tensorboard example

652c65f0 · Oiwi Parker Jones · 1b1114e0 · 652c65f0
Commit 652c65f0 authored 7 years ago by Oiwi Parker Jones
--- a/talks/tensorflow/demos/tensorboard_demo.ipynb
+++ b/talks/tensorflow/demos/tensorboard_demo.ipynb
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Extracting /tmp/TensorBoard_demo/data/train-images-idx3-ubyte.gz\n",
+      "Extracting /tmp/TensorBoard_demo/data/train-labels-idx1-ubyte.gz\n",
+      "Extracting /tmp/TensorBoard_demo/data/t10k-images-idx3-ubyte.gz\n",
+      "Extracting /tmp/TensorBoard_demo/data/t10k-labels-idx1-ubyte.gz\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import os.path\n",
+    "import shutil\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "LOGDIR = \"/tmp/TensorBoard_demo/\"\n",
+    "LABELS = os.path.join(os.getcwd(), \"labels_1024.tsv\")\n",
+    "SPRITES = os.path.join(os.getcwd(), \"sprite_1024.png\")\n",
+    "### MNIST EMBEDDINGS ###\n",
+    "mnist = tf.contrib.learn.datasets.mnist.read_data_sets(train_dir=LOGDIR + \"data\", one_hot=True)\n",
+    "### Get a sprite and labels file for the embedding projector ###\n",
+    "\n",
+    "if not (os.path.isfile(LABELS) and os.path.isfile(SPRITES)):\n",
+    "  print(\"Necessary data files were not found: LABELS and SPRITES\")\n",
+    "  exit(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def conv_layer(input, size_in, size_out, name=\"conv\"):\n",
+    "  with tf.name_scope(name):\n",
+    "    w = tf.Variable(tf.truncated_normal([5, 5, size_in, size_out], stddev=0.1), name=\"W\")\n",
+    "    b = tf.Variable(tf.constant(0.1, shape=[size_out]), name=\"B\")\n",
+    "    conv = tf.nn.conv2d(input, w, strides=[1, 1, 1, 1], padding=\"SAME\")\n",
+    "    act = tf.nn.relu(conv + b)\n",
+    "    tf.summary.histogram(\"weights\", w)\n",
+    "    tf.summary.histogram(\"biases\", b)\n",
+    "    tf.summary.histogram(\"activations\", act)\n",
+    "    return tf.nn.max_pool(act, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding=\"SAME\")\n",
+    "\n",
+    "\n",
+    "def fc_layer(input, size_in, size_out, name=\"fc\"):\n",
+    "  with tf.name_scope(name):\n",
+    "    w = tf.Variable(tf.truncated_normal([size_in, size_out], stddev=0.1), name=\"W\")\n",
+    "    b = tf.Variable(tf.constant(0.1, shape=[size_out]), name=\"B\")\n",
+    "    act = tf.matmul(input, w) + b\n",
+    "    tf.summary.histogram(\"weights\", w)\n",
+    "    tf.summary.histogram(\"biases\", b)\n",
+    "    tf.summary.histogram(\"activations\", act)\n",
+    "    return act"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def mnist_model(learning_rate):\n",
+    "  tf.reset_default_graph()\n",
+    "  sess = tf.Session()\n",
+    "\n",
+    "  # Setup placeholders, and reshape the data\n",
+    "  x = tf.placeholder(tf.float32, shape=[None, 784], name=\"x\")\n",
+    "  x_image = tf.reshape(x, [-1, 28, 28, 1])\n",
+    "  tf.summary.image('input', x_image, 3)\n",
+    "  y = tf.placeholder(tf.float32, shape=[None, 10], name=\"labels\")\n",
+    "\n",
+    "  conv_out = conv_layer(x_image, 1, 16, \"conv\")\n",
+    "\n",
+    "  flattened = tf.reshape(conv_out, [-1, 7 * 7 * 64])\n",
+    "\n",
+    "  embedding_input = flattened\n",
+    "  embedding_size = 7*7*64\n",
+    "  logits = fc_layer(flattened, 7*7*64, 10, \"fc\")\n",
+    "\n",
+    "  with tf.name_scope(\"xent\"):\n",
+    "    xent = tf.reduce_mean(\n",
+    "        tf.nn.softmax_cross_entropy_with_logits(\n",
+    "            logits=logits, labels=y), name=\"xent\")\n",
+    "    tf.summary.scalar(\"xent\", xent)\n",
+    "\n",
+    "  with tf.name_scope(\"train\"):\n",
+    "    train_step = tf.train.AdamOptimizer(learning_rate).minimize(xent)\n",
+    "\n",
+    "  with tf.name_scope(\"accuracy\"):\n",
+    "    correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))\n",
+    "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
+    "    tf.summary.scalar(\"accuracy\", accuracy)\n",
+    "\n",
+    "  summ = tf.summary.merge_all()\n",
+    "\n",
+    "\n",
+    "  embedding = tf.Variable(tf.zeros([1024, embedding_size]), name=\"test_embedding\")\n",
+    "  assignment = embedding.assign(embedding_input)\n",
+    "  saver = tf.train.Saver()\n",
+    "\n",
+    "  sess.run(tf.global_variables_initializer())\n",
+    "  writer = tf.summary.FileWriter(LOGDIR)\n",
+    "  writer.add_graph(sess.graph)\n",
+    "\n",
+    "  config = tf.contrib.tensorboard.plugins.projector.ProjectorConfig()\n",
+    "  embedding_config = config.embeddings.add()\n",
+    "  embedding_config.tensor_name = embedding.name\n",
+    "  embedding_config.sprite.image_path = SPRITES\n",
+    "  embedding_config.metadata_path = LABELS\n",
+    "  # Specify the width and height of a single thumbnail.\n",
+    "  embedding_config.sprite.single_image_dim.extend([28, 28])\n",
+    "  tf.contrib.tensorboard.plugins.projector.visualize_embeddings(writer, config)\n",
+    "\n",
+    "  for i in range(1,2001):\n",
+    "    batch = mnist.train.next_batch(100)\n",
+    "    if i % 5 == 0:\n",
+    "      [train_accuracy, s] = sess.run([accuracy, summ], feed_dict={x: batch[0], y: batch[1]})\n",
+    "      writer.add_summary(s, i)\n",
+    "    if i % 500 == 0:\n",
+    "      sess.run(assignment, feed_dict={x: mnist.test.images[:1024], y: mnist.test.labels[:1024]})\n",
+    "      saver.save(sess, os.path.join(LOGDIR, \"model.ckpt\"), i)\n",
+    "    sess.run(train_step, feed_dict={x: batch[0], y: batch[1]})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done training!\n",
+      "Run `tensorboard --logdir=/tmp/TensorBoard_demo/` to see the results.\n",
+      "Running on mac? If you want to get rid of the dialogue asking to give network permissions to TensorBoard, you can provide this flag: --host=localhost\n"
+     ]
+    }
+   ],
+   "source": [
+    "learning_rate = 1E-3\n",
+    "\n",
+    "mnist_model(learning_rate)\n",
+    "print('Done training!')\n",
+    "print('Run `tensorboard --logdir=%s` to see the results.' % LOGDIR)\n",
+    "print('Running on mac? If you want to get rid of the dialogue asking to give '\n",
+    "        'network permissions to TensorBoard, you can provide this flag: '\n",
+    "        '--host=localhost')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting TensorBoard b'47' at http://0.0.0.0:6006\n",
+      "(Press CTRL+C to quit)\n",
+      "WARNING:tensorflow:Found more than one graph event per run, or there was a metagraph containing a graph_def, as well as one or more graph events.  Overwriting the graph with the newest event.\n",
+      "WARNING:tensorflow:Found more than one graph event per run, or there was a metagraph containing a graph_def, as well as one or more graph events.  Overwriting the graph with the newest event.\n",
+      "WARNING:tensorflow:Found more than one graph event per run, or there was a metagraph containing a graph_def, as well as one or more graph events.  Overwriting the graph with the newest event.\n",
+      "WARNING:tensorflow:path ../external/data/plugin/text/runs not found, sending 404\n",
+      "WARNING:tensorflow:path ../external/data/plugin/text/runs not found, sending 404\n",
+      "WARNING:tensorflow:path ../external/data/plugin/text/runs not found, sending 404\n",
+      "WARNING:tensorflow:path ../external/data/plugin/text/runs not found, sending 404\n",
+      "^C\n"
+     ]
+    }
+   ],
+   "source": [
+    "!tensorboard --logdir=/tmp/TensorBoard_demo/"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [conda env:tensorflow]",
+   "language": "python",
+   "name": "conda-env-tensorflow-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
+%% Cell type:code id: tags:
+
+``` python
+import os
+import os.path
+import shutil
+import tensorflow as tf
+
+LOGDIR = "/tmp/TensorBoard_demo/"
+LABELS = os.path.join(os.getcwd(), "labels_1024.tsv")
+SPRITES = os.path.join(os.getcwd(), "sprite_1024.png")
+### MNIST EMBEDDINGS ###
+mnist = tf.contrib.learn.datasets.mnist.read_data_sets(train_dir=LOGDIR + "data", one_hot=True)
+### Get a sprite and labels file for the embedding projector ###
+
+if not (os.path.isfile(LABELS) and os.path.isfile(SPRITES)):
+  print("Necessary data files were not found: LABELS and SPRITES")
+  exit(1)
+```
+
+%% Output
+
+    Extracting /tmp/TensorBoard_demo/data/train-images-idx3-ubyte.gz
+    Extracting /tmp/TensorBoard_demo/data/train-labels-idx1-ubyte.gz
+    Extracting /tmp/TensorBoard_demo/data/t10k-images-idx3-ubyte.gz
+    Extracting /tmp/TensorBoard_demo/data/t10k-labels-idx1-ubyte.gz
+
+%% Cell type:code id: tags:
+
+``` python
+def conv_layer(input, size_in, size_out, name="conv"):
+  with tf.name_scope(name):
+    w = tf.Variable(tf.truncated_normal([5, 5, size_in, size_out], stddev=0.1), name="W")
+    b = tf.Variable(tf.constant(0.1, shape=[size_out]), name="B")
+    conv = tf.nn.conv2d(input, w, strides=[1, 1, 1, 1], padding="SAME")
+    act = tf.nn.relu(conv + b)
+    tf.summary.histogram("weights", w)
+    tf.summary.histogram("biases", b)
+    tf.summary.histogram("activations", act)
+    return tf.nn.max_pool(act, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")
+
+
+def fc_layer(input, size_in, size_out, name="fc"):
+  with tf.name_scope(name):
+    w = tf.Variable(tf.truncated_normal([size_in, size_out], stddev=0.1), name="W")
+    b = tf.Variable(tf.constant(0.1, shape=[size_out]), name="B")
+    act = tf.matmul(input, w) + b
+    tf.summary.histogram("weights", w)
+    tf.summary.histogram("biases", b)
+    tf.summary.histogram("activations", act)
+    return act
+```
+
+%% Cell type:code id: tags:
+
+``` python
+def mnist_model(learning_rate):
+  tf.reset_default_graph()
+  sess = tf.Session()
+
+  # Setup placeholders, and reshape the data
+  x = tf.placeholder(tf.float32, shape=[None, 784], name="x")
+  x_image = tf.reshape(x, [-1, 28, 28, 1])
+  tf.summary.image('input', x_image, 3)
+  y = tf.placeholder(tf.float32, shape=[None, 10], name="labels")
+
+  conv_out = conv_layer(x_image, 1, 16, "conv")
+
+  flattened = tf.reshape(conv_out, [-1, 7 * 7 * 64])
+
+  embedding_input = flattened
+  embedding_size = 7*7*64
+  logits = fc_layer(flattened, 7*7*64, 10, "fc")
+
+  with tf.name_scope("xent"):
+    xent = tf.reduce_mean(
+        tf.nn.softmax_cross_entropy_with_logits(
+            logits=logits, labels=y), name="xent")
+    tf.summary.scalar("xent", xent)
+
+  with tf.name_scope("train"):
+    train_step = tf.train.AdamOptimizer(learning_rate).minimize(xent)
+
+  with tf.name_scope("accuracy"):
+    correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))
+    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
+    tf.summary.scalar("accuracy", accuracy)
+
+  summ = tf.summary.merge_all()
+
+
+  embedding = tf.Variable(tf.zeros([1024, embedding_size]), name="test_embedding")
+  assignment = embedding.assign(embedding_input)
+  saver = tf.train.Saver()
+
+  sess.run(tf.global_variables_initializer())
+  writer = tf.summary.FileWriter(LOGDIR)
+  writer.add_graph(sess.graph)
+
+  config = tf.contrib.tensorboard.plugins.projector.ProjectorConfig()
+  embedding_config = config.embeddings.add()
+  embedding_config.tensor_name = embedding.name
+  embedding_config.sprite.image_path = SPRITES
+  embedding_config.metadata_path = LABELS
+  # Specify the width and height of a single thumbnail.
+  embedding_config.sprite.single_image_dim.extend([28, 28])
+  tf.contrib.tensorboard.plugins.projector.visualize_embeddings(writer, config)
+
+  for i in range(1,2001):
+    batch = mnist.train.next_batch(100)
+    if i % 5 == 0:
+      [train_accuracy, s] = sess.run([accuracy, summ], feed_dict={x: batch[0], y: batch[1]})
+      writer.add_summary(s, i)
+    if i % 500 == 0:
+      sess.run(assignment, feed_dict={x: mnist.test.images[:1024], y: mnist.test.labels[:1024]})
+      saver.save(sess, os.path.join(LOGDIR, "model.ckpt"), i)
+    sess.run(train_step, feed_dict={x: batch[0], y: batch[1]})
+```
+
+%% Cell type:code id: tags:
+
+``` python
+learning_rate = 1E-3
+
+mnist_model(learning_rate)
+print('Done training!')
+print('Run `tensorboard --logdir=%s` to see the results.' % LOGDIR)
+print('Running on mac? If you want to get rid of the dialogue asking to give '
+        'network permissions to TensorBoard, you can provide this flag: '
+        '--host=localhost')
+```
+
+%% Output
+
+    Done training!
+    Run `tensorboard --logdir=/tmp/TensorBoard_demo/` to see the results.
+    Running on mac? If you want to get rid of the dialogue asking to give network permissions to TensorBoard, you can provide this flag: --host=localhost
+
+%% Cell type:code id: tags:
+
+``` python
+!tensorboard --logdir=/tmp/TensorBoard_demo/
+```
+
+%% Output
+
+    Starting TensorBoard b'47' at http://0.0.0.0:6006
+    (Press CTRL+C to quit)
+    WARNING:tensorflow:Found more than one graph event per run, or there was a metagraph containing a graph_def, as well as one or more graph events.  Overwriting the graph with the newest event.
+    WARNING:tensorflow:Found more than one graph event per run, or there was a metagraph containing a graph_def, as well as one or more graph events.  Overwriting the graph with the newest event.
+    WARNING:tensorflow:Found more than one graph event per run, or there was a metagraph containing a graph_def, as well as one or more graph events.  Overwriting the graph with the newest event.
+    WARNING:tensorflow:path ../external/data/plugin/text/runs not found, sending 404
+    WARNING:tensorflow:path ../external/data/plugin/text/runs not found, sending 404
+    WARNING:tensorflow:path ../external/data/plugin/text/runs not found, sending 404
+    WARNING:tensorflow:path ../external/data/plugin/text/runs not found, sending 404
+    ^C
+
+%% Cell type:code id: tags:
+
+``` python
+```