13_convolutions.ipynb

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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hide\n",
    "from fastai2.vision.all import *\n",
    "from utils import *\n",
    "\n",
    "matplotlib.rc('image', cmap='Greys')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "[[chapter_convolutions]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convolutional Neural Networks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In <<chapter_mnist_basics>> we learned how to create a neural network recognizing images. We were able to achieve a bit over 98% accuracy at distinguishing 3s from 7s—but we also saw that fastai's built-in classes were able to get close to 100%. Let's start trying to close the gap.\n",
    "\n",
    "In this chapter, we will begin by digging into what convolutions are and building a CNN from scratch. We will then study a range of techniques to improve training stability and learn all the tweaks the library usually applies for us to get great results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Magic of Convolutions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the most powerful tools that machine learning practitioners have at their disposal is *feature engineering*. A *feature* is a transformation of the data which is designed to make it easier to model. For instance, the `add_datepart` function that we used for our tabular dataset preprocessing in <<chapter_tabular>> added date features to the Bulldozers dataset. What kinds of features might we be able to create from images?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> jargon: Feature engineering: Creating new transformations of the input data in order to make it easier to model."
   ]
  },
  {
   "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. On the other hand, the number 3 is characterized by a diagonal edge in one direction at the top left and bottom right of the digit, the opposite diagonal at the bottom left and top right, horizontal edges at the middle, top, and bottom, and so forth. So what if we could extract information about where the edges occur in each image, and then use that information as our features, instead of raw pixels?\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 something called a *convolution*. A convolution requires nothing more than multiplication, and addition—two operations that are responsible for the vast majority of work that we will see in every single deep learning model in this book!\n",
    "\n",
    "A convolution applies a *kernel* across an image. A kernel is a little matrix, such as the 3×3 matrix in the top right of <<basic_conv>>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"images/chapter9_conv_basic.png\" id=\"basic_conv\" caption=\"Applying a kernel to one location\" alt=\"Applying a kernel to one location\" width=\"700\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 7×7 grid to the left is the *image* we're going to apply the kernel to. The convolution operation multiplies each element of the kernel by each element of a 3×3 block of the image. The results of these multiplications are then added together. The diagram in <<basic_conv>> shows an example of applying a kernel to a single location in the image, the 3×3 block around cell 18.\n",
    "\n",
    "Let's do this with code. First, we create a little 3×3 matrix like so:"
   ]
  },
  {
   "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). And we'll need an image, of course:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = untar_data(URLs.MNIST_SAMPLE)"
   ]
  },
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   "execution_count": null,
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   "outputs": [],
   "source": [
    "#hide\n",
    "Path.BASE_PATH = path"
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      "text/plain": [
       "<Figure size 72x72 with 1 Axes>"
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   "source": [
    "im3 = Image.open(path/'train'/'3'/'12.png')\n",
    "show_image(im3);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we're going to take the top 3×3-pixel square of our image, and multiply each of those values by each item in our kernel. Then we'll add them up, like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-0., -0., -0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.]])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im3_t = tensor(im3)\n",
    "im3_t[0:3,0:3] * top_edge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(0.)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(im3_t[0:3,0:3] * top_edge).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not very interesting so far—all the pixels in the top-left corner are white. But let's pick a couple of more interesting spots:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row2_col17 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row2_col18 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row2_col19 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row3_col0 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row3_col1 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row3_col2 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row3_col3 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row3_col4 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row3_col5 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row3_col6 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row3_col7 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row3_col8 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row3_col9 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row3_col10 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row3_col11 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row3_col12 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row3_col13 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row3_col14 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row3_col15 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row3_col16 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row3_col17 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row3_col18 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row3_col19 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row4_col0 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row4_col1 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row4_col2 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row4_col3 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row4_col4 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row4_col5 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row4_col6 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row4_col7 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row4_col8 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row4_col9 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row4_col10 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row4_col11 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row4_col12 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row4_col13 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row4_col14 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row4_col15 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row4_col16 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row4_col17 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row4_col18 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row4_col19 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row5_col0 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row5_col1 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row5_col2 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row5_col3 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #f9f9f9;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row5_col4 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #b9b9b9;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row5_col5 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #c1c1c1;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row5_col6 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #858585;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row5_col7 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #777777;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row5_col8 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #090909;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row5_col9 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #5b5b5b;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row5_col10 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #777777;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row5_col11 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #777777;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row5_col12 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #777777;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row5_col13 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #777777;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row5_col14 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #919191;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row5_col15 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #e1e1e1;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row5_col16 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row5_col17 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row5_col18 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row5_col19 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row6_col0 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row6_col1 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row6_col2 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row6_col3 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #727272;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row6_col4 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #000000;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row6_col5 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #000000;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row6_col6 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #000000;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row6_col7 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #000000;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row6_col8 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #000000;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row6_col9 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #000000;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row6_col10 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #000000;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row6_col11 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #000000;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row6_col12 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #000000;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row6_col13 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #000000;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row6_col14 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #000000;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row6_col15 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #020202;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row6_col16 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #363636;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row6_col17 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #9d9d9d;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row6_col18 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #dfdfdf;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row6_col19 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row7_col0 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row7_col1 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row7_col2 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row7_col3 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #000000;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row7_col4 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #000000;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row7_col5 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #000000;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row7_col6 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #000000;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row7_col7 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #161616;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row7_col8 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #535353;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row7_col9 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #535353;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row7_col10 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #535353;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row7_col11 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #535353;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row7_col12 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #7c7c7c;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row7_col13 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #535353;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row7_col14 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #3d3d3d;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row7_col15 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #000000;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row7_col16 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #000000;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row7_col17 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #000000;\n",
       "            color:  #f1f1f1;\n",
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       "            font-size:  6pt;\n",
       "            background-color:  #999999;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row7_col19 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row8_col0 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
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       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
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       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
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       "            font-size:  6pt;\n",
       "            background-color:  #eaeaea;\n",
       "            color:  #000000;\n",
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       "            font-size:  6pt;\n",
       "            background-color:  #d0d0d0;\n",
       "            color:  #000000;\n",
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       "            font-size:  6pt;\n",
       "            background-color:  #eeeeee;\n",
       "            color:  #000000;\n",
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       "            font-size:  6pt;\n",
       "            background-color:  #eeeeee;\n",
       "            color:  #000000;\n",
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       "            font-size:  6pt;\n",
       "            background-color:  #f3f3f3;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row8_col8 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
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       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row8_col10 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row8_col11 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row8_col12 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row8_col13 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row8_col14 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #f9f9f9;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row8_col15 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #232323;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row8_col16 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #000000;\n",
       "            color:  #f1f1f1;\n",
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       "            font-size:  6pt;\n",
       "            background-color:  #000000;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row8_col18 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #000000;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row8_col19 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row9_col0 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row9_col1 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row9_col2 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row9_col3 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row9_col4 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row9_col5 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row9_col6 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row9_col7 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row9_col8 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row9_col9 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row9_col10 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row9_col11 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row9_col12 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row9_col13 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row9_col14 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #c2c2c2;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row9_col15 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #000000;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row9_col16 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #000000;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row9_col17 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #080808;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row9_col18 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #c4c4c4;\n",
       "            color:  #000000;\n",
       "        }    #T_508423a8_5672_11ea_9acc_8f0047ef1a35row9_col19 {\n",
       "            font-size:  6pt;\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }</style><table id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >0</th>        <th class=\"col_heading level0 col1\" >1</th>        <th class=\"col_heading level0 col2\" >2</th>        <th class=\"col_heading level0 col3\" >3</th>        <th class=\"col_heading level0 col4\" >4</th>        <th class=\"col_heading level0 col5\" >5</th>        <th class=\"col_heading level0 col6\" >6</th>        <th class=\"col_heading level0 col7\" >7</th>        <th class=\"col_heading level0 col8\" >8</th>        <th class=\"col_heading level0 col9\" >9</th>        <th class=\"col_heading level0 col10\" >10</th>        <th class=\"col_heading level0 col11\" >11</th>        <th class=\"col_heading level0 col12\" >12</th>        <th class=\"col_heading level0 col13\" >13</th>        <th class=\"col_heading level0 col14\" >14</th>        <th class=\"col_heading level0 col15\" >15</th>        <th class=\"col_heading level0 col16\" >16</th>        <th class=\"col_heading level0 col17\" >17</th>        <th class=\"col_heading level0 col18\" >18</th>        <th class=\"col_heading level0 col19\" >19</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row0_col0\" class=\"data row0 col0\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row0_col1\" class=\"data row0 col1\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row0_col2\" class=\"data row0 col2\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row0_col3\" class=\"data row0 col3\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row0_col4\" class=\"data row0 col4\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row0_col5\" class=\"data row0 col5\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row0_col6\" class=\"data row0 col6\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row0_col7\" class=\"data row0 col7\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row0_col8\" class=\"data row0 col8\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row0_col9\" class=\"data row0 col9\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row0_col10\" class=\"data row0 col10\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row0_col11\" class=\"data row0 col11\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row0_col12\" class=\"data row0 col12\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row0_col13\" class=\"data row0 col13\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row0_col14\" class=\"data row0 col14\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row0_col15\" class=\"data row0 col15\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row0_col16\" class=\"data row0 col16\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row0_col17\" class=\"data row0 col17\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row0_col18\" class=\"data row0 col18\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row0_col19\" class=\"data row0 col19\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row1_col0\" class=\"data row1 col0\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row1_col1\" class=\"data row1 col1\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row1_col2\" class=\"data row1 col2\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row1_col3\" class=\"data row1 col3\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row1_col4\" class=\"data row1 col4\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row1_col5\" class=\"data row1 col5\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row1_col6\" class=\"data row1 col6\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row1_col7\" class=\"data row1 col7\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row1_col8\" class=\"data row1 col8\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row1_col9\" class=\"data row1 col9\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row1_col10\" class=\"data row1 col10\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row1_col11\" class=\"data row1 col11\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row1_col12\" class=\"data row1 col12\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row1_col13\" class=\"data row1 col13\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row1_col14\" class=\"data row1 col14\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row1_col15\" class=\"data row1 col15\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row1_col16\" class=\"data row1 col16\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row1_col17\" class=\"data row1 col17\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row1_col18\" class=\"data row1 col18\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row1_col19\" class=\"data row1 col19\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row2_col0\" class=\"data row2 col0\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row2_col1\" class=\"data row2 col1\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row2_col2\" class=\"data row2 col2\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row2_col3\" class=\"data row2 col3\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row2_col4\" class=\"data row2 col4\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row2_col5\" class=\"data row2 col5\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row2_col6\" class=\"data row2 col6\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row2_col7\" class=\"data row2 col7\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row2_col8\" class=\"data row2 col8\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row2_col9\" class=\"data row2 col9\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row2_col10\" class=\"data row2 col10\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row2_col11\" class=\"data row2 col11\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row2_col12\" class=\"data row2 col12\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row2_col13\" class=\"data row2 col13\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row2_col14\" class=\"data row2 col14\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row2_col15\" class=\"data row2 col15\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row2_col16\" class=\"data row2 col16\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row2_col17\" class=\"data row2 col17\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row2_col18\" class=\"data row2 col18\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row2_col19\" class=\"data row2 col19\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row3_col0\" class=\"data row3 col0\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row3_col1\" class=\"data row3 col1\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row3_col2\" class=\"data row3 col2\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row3_col3\" class=\"data row3 col3\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row3_col4\" class=\"data row3 col4\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row3_col5\" class=\"data row3 col5\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row3_col6\" class=\"data row3 col6\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row3_col7\" class=\"data row3 col7\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row3_col8\" class=\"data row3 col8\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row3_col9\" class=\"data row3 col9\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row3_col10\" class=\"data row3 col10\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row3_col11\" class=\"data row3 col11\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row3_col12\" class=\"data row3 col12\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row3_col13\" class=\"data row3 col13\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row3_col14\" class=\"data row3 col14\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row3_col15\" class=\"data row3 col15\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row3_col16\" class=\"data row3 col16\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row3_col17\" class=\"data row3 col17\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row3_col18\" class=\"data row3 col18\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row3_col19\" class=\"data row3 col19\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row4_col0\" class=\"data row4 col0\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row4_col1\" class=\"data row4 col1\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row4_col2\" class=\"data row4 col2\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row4_col3\" class=\"data row4 col3\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row4_col4\" class=\"data row4 col4\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row4_col5\" class=\"data row4 col5\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row4_col6\" class=\"data row4 col6\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row4_col7\" class=\"data row4 col7\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row4_col8\" class=\"data row4 col8\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row4_col9\" class=\"data row4 col9\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row4_col10\" class=\"data row4 col10\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row4_col11\" class=\"data row4 col11\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row4_col12\" class=\"data row4 col12\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row4_col13\" class=\"data row4 col13\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row4_col14\" class=\"data row4 col14\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row4_col15\" class=\"data row4 col15\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row4_col16\" class=\"data row4 col16\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row4_col17\" class=\"data row4 col17\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row4_col18\" class=\"data row4 col18\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row4_col19\" class=\"data row4 col19\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row5_col0\" class=\"data row5 col0\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row5_col1\" class=\"data row5 col1\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row5_col2\" class=\"data row5 col2\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row5_col3\" class=\"data row5 col3\" >12</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row5_col4\" class=\"data row5 col4\" >99</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row5_col5\" class=\"data row5 col5\" >91</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row5_col6\" class=\"data row5 col6\" >142</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row5_col7\" class=\"data row5 col7\" >155</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row5_col8\" class=\"data row5 col8\" >246</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row5_col9\" class=\"data row5 col9\" >182</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row5_col10\" class=\"data row5 col10\" >155</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row5_col11\" class=\"data row5 col11\" >155</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row5_col12\" class=\"data row5 col12\" >155</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row5_col13\" class=\"data row5 col13\" >155</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row5_col14\" class=\"data row5 col14\" >131</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row5_col15\" class=\"data row5 col15\" >52</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row5_col16\" class=\"data row5 col16\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row5_col17\" class=\"data row5 col17\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row5_col18\" class=\"data row5 col18\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row5_col19\" class=\"data row5 col19\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row6_col0\" class=\"data row6 col0\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row6_col1\" class=\"data row6 col1\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row6_col2\" class=\"data row6 col2\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row6_col3\" class=\"data row6 col3\" >138</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row6_col4\" class=\"data row6 col4\" >254</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row6_col5\" class=\"data row6 col5\" >254</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row6_col6\" class=\"data row6 col6\" >254</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row6_col7\" class=\"data row6 col7\" >254</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row6_col8\" class=\"data row6 col8\" >254</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row6_col9\" class=\"data row6 col9\" >254</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row6_col10\" class=\"data row6 col10\" >254</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row6_col11\" class=\"data row6 col11\" >254</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row6_col12\" class=\"data row6 col12\" >254</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row6_col13\" class=\"data row6 col13\" >254</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row6_col14\" class=\"data row6 col14\" >254</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row6_col15\" class=\"data row6 col15\" >252</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row6_col16\" class=\"data row6 col16\" >210</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row6_col17\" class=\"data row6 col17\" >122</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row6_col18\" class=\"data row6 col18\" >33</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row6_col19\" class=\"data row6 col19\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row7_col0\" class=\"data row7 col0\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row7_col1\" class=\"data row7 col1\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row7_col2\" class=\"data row7 col2\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row7_col3\" class=\"data row7 col3\" >220</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row7_col4\" class=\"data row7 col4\" >254</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row7_col5\" class=\"data row7 col5\" >254</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row7_col6\" class=\"data row7 col6\" >254</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row7_col7\" class=\"data row7 col7\" >235</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row7_col8\" class=\"data row7 col8\" >189</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row7_col9\" class=\"data row7 col9\" >189</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row7_col10\" class=\"data row7 col10\" >189</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row7_col11\" class=\"data row7 col11\" >189</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row7_col12\" class=\"data row7 col12\" >150</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row7_col13\" class=\"data row7 col13\" >189</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row7_col14\" class=\"data row7 col14\" >205</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row7_col15\" class=\"data row7 col15\" >254</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row7_col16\" class=\"data row7 col16\" >254</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row7_col17\" class=\"data row7 col17\" >254</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row7_col18\" class=\"data row7 col18\" >75</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row7_col19\" class=\"data row7 col19\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row8_col0\" class=\"data row8 col0\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row8_col1\" class=\"data row8 col1\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row8_col2\" class=\"data row8 col2\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row8_col3\" class=\"data row8 col3\" >35</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row8_col4\" class=\"data row8 col4\" >74</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row8_col5\" class=\"data row8 col5\" >35</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row8_col6\" class=\"data row8 col6\" >35</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row8_col7\" class=\"data row8 col7\" >25</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row8_col8\" class=\"data row8 col8\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row8_col9\" class=\"data row8 col9\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row8_col10\" class=\"data row8 col10\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row8_col11\" class=\"data row8 col11\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row8_col12\" class=\"data row8 col12\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row8_col13\" class=\"data row8 col13\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row8_col14\" class=\"data row8 col14\" >13</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row8_col15\" class=\"data row8 col15\" >224</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row8_col16\" class=\"data row8 col16\" >254</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row8_col17\" class=\"data row8 col17\" >254</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row8_col18\" class=\"data row8 col18\" >153</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row8_col19\" class=\"data row8 col19\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row9_col0\" class=\"data row9 col0\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row9_col1\" class=\"data row9 col1\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row9_col2\" class=\"data row9 col2\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row9_col3\" class=\"data row9 col3\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row9_col4\" class=\"data row9 col4\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row9_col5\" class=\"data row9 col5\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row9_col6\" class=\"data row9 col6\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row9_col7\" class=\"data row9 col7\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row9_col8\" class=\"data row9 col8\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row9_col9\" class=\"data row9 col9\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row9_col10\" class=\"data row9 col10\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row9_col11\" class=\"data row9 col11\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row9_col12\" class=\"data row9 col12\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row9_col13\" class=\"data row9 col13\" >0</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row9_col14\" class=\"data row9 col14\" >90</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row9_col15\" class=\"data row9 col15\" >254</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row9_col16\" class=\"data row9 col16\" >254</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row9_col17\" class=\"data row9 col17\" >247</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row9_col18\" class=\"data row9 col18\" >53</td>\n",
       "                        <td id=\"T_508423a8_5672_11ea_9acc_8f0047ef1a35row9_col19\" class=\"data row9 col19\" >0</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fb709e80750>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide_output\n",
    "df = pd.DataFrame(im3_t[:10,:20])\n",
    "df.style.set_properties(**{'font-size':'6pt'}).background_gradient('Greys')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img alt=\"Top section of a digit\" width=\"490\" src=\"images/att_00059.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There's a top edge at cell 5,7. Let's repeat our calculation there:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(762.)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(im3_t[4:7,6:9] * top_edge).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There's a right edge at cell 8,18. What does that give us?:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(-29.)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(im3_t[7:10,17:20] * top_edge).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, this little calculation is returning a high number where the 3×3-pixel square represents a top edge (i.e., where there are low values at the top of the square, and high values immediately underneath). That's because the `-1` values in our kernel have little impact in that case, but the `1` values have a lot.\n",
    "\n",
    "Let's look a tiny bit at the math. 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 $a1+a2+a3-a7-a8-a9$. If we are in a part of the image where $a1$, $a2$, and $a3$ add up to the same as $a7$, $a8$, and $a9$, then the terms will cancel each other out and we will get 0. However, if $a1$ is greater than $a7$, $a2$ is greater than $a8$, and $a3$ is greater than $a9$, we will get a bigger number as a result. So this filter detects horizontal edges—more precisely, edges where we go from bright parts of the image at the top to darker parts at the bottom.\n",
    "\n",
    "Changing our filter to have the row of `1`s at the top and the `-1`s at the bottom would detect horizonal edges that go from dark to light. Putting the `1`s and `-1`s in columns versus rows would give us filters that detect vertical edges. Each set of weights will produce a different kind of outcome.\n",
    "\n",
    "Let's create a function to do this for one location, and check it matches our result from before:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def apply_kernel(row, col, kernel):\n",
    "    return (im3_t[row-1:row+2,col-1:col+2] * kernel).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(762.)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apply_kernel(5,7,top_edge)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But note that we can't apply it to the corner (e.g., location 0,0), since there isn't a complete 3×3 square there."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mapping a Convolution Kernel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can map `apply_kernel()` across the coordinate grid. That is, we'll be taking our 3×3 kernel, and applying it to each 3×3 section of our image. For instance, <<nopad_conv>> shows the positions a 3×3 kernel can be applied to in the first row of a 5×5 image."
   ]
  },
  {
   "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": "markdown",
   "metadata": {},
   "source": [
    "To get a grid of coordinates we can use a *nested list comprehension*, like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[(1, 1), (1, 2), (1, 3), (1, 4)],\n",
       " [(2, 1), (2, 2), (2, 3), (2, 4)],\n",
       " [(3, 1), (3, 2), (3, 3), (3, 4)],\n",
       " [(4, 1), (4, 2), (4, 3), (4, 4)]]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[[(i,j) for j in range(1,5)] for i in range(1,5)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> note: Nested List Comprehensions: Nested list comprehensions are used a lot in Python, so if you haven't seen them before, take a few minutes to make sure you understand what's happening here, and experiment with writing your own nested list comprehensions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's the result of applying our kernel over a coordinate grid:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 72x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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",
    "\n",
    "show_image(top_edge3);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking good! Our top edges are black, and bottom edges are white (since they are the *opposite* of top edges). Now that our image contains negative numbers too, `matplotlib` has automatically changed our colors so that white is the smallest number in the image, black the highest, and zeros appear as gray.\n",
    "\n",
    "We can try the same thing for left edges:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 72x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "left_edge = tensor([[-1,1,0],\n",
    "                    [-1,1,0],\n",
    "                    [-1,1,0]]).float()\n",
    "\n",
    "left_edge3 = tensor([[apply_kernel(i,j,left_edge) for j in rng] for i in rng])\n",
    "\n",
    "show_image(left_edge3);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we mentioned before, a convolution is the operation of applying such a kernel over a grid in this way. In the paper [\"A Guide to Convolution Arithmetic for Deep Learning\"](https://arxiv.org/abs/1603.07285) there are many great diagrams showing how image kernels can be applied. Here's an example from the paper showing (at the bottom) a light blue 4×4 image, with a dark blue 3×3 kernel being applied, creating a 2×2 green output activation map at the top. "
   ]
  },
  {
   "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": [
    "Look at the shape of the result. If the original image has a height of `h` and a width of `w`, how many 3×3 windows can we find? As you can see from the example, there are `h-2` by `w-2` windows, so the image we get has a result as a height of `h-2` and a width of `w-2`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We won't implement this convolution function from scratch, but use PyTorch's implementation instead (it is way faster than anything we could do in Python)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convolutions in PyTorch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convolution is such an important and widely used operation that PyTorch has it built in. It's called `F.conv2d` (recall that `F` is a fastai import from `torch.nn.functional`, as recommended by PyTorch). The PyTorch docs tell us that it includes these parameters:\n",
    "\n",
    "- input:: input tensor of shape `(minibatch, in_channels, iH, iW)`\n",
    "- weight:: filters of shape `(out_channels, in_channels, kH, kW)`\n",
    "\n",
    "Here `iH,iW` is the height and width of the image (i.e., `28,28`), and `kH,kW` is the height and width of our kernel (`3,3`). But apparently PyTorch is expecting rank-4 tensors for both these arguments, whereas currently we only have rank-2 tensors (i.e., matrices, or arrays with two axes).\n",
    "\n",
    "The reason for these extra axes is that PyTorch has a few tricks up its sleeve. The first trick is that PyTorch can apply a convolution to multiple images at the same time. That means we can call it on every item in a batch at once!\n",
    "\n",
    "The second trick is that PyTorch can apply multiple kernels at the same time. So let's create the diagonal-edge kernels too, and then stack all four of our edge kernels into a single tensor:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([4, 3, 3])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diag1_edge = tensor([[ 0,-1, 1],\n",
    "                     [-1, 1, 0],\n",
    "                     [ 1, 0, 0]]).float()\n",
    "diag2_edge = tensor([[ 1,-1, 0],\n",
    "                     [ 0, 1,-1],\n",
    "                     [ 0, 0, 1]]).float()\n",
    "\n",
    "edge_kernels = torch.stack([left_edge, top_edge, diag1_edge, diag2_edge])\n",
    "edge_kernels.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To test this, we'll need a `DataLoader` and a sample mini-batch. Let's use the data block API:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([64, 1, 28, 28])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnist = DataBlock((ImageBlock(cls=PILImageBW), CategoryBlock), \n",
    "                  get_items=get_image_files, \n",
    "                  splitter=GrandparentSplitter(),\n",
    "                  get_y=parent_label)\n",
    "\n",
    "dls = mnist.dataloaders(path)\n",
    "xb,yb = first(dls.valid)\n",
    "xb.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, fastai puts data on the GPU when using data blocks. Let's move it to the CPU for our examples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "xb,yb = to_cpu(xb),to_cpu(yb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One batch contains 64 images, each of 1 channel, with 28×28 pixels. `F.conv2d` can handle multichannel (i.e., color) images too. A *channel* is a single basic color in an image—for regular full-color images there are three channels, red, green, and blue. PyTorch represents an image as a rank-3 tensor, with dimensions `[channels, rows, columns]`.\n",
    "\n",
    "We'll see how to handle more than one channel later in this chapter. Kernels passed to `F.conv2d` need to be rank-4 tensors: `[channels_in, features_out, rows, columns]`. `edge_kernels` is currently missing one of these. We need to tell PyTorch that the number of input channels in the kernel is one, which we can do by inserting an axis of size one (this is known as a *unit axis*) in the first location, where the PyTorch docs show `in_channels` is expected. To insert a unit axis into a tensor, we use the `unsqueeze` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([4, 3, 3]), torch.Size([4, 1, 3, 3]))"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edge_kernels.shape,edge_kernels.unsqueeze(1).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is now the correct shape for `edge_kernels`. Let's pass this all to `conv2d`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "edge_kernels = edge_kernels.unsqueeze(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([64, 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 72x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_image(batch_features[0,0]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most important trick that PyTorch has up its sleeve is that it can use the GPU to do all this work in parallel—that is, applying multiple kernels, to multiple images, across multiple channels. Doing lots of work in parallel is critical to getting GPUs to work efficiently; if we did each of these operations one at a time, we'd often run hundreds of times slower (and if we used our manual convolution loop from the previous section, we'd be millions of times slower!). Therefore, to become a strong deep learning practitioner, one skill to practice is giving your GPU plenty of work to do at a time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It would be nice to not lose those two pixels on each axis. The way we do that is to add *padding*, which is simply additional pixels added around the outside of our image. Most commonly, pixels of zeros are added. "
   ]
  },
  {
   "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, which can make things a lot simpler when we construct our architectures. <<pad_conv>> shows how adding padding allows us to apply the kernels in the image corners."
   ]
  },
  {
   "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, as we can see in <<four_by_five_conv>>."
   ]
  },
  {
   "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`. An even number for `ks` would require a different amount of padding on the top/bottom and left/right, but in practice we almost never use an even filter size.\n",
    "\n",
    "So far, when we have applied the kernel to the grid, we have moved it one pixel over at a time. But we can jump further; for instance, we could move over two pixels after each kernel application, as in <<three_by_five_conv>>. This is known as a *stride-2* convolution. The most common kernel size in practice is 3×3, and the most common padding is 1. As you'll see, stride-2 convolutions are useful for decreasing the size of our outputs, and stride-1 convolutions are useful for adding layers without changing the output size."
   ]
  },
  {
   "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": [
    "In an image of size `h` by `w`, using a padding of 1 and a stride of 2 will give us a result of size `(h+1)//2` by `(w+1)//2`. The general formula for each dimension is `(n + 2*pad - ks)//stride + 1`, where `pad` is the padding, `ks`, the size of our kernel, and `stride` is the stride."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now take a look at how the pixel values of the result of our convolutions are computed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Understanding the Convolution Equations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To explain the math behing 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). In fact, it's so clever, and so helpful, we're going to show it here too!\n",
    "\n",
    "Here's our 3×3 pixel image, with each pixel labeled with a letter:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img alt=\"The image\" width=\"75\" src=\"images/att_00032.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And here's our kernel, with each weight labeled with a Greek letter:"
   ]
  },
  {
   "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": [
    "<<apply_kernel>> shows how we applied the kernel to each section of the image to yield each result."
   ]
  },
  {
   "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": [
    "The equation view is in <<eq_view>>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img alt=\"The equation\" width=\"436\" caption=\"The equation\" id=\"eq_view\" src=\"images/att_00036.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the bias term, *b*, is the same for each section of the image. You can consider the bias as part of the filter, just like the weights (α, β, γ, δ) are part of the filter."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's an interesting insight—a convolution can be represented as a special kind of matrix multiplication, as illustrated in <<conv_matmul>>. The weight matrix is just like the ones from traditional neural networks. However, this weight matrix has two special properties:\n",
    "\n",
    "1. The zeros shown in gray are untrainable. This means that they’ll stay zero throughout the optimization process.\n",
    "1. Some of the weights are equal, and while they are trainable (i.e., changeable), they must remain equal. These are called *shared weights*.\n",
    "\n",
    "The zeros correspond to the pixels that the filter can't touch. Each row of the weight matrix corresponds to one application of the filter."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img alt=\"Convolution as matrix multiplication\" width=\"683\" caption=\"Convolution as matrix multiplication\" id=\"conv_matmul\" src=\"images/att_00038.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we understand what a convolution is, let's use them to build a neural net."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Our First Convolutional Neural Network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is no reason to believe that some particular edge filters are the most useful kernels for image recognition. Furthermore, we've seen that in later layers convolutional kernels become complex transformations of features from lower levels, but we don't have a good idea of how to manually construct these.\n",
    "\n",
    "Instead, it would be best to learn the values of the kernels. We already know how to do this—SGD! In effect, the model will learn the features that are useful for classification.\n",
    "\n",
    "When we use convolutions instead of (or in addition to) regular linear layers we create a *convolutional neural network* (CNN)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating the CNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's go back to the  basic neural network we had in <<chapter_mnist_basics>>. It was defined like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "simple_net = nn.Sequential(\n",
    "    nn.Linear(28*28,30),\n",
    "    nn.ReLU(),\n",
    "    nn.Linear(30,1)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can view a model's definition:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (0): Linear(in_features=784, out_features=30, bias=True)\n",
       "  (1): ReLU()\n",
       "  (2): Linear(in_features=30, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simple_net"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now want to create a similar architecture to this linear model, but using convolutional layers instead of linear. `nn.Conv2d` is the module equivalent of `F.conv2d`. It's more convenient than `F.conv2d` when creating an architecture, because it creates the weight matrix for us automatically when we instantiate it.\n",
    "\n",
    "Here's a possible architecture:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "broken_cnn = sequential(\n",
    "    nn.Conv2d(1,30, kernel_size=3, padding=1),\n",
    "    nn.ReLU(),\n",
    "    nn.Conv2d(30,1, kernel_size=3, padding=1)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One thing to note here is that we didn't need to specify 28×28 as the input size. That's because a linear layer needs a weight in the weight matrix for every pixel, so it needs to know how many pixels there are, but a convolution is applied over each pixel automatically. The weights only depend on the number of input and output channels and the kernel size, as we saw in the previous section.\n",
    "\n",
    "Think about what the output shape is going to be, then let's try it and see:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([64, 1, 28, 28])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "broken_cnn(xb).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is not something we can use to do classification, since we need a single output activation per image, not a 28×28 map of activations. One way to deal with this is to use enough stride-2 convolutions such that the final layer is size 1. That is, after one stride-2 convolution the size will be 14×14, after two it will be 7×7, then 4×4, 2×2, and finally size 1.\n",
    "\n",
    "Let's try that now. First, we'll define a function with the basic parameters we'll use in each convolution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def conv(ni, nf, ks=3, act=True):\n",
    "    res = nn.Conv2d(ni, nf, stride=2, kernel_size=ks, padding=ks//2)\n",
    "    if act: res = nn.Sequential(res, nn.ReLU())\n",
    "    return res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> important: Refactoring: 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": "markdown",
   "metadata": {},
   "source": [
    "When we use a stride-2 convolution, we often increase the number of features at the same time. This is because we're decreasing the number of activations in the activation map by a factor of 4; we don't want to decrease the capacity of a layer by too much at a time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> jargon: channels and features: These two terms are largely used interchangably, and refer to the size of the second axis of a weight matrix, which is, the number of activations per grid cell after a convolution. _Features_ is never used to refer to the input data, but _channels_ can refer to either the input data (generally channels are colors) or activations inside the network."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is how we can build a simple CNN:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "simple_cnn = sequential(\n",
    "    conv(1 ,4),            #14x14\n",
    "    conv(4 ,8),            #7x7\n",
    "    conv(8 ,16),           #4x4\n",
    "    conv(16,32),           #2x2\n",
    "    conv(32,2, act=False), #1x1\n",
    "    Flatten(),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> j: I like to add comments like the ones here after each convolution to show how large the activation map will be after each layer. These comments assume that the input size is 28*28"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the network outputs two activations, which map to the two possible levels in our labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([64, 2])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simple_cnn(xb).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now create our `Learner`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = Learner(dls, simple_cnn, loss_func=F.cross_entropy, metrics=accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To see exactly what's going on in the model, we can use `summary`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential (Input shape: ['64 x 1 x 28 x 28'])\n",
       "================================================================\n",
       "Layer (type)         Output Shape         Param #    Trainable \n",
       "================================================================\n",
       "Conv2d               64 x 4 x 14 x 14     40         True      \n",
       "________________________________________________________________\n",
       "ReLU                 64 x 4 x 14 x 14     0          False     \n",
       "________________________________________________________________\n",
       "Conv2d               64 x 8 x 7 x 7       296        True      \n",
       "________________________________________________________________\n",
       "ReLU                 64 x 8 x 7 x 7       0          False     \n",
       "________________________________________________________________\n",
       "Conv2d               64 x 16 x 4 x 4      1,168      True      \n",
       "________________________________________________________________\n",
       "ReLU                 64 x 16 x 4 x 4      0          False     \n",
       "________________________________________________________________\n",
       "Conv2d               64 x 32 x 2 x 2      4,640      True      \n",
       "________________________________________________________________\n",
       "ReLU                 64 x 32 x 2 x 2      0          False     \n",
       "________________________________________________________________\n",
       "Conv2d               64 x 2 x 1 x 1       578        True      \n",
       "________________________________________________________________\n",
       "Flatten              64 x 2               0          False     \n",
       "________________________________________________________________\n",
       "\n",
       "Total params: 6,722\n",
       "Total trainable params: 6,722\n",
       "Total non-trainable params: 0\n",
       "\n",
       "Optimizer used: <function Adam at 0x7fbc9c258cb0>\n",
       "Loss function: <function cross_entropy at 0x7fbca9ba0170>\n",
       "\n",
       "Callbacks:\n",
       "  - TrainEvalCallback\n",
       "  - Recorder\n",
       "  - ProgressCallback"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the output of the final `Conv2d` layer is `64x2x1x1`. We need to remove those extra `1x1` axes; that's what `Flatten` does. It's basically the same as PyTorch's `squeeze` method, but as a module.\n",
    "\n",
    "Let's see if this trains! Since this is a deeper network than we've built from scratch before, we'll use a lower learning rate and more epochs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.072684</td>\n",
       "      <td>0.045110</td>\n",
       "      <td>0.990186</td>\n",
       "      <td>00:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.022580</td>\n",
       "      <td>0.030775</td>\n",
       "      <td>0.990186</td>\n",
       "      <td>00:05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.fit_one_cycle(2, 0.01)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Success! It's getting closer to the `resnet18` result we had, although it's not quite there yet, and it's taking more epochs, and we're needing to use a lower learning rate. We still have a few more tricks to learn, but we're getting closer and closer to being able to create a modern CNN from scratch."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Understanding Convolution Arithmetic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see from the summary that we have 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. The first layer is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (0): Conv2d(1, 4, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
       "  (1): ReLU()\n",
       ")"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = learn.model[0]\n",
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we have 1 input channel, 4 output channels, and a 3×3 kernel. Let's check the weights of the first convolution:"
   ]
  },
  {
   "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": [
    "m[0].weight.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The summary shows we have 40 parameters, and `4*1*3*3` is 36. What are the other four parameters? Let's see what the bias contains:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([4])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m[0].bias.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now use this information to clarify our statement in the previous section: \"When we use a stride-2 convolution, we often increase the number of features because we're decreasing the number of activations in the activation map by a factor of 4; we don't want to decrease the capacity of a layer by too much at a time.\"\n",
    "\n",
    "There is one bias for each channel. (Sometimes channels are called *features* or *filters* when they are not input channels.) The output shape is `64x4x14x14`, and this will therefore become the input shape to the next layer. The next layer, according to `summary`, has 296 parameters. Let's ignore the batch axis to keep things simple. So for each of `14*14=196` locations we are multiplying `296-8=288` weights (ignoring the bias for simplicity), so that's `196*288=56_448` multiplications at this layer. The next layer will have `7*7*(1168-16)=56_448` multiplications.\n",
    "\n",
    "What happened here is that our stride-2 convolution halved the *grid size* from `14x14` to `7x7`, and we doubled the *number of filters* from 8 to 16, resulting in no overall change in the amount of computation. If we left the number of channels the same in each stride-2 layer, the amount of computation being done in the net would get less and less as it gets deeper. But we know that the deeper layers have to compute semantically rich features (such as eyes or fur), so we wouldn't expect that doing *less* computation would make sense."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another way to think of this is based on receptive fields."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Receptive Fields"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The *receptive field* is the area of an image that is involved in the calculation of a layer. On the [book's website](https://book.fast.ai/), you'll find an Excel spreadsheet called *conv-example.xlsx* that shows the calculation of two stride-2 convolutional layers using an MNIST digit. Each layer has a single kernel. <<preced1>> shows 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 precendents*."
   ]
  },
  {
   "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": [
    "Here, the cell with the green border is the cell we clicked on, and 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). Let's now click *trace precedents* again, to see what cells are used to calculate these inputs. <<preced2>> shows what happens."
   ]
  },
  {
   "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, each of stride 2, so this is now tracing right back to the input image. 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 7×7 area is the *receptive field* in the input of the green activation in Conv2. We can also see that a second filter kernel is needed now, since we have two layers.\n",
    "\n",
    "As you see from this example, 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. A large receptive field means that a large amount of the input image is used to calculate each activation in that layer is. We now know that in the deeper layers of the network we have semantically rich features, corresponding to larger receptive fields. Therefore, we'd expect that we'd need more weights for each of our features to handle this increasing complexity. This is another way of saying the same thing we mentionedin the previous section: when we introduce a stride-2 conv in our network, we should also increase the number of channels."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When writing this particular chapter, we had a lot of questions we needed answers for, to be able to explain CNNs to you as best we could. Believe it or not, we found most of the answers on Twitter. We're going to take a quick break to talk to you about that now, before we move on to color images."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A Note About Twitter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are not, to say the least, big users of social networks in general. But our goal in writing this book is to help you become the best deep learning practitioner you can, and we would be remiss not to mention how important Twitter has been in our own deep learning journeys.\n",
    "\n",
    "You see, there's another part of Twitter, far away from Donald Trump and the Kardashians, which is the part of Twitter where deep learning researchers and practitioners talk shop every day. As we were writing this section, Jeremy wanted to double-checkthat what we were saying about stride-2 convolutions was accurate, so he asked on Twitter:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img alt=\"twitter 1\" width=\"500\" src=\"images/att_00064.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A few minutes later, this answer popped up:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img alt=\"twitter 2\" width=\"500\" src=\"images/att_00065.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Christian Szegedy is the first author of [Inception](https://arxiv.org/pdf/1409.4842.pdf), the 2014 ImageNet winner and source of many key insights used in modern neural networks. Two hours later, this appeared:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img alt=\"twitter 3\" width=\"500\" src=\"images/att_00066.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do you recognize that name? You saw it in <<chapter_production>>, when we were talking about the Turing Award winners who established the foundations of deep learning today!\n",
    "\n",
    "Jeremy also asked on Twitter for help checking our description of label smoothing in <<chapter_sizing_and_tta>> was accurate, and got a response again from directly from Christian Szegedy (label smoothing was originally introduced in the Inception paper):"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img alt=\"twitter 4\" width=\"500\" src=\"images/att_00067.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many of the top people in deep learning today are Twitter regulars, and are very open about interacting with the wider community. One good way to get started is to look at a list of Jeremy's [recent Twitter likes](https://twitter.com/jeremyphoward/likes), or [Sylvain's](https://twitter.com/GuggerSylvain/likes). That way, you can see a list of Twitter users that we think have interesting and useful things to say.\n",
    "\n",
    "Twitter is the main way we both stay up to date with interesting papers, software releases, and other deep learning news. For making connections with the deep learning community, we recommend getting involved both in the [fast.ai forums](https://forums.fast.ai) and on Twitter."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That said, let's get back to the meat of this chapter. Up until now, we have only shown you examples of pictures in black and white, with one value per pixel. In practice, most colored images have three values per pixel to define their color. We'll look at working with color images next."
   ]
  },
  {
   "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": [
    {
     "data": {
      "text/plain": [
       "torch.Size([3, 1000, 846])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im = image2tensor(Image.open('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 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_image(im);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first axis contains the channels, red, green, and blue:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_,axs = subplots(1,3)\n",
    "for bear,ax,color in zip(im,axs,('Reds','Greens','Blues')):\n",
    "    show_image(255-bear, ax=ax, cmap=color)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We saw what the convolution operation was for one filter on one channel of the image (our examples were done on a square). A convolutional layer will take an image with a certain number of channels (three for the first layer for regular RGB color images) and output an image with a different number of channels. Like our hidden size that represented the numbers of neurons in a linear layer, we can decide to have as many filters as we want, and each of them will be able to specialize, some to detect horizontal edges, others to detect vertical edges and so forth, to give something like we studied in <<chapter_production>>.\n",
    "\n",
    "In one sliding window, we have a certain number of channels and we need as many filters (we don't use the same kernel for all the channels). So our kernel doesn't have a size of 3 by 3, but `ch_in` (for channels in) by 3 by 3. On each channel, we multiply the elements of our window by the elements of the coresponding filter, then sum the results (as we saw before) and sum over all the filters. In the example given in <<rgbconv>>, the result of our conv layer on that window is red + green + blue."
   ]
  },
  {
   "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": [
    "So, in order to apply a convolution to a color picture we require a kernel tensor with a size that matches the first axis. At each location, the corresponding parts of the kernel and the image patch are multiplied together.\n",
    "\n",
    "These are then all added together, to produce a single number, for each grid location, for each output feature, as shown in <<rgbconv2>>."
   ]
  },
  {
   "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": [
    "Then 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 and a height and width given by the formula outlined earlier. This give us `ch_out` tensors of size `ch_in x ks x ks` that we represent in one big tensor of four dimensions. In PyTorch, the order of the dimensions for those weights is `ch_out x ch_in x ks x ks`.\n",
    "\n",
    "Additionally, we may want to have a bias for each filter. In the preceding example, the final result for our convolutional layer would be $y_{R} + y_{G} + y_{B} + b$ in that case. Like in a linear layer, there are as many bias as we have kernels, so the biases is a vector of size `ch_out`.\n",
    "\n",
    "There are no special mechanisms required when setting up a CNN for training with color images. Just make sure your first layer has three inputs.\n",
    "\n",
    "There are lots of ways of processing color images. For instance, you can change them to black and white, change from RGB to HSV (hue, saturation, and value) color space, and so forth. In general, it turns out experimentally that changing the encoding of colors won't make any difference to your model results, as long as you don't lose information in the transformation. So, transforming to black and white is a bad idea, since it removes the color information entirely (and this can be critical; for instance, a pet breed may have a distinctive color); but converting to HSV generally won't make any difference.\n",
    "\n",
    "Now you know what those pictures in <<chapter_intro>> of \"what a neural net learns\" from the [Zeiler and Fergus paper](https://arxiv.org/abs/1311.2901) mean! This is their picture of some of the layer 1 weights which we showed:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img alt=\"Layer 1 kernels found by Zeiler and Fergus\" width=\"120\" src=\"images/att_00031.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is taking the three slices of the convolutional kernel, for each output feature, and displaying them as images. We can see that even though the creators of the neural net never explicitly created kernels to find edges, for instance, the neural net automatically discovered these features using SGD.\n",
    "\n",
    "Now let's see how we can train these CNNs, and show you all the techniques fastai uses under the hood for efficient training."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Improving Training Stability"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we are so good at recognizing 3s from 7s, let's move on to something harder—recognizing all 10 digits. That means we'll need to use `MNIST` instead of `MNIST_SAMPLE`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = untar_data(URLs.MNIST)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hide\n",
    "Path.BASE_PATH = path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(#2) [Path('testing'),Path('training')]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path.ls()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data is in two folders named *training* and *testing*, so we have to tell `GrandparentSplitter` about that (it defaults to `train` and `valid`). We de do that in the `get_dls` function, which we create to make it easy to change our batch size later:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_dls(bs=64):\n",
    "    return DataBlock(\n",
    "        blocks=(ImageBlock(cls=PILImageBW), CategoryBlock), \n",
    "        get_items=get_image_files, \n",
    "        splitter=GrandparentSplitter('training','testing'),\n",
    "        get_y=parent_label,\n",
    "        batch_tfms=Normalize()\n",
    "    ).dataloaders(path, bs=bs)\n",
    "\n",
    "dls = get_dls()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember, it's always a good idea to look at your data before you use it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dls.show_batch(max_n=9, figsize=(4,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have our data ready, we can train a simple model on it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A Simple Baseline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Earlier in this chapter, we built a model based on a `conv` function like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def conv(ni, nf, ks=3, act=True):\n",
    "    res = nn.Conv2d(ni, nf, stride=2, kernel_size=ks, padding=ks//2)\n",
    "    if act: res = nn.Sequential(res, nn.ReLU())\n",
    "    return res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start with a basic CNN as a baseline. We'll use the same one as earlier, but with one tweak: we'll use more activations. Since we have more numbers to differentiate, it's likely we will need to learn more filters.\n",
    "\n",
    "As we discussed, we generally want to double the number of filters each time we have a stride-2 layer. One way to increase the number of filters throughout our network is to double the number of activations in the first layer–then every layer after that will end up twice as big as in the previous version as well.\n",
    "\n",
    "But there is a subtle problem with this. Consider the kernel that is being applied to each pixel. By default, we use a 3×3-pixel kernel. That means that there are a total of 3×3 = 9 pixels that the kernel is being applied to at each location. Previously, our first layer had four output filters. That meant that there were four values being computed from nine pixels at each location. Think about what happens if we double this output to eight filters. Then when we apply our kernel we will be using nine pixels to calculate eight numbers. That means it isn't really learning much at all: the output size is almost the same as the input size. Neural networks will only create useful features if they're forced to do so—that is, if the number of outputs from an operation is significantly smaller than the number of inputs.\n",
    "\n",
    "To fix this, we can use a larger kernel in the first layer. If we use a kernel of 5×5 pixels then there are 25 pixels being used at each kernel application. Creating eight filters from this will mean the neural net will have to find some useful features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def simple_cnn():\n",
    "    return sequential(\n",
    "        conv(1 ,8, ks=5),        #14x14\n",
    "        conv(8 ,16),             #7x7\n",
    "        conv(16,32),             #4x4\n",
    "        conv(32,64),             #2x2\n",
    "        conv(64,10, act=False),  #1x1\n",
    "        Flatten(),\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you'll see in a moment, we can look inside our models while they're training in order to try to find ways to make them train better. To do this we use the `ActivationStats` callback, which records the mean, standard deviation, and histogram of activations of every trainable layer (as we've seen, callbacks are used to add behavior to the training loop; we'll explore how they work in <<chapter_accel_sgd>>):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai2.callback.hook import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to train quickly, so that means training at a high learning rate. Let's see how we go at 0.06:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit(epochs=1):\n",
    "    learn = Learner(dls, simple_cnn(), loss_func=F.cross_entropy,\n",
    "                    metrics=accuracy, cbs=ActivationStats(with_hist=True))\n",
    "    learn.fit(epochs, 0.06)\n",
    "    return learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2.307071</td>\n",
       "      <td>2.305865</td>\n",
       "      <td>0.113500</td>\n",
       "      <td>00:16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This didn't train at all well! Let's find out why.\n",
    "\n",
    "One handy feature of the callbacks passed to `Learner` is that they are made available automatically, with the same name as the callback class, except in `camel_case`. So, our `ActivationStats` callback can be accessed through `activation_stats`. I'm sure you remember `learn.recorder`... can you guess how that is implemented? That's right, it's a callback called `Recorder`!\n",
    "\n",
    "`ActivationStats` includes some handy utilities for plotting the activations during training. `plot_layer_stats(idx)` plots the mean and standard deviation of the activations of layer number *`idx`*, along with the percentage of activations near zero. Here's the first layer's plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.activation_stats.plot_layer_stats(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generally our model should have a consistent, or at least smooth, mean and standard deviation of layer activations during training. Activations near zero are particularly problematic, because it means we have computation in the model that's doing nothing at all (since multiplying by zero gives zero). When you have some zeros in one layer, they will therefore generally carry over to the next layer... which will then create more zeros. Here's the penultimate layer of our network:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.activation_stats.plot_layer_stats(-2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, the problems get worse towards the end of the network, as the instability and zero activations compound over layers. Let's look at what we can do to make training more stable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Increase Batch Size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One way to make training more stable is to increase the batch size. Larger batches have gradients that are more accurate, since they're calculated from more data. On the downside, though, a larger batch size means fewer batches per epoch, which means less opportunities for your model to update weights. Let's see if a batch size of 512 helps:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dls = get_dls(512)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2.309385</td>\n",
       "      <td>2.302744</td>\n",
       "      <td>0.113500</td>\n",
       "      <td>00:08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see what the penultimate layer looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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b0lDuUETGxYRKnhtU8yxSVXJJs5Jnkeq1Zd9B0lnXyLPExoRKnlW2IVJdcjmzqjZEqtem3QcBmKeaZ4mJCZU81yQT1CSNrj4lzyLVIFfrnFH2LFK1Nu3pAmDeVJVtSDxMqOQZghk3NPIsUh1y5RpZlW2IVK3Nu7tIGMyerORZ4mHCJc+NtSnVPItUCY08i1S/TXsOMmtSAzXJCZdSiBQ14f7SG2uTmm1DpEpks+FPJc8iVWvT7i7maqYNiZEJlzw31KpsQ6Ra5Eacc0m0iFSf53Z3MV8zbUiMTLjkWSPPItWjv2xDNc8iVelAdx/bD/SwqL2p3KGIjJsJmDyn6OrL8Ksnt3H1r58udzgiMoTchYKqeRapTht2dAKwpL25zJGIjJ8JmDwnOdib5vrfbeTLdz/Jtv3d5Q5JRAZxqGxDybNINdqwswOAJRp5lhiZcMlzQ22Szp4Mz+3uIutwy5+eL3dIIjKIXM6sCwZFqtPT2ztJJoz5U5U8S3xMuOQ5qHlOs3lPsOLRTx7ejOuDWaQiZbU8t0hV27Czg/lTG6lNTbh0QmRQE+6vvbE2xZ6uPnrSWZbPbuWp7R08+MzucoclIkX0L5Ki3FlkWGY21cxuMbNOM9toZucN0m6ymX3PzLaH26qxiunp7Z0q2ZDYiSR5LrVDh23/2szuMbN9ZvZsFK+fr6Em2X/7H161hBmtdfz991bzqye3Rf1SIjJKmm1DZESuBHqBGcD5wFVmtrxIu/8AGoGFwMuBC8zs3VEHk8k6z+zqZLEuFpSYiWrkudQODdAJXAt8LKLXHqCx9lDyfMzsVm59/8nMm9rIB298RB/QIhUmlzyr5llkaGbWBJwLXO7uHe5+P3A7cEGR5mcBX3H3Lnd/FvgO8HdRx/T8noP0prMsbtPIs8TLqJPnEXZo3P1Bd78e2DDa1y4mlzybwZzJDcya1MBFJy2goyfN5j1dY/GSInKEMlphUKRURwEZd1+Xt28NMNhAlRXcPrZoI7OLzWy1ma3esWPHiAJ6OjfTxnSNPEu8RDHyPNIOPSIj7dgNtSkAZrbWUx+WcCyb0QLAum0dUYQkIhFR2YZIyZqBfQX79gEtRdreDfyLmbWY2VKCUeeiSwC6+zXuvtLdV7a3t48ooAfW7wQ0x7PETxTJ80g69IiNtGPnRp7nTTn0/8Sy8Kx4/fYDUYQkIhHJv2BQs+KIDKkDaC3Y1woU+2D7IHAQWA/cBtwIbI4ymGd2dvK93z3LW0+Yy9Sm2igPLVLxhk2ezexeM/NBtvsZWYcecw255HnqoeS5pb6GWZPqWa+RZ5GKkl+uocFnkSGtA1Jmtixv3wpgbWFDd9/t7ue7+0x3X07wWf9glMF8/s4nqEsl+fiZL4rysCJVITVcA3c/fajHw5rnlJktc/f14e6iHXo8NNbkkueGAfuXzWjRyLNIhclPmLPuJAeUaYpIjrt3mtnNwBVm9h7gOOBs4KTCtma2BNgbbn8DXAy8Ksp47v3Ldi545QKmt9RHeViRqjDqsg137wRyHbrJzE4m6NDXF2tvZgkzqwdqgrtWb2aRfefTVBecD8yfOrC866jpzTy1vUPLAItUkPxaZ9U9iwzrUqAB2E5QinGJu681s1PNLP+r1ROARwm+Af4icL67Rzag5e6ks05LfU1UhxSpKsOOPJfoUoLp57YDuwg7NICZnQr8zN1zVxScBtyT99yDwK+B06MI5JhZraw66xjOPHbmgP3LZjTT3Zdl054uFkzTtDoilWBg2YaSZ5GhuPtu4Jwi++8juP4od//HwI/HKo7ceW4qoW+KJJ4iSZ4H69DhY4Wd+l4Yu+9mEwnjopMXHbY/N+PG+m0dSp5FKkRWI88iVSfXV5NKniWmJtzy3INZGs64sU51zyIVI6MLBkWqTi55TpiSZ4mn2CTPrfU1tLfUsXGnFkoRqRTZbP5tZc8i1SB30quyDYmr2CTPAAunNfLMrs5yhyEiofw654xqnkWqQiYTjjwreZaYilXyvGBaExuVPItUjAFlGxp5FqkKGnmWuItV8rxwWiPb9vfQ1ZsudygiQsEFgxp5FqkK6bDeSiPPElfxSp7bglk2ntutumeRSjBwkZTyxSEipctdq6CRZ4mreCXP4RR1z+5U6YZIJcifnk5lGyLVIfctUVKzbUhMxSp5nj8tWHXw2V0aeRapBAMuGFTyLFIVchcMap5niatYJc+t9TVMa6rVRYMiFUKzbYhUn/6RZyXPElOxSp4BFkxr5BmVbYhUhPzRZlfyLFIVMmHRs5JniavYJc8L25rYqLINkYowsGyjjIGISMlyfVXJs8RV/JLnaU1s2ddNd1+m3KGIlJWZXWZmq82sx8yuG6btP5rZVjPbZ2bXmlldFDHkrzCommeR6tA/VZ0uGJSYil3yPHdKAwDP7z1Y5khEyu4F4HPAtUM1MrPXAf8CnAEsBBYDn4kigAGLpKhsQ6QqaKo6ibsYJs/BjBub9yh5lnhz95vd/VZg1zBNLwS+4+5r3X0P8FngoihiGLBIikaeRSpWNuvsOBAsMpZWzbPEXAyT53DkWcmzSKmWA2vy7q8BZpjZtNEeeOAiKUqeRSrV/u4+Xvb5/+Gmhzb191UlzxJXsUueZ7TWk0oYm/fookGREjUD+/Lu5263FDY0s4vDOurVO3bsGPbAKtsQqQ65RDmTddKa51liLnbJczJhzJpcr7INkdJ1AK1593O3DxQ2dPdr3H2lu69sb28f9sADyzZGGaWIjJlUIkgX0lnXPM8Se7FLngHmTm7UBYMipVsLrMi7vwLY5u7D1UoPSysMilSH/JHnXF9V8ixxFc/keUqDyjYk9swsZWb1QBJImlm9maWKNP0+8PdmdoyZTQE+BVwXRQxaJEWkOuRm1ujLZJU8S+zFMnmeM6WBbft76ElrrmeJtU8BBwmmoXtnePtTZjbfzDrMbD6Au98NfAW4B9gYbv8WRQBanlukNGY21cxuMbNOM9toZucN0q7OzL5lZtvMbLeZ3WFmc0b7+omEkbDghLf/gkHN8ywxFUnyXGqnDtt+zMweM7MDZvaMmX0sihhGIjdd3Za93eP90iIVw91XubsVbKvc/Tl3b3b35/Lafs3dZ7h7q7u/2917ooghv1JDZRsiQ7oS6AVmAOcDV5nZ8iLtPgS8EngpMBvYC3wjigBSiQRpXTAoEtnIc6mdGsCAdwFTgDOBy8zsHRHFUZLcdHW6aFCkvPITZs22IVKcmTUB5wKXu3uHu98P3A5cUKT5IuDn7r7N3buBHxFMNzlqyYQNHHlW8iwxNerkeYSdGnf/irv/0d3T7v4X4Dbg5NHGMRKHkmfVPYuUk2bbECnJUUDG3dfl7VtD8aT4O8DJZjbbzBoJBrR+FkUQqYSRzjhp1TxLzEUx8jySTj2AmRlwKsHV/ONmZms9yYRp5FmkzLRIikhJCudaJ7x/2FzrwDrgOeB5YD9wNHDFYAceydzsyaSRyeqCQZEokueRdOpCq8IYvjtYg5EuulCKVDLBzNZ6XtB0dSJlNWCRFNU8iwymcK51wvuHzbUOXAXUA9OAJuBmhhh5Hsnc7KmEBfM8Z3XBoMTbsMmzmd1rZj7Idj8j69T5x72MoPb5jUNdfDTSRRdKNXtyveZ6FimzAWUbGnkWGcw6IGVmy/L2raD4t7YrgOvcfXf42foN4OVm1jbaIJJh2YZGniXuhk2e3f30Ilfk57ZTGFmnBsDM/o5geqwz3H3zaH+JIzF7cgMv7FPyLFJOWiRFZHju3kkwgnyFmTWZ2cnA2cD1RZo/BLzLzCaZWQ1wKfCCu+8cbRy52TaUPEvcjbpsY4SdGjM7H/gC8Fp33zDa1z9Ssyc3sHVft74qFimj/NFmDTyLDOlSoAHYDtwIXOLua83sVDPryGv3UaAbWA/sAN4AvDmKAFK5muews6aUPEtMFVtN7EhcClxL0Kl3EXZqADM7FfiZuzeHbT9HUIv1kB2ql/qBu/9DRLGUZPbkBvoyzs6OHqa31o/nS4tIaOBsG8qeRQbj7ruBc4rsv4/g2qPc/V0EM2xELllQ85xQ8iwxFUnyPFinDh8r7NiLonjN0ZozOUiYn997UMmzSJkMWCRFQ88iFS0VzvOcS5418ixxFcvluSEYeQZ4QasMipTNgEVSNPIsUtGSBTXPGnmWuIpt8jxrUi551kWDIuUy4IJBjTyLVDSNPIsEYps8t9anaK5Labo6kTLKT5418CxS2ZIJoy9z6ILBhOZ5lpiKbfJsZsyeXM8WTVcnUjaZ7KHprlS2IVLZ+keeM5qqTuIttskzhHM9q+ZZpGyyWacmGXwAa7YNkcqWSoazbbhWGJR4U/Kssg2Rssm6U5NI9N8WkcqVSiT6a57NdMGgxFesk+c5kxvY1dlLd1+m3KGIxFLGnVQ48qzkWaSy5c/zrIsFJc5inzwDbNrdVeZIROIpKNsI/hvKZMscjIgMKah5zpLJui4WlFiLdfK8dHqwdstT2zuGaSkiYyHr9CfPGnkWqWzJhJHOaORZJNbJ85L2ZsxgvZJnkbLIuC4YFKkWuQsG01lXvbPEWqyT54baJHOnNCh5FimTbNZJaeRZpCokwwsGs66RZ4m3WCfPAMumt7B+24FyhyESS/kfwprnWaSypRJGOpslnXXN8SyxFvvkeen0Zjbs7CStq5VExl0mGyxYlEyYlucWqXCphJHJOFklzxJzSp6nN9ObzrJpj+Z7FhlvWXeSiWCxBZ2/ilS2/kVSsq4FUiTWYp88Lwtn3FDphsj4y3rwIZxIqOZZpNIlc8tz64JBibnYJ8+56ep00aDI+AtWKjOSZqp5FqlwqUSif3luXTAocRb75LmlvoZZk+o117NIGQRlG0bCVPMsUumCeZ6zmqpOYi/2yTPA4vYmNuxQ8iwy3rJZwrINjTyLVLpUuDx3VoukSMwpeQYWtwUzbrhGvkTGVcYdMzTbhkgVyNU8p7U8t8SckmdgUVsTB7rT7OrsLXcoIrGSm/IqYYYGnkWGZmZTzewWM+s0s41mdt4g7X5mZh15W6+ZPTra108lE/2zbaSSSp4lvlLlDqASLGpvAuCZnZ20NdeVORqR+DhU86xFUkRKcCXQC8wAjgPuNLM17r42v5G7vz7/vpndC/xqtC+eK9Xoy2Q1VZ3EWiQjz6WeDYdtP2xmG8xsv5m9YGb/YWZlTeKXtAUzbqjuWWR8ZTxvkRQlzyKDMrMm4FzgcnfvcPf7gduBC4Z53kLgVOD60caQWxilJ53VIikSa1GVbeSfDZ8PXGVmywdpewdwvLu3AscCK4APRhTHEZkzpYGapLFhZ2c5wxCJnWzWSRqabUNkeEcBGXdfl7dvDTDYZ23Ou4D73P2Z0QaQUvIsAkSQPI/0bNjdn3b3vbmnA1lg6WjjGI1kwlgwrYlndih5FhlPubKNpGbbEBlOM7CvYN8+oGWY570LuG6wB83sYjNbbWard+zYMeSB+kee+zJKniXWohh5HvHZsJmdZ2b7gZ0EI89XD9G25I49GovbmnhGI88i4yq3SErC0AWDIkPrAFoL9rUCgy6Pa2anADOBnwzWxt2vcfeV7r6yvb19yAByI8+9GnmWmIsieR7x2bC7/zAs2zgK+BawbYi2JXfs0VjU3sTGXV2quxQZR4eW51bZhsgw1gEpM1uWt28FsHaQ9gAXAje7eyQX9CSTQcrQk85qqjqJtWGTZzO718x8kO1+juBsOMfd1xN0/G8eSfBRWtzWRG8my+Y9XeUORSQ2sh58FazluUWG5u6dwM3AFWbWZGYnA2czyIWAZtYAvI0hSjZGqiY38pzJapEUibVhk2d3P93dbZDtFI7sbDhfClgy8tCjtXz2JAD++NyeMkciEh/ZbN4iKUqeRYZzKdAAbAduBC5x97VmdqqZFY4un0PwLfA9Ub24ap5FAqMu2ziCs+H3mNn08PYxwL8CvxxtHKN1zKxWpjbV8pt1O8sdikhsZFyLpIiUyt13u/s57t7k7vPd/Yfh/vvcvbmg7Y3uvsAjXDo3tzBKb0Y1zxJvUU1VV/RsGKDIGfHJwKNm1gncFW6fiCiOI5ZIGKcsbeO+9Tv09bHIODlU8xzcFpHKlUwcqnlW8ixxFsniJO6+m+AromKP3UdwUWHu/rujeM2xcNpR7SHQsxQAABkOSURBVNy+5gWe2Lq/v4xDRMZONhsukmIq2xCpdLk6Z/dDibRIHOmvP89py9oAVLohMk4yWSeZCL750cizSGXLH21OauBZYkzJc57prfW8eGYL960fu/mkRSqFmU01s1vMrNPMNprZeYO0W2VmfWbWkbctjiKG/kVSNPIsUvHyZ9jQyLPEmf76C5y0pI2HN+6hJ50pdygiY+1KoBeYAZwPXGVmgy1udJO7N+dtG6IIIOu5RVI08ixS6QaMPCt7kBjTn3+BVyyeSk86y5pNheu+iEwcZtYEnAtc7u4d7n4/cDtwwXjGkcnmXTCYHc9XFpGRqsnLmDXyLHGmv/4CL180FTP4/YZd5Q5FZCwdBWTcfV3evjXAYCPPZ5nZbjNba2aXDHZQM7vYzFab2eodO4Yvf+pfJEUrDIpUPI08iwT0519gcmMtR89sVfIsE10zwQIK+fYBLUXa/hg4GmgH3gt82sz+tthB3f0ad1/p7ivb29uHDSK3SEpCNc8iFS+/5jmlkWeJMf31F/GKxdNU9ywTXQfQWrCvFThQ2NDdH3f3F9w94+6/Bb4OvDWKIDK5eZ7NiHAtBxEZA/kjzwnTdBsSX0qei1Dds8TAOiBlZsvy9q0A1pbwXAci+eTsn21DZRsiFS9/tFllGxJn+vMv4oQFUwD403N7yhyJyNhw907gZuAKM2sys5OBs4HrC9ua2dlmNsUCLwc+CNwWRRy5RVKCso0ojigiYyWpqepEACXPRU1rrmPe1AYe2bS33KGIjKVLgQZgO3AjcIm7rzWzU82sI6/dO4CnCEo6vg982d2/F0UAGQ8WSUkmgvpnEalcqaQuGBSBiJbnnoiOmzeF1c/uLncYImPG3XcD5xTZfx/BBYW5+0UvDoxCNqx5VtmGSOXTIikiAf31D+K4eZPZsq+bbfu7yx2KyITk7rgHZRumRVJEKt6AmmddMCgxpuR5EMfNmwyg0g2RMZKbmi63PLfKNkQqWzKvbCO/hEMkbpQ8D2L57FZSCVPyLDJGcrmyZtsQqQ4pTVUnAih5HlR9TZKjZ7XyyHNKnkXGQq5MI7dIipbnFqlsyQGLpCh5lvhS8jyE4+dPZs3mvaQ1h5ZI5PrLNsxIGKp5FqlwA0aelTxLjCl5HsIJC6fS1ZvhiS2HLbomIqOUS5b7yzZU8yxS0TTyLBJQ8jyEly0MFkt5SFPWiUQuV6ZhZiQSmm1DpNLV5E3urJFniTMlz0OYNamBOZMbeHijVhoUiVruAsGkBaUbGnkWqWwDVhjUBYMSY0qeh7Fy4RQeenY3rlExkUjlRpoTKtsQKYmZTTWzW8ys08w2mtl5Q7Q93sx+Y2YdZrbNzD402tfPT5hVtiFxFlnyPJJOnfecWjN70sw2RxVH1FYunMr2Az1s3nOw3KGITCi5eZ0TZpiBzk9FhnUl0AvMAM4HrjKz5YWNzKwNuBu4GpgGLAX+e7QvnkgEF/fmbovEVZQjzyV16gIfA7ZHGEPkVi4I6p4ffEZ1zyJRyvjARVI0z7PI4MysCTgXuNzdO9z9fuB24IIizT8C/Nzdb3D3Hnc/4O5PRBFHbpVBjTxLnEWSPI+wU+eeswh4J/DFKGIYKy+a0cKUxhp++/SucociMqHkqjQShso2RIZ3FJBx93V5+9YAxQapXgHsNrPfmtl2M7vDzOYXO6iZXWxmq81s9Y4dO4YNIlf3rJFnibOoRp5H0qlzvgF8AqjoeohEwnjlkmn87umdqnsWiVB+2YZm2xAZVjOwr2DfPqClSNu5wIXAh4D5wDPAjcUO6u7XuPtKd1/Z3t4+bBC5EWeNPEucRZU8j6RTY2ZvBlLufstwBx7pWfFYeOWSNl7Y182zu7rK8voiE1H/IimJ3CIpZQ5IpLJ1AK0F+1qBYgsRHARucfeH3L0b+AxwkplNGm0QyWQ48qzZNiTGSkqezexeM/NBtvsZQacOSzy+AnyglNce6VnxWDh5yTQAfvv0zrK8vshE1D/bhpmmqhMZ3jogZWbL8vatANYWaftnIL9D5W6POuNVzbNIicmzu5/u7jbIdgoj69TLgIXAfWa2FbgZmGVmW81s4Wh+mbGyqK2Jma31/PYp1T2LRCV/qrpc/WRWCbRIUe7eSfB5eYWZNZnZycDZwPVFmn8XeLOZHWdmNcDlwP3uvne0ceSS5qSSZ4mxSMo2RtipHwPmAceF23uAbeHtTVHEEzUz46Sl0/jdhl36cBeJSCZcYTAZjjwDmnFDZGiXAg0Es1TdCFzi7mvN7FQz68g1cvdfEVxTdGfYdikw7PSxpUgqeRYhFeGxLgWuJeiouwg7NYCZnQr8zN2b3T0NbM09ycx2A1l331rkmBXjlYuncfMfn2f99g5eNLNoKbeIjMChso1DV+5nsk5Ncmxe72Bvhv9+fCu3/ul5Ht+yn3TGeesJc3nHy+ezqK1pbF5UJELuvhs4p8j++wiuPcrfdxVwVdQxpJJKnkUiS54H69ThY4d17LzH7iW4MriivWJxUPf8+w27lDyLRCBX4xwsvBB8EI/FwPP6bQf40s+e5L6ndtKbzjJ3SgOnLG2no6eP/7z/Ga7+zQZWzJvMG46dyWuOmcHitiZMF0OJFNU/VZ36iMRYlCPPE9q8qY3MmdzA7zfs4sKTFpY7HJGqlxt5TpqRDAvIoi7buOEPG/nM7Y/TWJfk/BPn89pjZvCKRdP6R7q37e/mtkee5/Y1L/DFnz3JF3/2JHOnNHDSkmmcuGgaxy+YwsJpjUqmRUL9U9Ul1SckvpQ8j8ArFk/jnr9sJ5t1TRAvMkr9i6QkDo1iRTnjxs/XbuVTtz7Gacva+fe3raC9pe6wNjNa67n4tCVcfNoSNu/p4p6/7OC+dTv4+dpt/Hj1ZgAmNdTwkjmTOHpWC0fNCLbF7U201NdEFqtItUiGs21o5FniTMnzCLxyyTR++sfNqnsWiUAmb5GUZMSzbdy/ficf/tEjrJg7masvOIH6Egqp505p5IJXLOCCVywgm3We2tHBwxv3sGbTXta+sJ/v/W4jvelsf/u25joWTGtk3pQG5kxpYM7kRmZOqmNGaz3TW+qZ2lSrulCZcGqSWiRFRMnzCLxi8VQAHnhqp5JnkVHqL9vIq3mOYpXBmx56jk/c8hhL25v59rtWlpQ4F0okrH+U+W9fHqxqnM5k2bi7i6e2d/D0jg427uxi4+5OHnp2D3f8ectho+YJg6lNtUxrqmNyYw1TGmuZ3FjDpIYaWhtqaKlP0VKformuhqa6JE21KZrqkjTUpmisSdJQm6QulVDJiFQUzbYhouR5ROZOaWTZ9GbuXruVvztlUbnDEalqhctzw+hrnq974BlW3fE4px3VzpXn/VWkpRWpZIIl7c0saT/82udM1tm2v5ut+7vZvr+bbft72NnRw86OXnZ39rCns48NOzvY09XHvoN9A0awh1OXSlBfk6S+JkFdKkltKkFtMjHgZ03SqEkmqEkmSCWNVCLYl7udShipZPAzkTBSiWC0Pxnezo3+JxLWX4Oe25dMGBZOJ5ibGSVpRiIRTOOZyHvMcsex3GPhv68ZZoeOmf94fjvIXUDKgOck8toe2jewjTHwmDI2NM+ziJLnEXvDS2bxf361nu0HupneUl/ucESqVqZghUGAbOk55WGu/92zrLrjcV57zAyuPO94alORTGNfkmTCmD25gdmTG0pq392XYX93H509GTq60xzo6eNgb4bO3gwHe9Mc7M3Q1ZehuzdDdzpLd1+Gnr4svZksPekMveksPeksveksXb1pejNZ0hmnL5MlnXX60sHPdDbYl8k66YyTzmZjswx6sQTdKEi6E0HSHdwP9uXuF0vWD7tP+Jzw/jXvWsmcEv8GqpVGnkWUPI/YG186i6//cj0/f2wrF7xyYbnDEalauUQ5GN0Mbh/JyHM6k+Wm1Zu4/La1vObo6Xzz/OOpSY5f4nwkgpHkJJSh+svdyWSdTPgznXWy2UP7slnCn4f2uTtZD0bYM1nHPWwTPpbJhscNn+8cahfcZkDbrDtO8O2DEzwnty/3WrnXcA9fL+v9ZT3ZvDbZMNbguYTHCx8n+JkNHjjseR6+ZiZsD2HMeb/fgOeR97yC47l7LOqAc8tzK3mWOFPyPEJHzWhh6fRm7nx0i5JnkVHIXyTF+keeS0+e12zayy1/ep6fr93Kln3dvHLxNP7veZWfOJebWVjOUe5ApCr1jzyrNEZiTP9/HoE3hqUbz+3qYv60xnKHI1KV+ss2EnllGyWOPG/Zd5C3fet3JBJw8pI2/u2sYzjj6BlKnEXGmGqeRUCfNEfgvBPnk0oY1z7wTLlDEalauVHmZN5UdaXO83z1rzeQdecX//gqvnPRyzjz2FlKnEXGgWqeRZQ8H5EZrfWc9dLZ/Hj1JvZ19ZU7HJGq1L9ISt5sG6WMPO840MONDz7HW46fw7yp+uZHZDzlTlKVPEucKXk+Qu85dTFdvRlueHBjuUMRqUr9i6QkgrrnYN/Qz3F3vnjXE/Rlslxy+tIxjlBECmnkWUTJ8xE7ZnYrpy5r47sPPEtPOlPucESqTv4iKaXWPH/vt89y85+e50NnHMWitqYxj1FEBkrpgkERXTA4Gu87bQnv/M4fuPVPz/P2l80vdzgSsdyUXrm5coN5coO5cnNz6mayTl84f25fxknn9mWdTP++Q89JZ8P5d/Pm5E1nDu3Lv9/eUsf7/3rijq4emm3DaKwL/iv68I8e4ZNvPJrTjmo/rP1T2zv43J1P8Jqjp/OBV0/c90WkkvWPPCeVPEt8KXkehZOXTmP57Fau+c0G3nbCvP66TTnE3enNBIs59GU8/Bks9tDXvz9LbzpIJoPt0O10xsMFIIKEsjeTpS89MFnN7U/nFooIE9C+zMBEN11sfzZLJnxOXyZ3+1CiPJ4SRv8qcKmEcfSs1gmdPPeXbZhx6tI2vvq2FVx5z1O89/ur+eklJ3HsnEkD2n/+zsdpqEnypXNfqr4mUiappEaeRZQ8j4KZcenpS3n/D//Ijx7axHknVs/ocybrdPam6ehO09mTprM3Q1fuZ2+art4MXbnVzvoydPdlg5+9GbrTwWpnPbmVz8KVznrSwe3c/d50kCSPBTOoSeSWIh64NHFN8tByxLlli5MJo74mQaouFSxbnChsW2zfoccKXysZ/sztK3a8ZG6J5NxjiQTJ/vaH4sstnxy3hDBXoZFbFvrcE+byqhe1c9Y37ud91z/Mp954NCctaaO1IcVtj7zAPX/ZwSfe8GLamuvKG7hIjKnmWUTJ86i94SUzOXHRVL5895O8bvkMpo3zB3s6k2V3Vy+7OnrZ3Rlse7p62dvVx56uXvYd7GNfVx/7Dvaxv7uPA91pDnSn6ehJl/waCYOGmiQNtUnqUknqaxLU1ySpSyWoSyVpqU9Rl0pSV5OgNpmgNtxfm8rdDvbXJI3aVDL8mduXoCZ1KMmtSR5qm5/81uTdziWeUt0OjTwf2tfWXMdV7zyBd3/3QS654Y+YwfSWOrbt7+FFM1q48KSF5QlWRACtMCgCSp5Hzcz43DnH8vqv38fbr/k97zxxPq85ZgZzpxz5FFruzoGeNNv397D9QDc7DvT0395+oIcd4bazo4c9Q0yV11yXYlJDTf+2qK2J1voaWupraKlP0VKfoqku2JrrkjTVpmisTdFQm6SpLkljTYr62iCZNX1FJxHL5NU85ztu3mQe/ORreGTTXn739C6e2LKf045q5y3Hz6EulSxHqCIS0gWDIkqeI7FsRgv/97zjufKep1h1x+OsuuNx5kxu4OhZrcyf2sj01jpa6lP9SWg6E5RAdPVm2H8wGCHe3dnHrs4gId55oJeDfYfP4FFfk2B6Sz3tLXUsbm/ixMVTmdZUR1tzLdOa65jSWMvUpmCb3FijRSOkonnebBuFapIJXrZwKi9bOHW8wxKRISSThhmxKzMTyafkOSJnHjuTM4+dyVPbO/j1uh386bk9/GXrAR54amfRRDinNpVgSmMNUxpraWuuY8H8Rtqa65jeWsf0lnqmtwS321vqaa1PaQRYJoxcOXzhyLOIFGdmU4HvAH8D7AT+1d1/WKTdKuCTQE/e7pe6+4bRxjBncgOzJzWM9jAiVU3Jc8SWTm9m6fRmYBEQjK519Wbo6EnTm87iHlytnKshrq/R19AST2etmMXJS6fR1lxb7lBEqsWVQC8wAzgOuNPM1rj72iJtb3L3d0YdwDtPXMDbXzYv6sOKVJXIvtc3s6lmdouZdZrZRjM7b4i2q8ysz8w68rbFUcVSScyMproUM1rrmTe1kfnTGpk9uYEpTbVKnKWsSu2zFviyme0Kt69YBF+BtNTXsGBaky7+FCmBmTUB5wKXu3uHu98P3A5cMJ5xJBKmaw8k9qL81Mo/Iz4fuMrMlg/R/iZ3b87bRv11koiMSKl99mLgHGAF8FLgTcD7xitIEQHgKCDj7uvy9q0BBvucPcvMdpvZWjO7ZLCDmtnFZrbazFbv2LEjynhFJqxIkudKOSMWkdKMsM9eCHzV3Te7+/PAV4GLxi1YEQFoBvYV7NsHtBRp+2PgaKAdeC/waTP722IHdfdr3H2lu69sbz98ZU8ROVxUI88jPSMGnRWLlNNI+uzy8LHh2qm/ioydDqC1YF8rcKCwobs/7u4vuHvG3X8LfB146zjEKBILUSXPIzkjBp0Vi5TbSPpsYdt9QHOxumf1V5Exsw5ImdmyvH0rgGIXCxZyQNPaiESkpOTZzO41Mx9ku58RnBGDzopFKsBI+mxh21agw3MTNYvImHP3TuBm4AozazKzk4GzgesL25rZ2WY2JbzY9+XAB4HbxjdikYmrpKnq3P30oR4P6ydTZrbM3deHu0s9I4YSz4offvjhnWa2cZhmbQTzX0rp9J6NXKW8ZwuO8Hn9o1gl9Nm14WMPDtNuAPXXMaX3beQq4T070v6acylwLbAd2AVc4u5rzexU4Gfu3hy2e0fYrg7YDHzZ3b833MHVZ8eM3rORq5T3rGiftagGj8zsRwRJ8HsI5p+8Czip2PyTZnY28BtgL/Ay4BbgE6V07hLiWO3uK0d7nDjRezZyE+E9K7XPmtk/AB8CXhO2/wXwDXf/VgQxVP37WA5630ZO71k09D6OnN6zkav09yzKqeouBRoIzohvJDwjBjCzU82sI6/tO4CnCL4i/j4lnhWLSKSK9tki/fVq4A7gUeAx4M5wn4iISOxEtsKgu+8mmAu22GP3EVx0lLtf9OJAERk/g/XZIv3VgY+Hm4iISKxNxKW9ril3AFVI79nI6T2Lht7HI6P3beT0nkVD7+PI6T0buYp+zyKreRYRERERmegm4siziIiIiMiYUPIsIiIiIlKiCZM8m9lUM7vFzDrNbKOZnVfumCpRuOBNt5l1hNtf8h47L3zvOs3sVjObWs5Yy8HMLguXl+4xs+sKHjvDzJ40sy4zu8fMFuQ9Vmdm15rZfjPbamYfGffgq4j6a2nUX4enPjs+1GeHp/46vInSXydM8gxcCfQCM4DzgavMbHl5Q6pYl7l7c7i9CCB8r64GLiB4D7uAb5YxxnJ5AfgcwQID/cysjWB1r8uBqcBq4Ka8JquAZQQTqv818HEzO3Mc4q1W6q+lU38dmvrs+FCfLY3669AmRH+dEMmzBSscngtc7u4d7n4/cDvBH6qU5nzgDnf/jbt3EPwBv8XMWsoc17hy95vd/VaC1bvyvQVY6+7/5e7dBB15hZm9OHz8XcBn3X2Puz8BfBu4aJzCrirqr5FQfw2pz4499dlRU38NTZT+OiGSZ+AoIOPu6/L2rQF0VlzcF81sp5k9YGanh/uWE7xnALj70wSjDEeVIb5KVPj+dAJPA8vNbAowO/9x9Pc3FPXXkVF/PTLqs9FRny2d+uuRqar+GtkiKWXWDOwr2LcPiN1ZXQn+GXicoOO+A7jDzI5D7+FwmoEdBfty709z3v3Cx+Rw+lsrnfrrkVOfjY7+3kqj/nrkqqq/TpSR5w6gtWBfK8Hy35LH3f/g7gfcvSdcEv0B4A3oPRzOUO9PR979wsfkcPpbK5H666ioz0ZHf28lUH8dlarqrxMleV4HpMxsWd6+FcDaMsVTTRwwgvdqRW6nmS0G6gjeWzn8/WkClhDUaO0BtuQ/jv7+hqL+euTUX0unPhsd9dkjo/5auqrqrxMieQ5rY24GrjCzJjM7GTgbuL68kVUWM5tsZq8zs3ozS5nZ+cBpwM+BG4CzzOzU8I/2CuBmd4/VmXH4vtQDSSCZe6+AW4Bjzezc8PFPA3929yfDp34f+JSZTQkvcHgvcF0ZfoWKp/5aGvXX0qjPjj312eGpv5ZmwvRXd58QG8HUJrcCncBzwHnljqnSNqAdeIjgq469wO+B1+Y9fl743nUCtwFTyx1zGd6jVQSjBfnbqvCx1wBPAgeBe4GFec+rI5h6Zz+wDfhIuX+XSt7UX0t6j9RfS3uf1GfH531Wnx36/VF/Le19mhD91cKgRERERERkGBOibENEREREZDwoeRYRERERKZGSZxERERGREil5FhEREREpkZJnEREREZESKXkWERERESmRkmcRERERkRIpeRYRERERKZGSZxERERGREv3/iu+ht1/5Ym4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.activation_stats.plot_layer_stats(-2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, we've got most of our activations near zero. Let's see what else we can do to improve training stability."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1cycle Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our initial weights are not well suited to the task we're trying to solve. Therefore, it is dangerous to begin training with a high learning rate: we may very well make the training diverge instantly, as we've seen. We probably don't want to end training with a high learning rate either, so that we don't skip over a minimum. But we want to train at a high learning rate for the rest of the training period, because we'll be able to train more quickly that way. Therefore, we should change the learning rate during training, from low, to high, and then back to low again.\n",
    "\n",
    "Leslie Smith (yes, the same guy that invented the learning rate finder!) developed this idea in his article [\"Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates\"](https://arxiv.org/abs/1708.07120). He designed a schedule for learning rate separated into two phases: one where the learning rate grows from the minimum value to the maximum value (*warmup*), and one where it decreases back to the minimum value (*annealing*). Smith called this combination of approaches *1cycle training*.\n",
    "\n",
    "1cycle training allows us to use a much higher maximum learning rate than other types of training, which gives two benefits:\n",
    "\n",
    "- By training with higher learning rates, we train faster—a phenomenon Smith named *super-convergence*.\n",
    "- By training with higher learning rates, we overfit less because we skip over the sharp local minima to end up in a smoother (and therefore more generalizable) part of the loss.\n",
    "\n",
    "The second point is an interesting and subtle one; it is based on the observation that a model that generalizes well is one whose loss would not change very much if you changed the input by a small amount. If a model trains at a large learning rate for quite a while, and can find a good loss when doing so, it must have found an area that also generalizes well, because it is jumping around a lot from batch to batch (that is basically the definition of a high learning rate). The problem is that, as we have discussed, just jumping to a high learning rate is more likely to result in diverging losses, rather than seeing your losses improve. So we don't jump straight to a high learning rate. Instead, we start at a low learning rate, where our losses do not diverge, and we allow the optimizer to gradually find smoother and smoother areas of our parameters by gradually going to higher and higher learning rates.\n",
    "\n",
    "Then, once we have found a nice smooth area for our parameters, we want to find the very best part of that area, which means we have to bring our learning rates down again. This is why 1cycle training has a gradual learning rate warmup, and a gradual learning rate cooldown. Many researchers have found that in practice this approach leads to more accurate models and trains more quickly. That is why it is the approach that is used by default for `fine_tune` in fastai.\n",
    "\n",
    "In <<chapter_accel_sgd>> we'll learn all about *momentum* in SGD. Briefly, momentum is a technique where the optimizer takes a step not only in the direction of the gradients, but also that continues in the direction of previous steps. Leslie Smith introduced the idea of *cyclical momentums* in [\"A Disciplined Approach to Neural Network Hyper-Parameters: Part 1\"](https://arxiv.org/pdf/1803.09820.pdf). It suggests that the momentum varies in the opposite direction of the learning rate: when we are at high learning rates, we use less momentum, and we use more again in the annealing phase.\n",
    "\n",
    "We can use 1cycle training in fastai by calling `fit_one_cycle`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit(epochs=1, lr=0.06):\n",
    "    learn = Learner(dls, simple_cnn(), loss_func=F.cross_entropy,\n",
    "                    metrics=accuracy, cbs=ActivationStats(with_hist=True))\n",
    "    learn.fit_one_cycle(epochs, lr)\n",
    "    return learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.210838</td>\n",
       "      <td>0.084827</td>\n",
       "      <td>0.974300</td>\n",
       "      <td>00:08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're finally making some progress! It's giving us a reasonable accuracy now.\n",
    "\n",
    "We can view the learning rate and momentum throughout training by calling `plot_sched` on `learn.recorder`. `learn.recorder` (as the name suggests) records everything that happens during training, including losses, metrics, and hyperparameters such as learning rate and momentum:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_sched()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Smith's original 1cycle paper used a linear warmup and linear annealing. As you can see, we adapted the approach in fastai by combining it with another popular approach: cosine annealing. `fit_one_cycle` provides the following parameters you can adjust:\n",
    "\n",
    "- `lr_max`:: The highest learning rate that will be used (this can also be a list of learning rates for each layer group, or a Python `slice` object containing the first and last layer group learning rates)\n",
    "- `div`:: How much to divide `lr_max` by to get the starting learning rate\n",
    "- `div_final`::  How much to divide `lr_max` by to get the ending learning rate\n",
    "- `pct_start`:: What percentage of the batches to use for the warmup\n",
    "- `moms`:: A tuple `(mom1,mom2,mom3)` where *`mom1`* is the initial momentum, *`mom2`* is the minimum momentum, and *`mom3`* is the final momentum\n",
    "\n",
    "Let's take a look at our layer stats again:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.activation_stats.plot_layer_stats(-2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The percentage of nonzero weights is getting much better, although it's still quite high.\n",
    "\n",
    "We can see even more about what's going on in our training using `color_dim`, passing it a layer index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.activation_stats.color_dim(-2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`color_dim` was developed by fast.ai in conjunction with a student, Stefano Giomo. Stefano, who refers to the idea as the *colorful dimension*, provides an [in-depth explanation](https://forums.fast.ai/t/the-colorful-dimension/42908) of the history and details behind the method. The basic idea is to create a histogram of the activations of a layer, which we would hope would follow a smooth pattern such as the normal distribution (colorful_dist)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"images/colorful_dist.jpeg\" id=\"colorful_dist\" caption=\"Histogram in 'colorful dimension'\" alt=\"Histogram in 'colorful dimension'\" width=\"800\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To create `color_dim`, we take the histogram shown on the left here, and convert it into just the colored representation shown at the bottom. Then we flip it on its side, as shown on the right. We found that the distribution is clearer if we take the log of the histogram values. Then, Stefano describes:\n",
    "\n",
    "> : The final plot for each layer is made by stacking the histogram of the activations from each batch along the horizontal axis. So each vertical slice in the visualisation represents the histogram of activations for a single batch. The color intensity corresponds to the height of the histogram, in other words the number of activations in each histogram bin.\n",
    "\n",
    "<<colorful_summ>> shows how this all fits together."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"images/colorful_summ.png\" id=\"colorful_summ\" caption=\"Summary of the colorful dimension (courtesy of Stefano Giomo)\" alt=\"Summary of the colorful dimension\" width=\"800\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This illustrates why log(f) is more colorful than *f* when *f* follows a normal distribution because taking a log changes the Gaussian in a quadratic, which isn't as narrow."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So with that in mind, let's take another look at the result for the penultimate layer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.activation_stats.color_dim(-2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shows a classic picture of \"bad training.\" We start with nearly all activations at zero—that's what we see at the far left, with all the dark blue. The bright yellow at the bottom represents the near-zero activations. Then, over the first few batches we see the number of nonzero activations exponentially increasing. But it goes too far, and collapses! We see the dark blue return, and the bottom becomes bright yellow again. It almost looks like training restarts from scratch. Then we see the activations increase again, and collapse again. After repeating this a few times, eventually we see a spread of activations throughout the range.\n",
    "\n",
    "It's much better if training can be smooth from the start. The cycles of exponential increase and then collapse tend to result in a lot of near-zero activations, resulting in slow training and poor final results. One way to solve this problem is to use batch normalization."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Batch Normalization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To fix the slow training and poor final results we ended up with in the previous section, we need to fix the initial large percentage of near-zero activations, and then try to maintain a good distribution of activations throughout training.\n",
    "\n",
    "Sergey Ioffe and Christian Szegedy presented a solution to this problem in the 2015 paper [\"Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift\"](https://arxiv.org/abs/1502.03167). In the abstract, they describe just the problem that we've seen:\n",
    "\n",
    "> : Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates and careful parameter initialization... We refer to this phenomenon as internal covariate shift, and address the problem by normalizing layer inputs.\n",
    "\n",
    "Their solution, they say is:\n",
    "\n",
    "> : Making normalization a part of the model architecture and performing the normalization for each training mini-batch. Batch Normalization allows us to use much higher learning rates and be less careful about initialization.\n",
    "\n",
    "The paper caused great excitement as soon as it was released, because it included the chart in <<batchnorm>>, which clearly demonstrated that batch normalization could train a model that was even more accurate than the current state of the art (the *Inception* architecture) and around 5x faster."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img alt=\"Impact of batch normalization\" width=\"553\" caption=\"Impact of batch normalization (courtesy of Sergey Ioffe and Christian Szegedy)\" id=\"batchnorm\" src=\"images/att_00046.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Batch normalization (often just called *batchnorm*) works by taking an average of the mean and standard deviations of the activations of a layer and using those to normalize the activations. However, this can cause problems because the network might want some activations to be really high in order to make accurate predictions. So they also added two learnable parameters (meaning they will be updated in the SGD step), usually called `gamma` and `beta`. After normalizing the activations to get some new activation vector `y`, a batchnorm layer returns `gamma*y + beta`.\n",
    "\n",
    "That's why our activations can have any mean or variance, independent from the mean and standard deviation of the results of the previous layer. Those statistics are learned separately, making training easier on our model. The behavior is different during training and validation: during training, we use the mean and standard deviation of the batch to normalize the data, while during validation we instead use a running mean of the statistics calculated during training.\n",
    "\n",
    "Let's add a batchnorm layer to `conv`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def conv(ni, nf, ks=3, act=True):\n",
    "    layers = [nn.Conv2d(ni, nf, stride=2, kernel_size=ks, padding=ks//2)]\n",
    "    layers.append(nn.BatchNorm2d(nf))\n",
    "    if act: layers.append(nn.ReLU())\n",
    "    return nn.Sequential(*layers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and fit our model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.130036</td>\n",
       "      <td>0.055021</td>\n",
       "      <td>0.986400</td>\n",
       "      <td>00:10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's a great result! Let's take a look at `color_dim`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.activation_stats.color_dim(-4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is just what we hope to see: a smooth development of activations, with no \"crashes.\" Batchnorm has really delivered on its promise here! In fact, batchnorm has been so successful that we see it (or something very similar) in nearly all modern neural networks.\n",
    "\n",
    "An interesting observation about models containing batch normalization layers is that they tend to generalize better than models that don't contain them. Although we haven't as yet seen a rigorous analysis of what's going on here, most researchers believe that the reason for this is that batch normalization adds some extra randomness to the training process. Each mini-batch will have a somewhat different mean and standard deviation than other mini-batches. Therefore, the activations will be normalized by different values each time. In order for the model to make accurate predictions, it will have to learn to become robust to these variations. In general, adding additional randomization to the training process often helps.\n",
    "\n",
    "Since things are going so well, let's train for a few more epochs and see how it goes. In fact, let's *increase* the learning rate, since the abstract of the batchnorm paper claimed we should be able to \"train at much higher learning rates\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.191731</td>\n",
       "      <td>0.121738</td>\n",
       "      <td>0.960900</td>\n",
       "      <td>00:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.083739</td>\n",
       "      <td>0.055808</td>\n",
       "      <td>0.981800</td>\n",
       "      <td>00:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.053161</td>\n",
       "      <td>0.044485</td>\n",
       "      <td>0.987100</td>\n",
       "      <td>00:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.034433</td>\n",
       "      <td>0.030233</td>\n",
       "      <td>0.990200</td>\n",
       "      <td>00:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.017646</td>\n",
       "      <td>0.025407</td>\n",
       "      <td>0.991200</td>\n",
       "      <td>00:10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = fit(5, lr=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.183244</td>\n",
       "      <td>0.084025</td>\n",
       "      <td>0.975800</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.080774</td>\n",
       "      <td>0.067060</td>\n",
       "      <td>0.978800</td>\n",
       "      <td>00:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.050215</td>\n",
       "      <td>0.062595</td>\n",
       "      <td>0.981300</td>\n",
       "      <td>00:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.030020</td>\n",
       "      <td>0.030315</td>\n",
       "      <td>0.990700</td>\n",
       "      <td>00:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.015131</td>\n",
       "      <td>0.025148</td>\n",
       "      <td>0.992100</td>\n",
       "      <td>00:12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = fit(5, lr=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this point, I think it's fair to say we know how to recognize digits! It's time to move on to something harder..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We've seen that convolutions are just a type of matrix multiplication, with two constraints on the weight matrix: some elements are always zero, and some elements are tied (forced to always have the same value). In <<chapter_intro>> we saw the eight requirements from the 1986 book *Parallel Distributed Processing*; one of them was \"A pattern of connectivity among units.\" That's exactly what these constraints do: they enforce a certain pattern of connectivity.\n",
    "\n",
    "These constraints allow us to use far fewer parameters in our model, without sacrificing the ability to represent complex visual features. That means we can train deeper models faster, with less overfitting. Although the universal approximation theorem shows that it should be *possible* to represent anything in a fully connected network in one hidden layer, we've seen now that in *practice* we can train much better models by being thoughtful about network architecture.\n",
    "\n",
    "Convolutions are by far the most common pattern of connectivity we see in neural nets (along with regular linear layers, which we refer to as *fully connected*), but it's likely that many more will be discovered.\n",
    "\n",
    "We've also seen how to interpret the activations of layers in the network to see whether training is going well or not, and how batchnorm helps regularize the training and makes it smoother. In the next chapter, we will use both of those layers to build the most popular architecture in computer vision: a residual network."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Questionnaire"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. What is a \"feature\"?\n",
    "1. Write out the convolutional kernel matrix for a top edge detector.\n",
    "1. Write out the mathematical operation applied by a 3×3 kernel to a single pixel in an image.\n",
    "1. What is the value of a convolutional kernel apply to a 3×3 matrix of zeros?\n",
    "1. What is \"padding\"?\n",
    "1. What is \"stride\"?\n",
    "1. Create a nested list comprehension to complete any task that you choose.\n",
    "1. What are the shapes of the `input` and `weight` parameters to PyTorch's 2D convolution?\n",
    "1. What is a \"channel\"?\n",
    "1. What is the relationship between a convolution and a matrix multiplication?\n",
    "1. What is a \"convolutional neural network\"?\n",
    "1. What is the benefit of refactoring parts of your neural network definition?\n",
    "1. What is `Flatten`? Where does it need to be included in the MNIST CNN? Why?\n",
    "1. What does \"NCHW\" mean?\n",
    "1. Why does the third layer of the MNIST CNN have `7*7*(1168-16)` multiplications?\n",
    "1. What is a \"receptive field\"?\n",
    "1. What is the size of the receptive field of an activation after two stride 2 convolutions? Why?\n",
    "1. Run *conv-example.xlsx* yourself and experiment with *trace precedents*.\n",
    "1. Have a look at Jeremy or Sylvain's list of recent Twitter \"like\"s, and see if you find any interesting resources or ideas there.\n",
    "1. How is a color image represented as a tensor?\n",
    "1. How does a convolution work with a color input?\n",
    "1. What method can we use to see that data in `DataLoaders`?\n",
    "1. Why do we double the number of filters after each stride-2 conv?\n",
    "1. Why do we use a larger kernel in the first conv with MNIST (with `simple_cnn`)?\n",
    "1. What information does `ActivationStats` save for each layer?\n",
    "1. How can we access a learner's callback after training?\n",
    "1. What are the three statistics plotted by `plot_layer_stats`? What does the x-axis represent?\n",
    "1. Why are activations near zero problematic?\n",
    "1. What are the upsides and downsides of training with a larger batch size?\n",
    "1. Why should we avoid using a high learning rate at the start of training?\n",
    "1. What is 1cycle training?\n",
    "1. What are the benefits of training with a high learning rate?\n",
    "1. Why do we want to use a low learning rate at the end of training?\n",
    "1. What is \"cyclical momentum\"?\n",
    "1. What callback tracks hyperparameter values during training (along with other information)?\n",
    "1. What does one column of pixels in the `color_dim` plot represent?\n",
    "1. What does \"bad training\" look like in `color_dim`? Why?\n",
    "1. What trainable parameters does a batch normalization layer contain?\n",
    "1. What statistics are used to normalize in batch normalization during training? How about during validation?\n",
    "1. Why do models with batch normalization layers generalize better?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Further Research"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. What features other than edge detectors have been used in computer vision (especially before deep learning became popular)?\n",
    "1. There are other normalization layers available in PyTorch. Try them out and see what works best. Learn about why other normalization layers have been developed, and how they differ from batch normalization.\n",
    "1. Try moving the activation function after the batch normalization layer in `conv`. Does it make a difference? See what you can find out about what order is recommended, and why."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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