CODEWITHSUNDEEP
pythonmachine-learningtensorflowartificial-intelligenceconvolution
Darius Šilkaitis
Darius Šilkaitis

Reputation: 31

How correctly calculate tf.nn.weighted_cross_entropy_with_logits pos_weight variable

I am using convolution neural network.

My data is quite imbalanced, I have two classes.

My first class contains: 551,462 image files

My second class contains: 52,377 image files

I want to use weighted_cross_entropy_with_logits, but I'm not sure I'm calculating pos_weight variable correctly.

Right now I'm using

classes_weights = tf.constant([0.0949784, 1.0])
cross_entropy = tf.reduce_mean(tf.nn.weighted_cross_entropy_with_logits(logits=logits, targets=y_, pos_weight=classes_weights))
train_step = tf.train.AdamOptimizer(LEARNING_RATE, epsilon=1e-03).minimize(
      cross_entropy
    , global_step=global_step
    )

Or should I use

classes_weights = 10.5287

Upvotes: 2

Views: 9175

Answers (2)

Sergei
Sergei

Reputation: 41

As @Salvador Dali said, the best source is the source code https://github.com/tensorflow/tensorflow/blob/5b10b3474bea72e29875264bb34be476e187039c/tensorflow/python/ops/nn_impl.py#L183

We have

log_weight = 1 + (pos_weight - 1) * targets

so it only applies if targets==1.

If targets==0 then log_weight = 1

If targets==1 then log_weight = pos_weight

So if we have ratio of positives to negatives x/y we need pos_weight to be y/x so both categories will contribute equally in total

Please note that each scalar in targets tensor corresponds to each category so each member of pos_weight corresponds to each category as well (not positive or negative probability for one category) .

Upvotes: 1

Salvador Dali
Salvador Dali

Reputation: 222471

From the documentation:

pos_weight: A coefficient to use on the positive examples.

and

The argument pos_weight is used as a multiplier for the positive targets:

So if your first class is positive, then pos_weights = 52,377 / 551,462, otherwise 551,462 / 52,377

Upvotes: 5

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