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+{
+ "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
+}