| /external/tensorflow/tensorflow/contrib/quantize/python/ |
| quantize_graph.py | 43 weight_bits: Number of bits to use for quantizing weights.
45 quant_delay: Number of steps after which weights and activations are
90 quant_delay: Number of steps after which weights and activations are
163 weight_bits: Number of bits to use for quantizing weights.
165 quant_delay: Number of steps after which weights and activations are
202 weight_bits: Number of bits to use for quantizing weights.
|
| /external/tensorflow/tensorflow/python/keras/_impl/keras/engine/ |
| topology_test.py | 75 weights = model.weights
76 self.assertListEqual(model.trainable_weights, weights)
81 self.assertListEqual(model.non_trainable_weights, weights)
84 self.assertListEqual(model.trainable_weights, weights)
89 self.assertListEqual(model.non_trainable_weights, weights)
94 weights = model.weights
96 self.assertListEqual(model.trainable_weights, weights)
101 self.assertListEqual(model.non_trainable_weights, weights)
[all...] |
| /device/google/taimen/ |
| thermal-engine.conf | 36 weights 1 -1
112 weights 1 -1
|
| /external/apache-commons-math/src/main/java/org/apache/commons/math/ode/nonstiff/ |
| ClassicalRungeKuttaIntegrator.java | 53 /** Internal weights Butcher array. */
60 /** Propagation weights Butcher array. */
|
| EulerIntegrator.java | 55 /** Internal weights Butcher array. */
59 /** Propagation weights Butcher array. */
|
| GillIntegrator.java | 54 /** Internal weights Butcher array. */
61 /** Propagation weights Butcher array. */
|
| ThreeEighthesIntegrator.java | 52 /** Internal weights Butcher array. */
59 /** Propagation weights Butcher array. */
|
| /external/freetype/include/freetype/ |
| ftlcdfil.h | 156 * weights are [0x08 0x4D 0x56 0x4D 0x08].
166 * onto surfaces. The light filter weights are
252 * This function can be used to enable LCD filter with custom weights,
259 * weights ::
261 * uses them to specify the filter weights.
273 * LCD filter weights can also be set per face using @FT_Face_Properties
281 unsigned char *weights );
289 * A typedef for passing the five LCD filter weights to
|
| /external/icu/icu4c/source/i18n/ |
| collationkeys.cpp | 282 // Shifted primary weights are lower than the common weight.
383 // Append reverse weights. The level will be re-reversed later.
401 // The backwards secondary level compares secondary weights backwards
428 // Primary+caseLevel: Ignore case level weights of primary ignorables.
429 // Otherwise: Ignore case level weights of secondary ignorables.
438 // lowerFirst: Compress common weights to nibbles 1..7..13, mixed=14, upper=15.
439 // If there are only common (=lowest) weights in the whole level,
462 // upperFirst: Compress common weights to nibbles 3..15, mixed=2, upper=1.
463 // The compressed common case weights only go up from the "low" value
491 // Tertiary weights without case bits
[all...] |
| /external/llvm/test/CodeGen/Hexagon/ |
| ifcvt-edge-weight.ll | 2 ; Check that the edge weights are updated correctly after if-conversion.
10 ; Check if the edge weights on edges to E and F are maintained correctly.
|
| /external/mesa3d/src/intel/common/ |
| gen_l3_config.h | 64 * L3 configuration represented as a vector of weights giving the desired
66 * between weights will have an influence on the selection of the closest L3
|
| /external/tensorflow/tensorflow/contrib/training/python/training/ |
| training_test.py | 456 # First, train only the weights of the model.
461 weights = variables_lib.get_variables_by_name('weights')
464 total_loss, optimizer, variables_to_train=weights)
504 # Finally, train both weights and bias to get lower loss.
524 # First, train only the weights of the model.
529 weights, biases = variables_lib.get_variables()
533 total_loss, optimizer, variables_to_train=[weights])
541 # Get the initial weights and biases values.
542 weights_values, biases_values = session.run([weights, biases]
[all...] |
| /external/tensorflow/tensorflow/core/framework/ |
| log_memory.proto | 24 // e.g., "affine2/weights/Assign".
45 // "affine2/weights/Assign".
|
| /external/tensorflow/tensorflow/docs_src/programmers_guide/ |
| summaries_and_tensorboard.md | 43 off a particular layer, or the distribution of gradients or weights. Collect
46 the gradient outputs and to the variable that holds your weights, respectively.
82 to visualize statistics, such as how the weights or accuracy varied during
108 # This Variable will hold the state of the weights for the layer
109 with tf.name_scope('weights'):
110 weights = weight_variable([input_dim, output_dim])
111 variable_summaries(weights)
116 preactivate = tf.matmul(input_tensor, weights) + biases
|
| /external/tensorflow/tensorflow/python/keras/_impl/keras/utils/ |
| layer_utils.py | 29 def count_params(weights):
30 """Count the total number of scalars composing the weights.
33 weights: An iterable containing the weights on which to compute params
36 The total number of scalars composing the weights
38 return int(np.sum([K.count_params(p) for p in set(weights)]))
236 When porting the weights of a convnet from one data format to the other,
239 followed by a `Dense` layer, the weights of that `Dense` layer
|
| /frameworks/ml/nn/runtime/test/specs/V1_0/ |
| fully_connected_float_2.mod.py | 19 weights = Parameter("op2", "TENSOR_FLOAT32", "{16, 8}", variable
48 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act_relu).To(out0)
|
| /frameworks/ml/nn/runtime/test/specs/V1_1/ |
| fully_connected_float_2_relaxed.mod.py | 19 weights = Parameter("op2", "TENSOR_FLOAT32", "{16, 8}", variable
48 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act_relu).To(out0)
|
| /external/tensorflow/tensorflow/python/framework/ |
| meta_graph_test.py | 304 name="weights")
321 name="weights")
346 name="weights")
357 self.assertEqual(["biases:0", "weights:0"], sorted(var_list.keys()))
359 self.assertEqual(["hidden1/biases:0", "hidden1/weights:0"],
403 self.assertEqual(["biases:0", "weights:0"], sorted(var_list.keys()))
405 self.assertEqual(["new_hidden1/biases:0", "new_hidden1/weights:0"],
418 self.assertEqual(["biases:0", "weights:0"], sorted(var_list.keys()))
420 self.assertEqual(["new_hidden2/biases:0", "new_hidden2/weights:0"],
433 self.assertEqual(["biases:0", "weights:0"], sorted(var_list.keys())
[all...] |
| /external/tensorflow/tensorflow/contrib/eager/python/ |
| evaluator.py | 336 label, predicted class, and optional weights.
343 weights_key: Optional key for looking up the value of the weights
344 in the `eval_data()` dict. Defaults to "weights". Note that weights
349 predicted_class_key="predicted_class", weights_key="weights"):
362 weights = eval_data.get(self.weights_key, None)
363 if weights is None:
368 self.avg_loss(eval_data[self.loss_key], weights=weights)
371 weights=weights
[all...] |
| /external/tensorflow/tensorflow/contrib/model_pruning/examples/cifar10/ |
| cifar10_pruning.py | 190 'weights', shape=[5, 5, 3, 64], stddev=5e-2, wd=0.0)
213 'weights', shape=[5, 5, 64, 64], stddev=5e-2, wd=0.0)
237 weights = _variable_with_weight_decay(
238 'weights', shape=[dim, 384], stddev=0.04, wd=0.004)
241 tf.matmul(reshape, pruning.apply_mask(weights, scope)) + biases,
247 weights = _variable_with_weight_decay(
248 'weights', shape=[384, 192], stddev=0.04, wd=0.004)
251 tf.matmul(local3, pruning.apply_mask(weights, scope)) + biases,
260 weights = _variable_with_weight_decay(
261 'weights', [192, NUM_CLASSES], stddev=1 / 192.0, wd=0.0
[all...] |
| /external/tensorflow/tensorflow/contrib/model_pruning/python/layers/ |
| layers.py | 122 `convolution` creates a variable called `weights`, representing the
166 weights_initializer: An initializer for the weights.
167 weights_regularizer: Optional regularizer for the weights.
191 'kernel': 'weights'
241 _add_variable_to_collections(layer.kernel, variables_collections, 'weights')
276 `fully_connected` creates a variable called `weights`, representing a fully
285 prior to the initial matrix multiply by `weights`.
298 weights_initializer: An initializer for the weights.
299 weights_regularizer: Optional regularizer for the weights.
323 'kernel': 'weights'
[all...] |
| /external/tensorflow/tensorflow/python/keras/_impl/keras/layers/ |
| wrappers.py | 91 def set_weights(self, weights):
92 self.layer.set_weights(weights)
265 def __init__(self, layer, merge_mode='concat', weights=None, **kwargs):
277 if weights:
278 nw = len(weights)
279 self.forward_layer.initial_weights = weights[:nw // 2]
280 self.backward_layer.initial_weights = weights[nw // 2:]
302 def set_weights(self, weights):
303 nw = len(weights)
304 self.forward_layer.set_weights(weights[:nw // 2]
[all...] |
| /external/deqp/external/vulkancts/modules/vulkan/texture/ |
| vktSampleVerifier.cpp | 333 const Vec2& weights,
359 const Interval iWeightInterval = m_filteringPrecision.roundOut(Interval(i == 0 ? 1.0f - weights[1] : weights[1]), false);
363 const Interval jWeightInterval = m_filteringPrecision.roundOut(iWeightInterval * Interval(j == 0 ? 1.0f - weights[0] : weights[0]), false);
382 const Vec3& weights,
411 const Interval iWeightInterval = m_filteringPrecision.roundOut(Interval(i == 0 ? 1.0f - weights[2] : weights[2]), false);
415 const Interval jWeightInterval = m_filteringPrecision.roundOut(iWeightInterval * Interval(j == 0 ? 1.0f - weights[1] : weights[1]), false)
[all...] |
| /external/tensorflow/tensorflow/python/ops/ |
| embedding_ops.py | 338 """Computes embeddings for the given ids and weights.
355 sp_weights: either a SparseTensor of float / double weights, or None to
356 indicate all weights should be taken to be 1. If specified, sp_weights
367 squares of the weights.
447 weights = sp_weights.values
448 if weights.dtype != embeddings.dtype:
449 weights = math_ops.cast(weights, embeddings.dtype)
451 # Reshape weights to allow broadcast
454 bcast_weights_shape = array_ops.concat([array_ops.shape(weights), ones]
[all...] |
| /frameworks/rs/driver/runtime/ |
| rs_sample.c | 268 getBilinearSample1D(const Allocation_t *alloc, float2 weights,
276 return getSample_RGBA(p, iPixel, next, weights.x, weights.y);
278 return getSample_A(p, iPixel, next, weights.x, weights.y);
281 return getSample_565(p, iPixel, next, weights.x, weights.y);
283 return getSample_RGB(p, iPixel, next, weights.x, weights.y);
285 return getSample_L(p, iPixel, next, weights.x, weights.y)
440 float2 weights; local
[all...] |
