Kernel Matrix Multiplication

Class15/.ipynb_checkpoints/Practice-checkpoint.ipynb

@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

Class15/Autoencoder_Sequential.ipynb

@@ -2,9 +2,17 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
@@ -150,7 +158,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "AutoEncoder.compile(optimizer='adam', loss='mse')"
+    "AutoEncoder.compile(optimizer='adam', loss='mse')## Doubt"
    ]
   },
   {
@@ -359,6 +367,7 @@
    ],
    "source": [
     "out = AutoEncoder.predict( np.array([sample]) )\n",
+    "print(out)\n",
     "out = np.array( out[0]*255, dtype=np.uint8 )\n",
     "plt.imshow( np.reshape(out, (28,28) ), cmap='gray' )"
    ]
@@ -373,21 +382,21 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.15"
+   "pygments_lexer": "ipython3",
+   "version": "3.6.0"
   }
  },
  "nbformat": 4,

Class15/Classification_on_MNIST.ipynb

@@ -304,24 +304,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1 [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
-      "0 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
-      "0 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
-      "8 [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
-      "9 [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n"
+     "ename": "NameError",
+     "evalue": "name 'Y_test' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-2-e412f025016e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mix\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mY_test\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mix\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m: name 'Y_test' is not defined"
      ]
     }
    ],
    "source": [
     "for ix in range(5):\n",
-    "    print y_test[ix], Y_test[ix]"
+    "    print(Y_test[ix])"
    ]
   },
   {
@@ -410,21 +410,21 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.15"
+   "pygments_lexer": "ipython3",
+   "version": "3.6.0"
   }
  },
  "nbformat": 4,

Class15/Kernel Coding.ipynb

@@ -0,0 +1,98 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def KernelMultiply(K,M):\n",
+    "    matrix_size  = M.shape[0]\n",
+    "    kernel_size = K.shape[0]\n",
+    "    new_matrix = np.zeros((matrix_size-kernel_size+1,matrix_size-kernel_size+1))\n",
+    "    for ix in list(range(new_matrix.shape[0])):\n",
+    "        for jx in list(range(new_matrix.shape[1])):\n",
+    "            for ax in list(range(ix,ix+K.shape[0])):\n",
+    "                for bx in list(range(jx,jx+K.shape[1])):\n",
+    "                    new_matrix[ix][jx] += M[ax][bx]*K[ix][jx]\n",
+    "                    \n",
+    "    return new_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "K= np.array(([1,1],[1,1]))\n",
+    "M = np.array(([1,1,1],[1,1,1],[1,1,1]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "S = KernelMultiply(K,M)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[4. 4.]\n",
+      " [4. 4.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(S)"
+   ]
+  },
+  {
+   "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": "3.6.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}