| /external/tensorflow/tensorflow/python/ops/ragged/ |
| D | ragged_reduce_op_test.py | 55 ragged_reduce_op=ragged_math_ops.reduce_sum,
62 ragged_reduce_op=ragged_math_ops.reduce_sum,
69 ragged_reduce_op=ragged_math_ops.reduce_sum,
76 ragged_reduce_op=ragged_math_ops.reduce_sum,
157 ragged_reduce_op=ragged_math_ops.reduce_sum,
164 ragged_reduce_op=ragged_math_ops.reduce_sum,
171 ragged_reduce_op=ragged_math_ops.reduce_sum,
178 ragged_reduce_op=ragged_math_ops.reduce_sum,
272 ragged_reduce_op=ragged_math_ops.reduce_sum,
303 ragged_reduce_op=ragged_math_ops.reduce_sum,
[all …]
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| D | ragged_map_fn_op_test.py | 61 fn=lambda x: array_ops.stack([mo.reduce_mean(x), mo.reduce_sum(x)]),
106 fn=lambda x: ragged_math_ops.reduce_sum(x, axis=1),
114 fn=lambda x: ragged_math_ops.reduce_sum(x, axis=0),
122 fn=ragged_math_ops.reduce_sum,
198 return mo.reduce_sum(f['batman']) + mo.reduce_sum(f['robin'])
280 fn = lambda x: ragged_math_ops.reduce_sum(x, axis=0)
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| D | ragged_math_ops.py | 567 def reduce_sum(input_tensor, axis=None, keepdims=None, name=None): function
571 reduce_op=math_ops.reduce_sum,
615 total = reduce_sum(input_tensor, axis, keepdims)
623 count = reduce_sum(ones, axis, keepdims)
650 reduce_sum(_cast(input_tensor, dtypes.int32), axis, keepdims),
662 _set_ragged_reduce_docstring(reduce_sum, 'sum', 'summed', '0',
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| /external/tensorflow/tensorflow/python/ops/ |
| D | random_grad.py | 76 return (None, math_ops.reduce_sum(
120 math_ops.reduce_sum(
206 grad_mean = math_ops.reduce_sum(grad * dmean, axis=extra_dims)
207 grad_stddev = math_ops.reduce_sum(grad * dstddev, axis=extra_dims)
208 grad_minval = math_ops.reduce_sum(grad * dminval, axis=extra_dims)
209 grad_maxval = math_ops.reduce_sum(grad * dmaxval, axis=extra_dims)
221 math_ops.reduce_sum(grad_mean, axis=rmean, keepdims=True), mean_shape)
224 math_ops.reduce_sum(grad_stddev, axis=rstddev, keepdims=True),
228 math_ops.reduce_sum(grad_minval, axis=rminval, keepdims=True),
232 math_ops.reduce_sum(grad_maxval, axis=rmaxval, keepdims=True),
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| D | math_grad.py | 236 math_ops.reduce_sum(indicators, op.inputs[1]), output_shape_kept_dims)
717 return (array_ops.reshape(math_ops.reduce_sum(partial_x * grad, rx), sx),
718 array_ops.reshape(math_ops.reduce_sum(partial_y * grad, ry), sy))
734 return (array_ops.reshape(math_ops.reduce_sum(partial_x * grad, rx), sx),
735 array_ops.reshape(math_ops.reduce_sum(partial_y * grad, ry), sy))
751 return (array_ops.reshape(math_ops.reduce_sum(partial_x * grad, rx), sx),
752 array_ops.reshape(math_ops.reduce_sum(partial_y * grad, ry), sy))
1082 return (array_ops.reshape(math_ops.reduce_sum(partial_a * grad, ra), sa),
1083 array_ops.reshape(math_ops.reduce_sum(partial_x * grad, rx), sx))
1118 array_ops.reshape(math_ops.reduce_sum(partial_x * grad, rx), sx))
[all …]
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| D | clip_ops.py | 148 gx = array_ops.reshape(math_ops.reduce_sum(xgrad, rx), sx)
149 gy = array_ops.reshape(math_ops.reduce_sum(ygrad, ry), sy)
150 gz = array_ops.reshape(math_ops.reduce_sum(zgrad, rz), sz)
220 l2sum = math_ops.reduce_sum(values * values, axes, keepdims=True)
279 half_squared_norm = math_ops.reduce_sum(array_ops.stack(half_squared_norms))
421 math_ops.reduce_sum(t * t, math_ops.range(array_ops.rank(t))))
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| D | linalg_grad.py | 367 math_ops.reduce_sum(grad_x, bx_start + rx), x_shape)
369 math_ops.reduce_sum(grad_y, by_start + ry), y_shape)
672 grad_a = array_ops.reshape(math_ops.reduce_sum(grad_a, axis=ra), a_shape)
673 grad_b = array_ops.reshape(math_ops.reduce_sum(grad_b, axis=rb), b_shape)
702 grad_a = array_ops.reshape(math_ops.reduce_sum(grad_a, axis=ra), a_shape)
703 grad_b = array_ops.reshape(math_ops.reduce_sum(grad_b, axis=rb), b_shape)
965 superdiag_grad = math_ops.reduce_sum(_LeftShift(rhs_conj) * grad, axis=-1)
966 maindiag_grad = math_ops.reduce_sum(rhs_conj * grad, axis=-1)
967 subdiag_grad = math_ops.reduce_sum(_RightShift(rhs_conj) * grad, axis=-1)
1044 diag = math_ops.reduce_sum(x * y_tr, axis=-1)
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| D | metrics_impl.py | 376 num_values = math_ops.reduce_sum(weights)
378 update_total_op = state_ops.assign_add(total, math_ops.reduce_sum(values))
581 math_ops.reduce_sum(
593 math_ops.reduce_sum(
605 math_ops.reduce_sum(
617 math_ops.reduce_sum(
800 return math_ops.reduce_sum(
829 return math_ops.reduce_sum(
834 return math_ops.reduce_sum(
839 return math_ops.reduce_sum(
[all …]
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| D | nccl_ops_test.py | 153 self._Test(partial(_NcclReduce, nccl_ops.reduce_sum), lambda x, y: x + y)
156 self._TestGradient(partial(_NcclReduce, nccl_ops.reduce_sum),
189 single_reduce_tensors = _NcclReduce(nccl_ops.reduce_sum, tensors, devices)
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| /external/tensorflow/tensorflow/python/keras/layers/preprocessing/ |
| D | reduction.py | 39 return math_ops.reduce_sum
100 input_sum = math_ops.reduce_sum(weighted_inputs, axis=self.axis)
101 weight_sum = math_ops.reduce_sum(weights, axis=self.axis)
110 input_sum = math_ops.reduce_sum(weighted_inputs, axis=self.axis)
112 squared_weights_sum = math_ops.reduce_sum(squared_weights, axis=self.axis)
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| /external/tensorflow/tensorflow/python/ops/distributions/ |
| D | dirichlet.py | 201 self._total_concentration = math_ops.reduce_sum(self._concentration, -1)
240 return gamma_sample / math_ops.reduce_sum(gamma_sample, -1, keepdims=True)
252 return math_ops.reduce_sum(math_ops.xlogy(self.concentration - 1., x), -1)
263 - math_ops.reduce_sum(
333 math_ops.reduce_sum(x, -1),
406 math_ops.reduce_sum(d1.concentration, axis=-1, keepdims=True))
410 return (math_ops.reduce_sum(concentration_diff * digamma_diff, axis=-1) -
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| D | multinomial.py | 266 x = math_ops.reduce_sum(array_ops.one_hot(x, depth=k), axis=-2) # [n, k]
285 return math_ops.reduce_sum(counts * nn_ops.log_softmax(self.logits), -1)
314 self.total_count, math_ops.reduce_sum(counts, -1),
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| D | dirichlet_multinomial.py | 222 self._total_concentration = math_ops.reduce_sum(self._concentration, -1)
275 x = math_ops.reduce_sum(array_ops.one_hot(draws, depth=k), -2)
353 self.total_count, math_ops.reduce_sum(counts, -1),
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| /external/tensorflow/tensorflow/python/kernel_tests/ |
| D | reduce_benchmark_test.py | 59 backprop.gradients_function(math_ops.reduce_sum, [0])(tensor)
68 backprop.gradients_function(math_ops.reduce_sum, [0])(tensor)
80 reduction = math_ops.reduce_sum(tensor)
97 reduction = math_ops.reduce_sum(tensor)
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| /external/tensorflow/tensorflow/python/eager/ |
| D | tape_test.py | 41 r = math_ops.reduce_sum(mm)
92 return math_ops.reduce_sum(mm)
106 return math_ops.reduce_sum(mm)
120 return math_ops.reduce_sum(mm)
135 return r + math_ops.reduce_sum(mm)
144 tf_rr = 2 * math_ops.reduce_sum(tf_mm)
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| /external/tensorflow/tensorflow/python/keras/engine/ |
| D | control_flow_test.py | 41 if math_ops.reduce_sum(inputs) > 0:
75 if math_ops.reduce_sum(inputs) > 0:
90 if math_ops.reduce_sum(inputs) > 0:
101 if math_ops.reduce_sum(inputs) > 0:
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| /external/tensorflow/tensorflow/python/keras/ |
| D | regularizers.py | 251 regularization += self.l1 * math_ops.reduce_sum(math_ops.abs(x))
253 regularization += self.l2 * math_ops.reduce_sum(math_ops.square(x))
288 return self.l1 * math_ops.reduce_sum(math_ops.abs(x))
322 return self.l2 * math_ops.reduce_sum(math_ops.square(x))
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| /external/tensorflow/tensorflow/python/ops/losses/ |
| D | losses_impl.py | 87 total_loss = math_ops.reduce_sum(losses)
125 return math_ops.reduce_sum(
130 return math_ops.reduce_sum(present, name=scope)
192 loss = math_ops.reduce_sum(weighted_losses)
195 loss, math_ops.reduce_sum(array_ops.ones_like(losses) * weights))
314 losses = 1 - math_ops.reduce_sum(radial_diffs, axis=(axis,), keepdims=True)
573 sum_squares_diff_per_batch = math_ops.reduce_sum(
582 sum_diff = math_ops.reduce_sum(diffs, axis=axis, keepdims=True)
591 loss = math_ops.reduce_sum(weighted_losses)
594 math_ops.reduce_sum(num_present_per_batch) > 0,
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| /external/tensorflow/tensorflow/python/grappler/ |
| D | layout_optimizer_test.py | 354 output = math_ops.reduce_sum(split[0])
420 reduce_sum = math_ops.reduce_sum(conv)
421 output = array_ops.identity(reduce_sum)
481 reduce_sum = math_ops.reduce_sum(conv, axis=[1, 2])
482 squeeze = array_ops.squeeze(reduce_sum)
512 reduce_sum = math_ops.reduce_sum(conv, axis=[1, 2], keepdims=True)
513 squeeze = array_ops.squeeze(reduce_sum, axis=[1, 2])
543 reduce_sum = math_ops.reduce_sum(conv, axis=[0, 1, 2], keepdims=True)
544 squeeze = array_ops.squeeze(reduce_sum, axis=[0, 1, 2])
574 reduce_sum = math_ops.reduce_sum(conv, axis=[1, 2, 3])
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| /external/tensorflow/tensorflow/python/keras/utils/ |
| D | kernelized_utils.py | 85 diff_squared_l2_norm = math_ops.reduce_sum(
115 diff_l1_norm = math_ops.reduce_sum(
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| /external/tensorflow/tensorflow/python/keras/layers/preprocessing/benchmarks/ |
| D | normalization_adapt_benchmark.py | 46 sum_v = math_ops.reduce_sum(values, axis=0)
47 sum_v2 = math_ops.reduce_sum(math_ops.square(values), axis=0)
49 batch_size = math_ops.reduce_sum(ones, axis=0)
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| /external/tensorflow/tensorflow/core/api_def/base_api/ |
| D | api_def_StopGradient.pbtxt | 22 denominator = tf.reduce_sum(numerator)
35 denominator = tf.reduce_sum(numerator)
49 denominator = tf.reduce_sum(numerator)
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| /external/tensorflow/tensorflow/python/keras/legacy_tf_layers/ |
| D | convolutional_test.py | 215 reg = lambda x: 0.1 * math_ops.reduce_sum(x)
228 reg = lambda x: 0.1 * math_ops.reduce_sum(x)
313 k_constraint = lambda x: x / math_ops.reduce_sum(x)
324 k_constraint = lambda x: x / math_ops.reduce_sum(x)
335 k_constraint = lambda x: x / math_ops.reduce_sum(x)
470 reg = lambda x: 0.1 * math_ops.reduce_sum(x)
483 reg = lambda x: 0.1 * math_ops.reduce_sum(x)
496 reg = lambda x: 0.1 * math_ops.reduce_sum(x)
516 d_constraint = lambda x: x / math_ops.reduce_sum(x)
517 p_constraint = lambda x: x / math_ops.reduce_sum(x)
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| /external/tensorflow/tensorflow/tools/compatibility/testdata/ |
| D | test_file_v0_11.py | 57 tf.reduce_sum(
60 tf.reduce_sum(
62 self.assertAllEqual(tf.reduce_sum(a, [0, 1]).eval(), 21.0)
221 tf.scalar_summary("scalar_reduce_var", tf.reduce_sum(var)),
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| /external/tensorflow/tensorflow/python/data/experimental/ops/ |
| D | resampling.py | 148 proportion_rejected = math_ops.reduce_sum((1 - accept_dist) * initial_dist)
221 num_examples_per_class_seen, math_ops.reduce_sum(
225 math_ops.reduce_sum(num_examples_per_class_seen))
|