dayyass

"""
In a binary classification problem, a neural network usually returns a vector of logits of shape [batch_size],
while in a multiclass classification problem, logits are represented as a matrix of shape [batch_size, n_classes].

For these tasks, different loss functions are used, and, therefore, the network training pipelines are also different,
which is not convenient when you need to test hypotheses for both problem statements (binary/multiclass).

Pipeline schemes:
- binary classification:
logits (of shape [batch_size]) -> BCEWithLogitsLoss

LeCoupa

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