Lesson 5-3 Tensorboard Network Operation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.examples.tutorials.mnist import input_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-2-f5a26485f7d7>:2: read_data_sets (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n",
      "WARNING:tensorflow:From /Users/alan/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:260: maybe_download (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please write your own downloading logic.\n",
      "WARNING:tensorflow:From /Users/alan/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:262: extract_images (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tf.data to implement this functionality.\n",
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "WARNING:tensorflow:From /Users/alan/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:267: extract_labels (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tf.data to implement this functionality.\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "WARNING:tensorflow:From /Users/alan/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:110: dense_to_one_hot (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tf.one_hot on tensors.\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n",
      "WARNING:tensorflow:From /Users/alan/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:290: DataSet.__init__ (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n",
      "WARNING:tensorflow:From <ipython-input-2-f5a26485f7d7>:43: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "\n",
      "Future major versions of TensorFlow will allow gradients to flow\n",
      "into the labels input on backprop by default.\n",
      "\n",
      "See `tf.nn.softmax_cross_entropy_with_logits_v2`.\n",
      "\n",
      "Iter0, Testing Accuracy: 0.8236\n",
      "Iter1, Testing Accuracy: 0.8864\n",
      "Iter2, Testing Accuracy: 0.9011\n",
      "Iter3, Testing Accuracy: 0.9059\n",
      "Iter4, Testing Accuracy: 0.9085\n",
      "Iter5, Testing Accuracy: 0.91\n",
      "Iter6, Testing Accuracy: 0.9117\n",
      "Iter7, Testing Accuracy: 0.9132\n",
      "Iter8, Testing Accuracy: 0.915\n",
      "Iter9, Testing Accuracy: 0.9156\n",
      "Iter10, Testing Accuracy: 0.9162\n",
      "Iter11, Testing Accuracy: 0.9173\n",
      "Iter12, Testing Accuracy: 0.9189\n",
      "Iter13, Testing Accuracy: 0.9205\n",
      "Iter14, Testing Accuracy: 0.9193\n",
      "Iter15, Testing Accuracy: 0.9209\n",
      "Iter16, Testing Accuracy: 0.9195\n",
      "Iter17, Testing Accuracy: 0.9203\n",
      "Iter18, Testing Accuracy: 0.9203\n",
      "Iter19, Testing Accuracy: 0.9217\n",
      "Iter20, Testing Accuracy: 0.9219\n",
      "Iter21, Testing Accuracy: 0.9222\n",
      "Iter22, Testing Accuracy: 0.9224\n",
      "Iter23, Testing Accuracy: 0.922\n",
      "Iter24, Testing Accuracy: 0.9224\n",
      "Iter25, Testing Accuracy: 0.9228\n",
      "Iter26, Testing Accuracy: 0.923\n",
      "Iter27, Testing Accuracy: 0.9228\n",
      "Iter28, Testing Accuracy: 0.9241\n",
      "Iter29, Testing Accuracy: 0.9241\n",
      "Iter30, Testing Accuracy: 0.924\n",
      "Iter31, Testing Accuracy: 0.9236\n",
      "Iter32, Testing Accuracy: 0.9234\n",
      "Iter33, Testing Accuracy: 0.9248\n",
      "Iter34, Testing Accuracy: 0.9243\n",
      "Iter35, Testing Accuracy: 0.9256\n",
      "Iter36, Testing Accuracy: 0.925\n",
      "Iter37, Testing Accuracy: 0.9254\n",
      "Iter38, Testing Accuracy: 0.9243\n",
      "Iter39, Testing Accuracy: 0.9252\n",
      "Iter40, Testing Accuracy: 0.9264\n",
      "Iter41, Testing Accuracy: 0.9257\n",
      "Iter42, Testing Accuracy: 0.9258\n",
      "Iter43, Testing Accuracy: 0.9267\n",
      "Iter44, Testing Accuracy: 0.9261\n",
      "Iter45, Testing Accuracy: 0.9265\n",
      "Iter46, Testing Accuracy: 0.9266\n",
      "Iter47, Testing Accuracy: 0.9268\n",
      "Iter48, Testing Accuracy: 0.9265\n",
      "Iter49, Testing Accuracy: 0.9268\n",
      "Iter50, Testing Accuracy: 0.9269\n"
     ]
    }
   ],
   "source": [
    "#load dataset\n",
    "mnist = input_data.read_data_sets(\"MNIST_data\",one_hot=True)\n",
    "\n",
    "\n",
    "#define batch size\n",
    "batch_size = 100\n",
    "#calculate number of batches\n",
    "n_batch = mnist.train.num_examples // batch_size\n",
    "\n",
    "def variable_summaries(var):\n",
    "    with tf.name_scope('summaries'):\n",
    "        mean = tf.reduce_mean(var)\n",
    "        tf.summary.scalar('mean',mean)\n",
    "        with tf.name_scope('stddev'):\n",
    "            stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))\n",
    "        tf.summary.scalar('stddev', stddev)\n",
    "        tf.summary.scalar('max', tf.reduce_max(var))\n",
    "        tf.summary.scalar('min', tf.reduce_min(var))\n",
    "        tf.summary.histogram('histogram', var)\n",
    "\n",
    "#name scope\n",
    "with tf.name_scope('input'):\n",
    "    x = tf.placeholder(tf.float32, [None,784],name='x-input')\n",
    "    y = tf.placeholder(tf.float32, [None,10],name='y-input')\n",
    "\n",
    "\n",
    "with tf.name_scope('layer'):\n",
    "    #create simple NeuroNet\n",
    "    with tf.name_scope('weights'):\n",
    "        W = tf.Variable(tf.zeros([784,10]),name='W')\n",
    "        variable_summaries(W)\n",
    "    with tf.name_scope('biases'):\n",
    "        b = tf.Variable(tf.zeros([10]),name='b')\n",
    "        variable_summaries(b)\n",
    "    with tf.name_scope('wx_plus_b'):\n",
    "        wx_plus_b = tf.matmul(x,W) + b\n",
    "    with tf.name_scope('softmax'):\n",
    "        prediction = tf.nn.softmax(wx_plus_b)\n",
    "\n",
    "\n",
    "with tf.name_scope('loss'):\n",
    "    #quadratic cost function\n",
    "    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction))\n",
    "    tf.summary.scalar('loss',loss)\n",
    "with tf.name_scope('train'):\n",
    "    #train with gradient descent\n",
    "    train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss)\n",
    "\n",
    "#initialize variables\n",
    "init = tf.global_variables_initializer()\n",
    "\n",
    "with tf.name_scope('accuracy'):\n",
    "    #find accuracy of trained model\n",
    "    with tf.name_scope('correct_prediction'):\n",
    "        correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(prediction,1))   #convert a list of booleans into a single boolean of accuracy\n",
    "    with tf.name_scope('accuracy'):\n",
    "        accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))\n",
    "        tf.summary.scalar('accuracy',accuracy)\n",
    "        \n",
    "#combine all summaries\n",
    "merged = tf.summary.merge_all()\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "    writer = tf.summary.FileWriter('logs/',sess.graph)\n",
    "    for epoch in range(51):\n",
    "        for batch in range(n_batch):\n",
    "            batch_xs,batch_ys = mnist.train.next_batch(batch_size)\n",
    "            summary, _ = sess.run([merged,train_step],feed_dict={x:batch_xs,y:batch_ys})\n",
    "        \n",
    "        writer.add_summary(summary,epoch)\n",
    "        acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels})\n",
    "        print(\"Iter\" + str(epoch) + \", Testing Accuracy: \" + str(acc))\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# alternative summary method:\n",
    "\n",
    "# for i in range(2001):\n",
    "#     #100 samples every batch\n",
    "#     batch_xs,batch_ys = mnist.train.next_batch(100)\n",
    "#     summary,_ = sess.run([merged,train_step],feed_dict={x:batch_xs,y:batch_ys})\n",
    "#     writer.add_summary(summary,i)\n",
    "#     if i%500 == 0:\n",
    "#         print(sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels}))"
   ]
  }
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