| /external/tensorflow/tensorflow/python/kernel_tests/ |
| D | ctc_loss_op_test.py | 313 num_labels = 6
318 [batch_size, max_label_length], minval=1, maxval=num_labels,
320 logits = random_ops.random_uniform([num_frames, batch_size, num_labels])
359 num_labels = 6
362 logits = random_ops.random_uniform([num_frames, batch_size, num_labels])
364 [batch_size, label_length], minval=1, maxval=num_labels,
412 num_labels = 6
415 logits = random_ops.random_uniform([num_frames, batch_size, num_labels])
417 [batch_size, label_length], minval=1, maxval=num_labels,
466 num_labels = 6
[all …]
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| /external/compiler-rt/test/dfsan/ |
| D | dump_labels.c | 39 unsigned long num_labels = 1 << (sizeof(dfsan_label) * 8); in main() local
40 for (unsigned long i = ijk_label + 1; i < num_labels - 2; ++i) { in main()
47 assert(l == num_labels - 2); in main()
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| /external/tensorflow/tensorflow/core/grappler/costs/ |
| D | analytical_cost_estimator_test.cc | 54 const int num_labels = 10; in CreateMiniGraph() local
61 auto labels = ops::RandomUniform(s.WithOpName("label"), {batch, num_labels}, in CreateMiniGraph()
77 {width * height * conv_filters, num_labels}, DT_FLOAT); in CreateMiniGraph()
78 auto b2 = ops::Variable(s.WithOpName("B2"), {num_labels}, DT_FLOAT); in CreateMiniGraph()
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| /external/tensorflow/tensorflow/python/ops/ |
| D | ctc_ops.py | 373 num_labels = _get_dim(label_seq, 1)
375 num_label_states = num_labels + 1
455 def _ilabel_to_state(labels, num_labels, ilabel_log_probs): argument
461 one_hot = array_ops.one_hot(labels, depth=num_labels)
471 def _state_to_olabel(labels, num_labels, states): argument
478 labels - 1, depth=(num_labels - 1),
489 def _state_to_olabel_unique(labels, num_labels, states, unique): argument
505 batch_offset = math_ops.range(batch_size, dtype=unique_y.dtype) * num_labels
511 shape=[batch_size * num_labels, num_frames])
512 scatter = array_ops.reshape(scatter, [batch_size, num_labels, num_frames])
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| /external/tensorflow/tensorflow/contrib/learn/python/learn/datasets/ |
| D | mnist.py | 78 num_labels = labels_dense.shape[0]
79 index_offset = numpy.arange(num_labels) * num_classes
80 labels_one_hot = numpy.zeros((num_labels, num_classes))
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| /external/tensorflow/tensorflow/examples/udacity/ |
| D | 4_convolutions.ipynb | 170 "num_labels = 10\n",
178 " labels = (np.arange(num_labels) == labels[:,None]).astype(np.float32)\n",
257 " tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels))\n",
272 " [num_hidden, num_labels], stddev=0.1))\n",
273 " layer4_biases = tf.Variable(tf.constant(1.0, shape=[num_labels]))\n",
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| D | 2_fullyconnected.ipynb | 180 "num_labels = 10\n",
185 " labels = (np.arange(num_labels) == labels[:,None]).astype(np.float32)\n",
264 " tf.truncated_normal([image_size * image_size, num_labels]))\n",
265 " biases = tf.Variable(tf.zeros([num_labels]))\n",
439 " tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels))\n",
445 " tf.truncated_normal([image_size * image_size, num_labels]))\n",
446 " biases = tf.Variable(tf.zeros([num_labels]))\n",
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| D | 3_regularization.ipynb | 179 "num_labels = 10\n",
184 " labels = (np.arange(num_labels) == labels[:,None]).astype(np.float32)\n",
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| /external/tensorflow/tensorflow/contrib/distribute/python/ |
| D | keras_utils_test.py | 376 def __init__(self, num_labels): argument
378 self.dense = keras.layers.Dense(num_labels)
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| D | keras_test.py | 73 def simple_subclassed_model(num_labels=_NUM_CLASS): argument
77 def __init__(self, num_labels): argument
79 self.dense = keras.layers.Dense(num_labels)
84 return _SimpleMLP(num_labels)
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