/external/tensorflow/tensorflow/python/kernel_tests/ |
D | parameterized_truncated_normal_op_test.py | 84 moments = [0.0] * (max_moment + 1) 87 for k in range(len(moments)): 88 moments[k] += value 90 for i in range(len(moments)): 91 moments[i] /= len(samples) 92 return moments 123 moments = calculate_moments(samples, self.max_moment) 126 for i in range(1, len(moments)): 128 z_test(moments, expected_moments, i, num_samples), self.z_limit)
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/external/tensorflow/tensorflow/core/lib/random/ |
D | random_distributions_test.cc | 79 std::vector<double> moments(max_moments + 1); in CheckSamplesMoments() local 80 double* const moments_data = &moments[0]; in CheckSamplesMoments() 102 moments[i] /= moments_sample_count[i]; in CheckSamplesMoments() 126 fabs((moments[i] - moments_i_mean) / sqrt(total_variance)); in CheckSamplesMoments() 132 << " measured moments: " << moments[i] in CheckSamplesMoments()
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/external/tensorflow/tensorflow/python/kernel_tests/random/ |
D | random_poisson_test.py | 69 moments = [0] * (max_moment + 1) 78 moments[i] += moment 82 moments[i] /= moments_sample_count[i] 102 (moments[i] - moments_i_mean) / np.sqrt(total_variance))
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D | random_gamma_test.py | 94 moments = [0] * (max_moment + 1) 103 moments[i] += moment 107 moments[i] /= moments_sample_count[i] 132 (moments[i] - moments_i_mean) / math.sqrt(total_variance))
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/external/tensorflow/tensorflow/core/api_def/base_api/ |
D | api_def_BatchNormWithGlobalNormalization.pbtxt | 13 This is the first output from tf.nn.moments, 21 This is the second output from tf.nn.moments,
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D | api_def_BatchNormWithGlobalNormalizationGrad.pbtxt | 13 This is the first output from tf.nn.moments, 21 This is the second output from tf.nn.moments,
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D | api_def_QuantizedBatchNormWithGlobalNormalization.pbtxt | 25 This is the first output from tf.nn.moments, 45 This is the second output from tf.nn.moments,
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/external/tensorflow/tensorflow/contrib/nn/python/ops/ |
D | alpha_dropout_test.py | 39 t_mean, t_std = nn_impl.moments(t, axes=[0, 1]) 40 output_mean, output_std = nn_impl.moments(output, axes=[0, 1])
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/external/tensorflow/tensorflow/contrib/gan/python/features/python/ |
D | virtual_batchnorm_test.py | 59 mom_mean, mom_var = nn.moments(tensors, axes) 82 mom_mean, mom_variance = nn.moments(full_batch, reduction_axes)
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/external/tensorflow/tensorflow/contrib/layers/python/layers/ |
D | normalization.py | 153 mean, variance = nn.moments(inputs, moments_axes, keep_dims=True)
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D | layers.py | 774 mean, variance = nn.moments(inputs, moments_axes, keep_dims=True) 778 mean, variance = nn.moments(inputs, moments_axes) 2173 mean, variance = nn.moments(inputs, norm_axes, keep_dims=True)
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/external/ImageMagick/MagickCore/ |
D | statistic.c | 1784 *moments; in GetImagePerceptualHash() local 1811 moments=GetImageMoments(hash_image,exception); in GetImagePerceptualHash() 1813 if (moments == (ChannelMoments *) NULL) in GetImagePerceptualHash() 1822 (-MagickLog10(moments[channel].invariant[i])); in GetImagePerceptualHash() 1823 moments=(ChannelMoments *) RelinquishMagickMemory(moments); in GetImagePerceptualHash() 1842 moments=GetImageMoments(hash_image,exception); in GetImagePerceptualHash() 1844 if (moments == (ChannelMoments *) NULL) in GetImagePerceptualHash() 1853 (-MagickLog10(moments[channel].invariant[i])); in GetImagePerceptualHash() 1854 moments=(ChannelMoments *) RelinquishMagickMemory(moments); in GetImagePerceptualHash()
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/external/tensorflow/tensorflow/python/keras/_impl/keras/ |
D | optimizers.py | 190 moments = [K.zeros(shape) for shape in shapes] 191 self.weights = [self.iterations] + moments 192 for p, g, m in zip(params, grads, moments):
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/external/tensorflow/tensorflow/contrib/gan/python/eval/python/ |
D | sliced_wasserstein_impl.py | 127 mean, variance = nn.moments(patches, [1, 2, 3], keep_dims=True)
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/external/tensorflow/tensorflow/python/ops/ |
D | batch_norm_benchmark.py | 99 mean, variance = nn_impl.moments(tensor, axes, keep_dims=keep_dims)
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D | nn_impl.py | 652 def moments( function
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D | nn_fused_batchnorm_test.py | 100 mean, var = nn_impl.moments(
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D | nn_batchnorm_test.py | 452 return nn_impl.moments(x, axes, keep_dims=keep_dims)
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/external/python/cpython2/Doc/library/ |
D | htmllib.rst | 53 The parser will call these at appropriate moments: :meth:`start_tag` or
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/external/tensorflow/tensorflow/python/layers/ |
D | normalization.py | 563 mean, variance = nn.moments(inputs, reduction_axes, keep_dims=keep_dims)
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/external/valgrind/ |
D | README_MISSING_SYSCALL_OR_IOCTL | 197 much the same as writing ioctl wrappers. Please take a few moments to study
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/external/tensorflow/tensorflow/contrib/timeseries/python/timeseries/ |
D | math_utils.py | 860 mean, variance = nn.moments(
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/external/tensorflow/tensorflow/tools/api/golden/ |
D | tensorflow.nn.pbtxt | 216 name: "moments"
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/external/ImageMagick/Magick++/lib/Magick++/ |
D | Image.h | 1079 ImageMoments moments(void) const;
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/external/tensorflow/tensorflow/docs_src/api_guides/python/ |
D | nn.md | 206 * @{tf.nn.moments}
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