Perform an evaluation of `model` on the examples from `dataset`.
(model, dataset)
| 164 | |
| 165 | |
| 166 | def test(model, dataset): |
| 167 | """Perform an evaluation of `model` on the examples from `dataset`.""" |
| 168 | avg_loss = tfe.metrics.Mean('loss', dtype=tf.float32) |
| 169 | accuracy = tfe.metrics.Accuracy('accuracy', dtype=tf.float32) |
| 170 | |
| 171 | for (images, labels) in dataset: |
| 172 | logits = model(images, training=False) |
| 173 | avg_loss(loss(logits, labels)) |
| 174 | accuracy( |
| 175 | tf.argmax(logits, axis=1, output_type=tf.int64), |
| 176 | tf.cast(labels, tf.int64)) |
| 177 | print('Test set: Average loss: %.4f, Accuracy: %4f%%\n' % |
| 178 | (avg_loss.result(), 100 * accuracy.result())) |
| 179 | with tf.contrib.summary.always_record_summaries(): |
| 180 | tf.contrib.summary.scalar('loss', avg_loss.result()) |
| 181 | tf.contrib.summary.scalar('accuracy', accuracy.result()) |
| 182 | |
| 183 | |
| 184 | def train_and_export(flags_obj): |