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2winsJonathan DEKHTIAR
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Jonathan DEKHTIAR
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Issue #642 - Add AtrousDeConv2dLayer (#662)
* Update visualize.py * Update README.md Add an example for Adversarial Learning * Update more.rst Update the URLs * Update more.rst * Update example.rst Add the same link of BEGAN implementation. * Update example.rst * Update example.rst * Create tutorial_tfslim.py fixes #552 * Update tutorial_tfslim.py * Update utils.py Fix #565 * Update utils.py * Create test_utils_predict.py related with #288, #565, #566 * Create test_utils_predict.py * Update utils.py * Update test_utils_predict.py * Update CHANGELOG.md related to #566 * Update test_utils_predict.py * Update CHANGELOG.md * Update CHANGELOG.md * Update CHANGELOG.md * Update test_utils_predict.py * Update test_utils_predict.py * Update test_utils_predict.py * Update test_utils_predict.py * Update test_utils_predict.py * Update test_utils_predict.py (fix Bad Coding Style) * Update test_utils_predict.py * Update CHANGELOG.md * Update CHANGELOG.md * Update CHANGELOG.md * Update CHANGELOG.md * Update convolution.py (Add AtrousConv2dTransLayer) * Add AtrousConv2dTransLayer * Fix some mistakes * Follow protocols * Fix coding style (yapf) * AtrousConv2dLayer fixed * AtrousConv2dTransposeLayer refactored * Fix coding style (yapf) * Fix error * Bias Add using premade tf func * Old TF Code Removed * Renamed to AtrousDeConv2dLayer * Update CHANGELOG.md * Release 1.8.6rc2 * Documentation Fix
1 parent bfffb58 commit 701d4d3

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‎.circleci/config.yml

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@@ -217,7 +217,7 @@ jobs:
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CONTAINER_TAG="$VERSION_PREFIX"-gpu-py3
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docker tag latest_py3_gpu:latest tensorlayer/tensorlayer:"$CONTAINER_TAG"
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docker push tensorlayer/tensorlayer:"$CONTAINER_TAG"
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CONTAINER_TAG="$VERSION_PREFIX"-py3-gpu
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docker tag latest_py3_gpu:latest tensorlayer/tensorlayer:"$CONTAINER_TAG"
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docker push tensorlayer/tensorlayer:"$CONTAINER_TAG"
@@ -411,7 +411,7 @@ jobs:
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CONTAINER_TAG="$TL_VERSION"-cpu-py3
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docker tag latest_py3_cpu:latest tensorlayer/tensorlayer:"$CONTAINER_TAG"
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docker push tensorlayer/tensorlayer:"$CONTAINER_TAG"
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CONTAINER_TAG="$TL_VERSION"-py3-cpu
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docker tag latest_py3_cpu:latest tensorlayer/tensorlayer:"$CONTAINER_TAG"
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docker push tensorlayer/tensorlayer:"$CONTAINER_TAG"
@@ -461,11 +461,26 @@ jobs:
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CONTAINER_TAG="$TL_VERSION"-gpu-py3
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docker tag latest_py3_gpu:latest tensorlayer/tensorlayer:"$CONTAINER_TAG"
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docker push tensorlayer/tensorlayer:"$CONTAINER_TAG"
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CONTAINER_TAG="$TL_VERSION"-py3-gpu
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docker tag latest_py3_gpu:latest tensorlayer/tensorlayer:"$CONTAINER_TAG"
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docker push tensorlayer/tensorlayer:"$CONTAINER_TAG"
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init_tag_build:
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working_directory: ~/build
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docker:
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- image: docker:git
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steps:
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- checkout
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- setup_remote_docker:
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reusable: true
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exclusive: true
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- run:
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name: Init Tag Deploy Build
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command: |
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echo "start tag workflow"
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###################################################################################
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###################################################################################
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###################################################################################
@@ -546,12 +561,21 @@ workflows:
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###################################################################################
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# TAGS BUILDS with TensorLayer installed from PyPI #
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###################################################################################
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- init_tag_build:
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filters:
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tags:
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only: /\d+\.\d+(\.\d+)?(\S*)?$/
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branches:
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ignore: /.*/
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- hold:
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type: approval
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requires:
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- init_tag_build
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filters:
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tags:
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only: /.*/
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only: /\d+\.\d+(\.\d+)?(\S*)?$/
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branches:
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ignore: /.*/
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- hold
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filters:
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tags:
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only: /.*/
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only: /\d+\.\d+(\.\d+)?(\S*)?$/
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branches:
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ignore: /.*/
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- hold
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filters:
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tags:
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only: /.*/
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only: /\d+\.\d+(\.\d+)?(\S*)?$/
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branches:
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ignore: /.*/
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- hold
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filters:
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tags:
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only: /.*/
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only: /\d+\.\d+(\.\d+)?(\S*)?$/
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branches:
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ignore: /.*/
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- hold
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filters:
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tags:
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only: /.*/
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only: /\d+\.\d+(\.\d+)?(\S*)?$/
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branches:
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ignore: /.*/

‎CHANGELOG.md

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@@ -94,6 +94,7 @@ To release a new version, please update the changelog as followed:
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- `AMSGrad` added on Optimizers page added (by @DEKHTIARJonathan in #636)
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- Layer:
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- ElementwiseLambdaLayer added to use custom function to connect multiple layer inputs (by @One-sixth in #579)
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- AtrousDeConv2dLayer added (by @2wins in #662)
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- Optimizer:
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- AMSGrad Optimizer added based on `On the Convergence of Adam and Beyond (ICLR 2018)` (by @DEKHTIARJonathan in #636)
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- Setup:
@@ -291,6 +292,6 @@ To release a new version, please update the changelog as followed:
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### Contributors
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@zsdonghao @luomai @DEKHTIARJonathan
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[Unreleased]: https://github.com/tensorlayer/tensorlayer/compare/1.8.6rc1...master
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[1.8.6]: https://github.com/tensorlayer/tensorlayer/compare/1.8.6rc1...1.8.5
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[Unreleased]: https://github.com/tensorlayer/tensorlayer/compare/1.8.5...master
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[1.8.6]: https://github.com/tensorlayer/tensorlayer/compare/1.8.6rc2...1.8.5
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[1.8.5]: https://github.com/tensorlayer/tensorlayer/compare/1.8.4...1.8.5

‎README.md

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[![Chinese Book](https://img.shields.io/badge/book-中文-blue.svg)](http://www.broadview.com.cn/book/5059/)
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[![PyPI version](https://badge.fury.io/py/tensorlayer.svg)](https://pypi.org/project/tensorlayer/)
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[![Github commits (since latest release)](https://img.shields.io/github/commits-since/tensorlayer/tensorlayer/latest.svg)](https://github.com/tensorlayer/tensorlayer/compare/1.8.6rc1...master)
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[![Github commits (since latest release)](https://img.shields.io/github/commits-since/tensorlayer/tensorlayer/latest.svg)](https://github.com/tensorlayer/tensorlayer/compare/1.8.6rc2...master)
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[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/tensorlayer.svg)](https://pypi.org/project/tensorlayer/)
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[![Supported TF Version](https://img.shields.io/badge/tensorflow-1.6.0+-blue.svg)](https://github.com/tensorflow/tensorflow/releases)
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‎README.rst

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:target: https://pypi.org/project/tensorlayer/
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.. image:: https://img.shields.io/github/commits-since/tensorlayer/tensorlayer/latest.svg
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:target: https://github.com/tensorlayer/tensorlayer/compare/1.8.6rc1...master
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:target: https://github.com/tensorlayer/tensorlayer/compare/1.8.6rc2...master
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.. image:: https://img.shields.io/pypi/pyversions/tensorlayer.svg
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:target: https://pypi.org/project/tensorlayer/

‎docs/modules/layers.rst

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@@ -254,6 +254,7 @@ Layer list
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DownSampling2dLayer
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AtrousConv1dLayer
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AtrousConv2dLayer
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AtrousDeConv2dLayer
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Conv1d
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Conv2d
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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.. autoclass:: AtrousConv2dLayer
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2D Atrous transposed convolution
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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.. autoclass:: AtrousDeConv2dLayer
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Convolutional layer (Simplified)
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-----------------------------------

‎example/tutorial_binarynet_cifar10_tfrecord.py

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@@ -108,26 +108,26 @@ def read_and_decode(filename, is_train=None):
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if is_train ==True:
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# 1. Randomly crop a [height, width] section of the image.
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img = tf.random_crop(img, [24, 24, 3])
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# 2. Randomly flip the image horizontally.
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img = tf.image.random_flip_left_right(img)
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# 3. Randomly change brightness.
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img = tf.image.random_brightness(img, max_delta=63)
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# 4. Randomly change contrast.
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img = tf.image.random_contrast(img, lower=0.2, upper=1.8)
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# 5. Subtract off the mean and divide by the variance of the pixels.
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try: # TF 0.12+
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img = tf.image.per_image_standardization(img)
120-
except Exception: # earlier TF versions
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img = tf.image.per_image_whitening(img)
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img = tf.image.per_image_standardization(img)
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123124
elif is_train == False:
124125
# 1. Crop the central [height, width] of the image.
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img = tf.image.resize_image_with_crop_or_pad(img, 24, 24)
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# 2. Subtract off the mean and divide by the variance of the pixels.
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try: # TF 0.12+
128-
img = tf.image.per_image_standardization(img)
129-
except Exception: # earlier TF versions
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img = tf.image.per_image_whitening(img)
129+
img = tf.image.per_image_standardization(img)
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elif is_train == None:
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img = img
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‎example/tutorial_cifar10_tfrecord.py

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@@ -109,26 +109,26 @@ def read_and_decode(filename, is_train=None):
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if is_train ==True:
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# 1. Randomly crop a [height, width] section of the image.
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img = tf.random_crop(img, [24, 24, 3])
112+
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# 2. Randomly flip the image horizontally.
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img = tf.image.random_flip_left_right(img)
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# 3. Randomly change brightness.
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img = tf.image.random_brightness(img, max_delta=63)
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# 4. Randomly change contrast.
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img = tf.image.random_contrast(img, lower=0.2, upper=1.8)
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# 5. Subtract off the mean and divide by the variance of the pixels.
119-
try: # TF 0.12+
120-
img = tf.image.per_image_standardization(img)
121-
except Exception: # earlier TF versions
122-
img = tf.image.per_image_whitening(img)
123+
img = tf.image.per_image_standardization(img)
123124

124125
elif is_train == False:
125126
# 1. Crop the central [height, width] of the image.
126127
img = tf.image.resize_image_with_crop_or_pad(img, 24, 24)
128+
127129
# 2. Subtract off the mean and divide by the variance of the pixels.
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try: # TF 0.12+
129-
img = tf.image.per_image_standardization(img)
130-
except Exception: # earlier TF versions
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img = tf.image.per_image_whitening(img)
130+
img = tf.image.per_image_standardization(img)
131+
132132
elif is_train == None:
133133
img = img
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‎example/tutorial_dorefanet_cifar10_tfrecord.py

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@@ -108,26 +108,26 @@ def read_and_decode(filename, is_train=None):
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if is_train ==True:
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# 1. Randomly crop a [height, width] section of the image.
110110
img = tf.random_crop(img, [24, 24, 3])
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# 2. Randomly flip the image horizontally.
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img = tf.image.random_flip_left_right(img)
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# 3. Randomly change brightness.
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img = tf.image.random_brightness(img, max_delta=63)
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# 4. Randomly change contrast.
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img = tf.image.random_contrast(img, lower=0.2, upper=1.8)
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# 5. Subtract off the mean and divide by the variance of the pixels.
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try: # TF 0.12+
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img = tf.image.per_image_standardization(img)
120-
except Exception: # earlier TF versions
121-
img = tf.image.per_image_whitening(img)
122+
img = tf.image.per_image_standardization(img)
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123124
elif is_train == False:
124125
# 1. Crop the central [height, width] of the image.
125126
img = tf.image.resize_image_with_crop_or_pad(img, 24, 24)
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# 2. Subtract off the mean and divide by the variance of the pixels.
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try: # TF 0.12+
128-
img = tf.image.per_image_standardization(img)
129-
except Exception: # earlier TF versions
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img = tf.image.per_image_whitening(img)
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img = tf.image.per_image_standardization(img)
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131131
elif is_train == None:
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img = img
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‎example/tutorial_inceptionV3_tfslim.py

+1-4
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@@ -129,10 +129,7 @@ def print_prob(prob):
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"Please download inception_v3 ckpt from https://github.com/tensorflow/models/tree/master/research/slim"
130130
)
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try: # TF12+
133-
saver.restore(sess, MODEL_PATH)
134-
except Exception: # TF11
135-
saver.restore(sess, MODEL_PATH)
132+
saver.restore(sess, MODEL_PATH)
136133
print("Model Restored")
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138135
y = network.outputs

‎example/tutorial_ptb_lstm.py

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# n_examples = batch_size * num_steps
259259
# so
260260
# cost is the averaged cost of each mini-batch (concurrent process).
261-
loss = tf.contrib.legacy_seq2seq.sequence_loss_by_example( # loss = tf.nn.seq2seq.sequence_loss_by_example( # TF0.12
262-
[outputs], [tf.reshape(targets, [-1])], [tf.ones_like(tf.reshape(targets, [-1]), dtype=tf.float32)])
261+
loss = tf.contrib.legacy_seq2seq.sequence_loss_by_example(
262+
[outputs], [tf.reshape(targets, [-1])], [tf.ones_like(tf.reshape(targets, [-1]), dtype=tf.float32)]
263+
)
263264
# [tf.ones([batch_size * num_steps])])
264265
cost = tf.reduce_sum(loss) / batch_size
265266
return cost

‎example/tutorial_ptb_lstm_state_is_tuple.py

+3-2
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264264
# n_examples = batch_size * num_steps
265265
# so
266266
# cost is the averaged cost of each mini-batch (concurrent process).
267-
loss = tf.contrib.legacy_seq2seq.sequence_loss_by_example( # loss = tf.nn.seq2seq.sequence_loss_by_example( # TF0.12
268-
[outputs], [tf.reshape(targets, [-1])], [tf.ones_like(tf.reshape(targets, [-1]), dtype=tf.float32)])
267+
loss = tf.contrib.legacy_seq2seq.sequence_loss_by_example(
268+
[outputs], [tf.reshape(targets, [-1])], [tf.ones_like(tf.reshape(targets, [-1]), dtype=tf.float32)]
269+
)
269270
# [tf.ones([batch_size * num_steps])])
270271
cost = tf.reduce_sum(loss) / batch_size
271272
return cost

‎example/tutorial_ternaryweight_cifar10_tfrecord.py

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@@ -107,26 +107,26 @@ def read_and_decode(filename, is_train=None):
107107
if is_train ==True:
108108
# 1. Randomly crop a [height, width] section of the image.
109109
img = tf.random_crop(img, [24, 24, 3])
110+
110111
# 2. Randomly flip the image horizontally.
111112
img = tf.image.random_flip_left_right(img)
113+
112114
# 3. Randomly change brightness.
113115
img = tf.image.random_brightness(img, max_delta=63)
116+
114117
# 4. Randomly change contrast.
115118
img = tf.image.random_contrast(img, lower=0.2, upper=1.8)
119+
116120
# 5. Subtract off the mean and divide by the variance of the pixels.
117-
try: # TF 0.12+
118-
img = tf.image.per_image_standardization(img)
119-
except Exception: # earlier TF versions
120-
img = tf.image.per_image_whitening(img)
121+
img = tf.image.per_image_standardization(img)
121122

122123
elif is_train == False:
123124
# 1. Crop the central [height, width] of the image.
124125
img = tf.image.resize_image_with_crop_or_pad(img, 24, 24)
126+
125127
# 2. Subtract off the mean and divide by the variance of the pixels.
126-
try: # TF 0.12+
127-
img = tf.image.per_image_standardization(img)
128-
except Exception: # earlier TF versions
129-
img = tf.image.per_image_whitening(img)
128+
img = tf.image.per_image_standardization(img)
129+
130130
elif is_train == None:
131131
img = img
132132

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