7_Visualization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Visualization Using TensorBoard\n",
    "Computation graphs are hard to imagine. The progress of training is also hard to imagine. Let's visualize them. Fortunately, TensorFlow makes this very very easy with TensorBoard. In any TensorFlow sessions, you can save the computation graph and the summary statistics (e.g. loss, accuracy) you would like to track. These will be saved in a log folder. Tensorboard loads the files in the log folder and visualize them nicely. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the section 4, we had the following code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a: 5\n"
     ]
    }
   ],
   "source": [
    "a = tf.add(2,3)       \n",
    "\n",
    "with tf.Session() as sess:\n",
    "    print('a:',sess.run(a))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's rewrite this code using more TensorFlow:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sum_: 5\n"
     ]
    }
   ],
   "source": [
    "a = tf.constant(2, name = 'a')\n",
    "b = tf.constant(3, name = 'b')\n",
    "sum_ = tf.add(a,b, name = 'sum_')       \n",
    "\n",
    "with tf.Session() as sess:\n",
    "    print('sum_:',sess.run(sum_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How does the computation graph of this code look like? Let's visualize it using TensorBoard. To do this, you just need to add one line to the code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sum_: 5\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph() # Clearing all tensors before this\n",
    "a = tf.constant(2, name = 'a')\n",
    "b = tf.constant(3, name = 'b')\n",
    "sum_ = tf.add(a,b, name = 'sum_')       \n",
    "\n",
    "with tf.Session() as sess:\n",
    "    print('sum_:',sess.run(sum_))\n",
    "    writer = tf.summary.FileWriter('log/simple', sess.graph) # NEW!\n",
    "    writer.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This should create a new directory called `log` to where this Jupyter Notebook is located. Now, run the following on your local terminal, \n",
    "\n",
    "`tensorboard --logdir=log/simple`\n",
    "\n",
    "Then, head to `http://localhost:6006` and see for yourself that the graph is being visualized!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.reset_default_graph() # Clearing all tensors before this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Import MINST data\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "mnist = input_data.read_data_sets('MNIST_data', one_hot=True)\n",
    "\n",
    "# Variables\n",
    "M = 784\n",
    "K = 10\n",
    "X = tf.placeholder(tf.float32, shape=[None, M])\n",
    "Y = tf.placeholder(tf.float32, shape=[None, K])\n",
    "W = tf.Variable(tf.zeros([M, K]))\n",
    "b = tf.Variable(tf.zeros([K]))\n",
    "\n",
    "# Model\n",
    "Yhat = tf.nn.softmax(tf.matmul(X, W) + b)\n",
    "\n",
    "# Loss\n",
    "loss = tf.reduce_mean(-tf.reduce_sum(Y*tf.log(Yhat), axis=1))\n",
    "\n",
    "# Optimizer\n",
    "learning_rate = 0.01\n",
    "optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train\n",
    "num_epochs  = 25\n",
    "batch_size  = 100\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    writer = tf.summary.FileWriter('log/logistic1', sess.graph) # NEW!\n",
    "    writer.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Better: Name the variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.reset_default_graph() # Clearing all tensors before this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Import MINST data\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "mnist = input_data.read_data_sets('MNIST_data', one_hot=True)\n",
    "\n",
    "# Variables\n",
    "M = 784\n",
    "K = 10\n",
    "X = tf.placeholder(tf.float32, shape=[None, M], name='Input-Images')\n",
    "Y = tf.placeholder(tf.float32, shape=[None, K], name='Output-Labels')\n",
    "W = tf.get_variable('Weights', initializer=tf.zeros([M, K]))\n",
    "b = tf.get_variable('Bias', initializer=tf.zeros([K]))\n",
    "\n",
    "# Model\n",
    "Yhat = tf.nn.softmax(tf.matmul(X, W) + b, name='Prediction-Labels')\n",
    "\n",
    "# Loss\n",
    "loss = tf.reduce_mean(-tf.reduce_sum(Y*tf.log(Yhat), axis=1), name=\"Cross-Entropy-Loss\")\n",
    "\n",
    "# Optimizer\n",
    "learning_rate = 0.01\n",
    "optimizer = tf.train.GradientDescentOptimizer(learning_rate, name=\"SGD-Optimizer\")\n",
    "update    = optimizer.minimize(loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train\n",
    "num_epochs  = 25\n",
    "batch_size  = 100\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    writer = tf.summary.FileWriter('log/logistic2', sess.graph) # NEW!\n",
    "    writer.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Even Better: Group Variables Together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.reset_default_graph() # Clearing all tensors before this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "# Import MINST data\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "mnist = input_data.read_data_sets('MNIST_data', one_hot=True)\n",
    "\n",
    "M = 784\n",
    "K = 10\n",
    "with tf.name_scope('data'):\n",
    "    X = tf.placeholder(tf.float32, shape=[None, M], name='Input-Images')\n",
    "    Y = tf.placeholder(tf.float32, shape=[None, K], name='Output-Labels')\n",
    "    W = tf.get_variable('Weights', initializer=tf.zeros([M, K]))\n",
    "    b = tf.get_variable('Bias', initializer=tf.zeros([K]))\n",
    "\n",
    "with tf.name_scope('model'):\n",
    "    Yhat = tf.nn.softmax(tf.matmul(X, W) + b, name='Prediction-Labels')\n",
    "\n",
    "with tf.name_scope('loss'):\n",
    "    loss = tf.reduce_mean(-tf.reduce_sum(Y*tf.log(Yhat), axis=1), name=\"Cross-Entropy-Loss\")\n",
    "\n",
    "with tf.name_scope('optimizer'):\n",
    "    learning_rate = 0.01\n",
    "    optimizer = tf.train.GradientDescentOptimizer(learning_rate, name=\"SGD-Optimizer\")\n",
    "    update    = optimizer.minimize(loss)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train\n",
    "num_epochs  = 25\n",
    "batch_size  = 100\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    writer = tf.summary.FileWriter('log/logistic3', sess.graph) # NEW!\n",
    "    writer.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Even Even Better: Create Summaries to Track Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.reset_default_graph() # Clearing all tensors before this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "# Import MINST data\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "mnist = input_data.read_data_sets('MNIST_data', one_hot=True)\n",
    "\n",
    "M = 784\n",
    "K = 10\n",
    "with tf.name_scope('data'):\n",
    "    X = tf.placeholder(tf.float32, shape=[None, M], name='Input-Images')\n",
    "    Y = tf.placeholder(tf.float32, shape=[None, K], name='Output-Labels')\n",
    "    W = tf.get_variable('Weights', initializer=tf.zeros([M, K]))\n",
    "    b = tf.get_variable('Bias', initializer=tf.zeros([K]))\n",
    "\n",
    "with tf.name_scope('model'):\n",
    "    Yhat = tf.nn.softmax(tf.matmul(X, W) + b, name='Prediction-Labels')\n",
    "\n",
    "with tf.name_scope('loss'):\n",
    "    loss = tf.reduce_mean(-tf.reduce_sum(Y*tf.log(Yhat), axis=1), name=\"Cross-Entropy-Loss\")\n",
    "\n",
    "with tf.name_scope('optimizer'):\n",
    "    learning_rate = 0.01\n",
    "    optimizer = tf.train.GradientDescentOptimizer(learning_rate, name=\"SGD-Optimizer\")\n",
    "    update    = optimizer.minimize(loss)\n",
    "\n",
    "with tf.name_scope('summaries'): # NEW!\n",
    "    tf.summary.scalar('loss', loss)\n",
    "    tf.summary.histogram('histogram-loss', loss)\n",
    "    summary_op = tf.summary.merge_all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 Cost: 1.18415000103\n",
      "Epoch: 1 Cost: 0.665413012992\n",
      "Epoch: 2 Cost: 0.552853881121\n",
      "Epoch: 3 Cost: 0.498638092279\n",
      "Epoch: 4 Cost: 0.46549880976\n",
      "Epoch: 5 Cost: 0.442564203848\n",
      "Epoch: 6 Cost: 0.425497464917\n",
      "Epoch: 7 Cost: 0.412214421332\n",
      "Epoch: 8 Cost: 0.40141326273\n",
      "Epoch: 9 Cost: 0.392426092245\n",
      "Epoch: 10 Cost: 0.384764256748\n",
      "Epoch: 11 Cost: 0.378108848442\n",
      "Epoch: 12 Cost: 0.3723855522\n",
      "Epoch: 13 Cost: 0.367273085605\n",
      "Epoch: 14 Cost: 0.362724641453\n",
      "Epoch: 15 Cost: 0.358594706519\n",
      "Epoch: 16 Cost: 0.354899908684\n",
      "Epoch: 17 Cost: 0.351458825957\n",
      "Epoch: 18 Cost: 0.348327035796\n",
      "Epoch: 19 Cost: 0.345387803154\n",
      "Epoch: 20 Cost: 0.342760479911\n",
      "Epoch: 21 Cost: 0.340282042704\n",
      "Epoch: 22 Cost: 0.337932195609\n",
      "Epoch: 23 Cost: 0.335765975389\n",
      "Epoch: 24 Cost: 0.333698157641\n"
     ]
    }
   ],
   "source": [
    "# Train\n",
    "num_epochs  = 25\n",
    "batch_size  = 100\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    writer = tf.summary.FileWriter('log/logistic4', sess.graph)\n",
    "    \n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    total_batch = int(mnist.train.num_examples/batch_size)\n",
    "    for epoch in range(num_epochs):\n",
    "        average_cost = 0\n",
    "        for batch in range(total_batch):\n",
    "            batch_X, batch_Y = mnist.train.next_batch(batch_size)\n",
    "            _, c = sess.run([update, loss], feed_dict={X: batch_X,\n",
    "                                                       Y: batch_Y})\n",
    "            average_cost += c / total_batch\n",
    "            summary = sess.run(summary_op, feed_dict={X: batch_X,\n",
    "                                                    Y: batch_Y})\n",
    "            global_step = epoch*total_batch + batch\n",
    "            writer.add_summary(summary, global_step=global_step)\n",
    "        print(\"Epoch:\",epoch,\"Cost:\",average_cost)\n",
    "        \n",
    "        \n",
    "        \n",
    "    writer.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's it! A model like logistic regression might be too simple to need a visualization. But imagine you're training a new neural network model architecture you came up with. How would you know if your model is doing well, or that there aren't any bugs in the code in the first place? TensorBoard is a great way to intuitively understand your model performance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise\n",
    "Nicely visualize the linear regression you implemented in the previous exercise.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.reset_default_graph() # Clearing all tensors before this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_boston\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "boston = load_boston()\n",
    "df = pd.DataFrame(boston.data)\n",
    "df.columns = boston.feature_names\n",
    "df['PRICE'] = boston.target\n",
    "\n",
    "M = df.shape[1]\n",
    "N = df.shape[0]\n",
    "with tf.name_scope('data'):\n",
    "    X = tf.placeholder(tf.float32, shape=[None, M],name=\"House-Features\")\n",
    "    Y = tf.placeholder(tf.float32, shape=[None, 1],name=\"Housing-Price\")\n",
    "    W = tf.Variable(tf.ones([M, 1]),name=\"Weights\")\n",
    "with tf.name_scope('model'):\n",
    "    Yhat = tf.matmul(X,W,name=\"Price-Predictions\")\n",
    "with tf.name_scope('loss'):\n",
    "    loss = tf.reduce_mean((Y-Yhat)**2,name=\"Loss\")\n",
    "with tf.name_scope('optimizer'):\n",
    "    optimizer = tf.train.GradientDescentOptimizer(0.01)\n",
    "    update = optimizer.minimize(loss, name=\"SGD-Optimizer\")\n",
    "\n",
    "with tf.name_scope('summaries'):\n",
    "    tf.summary.scalar('loss', loss)\n",
    "    tf.summary.histogram('histogram-loss', loss)\n",
    "    summary_op = tf.summary.merge_all()\n",
    "    \n",
    "X_np = df.iloc[:,:-1].as_matrix()\n",
    "X_np = (X_np - np.mean(X_np,axis=0)) / np.std(X_np,axis=0)\n",
    "X_np = np.hstack((X_np,np.ones([X_np.shape[0],1])))\n",
    "y_np = df.iloc[:,-1].as_matrix()[:,np.newaxis]\n",
    "np.random.seed(1)\n",
    "train_indices = np.random.rand(X_np.shape[0]) < 0.80\n",
    "X_train = X_np[train_indices]\n",
    "X_test  = X_np[~train_indices]\n",
    "y_train = y_np[train_indices]\n",
    "y_test  = y_np[~train_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 Cost: 604.989\n",
      "Epoch: 1 Cost: 566.648\n",
      "Epoch: 2 Cost: 533.349\n",
      "Epoch: 3 Cost: 504.086\n",
      "Epoch: 4 Cost: 478.079\n",
      "Epoch: 5 Cost: 454.72\n",
      "Epoch: 6 Cost: 433.534\n",
      "Epoch: 7 Cost: 414.152\n",
      "Epoch: 8 Cost: 396.281\n",
      "Epoch: 9 Cost: 379.692\n",
      "Epoch: 10 Cost: 364.203\n",
      "Epoch: 11 Cost: 349.67\n",
      "Epoch: 12 Cost: 335.978\n",
      "Epoch: 13 Cost: 323.032\n",
      "Epoch: 14 Cost: 310.756\n",
      "Epoch: 15 Cost: 299.089\n",
      "Epoch: 16 Cost: 287.978\n",
      "Epoch: 17 Cost: 277.381\n",
      "Epoch: 18 Cost: 267.259\n",
      "Epoch: 19 Cost: 257.581\n",
      "Epoch: 20 Cost: 248.321\n",
      "Epoch: 21 Cost: 239.453\n",
      "Epoch: 22 Cost: 230.957\n",
      "Epoch: 23 Cost: 222.812\n",
      "Epoch: 24 Cost: 215.002\n",
      "Epoch: 25 Cost: 207.51\n",
      "Epoch: 26 Cost: 200.322\n",
      "Epoch: 27 Cost: 193.424\n",
      "Epoch: 28 Cost: 186.802\n",
      "Epoch: 29 Cost: 180.446\n",
      "Epoch: 30 Cost: 174.343\n",
      "Epoch: 31 Cost: 168.483\n",
      "Epoch: 32 Cost: 162.856\n",
      "Epoch: 33 Cost: 157.452\n",
      "Epoch: 34 Cost: 152.263\n",
      "Epoch: 35 Cost: 147.278\n",
      "Epoch: 36 Cost: 142.491\n",
      "Epoch: 37 Cost: 137.893\n",
      "Epoch: 38 Cost: 133.476\n",
      "Epoch: 39 Cost: 129.233\n",
      "Epoch: 40 Cost: 125.157\n",
      "Epoch: 41 Cost: 121.242\n",
      "Epoch: 42 Cost: 117.48\n",
      "Epoch: 43 Cost: 113.867\n",
      "Epoch: 44 Cost: 110.395\n",
      "Epoch: 45 Cost: 107.06\n",
      "Epoch: 46 Cost: 103.855\n",
      "Epoch: 47 Cost: 100.777\n",
      "Epoch: 48 Cost: 97.8185\n",
      "Epoch: 49 Cost: 94.9762\n",
      "Epoch: 50 Cost: 92.2452\n",
      "Epoch: 51 Cost: 89.6211\n",
      "Epoch: 52 Cost: 87.0995\n",
      "Epoch: 53 Cost: 84.6765\n",
      "Epoch: 54 Cost: 82.3482\n",
      "Epoch: 55 Cost: 80.1107\n",
      "Epoch: 56 Cost: 77.9605\n",
      "Epoch: 57 Cost: 75.8941\n",
      "Epoch: 58 Cost: 73.9082\n",
      "Epoch: 59 Cost: 71.9997\n",
      "Epoch: 60 Cost: 70.1654\n",
      "Epoch: 61 Cost: 68.4025\n",
      "Epoch: 62 Cost: 66.7081\n",
      "Epoch: 63 Cost: 65.0794\n",
      "Epoch: 64 Cost: 63.514\n",
      "Epoch: 65 Cost: 62.0093\n",
      "Epoch: 66 Cost: 60.5629\n",
      "Epoch: 67 Cost: 59.1725\n",
      "Epoch: 68 Cost: 57.836\n",
      "Epoch: 69 Cost: 56.5511\n",
      "Epoch: 70 Cost: 55.3159\n",
      "Epoch: 71 Cost: 54.1284\n",
      "Epoch: 72 Cost: 52.9866\n",
      "Epoch: 73 Cost: 51.889\n",
      "Epoch: 74 Cost: 50.8335\n",
      "Epoch: 75 Cost: 49.8187\n",
      "Epoch: 76 Cost: 48.843\n",
      "Epoch: 77 Cost: 47.9047\n",
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      "Epoch: 590 Cost: 22.2015\n",
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      "Epoch: 788 Cost: 22.1087\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 789 Cost: 22.1084\n",
      "Epoch: 790 Cost: 22.1082\n",
      "Epoch: 791 Cost: 22.1079\n",
      "Epoch: 792 Cost: 22.1076\n",
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      "Epoch: 800 Cost: 22.1054\n",
      "Epoch: 801 Cost: 22.1051\n",
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      "Epoch: 820 Cost: 22.1001\n",
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      "Epoch: 922 Cost: 22.0789\n",
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      "Epoch: 924 Cost: 22.0786\n",
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      "Epoch: 927 Cost: 22.078\n",
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      "Epoch: 930 Cost: 22.0775\n",
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      "Epoch: 978 Cost: 22.0702\n",
      "Epoch: 979 Cost: 22.0701\n",
      "Epoch: 980 Cost: 22.0699\n",
      "Epoch: 981 Cost: 22.0698\n",
      "Epoch: 982 Cost: 22.0697\n",
      "Epoch: 983 Cost: 22.0695\n",
      "Epoch: 984 Cost: 22.0694\n",
      "Epoch: 985 Cost: 22.0693\n",
      "Epoch: 986 Cost: 22.0691\n",
      "Epoch: 987 Cost: 22.069\n",
      "Epoch: 988 Cost: 22.0688\n",
      "Epoch: 989 Cost: 22.0687\n",
      "Epoch: 990 Cost: 22.0686\n",
      "Epoch: 991 Cost: 22.0684\n",
      "Epoch: 992 Cost: 22.0683\n",
      "Epoch: 993 Cost: 22.0682\n",
      "Epoch: 994 Cost: 22.068\n",
      "Epoch: 995 Cost: 22.0679\n",
      "Epoch: 996 Cost: 22.0678\n",
      "Epoch: 997 Cost: 22.0676\n",
      "Epoch: 998 Cost: 22.0675\n",
      "Epoch: 999 Cost: 22.0674\n",
      "Test Loss: 22.3642\n"
     ]
    }
   ],
   "source": [
    "num_epochs  = 1000\n",
    "losses = []\n",
    "with tf.Session() as sess:\n",
    "    writer = tf.summary.FileWriter('log/linear', sess.graph)\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    for epoch in range(num_epochs):\n",
    "        _, c, summary = sess.run([update, loss, summary_op], feed_dict={X: X_train, \n",
    "                                                      Y: y_train})\n",
    "        losses.append(c)\n",
    "        print(\"Epoch:\",epoch,\"Cost:\",c)\n",
    "        writer.add_summary(summary, global_step=epoch)\n",
    "    test_loss = sess.run(loss, feed_dict={X: X_test, \n",
    "                                          Y: y_test})\n",
    "    print(\"Test Loss:\",test_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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