| /external/tensorflow/tensorflow/contrib/tensor_forest/hybrid/python/ops/ |
| D | training_ops.py | 113 dl_dx = math_ops.reduce_mean(dl_du * du_df * df_dx, 1)
114 dl_dt = math_ops.reduce_mean(dl_du * du_df * df_dt, 0)
115 dl_db = math_ops.reduce_mean(array_ops.squeeze(dl_du * du_df * df_db, [2]), 0)
192 dl_dx = math_ops.reduce_mean(dl_du * du_df * df_dx, 1)
193 dl_dt = math_ops.reduce_mean(dl_du * du_df * df_dt, 0)
194 dl_db = math_ops.reduce_mean(array_ops.squeeze(dl_du * du_df * df_db, [2]), 0)
270 dl_dx = math_ops.reduce_mean(dl_du * du_df * df_dx, 1)
271 dl_dt = math_ops.reduce_mean(dl_du * du_df * df_dt, 0)
272 dl_db = math_ops.reduce_mean(array_ops.squeeze(dl_du * du_df * df_db, [2]), 0)
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| /external/tensorflow/tensorflow/python/ops/ragged/ |
| D | ragged_reduce_op_test.py | 116 ragged_reduce_op=ragged_math_ops.reduce_mean,
174 ragged_reduce_op=ragged_math_ops.reduce_mean,
200 ragged_reduce_op=ragged_math_ops.reduce_mean,
293 ragged_reduce_op=ragged_math_ops.reduce_mean,
298 ragged_reduce_op=ragged_math_ops.reduce_mean,
303 ragged_reduce_op=ragged_math_ops.reduce_mean,
324 reduced = ragged_math_ops.reduce_mean(rt_input, axis=1)
330 reduced = ragged_math_ops.reduce_mean(tensor, axis=1)
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| /external/tensorflow/tensorflow/contrib/distributions/python/kernel_tests/ |
| D | mvn_diag_plus_low_rank_test.py | 180 sample_mean = math_ops.reduce_mean(samps, 0)
184 sample_kl_identity = math_ops.reduce_mean(
188 sample_kl_scaled = math_ops.reduce_mean(
192 sample_kl_diag = math_ops.reduce_mean(
196 sample_kl_chol = math_ops.reduce_mean(
207 sample_kl_identity_diag_baseline = math_ops.reduce_mean(
212 sample_kl_scaled_diag_baseline = math_ops.reduce_mean(
217 sample_kl_diag_diag_baseline = math_ops.reduce_mean(
221 sample_kl_chol_diag_baseline = math_ops.reduce_mean(
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| D | independent_test.py | 109 sample_mean = math_ops.reduce_mean(x, axis=0)
110 sample_var = math_ops.reduce_mean(
113 sample_entropy = -math_ops.reduce_mean(ind.log_prob(x), axis=0)
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| D | onehot_categorical_test.py | 186 kl_sample = math_ops.reduce_mean(p.log_prob(x) - q.log_prob(x), 0)
202 sample_mean = math_ops.reduce_mean(x, 0)
230 sample_mean = math_ops.reduce_mean(x, 0) # elementwise mean
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| /external/tensorflow/tensorflow/contrib/distributions/python/ops/ |
| D | test_util.py | 130 sample_mean = math_ops.reduce_mean(x, axis=0)
131 sample_variance = math_ops.reduce_mean(
295 return math_ops.reduce_mean(
361 sample_mean = math_ops.reduce_mean(x, axis=0)
362 sample_covariance = math_ops.reduce_mean(
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| /external/tensorflow/tensorflow/contrib/gan/python/eval/python/ |
| D | classifier_metrics_impl.py | 393 q = math_ops.reduce_mean(p, axis=0)
396 log_score = math_ops.reduce_mean(kl)
569 m = math_ops.reduce_mean(real_activations, 0)
570 m_w = math_ops.reduce_mean(generated_activations, 0)
705 m = math_ops.reduce_mean(real_activations, 0)
706 m_w = math_ops.reduce_mean(generated_activations, 0)
1099 return (-2 * math_ops.reduce_mean(k_rg) +
1106 mn = math_ops.reduce_mean(ests)
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| D | sliced_wasserstein_impl.py | 172 wdist = math_ops.reduce_mean(math_ops.abs(proj_a - proj_b))
174 return math_ops.reduce_mean(means)
196 wdist = math_ops.reduce_mean(math_ops.abs(proj_a - proj_b))
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| /external/tensorflow/tensorflow/contrib/bayesflow/python/ops/ |
| D | monte_carlo_impl.py | 331 return math_ops.reduce_mean(f(samples), axis=axis, keepdims=keep_dims)
351 return math_ops.reduce_mean(fx, axis=axis, keepdims=keep_dims)
356 return math_ops.reduce_mean(values, axis=[0])
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| /external/tensorflow/tensorflow/contrib/tensor_forest/hybrid/python/ |
| D | hybrid_model.py | 70 return math_ops.reduce_mean(
116 mean_squared_error = math_ops.reduce_mean(diff * diff)
120 loss = math_ops.reduce_mean(
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| /external/tensorflow/tensorflow/contrib/boosted_trees/estimator_batch/ |
| D | custom_loss_head.py | 58 average_loss = math_ops.reduce_mean(weighted_loss)
59 return average_loss, average_loss / math_ops.reduce_mean(weight_tensor)
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| /external/tensorflow/tensorflow/contrib/layers/python/layers/ |
| D | summaries.py | 92 standard_ops.reduce_mean(
99 standard_ops.reduce_mean(
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| D | target_column.py | 230 return math_ops.reduce_mean(loss_unweighted, name=name)
232 return math_ops.reduce_mean(loss_weighted, name=name)
255 return math_ops.reduce_mean(loss_unweighted, name="loss")
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| /external/tensorflow/tensorflow/examples/tutorials/mnist/ |
| D | mnist_with_summaries.py | 69 mean = tf.reduce_mean(var)
72 stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
135 accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
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| /external/tensorflow/tensorflow/contrib/boosted_trees/python/training/functions/ |
| D | gbdt_batch_test.py | 260 loss=math_ops.reduce_mean(
353 loss=math_ops.reduce_mean(
524 loss=math_ops.reduce_mean(
628 loss=math_ops.reduce_mean(
732 loss=math_ops.reduce_mean(
803 loss=math_ops.reduce_mean(
867 loss=math_ops.reduce_mean(
1109 loss=math_ops.reduce_mean(
1213 loss=math_ops.reduce_mean(
1305 loss=math_ops.reduce_mean(
[all …]
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| /external/tensorflow/tensorflow/contrib/losses/python/metric_learning/ |
| D | metric_loss_ops.py | 115 return math_ops.reduce_mean(
272 reg_anchor = math_ops.reduce_mean(
274 reg_positive = math_ops.reduce_mean(
296 xent_loss = math_ops.reduce_mean(xent_loss, name='xentropy')
380 reg_anchor = math_ops.reduce_mean(
382 reg_positive = math_ops.reduce_mean(
402 xent_loss = math_ops.reduce_mean(xent_loss, name='xentropy')
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| /external/tensorflow/tensorflow/contrib/gan/python/features/python/ |
| D | virtual_batchnorm_impl.py | 67 shift = array_ops.stop_gradient(math_ops.reduce_mean(y, axes, keepdims=True))
69 shifted_mean = math_ops.reduce_mean(y - shift, axes, keepdims=True)
71 mean_squared = math_ops.reduce_mean(math_ops.square(y), axes, keepdims=True)
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| /external/tensorflow/tensorflow/python/keras/layers/ |
| D | tensorflow_op_layer_test.py | 86 x = math_ops.reduce_mean(inputs, axis=1, keepdims=True)
93 x = math_ops.reduce_mean(inputs, axis=1, keepdims=True)
187 outputs = math_ops.reduce_mean(inputs, axis=1)
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| /external/tensorflow/tensorflow/tools/compatibility/testdata/ |
| D | test_file_v0_11.py | 70 tf.reduce_mean(
73 tf.reduce_mean(
75 self.assertAllEqual(tf.reduce_mean(a, [0, 1]).eval(), 3.5)
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| /external/tensorflow/tensorflow/compiler/tests/ |
| D | reduce_ops_test.py | 150 self._testReduction(math_ops.reduce_mean, np.mean, np.float32,
155 self._testReduction(math_ops.reduce_mean, np.mean, np.float16, self.ONES,
159 self._testReduction(math_ops.reduce_mean, np.mean, np.complex64,
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| /external/tensorflow/tensorflow/python/debug/examples/ |
| D | debug_mnist.py | 109 cross_entropy = tf.reduce_mean(diff)
119 accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
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| /external/tensorflow/tensorflow/contrib/labeled_tensor/ |
| D | __init__.py | 128 reduce_mean = _ops.reduce_mean variable
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| /external/tensorflow/tensorflow/contrib/autograph/examples/benchmarks/ |
| D | cartpole_benchmark.py | 175 grad_list[i] = tf.reduce_mean(g * r, axis=0)
197 mean_steps_per_iteration.append(tf.reduce_mean(steps_per_game))
357 grad_list[i] = tf.reduce_mean(g * r, axis=0)
376 mean_steps_per_iteration.append(tf.reduce_mean(steps_per_game))
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| /external/tensorflow/tensorflow/python/kernel_tests/distributions/ |
| D | multinomial_test.py | 275 sample_mean = math_ops.reduce_mean(x, 0)
277 sample_cov = math_ops.reduce_mean(math_ops.matmul(
314 sample_mean = math_ops.reduce_mean(x, 0)
344 sample_mean = math_ops.reduce_mean(x, 0)
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| D | dirichlet_test.py | 171 sample_mean = math_ops.reduce_mean(x, 0)
173 sample_cov = math_ops.reduce_mean(math_ops.matmul(
282 kl_sample = math_ops.reduce_mean(d1.log_prob(x) - d2.log_prob(x), 0)
|