2. Machine Learning Basics - Linear regression.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function abs in module tensorflow.python.ops.math_ops:\n",
      "\n",
      "abs(x, name=None)\n",
      "    Computes the absolute value of a tensor.\n",
      "    \n",
      "    Given a tensor of real numbers `x`, this operation returns a tensor\n",
      "    containing the absolute value of each element in `x`. For example, if x is\n",
      "    an input element and y is an output element, this operation computes\n",
      "    \\\\(y = |x|\\\\).\n",
      "    \n",
      "    Args:\n",
      "      x: A `Tensor` or `SparseTensor` of type `float32`, `float64`, `int32`, or\n",
      "        `int64`.\n",
      "      name: A name for the operation (optional).\n",
      "    \n",
      "    Returns:\n",
      "      A `Tensor` or `SparseTensor` the same size and type as `x` with absolute\n",
      "        values.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(tf.abs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# initialize variables/model parameters\n",
    "W = tf.Variable(tf.zeros([2, 1]), name=\"weights\")\n",
    "b = tf.Variable(0., name=\"bias\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def inference(X):\n",
    "    return tf.matmul(X, W) + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def loss(X, Y):\n",
    "    Y_predicted = inference(X)\n",
    "    return tf.reduce_sum(tf.squared_difference(Y, Y_predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def inputs():\n",
    "    weight_age = [[84, 46], [73, 20], [65, 52], [70, 30], [76, 57], [69, 25], [63, 28], [72, 36], [79, 57], [75, 44], [27, 24], [89, 31], [65, 52], [57, 23], [59, 60], [69, 48], [60, 34], [79, 51], [75, 50], [82, 34], [59, 46], [67, 23], [85, 37], [55, 40], [63, 30]]\n",
    "    blood_fat_content = [354, 190, 405, 263, 451, 302, 288, 385, 402, 365, 209, 290, 346, 254, 395, 434, 220, 374, 308, 220, 311, 181, 274, 303, 244]\n",
    "\n",
    "    return tf.to_float(weight_age), tf.to_float(blood_fat_content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def train(total_loss):\n",
    "    learning_rate = 0.0000001\n",
    "    return tf.train.GradientDescentOptimizer(learning_rate).minimize(total_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(sess, X, Y):\n",
    "    print (sess.run(inference([[80., 25.]]))) # ~ 303\n",
    "    print (sess.run(inference([[65., 25.]]))) # ~ 256"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X,Y = inputs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from sklearn import datasets\n",
    "from tensorflow.python.framework import ops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ops.reset_default_graph()\n",
    "\n",
    "# Create graph\n",
    "sess = tf.Session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
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      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_vals = np.array([x[3] for x in iris.data])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
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       "        2.3,  2.5,  2.3,  1.9,  2. ,  2.3,  1.8])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_vals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "y_vals = np.array([y[0] for y in iris.data])"
   ]
  },
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   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
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       "        6.4,  6.3,  6.1,  7.7,  6.3,  6.4,  6. ,  6.9,  6.7,  6.9,  5.8,\n",
       "        6.8,  6.7,  6.7,  6.3,  6.5,  6.2,  5.9])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_vals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "batch_size = 25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Initialize placeholders\n",
    "x_data = tf.placeholder(shape=[None, 1], dtype=tf.float32)\n",
    "y_target = tf.placeholder(shape=[None, 1], dtype=tf.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create variables for linear regression\n",
    "A = tf.Variable(tf.random_normal(shape=[1,1]))\n",
    "b = tf.Variable(tf.random_normal(shape=[1,1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_output = tf.add(tf.matmul(x_data, A), b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Declare loss function (L2 loss)\n",
    "loss = tf.reduce_mean(tf.square(y_target - model_output))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize variables\n",
    "init = tf.global_variables_initializer()\n",
    "sess.run(init)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "my_opt = tf.train.GradientDescentOptimizer(0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_step = my_opt.minimize(loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step #25 A = [[ 1.86538982]] b = [[ 3.24608445]]\n",
      "Loss = 1.07648\n",
      "Step #50 A = [[ 1.44703341]] b = [[ 3.8815465]]\n",
      "Loss = 0.431488\n",
      "Step #75 A = [[ 1.22449696]] b = [[ 4.24575472]]\n",
      "Loss = 0.275474\n",
      "Step #100 A = [[ 1.04554486]] b = [[ 4.47205067]]\n",
      "Loss = 0.275653\n"
     ]
    }
   ],
   "source": [
    "# Training loop\n",
    "loss_vec = []\n",
    "for i in range(100):\n",
    "    rand_index = np.random.choice(len(x_vals), size=batch_size)\n",
    "    rand_x = np.transpose([x_vals[rand_index]])\n",
    "    rand_y = np.transpose([y_vals[rand_index]])\n",
    "    sess.run(train_step, feed_dict={x_data: rand_x, y_target: rand_y})\n",
    "    temp_loss = sess.run(loss, feed_dict={x_data: rand_x, y_target: rand_y})\n",
    "    loss_vec.append(temp_loss)\n",
    "    if (i+1)%25==0:\n",
    "        print('Step #' + str(i+1) + ' A = ' + str(sess.run(A)) + ' b = ' + str(sess.run(b)))\n",
    "        print('Loss = ' + str(temp_loss))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "150"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x_vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 6.3],\n",
       "       [ 6.7],\n",
       "       [ 6. ],\n",
       "       [ 5.7],\n",
       "       [ 5.6],\n",
       "       [ 6.3],\n",
       "       [ 6.3],\n",
       "       [ 6.6],\n",
       "       [ 5.2],\n",
       "       [ 4.9],\n",
       "       [ 5.1],\n",
       "       [ 5.8],\n",
       "       [ 4.5],\n",
       "       [ 5.2],\n",
       "       [ 6.3],\n",
       "       [ 6.8],\n",
       "       [ 6.2],\n",
       "       [ 6.3],\n",
       "       [ 5.1],\n",
       "       [ 6.4],\n",
       "       [ 5.5],\n",
       "       [ 6.3],\n",
       "       [ 5.8],\n",
       "       [ 6. ],\n",
       "       [ 5.6]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rand_y "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "[slope] = sess.run(A)\n",
    "[y_intercept] = sess.run(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.04554486], dtype=float32)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4.47205067], dtype=float32)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_intercept"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IjpuLltJuxFRaD59CuxFTubloaaqrZIDpb7zPy73OZPuBTeGvf60c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\nA7Da+9gYSPnQnSmSy/sOub3/tu+5qyb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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29dafba5ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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6a+gcM3vXzP5tZo+bWWLQuo/Kp3plR0NDIiKhFdUjmABgPpzx/HoAFphZLXfduRGuV5nT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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29dafc616a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get best fit line\n",
    "best_fit = []\n",
    "for i in x_vals:\n",
    "  best_fit.append(slope*i+y_intercept)\n",
    "\n",
    "# Plot the result\n",
    "plt.plot(x_vals, y_vals, 'o', label='Data Points')\n",
    "plt.plot(x_vals, best_fit, 'r-', label='Best fit line', linewidth=3)\n",
    "plt.legend(loc='upper left')\n",
    "plt.title('Sepal Length vs Pedal Width')\n",
    "plt.xlabel('Pedal Width')\n",
    "plt.ylabel('Sepal Length')\n",
    "plt.show()\n",
    "\n",
    "# Plot loss over time\n",
    "plt.plot(loss_vec, 'k-')\n",
    "plt.title('L2 Loss per Generation')\n",
    "plt.xlabel('Generation')\n",
    "plt.ylabel('L2 Loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}