TF8 Neural Networks.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "sess = tf.Session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "from tensorflow.python.framework import ops\n",
    "ops.reset_default_graph()\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "x_vals = np.array([x[0:3] for x in iris.data])\n",
    "y_vals = np.array([x[3] for x in iris.data])\n",
    "sess = tf.Session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_indices = np.random.choice(len(x_vals), round(len(x_vals)*0.8), replace=False)\n",
    "\n",
    "test_indices = np.array(list(set(range(len(x_vals))) - set(train_indices)))\n",
    "\n",
    "x_vals_train = x_vals[train_indices]\n",
    "x_vals_test = x_vals[test_indices]\n",
    "y_vals_train = y_vals[train_indices]\n",
    "y_vals_test = y_vals[test_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Converting data in range of 0 - 1\n",
    "def normalize_cols(m):\n",
    "    col_max = m.max(axis=0)\n",
    "    col_min = m.min(axis=0)\n",
    "    return (m-col_min) / (col_max - col_min)\n",
    "\n",
    "x_vals_train = np.nan_to_num(normalize_cols(x_vals_train))\n",
    "x_vals_test = np.nan_to_num(normalize_cols(x_vals_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "batch_size = 50\n",
    "x_data = tf.placeholder(shape=[None, 3], dtype=tf.float32)\n",
    "y_target = tf.placeholder(shape=[None, 1], dtype=tf.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hidden_layer_nodes = 5\n",
    "A1 = tf.Variable(tf.random_normal(shape=[3,hidden_layer_nodes]))\n",
    "b1 = tf.Variable(tf.random_normal(shape=[hidden_layer_nodes]))\n",
    "A2 = tf.Variable(tf.random_normal(shape=[hidden_layer_nodes,1]))\n",
    "b2 = tf.Variable(tf.random_normal(shape=[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hidden_output = tf.nn.relu(tf.add(tf.matmul(x_data, A1), b1))\n",
    "final_output = tf.nn.relu(tf.add(tf.matmul(hidden_output, A2),b2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "loss = tf.reduce_mean(tf.square(y_target - final_output))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\ZekeLabs\\Anaconda3-N\\lib\\site-packages\\tensorflow\\python\\util\\tf_should_use.py:175: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\n",
      "Instructions for updating:\n",
      "Use `tf.global_variables_initializer` instead.\n"
     ]
    }
   ],
   "source": [
    "my_opt = tf.train.GradientDescentOptimizer(0.01)\n",
    "#my_opt = tf.train.GradientDescentOptimizer(0.01)\n",
    "train_step = my_opt.minimize(loss)\n",
    "init = tf.initialize_all_variables()\n",
    "sess.run(init)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation: 5. Loss = 0.641344\n",
      "Generation: 10. Loss = 0.789603\n",
      "Generation: 15. Loss = 0.630999\n",
      "Generation: 20. Loss = 0.716421\n",
      "Generation: 25. Loss = 0.489489\n",
      "Generation: 30. Loss = 0.501359\n",
      "Generation: 35. Loss = 0.372795\n",
      "Generation: 40. Loss = 0.42515\n",
      "Generation: 45. Loss = 0.292136\n",
      "Generation: 50. Loss = 0.275407\n"
     ]
    }
   ],
   "source": [
    "loss_vec = []\n",
    "test_loss = []\n",
    "for i in range(50):\n",
    "    # First we select a random set of indices for the batch.\n",
    "    rand_index = np.random.choice(len(x_vals_train), size=batch_size)\n",
    "    # We then select the training values\n",
    "    rand_x = x_vals_train[rand_index]\n",
    "    rand_y = np.transpose([y_vals_train[rand_index]])\n",
    "    # Now we run the training step\n",
    "    sess.run(train_step, feed_dict={x_data: rand_x, y_target: rand_y})\n",
    "    # We save the training loss\n",
    "    temp_loss = sess.run(loss, feed_dict={x_data: rand_x, y_target: rand_y})\n",
    "    loss_vec.append(np.sqrt(temp_loss))\n",
    "\n",
    "    # Finally, we run the test-set loss and save it.\n",
    "    test_temp_loss = sess.run(loss, feed_dict={x_data: x_vals_test, y_target: np.transpose([y_vals_test])})\n",
    "    test_loss.append(np.sqrt(test_temp_loss))\n",
    "    if (i+1)%5==0:\n",
    "        print('Generation: ' + str(i+1) + '. Loss = ' + str(temp_loss))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20f5272e2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(loss_vec, 'k-', label='Train Loss')\n",
    "plt.plot(test_loss, 'r--', label='Test Loss')\n",
    "plt.title('Loss (MSE) per Generation')\n",
    "plt.xlabel('Generation')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend(loc='upper right')\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": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}