| /external/tensorflow/tensorflow/contrib/losses/python/losses/ |
| D | loss_ops_test.py | 52 loss = loss_ops.absolute_difference(self._predictions, self._predictions)
54 self.assertAlmostEqual(0.0, loss.eval(), 3)
57 loss = loss_ops.absolute_difference(self._predictions, self._labels)
59 self.assertAlmostEqual(5.5, loss.eval(), 3)
63 loss = loss_ops.absolute_difference(self._predictions, self._labels,
66 self.assertAlmostEqual(5.5 * weights, loss.eval(), 3)
70 loss = loss_ops.absolute_difference(self._predictions, self._labels,
73 self.assertAlmostEqual(5.5 * weights, loss.eval(), 3)
77 loss = loss_ops.absolute_difference(self._predictions, self._labels,
80 self.assertAlmostEqual(5.6, loss.eval(), 3)
[all …]
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| /external/tensorflow/tensorflow/python/kernel_tests/ |
| D | losses_test.py | 56 loss = losses.absolute_difference(self._predictions, self._predictions)
58 self.assertAlmostEqual(0.0, self.evaluate(loss), 3)
61 loss = losses.absolute_difference(self._labels, self._predictions)
63 self.assertAlmostEqual(5.5, self.evaluate(loss), 3)
67 loss = losses.absolute_difference(self._labels, self._predictions, weights)
69 self.assertAlmostEqual(5.5 * weights, self.evaluate(loss), 3)
73 loss = losses.absolute_difference(self._labels, self._predictions,
76 self.assertAlmostEqual(5.5 * weights, self.evaluate(loss), 3)
80 loss = losses.absolute_difference(self._labels, self._predictions, weights)
82 self.assertAlmostEqual(5.6, self.evaluate(loss), 3)
[all …]
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| /external/tensorflow/tensorflow/python/keras/ |
| D | losses_test.py | 147 loss = keras.backend.eval(keras.losses.categorical_hinge(y_true, y_pred))
148 self.assertAllClose(expected_loss, np.mean(loss))
167 loss = _MSEMAELoss(0.3)
171 model.compile(optimizer='sgd', loss={'model_output': loss})
194 loss = mse_obj(y_true, y_pred, sample_weight=sample_weight)
200 self.assertAllClose(self.evaluate(loss), 16, 1e-2)
215 loss = mse_obj(y_true, y_true)
216 self.assertAlmostEqual(self.evaluate(loss), 0.0, 3)
224 loss = mse_obj(y_true, y_pred)
225 self.assertAlmostEqual(self.evaluate(loss), 49.5, 3)
[all …]
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| /external/tensorflow/tensorflow/contrib/gan/python/losses/python/ |
| D | losses_impl_test.py | 51 loss = self._g_loss_fn(self._discriminator_gen_outputs)
52 self.assertEqual(self._discriminator_gen_outputs.dtype, loss.dtype)
53 self.assertEqual(self._generator_loss_name, loss.op.name)
55 self.assertAlmostEqual(self._expected_g_loss, loss.eval(), 5)
58 loss = self._d_loss_fn(
60 self.assertEqual(self._discriminator_gen_outputs.dtype, loss.dtype)
61 self.assertEqual(self._discriminator_loss_name, loss.op.name)
63 self.assertAlmostEqual(self._expected_d_loss, loss.eval(), 5)
79 loss = self._g_loss_fn(
81 self.assertAllEqual([4], loss.shape)
[all …]
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| D | losses_impl.py | 108 loss = - discriminator_gen_outputs
109 loss = losses.compute_weighted_loss(
110 loss, weights, scope, loss_collection, reduction)
113 summary.scalar('generator_wass_loss', loss)
115 return loss
163 loss = loss_on_generated - loss_on_real
164 util.add_loss(loss, loss_collection)
169 summary.scalar('discriminator_wass_loss', loss)
171 return loss
236 loss = loss_on_generated + loss_on_real
[all …]
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| /external/tensorflow/tensorflow/contrib/kernel_methods/python/ |
| D | losses_test.py | 98 loss = losses.sparse_multiclass_hinge_loss(labels, logits)
100 loss.eval()
108 loss = losses.sparse_multiclass_hinge_loss(labels, logits)
109 self.assertAlmostEqual(loss.eval(), 0.0, 3)
117 loss = losses.sparse_multiclass_hinge_loss(labels, logits)
118 self.assertAlmostEqual(loss.eval(), 0.0, 3)
137 loss = losses.sparse_multiclass_hinge_loss(labels, logits)
138 result = loss.eval(feed_dict={logits: logits_np, labels: labels_np})
147 loss = losses.sparse_multiclass_hinge_loss(labels, logits)
149 self.assertAlmostEqual(loss.eval(), 0.4333, 3)
[all …]
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| /external/tensorflow/tensorflow/contrib/layers/python/layers/ |
| D | optimizers_test.py | 42 loss = math_ops.abs(var * x)
49 return x, var, loss, global_step
70 x, var, loss, global_step = _setup_model()
72 loss, global_step, learning_rate=0.1, optimizer=optimizer)
86 x, var, loss, global_step = _setup_model()
88 loss, global_step, learning_rate=None, optimizer=optimizer_fn)
100 _, _, loss, global_step = _setup_model()
103 loss, global_step, learning_rate=0.1, optimizer=optimizer)
107 _, _, loss, global_step = _setup_model()
110 loss, global_step, learning_rate=0.1, optimizer="SGD",
[all …]
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| D | regularizers_test.py | 78 loss = regularizers.l1_l2_regularizer(1.0, 1.0)(tensor)
79 self.assertEquals(loss.op.name, 'l1_l2_regularizer')
80 self.assertAlmostEqual(loss.eval(), num_elem + num_elem / 2, 5)
86 loss = regularizers.l1_l2_regularizer(0.0, 1.0)(tensor)
88 self.assertEquals(loss.op.name, 'l1_l2_regularizer')
89 self.assertAlmostEqual(loss.eval(), num_elem / 2, 5)
95 loss = regularizers.l1_l2_regularizer(1.0, 0.0)(tensor)
97 self.assertEquals(loss.op.name, 'l1_l2_regularizer')
98 self.assertAlmostEqual(loss.eval(), num_elem, 5)
103 loss = regularizers.l1_l2_regularizer(0.0, 0.0)(tensor)
[all …]
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| D | target_column_test.py | 38 5. / 3, sess.run(target_column.loss(prediction, labels, {})))
49 sess.run(target_column.loss(prediction, labels, features)),
68 sess.run(target_column.loss(logits, labels, {})),
82 sess.run(target_column.loss(logits, labels, features)),
106 sess.run(target_column.loss(logits, labels, {})))
118 1.5514446, sess.run(target_column.loss(logits, labels, features)))
146 loss = target_column.loss(predictions, labels, {})
152 self.assertAlmostEqual(0.25, sess.run(loss))
160 loss = target_column.loss(predictions, labels, features)
166 self.assertAlmostEqual(8.6 / 12, sess.run(loss), places=3)
|
| /external/tensorflow/tensorflow/contrib/compiler/ |
| D | xla_test.py | 274 loss = constant_op.constant(_EXPECTED_LOSS)
276 mode=mode, loss=loss, train_op=array_ops.identity(loss))
311 loss = constant_op.constant(_EXPECTED_LOSS)
312 mock_xla_compile.return_value = [loss]
322 self.assertEqual(sess.run(estimator_spec.loss), sess.run(loss))
323 self.assertEqual(sess.run(estimator_spec.train_op), sess.run(loss))
335 loss = constant_op.constant(_EXPECTED_LOSS)
336 mock_xla_compile.return_value = [loss]
346 self.assertEqual(sess.run(estimator_spec.loss), sess.run(loss))
347 self.assertEqual(sess.run(estimator_spec.train_op), sess.run(loss))
[all …]
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| /external/tensorflow/tensorflow/python/keras/mixed_precision/experimental/ |
| D | loss_scale_optimizer.py | 72 def _compute_gradients(self, loss, var_list, grad_loss=None): argument
73 loss = self._scale_loss(loss)
74 …grads_and_vars = self._optimizer._compute_gradients(loss, var_list, # pylint: disable=protected-a…
81 def get_gradients(self, loss, params): argument
82 loss = self._scale_loss(loss)
83 grads = self._optimizer.get_gradients(loss, params)
86 def _scale_loss(self, loss): argument
88 if callable(loss):
89 return lambda: loss() * self._loss_scale
91 return loss * self._loss_scale
|
| /external/tensorflow/tensorflow/contrib/training/python/training/ |
| D | training_test.py | 99 loss = losses.log_loss(tf_labels, tf_predictions)
101 train_op = training.create_train_op(loss, optimizer)
116 loss = losses.log_loss(tf_labels, tf_predictions)
119 train_op = training.create_train_op(loss, optimizer)
150 loss = losses.log_loss(tf_labels, tf_predictions)
152 train_op = training.create_train_op(loss, optimizer, update_ops=[])
183 loss = losses.log_loss(tf_labels, tf_predictions)
185 train_op = training.create_train_op(loss, optimizer)
206 loss = losses.log_loss(tf_labels, tf_predictions)
208 train_op = training.create_train_op(loss, optimizer, global_step=None)
[all …]
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| /external/tensorflow/tensorflow/contrib/slim/python/slim/ |
| D | learning_test.py | 256 loss = learning.train(
258 self.assertLess(loss, .1)
443 loss = learning.train(
445 self.assertIsNotNone(loss)
446 self.assertLess(loss, .015)
462 loss = learning.train(
464 self.assertIsNotNone(loss)
465 self.assertLess(loss, .015)
482 loss = learning.train(
488 self.assertIsNotNone(loss)
[all …]
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| /external/tensorflow/tensorflow/python/ops/ |
| D | nn_xent_test.py | 61 loss = nn_impl.sigmoid_cross_entropy_with_logits(
63 self.assertEqual("mylogistic", loss.op.name)
70 loss = nn_impl.sigmoid_cross_entropy_with_logits(
73 tf_loss = self.evaluate(loss)
81 loss = nn_impl.sigmoid_cross_entropy_with_logits(
84 tf_loss = self.evaluate(loss)
92 loss = nn_impl.sigmoid_cross_entropy_with_logits(
94 err = gradient_checker.compute_gradient_error(logits, sizes, loss, sizes)
103 loss = nn_impl.sigmoid_cross_entropy_with_logits(
105 grads = gradients_impl.gradients(loss, logits)[0].eval()
[all …]
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| /external/tensorflow/tensorflow/contrib/seq2seq/python/kernel_tests/ |
| D | loss_test.py | 23 from tensorflow.contrib.seq2seq.python.ops import loss
61 average_loss_per_example = loss.sequence_loss(
68 average_loss_per_sequence = loss.sequence_loss(
76 average_loss_per_batch = loss.sequence_loss(
84 total_loss = loss.sequence_loss(
96 seq_loss = loss.SequenceLoss(average_across_timesteps=True,
105 seq_loss = loss.SequenceLoss(average_across_timesteps=False,
115 seq_loss = loss.SequenceLoss(average_across_timesteps=True,
125 seq_loss = loss.SequenceLoss(average_across_timesteps=False,
139 seq_loss = loss.SequenceLoss(average_across_timesteps=False,
[all …]
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| /external/tensorflow/tensorflow/contrib/opt/python/training/ |
| D | external_optimizer_test.py | 78 loss = math_ops.reduce_sum(
80 loss += math_ops.reduce_sum(
82 loss += math_ops.reduce_sum(
86 optimizer = MockOptimizerInterface(loss)
105 loss = math_ops.reduce_sum(math_ops.square(vector - minimum_location)) / 2.
108 optimizer = MockOptimizerInterface(loss)
115 extra_fetches = [loss]
236 loss = math_ops.reduce_sum(math_ops.square(vector))
243 loss, equalities=equalities, inequalities=inequalities, method='SLSQP')
255 loss = math_ops.reduce_sum(math_ops.square(vector))
[all …]
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| /external/tensorflow/tensorflow/contrib/learn/python/learn/estimators/ |
| D | model_fn.py | 89 loss=None, argument
146 get_graph_from_inputs((predictions, loss, train_op))
157 if loss is None:
161 loss = ops.convert_to_tensor(loss)
162 loss_shape = loss.get_shape()
164 raise ValueError('Loss must be scalar: %s.' % loss)
166 loss = array_ops.reshape(loss, [])
203 loss=loss,
301 loss=self.loss,
|
| /external/deqp-deps/glslang/Test/ |
| D | hlsl.promotions.frag | 29 float3 Fn_R_F3D(out float3 p) { p = d3; return d3; } // valid, but loss of precision on downconve…
34 int3 Fn_R_I3D(out int3 p) { p = d3; return d3; } // valid, but loss of precision on downconvers…
39 uint3 Fn_R_U3D(out uint3 p) { p = d3; return d3; } // valid, but loss of precision on downconver…
57 float3 r03 = d3; // valid, but loss of precision on downconversion.
62 int3 r13 = d3; // valid, but loss of precision on downconversion.
67 uint3 r23 = d3; // valid, but loss of precision on downconversion.
83 r03 *= d3; // valid, but loss of precision on downconversion.
88 r13 *= d3; // valid, but loss of precision on downconversion.
93 r23 *= d3; // valid, but loss of precision on downconversion.
106 r03 *= ds; // valid, but loss of precision on downconversion.
[all …]
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| /external/tensorflow/tensorflow/python/keras/engine/ |
| D | training_test.py | 65 def _do_test_compile_with_model_and_single_loss(self, model, loss): argument
66 model.compile(optimizer='adam', loss=loss)
67 self.assertEqual(model.loss, loss)
69 loss = losses.get(loss)
70 if not isinstance(loss, list):
71 loss_list = [loss] * len(model.outputs)
84 def test_compile_with_single_output(self, loss): argument
87 self._do_test_compile_with_model_and_single_loss(model, loss)
93 def test_compile_with_multi_output(self, loss): argument
95 self._do_test_compile_with_model_and_single_loss(model, loss)
[all …]
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| /external/tensorflow/tensorflow/contrib/distribute/python/ |
| D | keras_test.py | 116 loss='categorical_crossentropy',
350 loss='categorical_crossentropy',
380 loss='categorical_crossentropy',
459 loss='categorical_crossentropy',
614 loss = 'mse'
616 model.compile(optimizer, loss, metrics=metrics)
646 loss = 'mse'
647 model.compile(optimizer, loss)
681 loss = 'mse'
682 model.compile(optimizer, loss)
[all …]
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| /external/tensorflow/tensorflow/python/keras/optimizer_v2/ |
| D | optimizer_v2_test.py | 66 loss = lambda: 5 * var0 + 3 * var1 # pylint: disable=cell-var-from-loop function
74 opt_op = sgd.minimize(loss, var_list=[var0, var1])
87 def loss(): function
97 opt_op = sgd.minimize(loss, [var0, var1])
108 sgd.minimize(loss, [var0, var1])
120 sgd.minimize(loss, [var0, var1])
130 loss = lambda: 5 * var0 + 3 * var1 # pylint: disable=cell-var-from-loop function
139 opt_op = sgd.minimize(loss, var_list=[var0, var1], grad_loss=grad_loss)
154 loss = lambda: 5 * var0 # pylint: disable=cell-var-from-loop function
158 sgd_op.minimize(loss, var_list=[var1])
[all …]
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| /external/tensorflow/tensorflow/python/saved_model/model_utils/ |
| D | export_output_test.py | 245 loss = {'my_loss': constant_op.constant([0])}
254 outputter = MockSupervisedOutput(loss, predictions, metrics)
255 self.assertEqual(outputter.loss['loss/my_loss'], loss['my_loss'])
265 loss['my_loss'], predictions['output1'], metrics['metrics'])
266 self.assertEqual(outputter.loss, {'loss': loss['my_loss']})
275 self.assertEqual(len(outputter.loss), 1)
292 loss = {('my', 'loss'): constant_op.constant([0])}
303 outputter = MockSupervisedOutput(loss, predictions, metrics)
304 self.assertEqual(set(outputter.loss.keys()), set(['loss/my/loss']))
317 loss = {'loss': constant_op.constant([0])}
[all …]
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| /external/tensorflow/tensorflow/examples/udacity/ |
| D | 6_lstm.ipynb | 577 " loss = tf.reduce_mean(\n",
586 " gradients, v = zip(*optimizer.compute_gradients(loss))\n",
668 " [optimizer, loss, train_prediction, learning_rate], feed_dict=feed_dict)\n",
673 " # The mean loss is an estimate of the loss over the last few batches.\n",
675 " 'Average loss at step %d: %f learning rate: %f' % (step, mean_loss, lr))\n",
708 "Average loss at step 0 : 3.29904174805 learning rate: 10.0\n",
718 "Average loss at step 100 : 2.59553678274 learning rate: 10.0\n",
721 "Average loss at step 200 : 2.24747137785 learning rate: 10.0\n",
724 "Average loss at step 300 : 2.09438110709 learning rate: 10.0\n",
727 "Average loss at step 400 : 1.99440989017 learning rate: 10.0\n",
[all …]
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| D | 4_convolutions.ipynb | 288 " loss = tf.reduce_mean(\n",
292 " optimizer = tf.train.GradientDescentOptimizer(0.05).minimize(loss)\n",
349 " [optimizer, loss, train_prediction], feed_dict=feed_dict)\n",
351 " print('Minibatch loss at step %d: %f' % (step, l))\n",
362 "Minibatch loss at step 0 : 3.51275\n",
365 "Minibatch loss at step 50 : 1.48703\n",
368 "Minibatch loss at step 100 : 1.04377\n",
371 "Minibatch loss at step 150 : 0.601682\n",
374 "Minibatch loss at step 200 : 0.898649\n",
377 "Minibatch loss at step 250 : 1.3637\n",
[all …]
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| /external/autotest/client/common_lib/cros/network/ |
| D | ping_runner.py | 122 loss = _regex_float_from_string('([0-9]+\.[0-9]+)% packet loss',
124 if None in (sent, received, loss):
129 return PingResult(sent, received, loss,
137 return PingResult(sent, received, loss)
215 loss = _regex_float_from_string('([0-9]+(\.[0-9]+)?)% packet loss',
217 if None in (sent, received, loss):
223 return PingResult(sent, received, loss,
231 return PingResult(sent, received, loss)
261 def __init__(self, sent, received, loss, argument
278 self.loss = loss
[all …]
|