Refactor tensorboard_basic for TF1.0

Signed-off-by: Norman Heckscher <norman.heckscher@gmail.com>

Author: normanheckscher

examples/4_Utils/tensorboard_basic.py

@@ -52,18 +52,18 @@
 init = tf.initialize_all_variables()
 
 # Create a summary to monitor cost tensor
-tf.scalar_summary("loss", cost)
+tf.summary.scalar("loss", cost)
 # Create a summary to monitor accuracy tensor
-tf.scalar_summary("accuracy", acc)
+tf.summary.scalar("accuracy", acc)
 # Merge all summaries into a single op
-merged_summary_op = tf.merge_all_summaries()
+merged_summary_op = tf.summary.merge_all()
 
 # Launch the graph
 with tf.Session() as sess:
     sess.run(init)
 
     # op to write logs to Tensorboard
-    summary_writer = tf.train.SummaryWriter(logs_path, graph=tf.get_default_graph())
+    summary_writer = tf.summary.FileWriter(logs_path, graph=tf.get_default_graph())
 
     # Training cycle
     for epoch in range(training_epochs):

notebooks/4_Utils/tensorboard_basic.ipynb

@@ -20,33 +20,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Extracting /tmp/data/train-images-idx3-ubyte.gz\n",
-      "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n",
-      "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n",
-      "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import tensorflow as tf\n",
     "\n",
     "# Import MINST data\n",
     "from tensorflow.examples.tutorials.mnist import input_data\n",
-    "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)"
+    "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 6,
    "metadata": {
     "collapsed": true
    },
@@ -72,9 +61,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 9,
    "metadata": {
-    "collapsed": true
+    "collapsed": false
    },
    "outputs": [],
    "source": [
@@ -95,19 +84,19 @@
     "    acc = tf.reduce_mean(tf.cast(acc, tf.float32))\n",
     "\n",
     "# Initializing the variables\n",
-    "init = tf.initialize_all_variables()\n",
+    "init = tf.global_variables_initializer()\n",
     "\n",
     "# Create a summary to monitor cost tensor\n",
-    "tf.scalar_summary(\"loss\", cost)\n",
+    "tf.summary.scalar(\"loss\", cost)\n",
     "# Create a summary to monitor accuracy tensor\n",
-    "tf.scalar_summary(\"accuracy\", acc)\n",
+    "tf.summary.scalar(\"accuracy\", acc)\n",
     "# Merge all summaries into a single op\n",
-    "merged_summary_op = tf.merge_all_summaries()"
+    "merged_summary_op = tf.summary.merge_all()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false
    },
@@ -116,31 +105,31 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch: 0001 cost= 1.182138957\n",
-      "Epoch: 0002 cost= 0.664735104\n",
-      "Epoch: 0003 cost= 0.552622685\n",
-      "Epoch: 0004 cost= 0.498596912\n",
-      "Epoch: 0005 cost= 0.465510372\n",
-      "Epoch: 0006 cost= 0.442504281\n",
-      "Epoch: 0007 cost= 0.425473650\n",
-      "Epoch: 0008 cost= 0.412175615\n",
-      "Epoch: 0009 cost= 0.401374554\n",
-      "Epoch: 0010 cost= 0.392403109\n",
-      "Epoch: 0011 cost= 0.384748503\n",
-      "Epoch: 0012 cost= 0.378154479\n",
-      "Epoch: 0013 cost= 0.372405099\n",
-      "Epoch: 0014 cost= 0.367272844\n",
-      "Epoch: 0015 cost= 0.362745077\n",
-      "Epoch: 0016 cost= 0.358575674\n",
-      "Epoch: 0017 cost= 0.354862829\n",
-      "Epoch: 0018 cost= 0.351437834\n",
-      "Epoch: 0019 cost= 0.348300697\n",
-      "Epoch: 0020 cost= 0.345401101\n",
-      "Epoch: 0021 cost= 0.342762216\n",
-      "Epoch: 0022 cost= 0.340199728\n",
-      "Epoch: 0023 cost= 0.337916089\n",
-      "Epoch: 0024 cost= 0.335764083\n",
-      "Epoch: 0025 cost= 0.333645939\n",
+      "Epoch: 0001 cost= 1.182138961\n",
+      "Epoch: 0002 cost= 0.664609327\n",
+      "Epoch: 0003 cost= 0.552565036\n",
+      "Epoch: 0004 cost= 0.498541865\n",
+      "Epoch: 0005 cost= 0.465393374\n",
+      "Epoch: 0006 cost= 0.442491178\n",
+      "Epoch: 0007 cost= 0.425474149\n",
+      "Epoch: 0008 cost= 0.412152022\n",
+      "Epoch: 0009 cost= 0.401320939\n",
+      "Epoch: 0010 cost= 0.392305281\n",
+      "Epoch: 0011 cost= 0.384732356\n",
+      "Epoch: 0012 cost= 0.378109478\n",
+      "Epoch: 0013 cost= 0.372409370\n",
+      "Epoch: 0014 cost= 0.367236996\n",
+      "Epoch: 0015 cost= 0.362727492\n",
+      "Epoch: 0016 cost= 0.358627345\n",
+      "Epoch: 0017 cost= 0.354815522\n",
+      "Epoch: 0018 cost= 0.351413656\n",
+      "Epoch: 0019 cost= 0.348314827\n",
+      "Epoch: 0020 cost= 0.345429416\n",
+      "Epoch: 0021 cost= 0.342749324\n",
+      "Epoch: 0022 cost= 0.340224642\n",
+      "Epoch: 0023 cost= 0.337897302\n",
+      "Epoch: 0024 cost= 0.335720168\n",
+      "Epoch: 0025 cost= 0.333691911\n",
       "Optimization Finished!\n",
       "Accuracy: 0.9143\n",
       "Run the command line:\n",
@@ -155,7 +144,7 @@
     "    sess.run(init)\n",
     "\n",
     "    # op to write logs to Tensorboard\n",
-    "    summary_writer = tf.train.SummaryWriter(logs_path, graph=tf.get_default_graph())\n",
+    "    summary_writer = tf.summary.FileWriter(logs_path, graph=tf.get_default_graph())\n",
     "\n",
     "    # Training cycle\n",
     "    for epoch in range(training_epochs):\n",
@@ -234,7 +223,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "IPython (Python 2.7)",
+   "display_name": "Python 2",
    "language": "python",
    "name": "python2"
   },
@@ -248,7 +237,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.11"
+   "version": "2.7.13"
   }
  },
  "nbformat": 4,