Created
April 19, 2020 13:38
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| def SparseCategoricalCrossentropy(labels,logits): | |
| ''' labels: shape [n_batch] contains true classes as numbers from 0 to n_classes-1 | |
| logits: shape [n_batch,n_classes], predicted log probabilities ''' | |
| Z = tf.reduce_logsumexp(logits,axis=-1) | |
| lookup_labels = tf.stack([tf.range(tf.shape(labels)[0]),tf.cast(labels,tf.int32)],1) | |
| true_logits = tf.gather_nd(logits,lookup_labels,batch_dims=0) | |
| return -true_logits + Z |
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