CODEWITHSUNDEEP
machine-learningtensorflowdeep-learning
opus111
opus111

Reputation: 2854

Tensorflow Estimator: training with weighted examples

I am training a model with Tensorflow Estimator, and my data is not balanced. I want to correct for this by weighting each training example.

In raw Tensorflow one might do it like this. Is there an easy way to do this in Estimator? Perhaps building a custom input_fn?

Upvotes: 4

Views: 1945

Answers (2)

Rodrigo Pereira
Rodrigo Pereira

Reputation: 233

If you are building a custom estimator model, you should forward the class weight for each sample of your dataset as a feature to your model_fn and when defining the loss function op you can pass the class weight on weight parameter.

Example:

 tf.losses.softmax_cross_entropy(target, logits,weights=features['weight'])

Upvotes: 1

Maxim
Maxim

Reputation: 53758

I assume you're doing classification. If so, use tf.estimator.DNNClassifier:

weight_column: A string or a _NumericColumn created by tf.feature_column.numeric_column defining feature column representing weights. It is used to down weight or boost examples during training. It will be multiplied by the loss of the example. If it is a string, it is used as a key to fetch weight tensor from the features. If it is a _NumericColumn, raw tensor is fetched by key weight_column.key, then weight_column.normalizer_fn is applied on it to get weight tensor.

Upvotes: 1

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