| /external/libopus/training/ |
| D | rnn_train.py | 111 epochs=10, validation_data=(x_train, y_train))
117 epochs=50, initial_epoch=10)
127 epochs=100, initial_epoch=50)
133 epochs=150, initial_epoch=100)
139 epochs=200, initial_epoch=150)
145 epochs=201, initial_epoch=200)
151 epochs=202, initial_epoch=201, validation_data=(x_train, y_train))
157 epochs=203, initial_epoch=202, validation_data=(x_train, y_train))
163 epochs=204, initial_epoch=203, validation_data=(x_train, y_train))
169 epochs=205, initial_epoch=204, validation_data=(x_train, y_train))
[all …]
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| /external/tensorflow/tensorflow/python/keras/engine/ |
| D | training_generator_v1.py | 42 epochs=1, argument
134 model, data, steps_per_epoch, epochs=epochs, steps_name=steps_name)
141 epochs=epochs - initial_epoch,
171 epochs=epochs,
197 for epoch in range(initial_epoch, epochs):
231 % (steps_name, steps_per_epoch * epochs))
244 'dataset.' % (steps_name, steps_per_epoch * epochs))
271 epochs=epochs,
322 if reset_dataset_after_each_epoch and epoch < epochs - 1:
425 epochs=1, argument
[all …]
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| D | training_arrays_v1.py | 47 epochs=1, argument
139 model, inputs, steps_per_epoch, epochs=epochs, steps_name=steps_name)
196 epochs=epochs,
218 epochs=epochs,
254 for epoch in range(initial_epoch, epochs):
305 % (steps_name, steps_per_epoch * epochs))
318 'dataset.' % (steps_name, steps_per_epoch * epochs))
430 if val_iterator and epoch < epochs - 1:
438 if reset_dataset_after_each_epoch and epoch < epochs - 1:
603 epochs=1, argument
[all …]
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| D | training_distributed_v1.py | 120 epochs=100, argument
203 epochs=epochs,
222 for epoch in range(initial_epoch, epochs):
243 steps_per_epoch * epochs)
362 epochs=1,
504 epochs=1,
582 epochs=1, argument
616 epochs=epochs)
650 model, dataset, steps_per_epoch, epochs, steps_name='steps_per_epoch')
660 epochs=epochs,
[all …]
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| D | training.py | 884 epochs=1, argument
1144 epochs=epochs,
1161 epochs=epochs,
1212 epochs=1,
1469 epochs=1,
1484 epochs=1,
1715 epochs=1,
1730 epochs=1,
1943 epochs=1, argument
1967 epochs=epochs,
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| D | data_adapter.py | 238 epochs=1, argument
284 indices_dataset = indices_dataset.repeat(epochs)
330 flat_dataset = flat_dataset.shuffle(1024).repeat(epochs)
1101 epochs=1, argument
1134 self._epochs = epochs
1154 epochs=epochs - initial_epoch,
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| D | training_v1.py | 619 epochs=1, argument
786 epochs = kwargs.pop('nb_epoch')
798 epochs=epochs,
1224 epochs=1, argument
1248 epochs=epochs,
2110 epochs=1, argument
2190 if epochs > 1:
2191 ds = ds.repeat(epochs)
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| D | training_utils_v1.py | 1663 epochs=1, argument
1698 if steps is not None and steps * epochs > size:
1699 if epochs > 1:
1704 (size, epochs, steps_name, steps, steps * epochs,
1705 steps_name, size // epochs))
1924 epochs=1, argument
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| /external/zstd/lib/dictBuilder/ |
| D | cover.c | 667 COVER_epoch_info_t epochs; in COVER_computeEpochs() local
668 epochs.num = MAX(1, maxDictSize / k / passes); in COVER_computeEpochs()
669 epochs.size = nbDmers / epochs.num; in COVER_computeEpochs()
670 if (epochs.size >= minEpochSize) { in COVER_computeEpochs()
671 assert(epochs.size * epochs.num <= nbDmers); in COVER_computeEpochs()
672 return epochs; in COVER_computeEpochs()
674 epochs.size = MIN(minEpochSize, nbDmers); in COVER_computeEpochs()
675 epochs.num = nbDmers / epochs.size; in COVER_computeEpochs()
676 assert(epochs.size * epochs.num <= nbDmers); in COVER_computeEpochs()
677 return epochs; in COVER_computeEpochs()
[all …]
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| D | fastcover.c | 409 const COVER_epoch_info_t epochs = COVER_computeEpochs( in FASTCOVER_buildDictionary() local
415 (U32)epochs.num, (U32)epochs.size); in FASTCOVER_buildDictionary()
419 for (epoch = 0; tail > 0; epoch = (epoch + 1) % epochs.num) { in FASTCOVER_buildDictionary()
420 const U32 epochBegin = (U32)(epoch * epochs.size); in FASTCOVER_buildDictionary()
421 const U32 epochEnd = epochBegin + epochs.size; in FASTCOVER_buildDictionary()
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| /external/tensorflow/tensorflow/python/profiler/integration_test/ |
| D | profiler_api_test.py | 85 model.fit(x=train_ds, epochs=2, steps_per_epoch=steps)
96 model.fit(x=train_ds, epochs=2, steps_per_epoch=steps)
154 model.fit(x=train_ds, epochs=2, steps_per_epoch=steps)
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| /external/tensorflow/tensorflow/python/debug/examples/v1/ |
| D | debug_keras.py | 61 model.fit(dataset, epochs=FLAGS.epochs, steps_per_epoch=steps_per_epoch)
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| /external/tensorflow/tensorflow/compiler/xla/g3doc/tutorials/ |
| D | autoclustering_xla.ipynb | 201 "def train_model(model, x_train, y_train, x_test, y_test, epochs=25):\n",
202 …" model.fit(x_train, y_train, batch_size=256, epochs=epochs, validation_data=(x_test, y_test), sh…
207 " train_model(model, x_train, y_train, x_test, y_test, epochs=1)\n",
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| /external/tensorflow/tensorflow/lite/g3doc/examples/on_device_training/ |
| D | overview.ipynb | 329 …s model training for 100 epochs, processing batches of 100 images at a time, and displaying the lo…
343 "Finished 10 epochs\n",
345 "Finished 20 epochs\n",
347 "Finished 30 epochs\n",
349 "Finished 40 epochs\n",
351 "Finished 50 epochs\n",
353 "Finished 60 epochs\n",
355 "Finished 70 epochs\n",
357 "Finished 80 epochs\n",
359 "Finished 90 epochs\n",
[all …]
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| /external/tensorflow/tensorflow/lite/g3doc/examples/modify/model_maker/ |
| D | text_classification.ipynb | 294 …67,349 sentences in the training dataset. We will train the model for 10 epochs, which means going…
305 "model = text_classifier.create(train_data, model_spec=spec, epochs=10)"
491 "model = text_classifier.create(train_data, model_spec=mb_spec, epochs=3)"
629 …"2. Trains the classifier model. The default epochs and the default batch size are set by the `de…
753 …"You can also tune the training hyperparameters like `epochs` and `batch_size` that affect the mod…
755 "* `epochs`: more epochs could achieve better accuracy, but may lead to overfitting.\n",
758 "For example, you can train with more epochs."
769 "model = text_classifier.create(new_train_data, model_spec=new_model_spec, epochs=20)"
778 "Evaluate the newly retrained model with 20 training epochs."
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| D | image_classification.ipynb | 648 …e classifier model. By default, the training parameters such as training epochs, batch size, learn…
840 …"We could also change the training hyperparameters like `epochs`, `dropout_rate` and `batch_size` …
843 …"* `epochs`: more epochs could achieve better accuracy until it converges but training for too m…
854 …he default warmup_steps is used which is the total training steps in two epochs. Only used when `u…
857 …"Parameters which are None by default like `epochs` will get the concrete default parameters in [m…
859 "For example, we could train with more epochs.\n"
870 "model = image_classifier.create(train_data, validation_data=validation_data, epochs=10)"
879 "Evaluate the newly retrained model with 10 training epochs."
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| D | question_answer.ipynb | 325 …"2. Train the question answer model. The default epochs and the default batch size are set accordi…
541 …"You can also tune the training hyperparameters like `epochs` and `batch_size` to impact the model…
543 … "* `epochs`: more epochs could achieve better performance, but may lead to overfitting.\n",
546 "For example, you can train with more epochs and with a bigger batch size like:\n",
549 "model = question_answer.create(train_data, model_spec=spec, epochs=5, batch_size=64)\n",
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| D | object_detection.ipynb | 288 …"* The EfficientDet-Lite0 model uses `epochs = 50` by default, which means it will go through the …
733 …"2. Trains the model. The default epochs and the default batch size are set by the `epochs` and `…
734 …"You can also tune the training hyperparameters like `epochs` and `batch_size` that affect the mod…
736 …"* `epochs`: Integer, 50 by default. More epochs could achieve better accuracy, but may lead to …
740 …"For example, you can train with less epochs and only the head layer. You can increase the number …
743 …"model = object_detector.create(train_data, model_spec=spec, epochs=10, validation_data=validation…
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| /external/rnnoise/src/ |
| D | rnn_train.py | 64 epochs=200,
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| /external/libopus/scripts/ |
| D | rnn_train.py | 65 epochs=200,
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| /external/tensorflow/tensorflow/python/keras/ |
| D | callbacks.py | 78 epochs=None, argument
126 epochs=epochs,
140 epochs=None, argument
174 'epochs': epochs,
1006 self.epochs = 1
1015 self.epochs = params['epochs']
1048 if self.verbose and self.epochs > 1:
1049 print('Epoch %d/%d' % (epoch + 1, self.epochs))
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| /external/tensorflow/tensorflow/lite/g3doc/tutorials/ |
| D | model_maker_object_detection.ipynb | 284 …"* The EfficientDet-Lite0 model uses `epochs = 50` by default, which means it will go through the …
740 …"2. Trains the model. The default epochs and the default batch size are set by the `epochs` and `…
741 …"You can also tune the training hyperparameters like `epochs` and `batch_size` that affect the mod…
743 …"* `epochs`: Integer, 50 by default. More epochs could achieve better accuracy, but may lead to …
747 …"For example, you can train with less epochs and only the head layer. You can increase the number …
750 …"model = object_detector.create(train_data, model_spec=spec, epochs=10, validation_data=validation…
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| /external/tensorflow/tensorflow/python/keras/layers/normalization/ |
| D | layer_normalization_test.py | 43 model.fit(x, x, epochs=4, verbose=0)
132 model.fit(x, x, epochs=4, verbose=0)
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| /external/rnnoise/training/ |
| D | rnn_train.py | 114 epochs=120,
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| /external/tensorflow/tensorflow/python/distribute/ |
| D | README.md | 44 model.fit(dataset, epochs=2)
|