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Dealing with imbalanced data - class weights

ratio = 31.0 / (500.0 + 31.0)
class_weight = tf.constant([[ratio, 1.0 - ratio]])
logits = ... # shape [batch_size, 2]

weight_per_label = tf.transpose( tf.matmul(labels
                           , tf.transpose(class_weight)) ) #shape [1, batch_size]
# this is the weight for each datapoint, depending on its label

xent = tf.mul(weight_per_label
         , tf.nn.softmax_cross_entropy_with_logits(logits, labels, name="xent_raw") #shape [1, batch_size]
loss = tf.reduce_mean(xent) #shape 1
classes_weights = tf.constant([0.1, 1.0])
cross_entropy = tf.nn.weighted_cross_entropy_with_logits(logits=logits, targets=labels, pos_weight=classes_weights)

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