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BUGFIX Updated the auto-encoder example. Fixes #18712 #19321

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Oct 12, 2020
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BUGFIX Updated the auto-encoder example. Fixes #18712 #19321

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bfae021
Updated the auto-encoder example. Fixes #18712
iadi7ya Oct 9, 2020
a8690fb
add Aditya Trivedi to contributors
iadi7ya Oct 9, 2020
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1 change: 1 addition & 0 deletions CONTRIBUTORS.md
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Original file line number Diff line number Diff line change
Expand Up @@ -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)
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61 changes: 28 additions & 33 deletions example/autoencoder/convolutional_autoencoder.ipynb
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Expand Up @@ -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",
" )"
")"
]
},
{
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