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@@ 162-190 (lines=29) @@
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| 159 |
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return |
| 160 |
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injector = BatchInjector(data_x=train_x, data_y=train_y, batch_size=batch_size) |
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i = 0 |
| 162 |
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while _pretrain_criterion.continue_learning(): |
| 163 |
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batch_x, batch_y = injector.next_batch() |
| 164 |
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if summaries_dir is not None and (i % summary_interval == 0): |
| 165 |
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summary, loss = session.run( |
| 166 |
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[current_layer.merged, current_layer.encode_loss], |
| 167 |
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feed_dict={self.x: x, self.y_: y} |
| 168 |
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) |
| 169 |
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train_writer.add_summary(summary, i) |
| 170 |
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logger.info('Pre-training Layer %d, Step %d, training loss %g' % (j, i, loss)) |
| 171 |
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if test_x is not None and test_y is not None: |
| 172 |
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summary, loss = session.run( |
| 173 |
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[current_layer.merged, current_layer.encode_loss], |
| 174 |
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feed_dict={self.x: test_x, self.y_: test_y} |
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) |
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test_writer.add_summary(summary, i) |
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logger.info('Pre-training Layer %d, Step %d, test loss %g' % (j, i, loss)) |
| 178 |
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if pretrain_criterion == 'monitor_based': |
| 179 |
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summary, loss = session.run( |
| 180 |
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[current_layer.merged, current_layer.encode_loss], |
| 181 |
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feed_dict={self.x: valid_x, self.y_: valid_y} |
| 182 |
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) |
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valid_writer.add_summary(summary, i) |
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logger.info('Pre-training Layer %d, Step %d, valid loss %g' % (j, i, loss)) |
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loss, _ = session.run( |
| 186 |
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[current_layer.encode_loss, self.encode_opts[j]], |
| 187 |
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feed_dict={self.x: batch_x, self.y_: batch_y} |
| 188 |
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) |
| 189 |
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logger.info('Pre-training Layer %d, Step %d, training loss %g' % (j, i, loss)) |
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i += 1 |
| 191 |
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if pretrain_criterion == 'monitor_based': |
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tf.train.Saver().restore(session, layer_summaries_dir + '/best.ckpt') |
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if summaries_dir is not None: |
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@@ 226-246 (lines=21) @@
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| 223 |
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return |
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injector = BatchInjector(data_x=train_x, data_y=train_y, batch_size=batch_size) |
| 225 |
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i = 0 |
| 226 |
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while _tuning_criterion.continue_learning(): |
| 227 |
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batch_x, batch_y = injector.next_batch() |
| 228 |
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if summaries_dir is not None and (i % summary_interval == 0): |
| 229 |
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summary, loss, accuracy = session.run([self.merged, self.loss, self.accuracy], |
| 230 |
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feed_dict={self.x: train_x, self.y_: train_y}) |
| 231 |
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train_writer.add_summary(summary, i) |
| 232 |
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logger.info('Fine-Tuning: Step %d, training accuracy %g, loss %g' % (i, accuracy, loss)) |
| 233 |
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if (test_x is not None) and (test_y is not None): |
| 234 |
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merged, accuracy = session.run([self.merged, self.accuracy], |
| 235 |
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feed_dict={self.x: test_x, self.y_: test_y}) |
| 236 |
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test_writer.add_summary(merged, i) |
| 237 |
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logger.info('Fine-Tuning: Step %d, test accuracy %g' % (i, accuracy)) |
| 238 |
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if tuning_criterion == 'monitor_based': |
| 239 |
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merged, accuracy = session.run([self.merged, self.accuracy], |
| 240 |
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feed_dict={self.x: valid_x, self.y_: valid_y}) |
| 241 |
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valid_writer.add_summary(merged, i) |
| 242 |
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logger.info('Fine-Tuning: Step %d, valid accuracy %g' % (i, accuracy)) |
| 243 |
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loss, accuracy, _ = session.run([self.loss, self.accuracy, self.fine_tuning], |
| 244 |
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feed_dict={self.x: batch_x, self.y_: batch_y}) |
| 245 |
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logger.info('Fine-Tuning: Step %d, batch accuracy %g, loss %g' % (i, accuracy, loss)) |
| 246 |
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i += 1 |
| 247 |
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if tuning_criterion == 'monitor_based': |
| 248 |
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tf.train.Saver().restore(session, tuning_summaries_dir + '/best.ckpt') |
| 249 |
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if summaries_dir is not None: |
TinghuiWang /
pyActLearn
