/cts/tests/openglperf2/assets/fragment/ |
blur | 19 float weights[11]; 20 weights[0] = 0.047748641153356156; 21 weights[1] = 0.05979670798364139; 22 weights[2] = 0.07123260215138659; 23 weights[3] = 0.08071711293576822; 24 weights[4] = 0.08700369673862933; 25 weights[5] = 0.08920620580763855; 26 weights[6] = 0.08700369673862933; 27 weights[7] = 0.08071711293576822; 28 weights[8] = 0.07123260215138659 [all...] |
/external/tensorflow/tensorflow/python/kernel_tests/ |
weights_broadcast_test.py | 40 def _test_valid(self, weights, values): 42 weights=weights, values=values) 46 weights=weights_placeholder, values=values_placeholder) 50 weights_placeholder: weights, 55 self._test_valid(weights=5, values=_test_values((3, 2, 4))) 59 weights=np.asarray((5,)).reshape((1, 1, 1)), 64 weights=np.asarray((5, 7, 11, 3)).reshape((1, 1, 4)), 69 weights=np.asarray((5, 11)).reshape((1, 2, 1)), 74 weights=np.asarray((5, 7, 11, 3, 2, 13, 7, 5)).reshape((1, 2, 4)) [all...] |
losses_test.py | 51 self._predictions, self._predictions, weights=None) 64 weights = 2.3 65 loss = losses.absolute_difference(self._labels, self._predictions, weights) 67 self.assertAlmostEqual(5.5 * weights, loss.eval(), 3) 70 weights = 2.3 72 constant_op.constant(weights)) 74 self.assertAlmostEqual(5.5 * weights, loss.eval(), 3) 77 weights = constant_op.constant((1.2, 0.0), shape=(2, 1)) 78 loss = losses.absolute_difference(self._labels, self._predictions, weights) 83 weights = constant_op.constant([1.2, 0.0], shape=[2, 1] [all...] |
/external/tensorflow/tensorflow/tools/api/golden/ |
tensorflow.metrics.pbtxt | 5 argspec: "args=[\'labels\', \'predictions\', \'weights\', \'metrics_collections\', \'updates_collections\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], " 9 argspec: "args=[\'labels\', \'predictions\', \'weights\', \'num_thresholds\', \'metrics_collections\', \'updates_collections\', \'curve\', \'name\', \'summation_method\'], varargs=None, keywords=None, defaults=[\'None\', \'200\', \'None\', \'None\', \'ROC\', \'None\', \'trapezoidal\'], " 13 argspec: "args=[\'labels\', \'predictions\', \'k\', \'weights\', \'metrics_collections\', \'updates_collections\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], " 17 argspec: "args=[\'labels\', \'predictions\', \'weights\', \'metrics_collections\', \'updates_collections\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], " 21 argspec: "args=[\'labels\', \'predictions\', \'thresholds\', \'weights\', \'metrics_collections\', \'updates_collections\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], " 25 argspec: "args=[\'labels\', \'predictions\', \'weights\', \'metrics_collections\', \'updates_collections\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], " 29 argspec: "args=[\'labels\', \'predictions\', \'thresholds\', \'weights\', \'metrics_collections\', \'updates_collections\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], " 33 argspec: "args=[\'values\', \'weights\', \'metrics_collections\', \'updates_collections\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], " 37 argspec: "args=[\'labels\', \'predictions\', \'weights\', \'metrics_collections\', \'updates_collections\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], " 41 argspec: "args=[\'labels\', \'predictions\', \'dim\', \'weights\', \'metrics_collections\', \'updates_collections\', \'name\'], varargs=None, keywords=None, defau (…) [all...] |
tensorflow.keras.applications.pbtxt | 41 argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], " 45 argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], " 49 argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], " 53 argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], " 57 argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], " 61 argspec: "args=[\'input_shape\', \'alpha\', \'depth_multiplier\', \'dropout\', \'include_top\', \'weights\', \'input_tensor\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'None\', \'1.0\', \'1\', \'0.001\', \'True\', \'imagenet\', \'None\', \'None\', \'1000\'], " 65 argspec: "args=[\'input_shape\', \'include_top\', \'weights\', \'input_tensor\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'None\', \'True\', \'imagenet\', \'None\', \'None\', \'1000\'], " 69 argspec: "args=[\'input_shape\', \'include_top\', \'weights\', \'input_tensor\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'None\', \'True\', \'imagenet\', \'None\', \'None\', \'1000\'], " 73 argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], " 77 argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=None, defa (…) [all...] |
tensorflow.losses.pbtxt | 9 argspec: "args=[\'labels\', \'predictions\', \'weights\', \'scope\', \'loss_collection\', \'reduction\'], varargs=None, keywords=None, defaults=[\'1.0\', \'None\', \'losses\', \'weighted_sum_by_nonzero_weights\'], " 17 argspec: "args=[\'losses\', \'weights\', \'scope\', \'loss_collection\', \'reduction\'], varargs=None, keywords=None, defaults=[\'1.0\', \'None\', \'losses\', \'weighted_sum_by_nonzero_weights\'], " 21 argspec: "args=[\'labels\', \'predictions\', \'axis\', \'weights\', \'scope\', \'loss_collection\', \'reduction\', \'dim\'], varargs=None, keywords=None, defaults=[\'None\', \'1.0\', \'None\', \'losses\', \'weighted_sum_by_nonzero_weights\', \'None\'], " 41 argspec: "args=[\'labels\', \'logits\', \'weights\', \'scope\', \'loss_collection\', \'reduction\'], varargs=None, keywords=None, defaults=[\'1.0\', \'None\', \'losses\', \'weighted_sum_by_nonzero_weights\'], " 45 argspec: "args=[\'labels\', \'predictions\', \'weights\', \'delta\', \'scope\', \'loss_collection\', \'reduction\'], varargs=None, keywords=None, defaults=[\'1.0\', \'1.0\', \'None\', \'losses\', \'weighted_sum_by_nonzero_weights\'], " 49 argspec: "args=[\'labels\', \'predictions\', \'weights\', \'epsilon\', \'scope\', \'loss_collection\', \'reduction\'], varargs=None, keywords=None, defaults=[\'1.0\', \'1e-07\', \'None\', \'losses\', \'weighted_sum_by_nonzero_weights\'], " 53 argspec: "args=[\'labels\', \'predictions\', \'weights\', \'scope\', \'loss_collection\'], varargs=None, keywords=None, defaults=[\'1.0\', \'None\', \'losses\'], " 57 argspec: "args=[\'labels\', \'predictions\', \'weights\', \'scope\', \'loss_collection\', \'reduction\'], varargs=None, keywords=None, defaults=[\'1.0\', \'None\', \'losses\', \'weighted_sum_by_nonzero_weights\'], " 61 argspec: "args=[\'multi_class_labels\', \'logits\', \'weights\', \'label_smoothing\', \'scope\', \'loss_collection\', \'reduction\'], varargs=None, keywords=None, defaults=[\'1.0\', \'0\', \'None\', \'losses\', \'weighted_sum_by_nonzero_weights\'], " 65 argspec: "args=[\'onehot_labels\', \'logits\', \'weights\', \'label_smoothing\', \'scope\', \'loss_collection\', \'reduction\'], varargs=None, keywords=Non (…) [all...] |
tensorflow.keras.applications.densenet.pbtxt | 5 argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], " 9 argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], " 13 argspec: "args=[\'include_top\', \'weights\', \'input_tensor\', \'input_shape\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'True\', \'imagenet\', \'None\', \'None\', \'None\', \'1000\'], "
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tensorflow.keras.applications.nasnet.pbtxt | 5 argspec: "args=[\'input_shape\', \'include_top\', \'weights\', \'input_tensor\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'None\', \'True\', \'imagenet\', \'None\', \'None\', \'1000\'], " 9 argspec: "args=[\'input_shape\', \'include_top\', \'weights\', \'input_tensor\', \'pooling\', \'classes\'], varargs=None, keywords=None, defaults=[\'None\', \'True\', \'imagenet\', \'None\', \'None\', \'1000\'], "
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/external/tensorflow/tensorflow/contrib/tensor_forest/client/ |
eval_metrics.py | 45 def _accuracy(predictions, targets, weights=None): 46 return metric_ops.streaming_accuracy(predictions, targets, weights=weights) 49 def _r2(probabilities, targets, weights=None): 56 return metric_ops.streaming_mean(score, weights=weights) 64 def _sigmoid_entropy(probabilities, targets, weights=None): 70 weights=weights) 73 def _softmax_entropy(probabilities, targets, weights=None) [all...] |
/external/tensorflow/tensorflow/python/ops/ |
weights_broadcast_ops.py | 60 _ASSERT_BROADCASTABLE_ERROR_PREFIX = "weights can not be broadcast to values." 63 def assert_broadcastable(weights, values): 64 """Asserts `weights` can be broadcast to `values`. 67 let weights be either scalar, or the same rank as the target values, with each 71 weights: `Tensor` of weights. 72 values: `Tensor` of values to which weights are applied. 75 `Operation` raising `InvalidArgumentError` if `weights` has incorrect shape. 76 `no_op` if static checks determine `weights` has correct shape. 79 ValueError: If static checks determine `weights` has incorrect shape [all...] |
/external/tensorflow/tensorflow/core/api_def/base_api/ |
api_def_Bincount.pbtxt | 16 name: "weights" 19 shape as `arr`, or a length-0 `Tensor`, in which case it acts as all weights 26 1D `Tensor` with length equal to `size`. The counts or summed weights for 32 Outputs a vector with length `size` and the same dtype as `weights`. If 33 `weights` are empty, then index `i` stores the number of times the value `i` is 34 counted in `arr`. If `weights` are non-empty, then index `i` stores the sum of 35 the value in `weights` at each index where the corresponding value in `arr` is
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/external/freetype/src/base/ |
ftlcdfil.c | 35 /* add padding according to filter weights */ 71 FT_LcdFiveTapFilter weights ) 90 /* the values in `weights' can exceed 0xFF */ 99 fir[2] = weights[2] * val; 100 fir[3] = weights[3] * val; 101 fir[4] = weights[4] * val; 104 fir[1] = fir[2] + weights[1] * val; 105 fir[2] = fir[3] + weights[2] * val; 106 fir[3] = fir[4] + weights[3] * val; 107 fir[4] = weights[4] * val [all...] |
/external/tensorflow/tensorflow/contrib/opt/ |
README.md | 7 * L2-norm clipping of weights
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/external/apache-commons-math/src/main/java/org/apache/commons/math/stat/descriptive/ |
WeightedEvaluation.java | 29 * using the supplied weights. 32 * @param weights array of weights 35 double evaluate(double[] values, double[] weights); 39 * in the input array, using corresponding entries in the supplied weights array. 42 * @param weights array of weights 47 double evaluate(double[] values, double[] weights, int begin, int length);
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/external/tensorflow/tensorflow/python/keras/_impl/keras/applications/ |
densenet_test.py | 28 model = keras.applications.DenseNet121(weights=None) 32 model = keras.applications.DenseNet121(weights=None, include_top=False) 36 model = keras.applications.DenseNet121(weights=None, 43 keras.applications.DenseNet121(weights='unknown', 46 keras.applications.DenseNet121(weights='imagenet', 53 model = keras.applications.DenseNet169(weights=None) 57 model = keras.applications.DenseNet169(weights=None, include_top=False) 61 model = keras.applications.DenseNet169(weights=None, 68 keras.applications.DenseNet169(weights='unknown', 71 keras.applications.DenseNet169(weights='imagenet' [all...] |
mobilenet_test.py | 30 model = keras.applications.MobileNet(weights=None) 34 model = keras.applications.MobileNet(weights=None, include_top=False) 38 model = keras.applications.MobileNet(weights=None, 45 keras.applications.MobileNet(weights='unknown', 48 keras.applications.MobileNet(weights='imagenet', 58 model = keras.applications.MobileNet(weights=None) 64 model = keras.applications.MobileNet(weights=None, 70 model = keras.applications.MobileNet(weights=None, 82 weights=None, 89 weights=None [all...] |
/external/tensorflow/tensorflow/contrib/losses/python/losses/ |
loss_ops.py | 42 def _scale_losses(losses, weights): 47 weights: A `Tensor` of size [1], [batch_size] or [batch_size, d1, ... dN]. 49 that of `weights` at which point the reduced `losses` are element-wise 50 multiplied by `weights` and a final reduce_sum is computed on the result. 52 `weights` to be the same size as `losses`, performing an element-wise 60 start_index = max(0, weights.get_shape().ndims) 64 reduced_losses = math_ops.multiply(reduced_losses, weights) 110 def compute_weighted_loss(losses, weights=1.0, scope=None): 115 weights: A tensor of size [1] or [batch_size, d1, ... dK] where K < N. 122 ValueError: If `weights` is `None` or the shape is not compatible wit [all...] |
/external/icu/icu4c/source/i18n/ |
collationweights.h | 31 * Allocates n collation element weights between two exclusive limits. 56 * what ranges to use for a given number of weights between (excluding) 60 * weights greater than this one. 62 * weights less than this one. 63 * @param n The number of collation element weights w necessary such that 71 * iterate through the weights. 94 * Takes two CE weights and calculates the 95 * possible ranges of weights between the two limits, excluding them. 96 * For weights with up to 4 bytes there are up to 2*4-1=7 ranges.
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/external/tensorflow/tensorflow/contrib/layers/python/layers/ |
regularizers.py | 38 """Returns a function that can be used to apply L1 regularization to weights. 47 A function with signature `l1(weights)` that apply L1 regularization. 62 def l1(weights, name=None): 63 """Applies L1 regularization to weights.""" 64 with ops.name_scope(scope, 'l1_regularizer', [weights]) as name: 66 dtype=weights.dtype.base_dtype, 70 standard_ops.reduce_sum(standard_ops.abs(weights)), 77 """Returns a function that can be used to apply L2 regularization to weights. 86 A function with signature `l2(weights)` that applies L2 regularization. 101 def l2(weights) [all...] |
/external/tensorflow/tensorflow/python/ops/losses/ |
losses_impl.py | 41 `MEAN`: Scalar `SUM` divided by sum of weights. 44 weights. 115 def _num_present(losses, weights, per_batch=False): 116 """Computes the number of elements in the loss function induced by `weights`. 118 A given weights tensor induces different numbers of usable elements in the 119 `losses` tensor. The `weights` tensor is broadcast across `losses` for all 121 `[4, 5, 6, 3]` and `weights` is a tensor of shape `[4, 5]`, then `weights` is, 123 tile, the total number of present elements is the number of non-zero weights. 127 weights: `Tensor` of shape `[]`, `[batch_size]` o [all...] |
/external/tensorflow/tensorflow/core/lib/random/ |
distribution_sampler_test.cc | 34 float TestWeights(const std::vector<float>& weights, int trials_per_bin) { 35 int iters = weights.size() * trials_per_bin; 36 std::unique_ptr<float[]> counts(new float[weights.size()]); 37 memset(counts.get(), 0, sizeof(float) * weights.size()); 38 DistributionSampler sampler(weights); 43 EXPECT_LT(r, weights.size()); 48 for (size_t i = 0; i < weights.size(); i++) { 50 float err = (counts[i] - weights[i]); 51 chi2 += (err * err) / weights[i]; 89 std::vector<float> weights(n, 0) [all...] |
/external/apache-commons-math/src/main/java/org/apache/commons/math/stat/descriptive/moment/ |
Variance.java | 266 * Σ(weights[i]*(values[i] - weightedMean)<sup>2</sup>)/(Σ(weights[i]) - 1) 271 * weights are equal, unless all weights are equal to 1. The formula assumes that 272 * weights are to be treated as "expansion values," as will be the case if for example 273 * the weights represent frequency counts. To normalize weights so that the denominator 275 * <code>evaluate(values, MathUtils.normalizeArray(weights, values.length)); </code> 282 * <li>the weights array is null</li> 283 * <li>the weights array does not have the same length as the values array</li [all...] |
/external/tensorflow/tensorflow/examples/image_retraining/ |
README.md | 7 with quantized weights and activations instead of taking a pre-trained floating 8 point model and then quantizing weights and activations.
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/external/tensorflow/tensorflow/contrib/slim/python/slim/nets/ |
vgg_test.py | 80 'vgg_a/conv1/conv1_1/weights', 82 'vgg_a/conv2/conv2_1/weights', 84 'vgg_a/conv3/conv3_1/weights', 86 'vgg_a/conv3/conv3_2/weights', 88 'vgg_a/conv4/conv4_1/weights', 90 'vgg_a/conv4/conv4_2/weights', 92 'vgg_a/conv5/conv5_1/weights', 94 'vgg_a/conv5/conv5_2/weights', 96 'vgg_a/fc6/weights', 98 'vgg_a/fc7/weights', [all...] |
/external/apache-commons-math/src/main/java/org/apache/commons/math/stat/descriptive/summary/ |
Product.java | 139 * <li>the weights array is null</li> 140 * <li>the weights array does not have the same length as the values array</li> 141 * <li>the weights array contains one or more infinite values</li> 142 * <li>the weights array contains one or more NaN values</li> 143 * <li>the weights array contains negative values</li> 148 * weighted product = ∏values[i]<sup>weights[i]</sup> 150 * that is, the weights are applied as exponents when computing the weighted product.</p> 153 * @param weights the weights array 160 public double evaluate(final double[] values, final double[] weights, [all...] |