| /external/tensorflow/tensorflow/contrib/slim/python/slim/ |
| D | learning_test.py | 31 from tensorflow.contrib.slim.python.slim import learning
70 [gradients_to_variables] = learning.clip_gradient_norms(
85 [gradients_to_variables] = learning.clip_gradient_norms(
104 gradients_to_variables = learning.clip_gradient_norms(
131 learning.multiply_gradients(grad_to_var, gradient_multipliers)
138 learning.multiply_gradients([grad_to_var], {})
145 learning.multiply_gradients([grad_to_var], 3)
153 learning.multiply_gradients(grad_to_var, gradient_multipliers)
161 [grad_to_var] = learning.multiply_gradients([grad_to_var],
183 [grad_to_var] = learning.multiply_gradients([grad_to_var],
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| /external/tensorflow/tensorflow/examples/udacity/ |
| D | 6_lstm.ipynb | 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",
730 "Average loss at step 500 : 1.9320810616 learning rate: 10.0\n",
733 "Average loss at step 600 : 1.90935629249 learning rate: 10.0\n",
736 "Average loss at step 700 : 1.85583009005 learning rate: 10.0\n",
739 "Average loss at step 800 : 1.82152368546 learning rate: 10.0\n",
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| /external/tensorflow/tensorflow/docs_src/get_started/ |
| D | get_started_for_beginners.md | 3 This document explains how to use machine learning to classify (categorize)
9 * You know little to nothing about machine learning.
13 If you are already familiar with basic machine learning concepts
20 Iris flower you find. Machine learning provides many ways to classify flowers.
21 For instance, a sophisticated machine learning program could classify flowers
53 one of the canonical introductions to machine learning classification problems.
70 [**label**](https://developers.google.com/machine-learning/glossary/#label);
72 [**features**](https://developers.google.com/machine-learning/glossary/#feature).
76 * An [**example**](https://developers.google.com/machine-learning/glossary/#example)
81 Each label is naturally a string (for example, "setosa"), but machine learning
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| D | index.md | 3 TensorFlow is a tool for machine learning. While it contains a wide range of
10 new to machine learning.
12 experience in machine learning.
|
| D | premade_estimators.md | 114 [label](https://developers.google.com/machine-learning/glossary/#label).
181 [pre-made Estimators](https://developers.google.com/machine-learning/glossary/#pre-made_Estimator)
184 [custom Estimators](https://developers.google.com/machine-learning/glossary/#custom_Estimator).
209 * [`features`](https://developers.google.com/machine-learning/glossary/#feature) - A Python diction…
213 [label](https://developers.google.com/machine-learning/glossary/#label) for
273 A [**feature column**](https://developers.google.com/machine-learning/glossary/#feature_columns)
364 [epoch](https://developers.google.com/machine-learning/glossary/#epoch) of data.
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| /external/tensorflow/tensorflow/contrib/boosted_trees/proto/ |
| D | learner.proto | 31 // LearningRateConfig describes all supported learning rate tuners.
40 // Config for a fixed learning rate.
45 // Config for a tuned learning rate.
47 // Max learning rate. Must be strictly positive.
50 // Number of learning rate values to consider between [0, max_learning_rate).
133 // Learning rate. By default we use fixed learning rate of 0.1.
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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/contrib/slim/ |
| D | BUILD | 49 name = "learning",
50 srcs = ["python/slim/learning.py"],
76 ":learning",
137 ":learning",
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| D | README.md | 58 * [learning](https://www.tensorflow.org/code/tensorflow/contrib/slim/python/slim/learning.py):
118 trained or fine-tuned during learning and are loaded
121 are all other variables that are used during learning or evaluation but are not
123 a variable using during learning and evaluation but it is not actually part of
429 learning models, require the use of multiple loss functions simultaneously. In
518 [learning.py](https://www.tensorflow.org/code/tensorflow/contrib/slim/python/slim/learning.py).
523 call `slim.learning.create_train_op` and `slim.learning.train` to perform the
537 train_op = slim.learning.create_train_op(total_loss, optimizer)
540 slim.learning.train(
548 In this example, `slim.learning.train` is provided with the `train_op` which is
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| /external/tensorflow/tensorflow/contrib/model_pruning/ |
| D | BUILD | 57 name = "learning",
58 srcs = ["python/learning.py"],
123 ":learning",
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| /external/tensorflow/tensorflow/docs_src/about/ |
| D | bib.md | 9 **Abstract:** TensorFlow is an interface for expressing machine learning
19 used for conducting research and for deploying machine learning
100 TensorFlow: Large-scale machine learning on heterogeneous systems,
110 **Abstract:** TensorFlow is a machine learning system that operates at
127 it has become widely used for machine learning research.
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| /external/tensorflow/tensorflow/docs_src/tutorials/ |
| D | linear.md | 11 deep learning to get the advantages of both.
19 with basic machine learning concepts, and also with
27 For example, if you have [data](https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adu…
34 example, [**logistic regression**](https://developers.google.com/machine-learning/glossary/#logisti…
48 with learning rates, etc.
52 * provide an excellent starting point for learning about machine learning.
222 ### Wide and deep learning
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| D | index.md | 24 These tutorials focus on machine learning problems dealing with sequence data.
55 Although TensorFlow specializes in machine learning, the core of TensorFlow is
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| /external/tensorflow/tensorflow/contrib/model_pruning/python/ |
| D | learning.py | 55 train_step = _slim.learning.train_step
162 total_loss, _ = _slim.learning.train(
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| /external/tensorflow/tensorflow/docs_src/mobile/ |
| D | mobile_intro.md | 3 TensorFlow was designed from the ground up to be a good deep learning solution
5 understand how machine learning can work on mobile platforms and how to
21 ## Common use cases for mobile machine learning
25 Traditionally, deep learning has been associated with data centers and giant
32 Here are some common use cases for on-device deep learning:
145 that deep learning can offer very natural-sounding speech.
147 ## Mobile machine learning and the cloud
184 1. Determine whether your problem is solvable by mobile machine learning
190 ### Is your problem solvable by mobile machine learning?
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| D | index.md | 3 TensorFlow was designed to be a good deep learning solution for mobile
4 platforms. Currently we have two solutions for deploying machine learning
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| /external/tensorflow/tensorflow/docs_src/mobile/tflite/ |
| D | index.md | 4 devices. It enables on-device machine learning inference with low latency and a
69 We believe the next wave of machine learning applications will have significant
144 models. This technique is called transfer learning, which starts with a model
146 similar problem. Deep learning from scratch can take days, but transfer learning
192 Future plans include using specialized machine learning hardware to get the best
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| /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/tensorflow/tools/dist_test/server/ |
| D | Dockerfile.test | 66 http://mlr.cs.umass.edu/ml/machine-learning-databases/adult/adult.data
68 http://mlr.cs.umass.edu/ml/machine-learning-databases/adult/adult.test
|
| /external/tensorflow/tensorflow/tools/ci_build/gpu_build/ |
| D | BUILD | 3 # learning applications.
|
| /external/tensorflow/tensorflow/compiler/tf2xla/python/ |
| D | BUILD | 5 "//learning/tfx:__subpackages__",
|
| /external/tensorflow/tensorflow/c/ |
| D | generate-pc.sh | 63 Description: Library for computation using data flow graphs for scalable machine learning
|
| /external/tensorflow/tensorflow/python/ |
| D | BUILD | 962 visibility = ["//learning/brain/python/ops:__pkg__"],
1316 "//learning/brain/python/ops:__pkg__",
1327 "//learning/brain/python/ops:__pkg__",
1338 "//learning/brain/python/ops:__pkg__",
1345 visibility = ["//learning/brain/python/ops:__pkg__"],
1359 "//learning/brain/python/ops:__pkg__",
1375 "//learning/brain/python/ops:__pkg__",
1383 "//learning/brain/python/ops:__pkg__",
1391 visibility = ["//learning/brain/python/ops:__pkg__"],
1397 "//learning/brain/python/ops:__pkg__",
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| /external/tensorflow/tensorflow/contrib/kfac/examples/ |
| D | BUILD | 2 "//learning/brain/contrib/kfac/examples:__subpackages__",
|
| /external/tensorflow/tensorflow/core/api_def/base_api/ |
| D | api_def_ResourceApplyProximalGradientDescent.pbtxt | 40 summary: "Update \'*var\' as FOBOS algorithm with fixed learning rate."
|