can reproduce this problem for an even more easily separable dataset: The ideal tree for it should be as follows: However, when I run DecisionTreeClassifier with the maximal depth = 2 in scikit-learn many times, it splits the dataset randomly and never gets it right. This is an example of 4 different runs: The problem is that scikit-learn has only two ...


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Your call to model.predict() is returning the logits for softmax. This is useful for training purposes. To get probabilties, you need to apply softmax on the logits. import torch.nn.functional as F logits = model.predict() probabilities = F.softmax(logits, dim=-1) Now you can apply your threshold same as for the Keras model.


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TL DR; I do not think the inverse of any reasonable neural network would exist. Assume that you are using 32-bit floating-point numbers in the MNIST example. The number of distinct numbers that a 32bit float can represent is finite (say x) The number of different images you can put into the neural network = x ^^ 784. But the total number of distinct outputs ...


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For this application, you can frame it as text classification. Look at SpaCy. You just need to create embeddings for your text and put a Softmax in the end. You can get those embeddings from BERT or anything else out there. You can in fact just use GLOVE vectors and others like it, concatenate them and then train a classifier.


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Yes, there is. You can try Spacy. Here you go. import spacy from spacytextblob.spacytextblob import SpacyTextBlob nlp = spacy.load('en_core_web_sm') spacy_text_blob = SpacyTextBlob() nlp.add_pipe(spacy_text_blob) text = "i'm good" doc = nlp(text) print(doc._.sentiment.polarity) # 0.7 text = "i'm bad" doc = nlp(text) print(doc._....


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The solution for my problem was implementing Batch Renormalization: BatchNormalization(renorm=True). In addition normalizing the inputs helped a lot improving the overall performance of the neural network.


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External sampling and outcome sampling are two ways of defining the sets $Q_1, \dots, Q_n$. I think your mistake is that you think of the $Q_i$ as fixed and taken as input in these shampling schemes. It is not the case. In external sampling, there is as many sets $Q_{\tau}$ as there are pure strategies for the opponent and the chance player (a pure strategy ...


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