[GitHub] anirudhacharya commented on a change in pull request #12933: Update autoencoder example

[GitBox <gi...@apache.org>]

anirudhacharya commented on a change in pull request #12933: Update autoencoder example
URL: https://github.com/apache/incubator-mxnet/pull/12933#discussion_r230610928
 
 

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 File path: example/autoencoder/convolutional_autoencoder.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Convolutional Autoencoder"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![](https://cdn-images-1.medium.com/max/800/1*LSYNW5m3TN7xRX61BZhoZA.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this example we will demonstrate how you can create a convolutional autoencoder in Gluon"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import random\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import mxnet as mx\n",
+    "from mxnet import autograd, nd, gluon"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data\n",
+    "\n",
+    "We will use the FashionMNIST dataset which is of a similar format than MNIST but is richer and has more variance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "batch_size = 512\n",
+    "ctx = mx.gpu() if len(mx.test_utils.list_gpus()) > 0 else mx.cpu()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "transform = lambda x,y: (x.transpose((2,0,1)).astype('float32')/255., y)\n",
+    "\n",
+    "train_dataset = gluon.data.vision.FashionMNIST(train=True)\n",
+    "test_dataset = gluon.data.vision.FashionMNIST(train=False)\n",
+    "\n",
+    "train_dataset_t = train_dataset.transform(transform)\n",
+    "test_dataset_t = test_dataset.transform(transform)\n",
+    "\n",
+    "train_data = gluon.data.DataLoader(train_dataset_t, batch_size=batch_size, last_batch='rollover', shuffle=True, num_workers=5)\n",
+    "test_data = gluon.data.DataLoader(test_dataset_t, batch_size=batch_size, last_batch='rollover', shuffle=True, num_workers=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1440x720 with 10 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20,10))\n",
+    "for i in range(10):\n",
+    "    ax = plt.subplot(1, 10, i+1)\n",
+    "    ax.imshow(train_dataset[i][0].squeeze().asnumpy(), cmap='gray')\n",
+    "    ax.axis('off')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Network"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "net = gluon.nn.HybridSequential(prefix='autoencoder_')\n",
+    "with net.name_scope():\n",
+    "    # Encoder 1x28x28 -> 32x1x1\n",
 
 Review comment:
   Just a question. So here, the dimension of the latent space is 32 right. and the decoder reconstructs the image from this 32 dimensional vector. Should the dimension of the latent space be configurable with a parameter? 
   
   It gives more room for a potential user to play around with.

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