Max margin loss in TensorFlow

Question

I\'m trying to implement a max margin loss in TensorFlow. the idea is that I have some positive example and i sample some negative examples and want to compute something like

Answer 1

First we need to clean the input:

• we want an array of positive scores, of shape [B, 1]
• we want a matrix of negative scores, of shape [B, N]

import tensorflow as tf

B = 2
N = 2
scores = tf.constant([0.5, 0.2, -0.1, 1., -0.5, 0.3])  # shape B * (N+1)

scores = tf.reshape(scores, [B, N+1])

scores_pos = tf.slice(scores, [0, 0], [B, 1])

scores_neg = tf.slice(scores, [0, 1], [B, N])

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Now we only have to compute the matrix of the loss, i.e. all the individual loss for every pair (positive, negative), and compute its sum.

loss_matrix = tf.maximum(0., 1. - scores_pos + scores_neg)  # we could also use tf.nn.relu here
loss = tf.reduce_sum(loss_matrix)