Merge pull request aws#83 from awslabs/image_classification_notebooks

Image classification notebooks

Author: gurumurthys

introduction_to_amazon_algorithms/imageclassification_caltech/Image-classification-fulltraining.ipynb

@@ -46,7 +46,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "%%time\n",
@@ -75,7 +77,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "import os \n",
@@ -132,7 +136,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# The algorithm supports multiple network depth (number of layers). They are 18, 34, 50, 101, 152 and 200\n",
@@ -148,7 +154,7 @@
     "# batch size for training\n",
     "mini_batch_size =  \"64\"\n",
     "# number of epochs\n",
-    "epochs = \"20\"\n",
+    "epochs = \"2\"\n",
     "# learning rate\n",
     "learning_rate = \"0.01\""
    ]
@@ -165,6 +171,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
+    "collapsed": true,
     "scrolled": true
    },
    "outputs": [],
@@ -246,7 +253,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# create the Amazon SageMaker training job\n",
@@ -273,7 +282,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "training_info = sagemaker.describe_training_job(TrainingJobName=job_name)\n",
@@ -289,7 +300,7 @@
     "\n",
     "> `Training job ended with status: Completed`\n",
     "\n",
-    "then that means training sucessfully completed and the output LDA model was stored in the output path specified by `training_params['OutputDataConfig']`.\n",
+    "then that means training sucessfully completed and the output model was stored in the output path specified by `training_params['OutputDataConfig']`.\n",
     "\n",
     "You can also view information about and the status of a training job using the AWS SageMaker console. Just click on the \"Jobs\" tab."
    ]
@@ -324,7 +335,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "%%time\n",
@@ -370,7 +383,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "from time import gmtime, strftime\n",
@@ -400,7 +415,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "%%time\n",
@@ -428,7 +445,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# get the status of the endpoint\n",
@@ -474,7 +493,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "import boto3\n",
@@ -491,7 +512,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "!wget -O /tmp/test.jpg http://www.vision.caltech.edu/Image_Datasets/Caltech256/images/008.bathtub/008_0007.jpg\n",
@@ -504,7 +527,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "import json\n",
@@ -538,7 +563,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "#sage.delete_endpoint(EndpointName=endpoint_name)\n",
@@ -548,7 +575,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": []
   }

introduction_to_amazon_algorithms/imageclassification_caltech/Image-classification-transfer-learning.ipynb

@@ -167,7 +167,7 @@
     "# batch size for training\n",
     "mini_batch_size =  128\n",
     "# number of epochs\n",
-    "epochs = 10\n",
+    "epochs = 2\n",
     "# learning rate\n",
     "learning_rate = 0.01\n",
     "top_k=2\n",
@@ -317,7 +317,7 @@
     "\n",
     "> `Training job ended with status: Completed`\n",
     "\n",
-    "then that means training sucessfully completed and the output LDA model was stored in the output path specified by `training_params['OutputDataConfig']`.\n",
+    "then that means training sucessfully completed and the output model was stored in the output path specified by `training_params['OutputDataConfig']`.\n",
     "\n",
     "You can also view information about and the status of a training job using the AWS SageMaker console. Just click on the \"Jobs\" tab."
    ]