| /external/skqp/infra/bots/ |
| D | infra_tests.py | 28 def python_unit_tests(train): argument
29 if train:
36 def recipe_test(train): argument
39 if train:
46 def gen_tasks_test(train): argument
48 if not train:
64 train = False
66 train = True
75 err = t(train)
87 if train:
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| D | Makefile | 4 train: target
5 python infra_tests.py --train
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| /external/skia/infra/bots/ |
| D | infra_tests.py | 29 def python_unit_tests(train): argument
30 if train:
38 def recipe_test(train): argument
41 if train:
48 def gen_tasks_test(train): argument
50 if not train:
61 train = False
63 train = True
72 err = t(train)
84 if train:
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| D | Makefile | 4 train: target
5 python infra_tests.py --train
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| /external/tensorflow/tensorflow/examples/speech_commands/ |
| D | train_test.py | 26 from tensorflow.examples.speech_commands import train
121 train.FLAGS = self._getDefaultFlags()
122 train.main('')
125 os.path.join(train.FLAGS.train_dir,
126 train.FLAGS.model_architecture + '.pbtxt')))
129 os.path.join(train.FLAGS.train_dir,
130 train.FLAGS.model_architecture + '_labels.txt')))
133 os.path.join(train.FLAGS.train_dir,
134 train.FLAGS.model_architecture + '.ckpt-1.meta')))
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| /external/tensorflow/tensorflow/core/kernels/ |
| D | training_ops_test.cc | 110 Graph* train; in BM_SGD() local
111 SGD(params, &init, &train); in BM_SGD()
112 test::Benchmark("cpu", train, GetOptions(), init, nullptr, "", in BM_SGD()
146 Graph* train; in BM_Adagrad() local
147 Adagrad(params, &init, &train); in BM_Adagrad()
148 test::Benchmark("cpu", train, GetOptions(), init, nullptr, "", in BM_Adagrad()
184 Graph* train; in BM_SparseAdagrad() local
185 SparseAdagrad(m, n, &init, &train); in BM_SparseAdagrad()
186 test::Benchmark("cpu", train, GetMultiThreadedOptions(), init, nullptr, "", in BM_SparseAdagrad()
228 Graph* train; in BM_Momentum() local
[all …]
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| D | sdca_ops_test.cc | 233 Graph* train = nullptr; in BM_SDCA() local
236 20 /* dense features per group */, &init, &train); in BM_SDCA()
238 test::Benchmark("cpu", train, GetSingleThreadedOptions(), init).Run(iters); in BM_SDCA()
244 Graph* train = nullptr; in BM_SDCA_LARGE_DENSE() local
247 200000 /* dense features per group */, &init, &train); in BM_SDCA_LARGE_DENSE()
249 test::Benchmark("cpu", train, GetSingleThreadedOptions(), init).Run(iters); in BM_SDCA_LARGE_DENSE()
255 Graph* train = nullptr; in BM_SDCA_LARGE_SPARSE() local
258 0 /* dense features per group */, &init, &train); in BM_SDCA_LARGE_SPARSE()
260 test::Benchmark("cpu", train, GetMultiThreadedOptions(), init).Run(iters); in BM_SDCA_LARGE_SPARSE()
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| /external/tensorflow/tensorflow/python/data/experimental/kernel_tests/serialization/ |
| D | checkpoint_input_pipeline_hook_test.py | 86 est.train(_input_fn, steps=2, hooks=[self._build_iterator_saver_hook(est)])
88 est.train(_input_fn, steps=2, hooks=[self._build_iterator_saver_hook(est)])
101 est.train(_input_fn, steps=2, hooks=[self._build_iterator_saver_hook(est)])
103 est.train(_input_fn, steps=2, hooks=[self._build_iterator_saver_hook(est)])
114 est.train(_input_fn, steps=2, hooks=[self._build_iterator_saver_hook(est)])
116 est.train(_input_fn, steps=2, hooks=[self._build_iterator_saver_hook(est)])
119 est.train(_input_fn, steps=2)
131 est.train(
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| /external/tensorflow/tensorflow/lite/micro/examples/magic_wand/train/ |
| D | train_magic_wand_model.ipynb | 20 …"This notebook demonstrates how to train a 20kb gesture recognition model for [TensorFlow Lite for…
26 …nsorflow/tensorflow/blob/master/tensorflow/lite/micro/examples/magic_wand/train/train_magic_wand_m…
29 …nsorflow/tensorflow/blob/master/tensorflow/lite/micro/examples/magic_wand/train/train_magic_wand_m…
79 "!cp -r tensorflow/tensorflow/lite/micro/examples/magic_wand/train train"
104 "# Download the data we will use to train the model\n",
106 "# Extract the data into the train directory\n",
107 "!tar xvzf data.tar.gz -C train 1\u003e/dev/null"
130 "# The scripts must be run from within the train directory\n",
131 "%cd train\n",
187 "!python train.py --model CNN --person true"
[all …]
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| D | train_test.py | 27 from train import build_cnn
28 from train import build_lstm
29 from train import load_data
30 from train import reshape_function
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| D | README.md | 5 The scripts in this directory can be used to train a TensorFlow model that
9 The following document contains instructions on using the scripts to train a
31 notebook demonstrates how to train the model. It's the easiest way to get
36 …nsorflow/tensorflow/blob/master/tensorflow/lite/micro/examples/magic_wand/train/train_magic_wand_m…
39 …nsorflow/tensorflow/blob/master/tensorflow/lite/micro/examples/magic_wand/train/train_magic_wand_m…
53 There are two ways to train the model:
69 $ python train.py --model CNN --person false
82 $ python train.py --model CNN --person true
191 Finally, run the commands described earlier to train a new model.
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| /external/zstd/tests/ |
| D | playTests.sh | 793 zstd --train "$TESTDIR"/*.c "$PRGDIR"/*.c -o tmpDict
811 zstd --train "$TESTDIR"/*.c "$PRGDIR"/*.c "$PRGDIR"/*.h -o tmpDictC
814 zstd --train "$TESTDIR"/*.c "$PRGDIR"/*.c --dictID=1 -o tmpDict1
817 zstd --train "$TESTDIR"/*.c "$PRGDIR"/*.c --dictID -o 1 tmpDict1 && die "wrong order : --dictID mus…
819 zstd --train "$TESTDIR"/*.c "$PRGDIR"/*.c -o tmpDict2 --maxdict=4K -v
821 zstd --train "$TESTDIR"/*.c "$PRGDIR"/*.c -o tmpDict3 --maxdict=1K -v
823 zstd --train "$TESTDIR"/*.c "$PRGDIR"/*.c -o tmpDict3 --maxdict -v 4K && die "wrong order : --maxdi…
844 zstd --train-legacy -q tmp && die "Dictionary training should fail : not enough input source"
846 zstd --train-legacy -q tmp && die "Dictionary training should fail : source is pure noise"
849 zstd -o tmpDict --train "$TESTDIR"/*.c "$PRGDIR"/*.c
[all …]
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| /external/tensorflow/tensorflow/python/ops/ |
| D | batch_norm_benchmark.py | 68 def build_graph(device, input_shape, axes, num_layers, mode, scale, train): argument
98 if train:
111 if train:
127 train, num_iters): argument
146 train)
155 (device, len(input_shape), len(axes), num_layers, mode, scale, train,
169 train=train,
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| /external/skia/infra/bots/recipes/ |
| D | README.md | 14 When you change a recipe, you generally need to re-train the simulation test:
16 $ python infra/bots/recipes.py test train
20 $ cd infra/bots; make train
|
| /external/skqp/infra/bots/recipes/ |
| D | README.md | 14 When you change a recipe, you generally need to re-train the simulation test:
16 $ python infra/bots/recipes.py test train
20 $ cd infra/bots; make train
|
| /external/tensorflow/tensorflow/lite/experimental/examples/lstm/ |
| D | bidirectional_sequence_rnn_test.py | 162 opt = tf.train.AdamOptimizer(
169 batch_x, batch_y = self.mnist.train.next_batch(
216 saver = tf.train.Saver()
235 b1, _ = self.mnist.train.next_batch(batch_size=1, fake_data=True)
289 saver = tf.compat.v1.train.Saver()
308 saver = tf.compat.v1.train.Saver()
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| D | unidirectional_sequence_lstm_test.py | 131 opt = tf.compat.v1.train.AdamOptimizer(
138 batch_x, batch_y = self.mnist.train.next_batch(
173 saver = tf.compat.v1.train.Saver()
192 b1, _ = self.mnist.train.next_batch(batch_size=1, fake_data=True)
249 saver = tf.compat.v1.train.Saver()
272 saver = tf.compat.v1.train.Saver()
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| D | unidirectional_sequence_rnn_test.py | 127 opt = tf.compat.v1.train.AdamOptimizer(
133 batch_x, batch_y = self.mnist.train.next_batch(
168 saver = tf.compat.v1.train.Saver()
187 b1, _ = self.mnist.train.next_batch(batch_size=1, fake_data=True)
240 saver = tf.train.Saver()
263 saver = tf.compat.v1.train.Saver()
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| D | input_data.py | 284 train = fake()
287 return _Datasets(train=train, validation=validation, test=test)
329 train = _DataSet(train_images, train_labels, **options)
333 return _Datasets(train=train, validation=validation, test=test)
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| D | bidirectional_sequence_lstm_test.py | 141 opt = tf.compat.v1.train.AdamOptimizer(
148 batch_x, batch_y = self.mnist.train.next_batch(
188 saver = tf.compat.v1.train.Saver()
207 b1, _ = self.mnist.train.next_batch(batch_size=1, fake_data=True)
264 saver = tf.compat.v1.train.Saver()
283 saver = tf.compat.v1.train.Saver()
|
| /external/tensorflow/tensorflow/python/keras/integration_test/ |
| D | saved_model_test.py | 44 class _ModelWithOptimizer(tf.train.Checkpoint):
88 root = tf.train.Checkpoint(
137 def train(self, x, y): member in LoadTest.test_optimizer._HasOptimizer
148 root.train(**train_input)
154 root.train(**train_input)
155 imported.train(**train_input)
160 root = tf.train.Checkpoint(
|
| /external/tensorflow/tensorflow/core/platform/ |
| D | ram_file_system_test.py | 126 estimator.train(input_fn=input_fn, steps=10)
127 estimator.train(input_fn=input_fn, steps=10)
128 estimator.train(input_fn=input_fn, steps=10)
129 estimator.train(input_fn=input_fn, steps=10)
|
| /external/toolchain-utils/bestflags/examples/omnetpp/ |
| D | test_omnetpp | 6 (time ./omnetpp$1 ../../data/train/input/omnetpp.ini) 1>log-file 2>time.txt
11 diff ../../data/train/output/omnetpp.sca.result omnetpp.sca
|
| /external/skia/infra/bots/recipe_modules/ |
| D | README.md | 19 When you change a recipe module, you generally need to re-train the simulation
22 $ python infra/bots/infra_tests.py --train
26 $ cd infra/bots; make train
|
| /external/skqp/infra/bots/recipe_modules/ |
| D | README.md | 19 When you change a recipe module, you generally need to re-train the simulation
22 $ python infra/bots/recipes.py test run --train
26 $ cd infra/bots; make train
|