Searched refs:pop_mean (Results 1 – 4 of 4) sorted by relevance
| /external/tensorflow/tensorflow/python/ops/ |
| D | nn_grad.py | 895 pop_mean = op.inputs[3]
909 "reserve_space_1": pop_mean,
943 pop_mean, argument
1016 shape = [1, array_ops.size(pop_mean), 1, 1]
1017 pop_mean = array_ops.reshape(pop_mean, shape)
1022 shape = [1, array_ops.size(pop_mean), 1, 1, 1]
1023 pop_mean = array_ops.reshape(pop_mean, shape)
1030 grad_y * (x - pop_mean) * var_rsqrt, axis=reduce_axis)
1056 pop_mean = op.inputs[3]
1066 grad_y, x, scale, pop_mean, pop_var, epsilon, data_format, is_training)
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| D | nn_fused_batchnorm_test.py | 241 pop_mean = None
244 pop_mean = np.random.random_sample(scale_shape).astype(scale_dtype)
250 mean=pop_mean,
267 mean=pop_mean,
307 pop_mean = None
310 pop_mean = np.random.random_sample(scale_shape).astype(scale_dtype)
316 mean=pop_mean,
328 grad_internal = nn_grad._BatchNormGrad(grad_y, x, scale, pop_mean,
358 mean=pop_mean,
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| /external/tensorflow/tensorflow/core/kernels/ |
| D | fused_batch_norm_op.cu.cc | 46 typename TTypes<U>::ConstVec pop_mean(pop_mean_input.vec<U>()); in operator ()() local
52 const int depth = pop_mean.dimension(0); in operator ()()
97 pop_mean.reshape(one_by_depth).broadcast(rest_by_one))) in operator ()()
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| D | fused_batch_norm_op.cc | 527 typename TTypes<U>::ConstVec pop_mean(pop_mean_input.vec<U>()); in operator ()() local
532 const int depth = pop_mean.dimension(0); in operator ()()
598 pop_mean.reshape(one_by_depth).broadcast(rest_by_one)); in operator ()()
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