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Posted to commits@mxnet.apache.org by xi...@apache.org on 2020/10/12 05:21:20 UTC
[incubator-mxnet] branch master updated: BUGFIX Updated the
auto-encoder example. Fixes #18712 (#19321)
This is an automated email from the ASF dual-hosted git repository.
xidulu pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git
The following commit(s) were added to refs/heads/master by this push:
new 4dc9947 BUGFIX Updated the auto-encoder example. Fixes #18712 (#19321)
4dc9947 is described below
commit 4dc99472a510299017ed19347ed6d41f2f6680d2
Author: Aditya Trivedi <ia...@gmail.com>
AuthorDate: Mon Oct 12 10:49:11 2020 +0530
BUGFIX Updated the auto-encoder example. Fixes #18712 (#19321)
* Updated the auto-encoder example. Fixes #18712
* add Aditya Trivedi to contributors
---
CONTRIBUTORS.md | 1 +
.../autoencoder/convolutional_autoencoder.ipynb | 61 ++++++++++------------
2 files changed, 29 insertions(+), 33 deletions(-)
diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md
index c9fe77c..54fb107 100644
--- a/CONTRIBUTORS.md
+++ b/CONTRIBUTORS.md
@@ -137,6 +137,7 @@ List of Contributors
--------------------
* [Top-100 Contributors](https://github.com/apache/incubator-mxnet/graphs/contributors)
- To contributors: please add your name to the list when you submit a patch to the project:)
+* [Aditya Trivedi](https://github.com/iadi7ya)
* [Feng Wang](https://github.com/happynear)
- Feng makes MXNet compatible with Windows Visual Studio.
* [Jack Deng](https://github.com/jdeng)
diff --git a/example/autoencoder/convolutional_autoencoder.ipynb b/example/autoencoder/convolutional_autoencoder.ipynb
index a49eba0..a18ee55 100644
--- a/example/autoencoder/convolutional_autoencoder.ipynb
+++ b/example/autoencoder/convolutional_autoencoder.ipynb
@@ -108,41 +108,36 @@
"metadata": {},
"outputs": [],
"source": [
- "net = gluon.nn.HybridSequential(prefix='autoencoder_')\n",
- "with net.name_scope():\n",
- " # Encoder 1x28x28 -> 32x1x1\n",
- " encoder = gluon.nn.HybridSequential(prefix='encoder_')\n",
- " with encoder.name_scope():\n",
- " encoder.add(\n",
- " gluon.nn.Conv2D(channels=4, kernel_size=3, padding=1, strides=(2,2), activation='relu'),\n",
- " gluon.nn.BatchNorm(),\n",
- " gluon.nn.Conv2D(channels=8, kernel_size=3, padding=1, strides=(2,2), activation='relu'),\n",
- " gluon.nn.BatchNorm(),\n",
- " gluon.nn.Conv2D(channels=16, kernel_size=3, padding=1, strides=(2,2), activation='relu'),\n",
- " gluon.nn.BatchNorm(),\n",
- " gluon.nn.Conv2D(channels=32, kernel_size=3, padding=0, strides=(2,2),activation='relu'),\n",
- " gluon.nn.BatchNorm()\n",
- " )\n",
- " decoder = gluon.nn.HybridSequential(prefix='decoder_')\n",
- " # Decoder 32x1x1 -> 1x28x28\n",
- " with decoder.name_scope():\n",
- " decoder.add(\n",
- " gluon.nn.Conv2D(channels=32, kernel_size=3, padding=2, activation='relu'),\n",
- " gluon.nn.HybridLambda(lambda F, x: F.UpSampling(x, scale=2, sample_type='nearest')),\n",
- " gluon.nn.BatchNorm(),\n",
- " gluon.nn.Conv2D(channels=16, kernel_size=3, padding=1, activation='relu'),\n",
- " gluon.nn.HybridLambda(lambda F, x: F.UpSampling(x, scale=2, sample_type='nearest')),\n",
- " gluon.nn.BatchNorm(),\n",
- " gluon.nn.Conv2D(channels=8, kernel_size=3, padding=2, activation='relu'),\n",
- " gluon.nn.HybridLambda(lambda F, x: F.UpSampling(x, scale=2, sample_type='nearest')),\n",
- " gluon.nn.BatchNorm(),\n",
- " gluon.nn.Conv2D(channels=4, kernel_size=3, padding=1, activation='relu'),\n",
- " gluon.nn.Conv2D(channels=1, kernel_size=3, padding=1, activation='sigmoid')\n",
- " )\n",
- " net.add(\n",
+ "net = gluon.nn.HybridSequential()\n",
+ "encoder = gluon.nn.HybridSequential()\n",
+ "encoder.add(\n",
+ " gluon.nn.Conv2D(channels=4, kernel_size=3, padding=1, strides=(2,2), activation='relu'),\n",
+ " gluon.nn.BatchNorm(),\n",
+ " gluon.nn.Conv2D(channels=8, kernel_size=3, padding=1, strides=(2,2), activation='relu'),\n",
+ " gluon.nn.BatchNorm(),\n",
+ " gluon.nn.Conv2D(channels=16, kernel_size=3, padding=1, strides=(2,2), activation='relu'),\n",
+ " gluon.nn.BatchNorm(),\n",
+ " gluon.nn.Conv2D(channels=32, kernel_size=3, padding=0, strides=(2,2),activation='relu'),\n",
+ " gluon.nn.BatchNorm()\n",
+ ")\n",
+ "decoder = gluon.nn.HybridSequential()\n",
+ "decoder.add(\n",
+ " gluon.nn.Conv2D(channels=32, kernel_size=3, padding=2, activation='relu'),\n",
+ " gluon.nn.HybridLambda(lambda F, x: F.UpSampling(x, scale=2, sample_type='nearest')),\n",
+ " gluon.nn.BatchNorm(),\n",
+ " gluon.nn.Conv2D(channels=16, kernel_size=3, padding=1, activation='relu'),\n",
+ " gluon.nn.HybridLambda(lambda F, x: F.UpSampling(x, scale=2, sample_type='nearest')),\n",
+ " gluon.nn.BatchNorm(),\n",
+ " gluon.nn.Conv2D(channels=8, kernel_size=3, padding=2, activation='relu'),\n",
+ " gluon.nn.HybridLambda(lambda F, x: F.UpSampling(x, scale=2, sample_type='nearest')),\n",
+ " gluon.nn.BatchNorm(),\n",
+ " gluon.nn.Conv2D(channels=4, kernel_size=3, padding=1, activation='relu'),\n",
+ " gluon.nn.Conv2D(channels=1, kernel_size=3, padding=1, activation='sigmoid')\n",
+ ")\n",
+ "net.add(\n",
" encoder,\n",
" decoder\n",
- " )"
+ ")"
]
},
{