Cifar-10.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np # imports a fast numerical programming library\n",
    "import scipy as sp # imports a statistical programming library\n",
    "import matplotlib as mpl # imports the standard plotting library\n",
    "import matplotlib.cm as cm # imports plotting colormaps\n",
    "import matplotlib.pyplot as plt # imports the MATLAB compatible plotting API"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lists\n",
    "\n",
    "Python indexes start at 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3.0, 5.0, 4.0, 2.0]\n",
      "[1.0, 3.0]\n"
     ]
    }
   ],
   "source": [
    "float_list = [1., 3., 5., 4., 2.]\n",
    "print(float_list[1:5])\n",
    "print(float_list[0:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "float_list[-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is a key for some of the indexing operations:\n",
    "\n",
    "![](images/ops3_v2.png)\n",
    "\n",
    "We can iterate through a list using a loop. Here's a for loop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "3.0\n",
      "5.0\n",
      "4.0\n",
      "2.0\n"
     ]
    }
   ],
   "source": [
    "for ele in float_list:\n",
    "    print(ele)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 1.0\n",
      "1 3.0\n",
      "2 5.0\n",
      "3 4.0\n",
      "4 2.0\n"
     ]
    }
   ],
   "source": [
    "for i, ele in enumerate(float_list):\n",
    "    print(i,ele)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lists can be constructed in a compact way using a list comprehension. Here's a simple example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.0, 9.0, 25.0, 16.0, 4.0]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "squaredlist = [i*i for i in float_list]\n",
    "squaredlist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[32.0, 8.0]\n"
     ]
    }
   ],
   "source": [
    "comp_list1 = [2*i for i in squaredlist if i % 2 == 0]\n",
    "print(comp_list1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions\n",
    "\n",
    "A *function* object is a reusable block of code that does a specific task.  Functions are all over Python, either on their own or on other objects.  To invoke a function `func`, you call it as `func(arguments)`.\n",
    "\n",
    "We've seen built-in Python functions and methods.  For example, `len` and `print` are built-in Python functions.  Last time we called `np.mean` to calculate the mean of three numbers, where `mean` is a function in the numpy module and numpy was abbreviated as `np`. This syntax allow us to have multiple \"mean\" functions in different modules; calling this one as `np.mean` guarantees that we will pick up numpy's mean function, as opposed to a mean function from a different module.\n",
    "\n",
    "### Methods\n",
    "A function that belongs to an object is called a *method*. An example of this is `append` on an existing list. In other words, a *method* is a function on an *instance* of a type of object (also called *class*, in this case, list type)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np # imports a fast numerical programming library\n",
    "np.mean([1, 2, 3, 4, 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.0, 2.09, 4.0, 2.0, 0.444]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1.0, 2.09, 4.0, 2.0, 0.444, 56.7]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "float_list = [1.0, 2.09, 4.0, 2.0, 0.444]\n",
    "print(float_list)\n",
    "float_list.append(56.7) \n",
    "float_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### User-defined functions\n",
    "\n",
    "We'll now learn to write our own user-defined functions.  Below is the syntax for defining a basic function with one input argument and one output. You can also define functions with no input or output arguments, or multiple input or output arguments.\n",
    "\n",
    "```\n",
    "def name_of_function(arg):\n",
    "    ...\n",
    "    return(output)\n",
    "```\n",
    "\n",
    "We can write functions with one input and one output argument.  Here are two such functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 125)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def square(x):\n",
    "    x_sqr = x*x\n",
    "    return(x_sqr)\n",
    "\n",
    "def cube(x):\n",
    "    x_cub = x*x*x\n",
    "    return(x_cub)\n",
    "\n",
    "square(5),cube(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lambda functions\n",
    "\n",
    "Often we quickly define mathematical functions with a one-line function called a *lambda* function.  Lambda functions are great because they enable us to write functions without having to name them, ie, they're *anonymous*.  \n",
    "No return statement is needed. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "square = lambda x: x*x\n",
    "print(square(3))\n",
    "\n",
    "\n",
    "hypotenuse = lambda x, y: x*x + y*y\n",
    "\n",
    "## Same as\n",
    "\n",
    "# def hypotenuse(x, y):\n",
    "#     return(x*x + y*y)\n",
    "\n",
    "hypotenuse(3,4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Default Arguments\n",
    "\n",
    "Functions may also have *default* argument values.  Functions with default values are used extensively in many libraries.  The default values are assigned when the function is defined."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "With x and y: (10, 2, 20)\n",
      "With x only: (10, 1, 10)\n"
     ]
    }
   ],
   "source": [
    "# This function can be called with x and y, in which case it will return x*y;\n",
    "# or it can be called with x only, in which case it will return x*1.\n",
    "def get_multiple(x, y=1):\n",
    "    return x, y, x*y\n",
    "\n",
    "print(\"With x and y:\", get_multiple(10, 2))\n",
    "print(\"With x only:\", get_multiple(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3, 1, 3), (3, 4, 12))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_multiple(x=3), get_multiple(x=3, y=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3, 4, 12), (3, 4, 12))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_multiple(y=4, x=3), get_multiple(3, y=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "positional argument follows keyword argument (<ipython-input-11-90f2b29dc57a>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-11-90f2b29dc57a>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    get_multiple(y=4, 3)\u001b[0m\n\u001b[0m                     ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m positional argument follows keyword argument\n"
     ]
    }
   ],
   "source": [
    "get_multiple(y=4, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scientific Python code uses a fast array structure, called the numpy array. Those who have worked in Matlab will find this very natural.   For reference, the numpy documention can be found [here](https://docs.scipy.org/doc/numpy/reference/).  \n",
    "\n",
    "Let's make a numpy array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_array = np.array([1, 2, 3, 4])\n",
    "my_array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**In general you should manipulate numpy arrays by using numpy module functions** (`np.mean`, for example). This is for efficiency purposes, and a discussion follows below this section.\n",
    "\n",
    "You can calculate the mean of the array elements either by calling the method `.mean` on a numpy array or by applying the function np.mean with the numpy array as an argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.5\n",
      "2.5\n"
     ]
    }
   ],
   "source": [
    "print(my_array.mean())\n",
    "print(np.mean(my_array))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The way we constructed the numpy array above seems redundant..after all we already had a regular python list. Indeed, it is the other ways we have to construct numpy arrays that make them super useful. \n",
    "\n",
    "There are many such numpy array *constructors*. Here are some commonly used constructors. Look them up in the documentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones(10) # generates 10 floating point ones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dtype(float).itemsize # in bytes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Numpy supports vector operations\n",
    "\n",
    "What does this mean? It means that instead of adding two arrays, element by element, you can just say: add the two arrays. Note that this behavior is very different from python lists."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2., 2., 2., 2., 2.])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first = np.ones(5)\n",
    "second = np.ones(5)\n",
    "first + second"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_list = [1., 1., 1., 1., 1.]\n",
    "second_list = [1., 1., 1., 1., 1.]\n",
    "first_list + second_list #not what u want"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On some computer chips this addition actually happens in parallel, so speedups can be high. But even on regular chips, the advantage of greater readability is important."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numpy supports a concept known as *broadcasting*, which dictates how arrays of different sizes are combined together. There are too many rules to list here, but importantly, multiplying an array by a number multiplies each element by the number. Adding a number adds the number to each element."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2., 2., 2., 2., 2.])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Broadcasting and vectorized operations are used throughout numpy. Suppose you wanted points on the line $$y = 4x + 3$$ between 0 and 1. Here's how:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = lambda x: 4*x + 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.01010101, 0.02020202, 0.03030303, 0.04040404,\n",
       "       0.05050505, 0.06060606, 0.07070707, 0.08080808, 0.09090909,\n",
       "       0.1010101 , 0.11111111, 0.12121212, 0.13131313, 0.14141414,\n",
       "       0.15151515, 0.16161616, 0.17171717, 0.18181818, 0.19191919,\n",
       "       0.2020202 , 0.21212121, 0.22222222, 0.23232323, 0.24242424,\n",
       "       0.25252525, 0.26262626, 0.27272727, 0.28282828, 0.29292929,\n",
       "       0.3030303 , 0.31313131, 0.32323232, 0.33333333, 0.34343434,\n",
       "       0.35353535, 0.36363636, 0.37373737, 0.38383838, 0.39393939,\n",
       "       0.4040404 , 0.41414141, 0.42424242, 0.43434343, 0.44444444,\n",
       "       0.45454545, 0.46464646, 0.47474747, 0.48484848, 0.49494949,\n",
       "       0.50505051, 0.51515152, 0.52525253, 0.53535354, 0.54545455,\n",
       "       0.55555556, 0.56565657, 0.57575758, 0.58585859, 0.5959596 ,\n",
       "       0.60606061, 0.61616162, 0.62626263, 0.63636364, 0.64646465,\n",
       "       0.65656566, 0.66666667, 0.67676768, 0.68686869, 0.6969697 ,\n",
       "       0.70707071, 0.71717172, 0.72727273, 0.73737374, 0.74747475,\n",
       "       0.75757576, 0.76767677, 0.77777778, 0.78787879, 0.7979798 ,\n",
       "       0.80808081, 0.81818182, 0.82828283, 0.83838384, 0.84848485,\n",
       "       0.85858586, 0.86868687, 0.87878788, 0.88888889, 0.8989899 ,\n",
       "       0.90909091, 0.91919192, 0.92929293, 0.93939394, 0.94949495,\n",
       "       0.95959596, 0.96969697, 0.97979798, 0.98989899, 1.        ])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.linspace(0,1, 100)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.arange(0,1,0.1)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3. , 3.4, 3.8, 4.2, 4.6, 5. , 5.4, 5.8, 6.2, 6.6])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f(x) # operates vectorially"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot these against each other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'line')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, f(x), '.-', alpha=0.3)\n",
    "plt.xlim([0.1,0.9])\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\")\n",
    "plt.title(\"line\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2D arrays\n",
    "Similarly, we can create two-dimensional arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1  2  3  4]\n",
      " [ 5  6  7  8]\n",
      " [ 9 10 11 12]]\n"
     ]
    }
   ],
   "source": [
    "my_array2d = np.array([ [1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12] ])\n",
    "print(my_array2d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 4)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_array2d.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_array2d[2, 3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](images/numpyarrayindexing.png)\n",
    "\n",
    ">Our convention for identifying cells in a 3D block will be to start in the near, upper-left corner. We name cells in the order (away, down, right). (a) The three directions in sequence. (b) Finding a cell in a 3D volume. The first index tells us how far to move away, the second index how far to move down, and the third index how far to move right.\n",
    "\n",
    "From Glassner, Andrew. Deep Learning, Vol. 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#downward\n",
    "my_array2d[0], my_array2d[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([15, 18, 21, 24])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(my_array2d, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10, 26, 42])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(my_array2d, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 1. 1. 1.]\n",
      " [1. 1. 1. 1.]\n",
      " [1. 1. 1. 1.]]\n"
     ]
    }
   ],
   "source": [
    "# 3 x 4 array of ones\n",
    "ones_2d = np.ones([3, 4])\n",
    "print(ones_2d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 0. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 0. 1.]]\n"
     ]
    }
   ],
   "source": [
    "# 3 x 3 identity matrix\n",
    "my_identity = np.eye(3)\n",
    "print(my_identity)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An often seen idiom allocates a two-dimensional array, and then fills in one-dimensional arrays from some function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.],\n",
       "       [ 0.,  0.],\n",
       "       [ 0.,  0.],\n",
       "       [ 0.,  0.],\n",
       "       [ 0.,  0.]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twod = np.zeros((5, 2))\n",
    "twod"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.62349039,  0.71825025],\n",
       "       [ 0.17007564,  0.66104173],\n",
       "       [ 0.15565617,  0.86803246],\n",
       "       [ 0.7522172 ,  0.49560275],\n",
       "       [ 0.65087571,  0.31589517]])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(twod.shape[0]):\n",
    "    twod[i, :] = np.random.random(2)\n",
    "twod"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Images\n",
    "\n",
    "Lets generalize from 2D to 3D, to cover an image with channels, and then a dataset of many images. (cifar-10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.datasets import cifar10\n",
    "(x_train, y_train), (x_test, y_test) = cifar10.load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](images/cifar-snapshot.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',\n",
    "               'dog', 'frog', 'horse', 'ship', 'truck']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(x_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As seen above, we go depth first, so the images are stacked deep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 32, 32, 3)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](images/imagestacking.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "in cifar-10 each image actually has 3 channels. So its not images we are stacking, but image tensors. The channels can be arranged in 2 ways.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32, 32, 3)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train[0].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](images/giraffechannels.png)\n",
    "\n",
    ">Two ways to stack images of size 100 wide by 200 high. We read the sizes of the blocks by the number of layers going away, then down, then right. (a) Stacking images from front to back. This block has dimensions 3 by 200 by 100. This is the channels_first organization. (b) Stacking images from left to right. This block has dimensions 200 by 100 by 3. This is the channels_last organization.\n",
    "\n",
    "From Glassner, Andrew. Deep Learning, Vol. 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32, 32)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train[0][:,:,0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 1)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1,)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6], dtype=uint8)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from fast.ai\n",
    "from matplotlib import patches, patheffects\n",
    "def draw_outline(o, lw):\n",
    "    o.set_path_effects([patheffects.Stroke(\n",
    "        linewidth=lw, foreground='black'), patheffects.Normal()])\n",
    "def show_img(im, figsize=None, ax=None):\n",
    "    if not ax: fig,ax = plt.subplots(figsize=figsize)\n",
    "    ax.imshow(im)\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "    return ax\n",
    "def draw_text(ax, xy, txt, sz=14):\n",
    "    text = ax.text(*xy, txt,\n",
    "        verticalalignment='top', color='white', fontsize=sz, weight='bold')\n",
    "    draw_outline(text, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(5, 5, figsize=(8, 8))\n",
    "for i, ax in enumerate(axes.flat):\n",
    "    img = x_train[i]\n",
    "    label = class_names[y_train[i][0]]\n",
    "    ax = show_img(img, ax=ax)\n",
    "    draw_text(ax, (0,0), label)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}