/external/tensorflow/tensorflow/compiler/tests/ |
categorical_op_test.py | 53 logits: Numpy ndarray of shape [batch_size, num_classes]. 57 Frequencies from sampled classes; shape [batch_size, num_classes]. 65 batch_size, num_classes = logits.shape 71 self.assertLess(max(cnts.keys()), num_classes) 75 for k in range(num_classes)]
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jit_test.py | 206 num_classes = 10 212 w = np.random.random_sample((image_size, num_classes)).astype(np.float32) 213 b = np.random.random_sample((num_classes)).astype(np.float32) 221 num_classes = 10 233 dw = np.random.random_sample((image_size, num_classes)).astype(np.float32) 234 db = np.random.random_sample((num_classes)).astype(np.float32)
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/external/tensorflow/tensorflow/contrib/eager/python/examples/resnet50/ |
resnet50_graph_test.py | 43 num_classes = 1000 45 low=0, high=num_classes, size=[batch_size]).astype(np.int32) 46 one_hot = np.zeros((batch_size, num_classes)).astype(np.float32)
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resnet50_test.py | 45 num_classes = 1000 48 [batch_size], minval=0, maxval=num_classes, dtype=tf.int32) 49 one_hot = tf.one_hot(labels, num_classes)
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/external/tensorflow/tensorflow/core/kernels/ |
sparse_xent_op.h | 53 // for j = 0 .. num_classes. This value must be summed over all j for 94 // for j = 0 .. num_classes. 136 // logits: batch_size, num_classes. 137 // labels: num_classes. 140 // backprop: output tensor for the backprop, dims: batch_size, num_classes. 165 const int num_classes = logits.dimension(kClassDim); local 179 one_by_class[1] = num_classes; 187 one_by_class.set(1, num_classes);
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/external/tensorflow/tensorflow/python/keras/_impl/keras/ |
callbacks_test.py | 48 NUM_CLASSES = 2 71 num_classes=NUM_CLASSES) 83 model.add(keras.layers.Dense(NUM_CLASSES, activation='softmax')) 229 num_classes=NUM_CLASSES) 236 model.add(keras.layers.Dense(NUM_CLASSES, activation='softmax')) 301 num_classes=NUM_CLASSES) 308 model.add(keras.layers.Dense(NUM_CLASSES, activation='softmax') [all...] |
regularizers_test.py | 27 NUM_CLASSES = 2 35 num_classes=NUM_CLASSES) 36 y_train = keras.utils.to_categorical(y_train, NUM_CLASSES) 37 y_test = keras.utils.to_categorical(y_test, NUM_CLASSES) 43 model.add(keras.layers.Dense(NUM_CLASSES,
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models_test.py | 232 num_classes = 2 236 model.add(keras.layers.Dense(num_classes)) 239 y = np.random.random((batch_size, num_classes)) 271 num_classes = 2 276 model.add(keras.layers.Dense(num_classes)) 285 model.add(keras.layers.Dense(num_classes)) 328 num_classes = 2 335 model.add(keras.layers.Dense(num_classes))
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/external/tensorflow/tensorflow/contrib/tensor_forest/kernels/v4/ |
grow_stats.cc | 273 const float num_classes = params_.num_outputs(); local 274 const float gini_diff_range = weight_sum_ * (1.0 - 1.0 / num_classes); 420 const int32 num_classes = params_.num_outputs(); local 426 for (int i = 0; i < num_classes; ++i) { 436 for (int i = 0; i < num_classes; ++i) { 584 const int32 num_classes = params_.num_outputs(); local 586 WeightedSmoothedGini(*left_sum, left_square, num_classes); 588 WeightedSmoothedGini(*right_sum, right_square, num_classes); 775 const int32 num_classes = params_.num_outputs(); local 777 WeightedSmoothedGini(*left_sum, left_square, num_classes); [all...] |
/device/linaro/bootloader/edk2/BaseTools/Source/C/VfrCompile/Pccts/support/genmk/ |
genmk.c | 57 static int num_classes = 0; /* ANTLR classes */
variable 272 require(num_classes<MAX_CLASSES, "exceeded max # of grammar classes");
273 classes[num_classes++] = t;
411 if ( !gen_CPP && num_classes>0 ) {
419 if ( gen_CPP && num_classes==0 ) {
546 pclasses(classes, num_classes, CPP_FILE_SUFFIX_NO_DOT);
583 pclasses(classes, num_classes, "o");
623 pclasses(classes, num_classes, CPP_FILE_SUFFIX_NO_DOT);
625 pclasses(classes, num_classes, "h");
741 for (i=0; i<num_classes; i++) [all...] |
/external/tensorflow/tensorflow/contrib/factorization/python/ops/ |
gmm_test.py | 181 num_classes = 20 183 for _ in range(num_classes)], dtype=np.float32) 186 gmm = gmm_lib.GMM(num_classes,
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/external/tensorflow/tensorflow/contrib/training/python/training/ |
sampling_ops.py | 260 def _estimate_data_distribution(labels, num_classes, smoothing_constant=10): 268 initial_value=[smoothing_constant] * num_classes, 277 labels, num_classes, dtype=dtypes.int64), 0)) 362 # Label's classes must be integers 0 <= x < num_classes.
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/external/tensorflow/tensorflow/contrib/tensor_forest/hybrid/python/models/ |
forest_to_data_then_nn_test.py | 38 num_classes=2,
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k_feature_decisions_to_data_then_nn_test.py | 38 num_classes=2,
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/prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setools/ |
permmap.py | 85 num_classes = 0 98 num_classes = int(entry[0]) 104 if num_classes < 1: 132 if class_count > num_classes:
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/external/tensorflow/tensorflow/contrib/losses/python/metric_learning/ |
metric_loss_ops_test.py | 97 num_classes = 4 101 0, num_classes, size=(num_data)).astype(np.float32) 153 num_classes = 4 157 0, num_classes, size=(num_data)).astype(np.float32) 223 num_classes = 5 231 0, num_classes, size=(num_data)).astype(np.float32) 296 num_classes = 10 303 labels = np.random.randint(0, 2, (num_data, num_classes))
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/external/tensorflow/tensorflow/python/keras/_impl/keras/wrappers/ |
scikit_learn_test.py | 31 NUM_CLASSES = 2 42 model.add(keras.layers.Dense(NUM_CLASSES)) 55 num_classes=NUM_CLASSES) 65 assert prediction in range(NUM_CLASSES) 68 assert proba.shape == (TEST_SAMPLES, NUM_CLASSES) 91 num_classes=NUM_CLASSES)
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/external/tensorflow/tensorflow/python/ops/ |
nn_impl.py | [all...] |
/prebuilts/gcc/linux-x86/host/x86_64-linux-glibc2.15-4.8/sysroot/usr/include/X11/extensions/ |
XInput.h | 81 _i< ((XDevice *) d)->num_classes; \ 370 int num_classes; member in struct:__anon61680 774 int num_classes; member in struct:_XDeviceInfo 848 int num_classes; member in struct:__anon61711 888 int num_classes; member in struct:__anon61714 [all...] |
/external/tensorflow/tensorflow/python/kernel_tests/ |
metrics_test.py | 60 labels: Dense 2D binary indicator, shape [batch_size, num_classes]. 63 `SparseTensorValue` of shape [batch_size, num_classes], where num_classes 94 labels: Dense 2D binary indicator, shape [batch_size, num_classes]. Each [all...] |
/external/tensorflow/tensorflow/contrib/boosted_trees/python/kernel_tests/ |
model_ops_test.py | 109 learner_config.num_classes = 2 176 learner_config.num_classes = 3 217 learner_config.num_classes = 2
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/external/tensorflow/tensorflow/contrib/learn/python/learn/estimators/ |
dynamic_rnn_estimator.py | 266 `[batch_size, num_classes]`. 549 num_classes=None, 609 num_classes: the number of classes for a classification problem. Only 642 `num_classes` is not specified. 659 if not num_classes: 660 raise ValueError('For CLASSIFICATION problem_type, num_classes must be ' 662 target_column = layers.multi_class_target(n_classes=num_classes)
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state_saving_rnn_estimator.py | 538 num_classes=None, 569 num_classes: The number of classes for categorization. Used only and 609 `num_classes` is not specified. 616 if not num_classes: 617 raise ValueError('For CLASSIFICATION problem_type, num_classes must be ' 619 target_column = layers.multi_class_target(n_classes=num_classes)
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/external/tensorflow/tensorflow/contrib/tensor_forest/hybrid/python/ |
hybrid_model.py | 82 output_size = 1 if self.is_regression else self.params.num_classes
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/external/tensorflow/tensorflow/examples/learn/ |
random_forest_mnist.py | 40 num_classes=10,
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