tf.losses and tf.metrics return different values

[shiheng...@gmail.com]

Hi all,

I am using custom estimator to build a CNN network. In the EVAL part, I returned the training loss and also the loss computed by tf.metrics.mean_squared_error. The code is the following: 

loss1 = tf.losses.mean_squared_error(labels, dense)
loss = loss1
if mode == tf.estimator.ModeKeys.TRAIN:
    optimizer = tf.train.AdamOptimizer(learning_rate=0.000001)
    train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step())
    return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)
if mode == tf.estimator.ModeKeys.EVAL:
    metrics = {'pure_loss': tf.metrics.mean_squared_error(labels, tf.cast(dense, tf.float64), name='loss1')}
    return tf.estimator.EstimatorSpec(mode, loss=loss, eval_metric_ops=metrics)

These two losses should be the same but the problem is that they are not the same. Here is the result from one run: 

{'loss': 0.9698009, 'pure_loss': 1.1101756, 'global_step': 3}

{'loss': 1.0194854, 'pure_loss': 1.1081429, 'global_step': 6}

{'loss': 1.0608621, 'pure_loss': 1.1064296, 'global_step': 9}

{'loss': 1.3501577, 'pure_loss': 1.1048187, 'global_step': 12}

{'loss': 1.156745, 'pure_loss': 1.1032388, 'global_step': 15}

{'loss': 1.094354, 'pure_loss': 1.1016937, 'global_step': 18}

{'loss': 1.1235374, 'pure_loss': 1.100199, 'global_step': 21}

{'loss': 1.005026, 'pure_loss': 1.0985746, 'global_step': 24}

{'loss': 1.403479, 'pure_loss': 1.0968639, 'global_step': 27}

{'loss': 1.100715, 'pure_loss': 1.0952694, 'global_step': 30}

Any ideas about that? 

Actually, I want to use loss for total loss and pure_loss for loss without regularization. But it failed so I removed regularization and then found out they are not the same. Is there any problem with my code? 

Thanks all. 

[Martin Wicke]

I don't think in this example loss and metrics are even computed on the same data. We probably shouldn't even be putting the loss into the metrics dict, it's pretty confusing. Can you file an issue?

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[Duan Shiheng]

It seems that losses calculate the batch loss while metrics are for losses for whole input dataset. Is that the reason? 

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