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  /external/skia/infra/bots/
Makefile 4 train:
5 python infra_tests.py --train
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
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)
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)
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)
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);
  /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')
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)
  /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
  /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)
  /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)
  /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__
  /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
  /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()
  /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)
  /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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