TensorFlow.ipynb

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   "source": [
    "### Load tensorFlow"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BRBQvYdMhTjB",
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   },
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {
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   "outputs": [],
   "source": [
    "#Reset Default graph - Needed only for Jupyter notebook\n",
    "tf.reset_default_graph()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
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   },
   "source": [
    "### Collect Data"
   ]
  },
  {
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   "outputs": [],
   "source": [
    "from tensorflow.python.keras.datasets import boston_housing\n",
    "\n",
    "#Load data\n",
    "(features, actual_prices),_ = boston_housing.load_data(test_split=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0izmyrzshTjT",
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     "slide_type": "fragment"
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   },
   "outputs": [],
   "source": [
    "print('Number of examples: ', features.shape[0])\n",
    "print('Number of features for each example: ', features.shape[1])\n",
    "print('Shape of actual prices data: ', actual_prices.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4gr5rVjNhTjX",
    "slideshow": {
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    }
   },
   "source": [
    "# Building the graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BEyeiK7dhTjY",
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    }
   },
   "source": [
    "Define input data placeholders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
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    "colab_type": "code",
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   "outputs": [],
   "source": [
    "#Input features\n",
    "x = tf.placeholder(shape=[None,13],dtype=tf.float32, name='x-input')\n",
    "\n",
    "#Actual Prices\n",
    "y_ = tf.placeholder(shape=[None],dtype=tf.float32, name='y-input')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "id": "HG8jzFozhTjf",
    "slideshow": {
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    }
   },
   "source": [
    "Define Weights and Bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
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   },
   "outputs": [],
   "source": [
    "W = tf.Variable(tf.zeros(shape=[13,1]), name=\"Weights\")\n",
    "b = tf.Variable(tf.zeros(shape=[1]), name=\"Bias\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lolG0RDchTjn",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Prediction"
   ]
  },
  {
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   "execution_count": 0,
   "metadata": {
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   },
   "outputs": [],
   "source": [
    "y = tf.add(tf.matmul(x,W),b,name='output')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "V4k0IxlNhTjv",
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   },
   "source": [
    "Loss (Cost) Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
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    "colab_type": "code",
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   },
   "outputs": [],
   "source": [
    "loss = tf.reduce_mean(tf.square(y-y_),name='Loss')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9OFlN_61hTj0",
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   },
   "source": [
    "GradientDescent Optimizer to minimize Loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lIPqs9IghTj1",
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   },
   "outputs": [],
   "source": [
    "train_op = tf.train.GradientDescentOptimizer(0.03).minimize(loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KVARKESwhTj5",
    "slideshow": {
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   },
   "source": [
    "# Executing the Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
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   "outputs": [],
   "source": [
    "#Lets start graph Execution\n",
    "sess = tf.Session()\n",
    "\n",
    "# variables need to be initialized before we can use them\n",
    "sess.run(tf.global_variables_initializer())\n",
    "\n",
    "#how many times data need to be shown to model\n",
    "training_epochs = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
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    "colab_type": "code",
    "id": "Vba1py2rhTkG",
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   },
   "outputs": [],
   "source": [
    "for epoch in range(training_epochs):\n",
    "            \n",
    "    #Calculate train_op and loss\n",
    "    _, train_loss = sess.run([train_op,loss],feed_dict={x:features, y_:actual_prices})\n",
    "    \n",
    "    if epoch % 10 == 0:\n",
    "        print ('Training loss at step: ', epoch, ' is ', train_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
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    "id": "q8wIKHdKQ36Q"
   },
   "outputs": [],
   "source": [
    "features[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CeZwYgeShTkJ",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "sess.close()"
   ]
  }
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