| /external/tensorflow/tensorflow/contrib/model_pruning/examples/cifar10/ |
| cifar10_pruning.py | 127 tf.add_to_collection('losses', weight_decay)
290 tf.add_to_collection('losses', cross_entropy_mean)
294 return tf.add_n(tf.get_collection('losses'), name='total_loss')
298 """Add summaries for losses in CIFAR-10 model.
300 Generates moving average for all losses and associated summaries for
306 loss_averages_op: op for generating moving averages of losses.
308 # Compute the moving average of all individual losses and the total loss.
310 losses = tf.get_collection('losses')
311 loss_averages_op = loss_averages.apply(losses + [total_loss]
[all...] |
| /external/tensorflow/tensorflow/python/ |
| __init__.py | 94 from tensorflow.python.ops.losses import losses
270 'losses',
303 losses, math_ops, metrics, nn, profiler, resource_loader, sets, script_ops,
|
| /external/tensorflow/tensorflow/python/training/ |
| evaluation_test.py | 35 from tensorflow.python.ops.losses import losses
91 loss_op = losses.log_loss(labels=tf_labels, predictions=tf_predictions)
|
| /external/tensorflow/tensorflow/contrib/gan/python/estimator/python/ |
| gan_estimator_test.py | 30 from tensorflow.contrib.gan.python.losses.python import tuple_losses as losses
202 generator_loss_fn=losses.wasserstein_generator_loss,
203 discriminator_loss_fn=losses.wasserstein_discriminator_loss,
|
| /external/tensorflow/tensorflow/contrib/slim/ |
| __init__.py | 26 from tensorflow.contrib import losses
|
| /external/tensorflow/tensorflow/examples/tutorials/mnist/ |
| mnist.py | 97 return tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
|
| mnist_softmax_xla.py | 53 # So here we use tf.losses.sparse_softmax_cross_entropy on the raw
55 cross_entropy = tf.losses.sparse_softmax_cross_entropy(labels=y_, logits=y)
|
| /external/tensorflow/tensorflow/python/keras/_impl/keras/ |
| __init__.py | 31 from tensorflow.python.keras._impl.keras import losses
|
| /external/tensorflow/tensorflow/contrib/eager/python/examples/resnet50/ |
| resnet50_graph_test.py | 81 loss = tf.losses.softmax_cross_entropy(
145 loss = tf.losses.softmax_cross_entropy(
|
| /external/tensorflow/tensorflow/contrib/kfac/examples/ |
| mlp.py | 229 losses, accuracies = zip(*tower_results)
232 loss = tf.reduce_mean(losses)
|
| /external/tensorflow/tensorflow/contrib/nccl/python/ops/ |
| nccl_ops_test.py | 122 losses = _DeviceTensors(tensors, [t.device for t in reduce_tensors])
124 reduce_tensors, inputs, losses, colocate_gradients_with_ops=True)
|
| /external/tensorflow/tensorflow/python/keras/_impl/keras/layers/ |
| convolutional_recurrent_test.py | 164 self.assertEqual(len(layer.losses), 3)
166 self.assertEqual(len(layer.losses), 4)
|
| wrappers.py | 85 def losses(self): member in class:Wrapper
86 return self.layer.losses + self._losses
474 def losses(self): member in class:Bidirectional
475 if hasattr(self.forward_layer, 'losses'):
476 return self.forward_layer.losses + self.backward_layer.losses
|
| /external/tensorflow/tensorflow/python/layers/ |
| network.py | 678 def losses(self): member in class:GraphNetwork
679 """Retrieve the network's losses.
681 Will only include losses that are either
683 (e.g. will not include losses that depend on tensors
689 losses = []
691 losses += layer.losses
693 return losses
702 reachable = layers_util.get_reachable_from_inputs(relevant_inputs, losses)
703 relevant_conditional_losses = [x for x in losses if x in reachable
[all...] |
| network_test.py | 125 self.assertEqual(len(layer.losses), 2)
132 self.assertEqual(len(layer.losses), 3)
138 self.assertEqual(len(network.losses), 2)
145 self.assertEqual(len(network.losses), 2)
149 self.assertEqual(len(network.losses), 3)
155 self.assertEqual(len(network.losses), 4)
159 self.assertEqual(len(network.losses), 5)
417 # losses
418 self.assertEqual(len(net.losses), 1)
|
| /external/tensorflow/tensorflow/contrib/kfac/python/ops/ |
| layer_collection.py | 122 """Registry of information about layers and losses.
131 losses: a list of LossFunction objects. The loss to be optimized is their
156 def losses(self): member in class:LayerCollection
412 if not self.losses:
414 inputs_to_losses = nest.flatten(tuple(loss.inputs for loss in self.losses))
418 return math_ops.add_n(tuple(loss.evaluate() for loss in self.losses))
422 tuple(loss.evaluate_on_sample() for loss in self.losses))
|
| /external/tensorflow/tensorflow/python/keras/_impl/keras/engine/ |
| training_eager.py | 27 from tensorflow.python.keras._impl.keras import losses
39 if output_shape[-1] == 1 or loss_func == losses.binary_crossentropy:
42 elif loss_func == losses.sparse_categorical_crossentropy:
113 specified loss function. The total loss includes regularization losses and
169 # Add regularization losses
172 if layer.losses:
173 custom_losses += layer.losses
|
| /external/tensorflow/tensorflow/contrib/kfac/python/kernel_tests/ |
| layer_collection_test.py | 93 self.assertFalse(lc.losses)
240 self.assertEqual(1, len(lc.losses))
245 self.assertEqual(1, len(lc.losses))
249 self.assertEqual(2, len(lc.losses))
260 self.assertEqual(2, len(lc.losses))
301 self.assertEqual(len(lc.losses), 1)
302 loss = lc.losses[0]
|
| /external/tensorflow/tensorflow/contrib/gan/python/losses/python/ |
| tuple_losses_impl.py | 17 The losses and penalties in this file all correspond to losses in
18 `losses_impl.py`. Losses in that file take individual arguments, whereas in this
29 loss_collection=ops.GraphKeys.LOSSES,
30 reduction=losses.Reduction.SUM_BY_NONZERO_WEIGHTS,
41 loss_collection=ops.GraphKeys.LOSSES,
42 reduction=losses.Reduction.SUM_BY_NONZERO_WEIGHTS,
50 # `tfgan.losses.wargs` losses take individual arguments.
51 w_loss = tfgan.losses.wargs.wasserstein_discriminator_loss
[all...] |
| /external/tensorflow/tensorflow/examples/get_started/regression/ |
| custom_regression.py | 52 average_loss = tf.losses.mean_squared_error(labels, predictions)
|
| /external/tensorflow/tensorflow/examples/learn/ |
| iris_custom_decay_dnn.py | 50 loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
|
| iris_custom_model.py | 51 loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
|
| mnist.py | 80 loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
|
| multiple_gpu.py | 69 loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
|
| text_classification_character_rnn.py | 62 loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
|
