{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|default_exp conv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convolutions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "import torch\n",
    "from torch import nn\n",
    "\n",
    "from torch.utils.data import default_collate\n",
    "from typing import Mapping\n",
    "\n",
    "from miniai.training import *\n",
    "from miniai.datasets import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle,gzip,math,os,time,shutil,torch,matplotlib as mpl, numpy as np\n",
    "import pandas as pd,matplotlib.pyplot as plt\n",
    "from pathlib import Path\n",
    "from torch import tensor\n",
    "\n",
    "from torch.utils.data import DataLoader\n",
    "from typing import Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mpl.rcParams['image.cmap'] = 'gray'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_data = Path('data')\n",
    "path_gz = path_data/'mnist.pkl.gz'\n",
    "with gzip.open(path_gz, 'rb') as f: ((x_train, y_train), (x_valid, y_valid), _) = pickle.load(f, encoding='latin-1')\n",
    "x_train, y_train, x_valid, y_valid = map(tensor, [x_train, y_train, x_valid, y_valid])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the context of an image, a feature is a visually distinctive attribute. For example, the number 7 is characterized by a horizontal edge near the top of the digit, and a top-right to bottom-left diagonal edge underneath that.\n",
    "\n",
    "It turns out that finding the edges in an image is a very common task in computer vision, and is surprisingly straightforward. To do it, we use a *convolution*. A convolution requires nothing more than multiplication, and addition."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Understanding the Convolution Equations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To explain the math behind convolutions, fast.ai student Matt Kleinsmith came up with the very clever idea of showing [CNNs from different viewpoints](https://medium.com/impactai/cnns-from-different-viewpoints-fab7f52d159c).\n",
    "\n",
    "Here's the input:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img alt=\"The image\" width=\"75\" src=\"images/att_00032.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's our kernel:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img alt=\"The kernel\" width=\"55\" src=\"images/att_00033.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the filter fits in the image four times, we have four results:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img alt=\"The activations\" width=\"52\" src=\"images/att_00034.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img alt=\"Applying the kernel\" width=\"366\" caption=\"Applying the kernel\" id=\"apply_kernel\" src=\"images/att_00035.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img alt=\"The equation\" width=\"436\" caption=\"The equation\" id=\"eq_view\" src=\"images/att_00036.png\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_imgs = x_train.view(-1,28,28)\n",
    "xv_imgs = x_valid.view(-1,28,28)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mpl.rcParams['figure.dpi'] = 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "im3 = x_imgs[7]\n",
    "show_image(im3);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_edge = tensor([[-1,-1,-1],\n",
    "                   [ 0, 0, 0],\n",
    "                   [ 1, 1, 1]]).float()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're going to call this our kernel (because that's what fancy computer vision researchers call these)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOsAAADrCAYAAACICmHVAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAADHUlEQVR4nO3ZsW0CQRBA0VuLFhwQuYnriZro6Zpw7CLGDWAJAoS/eC+8nWCSr13p1sxswP/38eoFgPuIFSLEChFihQixQoRYIeL0yPBay38eeLKZWbe+u1khQqwQIVaIECtEiBUixAoRYoUIsUKEWCFCrBAhVogQK0SIFSLEChFihQixQoRYIUKsECFWiBArRIgVIsQKEWKFCLFChFghQqwQIVaIECtEiBUixAoRYoUIsUKEWCFCrBAhVogQK0SIFSLEChFihQixQoRYIUKsECFWiBArRIgVIsQKEWKFCLFChFghQqwQIVaIECtEiBUixAoRYoUIsUKEWCFCrBAhVogQK0SIFSLEChFihQixQoRYIUKsECFWiBArRIgVIsQKEWKFiNMjw+fzebtcLs/aBd7e9Xr988zNChFihQixQoRYIUKsECFWiBArRIgVIsQKEWKFCLFChFghQqwQIVaIECtEiBUixAoRYoUIsUKEWCFCrBAhVogQK0SIFSLEChFihQixQoRYIUKsECFWiBArRIgVIsQKEWKFCLFChFghQqwQIVaIECtEiBUixAoRYoUIsUKEWCFCrBAhVogQK0SIFSLEChFihQixQoRYIUKsECFWiBArRIgVIsQKEWKFCLFChFghQqwQIVaIECtEiBUixAoRYoUIsUKEWCFCrBAhVogQK0Ssmbl7eN/3OY7jievAe9v3fTuOY906c7NChFghQqwQIVaIECtEiBUixAoRYoUIsUKEWCFCrBAhVogQK0SIFSLEChFihQixQoRYIUKsECFWiBArRIgVIsQKEWKFCLFChFghQqwQIVaIECtEiBUixAoRYoUIsUKEWCFCrBAhVogQK0SIFSLEChFihQixQoRYIUKsECFWiBArRIgVIsQKEWKFCLFChFghQqwQIVaIECtEiBUixAoRYoUIsUKEWCFCrBAhVogQK0SIFSLEChFihQixQoRYIUKsECFWiBArRIgVIsQKEWKFiDUz9w+v9bNt2/fz1oG39zUzn7cOHooVeB3PYIgQK0SIFSLEChFihQixQoRYIUKsECFWiPgFV2MhFpDbtPEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_image(top_edge, noframe=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The filter will take any window of size 3×3 in our images, and if we name the pixel values like this:\n",
    "\n",
    "$$\\begin{matrix} a1 & a2 & a3 \\\\ a4 & a5 & a6 \\\\ a7 & a8 & a9 \\end{matrix}$$\n",
    "\n",
    "it will return $-a1-a2-a3+a7+a8+a9$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  color: #000000;\n",
       "}\n",
       "#T_6d651_row5_col14, #T_6d651_row5_col15, #T_6d651_row5_col16, #T_6d651_row5_col17, #T_6d651_row5_col18, #T_6d651_row5_col19, #T_6d651_row6_col13, #T_6d651_row6_col20, #T_6d651_row7_col9, #T_6d651_row7_col10, #T_6d651_row7_col11, #T_6d651_row7_col12, #T_6d651_row7_col13, #T_6d651_row7_col20, #T_6d651_row7_col21, #T_6d651_row7_col22, #T_6d651_row8_col10, #T_6d651_row8_col11, #T_6d651_row8_col20, #T_6d651_row8_col21, #T_6d651_row8_col22, #T_6d651_row9_col20, #T_6d651_row10_col20, #T_6d651_row11_col20 {\n",
       "  font-size: 7pt;\n",
       "  background-color: #000000;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6d651_row5_col20 {\n",
       "  font-size: 7pt;\n",
       "  background-color: #626262;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6d651_row5_col21 {\n",
       "  font-size: 7pt;\n",
       "  background-color: #fcfcfc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_6d651_row6_col9 {\n",
       "  font-size: 7pt;\n",
       "  background-color: #dbdbdb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_6d651_row6_col10 {\n",
       "  font-size: 7pt;\n",
       "  background-color: #878787;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6d651_row6_col11 {\n",
       "  font-size: 7pt;\n",
       "  background-color: #212121;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6d651_row6_col12 {\n",
       "  font-size: 7pt;\n",
       "  background-color: #1e1e1e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6d651_row6_col14, #T_6d651_row7_col14 {\n",
       "  font-size: 7pt;\n",
       "  background-color: #020202;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6d651_row6_col15, #T_6d651_row6_col16, #T_6d651_row6_col17, #T_6d651_row6_col18, #T_6d651_row6_col19, #T_6d651_row7_col15, #T_6d651_row7_col16, #T_6d651_row7_col17, #T_6d651_row7_col18, #T_6d651_row7_col19, #T_6d651_row8_col18, #T_6d651_row8_col19, #T_6d651_row9_col19, #T_6d651_row10_col19, #T_6d651_row11_col18, #T_6d651_row11_col19, #T_6d651_row12_col17, #T_6d651_row12_col18, #T_6d651_row12_col19 {\n",
       "  font-size: 7pt;\n",
       "  background-color: #010101;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6d651_row6_col21 {\n",
       "  font-size: 7pt;\n",
       "  background-color: #727272;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6d651_row6_col22 {\n",
       "  font-size: 7pt;\n",
       "  background-color: #dcdcdc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_6d651_row8_col9 {\n",
       "  font-size: 7pt;\n",
       "  background-color: #777777;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6d651_row8_col12 {\n",
       "  font-size: 7pt;\n",
       "  background-color: #1a1a1a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6d651_row8_col13 {\n",
       "  font-size: 7pt;\n",
       "  background-color: #8f8f8f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6d651_row8_col14, #T_6d651_row8_col15, #T_6d651_row8_col16 {\n",
       "  font-size: 7pt;\n",
       "  background-color: #909090;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6d651_row8_col17, #T_6d651_row11_col17 {\n",
       "  font-size: 7pt;\n",
       "  background-color: #525252;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6d651_row9_col9 {\n",
       "  font-size: 7pt;\n",
       "  background-color: #fdfdfd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_6d651_row9_col10, #T_6d651_row9_col11, #T_6d651_row9_col22 {\n",
       "  font-size: 7pt;\n",
       "  background-color: #f1f1f1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_6d651_row9_col12 {\n",
       "  font-size: 7pt;\n",
       "  background-color: #f4f4f4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_6d651_row9_col17, #T_6d651_row11_col21 {\n",
       "  font-size: 7pt;\n",
       "  background-color: #f8f8f8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_6d651_row9_col18 {\n",
       "  font-size: 7pt;\n",
       "  background-color: #1f1f1f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6d651_row9_col21 {\n",
       "  font-size: 7pt;\n",
       "  background-color: #646464;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6d651_row10_col17 {\n",
       "  font-size: 7pt;\n",
       "  background-color: #c5c5c5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_6d651_row10_col18 {\n",
       "  font-size: 7pt;\n",
       "  background-color: #0c0c0c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6d651_row10_col21 {\n",
       "  font-size: 7pt;\n",
       "  background-color: #828282;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6d651_row11_col16 {\n",
       "  font-size: 7pt;\n",
       "  background-color: #c3c3c3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_6d651_row12_col14 {\n",
       "  font-size: 7pt;\n",
       "  background-color: #c1c1c1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_6d651_row12_col15 {\n",
       "  font-size: 7pt;\n",
       "  background-color: #323232;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6d651_row12_col16 {\n",
       "  font-size: 7pt;\n",
       "  background-color: #070707;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6d651_row12_col20 {\n",
       "  font-size: 7pt;\n",
       "  background-color: #3c3c3c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6d651_row12_col21 {\n",
       "  font-size: 7pt;\n",
       "  background-color: #fbfbfb;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_6d651\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_6d651_level0_col0\" class=\"col_heading level0 col0\" >0</th>\n",
       "      <th id=\"T_6d651_level0_col1\" class=\"col_heading level0 col1\" >1</th>\n",
       "      <th id=\"T_6d651_level0_col2\" class=\"col_heading level0 col2\" >2</th>\n",
       "      <th id=\"T_6d651_level0_col3\" class=\"col_heading level0 col3\" >3</th>\n",
       "      <th id=\"T_6d651_level0_col4\" class=\"col_heading level0 col4\" >4</th>\n",
       "      <th id=\"T_6d651_level0_col5\" class=\"col_heading level0 col5\" >5</th>\n",
       "      <th id=\"T_6d651_level0_col6\" class=\"col_heading level0 col6\" >6</th>\n",
       "      <th id=\"T_6d651_level0_col7\" class=\"col_heading level0 col7\" >7</th>\n",
       "      <th id=\"T_6d651_level0_col8\" class=\"col_heading level0 col8\" >8</th>\n",
       "      <th id=\"T_6d651_level0_col9\" class=\"col_heading level0 col9\" >9</th>\n",
       "      <th id=\"T_6d651_level0_col10\" class=\"col_heading level0 col10\" >10</th>\n",
       "      <th id=\"T_6d651_level0_col11\" class=\"col_heading level0 col11\" >11</th>\n",
       "      <th id=\"T_6d651_level0_col12\" class=\"col_heading level0 col12\" >12</th>\n",
       "      <th id=\"T_6d651_level0_col13\" class=\"col_heading level0 col13\" >13</th>\n",
       "      <th id=\"T_6d651_level0_col14\" class=\"col_heading level0 col14\" >14</th>\n",
       "      <th id=\"T_6d651_level0_col15\" class=\"col_heading level0 col15\" >15</th>\n",
       "      <th id=\"T_6d651_level0_col16\" class=\"col_heading level0 col16\" >16</th>\n",
       "      <th id=\"T_6d651_level0_col17\" class=\"col_heading level0 col17\" >17</th>\n",
       "      <th id=\"T_6d651_level0_col18\" class=\"col_heading level0 col18\" >18</th>\n",
       "      <th id=\"T_6d651_level0_col19\" class=\"col_heading level0 col19\" >19</th>\n",
       "      <th id=\"T_6d651_level0_col20\" class=\"col_heading level0 col20\" >20</th>\n",
       "      <th id=\"T_6d651_level0_col21\" class=\"col_heading level0 col21\" >21</th>\n",
       "      <th id=\"T_6d651_level0_col22\" class=\"col_heading level0 col22\" >22</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_6d651_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_6d651_row0_col0\" class=\"data row0 col0\" >0.00</td>\n",
       "      <td id=\"T_6d651_row0_col1\" class=\"data row0 col1\" >0.00</td>\n",
       "      <td id=\"T_6d651_row0_col2\" class=\"data row0 col2\" >0.00</td>\n",
       "      <td id=\"T_6d651_row0_col3\" class=\"data row0 col3\" >0.00</td>\n",
       "      <td id=\"T_6d651_row0_col4\" class=\"data row0 col4\" >0.00</td>\n",
       "      <td id=\"T_6d651_row0_col5\" class=\"data row0 col5\" >0.00</td>\n",
       "      <td id=\"T_6d651_row0_col6\" class=\"data row0 col6\" >0.00</td>\n",
       "      <td id=\"T_6d651_row0_col7\" class=\"data row0 col7\" >0.00</td>\n",
       "      <td id=\"T_6d651_row0_col8\" class=\"data row0 col8\" >0.00</td>\n",
       "      <td id=\"T_6d651_row0_col9\" class=\"data row0 col9\" >0.00</td>\n",
       "      <td id=\"T_6d651_row0_col10\" class=\"data row0 col10\" >0.00</td>\n",
       "      <td id=\"T_6d651_row0_col11\" class=\"data row0 col11\" >0.00</td>\n",
       "      <td id=\"T_6d651_row0_col12\" class=\"data row0 col12\" >0.00</td>\n",
       "      <td id=\"T_6d651_row0_col13\" class=\"data row0 col13\" >0.00</td>\n",
       "      <td id=\"T_6d651_row0_col14\" class=\"data row0 col14\" >0.00</td>\n",
       "      <td id=\"T_6d651_row0_col15\" class=\"data row0 col15\" >0.00</td>\n",
       "      <td id=\"T_6d651_row0_col16\" class=\"data row0 col16\" >0.00</td>\n",
       "      <td id=\"T_6d651_row0_col17\" class=\"data row0 col17\" >0.00</td>\n",
       "      <td id=\"T_6d651_row0_col18\" class=\"data row0 col18\" >0.00</td>\n",
       "      <td id=\"T_6d651_row0_col19\" class=\"data row0 col19\" >0.00</td>\n",
       "      <td id=\"T_6d651_row0_col20\" class=\"data row0 col20\" >0.00</td>\n",
       "      <td id=\"T_6d651_row0_col21\" class=\"data row0 col21\" >0.00</td>\n",
       "      <td id=\"T_6d651_row0_col22\" class=\"data row0 col22\" >0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6d651_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_6d651_row1_col0\" class=\"data row1 col0\" >0.00</td>\n",
       "      <td id=\"T_6d651_row1_col1\" class=\"data row1 col1\" >0.00</td>\n",
       "      <td id=\"T_6d651_row1_col2\" class=\"data row1 col2\" >0.00</td>\n",
       "      <td id=\"T_6d651_row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
       "      <td id=\"T_6d651_row1_col4\" class=\"data row1 col4\" >0.00</td>\n",
       "      <td id=\"T_6d651_row1_col5\" class=\"data row1 col5\" >0.00</td>\n",
       "      <td id=\"T_6d651_row1_col6\" class=\"data row1 col6\" >0.00</td>\n",
       "      <td id=\"T_6d651_row1_col7\" class=\"data row1 col7\" >0.00</td>\n",
       "      <td id=\"T_6d651_row1_col8\" class=\"data row1 col8\" >0.00</td>\n",
       "      <td id=\"T_6d651_row1_col9\" class=\"data row1 col9\" >0.00</td>\n",
       "      <td id=\"T_6d651_row1_col10\" class=\"data row1 col10\" >0.00</td>\n",
       "      <td id=\"T_6d651_row1_col11\" class=\"data row1 col11\" >0.00</td>\n",
       "      <td id=\"T_6d651_row1_col12\" class=\"data row1 col12\" >0.00</td>\n",
       "      <td id=\"T_6d651_row1_col13\" class=\"data row1 col13\" >0.00</td>\n",
       "      <td id=\"T_6d651_row1_col14\" class=\"data row1 col14\" >0.00</td>\n",
       "      <td id=\"T_6d651_row1_col15\" class=\"data row1 col15\" >0.00</td>\n",
       "      <td id=\"T_6d651_row1_col16\" class=\"data row1 col16\" >0.00</td>\n",
       "      <td id=\"T_6d651_row1_col17\" class=\"data row1 col17\" >0.00</td>\n",
       "      <td id=\"T_6d651_row1_col18\" class=\"data row1 col18\" >0.00</td>\n",
       "      <td id=\"T_6d651_row1_col19\" class=\"data row1 col19\" >0.00</td>\n",
       "      <td id=\"T_6d651_row1_col20\" class=\"data row1 col20\" >0.00</td>\n",
       "      <td id=\"T_6d651_row1_col21\" class=\"data row1 col21\" >0.00</td>\n",
       "      <td id=\"T_6d651_row1_col22\" class=\"data row1 col22\" >0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6d651_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_6d651_row2_col0\" class=\"data row2 col0\" >0.00</td>\n",
       "      <td id=\"T_6d651_row2_col1\" class=\"data row2 col1\" >0.00</td>\n",
       "      <td id=\"T_6d651_row2_col2\" class=\"data row2 col2\" >0.00</td>\n",
       "      <td id=\"T_6d651_row2_col3\" class=\"data row2 col3\" >0.00</td>\n",
       "      <td id=\"T_6d651_row2_col4\" class=\"data row2 col4\" >0.00</td>\n",
       "      <td id=\"T_6d651_row2_col5\" class=\"data row2 col5\" >0.00</td>\n",
       "      <td id=\"T_6d651_row2_col6\" class=\"data row2 col6\" >0.00</td>\n",
       "      <td id=\"T_6d651_row2_col7\" class=\"data row2 col7\" >0.00</td>\n",
       "      <td id=\"T_6d651_row2_col8\" class=\"data row2 col8\" >0.00</td>\n",
       "      <td id=\"T_6d651_row2_col9\" class=\"data row2 col9\" >0.00</td>\n",
       "      <td id=\"T_6d651_row2_col10\" class=\"data row2 col10\" >0.00</td>\n",
       "      <td id=\"T_6d651_row2_col11\" class=\"data row2 col11\" >0.00</td>\n",
       "      <td id=\"T_6d651_row2_col12\" class=\"data row2 col12\" >0.00</td>\n",
       "      <td id=\"T_6d651_row2_col13\" class=\"data row2 col13\" >0.00</td>\n",
       "      <td id=\"T_6d651_row2_col14\" class=\"data row2 col14\" >0.00</td>\n",
       "      <td id=\"T_6d651_row2_col15\" class=\"data row2 col15\" >0.00</td>\n",
       "      <td id=\"T_6d651_row2_col16\" class=\"data row2 col16\" >0.00</td>\n",
       "      <td id=\"T_6d651_row2_col17\" class=\"data row2 col17\" >0.00</td>\n",
       "      <td id=\"T_6d651_row2_col18\" class=\"data row2 col18\" >0.00</td>\n",
       "      <td id=\"T_6d651_row2_col19\" class=\"data row2 col19\" >0.00</td>\n",
       "      <td id=\"T_6d651_row2_col20\" class=\"data row2 col20\" >0.00</td>\n",
       "      <td id=\"T_6d651_row2_col21\" class=\"data row2 col21\" >0.00</td>\n",
       "      <td id=\"T_6d651_row2_col22\" class=\"data row2 col22\" >0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6d651_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_6d651_row3_col0\" class=\"data row3 col0\" >0.00</td>\n",
       "      <td id=\"T_6d651_row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
       "      <td id=\"T_6d651_row3_col2\" class=\"data row3 col2\" >0.00</td>\n",
       "      <td id=\"T_6d651_row3_col3\" class=\"data row3 col3\" >0.00</td>\n",
       "      <td id=\"T_6d651_row3_col4\" class=\"data row3 col4\" >0.00</td>\n",
       "      <td id=\"T_6d651_row3_col5\" class=\"data row3 col5\" >0.00</td>\n",
       "      <td id=\"T_6d651_row3_col6\" class=\"data row3 col6\" >0.00</td>\n",
       "      <td id=\"T_6d651_row3_col7\" class=\"data row3 col7\" >0.00</td>\n",
       "      <td id=\"T_6d651_row3_col8\" class=\"data row3 col8\" >0.00</td>\n",
       "      <td id=\"T_6d651_row3_col9\" class=\"data row3 col9\" >0.00</td>\n",
       "      <td id=\"T_6d651_row3_col10\" class=\"data row3 col10\" >0.00</td>\n",
       "      <td id=\"T_6d651_row3_col11\" class=\"data row3 col11\" >0.00</td>\n",
       "      <td id=\"T_6d651_row3_col12\" class=\"data row3 col12\" >0.00</td>\n",
       "      <td id=\"T_6d651_row3_col13\" class=\"data row3 col13\" >0.00</td>\n",
       "      <td id=\"T_6d651_row3_col14\" class=\"data row3 col14\" >0.00</td>\n",
       "      <td id=\"T_6d651_row3_col15\" class=\"data row3 col15\" >0.00</td>\n",
       "      <td id=\"T_6d651_row3_col16\" class=\"data row3 col16\" >0.00</td>\n",
       "      <td id=\"T_6d651_row3_col17\" class=\"data row3 col17\" >0.00</td>\n",
       "      <td id=\"T_6d651_row3_col18\" class=\"data row3 col18\" >0.00</td>\n",
       "      <td id=\"T_6d651_row3_col19\" class=\"data row3 col19\" >0.00</td>\n",
       "      <td id=\"T_6d651_row3_col20\" class=\"data row3 col20\" >0.00</td>\n",
       "      <td id=\"T_6d651_row3_col21\" class=\"data row3 col21\" >0.00</td>\n",
       "      <td id=\"T_6d651_row3_col22\" class=\"data row3 col22\" >0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6d651_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_6d651_row4_col0\" class=\"data row4 col0\" >0.00</td>\n",
       "      <td id=\"T_6d651_row4_col1\" class=\"data row4 col1\" >0.00</td>\n",
       "      <td id=\"T_6d651_row4_col2\" class=\"data row4 col2\" >0.00</td>\n",
       "      <td id=\"T_6d651_row4_col3\" class=\"data row4 col3\" >0.00</td>\n",
       "      <td id=\"T_6d651_row4_col4\" class=\"data row4 col4\" >0.00</td>\n",
       "      <td id=\"T_6d651_row4_col5\" class=\"data row4 col5\" >0.00</td>\n",
       "      <td id=\"T_6d651_row4_col6\" class=\"data row4 col6\" >0.00</td>\n",
       "      <td id=\"T_6d651_row4_col7\" class=\"data row4 col7\" >0.00</td>\n",
       "      <td id=\"T_6d651_row4_col8\" class=\"data row4 col8\" >0.00</td>\n",
       "      <td id=\"T_6d651_row4_col9\" class=\"data row4 col9\" >0.00</td>\n",
       "      <td id=\"T_6d651_row4_col10\" class=\"data row4 col10\" >0.00</td>\n",
       "      <td id=\"T_6d651_row4_col11\" class=\"data row4 col11\" >0.00</td>\n",
       "      <td id=\"T_6d651_row4_col12\" class=\"data row4 col12\" >0.00</td>\n",
       "      <td id=\"T_6d651_row4_col13\" class=\"data row4 col13\" >0.00</td>\n",
       "      <td id=\"T_6d651_row4_col14\" class=\"data row4 col14\" >0.00</td>\n",
       "      <td id=\"T_6d651_row4_col15\" class=\"data row4 col15\" >0.00</td>\n",
       "      <td id=\"T_6d651_row4_col16\" class=\"data row4 col16\" >0.00</td>\n",
       "      <td id=\"T_6d651_row4_col17\" class=\"data row4 col17\" >0.00</td>\n",
       "      <td id=\"T_6d651_row4_col18\" class=\"data row4 col18\" >0.00</td>\n",
       "      <td id=\"T_6d651_row4_col19\" class=\"data row4 col19\" >0.00</td>\n",
       "      <td id=\"T_6d651_row4_col20\" class=\"data row4 col20\" >0.00</td>\n",
       "      <td id=\"T_6d651_row4_col21\" class=\"data row4 col21\" >0.00</td>\n",
       "      <td id=\"T_6d651_row4_col22\" class=\"data row4 col22\" >0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6d651_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_6d651_row5_col0\" class=\"data row5 col0\" >0.00</td>\n",
       "      <td id=\"T_6d651_row5_col1\" class=\"data row5 col1\" >0.00</td>\n",
       "      <td id=\"T_6d651_row5_col2\" class=\"data row5 col2\" >0.00</td>\n",
       "      <td id=\"T_6d651_row5_col3\" class=\"data row5 col3\" >0.00</td>\n",
       "      <td id=\"T_6d651_row5_col4\" class=\"data row5 col4\" >0.00</td>\n",
       "      <td id=\"T_6d651_row5_col5\" class=\"data row5 col5\" >0.00</td>\n",
       "      <td id=\"T_6d651_row5_col6\" class=\"data row5 col6\" >0.00</td>\n",
       "      <td id=\"T_6d651_row5_col7\" class=\"data row5 col7\" >0.00</td>\n",
       "      <td id=\"T_6d651_row5_col8\" class=\"data row5 col8\" >0.00</td>\n",
       "      <td id=\"T_6d651_row5_col9\" class=\"data row5 col9\" >0.00</td>\n",
       "      <td id=\"T_6d651_row5_col10\" class=\"data row5 col10\" >0.00</td>\n",
       "      <td id=\"T_6d651_row5_col11\" class=\"data row5 col11\" >0.15</td>\n",
       "      <td id=\"T_6d651_row5_col12\" class=\"data row5 col12\" >0.17</td>\n",
       "      <td id=\"T_6d651_row5_col13\" class=\"data row5 col13\" >0.41</td>\n",
       "      <td id=\"T_6d651_row5_col14\" class=\"data row5 col14\" >1.00</td>\n",
       "      <td id=\"T_6d651_row5_col15\" class=\"data row5 col15\" >0.99</td>\n",
       "      <td id=\"T_6d651_row5_col16\" class=\"data row5 col16\" >0.99</td>\n",
       "      <td id=\"T_6d651_row5_col17\" class=\"data row5 col17\" >0.99</td>\n",
       "      <td id=\"T_6d651_row5_col18\" class=\"data row5 col18\" >0.99</td>\n",
       "      <td id=\"T_6d651_row5_col19\" class=\"data row5 col19\" >0.99</td>\n",
       "      <td id=\"T_6d651_row5_col20\" class=\"data row5 col20\" >0.68</td>\n",
       "      <td id=\"T_6d651_row5_col21\" class=\"data row5 col21\" >0.02</td>\n",
       "      <td id=\"T_6d651_row5_col22\" class=\"data row5 col22\" >0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6d651_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_6d651_row6_col0\" class=\"data row6 col0\" >0.00</td>\n",
       "      <td id=\"T_6d651_row6_col1\" class=\"data row6 col1\" >0.00</td>\n",
       "      <td id=\"T_6d651_row6_col2\" class=\"data row6 col2\" >0.00</td>\n",
       "      <td id=\"T_6d651_row6_col3\" class=\"data row6 col3\" >0.00</td>\n",
       "      <td id=\"T_6d651_row6_col4\" class=\"data row6 col4\" >0.00</td>\n",
       "      <td id=\"T_6d651_row6_col5\" class=\"data row6 col5\" >0.00</td>\n",
       "      <td id=\"T_6d651_row6_col6\" class=\"data row6 col6\" >0.00</td>\n",
       "      <td id=\"T_6d651_row6_col7\" class=\"data row6 col7\" >0.00</td>\n",
       "      <td id=\"T_6d651_row6_col8\" class=\"data row6 col8\" >0.00</td>\n",
       "      <td id=\"T_6d651_row6_col9\" class=\"data row6 col9\" >0.17</td>\n",
       "      <td id=\"T_6d651_row6_col10\" class=\"data row6 col10\" >0.54</td>\n",
       "      <td id=\"T_6d651_row6_col11\" class=\"data row6 col11\" >0.88</td>\n",
       "      <td id=\"T_6d651_row6_col12\" class=\"data row6 col12\" >0.88</td>\n",
       "      <td id=\"T_6d651_row6_col13\" class=\"data row6 col13\" >0.98</td>\n",
       "      <td id=\"T_6d651_row6_col14\" class=\"data row6 col14\" >0.99</td>\n",
       "      <td id=\"T_6d651_row6_col15\" class=\"data row6 col15\" >0.98</td>\n",
       "      <td id=\"T_6d651_row6_col16\" class=\"data row6 col16\" >0.98</td>\n",
       "      <td id=\"T_6d651_row6_col17\" class=\"data row6 col17\" >0.98</td>\n",
       "      <td id=\"T_6d651_row6_col18\" class=\"data row6 col18\" >0.98</td>\n",
       "      <td id=\"T_6d651_row6_col19\" class=\"data row6 col19\" >0.98</td>\n",
       "      <td id=\"T_6d651_row6_col20\" class=\"data row6 col20\" >0.98</td>\n",
       "      <td id=\"T_6d651_row6_col21\" class=\"data row6 col21\" >0.62</td>\n",
       "      <td id=\"T_6d651_row6_col22\" class=\"data row6 col22\" >0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6d651_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_6d651_row7_col0\" class=\"data row7 col0\" >0.00</td>\n",
       "      <td id=\"T_6d651_row7_col1\" class=\"data row7 col1\" >0.00</td>\n",
       "      <td id=\"T_6d651_row7_col2\" class=\"data row7 col2\" >0.00</td>\n",
       "      <td id=\"T_6d651_row7_col3\" class=\"data row7 col3\" >0.00</td>\n",
       "      <td id=\"T_6d651_row7_col4\" class=\"data row7 col4\" >0.00</td>\n",
       "      <td id=\"T_6d651_row7_col5\" class=\"data row7 col5\" >0.00</td>\n",
       "      <td id=\"T_6d651_row7_col6\" class=\"data row7 col6\" >0.00</td>\n",
       "      <td id=\"T_6d651_row7_col7\" class=\"data row7 col7\" >0.00</td>\n",
       "      <td id=\"T_6d651_row7_col8\" class=\"data row7 col8\" >0.00</td>\n",
       "      <td id=\"T_6d651_row7_col9\" class=\"data row7 col9\" >0.70</td>\n",
       "      <td id=\"T_6d651_row7_col10\" class=\"data row7 col10\" >0.98</td>\n",
       "      <td id=\"T_6d651_row7_col11\" class=\"data row7 col11\" >0.98</td>\n",
       "      <td id=\"T_6d651_row7_col12\" class=\"data row7 col12\" >0.98</td>\n",
       "      <td id=\"T_6d651_row7_col13\" class=\"data row7 col13\" >0.98</td>\n",
       "      <td id=\"T_6d651_row7_col14\" class=\"data row7 col14\" >0.99</td>\n",
       "      <td id=\"T_6d651_row7_col15\" class=\"data row7 col15\" >0.98</td>\n",
       "      <td id=\"T_6d651_row7_col16\" class=\"data row7 col16\" >0.98</td>\n",
       "      <td id=\"T_6d651_row7_col17\" class=\"data row7 col17\" >0.98</td>\n",
       "      <td id=\"T_6d651_row7_col18\" class=\"data row7 col18\" >0.98</td>\n",
       "      <td id=\"T_6d651_row7_col19\" class=\"data row7 col19\" >0.98</td>\n",
       "      <td id=\"T_6d651_row7_col20\" class=\"data row7 col20\" >0.98</td>\n",
       "      <td id=\"T_6d651_row7_col21\" class=\"data row7 col21\" >0.98</td>\n",
       "      <td id=\"T_6d651_row7_col22\" class=\"data row7 col22\" >0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6d651_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_6d651_row8_col0\" class=\"data row8 col0\" >0.00</td>\n",
       "      <td id=\"T_6d651_row8_col1\" class=\"data row8 col1\" >0.00</td>\n",
       "      <td id=\"T_6d651_row8_col2\" class=\"data row8 col2\" >0.00</td>\n",
       "      <td id=\"T_6d651_row8_col3\" class=\"data row8 col3\" >0.00</td>\n",
       "      <td id=\"T_6d651_row8_col4\" class=\"data row8 col4\" >0.00</td>\n",
       "      <td id=\"T_6d651_row8_col5\" class=\"data row8 col5\" >0.00</td>\n",
       "      <td id=\"T_6d651_row8_col6\" class=\"data row8 col6\" >0.00</td>\n",
       "      <td id=\"T_6d651_row8_col7\" class=\"data row8 col7\" >0.00</td>\n",
       "      <td id=\"T_6d651_row8_col8\" class=\"data row8 col8\" >0.00</td>\n",
       "      <td id=\"T_6d651_row8_col9\" class=\"data row8 col9\" >0.43</td>\n",
       "      <td id=\"T_6d651_row8_col10\" class=\"data row8 col10\" >0.98</td>\n",
       "      <td id=\"T_6d651_row8_col11\" class=\"data row8 col11\" >0.98</td>\n",
       "      <td id=\"T_6d651_row8_col12\" class=\"data row8 col12\" >0.90</td>\n",
       "      <td id=\"T_6d651_row8_col13\" class=\"data row8 col13\" >0.52</td>\n",
       "      <td id=\"T_6d651_row8_col14\" class=\"data row8 col14\" >0.52</td>\n",
       "      <td id=\"T_6d651_row8_col15\" class=\"data row8 col15\" >0.52</td>\n",
       "      <td id=\"T_6d651_row8_col16\" class=\"data row8 col16\" >0.52</td>\n",
       "      <td id=\"T_6d651_row8_col17\" class=\"data row8 col17\" >0.74</td>\n",
       "      <td id=\"T_6d651_row8_col18\" class=\"data row8 col18\" >0.98</td>\n",
       "      <td id=\"T_6d651_row8_col19\" class=\"data row8 col19\" >0.98</td>\n",
       "      <td id=\"T_6d651_row8_col20\" class=\"data row8 col20\" >0.98</td>\n",
       "      <td id=\"T_6d651_row8_col21\" class=\"data row8 col21\" >0.98</td>\n",
       "      <td id=\"T_6d651_row8_col22\" class=\"data row8 col22\" >0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6d651_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_6d651_row9_col0\" class=\"data row9 col0\" >0.00</td>\n",
       "      <td id=\"T_6d651_row9_col1\" class=\"data row9 col1\" >0.00</td>\n",
       "      <td id=\"T_6d651_row9_col2\" class=\"data row9 col2\" >0.00</td>\n",
       "      <td id=\"T_6d651_row9_col3\" class=\"data row9 col3\" >0.00</td>\n",
       "      <td id=\"T_6d651_row9_col4\" class=\"data row9 col4\" >0.00</td>\n",
       "      <td id=\"T_6d651_row9_col5\" class=\"data row9 col5\" >0.00</td>\n",
       "      <td id=\"T_6d651_row9_col6\" class=\"data row9 col6\" >0.00</td>\n",
       "      <td id=\"T_6d651_row9_col7\" class=\"data row9 col7\" >0.00</td>\n",
       "      <td id=\"T_6d651_row9_col8\" class=\"data row9 col8\" >0.00</td>\n",
       "      <td id=\"T_6d651_row9_col9\" class=\"data row9 col9\" >0.02</td>\n",
       "      <td id=\"T_6d651_row9_col10\" class=\"data row9 col10\" >0.11</td>\n",
       "      <td id=\"T_6d651_row9_col11\" class=\"data row9 col11\" >0.11</td>\n",
       "      <td id=\"T_6d651_row9_col12\" class=\"data row9 col12\" >0.09</td>\n",
       "      <td id=\"T_6d651_row9_col13\" class=\"data row9 col13\" >0.00</td>\n",
       "      <td id=\"T_6d651_row9_col14\" class=\"data row9 col14\" >0.00</td>\n",
       "      <td id=\"T_6d651_row9_col15\" class=\"data row9 col15\" >0.00</td>\n",
       "      <td id=\"T_6d651_row9_col16\" class=\"data row9 col16\" >0.00</td>\n",
       "      <td id=\"T_6d651_row9_col17\" class=\"data row9 col17\" >0.05</td>\n",
       "      <td id=\"T_6d651_row9_col18\" class=\"data row9 col18\" >0.88</td>\n",
       "      <td id=\"T_6d651_row9_col19\" class=\"data row9 col19\" >0.98</td>\n",
       "      <td id=\"T_6d651_row9_col20\" class=\"data row9 col20\" >0.98</td>\n",
       "      <td id=\"T_6d651_row9_col21\" class=\"data row9 col21\" >0.67</td>\n",
       "      <td id=\"T_6d651_row9_col22\" class=\"data row9 col22\" >0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6d651_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_6d651_row10_col0\" class=\"data row10 col0\" >0.00</td>\n",
       "      <td id=\"T_6d651_row10_col1\" class=\"data row10 col1\" >0.00</td>\n",
       "      <td id=\"T_6d651_row10_col2\" class=\"data row10 col2\" >0.00</td>\n",
       "      <td id=\"T_6d651_row10_col3\" class=\"data row10 col3\" >0.00</td>\n",
       "      <td id=\"T_6d651_row10_col4\" class=\"data row10 col4\" >0.00</td>\n",
       "      <td id=\"T_6d651_row10_col5\" class=\"data row10 col5\" >0.00</td>\n",
       "      <td id=\"T_6d651_row10_col6\" class=\"data row10 col6\" >0.00</td>\n",
       "      <td id=\"T_6d651_row10_col7\" class=\"data row10 col7\" >0.00</td>\n",
       "      <td id=\"T_6d651_row10_col8\" class=\"data row10 col8\" >0.00</td>\n",
       "      <td id=\"T_6d651_row10_col9\" class=\"data row10 col9\" >0.00</td>\n",
       "      <td id=\"T_6d651_row10_col10\" class=\"data row10 col10\" >0.00</td>\n",
       "      <td id=\"T_6d651_row10_col11\" class=\"data row10 col11\" >0.00</td>\n",
       "      <td id=\"T_6d651_row10_col12\" class=\"data row10 col12\" >0.00</td>\n",
       "      <td id=\"T_6d651_row10_col13\" class=\"data row10 col13\" >0.00</td>\n",
       "      <td id=\"T_6d651_row10_col14\" class=\"data row10 col14\" >0.00</td>\n",
       "      <td id=\"T_6d651_row10_col15\" class=\"data row10 col15\" >0.00</td>\n",
       "      <td id=\"T_6d651_row10_col16\" class=\"data row10 col16\" >0.00</td>\n",
       "      <td id=\"T_6d651_row10_col17\" class=\"data row10 col17\" >0.33</td>\n",
       "      <td id=\"T_6d651_row10_col18\" class=\"data row10 col18\" >0.95</td>\n",
       "      <td id=\"T_6d651_row10_col19\" class=\"data row10 col19\" >0.98</td>\n",
       "      <td id=\"T_6d651_row10_col20\" class=\"data row10 col20\" >0.98</td>\n",
       "      <td id=\"T_6d651_row10_col21\" class=\"data row10 col21\" >0.56</td>\n",
       "      <td id=\"T_6d651_row10_col22\" class=\"data row10 col22\" >0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6d651_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_6d651_row11_col0\" class=\"data row11 col0\" >0.00</td>\n",
       "      <td id=\"T_6d651_row11_col1\" class=\"data row11 col1\" >0.00</td>\n",
       "      <td id=\"T_6d651_row11_col2\" class=\"data row11 col2\" >0.00</td>\n",
       "      <td id=\"T_6d651_row11_col3\" class=\"data row11 col3\" >0.00</td>\n",
       "      <td id=\"T_6d651_row11_col4\" class=\"data row11 col4\" >0.00</td>\n",
       "      <td id=\"T_6d651_row11_col5\" class=\"data row11 col5\" >0.00</td>\n",
       "      <td id=\"T_6d651_row11_col6\" class=\"data row11 col6\" >0.00</td>\n",
       "      <td id=\"T_6d651_row11_col7\" class=\"data row11 col7\" >0.00</td>\n",
       "      <td id=\"T_6d651_row11_col8\" class=\"data row11 col8\" >0.00</td>\n",
       "      <td id=\"T_6d651_row11_col9\" class=\"data row11 col9\" >0.00</td>\n",
       "      <td id=\"T_6d651_row11_col10\" class=\"data row11 col10\" >0.00</td>\n",
       "      <td id=\"T_6d651_row11_col11\" class=\"data row11 col11\" >0.00</td>\n",
       "      <td id=\"T_6d651_row11_col12\" class=\"data row11 col12\" >0.00</td>\n",
       "      <td id=\"T_6d651_row11_col13\" class=\"data row11 col13\" >0.00</td>\n",
       "      <td id=\"T_6d651_row11_col14\" class=\"data row11 col14\" >0.00</td>\n",
       "      <td id=\"T_6d651_row11_col15\" class=\"data row11 col15\" >0.00</td>\n",
       "      <td id=\"T_6d651_row11_col16\" class=\"data row11 col16\" >0.34</td>\n",
       "      <td id=\"T_6d651_row11_col17\" class=\"data row11 col17\" >0.74</td>\n",
       "      <td id=\"T_6d651_row11_col18\" class=\"data row11 col18\" >0.98</td>\n",
       "      <td id=\"T_6d651_row11_col19\" class=\"data row11 col19\" >0.98</td>\n",
       "      <td id=\"T_6d651_row11_col20\" class=\"data row11 col20\" >0.98</td>\n",
       "      <td id=\"T_6d651_row11_col21\" class=\"data row11 col21\" >0.05</td>\n",
       "      <td id=\"T_6d651_row11_col22\" class=\"data row11 col22\" >0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6d651_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_6d651_row12_col0\" class=\"data row12 col0\" >0.00</td>\n",
       "      <td id=\"T_6d651_row12_col1\" class=\"data row12 col1\" >0.00</td>\n",
       "      <td id=\"T_6d651_row12_col2\" class=\"data row12 col2\" >0.00</td>\n",
       "      <td id=\"T_6d651_row12_col3\" class=\"data row12 col3\" >0.00</td>\n",
       "      <td id=\"T_6d651_row12_col4\" class=\"data row12 col4\" >0.00</td>\n",
       "      <td id=\"T_6d651_row12_col5\" class=\"data row12 col5\" >0.00</td>\n",
       "      <td id=\"T_6d651_row12_col6\" class=\"data row12 col6\" >0.00</td>\n",
       "      <td id=\"T_6d651_row12_col7\" class=\"data row12 col7\" >0.00</td>\n",
       "      <td id=\"T_6d651_row12_col8\" class=\"data row12 col8\" >0.00</td>\n",
       "      <td id=\"T_6d651_row12_col9\" class=\"data row12 col9\" >0.00</td>\n",
       "      <td id=\"T_6d651_row12_col10\" class=\"data row12 col10\" >0.00</td>\n",
       "      <td id=\"T_6d651_row12_col11\" class=\"data row12 col11\" >0.00</td>\n",
       "      <td id=\"T_6d651_row12_col12\" class=\"data row12 col12\" >0.00</td>\n",
       "      <td id=\"T_6d651_row12_col13\" class=\"data row12 col13\" >0.00</td>\n",
       "      <td id=\"T_6d651_row12_col14\" class=\"data row12 col14\" >0.36</td>\n",
       "      <td id=\"T_6d651_row12_col15\" class=\"data row12 col15\" >0.83</td>\n",
       "      <td id=\"T_6d651_row12_col16\" class=\"data row12 col16\" >0.96</td>\n",
       "      <td id=\"T_6d651_row12_col17\" class=\"data row12 col17\" >0.98</td>\n",
       "      <td id=\"T_6d651_row12_col18\" class=\"data row12 col18\" >0.98</td>\n",
       "      <td id=\"T_6d651_row12_col19\" class=\"data row12 col19\" >0.98</td>\n",
       "      <td id=\"T_6d651_row12_col20\" class=\"data row12 col20\" >0.80</td>\n",
       "      <td id=\"T_6d651_row12_col21\" class=\"data row12 col21\" >0.04</td>\n",
       "      <td id=\"T_6d651_row12_col22\" class=\"data row12 col22\" >0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(im3[:13,:23])\n",
    "df.style.format(precision=2).set_properties(**{'font-size':'7pt'}).background_gradient('Greys')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(2.9727)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(im3[3:6,14:17] * top_edge).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(-2.9570)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(im3[7:10,14:17] * top_edge).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def apply_kernel(row, col, kernel): return (im3[row-1:row+2,col-1:col+2] * kernel).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(2.9727)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apply_kernel(4,15,top_edge)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"images/chapter9_nopadconv.svg\" id=\"nopad_conv\" caption=\"Applying a kernel across a grid\" alt=\"Applying a kernel across a grid\" width=\"400\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[(0, 0), (0, 1), (0, 2), (0, 3), (0, 4)],\n",
       " [(1, 0), (1, 1), (1, 2), (1, 3), (1, 4)],\n",
       " [(2, 0), (2, 1), (2, 2), (2, 3), (2, 4)],\n",
       " [(3, 0), (3, 1), (3, 2), (3, 3), (3, 4)],\n",
       " [(4, 0), (4, 1), (4, 2), (4, 3), (4, 4)]]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[[(i,j) for j in range(5)] for i in range(5)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rng = range(1,27)\n",
    "top_edge3 = tensor([[apply_kernel(i,j,top_edge) for j in rng] for i in rng])\n",
    "show_image(top_edge3);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "left_edge = tensor([[-1,0,1],\n",
    "                    [-1,0,1],\n",
    "                    [-1,0,1]]).float()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_image(left_edge, noframe=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "left_edge3 = tensor([[apply_kernel(i,j,left_edge) for j in rng] for i in rng])\n",
    "show_image(left_edge3);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img alt=\"Result of applying a 3×3 kernel to a 4×4 image\" width=\"782\" caption=\"Result of applying a 3×3 kernel to a 4×4 image (courtesy of Vincent Dumoulin and Francesco Visin)\" id=\"three_ex_four_conv\" src=\"images/att_00028.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convolutions in PyTorch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn.functional as F\n",
    "import torch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What to do if you have [2 months to complete your thesis](https://github.com/Yangqing/caffe/wiki/Convolution-in-Caffe:-a-memo)? Use [im2col](https://hal.inria.fr/inria-00112631/)."
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's a sample [numpy implementation](https://github.com/3outeille/CNNumpy/blob/5394f13e7ed67a808a3e39fd381f168825d65ff5/src/fast/utils.py#L360)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([9, 676])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inp = im3[None,None,:,:].float()\n",
    "inp_unf = F.unfold(inp, (3,3))[0]\n",
    "inp_unf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([9])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w = left_edge.view(-1)\n",
    "w.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([676])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out_unf = w@inp_unf\n",
    "out_unf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out = out_unf.view(26,26)\n",
    "show_image(out);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.14 ms ± 150 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit -n 1 tensor([[apply_kernel(i,j,left_edge) for j in rng] for i in rng]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27.2 µs ± 1.51 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit -n 100 (w@F.unfold(inp, (3,3))[0]).view(26,26);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15.7 µs ± 1.06 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit -n 100 F.conv2d(inp, left_edge[None,None])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "diag1_edge = tensor([[ 0,-1, 1],\n",
    "                     [-1, 1, 0],\n",
    "                     [ 1, 0, 0]]).float()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_image(diag1_edge, noframe=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "diag2_edge = tensor([[ 1,-1, 0],\n",
    "                     [ 0, 1,-1],\n",
    "                     [ 0, 0, 1]]).float()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_image(diag2_edge, noframe=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([16, 1, 28, 28])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xb = x_imgs[:16][:,None]\n",
    "xb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([4, 1, 3, 3])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edge_kernels = torch.stack([left_edge, top_edge, diag1_edge, diag2_edge])[:,None]\n",
    "edge_kernels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([16, 4, 26, 26])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_features = F.conv2d(xb, edge_kernels)\n",
    "batch_features.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output shape shows we gave 64 images in the mini-batch, 4 kernels, and 26×26 edge maps (we started with 28×28 images, but lost one pixel from each side as discussed earlier). We can see we get the same results as when we did this manually:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img0 = xb[1,0]\n",
    "show_image(img0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_images([batch_features[1,i] for i in range(4)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Strides and Padding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With appropriate padding, we can ensure that the output activation map is the same size as the original image."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"images/chapter9_padconv.svg\" id=\"pad_conv\" caption=\"A convolution with padding\" alt=\"A convolution with padding\" width=\"600\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With a 5×5 input, 4×4 kernel, and 2 pixels of padding, we end up with a 6×6 activation map."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img alt=\"A 4×4 kernel with 5×5 input and 2 pixels of padding\" width=\"783\" caption=\"A 4×4 kernel with 5×5 input and 2 pixels of padding (courtesy of Vincent Dumoulin and Francesco Visin)\" id=\"four_by_five_conv\" src=\"images/att_00029.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we add a kernel of size `ks` by `ks` (with `ks` an odd number), the necessary padding on each side to keep the same shape is `ks//2`.\n",
    "\n",
    "We could move over two pixels after each kernel application. This is known as a *stride-2* convolution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img alt=\"A 3×3 kernel with 5×5 input, stride-2 convolution, and 1 pixel of padding\" width=\"774\" caption=\"A 3×3 kernel with 5×5 input, stride-2 convolution, and 1 pixel of padding (courtesy of Vincent Dumoulin and Francesco Visin)\" id=\"three_by_five_conv\" src=\"images/att_00030.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating the CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "n,m = x_train.shape\n",
    "c = y_train.max()+1\n",
    "nh = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = nn.Sequential(nn.Linear(m,nh), nn.ReLU(), nn.Linear(nh,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "broken_cnn = nn.Sequential(\n",
    "    nn.Conv2d(1,30, kernel_size=3, padding=1),\n",
    "    nn.ReLU(),\n",
    "    nn.Conv2d(30,10, kernel_size=3, padding=1)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([16, 10, 28, 28])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "broken_cnn(xb).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "def conv(ni, nf, ks=3, stride=2, act=True):\n",
    "    res = nn.Conv2d(ni, nf, stride=stride, kernel_size=ks, padding=ks//2)\n",
    "    if act: res = nn.Sequential(res, nn.ReLU())\n",
    "    return res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Refactoring parts of your neural networks like this makes it much less likely you'll get errors due to inconsistencies in your architectures, and makes it more obvious to the reader which parts of your layers are actually changing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "simple_cnn = nn.Sequential(\n",
    "    conv(1 ,4),            #14x14\n",
    "    conv(4 ,8),            #7x7\n",
    "    conv(8 ,16),           #4x4\n",
    "    conv(16,16),           #2x2\n",
    "    conv(16,10, act=False), #1x1\n",
    "    nn.Flatten(),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([16, 10])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simple_cnn(xb).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_imgs = x_train.view(-1,1,28,28)\n",
    "xv_imgs = x_valid.view(-1,1,28,28)\n",
    "train_ds,valid_ds = Dataset(x_imgs, y_train),Dataset(xv_imgs, y_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "def_device = 'mps' if torch.backends.mps.is_available() else 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "\n",
    "def to_device(x, device=def_device):\n",
    "    if isinstance(x, torch.Tensor): return x.to(device)\n",
    "    if isinstance(x, Mapping): return {k:v.to(device) for k,v in x.items()}\n",
    "    return type(x)(to_device(o, device) for o in x)\n",
    "\n",
    "def collate_device(b): return to_device(default_collate(b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch import optim\n",
    "\n",
    "bs = 256\n",
    "lr = 0.4\n",
    "train_dl,valid_dl = get_dls(train_ds, valid_ds, bs, collate_fn=collate_device)\n",
    "opt = optim.SGD(simple_cnn.parameters(), lr=lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.3630618950843811 0.8875999997138977\n",
      "1 0.16439641580581665 0.9496000003814697\n",
      "2 0.24622697901725768 0.9316000004768371\n",
      "3 0.25093305287361145 0.9335999998092651\n",
      "4 0.13128829071521758 0.9618000007629395\n"
     ]
    }
   ],
   "source": [
    "loss,acc = fit(5, simple_cnn.to(def_device), F.cross_entropy, opt, train_dl, valid_dl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.08451943595409393 0.9756999996185303\n",
      "1 0.08082638642787933 0.9777999995231629\n",
      "2 0.08050601842403411 0.9778999995231629\n",
      "3 0.08200360851287841 0.9773999995231628\n",
      "4 0.08405050563812255 0.9761999994277955\n"
     ]
    }
   ],
   "source": [
    "opt = optim.SGD(simple_cnn.parameters(), lr=lr/4)\n",
    "loss,acc = fit(5, simple_cnn.to(def_device), F.cross_entropy, opt, train_dl, valid_dl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Understanding Convolution Arithmetic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In an input of size `64x1x28x28` the axes are `batch,channel,height,width`. This is often represented as `NCHW` (where `N` refers to batch size). Tensorflow, on the other hand, uses `NHWC` axis order (aka \"channels-last\"). Channels-last is faster for many models, so recently it's become more common to see this as an option in PyTorch too.\n",
    "\n",
    "We have 1 input channel, 4 output channels, and a 3×3 kernel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Conv2d(1, 4, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simple_cnn[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([4, 1, 3, 3])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conv1 = simple_cnn[0][0]\n",
    "conv1.weight.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([4])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conv1.bias.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The *receptive field* is the area of an image that is involved in the calculation of a layer. *conv-example.xlsx* shows the calculation of two stride-2 convolutional layers using an MNIST digit. Here's what we see if we click on one of the cells in the *conv2* section, which shows the output of the second convolutional layer, and click *trace precedents*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img alt=\"Immediate precedents of conv2 layer\" width=\"308\" caption=\"Immediate precedents of Conv2 layer\" id=\"preced1\" src=\"images/att_00068.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The blue highlighted cells are its *precedents*—that is, the cells used to calculate its value. These cells are the corresponding 3×3 area of cells from the input layer (on the left), and the cells from the filter (on the right). Click *trace precedents* again:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img alt=\"Secondary precedents of conv2 layer\" width=\"601\" caption=\"Secondary precedents of Conv2 layer\" id=\"preced2\" src=\"images/att_00069.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, we have just two convolutional layers. We can see that a 7×7 area of cells in the input layer is used to calculate the single green cell in the Conv2 layer. This is the *receptive field*\n",
    "\n",
    "The deeper we are in the network (specifically, the more stride-2 convs we have before a layer), the larger the receptive field for an activation in that layer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Color Images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A colour picture is a rank-3 tensor:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchvision.io import read_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([3, 1000, 846])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im = read_image('images/grizzly.jpg')\n",
    "im.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_image(im.permute(1,2,0));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_,axs = plt.subplots(1,3)\n",
    "for bear,ax,color in zip(im,axs,('Reds','Greens','Blues')): show_image(255-bear, ax=ax, cmap=color)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"images/chapter9_rgbconv.svg\" id=\"rgbconv\" caption=\"Convolution over an RGB image\" alt=\"Convolution over an RGB image\" width=\"550\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are then all added together, to produce a single number, for each grid location, for each output feature."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"images/chapter9_rgb_conv_stack.svg\" id=\"rgbconv2\" caption=\"Adding the RGB filters\" alt=\"Adding the RGB filters\" width=\"500\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have `ch_out` filters like this, so in the end, the result of our convolutional layer will be a batch of images with `ch_out` channels."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Export -"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nbdev; nbdev.nbdev_export()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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