/external/skia/infra/bots/ |
Makefile | 4 train: 5 python infra_tests.py --train
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infra_tests.py | 28 def python_unit_tests(train): 29 if train: 36 def recipe_test(train): 39 if train: 40 cmd.append('train') 46 def gen_tasks_test(train): 48 if not train: 64 train = False 65 if '--train' in sys.argv: 66 train = Tru [all...] |
/external/skqp/infra/bots/ |
Makefile | 4 train: 5 python infra_tests.py --train
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infra_tests.py | 28 def python_unit_tests(train): 29 if train: 36 def recipe_test(train): 39 if train: 40 cmd.append('train') 46 def gen_tasks_test(train): 48 if not train: 64 train = False 65 if '--train' in sys.argv: 66 train = Tru [all...] |
/external/tensorflow/tensorflow/examples/get_started/regression/ |
dnn_regression.py | 32 (train, test) = imports85.dataset() 38 train = train.map(normalize_price) 46 train.shuffle(1000).batch(128) 85 # Train the model. 86 model.train(input_fn=input_train, steps=STEPS)
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linear_regression_categorical.py | 32 (train, test) = imports85.dataset() 38 train = train.map(normalize_price) 46 train.shuffle(1000).batch(128) 89 # Train the model. 91 model.train(input_fn=input_train, steps=STEPS)
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linear_regression.py | 33 (train, test) = imports85.dataset() 39 train = train.map(to_thousands) 47 train.shuffle(1000).batch(128) 65 # Train the model. 67 model.train(input_fn=input_train, steps=STEPS)
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custom_regression.py | 59 if mode == tf.estimator.ModeKeys.TRAIN: 60 optimizer = params.get("optimizer", tf.train.AdamOptimizer) 63 loss=average_loss, global_step=tf.train.get_global_step()) 87 (train, test) = imports85.dataset() 93 train = train.map(normalize_price) 101 train.shuffle(1000).batch(128) 142 "optimizer": tf.train.AdamOptimizer, 146 # Train the model. 147 model.train(input_fn=input_train, steps=STEPS [all...] |
/frameworks/base/core/java/android/service/resolver/ |
IResolverRankerService.aidl | 28 void train(in List<ResolverTarget> targets, int selectedPosition);
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/external/tensorflow/tensorflow/examples/how_tos/reading_data/ |
fully_connected_preloaded_var.py | 46 """Train MNIST for a number of epochs.""" 56 dtype=data_sets.train.images.dtype, 57 shape=data_sets.train.images.shape) 59 dtype=data_sets.train.labels.dtype, 60 shape=data_sets.train.labels.shape) 66 image, label = tf.train.slice_input_producer( 69 images, labels = tf.train.batch( 88 saver = tf.train.Saver() 100 feed_dict={images_initializer: data_sets.train.images}) 102 feed_dict={labels_initializer: data_sets.train.labels} [all...] |
convert_to_records.py | 33 return tf.train.Feature(int64_list=tf.train.Int64List(value=[value])) 37 return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value])) 58 example = tf.train.Example( 59 features=tf.train.Features( 78 convert_to(data_sets.train, 'train')
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fully_connected_preloaded.py | 47 """Train MNIST for a number of epochs.""" 57 input_images = tf.constant(data_sets.train.images) 58 input_labels = tf.constant(data_sets.train.labels) 60 image, label = tf.train.slice_input_producer( 63 images, labels = tf.train.batch( 82 saver = tf.train.Saver() 97 coord = tf.train.Coordinator() 98 threads = tf.train.start_queue_runners(sess=sess, coord=coord)
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/external/toolchain-utils/bestflags/examples/omnetpp/ |
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
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/external/tensorflow/tensorflow/examples/learn/ |
mnist.py | 64 if mode == tf.estimator.ModeKeys.TRAIN: 83 if mode == tf.estimator.ModeKeys.TRAIN: 84 optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01) 85 train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step()) 103 x={X_FEATURE: mnist.train.images}, 104 y=mnist.train.labels.astype(np.int32), 109 x={X_FEATURE: mnist.train.images}, 110 y=mnist.train.labels.astype(np.int32), 117 X_FEATURE, shape=mnist.train.images.shape[1:])] 121 classifier.train(input_fn=train_input_fn, steps=200 [all...] |
text_classification_character_rnn.py | 63 if mode == tf.estimator.ModeKeys.TRAIN: 64 optimizer = tf.train.AdamOptimizer(learning_rate=0.01) 65 train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step()) 80 x_train = pandas.DataFrame(dbpedia.train.data)[1] 81 y_train = pandas.Series(dbpedia.train.target) 94 # Train. 101 classifier.train(input_fn=train_input_fn, steps=100)
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/external/tensorflow/tensorflow/contrib/model_pruning/examples/cifar10/ |
cifar10_train.py | 15 """A binary to train pruned CIFAR-10 using a single GPU. 33 data set, compile the program and train the model. 55 def train(): function 56 """Train CIFAR-10 for a number of steps.""" 72 train_op = cifar10.train(loss, global_step) 90 class _LoggerHook(tf.train.SessionRunHook): 99 return tf.train.SessionRunArgs(loss) # Asks for loss value. 114 with tf.train.MonitoredTrainingSession( 116 hooks=[tf.train.StopAtStepHook(last_step=FLAGS.max_steps), 117 tf.train.NanTensorHook(loss) [all...] |
/external/tensorflow/tensorflow/contrib/eager/python/examples/linear_regression/ |
linear_regression_graph_test.py | 53 optimization_step = tf.train.GradientDescentOptimizer( 59 def train(num_epochs): function in function:GraphLinearRegressionBenchmark.benchmarkGraphLinearRegression 69 train(1) 72 train(num_epochs)
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/external/tensorflow/tensorflow/contrib/gan/ |
__init__.py | 33 from tensorflow.contrib.gan.python import train 37 from tensorflow.contrib.gan.python.train import * 48 _allowed_symbols += train.__all__
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/external/tensorflow/tensorflow/contrib/kfac/examples/ |
mnist.py | 62 num_examples = len(mnist_data.train.labels) 63 images = mnist_data.train.images 64 labels = mnist_data.train.labels
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/external/tensorflow/tensorflow/core/kernels/ |
training_ops_test.cc | 82 Graph* train; local 83 SGD(params, &init, &train); 84 test::Benchmark("cpu", train, GetOptions(), init).Run(iters); 114 Graph* train; local 115 Adagrad(params, &init, &train); 116 test::Benchmark("cpu", train, GetOptions(), init).Run(iters); 148 Graph* train; local 149 Momentum(params, &init, &train); 150 test::Benchmark("cpu", train, GetOptions(), init).Run(iters); 191 Graph* train; local 231 Graph* train; local 268 Graph* train; local 305 Graph* train; local [all...] |
/external/tensorflow/tensorflow/contrib/gan/python/ |
train_test.py | 15 """Tests for gan.python.train.""" 25 from tensorflow.contrib.gan.python import train 130 return train.gan_model( 138 return train.gan_model( 162 return train.infogan_model( 171 return train.infogan_model( 196 return train.acgan_model( 205 return train.acgan_model( 230 return train.cyclegan_model( 238 return train.cyclegan_model [all...] |
/external/tensorflow/tensorflow/examples/tutorials/layers/ |
cnn_mnist.py | 82 inputs=dense, rate=0.4, training=mode == tf.estimator.ModeKeys.TRAIN) 99 # Calculate Loss (for both TRAIN and EVAL modes) 102 # Configure the Training Op (for TRAIN mode) 103 if mode == tf.estimator.ModeKeys.TRAIN: 104 optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001) 107 global_step=tf.train.get_global_step()) 121 train_data = mnist.train.images # Returns np.array 122 train_labels = np.asarray(mnist.train.labels, dtype=np.int32) 133 logging_hook = tf.train.LoggingTensorHook( 136 # Train the mode [all...] |
/external/tensorflow/tensorflow/contrib/eager/python/examples/mnist/ |
mnist_test.py | 46 optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01) 49 tf.train.get_or_create_global_step() 59 tf.train.get_or_create_global_step()
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/external/tensorflow/tensorflow/contrib/learn/python/learn/datasets/ |
text_datasets.py | 35 train_path = os.path.join(data_dir, 'dbpedia_csv/train.csv') 50 train_path = os.path.join(data_dir, 'dbpedia_csv', 'train.csv') 57 train_path = train_path.replace('train.csv', 'train_small.csv') 64 train = base.load_csv_without_header( 69 return base.Datasets(train=train, validation=None, test=test)
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/external/tensorflow/tensorflow/tools/dist_test/python/ |
mnist_replica.py | 109 cluster = tf.train.ClusterSpec({"ps": ps_spec, "worker": worker_spec}) 113 server = tf.train.Server( 132 tf.train.replica_device_setter( 164 opt = tf.train.AdamOptimizer(FLAGS.learning_rate) 172 opt = tf.train.SyncReplicasOptimizer( 195 sv = tf.train.Supervisor( 204 sv = tf.train.Supervisor( 247 batch_xs, batch_ys = mnist.train.next_batch(FLAGS.batch_size)
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