| /external/tensorflow/tensorflow/python/ops/ |
| nn_batchnorm_test.py | 250 """Test for tf.nn.moments(..., keep_dims=True / False).
340 def _npSuffStats(self, x, axes, shift, keep_dims):
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)
352 if not keep_dims:
356 def _opSuffStats(self, x, axes, shift, keep_dims):
357 return nn_impl.sufficient_statistics(x, axes, shift, keep_dims)
359 def _testSuffStats(self, x_shape, axes, shift, keep_dims, has_shape)
[all...] |
| string_ops.py | 127 keep_dims=False,
136 keep_dims=keep_dims,
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| batch_norm_benchmark.py | 84 keep_dims = mode == "py" or mode == "slow"
85 if keep_dims:
99 mean, variance = nn_impl.moments(tensor, axes, keep_dims=keep_dims)
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| sparse_ops.py | 795 def sparse_reduce_max(sp_input, axis=None, keep_dims=False,
804 `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
805 `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained
821 tf.sparse_reduce_max(x, 1, keep_dims=True) ==> [[2], [3]]
829 keep_dims: If true, retain reduced dimensions with length 1.
837 math_ops._ReductionDims(sp_input, axis, reduction_axes), keep_dims)
843 keep_dims=False,
852 `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
853 `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained
864 keep_dims: If true, retain reduced dimensions with length 1
[all...] |
| math_ops.py | [all...] |
| nn_impl.py | 570 def sufficient_statistics(x, axes, shift=None, keep_dims=False, name=None):
583 keep_dims: produce statistics with the same dimensionality as the input.
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")
657 keep_dims=False):
679 keep_dims: produce moments with the same dimensionality as the input.
697 if not keep_dims:
708 def weighted_moments(x, axes, frequency_weights, name=None, keep_dims=False):
718 keep_dims: Produce moments with the same dimensionality as the input.
738 # Note that we use keep_dims=True for our reductions regardless of the arg
[all...] |
| /external/tensorflow/tensorflow/contrib/lite/kernels/ |
| mean_test.cc | 53 std::initializer_list<int> axis, bool keep_dims) {
58 CreateMeanOptions(builder_, keep_dims).Union());
67 const TensorData& axis, bool keep_dims) {
72 CreateMeanOptions(builder_, keep_dims).Union());
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| mean.cc | 74 if (op_context->params->keep_dims) {
195 op_context.params->keep_dims, GetTensorData<int>(temp_index), \
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| /external/tensorflow/tensorflow/contrib/bayesflow/python/ops/ |
| monte_carlo_impl.py | 198 axis=0, keep_dims=False, name=None):
312 keep_dims: If True, retains averaged dimensions using size `1`.
331 return math_ops.reduce_mean(f(samples), axis=axis, keep_dims=keep_dims)
351 return math_ops.reduce_mean(fx, axis=axis, keep_dims=keep_dims)
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| /external/tensorflow/tensorflow/python/kernel_tests/ |
| norm_op_test.py | 72 tf_matrix, ord=ord_, axis=axis_, keep_dims=keep_dims_)
77 tf_matrix, ord=ord_, axis=axis_, keep_dims=keep_dims_)
111 for keep_dims in False, True:
114 keep_dims, use_static_shape)
116 _GetNormOpTest(dtype, shape, ord, axis, keep_dims,
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| reduce_join_op_test.py | 104 keep_dims=False,
113 keep_dims: Whether or not to retain reduced dimensions.
120 keep_dims=keep_dims,
146 keep_dims=False,
151 keep_dims=True,
159 keep_dims=True,
297 keep_dims=True)
303 keep_dims=True)
309 keep_dims=True, reduction_indices=None
[all...] |
| sparse_ops_test.py | 591 def _compare(self, sp_t, reduction_axes, ndims, keep_dims, do_sum):
597 np_ans = np.sum(np_ans, keepdims=keep_dims)
599 np_ans = np.max(np_ans, keepdims=keep_dims)
610 np_ans = np.sum(np_ans, axis=ra, keepdims=keep_dims)
612 np_ans = np.max(np_ans, axis=ra, keepdims=keep_dims)
617 keep_dims)
620 keep_dims)
626 keep_dims)
630 keep_dims)
[all...] |
| /external/tensorflow/tensorflow/core/kernels/ |
| reduction_ops_common.cc | 78 const bool keep_dims) {
92 } else if (keep_dims) {
|
| reduce_join_op.cc | 100 const TensorShape& input_shape, bool keep_dims) {
104 if (keep_dims) output_shape.AddDim(1);
119 OP_REQUIRES_OK(ctx, ctx->GetAttr("keep_dims", &keep_dims_));
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| /external/tensorflow/tensorflow/contrib/learn/python/learn/ops/ |
| embeddings_ops.py | 60 ids, math_ops.reduce_prod(shape, keep_dims=True))
|
| /external/tensorflow/tensorflow/contrib/lite/ |
| builtin_op_data.h | 195 bool keep_dims; member in struct:__anon39216
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| /external/tensorflow/tensorflow/contrib/factorization/python/ops/ |
| gmm_ops.py | 57 x -= math_ops.reduce_mean(x, 0, keep_dims=True)
60 math_ops.square(x), 0, keep_dims=True) / (num_points - 1)
316 math_ops.log(self._covs + 1e-3), 1, keep_dims=True)
354 self._probs[shard_id], axis=1, keep_dims=True)
378 self._w[shard_id], 0, keep_dims=True)
457 math_ops.reduce_logsumexp(op, axis=2, keep_dims=True), axis=0)
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| /external/tensorflow/tensorflow/contrib/gan/python/features/python/ |
| virtual_batchnorm_impl.py | 67 shift = array_ops.stop_gradient(math_ops.reduce_mean(y, axes, keep_dims=True))
69 shifted_mean = math_ops.reduce_mean(y - shift, axes, keep_dims=True)
71 mean_squared = math_ops.reduce_mean(math_ops.square(y), axes, keep_dims=True)
|
| /external/tensorflow/tensorflow/contrib/distributions/python/ops/bijectors/ |
| softmax_centered.py | 235 math_ops.reduce_logsumexp(x, axis=-1, keep_dims=True))
239 keep_dims=True))
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| /external/tensorflow/tensorflow/contrib/layers/python/layers/ |
| normalization.py | 153 mean, variance = nn.moments(inputs, moments_axes, keep_dims=True)
|
| /external/tensorflow/tensorflow/contrib/lite/toco/ |
| model.h | 1052 bool keep_dims = false; member in struct:toco::TensorFlowSumOperator
1205 bool keep_dims = false; member in struct:toco::TensorFlowMaxOperator
1218 bool keep_dims = false; member in struct:toco::TensorFlowMinOperator
1389 bool keep_dims = false; member in struct:toco::MeanOperator
[all...] |
| /external/tensorflow/tensorflow/contrib/nn/python/ops/ |
| sampling_ops.py | 91 keep_dims=False)
|
| /external/tensorflow/tensorflow/contrib/gan/python/eval/python/ |
| sliced_wasserstein_impl.py | 127 mean, variance = nn.moments(patches, [1, 2, 3], keep_dims=True)
164 math_ops.reduce_sum(math_ops.square(proj), 0, keep_dims=True))
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| /external/tensorflow/tensorflow/python/layers/ |
| normalization.py | 562 keep_dims = self.virtual_batch_size is not None or len(self.axis) > 1
563 mean, variance = nn.moments(inputs, reduction_axes, keep_dims=keep_dims)
594 axis=1, keep_dims=True)
596 axis=1, keep_dims=True)
[all...] |
| /external/tensorflow/tensorflow/contrib/kfac/python/ops/ |
| loss_functions.py | 664 vector * probs, axis=-1, keep_dims=True)
670 sqrt_probs * vector, axis=-1, keep_dims=True)
676 probs * vector, axis=-1, keep_dims=True)
|
