(logits)
| 15 | |
| 16 | |
| 17 | def cat_entropy(logits): |
| 18 | a0 = logits - tf.reduce_max(logits, 1, keepdims=True) |
| 19 | ea0 = tf.exp(a0) |
| 20 | z0 = tf.reduce_sum(ea0, 1, keepdims=True) |
| 21 | p0 = ea0 / z0 |
| 22 | return tf.reduce_sum(p0 * (tf.log(z0) - a0), 1) |
| 23 | |
| 24 | |
| 25 | def find_trainable_variables(key): |