p5_placeholders.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[12.0]\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf;\n",
    "x= tf.placeholder(tf.float32);\n",
    "fx =2*x*x;\n",
    "grads =tf.gradients(fx,x)\n",
    "with tf.Session() as sess:\n",
    "    print (sess.run(grads,feed_dict={x:3.0}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[12.0, 30.0]\n"
     ]
    }
   ],
   "source": [
    "x= tf.placeholder(tf.float32);\n",
    "y= tf.placeholder(tf.float32);\n",
    "fxy =2*x*x+3*y*y;\n",
    "grads =tf.gradients(fxy,[x,y])\n",
    "with tf.Session() as sess:\n",
    "    print (sess.run(grads,feed_dict={x:3.0,y:5.0}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf;\n",
    "import numpy as np;\n",
    "import matplotlib.pyplot as plt;\n",
    "x= tf.placeholder(tf.float32);\n",
    "fsigma = tf.nn.sigmoid(x);\n",
    "frelu = tf.nn.relu(x);\n",
    "ftanh = tf.nn.tanh(x)\n",
    "gradsigma =tf.gradients(fsigma,x)\n",
    "gradrelu = tf.gradients(frelu,x)\n",
    "gradtanh = tf.gradients(ftanh,x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "Unknown property cols",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-4-62ec557df66f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[0mxval\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlinspace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[1;31m# Gradient Calculation for Sigmoid;\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m     \u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0maxarr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mrows\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m;\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m     \u001b[0mgradsigmaval\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msigmaval\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0msess\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mgradsigma\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mfsigma\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mxval\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\conda\\conda\\envs\\py36\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36msubplot\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m   1081\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1082\u001b[0m     \u001b[0mfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgcf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1083\u001b[1;33m     \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_subplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1084\u001b[0m     \u001b[0mbbox\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbbox\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1085\u001b[0m     \u001b[0mbyebye\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\conda\\conda\\envs\\py36\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36madd_subplot\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1368\u001b[0m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_axstack\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1369\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1370\u001b[1;33m             \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msubplot_class_factory\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprojection_class\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1371\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_axstack\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1372\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msca\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\conda\\conda\\envs\\py36\\lib\\site-packages\\matplotlib\\axes\\_subplots.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, fig, *args, **kwargs)\u001b[0m\n\u001b[0;32m     68\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     69\u001b[0m         \u001b[1;31m# _axes_class is set in the subplot_class_factory\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 70\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_axes_class\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigbox\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     71\u001b[0m         \u001b[1;31m# add a layout box to this, for both the full axis, and the poss\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     72\u001b[0m         \u001b[1;31m# of the axis.  We need both because the axes may become smaller\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\conda\\conda\\envs\\py36\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, fig, rect, facecolor, frameon, sharex, sharey, label, xscale, yscale, **kwargs)\u001b[0m\n\u001b[0;32m    496\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_yscale\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0myscale\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    497\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 498\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    499\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    500\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxaxis\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\conda\\conda\\envs\\py36\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mupdate\u001b[1;34m(self, props)\u001b[0m\n\u001b[0;32m    914\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    915\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mcbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setattr_cm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0meventson\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 916\u001b[1;33m             \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0m_update_property\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mprops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    917\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    918\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mret\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\conda\\conda\\envs\\py36\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    914\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    915\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mcbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setattr_cm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0meventson\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 916\u001b[1;33m             \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0m_update_property\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mprops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    917\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    918\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mret\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\conda\\conda\\envs\\py36\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36m_update_property\u001b[1;34m(self, k, v)\u001b[0m\n\u001b[0;32m    910\u001b[0m                 \u001b[0mfunc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'set_'\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    911\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mcallable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 912\u001b[1;33m                     \u001b[1;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Unknown property %s'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    913\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    914\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: Unknown property cols"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,axarr = plt.subplots(1,3,figsize=(18,5),sharey=True)\n",
    "with tf.Session() as sess:\n",
    "    xval = np.linspace(-3,3,100)\n",
    "    # Gradient Calculation for Sigmoid;\n",
    "    f,axarr = plt.subplot(cols=3,rows=1);\n",
    "    gradsigmaval,sigmaval = (sess.run([gradsigma,fsigma],feed_dict={x:xval}))\n",
    "    \n",
    "    axarr[0].grid(color='g', linestyle='--', linewidth=1)\n",
    "    axarr[0].plot(xval,np.squeeze(gradsigmaval),label='sigmoid');\n",
    "    axarr[0].plot(xval,np.squeeze(sigmaval),label=\"gradient\");\n",
    "    axarr[0].set_title(\"sigmoid\");\n",
    "    axarr[0].legend(loc=\"upper right\")\n",
    "\n",
    "    # Gradient Calculation for tanh;\n",
    "    gradtanhval,tanhval = (sess.run([gradtanh,ftanh],feed_dict={x:xval}))\n",
    "    \n",
    "    axarr[1].grid(color='g', linestyle='--', linewidth=1)\n",
    "    axarr[1].plot(xval,np.squeeze(gradtanhval),label=\"tanh\");\n",
    "    axarr[1].plot(xval,np.squeeze(tanhval),label=\"gradient\");\n",
    "    axarr[1].set_title(\"tanh\");\n",
    "    axarr[1].legend(loc=\"upper right\")\n",
    "\n",
    "    # Gradient Calculation for relu;\n",
    "    xval = np.linspace(-1.5,1.5,100)\n",
    "    gradreluval,reluval = (sess.run([gradrelu,frelu],feed_dict={x:xval}))    \n",
    "    axarr[2].grid(color='g', linestyle='--', linewidth=1)\n",
    "    axarr[2].plot(xval,np.squeeze(gradreluval),label=\"relu\");\n",
    "    axarr[2].plot(xval,np.squeeze(reluval),label=\"gradient\");\n",
    "    axarr[1].set_title(\"relu\");\n",
    "    axarr[2].legend(loc=\"upper right\")\n",
    "    f.suptitle(\"Graphs describing gradients for various activations\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}