| /external/tensorflow/tensorflow/python/ops/ |
| D | math_ops.py | 1310 def _may_reduce_to_scalar(keepdims, axis, reduction_indices, output): argument
1312 if (not output.shape.is_fully_defined()) and (not keepdims) and (
1323 keepdims=None, argument
1365 keepdims = deprecation.deprecated_argument_lookup("keepdims", keepdims,
1367 if keepdims is None:
1368 keepdims = False
1370 return _may_reduce_to_scalar(keepdims, axis, reduction_indices,
1375 keepdims,
1384 keepdims=None, argument
1428 keepdims = deprecation.deprecated_argument_lookup("keepdims", keepdims,
[all …]
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| D | linalg_ops.py | 455 keepdims=None, argument
516 keepdims = deprecation.deprecated_argument_lookup('keepdims', keepdims,
518 if keepdims is None:
519 keepdims = False
552 tensor * math_ops.conj(tensor), axis, keepdims=True))
557 result = math_ops.reduce_sum(result, sum_axis, keepdims=True)
559 result = math_ops.reduce_max(result, axis[-1], keepdims=True)
562 result = math_ops.reduce_sum(result, axis[1], keepdims=True)
564 result = math_ops.reduce_max(result, max_axis, keepdims=True)
568 math_ops.reduce_sum(math_ops.pow(result, ord), axis, keepdims=True),
[all …]
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| D | nn_grad.py | 261 return grad - math_ops.reduce_sum(grad, 1, keepdims=True) * softmax
866 keepdims = False
869 keepdims = True
873 mean_grad_y = math_ops.reduce_mean(grad_y, reduce_axis, keepdims=keepdims)
874 mean_x = math_ops.reduce_mean(x, reduce_axis, keepdims=keepdims)
878 keepdims=keepdims)
882 grad_y * x_offset, axis=reduce_axis, keepdims=keepdims)
886 grad_y * x_offset, axis=reduce_axis, keepdims=keepdims)
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| D | nn_impl.py | 358 square_sum = math_ops.reduce_sum(math_ops.square(x), axis, keepdims=True)
614 m_ss = math_ops.reduce_sum(m_ss, axes, keepdims=keep_dims, name="mean_ss")
615 v_ss = math_ops.reduce_sum(v_ss, axes, keepdims=keep_dims, name="var_ss")
690 mean = math_ops.reduce_mean(y, axes, keepdims=True, name="mean")
695 keepdims=True,
741 frequency_weights * x, axes, name="weighted_input_sum", keepdims=True)
752 broadcasted_weights, axes, name="sum_of_weights", keepdims=True)
763 keepdims=True)
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| D | nn_batchnorm_test.py | 343 m_ss = np.sum(x - shift, axis=axis, keepdims=keep_dims)
344 v_ss = np.sum((x - shift) * (x - shift), axis=axis, keepdims=keep_dims)
346 m_ss = np.sum(x, axis=axis, keepdims=keep_dims)
347 v_ss = np.sum(x * x, axis=axis, keepdims=keep_dims)
468 keepdims=keep_dims) / num_elements
472 keepdims=keep_dims) / num_elements
499 keepdims=keep_dims) / num_elements
503 keepdims=keep_dims) / num_elements
668 return np.sum(weights_numpy * v, axis=ax, keepdims=keep_dims)
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| /external/tensorflow/tensorflow/python/kernel_tests/ |
| D | reduction_ops_test.py | 113 def _tf_reduce(self, x, reduction_axes, keepdims): argument
116 def _np_reduce(self, x, reduction_axes, keepdims): argument
131 def _compare(self, x, reduction_axes, keepdims, feed_dict=None): argument
132 np_ans = self._np_reduce(x, reduction_axes, keepdims)
134 tf_ans = self._tf_reduce(x, reduction_axes, keepdims)
143 self._compare(x, reduction_axes, keepdims=False, feed_dict=feed_dict)
144 self._compare(x, reduction_axes, keepdims=True, feed_dict=feed_dict)
174 def _tf_reduce(self, x, reduction_axes, keepdims): argument
175 return math_ops.reduce_sum(x, reduction_axes, keepdims)
177 def _np_reduce(self, x, reduction_axes, keepdims): argument
[all …]
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| D | reduction_ops_test_big.py | 30 def _tf_reduce(self, x, reduction_axes, keepdims): argument
37 def _tf_reduce_max(self, x, reduction_axes, keepdims): argument
38 return math_ops.reduce_max(x, reduction_axes, keepdims)
40 def _tf_reduce_all(self, x, reduction_axes, keepdims): argument
41 return math_ops.reduce_all(x, reduction_axes, keepdims)
43 def _tf_reduce_mean(self, x, reduction_axes, keepdims): argument
44 return math_ops.reduce_mean(x, reduction_axes, keepdims)
46 def _tf_reduce_sum(self, x, reduction_axes, keepdims): argument
47 return math_ops.reduce_sum(x, reduction_axes, keepdims)
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| /external/tensorflow/tensorflow/contrib/bayesflow/python/kernel_tests/ |
| D | mcmc_diagnostics_test.py | 384 def check_versus_numpy(self, x_, axis, biased, keepdims): argument
390 x, axis=axis, biased=biased, keepdims=keepdims)
391 np_var = np.var(x_, axis=axis, ddof=0 if biased else 1, keepdims=keepdims)
407 self.check_versus_numpy(x_=-1.234, axis=None, biased=True, keepdims=False)
411 self.check_versus_numpy(x_=-1.234, axis=None, biased=False, keepdims=False)
415 x_=rng.randn(2, 3, 4), axis=None, biased=True, keepdims=False)
419 x_=rng.randn(2, 3, 4), axis=1, biased=True, keepdims=True)
423 x_=rng.randn(2, 3, 4, 5), axis=1, biased=True, keepdims=True)
427 x_=rng.randn(2, 3, 4, 5), axis=1, biased=False, keepdims=False)
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| /external/tensorflow/tensorflow/contrib/bayesflow/python/ops/ |
| D | mcmc_diagnostics_impl.py | 351 math_ops.reduce_mean(state, sample_axis, keepdims=True),
355 _reduce_variance(state, sample_axis, keepdims=True, biased=True),
366 def _reduce_variance(x, axis=None, biased=True, keepdims=False): argument
369 mean = math_ops.reduce_mean(x, axis=axis, keepdims=True)
371 math_ops.squared_difference(x, mean), axis=axis, keepdims=keepdims)
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| /external/tensorflow/tensorflow/contrib/losses/python/metric_learning/ |
| D | metric_loss_ops.py | 56 keepdims=True),
61 keepdims=True)) - 2.0 * math_ops.matmul(
135 axis_minimums = math_ops.reduce_min(data, dim, keepdims=True)
138 data - axis_minimums, mask), dim, keepdims=True) + axis_minimums
154 axis_maximums = math_ops.reduce_max(data, dim, keepdims=True)
157 data - axis_maximums, mask), dim, keepdims=True) + axis_maximums
206 mask, dtype=dtypes.float32), 1, keepdims=True),
293 labels_remapped /= math_ops.reduce_sum(labels_remapped, 1, keepdims=True)
398 labels_remapped /= math_ops.reduce_sum(labels_remapped, 1, keepdims=True)
451 row_minimums = math_ops.reduce_min(diff, 1, keepdims=True)
[all …]
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| /external/tensorflow/tensorflow/python/keras/_impl/keras/ |
| D | constraints.py | 68 norms = K.sqrt(K.sum(K.square(w), axis=self.axis, keepdims=True))
108 K.epsilon() + K.sqrt(K.sum(K.square(w), axis=self.axis, keepdims=True)))
151 norms = K.sqrt(K.sum(K.square(w), axis=self.axis, keepdims=True))
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| D | backend.py | 1474 def max(x, axis=None, keepdims=False): argument
1488 return math_ops.reduce_max(x, axis, keepdims)
1492 def min(x, axis=None, keepdims=False): argument
1506 return math_ops.reduce_min(x, axis, keepdims)
1510 def sum(x, axis=None, keepdims=False): argument
1524 return math_ops.reduce_sum(x, axis, keepdims)
1528 def prod(x, axis=None, keepdims=False): argument
1542 return math_ops.reduce_prod(x, axis, keepdims)
1572 def var(x, axis=None, keepdims=False): argument
1591 devs_squared, axis, keepdims)
[all …]
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| D | activations.py | 48 e = K.exp(x - K.max(x, axis=axis, keepdims=True))
49 s = K.sum(e, axis=axis, keepdims=True)
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| /external/tensorflow/tensorflow/contrib/learn/python/learn/estimators/ |
| D | kmeans_test.py | 50 return x / np.sqrt(np.sum(x * x, axis=-1, keepdims=True))
222 keepdims=True) - 2 * np.dot(points, np.transpose(clusters)) +
223 np.transpose(np.sum(np.square(clusters), axis=1, keepdims=True)))
327 np.mean(normalize(self.points)[0:4, :], axis=0, keepdims=True))[
330 np.mean(normalize(self.points)[4:, :], axis=0, keepdims=True))[
394 np.mean(normalize(points)[0:2, :], axis=0, keepdims=True))[0],
396 np.mean(normalize(points)[2:4, :], axis=0, keepdims=True))[0],
398 np.mean(normalize(points)[4:, :], axis=0, keepdims=True))[0]
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| /external/tensorflow/tensorflow/contrib/factorization/python/ops/ |
| D | kmeans_test.py | 48 return x / np.sqrt(np.sum(x * x, axis=-1, keepdims=True))
213 np.sum(np.square(points), axis=1, keepdims=True) -
215 np.sum(np.square(clusters), axis=1, keepdims=True)))
320 keepdims=True))[0],
323 keepdims=True))[0]
387 np.mean(normalize(points)[0:2, :], axis=0, keepdims=True))[0],
389 np.mean(normalize(points)[2:4, :], axis=0, keepdims=True))[0],
391 keepdims=True))[0]
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| /external/tensorflow/tensorflow/python/layers/ |
| D | normalization_test.py | 1101 means = np.mean(sub_batched, axis=0, keepdims=True)
1102 variances = np.var(sub_batched, axis=0, keepdims=True)
1104 avg_means = np.mean(means, axis=1, keepdims=True)
1105 avg_variances = np.mean(variances, axis=1, keepdims=True)
1154 means = np.mean(sub_batched, axis=(0, 2, 3), keepdims=True)
1155 variances = np.var(sub_batched, axis=(0, 2, 3), keepdims=True)
1157 avg_means = np.mean(means, axis=1, keepdims=True)
1158 avg_variances = np.mean(variances, axis=1, keepdims=True)
1208 means = np.mean(sub_batched, axis=(0, 3, 4), keepdims=True)
1209 variances = np.var(sub_batched, axis=(0, 3, 4), keepdims=True)
[all …]
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| D | maxout.py | 109 gen_array_ops.reshape(inputs, shape), -1, keepdims=False)
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| /external/toolchain-utils/cros_utils/ |
| D | stats.py | 643 def lcov(x, y, keepdims=0): argument
2029 def ageometricmean(inarray, dimension=None, keepdims=0): argument
2052 if keepdims == 1:
2064 if keepdims == 1:
2071 def aharmonicmean(inarray, dimension=None, keepdims=0): argument
2092 if keepdims == 1:
2108 if keepdims == 1:
2117 if keepdims == 1:
2124 def amean(inarray, dimension=None, keepdims=0): argument
2146 if keepdims == 1:
[all …]
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| /external/tensorflow/tensorflow/tools/api/golden/ |
| D | tensorflow.keras.backend.pbtxt | 13 …argspec: "args=[\'x\', \'axis\', \'keepdims\'], varargs=None, keywords=None, defaults=[\'None\', \…
17 …argspec: "args=[\'x\', \'axis\', \'keepdims\'], varargs=None, keywords=None, defaults=[\'None\', \…
273 …argspec: "args=[\'x\', \'axis\', \'keepdims\'], varargs=None, keywords=None, defaults=[\'None\', \…
281 …argspec: "args=[\'x\', \'axis\', \'keepdims\'], varargs=None, keywords=None, defaults=[\'None\', \…
285 …argspec: "args=[\'x\', \'axis\', \'keepdims\'], varargs=None, keywords=None, defaults=[\'None\', \…
345 …argspec: "args=[\'x\', \'axis\', \'keepdims\'], varargs=None, keywords=None, defaults=[\'None\', \…
493 …argspec: "args=[\'x\', \'axis\', \'keepdims\'], varargs=None, keywords=None, defaults=[\'None\', \…
501 …argspec: "args=[\'x\', \'axis\', \'keepdims\'], varargs=None, keywords=None, defaults=[\'None\', \…
541 …argspec: "args=[\'x\', \'axis\', \'keepdims\'], varargs=None, keywords=None, defaults=[\'None\', \…
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| /external/tensorflow/tensorflow/contrib/distributions/python/kernel_tests/ |
| D | relaxed_onehot_categorical_test.py | 111 p = np.exp(logits)/np.sum(np.exp(logits), axis=1, keepdims=True)
113 term2 = np.sum(p/(np.power(x, temperature)), axis=1, keepdims=True)
114 term3 = np.prod(p/(np.power(x, temperature+1)), axis=1, keepdims=True)
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| D | sample_stats_test.py | 89 x -= x.mean(axis=axis, keepdims=True)
94 ).mean(axis=axis, keepdims=True))
320 x, q=q, interpolation=self._interpolation, keepdims=True, axis=0)
353 keepdims=True)
387 keepdims=True)
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| /external/tensorflow/tensorflow/contrib/kfac/python/kernel_tests/ |
| D | loss_functions_test.py | 76 probs = np.exp(logits) / np.sum(np.exp(logits), axis=1, keepdims=True)
144 keepdims=True))
182 probs = np.exp(logits) / np.sum(np.exp(logits), axis=1, keepdims=True)
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| /external/tensorflow/tensorflow/contrib/boosted_trees/python/utils/ |
| D | losses.py | 81 normalizers = math_ops.reduce_sum(unnormalized_probs, 1, keepdims=True)
123 math_ops.square(predictions - labels), 1, keepdims=True)
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| /external/tensorflow/tensorflow/python/ops/losses/ |
| D | losses_impl.py | 147 keepdims=True, name=scope)
314 losses = 1 - math_ops.reduce_sum(radial_diffs, axis=(axis,), keepdims=True)
546 keepdims=True)
553 diffs, reduction_indices=reduction_indices, keepdims=True)
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| /external/tensorflow/tensorflow/python/ops/distributions/ |
| D | util.py | 1044 lswe = math_ops.reduce_logsumexp(logx, axis=axis, keepdims=keep_dims)
1051 max_log_absw_x = math_ops.reduce_max(log_absw_x, axis=axis, keepdims=True)
1065 keepdims=keep_dims)
1174 probs /= np.linalg.norm(probs, ord=1, keepdims=True)
1183 probs /= linalg_ops.norm(probs, ord=1, axis=-1, keepdims=True,
|