| /external/tensorflow/tensorflow/core/protobuf/tpu/ |
| D | optimization_parameters.proto | 13 // Dynamic learning rate specification in the TPUEmbeddingConfiguration. The
14 // actual learning rates are provided as a scalar input list to the
18 // For tables where learning rates are dynamically computed and communicated
19 // to the TPU embedding program, a tag must be specified for the learning
25 // learning rate, and specifies exactly one tag if it uses dynamic learning
33 // the same dynamic learning rate, for example, their dynamic learning rate
39 // communicate dynamic learning rates to the TPU embedding program.
41 // equal to the number of unique tags. The learning rate associated with a
47 // Source of learning rate to use.
69 // computing the effective learning rate. When update_accumulator_first is set
[all …]
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| /external/tensorflow/tensorflow/lite/micro/tools/make/templates/ |
| D | library.properties | 5 sentence=Allows you to run machine learning models locally on your device.
6 … runs TensorFlow machine learning models on microcontrollers, allowing you to build AI/ML applicat…
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| /external/tensorflow/tensorflow/core/api_def/base_api/ |
| D | api_def_SendTPUEmbeddingGradients.pbtxt | 18 A TensorList of float32 scalars, one for each dynamic learning
21 Multiple tables can share the same dynamic learning rate tag as specified
22 in the configuration. If the learning rates for all tables are constant,
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| D | api_def_ResourceApplyProximalGradientDescent.pbtxt | 40 summary: "Update \'*var\' as FOBOS algorithm with fixed learning rate."
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| /external/tensorflow/tensorflow/lite/g3doc/guide/ |
| D | index.md | 4 mobile, embedded, and IoT devices. It enables on-device machine learning
16 ### Machine learning at the edge
18 TensorFlow Lite is designed to make it easy to perform machine learning on
20 from a server. For developers, performing machine learning on-device can help
60 * *[Pre-trained models](../models)* for common machine learning tasks that can
63 how to deploy machine learning models on supported platforms.
115 Want to keep learning about TensorFlow Lite? Here are some next steps:
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| D | get_started.md | 14 a machine learning network trained to solve a particular problem. There are many
31 variety of machine learning problems. These models have been converted to work
51 ### Re-train a model (transfer learning)
53 Transfer learning allows you to take a trained model and re-train it to perform
59 You can use transfer learning to customize pre-trained models to your
60 application. Learn how to perform transfer learning in the
175 Some devices provide hardware acceleration for machine learning operations. For
217 Embedded Linux is an important platform for deploying machine learning. To get
252 Machine learning optimization is an evolving field, and TensorFlow Lite's
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| /external/XNNPACK/ |
| D | METADATA | 2 …by deep learning practitioners and researchers; instead it provides low-level performance primitiv…
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| /external/iproute2/ip/ |
| D | iplink_vxlan.c | 82 __u8 learning = 1; in vxlan_parse_opt() local
246 learning = 0; in vxlan_parse_opt()
249 learning = 1; in vxlan_parse_opt()
316 learning = 0; in vxlan_parse_opt()
385 addattr8(n, 1024, IFLA_VXLAN_LEARNING, learning); in vxlan_parse_opt()
500 __u8 learning = rta_getattr_u8(tb[IFLA_VXLAN_LEARNING]); in vxlan_print_opt() local
502 print_bool(PRINT_JSON, "learning", NULL, learning); in vxlan_print_opt()
503 if (!learning) in vxlan_print_opt()
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| /external/tensorflow/tensorflow/lite/tools/pip_package/debian/ |
| D | control | 25 TensorFlow Lite is the official solution for running machine learning models on
26 mobile and embedded devices. It enables on-device machine learning inference
|
| /external/tensorflow/tensorflow/c/ |
| D | generate-pc.sh | 70 Description: Library for computation using data flow graphs for scalable machine learning
84 Description: Library for computation using data flow graphs for scalable machine learning
|
| /external/tflite-support/tensorflow_lite_support/java/ |
| D | README.md | 15 that runs the on-device machine learning model uses tensors in the form of
32 for popular machine learning tasks, such as image classification, question and
|
| /external/iproute2/bridge/ |
| D | link.c | 276 __s8 learning = -1; in brlink_modify() local
313 if (!on_off("learning", &learning, *argv)) in brlink_modify()
402 if (learning >= 0) in brlink_modify()
403 addattr8(&req.n, sizeof(req), IFLA_BRPORT_LEARNING, learning); in brlink_modify()
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| /external/tensorflow/third_party/dlpack/ |
| D | BUILD.bazel | 2 # DLPack is a protocol for sharing arrays between deep learning frameworks.
|
| D | workspace.bzl | 1 """DLPack is a protocol for sharing arrays between deep learning frameworks."""
|
| /external/tensorflow/tensorflow/tools/ci_build/gpu_build/ |
| D | BUILD | 3 # learning applications.
|
| /external/tensorflow/tensorflow/compiler/mlir/tfr/resources/ |
| D | BUILD | 14 "//learning/brain/experimental/mlir/tfr/...",
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| /external/tensorflow/tensorflow/compiler/mlir/xla/ |
| D | BUILD | 16 "//learning/brain/experimental/dtensor/...",
17 "//learning/brain/experimental/mlir/...",
18 "//learning/brain/google/xla/kernels/...",
19 "//learning/brain/google/xla/mlir/...",
20 "//learning/deepmind/partir/...",
21 "//learning/pathways/data_parallel/tf2xla/...",
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| /external/tensorflow/tensorflow/lite/ |
| D | README.md | 4 devices. It enables low-latency inference of on-device machine learning models
|
| /external/tensorflow/tensorflow/compiler/mlir/lite/quantization/ |
| D | BUILD | 28 "//learning/brain/experimental/mlir/quantization/...",
29 "//learning/brain/experimental/mlir/tflite/tfmrt/...",
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| /external/tensorflow/tensorflow/compiler/xla/pjrt/ |
| D | BUILD | 75 "//learning/pathways/data_parallel:__pkg__",
232 "//learning/brain/research/jax:__subpackages__",
233 "//learning/deepmind/tensorflow/tensorfn:__subpackages__",
234 "//learning/pathways:__subpackages__",
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| /external/tensorflow/tensorflow/python/ops/linalg/sparse/ |
| D | BUILD | 14 "//learning/brain/python/ops:__pkg__",
|
| /external/tensorflow/tensorflow/compiler/mlir/lite/sparsity/ |
| D | BUILD | 14 "//learning/brain/experimental/mlir/...",
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| /external/tensorflow/tensorflow/lite/g3doc/models/image_classification/ |
| D | overview.md | 62 [transfer learning](https://www.tensorflow.org/tutorials/images/transfer_learning)
64 learning does not require a very large training dataset.
101 <a href="https://developers.google.com/machine-learning/crash-course/multi-class-neural-networks/so…
188 You can also use transfer learning to re-train a model to
194 Learn how to perform transfer learning in the
284 * [Transfer learning](https://www.tensorflow.org/tutorials/images/transfer_learning)
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| /external/tensorflow/third_party/tensorrt/ |
| D | BUILD.tpl | 2 # A high-performance deep learning inference optimizer and runtime.
|
| /external/linux-kselftest/tools/testing/selftests/net/forwarding/ |
| D | bridge_vlan_unaware.sh | 87 learning() function
|