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  /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)]
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)
  /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)
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)
  /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);
  /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,
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))
  /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,
  /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.
  /external/tensorflow/tensorflow/contrib/tensor_forest/hybrid/python/models/
forest_to_data_then_nn_test.py 38 num_classes=2,
k_feature_decisions_to_data_then_nn_test.py 38 num_classes=2,
  /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:
  /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))
  /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)
  /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
  /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)
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)
  /external/tensorflow/tensorflow/contrib/tensor_forest/hybrid/python/
hybrid_model.py 82 output_size = 1 if self.is_regression else self.params.num_classes
  /external/tensorflow/tensorflow/examples/learn/
random_forest_mnist.py 40 num_classes=10,

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