| /external/tensorflow/tensorflow/python/ops/ragged/ |
| ragged_reduce_op_test.py | 56 ragged_reduce_op=ragged_math_ops.reduce_sum,
62 ragged_reduce_op=ragged_math_ops.reduce_sum,
68 ragged_reduce_op=ragged_math_ops.reduce_sum,
74 ragged_reduce_op=ragged_math_ops.reduce_sum,
154 ragged_reduce_op=ragged_math_ops.reduce_sum,
180 ragged_reduce_op=ragged_math_ops.reduce_sum,
209 ragged_reduce_op=ragged_math_ops.reduce_sum,
241 ragged_reduce_op=ragged_math_ops.reduce_sum,
246 ragged_reduce_op=ragged_math_ops.reduce_sum,
251 ragged_reduce_op=ragged_math_ops.reduce_sum,
[all...] |
| ragged_map_fn_op_test.py | 60 fn=lambda x: array_ops.stack([mo.reduce_mean(x), mo.reduce_sum(x)]),
104 fn=lambda x: ragged_math_ops.reduce_sum(x, axis=1),
112 fn=lambda x: ragged_math_ops.reduce_sum(x, axis=0),
120 fn=ragged_math_ops.reduce_sum,
181 return mo.reduce_sum(f['batman']) + mo.reduce_sum(f['robin'])
263 fn = lambda x: ragged_math_ops.reduce_sum(x, axis=0)
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| ragged_math_ops.py | 350 >>> ragged.reduce_sum(rt, axis=0).eval().tolist()
352 >>> ragged.reduce_sum(rt, axis=1).eval().tolist()
419 `reduce_sum`, `reduce_max`, etc.
501 def reduce_sum(input_tensor, axis=None, keepdims=None, name=None): function
503 return _ragged_reduce_aggregate(math_ops.reduce_sum,
532 total = reduce_sum(input_tensor, axis, keepdims)
539 count = reduce_sum(ones, axis, keepdims)
564 reduce_sum(_cast(input_tensor, dtypes.int32), axis, keepdims),
576 _set_ragged_reduce_docstring(reduce_sum, 'sum', 'summed', '0',
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| /external/tensorflow/tensorflow/contrib/seq2seq/python/ops/ |
| loss.py | 118 crossent = math_ops.reduce_sum(crossent)
119 total_size = math_ops.reduce_sum(weights)
122 crossent = math_ops.reduce_sum(crossent)
130 crossent = math_ops.reduce_sum(crossent, axis=reduce_axis)
131 total_size = math_ops.reduce_sum(weights, axis=reduce_axis)
135 crossent = math_ops.reduce_sum(crossent, axis=reduce_axis)
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| /external/tensorflow/tensorflow/python/kernel_tests/ |
| 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/keras/utils/ |
| kernelized_utils.py | 85 diff_squared_l2_norm = math_ops.reduce_sum(
115 diff_l1_norm = math_ops.reduce_sum(
|
| /external/tensorflow/tensorflow/python/eager/ |
| tape_test.py | 39 r = math_ops.reduce_sum(mm)
90 return math_ops.reduce_sum(mm)
104 return math_ops.reduce_sum(mm)
118 return math_ops.reduce_sum(mm)
133 return r + math_ops.reduce_sum(mm)
142 tf_rr = 2 * math_ops.reduce_sum(tf_mm)
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| /external/tensorflow/tensorflow/contrib/losses/python/losses/ |
| loss_ops.py | 50 The `losses` are reduced (tf.reduce_sum) until its dimension matches
52 multiplied by `weights` and a final reduce_sum is computed on the result.
64 reduced_losses = math_ops.reduce_sum(losses, axis=axis)
66 return math_ops.reduce_sum(reduced_losses)
80 total_loss = math_ops.reduce_sum(losses)
157 return num_per_batch if per_batch else math_ops.reduce_sum(num_per_batch)
162 num_nonzero_per_batch = math_ops.reduce_sum(
173 return num_per_batch if per_batch else math_ops.reduce_sum(num_per_batch)
583 sum_squares_diff_per_batch = math_ops.reduce_sum(
590 sum_diff = math_ops.reduce_sum(diffs, axis=axis
[all...] |
| /external/tensorflow/tensorflow/contrib/opt/python/training/ |
| external_optimizer_test.py | 78 loss = math_ops.reduce_sum(
80 loss += math_ops.reduce_sum(
82 loss += math_ops.reduce_sum(
105 loss = math_ops.reduce_sum(math_ops.square(vector - minimum_location)) / 2.
155 return math_ops.reduce_sum(s)
236 loss = math_ops.reduce_sum(math_ops.square(vector))
255 loss = math_ops.reduce_sum(math_ops.square(vector))
273 loss = math_ops.reduce_sum(math_ops.square(vector))
291 loss = math_ops.reduce_sum(math_ops.square(vector))
310 loss = math_ops.reduce_sum(math_ops.square(vector - minimum_location)) / 2
[all...] |
| /external/tensorflow/tensorflow/python/ops/distributions/ |
| dirichlet.py | 44 `tf.reduce_sum(value, -1) = 1`. It must have a shape compatible with
197 self._total_concentration = math_ops.reduce_sum(self._concentration, -1)
236 return gamma_sample / math_ops.reduce_sum(gamma_sample, -1, keepdims=True)
248 return math_ops.reduce_sum(math_ops.xlogy(self.concentration - 1., x), -1)
259 - math_ops.reduce_sum(
330 math_ops.reduce_sum(x, -1),
403 math_ops.reduce_sum(d1.concentration, axis=-1, keepdims=True))
407 return (math_ops.reduce_sum(concentration_diff * digamma_diff, axis=-1) -
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| /external/tensorflow/tensorflow/contrib/losses/python/metric_learning/ |
| metric_loss_ops.py | 53 math_ops.reduce_sum(math_ops.square(feature), axis=[1], keepdims=True),
54 math_ops.reduce_sum(
109 math_ops.reduce_sum(
203 math_ops.reduce_sum(
230 num_positives = math_ops.reduce_sum(mask_positives)
233 math_ops.reduce_sum(
273 math_ops.reduce_sum(math_ops.square(embeddings_anchor), 1))
275 math_ops.reduce_sum(math_ops.square(embeddings_positive), 1))
291 labels_remapped /= math_ops.reduce_sum(labels_remapped, 1, keepdims=True)
381 math_ops.reduce_sum(math_ops.square(embeddings_anchor), 1)
[all...] |
| /external/tensorflow/tensorflow/contrib/eager/python/examples/l2hmc/ |
| l2hmc.py | 182 return momentum, tf.reduce_sum(scale, axis=1)
195 return position, tf.reduce_sum(mask_inv * scale, axis=1)
208 return momentum, tf.reduce_sum(scale, axis=1)
221 return position, tf.reduce_sum(mask_inv * scale, axis=1)
274 return .5 * tf.reduce_sum(v**2, axis=1)
321 ip = tf.reduce_sum(x**2., axis=1)
322 return .5 * ip + eta * tf.reduce_sum(tf.cos(x / eta), axis=1)
336 x_loss = tf.reduce_sum((x - x_)**2, axis=1) * x_accept_prob + eps
337 z_loss = tf.reduce_sum((z - z_)**2, axis=1) * z_accept_prob + eps
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| /external/tensorflow/tensorflow/python/keras/ |
| constraints.py | 70 math_ops.reduce_sum(math_ops.square(w), axis=self.axis, keepdims=True))
111 math_ops.reduce_sum(
156 math_ops.reduce_sum(math_ops.square(w), axis=self.axis, keepdims=True))
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| regularizers.py | 61 regularization += math_ops.reduce_sum(self.l1 * math_ops.abs(x))
63 regularization += math_ops.reduce_sum(self.l2 * math_ops.square(x))
|
| /external/tensorflow/tensorflow/contrib/distributions/python/ops/ |
| mixture_same_family.py | 259 return math_ops.reduce_sum(
276 return math_ops.reduce_sum(
294 mean_cond_var = math_ops.reduce_sum(
297 var_cond_mean = math_ops.reduce_sum(
318 mean_cond_var = math_ops.reduce_sum(
321 var_cond_mean = math_ops.reduce_sum(
|
| /external/tensorflow/tensorflow/contrib/recurrent/python/kernel_tests/ |
| recurrent_test.py | 88 ys=[math_ops.reduce_sum(acc)], xs=[theta.x, inputs.coeff])
115 dxw, db = dxwb, math_ops.reduce_sum(dxwb, axis=0)
163 loss0 = math_ops.reduce_sum(acc0) + math_ops.reduce_sum(final0)
176 loss1 = math_ops.reduce_sum(acc1) + math_ops.reduce_sum(final1)
|
| /external/tensorflow/tensorflow/python/ops/ |
| clip_ops.py | 106 gx = array_ops.reshape(math_ops.reduce_sum(xgrad, rx), sx)
107 gy = array_ops.reshape(math_ops.reduce_sum(ygrad, ry), sy)
108 gz = array_ops.reshape(math_ops.reduce_sum(zgrad, rz), sz)
151 l2sum = math_ops.reduce_sum(values * values, axes, keepdims=True)
209 half_squared_norm = math_ops.reduce_sum(array_ops.stack(half_squared_norms))
343 math_ops.reduce_sum(t * t, math_ops.range(array_ops.rank(t))))
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| random_grad.py | 64 return (None, math_ops.reduce_sum(
|
| math_grad.py | 104 math_ops.reduce_sum(indicators, op.inputs[1]), output_shape_kept_dims)
536 return (array_ops.reshape(math_ops.reduce_sum(partial_x * grad, rx), sx),
537 array_ops.reshape(math_ops.reduce_sum(partial_y * grad, ry), sy))
553 return (array_ops.reshape(math_ops.reduce_sum(partial_x * grad, rx), sx),
554 array_ops.reshape(math_ops.reduce_sum(partial_y * grad, ry), sy))
705 return (array_ops.reshape(math_ops.reduce_sum(partial_a * grad, ra), sa),
706 array_ops.reshape(math_ops.reduce_sum(partial_x * grad, rx), sx))
740 array_ops.reshape(math_ops.reduce_sum(partial_x * grad, rx), sx))
760 array_ops.reshape(math_ops.reduce_sum(partial_q * grad, rq), sq))
780 array_ops.reshape(math_ops.reduce_sum(partial_x * grad, rx), sx)
[all...] |
| /external/tensorflow/tensorflow/contrib/boosted_trees/python/utils/ |
| losses.py | 123 labels = math_ops.reduce_sum(input_tensor=target_one_hot, axis=[1])
128 normalizers = math_ops.reduce_sum(unnormalized_probs, 1, keepdims=True)
133 probs_for_real_class = math_ops.reduce_sum(labels * softmax_predictions, 1)
169 unweighted_loss = math_ops.reduce_sum(
|
| /external/tensorflow/tensorflow/contrib/distributions/python/kernel_tests/ |
| normal_conjugate_posteriors_test.py | 42 s = math_ops.reduce_sum(x)
63 s = math_ops.reduce_sum(x)
85 s = math_ops.reduce_sum(x, axis=[1])
107 s = math_ops.reduce_sum(x)
|
| /external/tensorflow/tensorflow/contrib/sparsemax/python/ops/ |
| sparsemax_loss.py | 76 return math_ops.reduce_sum(sum_s + q_part_safe, axis=1)
|
| /external/tensorflow/tensorflow/python/ops/losses/ |
| losses_impl.py | 92 total_loss = math_ops.reduce_sum(losses)
130 return math_ops.reduce_sum(
135 return math_ops.reduce_sum(present, name=scope)
192 loss = math_ops.reduce_sum(weighted_losses)
197 math_ops.reduce_sum(array_ops.ones_like(losses) * weights))
315 losses = 1 - math_ops.reduce_sum(radial_diffs, axis=(axis,), keepdims=True)
572 sum_squares_diff_per_batch = math_ops.reduce_sum(
581 sum_diff = math_ops.reduce_sum(diffs, axis=axis, keepdims=True)
590 loss = math_ops.reduce_sum(weighted_losses)
593 math_ops.reduce_sum(num_present_per_batch) > 0
[all...] |
| /external/tensorflow/tensorflow/contrib/linear_optimizer/python/ops/ |
| sdca_ops.py | 264 math_ops.reduce_sum(
278 sums.append(math_ops.reduce_sum(math_ops.square(math_ops.cast(
455 num_total_ids = math_ops.reduce_sum(
627 math_ops.reduce_sum(math_ops.cast(values, dtypes.float64), 0))
667 return math_ops.reduce_sum(math_ops.multiply(
670 weights)) / math_ops.reduce_sum(weights)
673 return math_ops.reduce_sum(math_ops.multiply(
675 weights)) / math_ops.reduce_sum(weights)
688 return math_ops.reduce_sum(weighted_error) / math_ops.reduce_sum(
[all...] |
| /external/tensorflow/tensorflow/contrib/distributions/python/ops/bijectors/ |
| ordered.py | 120 return math_ops.reduce_sum(y[..., 1:], axis=-1)
124 return -math_ops.reduce_sum(
|
