| /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\'], "
|
| 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\'], "
|
| /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
|
| /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
|
| /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);
|
| /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.
|
| /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.
|
| /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...] |
