TinghuiWang /
pyActLearn
| @@ 205-223 (lines=19) @@ | ||
| 202 | test_writer = tf.summary.FileWriter(tuning_summaries_dir + '/test') |
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| 203 | valid_writer = tf.summary.FileWriter(tuning_summaries_dir + '/valid') |
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| 204 | # Setup Stopping Criterion |
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| 205 | if tuning_criterion == 'const_iterations': |
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| 206 | _tuning_criterion = ConstIterations(num_iters=pretrain_iter_num) |
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| 207 | train_x = x |
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| 208 | train_y = y |
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| 209 | elif tuning_criterion == 'monitor_based': |
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| 210 | num_samples = x.shape[0] |
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| 211 | valid_set_len = int(1 / 5 * num_samples) |
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| 212 | valid_x = x[num_samples - valid_set_len:num_samples, :] |
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| 213 | valid_y = y[num_samples - valid_set_len:num_samples, :] |
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| 214 | train_x = x[0:num_samples - valid_set_len, :] |
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| 215 | train_y = y[0:num_samples - valid_set_len, :] |
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| 216 | _tuning_criterion = MonitorBased(n_steps=pretrain_iter_num, |
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| 217 | monitor_fn=self.predict_accuracy, |
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| 218 | monitor_fn_args=(valid_x, valid_y), |
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| 219 | save_fn=tf.train.Saver().save, |
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| 220 | save_fn_args=(session, tuning_summaries_dir + '/best.ckpt')) |
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| 221 | else: |
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| 222 | logger.error('Wrong criterion %s specified.' % pretrain_criterion) |
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| 223 | return |
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| 224 | injector = BatchInjector(data_x=train_x, data_y=train_y, batch_size=batch_size) |
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| 225 | i = 0 |
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| 226 | while _tuning_criterion.continue_learning(): |
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| @@ 141-159 (lines=19) @@ | ||
| 138 | test_writer = tf.summary.FileWriter(layer_summaries_dir + '/test') |
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| 139 | valid_writer = tf.summary.FileWriter(layer_summaries_dir + '/valid') |
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| 140 | # Get Stopping Criterion |
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| 141 | if pretrain_criterion == 'const_iterations': |
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| 142 | _pretrain_criterion = ConstIterations(num_iters=pretrain_iter_num) |
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| 143 | train_x = x |
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| 144 | train_y = y |
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| 145 | elif pretrain_criterion == 'monitor_based': |
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| 146 | num_samples = x.shape[0] |
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| 147 | valid_set_len = int(1 / 5 * num_samples) |
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| 148 | valid_x = x[num_samples - valid_set_len:num_samples, :] |
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| 149 | valid_y = y[num_samples - valid_set_len:num_samples, :] |
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| 150 | train_x = x[0:num_samples - valid_set_len, :] |
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| 151 | train_y = y[0:num_samples - valid_set_len, :] |
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| 152 | _pretrain_criterion = MonitorBased(n_steps=pretrain_iter_num, |
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| 153 | monitor_fn=self.get_encode_loss, |
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| 154 | monitor_fn_args=(current_layer, valid_x, valid_y), |
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| 155 | save_fn=tf.train.Saver().save, |
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| 156 | save_fn_args=(session, layer_summaries_dir + '/best.ckpt')) |
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| 157 | else: |
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| 158 | logger.error('Wrong criterion %s specified.' % pretrain_criterion) |
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| 159 | return |
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| 160 | injector = BatchInjector(data_x=train_x, data_y=train_y, batch_size=batch_size) |
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| 161 | i = 0 |
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| 162 | while _pretrain_criterion.continue_learning(): |
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| @@ 134-149 (lines=16) @@ | ||
| 131 | valid_writer = tf.summary.FileWriter(summaries_dir + '/valid') |
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| 132 | session.run(tf.global_variables_initializer()) |
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| 133 | # Get Stopping Criterion |
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| 134 | if criterion == 'const_iteration': |
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| 135 | criterion = ConstIterations(num_iters=iter_num) |
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| 136 | elif criterion == 'monitor_based': |
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| 137 | num_samples = x.shape[0] |
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| 138 | valid_set_len = int(1/5 * num_samples) |
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| 139 | valid_x = x[num_samples-valid_set_len:num_samples, :] |
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| 140 | valid_y = y[num_samples-valid_set_len:num_samples, :] |
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| 141 | x = x[0:num_samples-valid_set_len, :] |
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| 142 | y = y[0:num_samples-valid_set_len, :] |
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| 143 | _criterion = MonitorBased(n_steps=iter_num, |
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| 144 | monitor_fn=self.predict_accuracy, monitor_fn_args=(valid_x, valid_y), |
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| 145 | save_fn=tf.train.Saver().save, |
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| 146 | save_fn_args=(session, summaries_dir + '/best.ckpt')) |
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| 147 | else: |
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| 148 | logger.error('Wrong criterion %s specified.' % criterion) |
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| 149 | return |
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| 150 | # Setup batch injector |
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| 151 | injector = BatchInjector(data_x=x, data_y=y, batch_size=batch_size) |
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| 152 | i = 0 |
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