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Mechanical replacement of http://storage.googleapis.com with https equivalent.

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PiperOrigin-RevId: 260023596
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Frank Chen 2019-07-25 14:40:06 -07:00 committed by TensorFlower Gardener
parent f8d304cfd8
commit ed0761ba56
6 changed files with 22 additions and 20 deletions
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5
WORKSPACE
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@ -104,7 +104,6 @@ http_archive(
build_file = "//:models.BUILD", build_file = "//:models.BUILD",
sha256 = "7efe12a8363f09bc24d7b7a450304a15655a57a7751929b2c1593a71183bb105", sha256 = "7efe12a8363f09bc24d7b7a450304a15655a57a7751929b2c1593a71183bb105",
urls = [ urls = [
"http://storage.googleapis.com/download.tensorflow.org/models/inception_v1.zip",
"https://storage.googleapis.com/download.tensorflow.org/models/inception_v1.zip", "https://storage.googleapis.com/download.tensorflow.org/models/inception_v1.zip",
], ],
) )
@ -114,7 +113,6 @@ http_archive(
build_file = "//:models.BUILD", build_file = "//:models.BUILD",
sha256 = "bddd81ea5c80a97adfac1c9f770e6f55cbafd7cce4d3bbe15fbeb041e6b8f3e8", sha256 = "bddd81ea5c80a97adfac1c9f770e6f55cbafd7cce4d3bbe15fbeb041e6b8f3e8",
urls = [ urls = [
"http://storage.googleapis.com/download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_android_export.zip",
"https://storage.googleapis.com/download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_android_export.zip", "https://storage.googleapis.com/download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_android_export.zip",
], ],
) )
@ -124,7 +122,6 @@ http_archive(
build_file = "//:models.BUILD", build_file = "//:models.BUILD",
sha256 = "859edcddf84dddb974c36c36cfc1f74555148e9c9213dedacf1d6b613ad52b96", sha256 = "859edcddf84dddb974c36c36cfc1f74555148e9c9213dedacf1d6b613ad52b96",
urls = [ urls = [
"http://storage.googleapis.com/download.tensorflow.org/models/mobile_multibox_v1a.zip",
"https://storage.googleapis.com/download.tensorflow.org/models/mobile_multibox_v1a.zip", "https://storage.googleapis.com/download.tensorflow.org/models/mobile_multibox_v1a.zip",
], ],
) )
@ -134,7 +131,6 @@ http_archive(
build_file = "//:models.BUILD", build_file = "//:models.BUILD",
sha256 = "3d374a730aef330424a356a8d4f04d8a54277c425e274ecb7d9c83aa912c6bfa", sha256 = "3d374a730aef330424a356a8d4f04d8a54277c425e274ecb7d9c83aa912c6bfa",
urls = [ urls = [
"http://storage.googleapis.com/download.tensorflow.org/models/stylize_v1.zip",
"https://storage.googleapis.com/download.tensorflow.org/models/stylize_v1.zip", "https://storage.googleapis.com/download.tensorflow.org/models/stylize_v1.zip",
], ],
) )
@ -144,7 +140,6 @@ http_archive(
build_file = "//:models.BUILD", build_file = "//:models.BUILD",
sha256 = "c3ec4fea3158eb111f1d932336351edfe8bd515bb6e87aad4f25dbad0a600d0c", sha256 = "c3ec4fea3158eb111f1d932336351edfe8bd515bb6e87aad4f25dbad0a600d0c",
urls = [ urls = [
"http://storage.googleapis.com/download.tensorflow.org/models/speech_commands_v0.01.zip",
"https://storage.googleapis.com/download.tensorflow.org/models/speech_commands_v0.01.zip", "https://storage.googleapis.com/download.tensorflow.org/models/speech_commands_v0.01.zip",
], ],
) )

17
tensorflow/examples/tutorials/deepdream/README.md
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@ -5,11 +5,18 @@ by [Alexander Mordvintsev](mailto:moralex@google.com)
This directory contains Jupyter notebook that demonstrates a number of Convolutional Neural Network This directory contains Jupyter notebook that demonstrates a number of Convolutional Neural Network
image generation techniques implemented with TensorFlow: image generation techniques implemented with TensorFlow:
- visualizing individual feature channels and their combinations to explore the space of patterns learned by the neural network (see [GoogLeNet](http://storage.googleapis.com/deepdream/visualz/tensorflow_inception/index.html) and [VGG16](http://storage.googleapis.com/deepdream/visualz/vgg16/index.html) galleries) - visualizing individual feature channels and their combinations to explore
- embedding TensorBoard graph visualizations into Jupyter notebooks the space of patterns learned by the neural network (see
- producing high-resolution images with tiled computation ([example](http://storage.googleapis.com/deepdream/pilatus_flowers.jpg)) [GoogLeNet](https://storage.googleapis.com/deepdream/visualz/tensorflow_inception/index.html)
- using Laplacian Pyramid Gradient Normalization to produce smooth and colorful visuals at low cost and
- generating DeepDream-like images with TensorFlow [VGG16](https://storage.googleapis.com/deepdream/visualz/vgg16/index.html)
galleries)
- embedding TensorBoard graph visualizations into Jupyter notebooks
- producing high-resolution images with tiled computation
([example](https://storage.googleapis.com/deepdream/pilatus_flowers.jpg))
- using Laplacian Pyramid Gradient Normalization to produce smooth and
colorful visuals at low cost
- generating DeepDream-like images with TensorFlow
You can view "deepdream.ipynb" directly on GitHub. Note that GitHub Jupyter notebook preview removes You can view "deepdream.ipynb" directly on GitHub. Note that GitHub Jupyter notebook preview removes
embedded graph visualizations. You can still see them online embedded graph visualizations. You can still see them online

8
tensorflow/examples/tutorials/deepdream/deepdream.ipynb
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@ -40,14 +40,14 @@
"source": [ "source": [
"This notebook demonstrates a number of Convolutional Neural Network image generation techniques implemented with TensorFlow for fun and science:\n", "This notebook demonstrates a number of Convolutional Neural Network image generation techniques implemented with TensorFlow for fun and science:\n",
"\n", "\n",
"- visualize individual feature channels and their combinations to explore the space of patterns learned by the neural network (see [GoogLeNet](http://storage.googleapis.com/deepdream/visualz/tensorflow_inception/index.html) and [VGG16](http://storage.googleapis.com/deepdream/visualz/vgg16/index.html) galleries)\n", "- visualize individual feature channels and their combinations to explore the space of patterns learned by the neural network (see [GoogLeNet](https://storage.googleapis.com/deepdream/visualz/tensorflow_inception/index.html) and [VGG16](https://storage.googleapis.com/deepdream/visualz/vgg16/index.html) galleries)\n",
"- embed TensorBoard graph visualizations into Jupyter notebooks\n", "- embed TensorBoard graph visualizations into Jupyter notebooks\n",
"- produce high-resolution images with tiled computation ([example](http://storage.googleapis.com/deepdream/pilatus_flowers.jpg))\n", "- produce high-resolution images with tiled computation ([example](https://storage.googleapis.com/deepdream/pilatus_flowers.jpg))\n",
"- use Laplacian Pyramid Gradient Normalization to produce smooth and colorful visuals at low cost\n", "- use Laplacian Pyramid Gradient Normalization to produce smooth and colorful visuals at low cost\n",
"- generate DeepDream-like images with TensorFlow (DogSlugs included)\n", "- generate DeepDream-like images with TensorFlow (DogSlugs included)\n",
"\n", "\n",
"\n", "\n",
"The network under examination is the [GoogLeNet architecture](http://arxiv.org/abs/1409.4842), trained to classify images into one of 1000 categories of the [ImageNet](http://image-net.org/) dataset. It consists of a set of layers that apply a sequence of transformations to the input image. The parameters of these transformations were determined during the training process by a variant of gradient descent algorithm. The internal image representations may seem obscure, but it is possible to visualize and interpret them. In this notebook we are going to present a few tricks that allow to make these visualizations both efficient to generate and even beautiful. Impatient readers can start with exploring the full galleries of images generated by the method described here for [GoogLeNet](http://storage.googleapis.com/deepdream/visualz/tensorflow_inception/index.html) and [VGG16](http://storage.googleapis.com/deepdream/visualz/vgg16/index.html) architectures." "The network under examination is the [GoogLeNet architecture](http://arxiv.org/abs/1409.4842), trained to classify images into one of 1000 categories of the [ImageNet](http://image-net.org/) dataset. It consists of a set of layers that apply a sequence of transformations to the input image. The parameters of these transformations were determined during the training process by a variant of gradient descent algorithm. The internal image representations may seem obscure, but it is possible to visualize and interpret them. In this notebook we are going to present a few tricks that allow to make these visualizations both efficient to generate and even beautiful. Impatient readers can start with exploring the full galleries of images generated by the method described here for [GoogLeNet](https://storage.googleapis.com/deepdream/visualz/tensorflow_inception/index.html) and [VGG16](https://storage.googleapis.com/deepdream/visualz/vgg16/index.html) architectures."
] ]
}, },
{ {
@ -1117,7 +1117,7 @@
"id": "mYsY6_Ngpfwl" "id": "mYsY6_Ngpfwl"
}, },
"source": [ "source": [
"Don't hesitate to use higher resolution inputs (also increase the number of octaves)! Here is an [example](http://storage.googleapis.com/deepdream/pilatus_flowers.jpg) of running the flower dream over the bigger image." "Don't hesitate to use higher resolution inputs (also increase the number of octaves)! Here is an [example](https://storage.googleapis.com/deepdream/pilatus_flowers.jpg) of running the flower dream over the bigger image."
] ]
}, },
{ {

2
tensorflow/lite/g3doc/guide/hosted_models.md
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@ -113,7 +113,7 @@ For more information about object detection, see
The object detection model we currently host is The object detection model we currently host is
**coco_ssd_mobilenet_v1_1.0_quant_2018_06_29**. **coco_ssd_mobilenet_v1_1.0_quant_2018_06_29**.
<a class="button button-primary" href="http://storage.googleapis.com/download.tensorflow.org/models/tflite/coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip">Download <a class="button button-primary" href="https://storage.googleapis.com/download.tensorflow.org/models/tflite/coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip">Download
model and labels</a> model and labels</a>
## Pose estimation ## Pose estimation

8
tensorflow/lite/g3doc/models/object_detection/overview.md
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@ -20,7 +20,7 @@ If you are using a platform other than Android or iOS, or you are already
familiar with the <a href="https://www.tensorflow.org/api_docs/python/tf/lite">TensorFlow Lite APIs</a>, you can familiar with the <a href="https://www.tensorflow.org/api_docs/python/tf/lite">TensorFlow Lite APIs</a>, you can
download our starter object detection model and the accompanying labels. download our starter object detection model and the accompanying labels.
<a class="button button-primary" href="http://storage.googleapis.com/download.tensorflow.org/models/tflite/coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip">Download <a class="button button-primary" href="https://storage.googleapis.com/download.tensorflow.org/models/tflite/coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip">Download
starter model and labels</a> starter model and labels</a>
For more information about the starter model, see For more information about the starter model, see
@ -185,7 +185,7 @@ Note: Object detection models accept input images of a specific size. This is li
We recommend starting with this pre-trained quantized COCO SSD MobileNet v1 We recommend starting with this pre-trained quantized COCO SSD MobileNet v1
model. model.
<a class="button button-primary" href="http://storage.googleapis.com/download.tensorflow.org/models/tflite/coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip">Download <a class="button button-primary" href="https://storage.googleapis.com/download.tensorflow.org/models/tflite/coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip">Download
starter model and labels</a> starter model and labels</a>
### Uses and limitations ### Uses and limitations
@ -193,7 +193,7 @@ starter model and labels</a>
The object detection model we provide can identify and locate up to 10 objects The object detection model we provide can identify and locate up to 10 objects
in an image. It is trained to recognize 80 classes of object. For a full list of in an image. It is trained to recognize 80 classes of object. For a full list of
classes, see the labels file in the classes, see the labels file in the
<a href="http://storage.googleapis.com/download.tensorflow.org/models/tflite/coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip">model <a href="https://storage.googleapis.com/download.tensorflow.org/models/tflite/coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip">model
zip</a>. zip</a>.
If you want to train a model to recognize new classes, see If you want to train a model to recognize new classes, see
@ -256,7 +256,7 @@ each object. There will always be 10 objects detected.
The pre-trained models we provide are trained to detect 80 classes of object. The pre-trained models we provide are trained to detect 80 classes of object.
For a full list of classes, see the labels file in the For a full list of classes, see the labels file in the
<a href="http://storage.googleapis.com/download.tensorflow.org/models/tflite/coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip">model <a href="https://storage.googleapis.com/download.tensorflow.org/models/tflite/coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip">model
zip</a>. zip</a>.
You can use a technique known as transfer learning to re-train a model to You can use a technique known as transfer learning to re-train a model to

2
tensorflow/workspace.bzl
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@ -829,7 +829,7 @@ def tf_repositories(path_prefix = "", tf_repo_name = ""):
strip_prefix = "ssd_mobilenet_v1_quantized_300x300_coco14_sync_2018_07_18", strip_prefix = "ssd_mobilenet_v1_quantized_300x300_coco14_sync_2018_07_18",
urls = [ urls = [
"https://storage.googleapis.com/mirror.tensorflow.org/storage.googleapis.com/download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_quantized_300x300_coco14_sync_2018_07_18.tar.gz", "https://storage.googleapis.com/mirror.tensorflow.org/storage.googleapis.com/download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_quantized_300x300_coco14_sync_2018_07_18.tar.gz",
"http://storage.googleapis.com/download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_quantized_300x300_coco14_sync_2018_07_18.tar.gz", "https://storage.googleapis.com/download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_quantized_300x300_coco14_sync_2018_07_18.tar.gz",
], ],
) )