| /external/tensorflow/tensorflow/contrib/factorization/python/ops/ |
| gmm_ops_test.py | 119 num_classes = 2
126 data, 'random', num_classes, random_seed=self.seed)
146 num_classes = 2
150 gmm_tool = gmm_ops.GmmAlgorithm([data], num_classes,
168 gmm_tool = gmm_ops.GmmAlgorithm([data], num_classes,
187 gmm_tool = gmm_ops.GmmAlgorithm([data], num_classes,
|
| /external/tensorflow/tensorflow/contrib/slim/python/slim/nets/ |
| vgg.py | 75 num_classes=1000,
87 num_classes: number of predicted classes.
124 num_classes, [1, 1],
140 num_classes=1000,
152 num_classes: number of predicted classes.
189 num_classes, [1, 1],
205 num_classes=1000,
217 num_classes: number of predicted classes.
254 num_classes, [1, 1],
|
| alexnet.py | 64 num_classes=1000,
84 num_classes: number of predicted classes.
124 num_classes, [1, 1],
|
| overfeat.py | 60 num_classes=1000,
80 num_classes: number of predicted classes.
120 num_classes, [1, 1],
|
| /external/tensorflow/tensorflow/contrib/boosted_trees/estimator_batch/ |
| dnn_tree_combined_estimator_test.py | 66 learner_config.num_classes = 2
82 learner_config.num_classes = 2
|
| /external/tensorflow/tensorflow/contrib/receptive_field/python/util/examples/ |
| rf_benchmark.py | 129 images, num_classes=None, is_training=False, global_pool=False)
132 images, num_classes=None, is_training=False, global_pool=False)
135 images, num_classes=None, is_training=False, global_pool=False)
138 images, num_classes=None, is_training=False, global_pool=False)
141 images, num_classes=None, is_training=False, global_pool=False)
144 images, num_classes=None, is_training=False, global_pool=False)
147 images, num_classes=None, is_training=False, global_pool=False)
150 images, num_classes=None, is_training=False, global_pool=False)
|
| /external/tensorflow/tensorflow/contrib/seq2seq/python/ops/ |
| loss.py | 87 num_classes = array_ops.shape(logits)[2]
88 logits_flat = array_ops.reshape(logits, [-1, num_classes])
|
| /external/tensorflow/tensorflow/contrib/tensor_forest/hybrid/python/ |
| hybrid_layer_test.py | 33 num_classes=3,
|
| /external/tensorflow/tensorflow/python/keras/_impl/keras/ |
| testing_utils.py | 31 num_classes):
38 num_classes: Integer, number of classes for the data and targets.
45 templates = 2 * num_classes * np.random.random((num_classes,) + input_shape)
46 y = np.random.randint(0, num_classes, size=(num_sample,))
|
| /external/tensorflow/tensorflow/contrib/slim/python/slim/ |
| evaluation_test.py | 53 def GenerateTestData(num_classes, batch_size):
54 inputs = np.random.rand(batch_size, num_classes)
57 labels = np.random.randint(low=0, high=num_classes, size=batch_size)
81 num_classes = 8
83 inputs, labels = GenerateTestData(num_classes, batch_size)
219 num_classes = 8
221 inputs, labels = GenerateTestData(num_classes, batch_size)
|
| /device/linaro/bootloader/edk2/BaseTools/Source/C/VfrCompile/Pccts/support/genmk/ |
| genmk_old.c | 48 static int num_classes = 0;
variable
156 require(num_classes<MAX_CLASSES, "exceeded max # of grammar classes");
157 classes[num_classes++] = t;
289 if ( !gen_CPP && num_classes>0 ) {
293 if ( gen_CPP && num_classes==0 ) {
399 pclasses(classes, num_classes, CPP_FILE_SUFFIX_NO_DOT);
423 pclasses(classes, num_classes, "o");
450 pclasses(classes, num_classes, CPP_FILE_SUFFIX_NO_DOT);
452 pclasses(classes, num_classes, "h");
536 for (i=0; i<num_classes; i++)
[all...] |
| /external/tensorflow/tensorflow/contrib/learn/python/learn/datasets/ |
| mnist.py | 68 def dense_to_one_hot(labels_dense, num_classes):
71 index_offset = numpy.arange(num_labels) * num_classes
72 labels_one_hot = numpy.zeros((num_labels, num_classes))
77 def extract_labels(f, one_hot=False, num_classes=10):
83 num_classes: Number of classes for the one hot encoding.
101 return dense_to_one_hot(labels, num_classes)
|
| /hardware/intel/common/libwsbm/src/ |
| wsbm_fencemgr.c | 77 uint32_t num_classes; member in struct:_WsbmFenceMgr
127 tmp->classes = calloc(tmp->info.num_classes, sizeof(*tmp->classes));
131 for (i = 0; i < tmp->info.num_classes; ++i) {
429 info.num_classes = numClass;
467 for (i = 0; i < mgr->info.num_classes; ++i) {
|
| wsbm_fencemgr.h | 140 uint32_t num_classes; member in struct:_WsbmFenceMgrCreateInfo
|
| /external/tensorflow/tensorflow/contrib/boosted_trees/python/training/functions/ |
| gbdt_batch_test.py | 152 learner_config.num_classes = 2
255 learner_config.num_classes = 2
359 learner_config.num_classes = 2
430 learner_config.num_classes = 2
493 learner_config.num_classes = 2
577 learner_config.num_classes = 2
612 learner_config.num_classes = 5
662 num_classes=learner_config.num_classes)[0]),
715 learner_config.num_classes =
[all...] |
| /external/tensorflow/tensorflow/contrib/learn/python/learn/estimators/ |
| state_saving_rnn_estimator_test.py | 385 num_classes = 2
420 num_classes=num_classes,
525 num_classes = 2
558 num_classes=num_classes,
598 num_classes = len(vocab)
641 num_classes=num_classes,
|
| /external/tensorflow/tensorflow/contrib/boosted_trees/python/kernel_tests/ |
| prediction_ops_test.py | 181 learner_config.num_classes = 2
208 learner_config.num_classes = 2
238 learner_config.num_classes = 3
282 learner_config.num_classes = 2
357 learner_config.num_classes = 2
419 learner_config.num_classes = 2
466 learner_config.num_classes = 2
516 learner_config.num_classes = 2
531 # For TREE_PER_CLASS strategy, predictions size is num_classes-1
562 learner_config.num_classes =
[all...] |
| training_ops_test.py | 37 def _gen_learner_config(num_classes,
50 config.num_classes = num_classes
139 num_classes=3,
290 num_classes=2,
437 num_classes=2,
628 num_classes=2,
769 num_classes=2,
835 num_classes=2,
942 num_classes=2
[all...] |
| /external/tensorflow/tensorflow/contrib/boosted_trees/python/utils/ |
| losses.py | 49 def per_example_maxent_loss(labels, weights, logits, num_classes, eps=1e-15):
60 num_classes: number of classes in classification task. Used to expand label
74 target_one_hot = array_ops.one_hot(indices=labels, depth=num_classes)
|
| /external/tensorflow/tensorflow/contrib/tensor_forest/hybrid/python/models/ |
| decisions_to_data_then_nn_test.py | 38 num_classes=2,
55 self.assertEquals(self.params.num_classes, 2)
|
| hard_decisions_to_data_then_nn.py | 57 output_size = 1 if self.is_regression else self.params.num_classes
|
| /external/tensorflow/tensorflow/python/kernel_tests/ |
| confusion_matrix_test.py | 50 num_classes=None):
55 num_classes=num_classes).eval()
102 lab, data, dtype=tf_dtype, num_classes=2)
188 labels=labels, predictions=predictions, num_classes=3, truth=None)
195 labels=labels, predictions=predictions, num_classes=3, truth=None)
202 labels=labels, predictions=predictions, num_classes=3, truth=None)
209 labels=labels, predictions=predictions, num_classes=3, truth=None)
|
| /external/tensorflow/tensorflow/contrib/tensor_forest/hybrid/python/layers/ |
| decisions_to_data_test.py | 37 num_classes=2,
|
| /external/tensorflow/tensorflow/python/keras/_impl/keras/preprocessing/ |
| image_test.py | 157 num_classes = 2
164 for cl in range(num_classes):
181 im_class = count % num_classes
203 self.assertEqual(len(dir_iterator.class_indices), num_classes)
|
| /external/tensorflow/tensorflow/python/keras/_impl/keras/engine/ |
| training_test.py | 306 num_classes = 5
315 model.add(keras.layers.Dense(num_classes))
323 num_classes=num_classes)
400 num_classes = 5
412 model.add(keras.layers.Dense(num_classes))
421 num_classes=num_classes)
425 y_train = keras.utils.to_categorical(y_train, num_classes)
426 y_test = keras.utils.to_categorical(y_test, num_classes)
[all...] |
