Update Keras loss function to include l2 loss, like estimator #23
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seemuch
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anj-s
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Nov 9, 2018
| entropy_loss = _softmax_crossentropy_with_logits(y_true, y_pred) | ||
| l2_loss = weight_decay * tf.add_n( | ||
| # loss is computed using fp32 for numerical stability. | ||
| [tf.nn.l2_loss(tf.cast(v, tf.float32)) for v in tf.trainable_variables() |
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Is this populated for Keras? I think we may need to add regularization losses to the layers explicitly. What is the difference in loss that you see with this change?
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