Modified notebook examples

notebooks/0_Prerequisite/ml_introduction.ipynb

@@ -26,23 +26,23 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "IPython (Python 2.7)",
+   "display_name": "PyCharm (jupyterconverter)",
    "language": "python",
-   "name": "python2"
+   "name": "pycharm-3058efaf"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.11"
+   "pygments_lexer": "ipython3",
+   "version": "3.6.8"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }

notebooks/0_Prerequisite/mnist_dataset_intro.ipynb

@@ -27,14 +27,27 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install tensorflow --user"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Import MNIST\n",
     "from tensorflow.examples.tutorials.mnist import input_data\n",
-    "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)\n",
+    "import os\n",
+    "data_path = \"./dataset/mnist_dataset_intro/\"\n",
+    "try:\n",
+    "    os.makedirs(data_path)\n",
+    "except FileExistsError:\n",
+    "    pass\n",
+    "mnist = input_data.read_data_sets(data_path, one_hot=True)\n",
     "\n",
     "# Load data\n",
     "X_train = mnist.train.images\n",
@@ -53,9 +66,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Get the next 64 images array and labels\n",
@@ -72,23 +83,32 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "PyCharm (jupyterconverter)",
    "language": "python",
-   "name": "python2"
+   "name": "pycharm-3058efaf"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.13"
+   "pygments_lexer": "ipython3",
+   "version": "3.6.8"
+  },
+  "pycharm": {
+   "stem_cell": {
+    "cell_type": "raw",
+    "source": [],
+    "metadata": {
+     "collapsed": false
+    }
+   }
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }

notebooks/0_Prerequisite/tensorflow_gpu_validator.ipynb

@@ -0,0 +1,88 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install tensorflow --user"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install wurlitzer --user\n",
+    "\n",
+    "import tensorflow as tf\n",
+    "# Creates a graph.\n",
+    "a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')\n",
+    "b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')\n",
+    "c = tf.matmul(a, b)\n",
+    "\n",
+    "tf.logging.set_verbosity(tf.logging.INFO)\n",
+    "# Creates a session with log_device_placement set to True.\n",
+    "sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))\n",
+    "\n",
+    "# Runs the op.\n",
+    "from wurlitzer import pipes\n",
+    "\n",
+    "with pipes() as (out, err):\n",
+    "    print(sess.run(c))\n",
+    "\n",
+    "print (out.read())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Creates a graph.\n",
+    "a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')\n",
+    "b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')\n",
+    "c = tf.matmul(a, b)\n",
+    "# Creates a session with log_device_placement set to True.\n",
+    "sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))\n",
+    "# Runs the op.\n",
+    "print(sess.run(c))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with tf.Session() as sess:\n",
+    "    devices = sess.list_devices()\n",
+    "    print(devices)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}