What are Loss functions Normalization functions and Evaluation functions in chainer

This recipe explains what are Loss functions Normalization functions and Evaluation functions in chainer

Recipe Objective - What are Loss functions, Normalization functions and Evaluation functions in chainer?

Loss Functions:-

1.chainer.functions.absolute_error - This function is used for element-wise absolute error.

2.chainer.functions.bernoulli_nll - This function is used to compute the negative log likelihood of the Bernoulli distribution.

3.chainer.functions.black_out - This function is used for Blackout loss function.

4.chainer.functions.connectionist_temporal_classification - This function is used for Connectionist Temporal Classification loss function.

5.chainer.functions.contrastive - This function is used to Computes contrastive loss.

6.chainer.functions.crf1d - This function is used to Calculates negative log-likelihood of linear-chain CRF.

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Normalization Functions:-

1.chainer.functions.batch_normalization - Batch Normalization function.

2.chainer.functions.batch_renormalization - Batch Renormalization function.

3.chainer.functions.decorrelated_batch_normalization - Decorrelated batch normalization function.

4.chainer.functions.fixed_batch_normalization - Batch normalization function with fixed statistics.

5.chainer.functions.group_normalization - Group normalization function.

Evaluation Functions:-

1.chainer.functions.accuracy - This function is used to Computes multiclass classification accuracy of the minibatch.

2.chainer.functions.binary_accuracy - This function is used to Computes binary classification accuracy of the minibatch.

3.chainer.functions.classification_summary - This function is used to Calculates Precision, Recall, F beta Score, and support.

3.chainer.functions.r2_score - This function is used to Computes R^2(coefficient of determination) regression score function.

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