From 266342ed97a7a3aca6035b5b196c6191df573374 Mon Sep 17 00:00:00 2001
From: Simon van Hemert <simon.vanhemert@hslu.ch>
Date: Fri, 13 Nov 2020 13:51:58 +0000
Subject: [PATCH] Block0
---
data/Block 0/Daten_Serie_1.zip | Bin 6591 -> 0 bytes
.../Checking_Correct_Installation.ipynb | 8 +-
.../Preliminaries_3_Numpy.ipynb | 506 ++++
.../Block 0/Solution - Basics Numpy.ipynb | 46 +-
notebooks/Block 0/data/child.csv | 31 +
notebooks/Block 0/data/d.fuel.dat | 61 +
notebooks/Block 0/data/geysir.dat | Bin 0 -> 1507 bytes
notebooks/Block 0/data/weather.csv | 7 +
notebooks/Block 0/data/zuendschluessel.dat | 20 +
...Introduction to Image Classification.ipynb | 807 ++++++
...Introduction to Image Classification.ipynb | 2206 +++++++++++++++++
...Introduction to Image Classification.ipynb | 1451 +++++++++++
12 files changed, 5116 insertions(+), 27 deletions(-)
delete mode 100644 data/Block 0/Daten_Serie_1.zip
create mode 100644 notebooks/Block 0/Examples script/Preliminaries_3_Numpy.ipynb
create mode 100644 notebooks/Block 0/data/child.csv
create mode 100644 notebooks/Block 0/data/d.fuel.dat
create mode 100644 notebooks/Block 0/data/geysir.dat
create mode 100644 notebooks/Block 0/data/weather.csv
create mode 100644 notebooks/Block 0/data/zuendschluessel.dat
create mode 100644 notebooks/Block 1/Exercises Block 1 - Introduction to Image Classification.ipynb
create mode 100644 notebooks/Block 1/Jupyter Notebook Block 1 - Introduction to Image Classification.ipynb
create mode 100644 notebooks/Block 1/Solutions to Exercises Block 1 - Introduction to Image Classification.ipynb
diff --git a/data/Block 0/Daten_Serie_1.zip b/data/Block 0/Daten_Serie_1.zip
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diff --git a/notebooks/Block 0/Checking_Correct_Installation.ipynb b/notebooks/Block 0/Checking_Correct_Installation.ipynb
index 67bad2a..da8803f 100644
--- a/notebooks/Block 0/Checking_Correct_Installation.ipynb
+++ b/notebooks/Block 0/Checking_Correct_Installation.ipynb
@@ -17,7 +17,7 @@
{
"data": {
"text/plain": [
- "'3.7.7 (default, May 6 2020, 11:45:54) [MSC v.1916 64 bit (AMD64)]'"
+ "'3.7.6 | packaged by conda-forge | (default, Mar 23 2020, 23:03:20) \\n[GCC 7.3.0]'"
]
},
"execution_count": 1,
@@ -80,7 +80,7 @@
{
"data": {
"text/plain": [
- "<matplotlib.collections.PathCollection at 0x17a7a929508>"
+ "<matplotlib.collections.PathCollection at 0x7fb7d492ebd0>"
]
},
"execution_count": 4,
@@ -89,7 +89,7 @@
},
{
"data": {
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+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -123,7 +123,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.7"
+ "version": "3.7.6"
}
},
"nbformat": 4,
diff --git a/notebooks/Block 0/Examples script/Preliminaries_3_Numpy.ipynb b/notebooks/Block 0/Examples script/Preliminaries_3_Numpy.ipynb
new file mode 100644
index 0000000..b07c25c
--- /dev/null
+++ b/notebooks/Block 0/Examples script/Preliminaries_3_Numpy.ipynb
@@ -0,0 +1,506 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 3.2 Funktions, Conditionals, and Iteration in Python\n",
+ "Let us create a Python function, and call it from a loop."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "--- Now running with i: 8\n",
+ "Hello World, x was < 10\n",
+ "Result from HelloWorld: 16\n",
+ "\n",
+ "--- Now running with i: 13\n",
+ "Hello World, x was >= 10 but < 20\n",
+ "Result from HelloWorld: 26\n",
+ "\n",
+ "--- Now running with i: 18\n",
+ "Hello World, x was >= 10 but < 20\n",
+ "Result from HelloWorld: 36\n",
+ "\n",
+ "--- Now running with i: 23\n",
+ "Hello World, x was >= 20\n",
+ "Result from HelloWorld: 46\n"
+ ]
+ }
+ ],
+ "source": [
+ "def HelloWorldXY(x, y):\n",
+ " if (x < 10):\n",
+ " print(\"Hello World, x was < 10\")\n",
+ " elif (x < 20):\n",
+ " print(\"Hello World, x was >= 10 but < 20\")\n",
+ " else:\n",
+ " print(\"Hello World, x was >= 20\")\n",
+ " return x + y\n",
+ "\n",
+ "for i in range(8, 25, 5): # i=8, 13, 18, 23 (start, stop, step)\n",
+ " print(\"\\n--- Now running with i: {}\".format(i))\n",
+ " r = HelloWorldXY(i,i)\n",
+ " print(\"Result from HelloWorld: {}\".format(r))\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If you want a loop starting at 0 to 2 (exclusive) you could do any of the following:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Iterate over the items. `range(2)` is like a list [0,1].\n",
+ "0\n",
+ "1\n",
+ "Iterate over an actual list.\n",
+ "0\n",
+ "1\n",
+ "While works\n",
+ "0\n",
+ "1\n",
+ "Python supports standard key words like continue and break\n",
+ "Entered while\n",
+ "while broken\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(\"Iterate over the items. `range(2)` is like a list [0,1].\")\n",
+ "for i in range(2):\n",
+ " print(i)\n",
+ "\n",
+ "print(\"Iterate over an actual list.\")\n",
+ "for i in [0,1]:\n",
+ " print(i)\n",
+ "\n",
+ "print(\"While works\")\n",
+ "i = 0\n",
+ "while i < 2:\n",
+ " print(i)\n",
+ " i += 1\n",
+ " \n",
+ "print(\"Python supports standard key words like continue and break\")\n",
+ "while True:\n",
+ " print(\"Entered while\")\n",
+ " break\n",
+ "print(\"while broken\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 3.3 Data in Numpy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Shape scaler () \n",
+ "Shape vector (3,) \n",
+ "Shape matrix (3, 3) \n",
+ "Shape tensor (3, 3, 2, 1)\n",
+ "Type scalar or array <class 'numpy.ndarray'> \n",
+ "Type after addition with integer <class 'numpy.int64'>\n",
+ "v[1:] = [ 2 10] \n",
+ "m[1:][2:] = \n",
+ " [[5 6]\n",
+ " [8 9]]\n",
+ "[ 1 2 10] [[ 1 2 10]] [[ 1 2 10]]\n",
+ "(3,) (1, 3) (1, 3)\n"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "\n",
+ "# Scalar\n",
+ "s = np.array(5)\n",
+ "# Vector\n",
+ "v = np.array([1, 2, 10])\n",
+ "# Matrix\n",
+ "m = np.array([[1,2,3], \n",
+ " [4,5,6], \n",
+ " [7,8,9]])\n",
+ "# Tensor:\n",
+ "t = np.array([[[[1],[2]], [[3],[4]], [[5],[6]]],\n",
+ " [[[7],[8]], [[9],[10]], [[11],[12]]],\n",
+ " [[[13],[14]], [[15],[16]], [[17],[17]]]])\n",
+ "\n",
+ "# Shape\n",
+ "print(\"Shape scaler\", s.shape, \"\\nShape vector\", v.shape, \"\\nShape matrix\", m.shape, \"\\nShape tensor\", t.shape)\n",
+ "\n",
+ "# Type\n",
+ "print(\"Type scalar or array\", type(s), \"\\nType after addition with integer\", type(s + 3))\n",
+ "\n",
+ "# Slicing\n",
+ "print(\"v[1:] = \", v[1:], \"\\nm[1:][2:] = \\n\", m[1:,1:])\n",
+ "\n",
+ "# Reshape arrays\n",
+ "x = v.reshape(1, 3)\n",
+ "y = v[None, :]\n",
+ "print(v, x, y)\n",
+ "print(v.shape, x.shape, y.shape)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 3.4 Element-wise Operations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[6, 7, 8, 9, 10]\n",
+ "[ 6 7 8 9 10]\n",
+ "[30 35 40 45 50] \n",
+ " [30 35 40 45 50] \n",
+ "\n",
+ "a =\n",
+ " [[1 3]\n",
+ " [5 7]] \n",
+ "b =\n",
+ " [[2 4]\n",
+ " [6 8]]\n",
+ "a + b =\n",
+ " [[ 3 7]\n",
+ " [11 15]]\n",
+ "a * b =\n",
+ " [[ 2 12]\n",
+ " [30 56]]\n"
+ ]
+ },
+ {
+ "ename": "ValueError",
+ "evalue": "operands could not be broadcast together with shapes (2,2) (5,) ",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-4-7ddd6b5f4e75>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"a * b =\\n\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[0;31m# Shape mismatch:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"a * values =\\n\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (2,2) (5,) "
+ ]
+ }
+ ],
+ "source": [
+ "# The Python way:\n",
+ "values = [1, 2, 3, 4, 5]\n",
+ "for i in range(len(values)):\n",
+ " values[i] += 5\n",
+ " \n",
+ "print(values)\n",
+ "\n",
+ "# The Numpy way:\n",
+ "values = np.array([1, 2, 3, 4, 5])\n",
+ "values += 5\n",
+ "\n",
+ "print(values)\n",
+ "\n",
+ "# Multiplication\n",
+ "x = np.multiply(values, 5)\n",
+ "y = values * 5\n",
+ "print(x, \"\\n\", y, \"\\n\")\n",
+ "\n",
+ "# Element wise matrix operations\n",
+ "a = np.array([[1,3],[5,7]])\n",
+ "b = np.array([[2,4],[6,8]])\n",
+ "print(\"a =\\n\", a, \"\\nb =\\n\", b)\n",
+ "print(\"a + b =\\n\", a + b)\n",
+ "print(\"a * b =\\n\", a * b)\n",
+ "# Shape mismatch:\n",
+ "print(\"a * values =\\n\", a * values)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Numpy Matrix Multiplication\n",
+ "Recap element-wise multiplication:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "m =\n",
+ " [[1 2 3]\n",
+ " [4 5 6]] \n",
+ "n =\n",
+ " [[0.25 0.5 0.75]\n",
+ " [1. 1.25 1.5 ]]\n",
+ "x =\n",
+ " [[0.25 1. 2.25]\n",
+ " [4. 6.25 9. ]] \n",
+ "y =\n",
+ " [[0.25 1. 2.25]\n",
+ " [4. 6.25 9. ]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Elementwise recap:\n",
+ "m = np.array([[1,2,3],[4,5,6]])\n",
+ "# Scalar multiplication\n",
+ "n = m * 0.25\n",
+ "# Python Elementwise matrix multiplication\n",
+ "x = m * n\n",
+ "# Numpy Elementwise matrix multiplication\n",
+ "y = np.multiply(m, n)\n",
+ "\n",
+ "print(\"m =\\n\", m, \"\\nn =\\n\", n)\n",
+ "print(\"x =\\n\", x, \"\\ny =\\n\", y)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Matrix Product:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "a =\n",
+ " [[1 2 3 4]\n",
+ " [5 6 7 8]] \n",
+ "a.shape =\n",
+ " (2, 4) \n",
+ "b =\n",
+ " [[ 1 2 3]\n",
+ " [ 4 5 6]\n",
+ " [ 7 8 9]\n",
+ " [10 11 12]] \n",
+ "b.shape =\n",
+ " (4, 3)\n",
+ "c = \n",
+ " [[ 70 80 90]\n",
+ " [158 184 210]] \n",
+ "c.shape =\n",
+ " (2, 3)\n",
+ "d = \n",
+ " [[ 70 80 90]\n",
+ " [158 184 210]] \n",
+ "d.shape =\n",
+ " (2, 3)\n"
+ ]
+ }
+ ],
+ "source": [
+ "\"\"\" Using np.matmul \"\"\"\n",
+ "a = np.array([[1,2,3,4],[5,6,7,8]])\n",
+ "b = np.array([[1,2,3],[4,5,6],[7,8,9],[10,11,12]])\n",
+ "\n",
+ "print(\"a =\\n\", a, \"\\na.shape =\\n\", a.shape, \"\\nb =\\n\", b, \"\\nb.shape =\\n\", b.shape)\n",
+ "\n",
+ "# Matrix product\n",
+ "c = np.matmul(a, b)\n",
+ "print(\"c = \\n\", c, \"\\nc.shape =\\n\", c.shape)\n",
+ "\n",
+ "# Dimension mismatch:\n",
+ "# print(np.matmul(b, a))\n",
+ "\"\"\" Using np.dot \"\"\"\n",
+ "d = np.dot(a, b)\n",
+ "print(\"d = \\n\", d, \"\\nd.shape =\\n\", d.shape)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Transpose"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "m = \n",
+ " [[ 1 2 3 4]\n",
+ " [ 5 6 7 8]\n",
+ " [ 9 10 11 12]] \n",
+ "m.T = \n",
+ " [[ 1 5 9]\n",
+ " [ 2 6 10]\n",
+ " [ 3 7 11]\n",
+ " [ 4 8 12]]\n",
+ "m = \n",
+ " [[ 1 2 3 4]\n",
+ " [ 5 6 7 200]\n",
+ " [ 9 10 11 12]] \n",
+ "m_t = \n",
+ " [[ 1 5 9]\n",
+ " [ 2 6 10]\n",
+ " [ 3 7 11]\n",
+ " [ 4 200 12]]\n",
+ "entries [3][1], [1][3], respectively are edited in both matrices\n"
+ ]
+ }
+ ],
+ "source": [
+ "m = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])\n",
+ "print(\"m = \\n\", m,\"\\nm.T = \\n\", m.T)\n",
+ "\n",
+ "# note how the transposed matrix is not a copy of the original:\n",
+ "m_t = m.T\n",
+ "m_t[3][1] = 200\n",
+ "print(\"m = \\n\", m, \"\\nm_t = \\n\", m_t)\n",
+ "print(\"entries [3][1], [1][3], respectively are edited in both matrices\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## A real use case"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[-0.27 0.45 0.64 0.31]] (1, 4)\n",
+ "[[ 0.02 0.001 -0.03 0.036]\n",
+ " [ 0.04 -0.003 0.025 0.009]\n",
+ " [ 0.012 -0.045 0.28 -0.067]] (3, 4)\n",
+ "Matrix multiplication gives:\n",
+ " [[-0.01299 0.00664 0.13494]] \n",
+ "or, equivalently:\n",
+ " [[-0.01299]\n",
+ " [ 0.00664]\n",
+ " [ 0.13494]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "inputs = np.array([[-0.27, 0.45, 0.64, 0.31]])\n",
+ "print(inputs, inputs.shape)\n",
+ "\n",
+ "weights = np.array([[0.02, 0.001, -0.03, 0.036], \n",
+ " [0.04, -0.003, 0.025, 0.009], \n",
+ " [0.012, -0.045, 0.28, -0.067]])\n",
+ "print(weights, weights.shape)\n",
+ "\n",
+ "print(\"Matrix multiplication gives:\\n\", np.matmul(inputs, weights.T), \"\\nor, equivalently:\\n\", np.matmul(weights, inputs.T))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Some more useful Numpy methods"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Showing some basic math on arrays\n",
+ "Max: 4\n",
+ "Average: 2.0\n",
+ "Max index: 2\n",
+ "\n",
+ "Use numpy to create a [3,3] dimension array with random number\n",
+ "[[0.92371879 0.58999086 0.76979433]\n",
+ " [0.48733651 0.44698554 0.91494542]\n",
+ " [0.59130531 0.69632003 0.32785335]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(\"\\nShowing some basic math on arrays\")\n",
+ "\n",
+ "b = np.array([0,1,4,3,2])\n",
+ "print(\"Max: {}\".format(np.max(b)))\n",
+ "print(\"Average: {}\".format(np.average(b)))\n",
+ "print(\"Max index: {}\".format(np.argmax(b)))\n",
+ "\n",
+ "print(\"\\nUse numpy to create a [3,3] dimension array with random number\")\n",
+ "c = np.random.rand(3, 3)\n",
+ "print(c)"
+ ]
+ }
+ ],
+ "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": "3.7.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/Block 0/Solution - Basics Numpy.ipynb b/notebooks/Block 0/Solution - Basics Numpy.ipynb
index fd04a3a..1aa12bc 100644
--- a/notebooks/Block 0/Solution - Basics Numpy.ipynb
+++ b/notebooks/Block 0/Solution - Basics Numpy.ipynb
@@ -162,7 +162,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 11,
"metadata": {},
"outputs": [
{
@@ -249,7 +249,7 @@
"Jun 25 21 23 27"
]
},
- "execution_count": 4,
+ "execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
@@ -259,7 +259,7 @@
"import pandas as pd\n",
"\n",
"# load the database using pandas.read_csv \n",
- "data = pd.read_csv(\"./Daten_Serie_1/weather.csv\")\n",
+ "data = pd.read_csv(\"./data/weather.csv\")\n",
"data"
]
},
@@ -272,7 +272,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 12,
"metadata": {},
"outputs": [
{
@@ -310,7 +310,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 13,
"metadata": {},
"outputs": [
{
@@ -404,7 +404,7 @@
"Jun 25 21 23 27"
]
},
- "execution_count": 6,
+ "execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
@@ -434,7 +434,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
@@ -468,7 +468,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 15,
"metadata": {},
"outputs": [
{
@@ -555,7 +555,7 @@
"Jun 25 21 23 27"
]
},
- "execution_count": 8,
+ "execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
@@ -566,7 +566,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 16,
"metadata": {},
"outputs": [
{
@@ -653,7 +653,7 @@
"Jan 2 5 -3 4"
]
},
- "execution_count": 9,
+ "execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
@@ -671,7 +671,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 18,
"metadata": {},
"outputs": [
{
@@ -1190,7 +1190,7 @@
"59 60 3690 19 Van"
]
},
- "execution_count": 10,
+ "execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
@@ -1201,7 +1201,7 @@
"\n",
"# load the database using pandas.read_csv with options: sep=\",\" and index_col=0\n",
"# data = None\n",
- "data = pd.read_csv(\"./Daten_Serie_1/d.fuel.dat\")\n",
+ "data = pd.read_csv(\"./data/d.fuel.dat\")\n",
"\n",
"data"
]
@@ -1217,7 +1217,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 19,
"metadata": {},
"outputs": [
{
@@ -1296,7 +1296,7 @@
"5 6 2285 26 Small"
]
},
- "execution_count": 11,
+ "execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
@@ -1308,7 +1308,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 20,
"metadata": {},
"outputs": [
{
@@ -1387,7 +1387,7 @@
"4 5 2440 32 Small"
]
},
- "execution_count": 12,
+ "execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
@@ -1409,7 +1409,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 21,
"metadata": {},
"outputs": [
{
@@ -1443,7 +1443,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 22,
"metadata": {},
"outputs": [
{
@@ -1485,9 +1485,9 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python [conda env:root]",
+ "display_name": "Python 3",
"language": "python",
- "name": "conda-root-py"
+ "name": "python3"
},
"language_info": {
"codemirror_mode": {
@@ -1499,7 +1499,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.3"
+ "version": "3.7.6"
}
},
"nbformat": 4,
diff --git a/notebooks/Block 0/data/child.csv b/notebooks/Block 0/data/child.csv
new file mode 100644
index 0000000..952aaec
--- /dev/null
+++ b/notebooks/Block 0/data/child.csv
@@ -0,0 +1,31 @@
+"","Average.disposable.income","Children.in.poor.homes","Educational.Deprivation","Overcrowding","Poor.environmental.conditions","Average.mean.literacy.score","Literacy.inequality","Youth.NEET.rate","Low.birth.weight","Infant.mortality","Breastfeeding.rates","Vaccination.rates..pertussis.","Vaccination.rates.measles.","Physical.activity","Mortality.rates","Suicide.rates","Smoking","Drunkenness","Teenage.births","Bullying","Liking.school"
+"Australia",20.81322093,11.79135221,2.2,19.7,10.5,520,1.610349213,7.4,6.4,5,92,92.2,94,NA,23.66,8.512646079,NA,NA,14.3,NA,NA
+"Austria",22.16244638,6.166094377,0.6,34.01460053,20.15418609,502,1.716941852,6.9,6.8,4.2,96,83,74,19.6,24.57,9.482761048,27.1,18.6,12.3,15.6,38.1
+"Belgium",21.40115256,9.97472,1,12.61450478,29.75452638,510.33,1.740257743,6.2,7.8,3.7,71.5,97,88,19.1,28.97,8.977713182,16.7,13.9,7.8,12.2,21.6
+"Canada",25.60624512,15.05758531,2.1,NA,NA,529.33,1.573911631,6.1,5.9,5.3,84.5,78,94,23.6,23.43,10.04753517,8.6,18.8,13.2,14,29.5
+"Czech Republic",10.84927021,10.27,1.2,58.89934714,29.74859392,502,1.743012402,5.3,6.7,3.4,95.6,98.7,96.6,22,24.92,6.212491765,21.5,16.8,11.4,5.5,11.7
+"Denmark",23.17589375,2.74,0.7,17.55296515,20.15146723,501,1.594974222,4.3,4.9,4.4,98,95,96,22.7,22.93,5.942230735,15,24.8,6.6,8,25.6
+"Finland",22.02765061,4.17,1,15.22887563,22.8023618,552.67,1.475431888,5.2,4.1,3,93,97,97,24.8,23.54,12.15868685,22,22.4,9.7,8,16.1
+"France",18.96038214,7.64,1.2,20.28461913,25.83029828,493,1.728003801,6.2,6.8,3.6,63,98,87.1,13.5,21.04,4.906704257,19,11.2,6.7,13.6,21.4
+"Germany",19.89406707,16.28927,0.5,19.9677627,37.37470837,505,1.719702291,4.4,6.8,3.9,96,97.8,94,17,21.23,5.507378351,19.5,15.1,9.8,13.9,34.9
+"Greece",17.18364717,13.230296,6.1,54.92205586,25.070654,464,1.722499339,9.8,8.8,3.8,79,88,88,15.5,23.15,1.263026085,16.5,10.3,8.7,22,25.6
+"Hungary",9.463130275,8.724202627,2.1,73.30602107,22.21378548,492.33,1.620709864,6.4,8.2,6.2,95.8,99.8,99.8,19.5,25.75,6.806579778,21.5,17.7,20.7,6.6,27.6
+"Iceland",22.28685182,8.25,0.4,21.65252618,15.53468756,493.67,1.654064252,NA,3.9,2.3,98,97,94,20.6,16.95,7.841311724,13.5,10.5,16.9,5.4,36.6
+"Ireland",22.3646892,16.2994156,2.9,16.42902829,19.30009971,508.67,1.590217406,4.4,4.9,4,41,90,84,31.1,21.97,9.517062801,19.5,16.3,13.5,8.6,24
+"Italy",17.18076094,15.5,1.2,47.85160178,32.57736342,468.67,1.752955059,11.2,6.7,4.7,81.1,94.7,87.3,15.1,21.76,2.437998092,20,10,6.8,7.9,12.8
+"Japan",22.47970482,13.68803298,5.6,22.54,32.38,517.33,1.652193881,8.4,9.5,2.8,96.6,93,94,NA,18.23,7.72849265,NA,NA,3.7,NA,NA
+"Korea",21.65191791,10.74683449,1.8,NA,NA,541.67,1.545776704,NA,4.3,5.3,81.3,97,90.2,NA,22.36,6.784772397,NA,NA,3.7,NA,NA
+"Luxembourg",34.24182159,12.39,1.1,16.89318543,25.57423111,485,1.70136067,2.2,4.9,2.6,88,NA,NA,15.2,14.84,6.357193604,19,11.7,8.6,13.8,20.7
+"Mexico",5.3350735,22.16451356,13.7,69.6,NA,408.67,1.755798339,NA,8.8,18.8,92,98.1,96.4,18.1,50.23,5.128753653,NA,NA,65.8,NA,NA
+"Netherlands",25.04101231,11.52675463,0.6,10.32650318,38.71287446,521,1.614444199,3.9,6.2,4.9,79,95.8,96.3,21.2,20.49,3.496134498,18.5,12.1,4.7,8.5,39.7
+"New Zealand",17.19710764,15,2.2,31,NA,524.33,1.676001258,8.5,6.1,5.1,87.8,88.6,82,NA,32.05,15.94956674,NA,NA,23.4,NA,NA
+"Norway",28.57437061,4.6,1.3,15.1136708,11.99278944,487,1.692809117,2.5,4.8,3.1,99,91,88,15.6,21.49,10.09402951,10.4,14,9.4,8.3,41.7
+"Poland",7.939398926,21.5,2.1,73.95766735,22.8419035,500.33,1.632216885,1.7,6.1,6.4,NA,99,97,17.3,28.13,8.863713432,16.4,19.9,14.5,9.6,21.1
+"Portugal",3.83946222,16.55039805,1.4,31.95230279,33.45789776,470.67,1.68947711,8.4,7.5,3.5,91,93.3,92.7,14.6,34.6,2.430484109,10.6,10.9,18.1,14.1,22.8
+"Slovak Republic",7.797595504,10.93,3.8,68.35628386,27.34869142,482,1.712179223,6.3,7.2,7.2,87,99.2,99.5,42.1,30.15,5.036519508,16.4,16.8,20,NA,13
+"Spain",16.430249,17.3,0.9,10.83183066,31.68334715,476.33,1.636258647,8.5,7.1,4.1,71,96.2,96.8,20.3,23.49,2.599717056,17.1,16.1,9.1,4.7,23.9
+"Sweden",19.91699786,3.969106338,1.6,20.01146001,15.74642214,504,1.63006314,4.7,4.2,2.4,97.6,99,95,16.4,19.27,6.783234397,8.5,13.8,6.8,4.2,24.1
+"Switzerland",24.65181522,9.43329321,0.7,NA,NA,513.67,1.650384595,7.2,7,4.2,92,96,86,13.1,20.33,7.280508221,15,11.4,4.5,12.1,27.3
+"Turkey",5.071859655,24.59,13.6,NA,NA,431.67,1.704249349,37.7,11.3,23.6,96.8,90,91,20,NA,NA,NA,NA,39.7,25.3,57.4
+"United Kingdom",22.69706157,10.08,1.8,21.49742729,29.06905181,501.67,1.690350618,9.3,7.5,5.1,77,91.4,83.8,18.7,21.17,2.987868617,15.9,22.1,24.8,9.7,35.7
+"United States",29.196531,20.59393236,4.8,26.2,25.4,481.5,1.725965403,6.1,8.1,6.8,74.2,85.7,91.5,26.8,32.67,7.696772862,8.1,10.2,49.8,11.9,26.4
diff --git a/notebooks/Block 0/data/d.fuel.dat b/notebooks/Block 0/data/d.fuel.dat
new file mode 100644
index 0000000..370e739
--- /dev/null
+++ b/notebooks/Block 0/data/d.fuel.dat
@@ -0,0 +1,61 @@
+"X","weight","mpg","type"
+1,2560,33,"Small"
+2,2345,33,"Small"
+3,1845,37,"Small"
+4,2260,32,"Small"
+5,2440,32,"Small"
+6,2285,26,"Small"
+7,2275,33,"Small"
+8,2350,28,"Small"
+9,2295,25,"Small"
+10,1900,34,"Small"
+11,2390,29,"Small"
+12,2075,35,"Small"
+13,2330,26,"Small"
+14,3320,20,"Sporty"
+15,2885,27,"Sporty"
+16,3310,19,"Sporty"
+17,2695,30,"Sporty"
+18,2170,33,"Sporty"
+19,2710,27,"Sporty"
+20,2775,24,"Sporty"
+21,2840,26,"Sporty"
+22,2485,28,"Sporty"
+23,2670,27,"Compact"
+24,2640,23,"Compact"
+25,2655,26,"Compact"
+26,3065,25,"Compact"
+27,2750,24,"Compact"
+28,2920,26,"Compact"
+29,2780,24,"Compact"
+30,2745,25,"Compact"
+31,3110,21,"Compact"
+32,2920,21,"Compact"
+33,2645,23,"Compact"
+34,2575,24,"Compact"
+35,2935,23,"Compact"
+36,2920,27,"Compact"
+37,2985,23,"Compact"
+38,3265,20,"Medium"
+39,2880,21,"Medium"
+40,2975,22,"Medium"
+41,3450,22,"Medium"
+42,3145,22,"Medium"
+43,3190,22,"Medium"
+44,3610,23,"Medium"
+45,2885,23,"Medium"
+46,3480,21,"Medium"
+47,3200,22,"Medium"
+48,2765,21,"Medium"
+49,3220,21,"Medium"
+50,3480,23,"Medium"
+51,3325,23,"Large"
+52,3855,18,"Large"
+53,3850,20,"Large"
+54,3195,18,"Van"
+55,3735,18,"Van"
+56,3665,18,"Van"
+57,3735,19,"Van"
+58,3415,20,"Van"
+59,3185,20,"Van"
+60,3690,19,"Van"
diff --git a/notebooks/Block 0/data/geysir.dat b/notebooks/Block 0/data/geysir.dat
new file mode 100644
index 0000000000000000000000000000000000000000..31afce4b2560b99614afc186853ba90709246511
GIT binary patch
literal 1507
XcmZQz7zLvtFd71*Aut*O!!!f{1>*n#
literal 0
HcmV?d00001
diff --git a/notebooks/Block 0/data/weather.csv b/notebooks/Block 0/data/weather.csv
new file mode 100644
index 0000000..8b56301
--- /dev/null
+++ b/notebooks/Block 0/data/weather.csv
@@ -0,0 +1,7 @@
+"Luzern","Basel","Chur","Zurich"
+"Jan",2,5,-3,4
+"Feb",5,6,1,0
+"Mar",10,11,13,8
+"Apr",16,12,14,17
+"May",21,23,21,20
+"Jun",25,21,23,27
diff --git a/notebooks/Block 0/data/zuendschluessel.dat b/notebooks/Block 0/data/zuendschluessel.dat
new file mode 100644
index 0000000..42bdbc4
--- /dev/null
+++ b/notebooks/Block 0/data/zuendschluessel.dat
@@ -0,0 +1,20 @@
+0.196 0.206 0.199 0.215
+0.201 0.205 0.198 0.200
+0.195 0.200 0.204 0.204
+0.210 0.188 0.203 0.200
+0.202 0.204 0.196 0.195
+0.195 0.200 0.196 0.201
+0.203 0.199 0.202 0.189
+0.205 0.191 0.205 0.209
+0.204 0.197 0.199 0.201
+0.198 0.203 0.205 0.213
+0.209 0.208 0.202 0.203
+0.202 0.199 0.196 0.196
+0.196 0.202 0.202 0.204
+0.186 0.200 0.193 0.194
+0.207 0.192 0.209 0.192
+0.200 0.197 0.212 0.202
+0.200 0.198 0.198 0.197
+0.206 0.200 0.194 0.200
+0.205 0.207 0.203 0.200
+0.204 0.205 0.199 0.196
diff --git a/notebooks/Block 1/Exercises Block 1 - Introduction to Image Classification.ipynb b/notebooks/Block 1/Exercises Block 1 - Introduction to Image Classification.ipynb
new file mode 100644
index 0000000..bd21818
--- /dev/null
+++ b/notebooks/Block 1/Exercises Block 1 - Introduction to Image Classification.ipynb
@@ -0,0 +1,807 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "jYysdyb-CaWM"
+ },
+ "source": [
+ "# Exercise 1 - K-Nearest Neighbor Classifier for MNIST"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "FbVhjPpzn6BM"
+ },
+ "source": [
+ "In this exercise, we'll apply KNN Classifiers to the MNIST dataset. The aim of the exercise is to get acquainted with the MNIST dataset. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "H0tMfX2vR0uD"
+ },
+ "source": [
+ "## Install and import dependencies\n",
+ "\n",
+ "We'll need [TensorFlow Datasets](https://www.tensorflow.org/datasets/), an API that simplifies downloading and accessing datasets, and provides several sample datasets to work with. We're also using a few helper libraries."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "P7mUJVqcINSM"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Collecting tensorflow_datasets\n",
+ " Downloading tensorflow_datasets-4.1.0-py3-none-any.whl (3.6 MB)\n",
+ "\u001b[K |████████████████████████████████| 3.6 MB 7.1 MB/s eta 0:00:01\n",
+ "\u001b[?25hCollecting future\n",
+ " Downloading future-0.18.2.tar.gz (829 kB)\n",
+ "\u001b[K |████████████████████████████████| 829 kB 43.0 MB/s eta 0:00:01\n",
+ "\u001b[?25hRequirement already satisfied, skipping upgrade: numpy in /opt/conda/lib/python3.7/site-packages (from tensorflow_datasets) (1.19.1)\n",
+ "Requirement already satisfied, skipping upgrade: requests>=2.19.0 in /opt/conda/lib/python3.7/site-packages (from tensorflow_datasets) (2.23.0)\n",
+ "Collecting importlib-resources; python_version < \"3.9\"\n",
+ " Downloading importlib_resources-3.3.0-py2.py3-none-any.whl (26 kB)\n",
+ "Collecting promise\n",
+ " Downloading promise-2.3.tar.gz (19 kB)\n",
+ "Requirement already satisfied, skipping upgrade: typing-extensions; python_version < \"3.8\" in /opt/conda/lib/python3.7/site-packages (from tensorflow_datasets) (3.7.4.3)\n",
+ "Collecting dill\n",
+ " Downloading dill-0.3.3-py2.py3-none-any.whl (81 kB)\n",
+ "\u001b[K |████████████████████████████████| 81 kB 3.2 MB/s eta 0:00:01\n",
+ "\u001b[?25hCollecting tensorflow-metadata\n",
+ " Downloading tensorflow_metadata-0.25.0-py3-none-any.whl (44 kB)\n",
+ "\u001b[K |████████████████████████████████| 44 kB 452 kB/s eta 0:00:01\n",
+ "\u001b[?25hRequirement already satisfied, skipping upgrade: six in /opt/conda/lib/python3.7/site-packages (from tensorflow_datasets) (1.14.0)\n",
+ "Requirement already satisfied, skipping upgrade: tqdm in /opt/conda/lib/python3.7/site-packages (from tensorflow_datasets) (4.45.0)\n",
+ "Requirement already satisfied, skipping upgrade: termcolor in /opt/conda/lib/python3.7/site-packages (from tensorflow_datasets) (1.1.0)\n",
+ "Requirement already satisfied, skipping upgrade: attrs>=18.1.0 in /opt/conda/lib/python3.7/site-packages (from tensorflow_datasets) (19.3.0)\n",
+ "Requirement already satisfied, skipping upgrade: protobuf>=3.6.1 in /opt/conda/lib/python3.7/site-packages (from tensorflow_datasets) (3.13.0)\n",
+ "Requirement already satisfied, skipping upgrade: absl-py in /opt/conda/lib/python3.7/site-packages (from tensorflow_datasets) (0.11.0)\n",
+ "Requirement already satisfied, skipping upgrade: idna<3,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests>=2.19.0->tensorflow_datasets) (2.9)\n",
+ "Requirement already satisfied, skipping upgrade: chardet<4,>=3.0.2 in /opt/conda/lib/python3.7/site-packages (from requests>=2.19.0->tensorflow_datasets) (3.0.4)\n",
+ "Requirement already satisfied, skipping upgrade: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests>=2.19.0->tensorflow_datasets) (2020.6.20)\n",
+ "Requirement already satisfied, skipping upgrade: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests>=2.19.0->tensorflow_datasets) (1.25.9)\n",
+ "Requirement already satisfied, skipping upgrade: zipp>=0.4; python_version < \"3.8\" in /opt/conda/lib/python3.7/site-packages (from importlib-resources; python_version < \"3.9\"->tensorflow_datasets) (3.1.0)\n",
+ "Collecting googleapis-common-protos<2,>=1.52.0\n",
+ " Downloading googleapis_common_protos-1.52.0-py2.py3-none-any.whl (100 kB)\n",
+ "\u001b[K |████████████████████████████████| 100 kB 2.8 MB/s eta 0:00:01\n",
+ "\u001b[?25hRequirement already satisfied, skipping upgrade: setuptools in /opt/conda/lib/python3.7/site-packages (from protobuf>=3.6.1->tensorflow_datasets) (46.1.3.post20200325)\n",
+ "Building wheels for collected packages: future, promise\n",
+ " Building wheel for future (setup.py) ... \u001b[?25ldone\n",
+ "\u001b[?25h Created wheel for future: filename=future-0.18.2-py3-none-any.whl size=491058 sha256=9a3231d2200886eb11d4d6723c8b24559a8d03b36ed0756946cff540d3e4e86a\n",
+ " Stored in directory: /home/jovyan/.cache/pip/wheels/56/b0/fe/4410d17b32f1f0c3cf54cdfb2bc04d7b4b8f4ae377e2229ba0\n",
+ " Building wheel for promise (setup.py) ... \u001b[?25ldone\n",
+ "\u001b[?25h Created wheel for promise: filename=promise-2.3-py3-none-any.whl size=21495 sha256=837ca263691051097e35d24a408853930691e949f9249c9ae00ecd6a856dcadb\n",
+ " Stored in directory: /home/jovyan/.cache/pip/wheels/29/93/c6/762e359f8cb6a5b69c72235d798804cae523bbe41c2aa8333d\n",
+ "Successfully built future promise\n",
+ "Installing collected packages: future, importlib-resources, promise, dill, googleapis-common-protos, tensorflow-metadata, tensorflow-datasets\n",
+ "\u001b[31mERROR: After October 2020 you may experience errors when installing or updating packages. This is because pip will change the way that it resolves dependency conflicts.\n",
+ "\n",
+ "We recommend you use --use-feature=2020-resolver to test your packages with the new resolver before it becomes the default.\n",
+ "\n",
+ "tensorflow-metadata 0.25.0 requires absl-py<0.11,>=0.9, but you'll have absl-py 0.11.0 which is incompatible.\u001b[0m\n",
+ "Successfully installed dill-0.3.3 future-0.18.2 googleapis-common-protos-1.52.0 importlib-resources-3.3.0 promise-2.3 tensorflow-datasets-4.1.0 tensorflow-metadata-0.25.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "!python3 -m pip install -U tensorflow_datasets"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2.1.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Import TensorFlow\n",
+ "\n",
+ "# FOR COLAB USERS:\n",
+ "# If you run this notebook in Colab, then execute the following line (uncomment it)\n",
+ "# %tensorflow_version 2.x\n",
+ "\n",
+ "\n",
+ "# If you run this noteook in your tensorflow 2.x environment, then \n",
+ "# verify you have version > 2.0\n",
+ "\n",
+ "import tensorflow as tf\n",
+ "print(tf.__version__)\n",
+ "\n",
+ "# Now you should get version 2.x\n",
+ "# If you still get version 1.x, then execute (uncomment) the following lines and run the cell\n",
+ "\n",
+ "#!pip uninstall tensorflow\n",
+ "#!pip install --upgrade pip\n",
+ "#!pip install --upgrade tensorflow\n",
+ "#!python3 -c \"import tensorflow as tf;print(tf.reduce_sum(tf.random.normal([1000, 1000])))\"\n",
+ "#try:\n",
+ "# import tensorflow as tf\n",
+ "#except Exception:\n",
+ "# pass\n",
+ "#print(tf.__version__)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "dzLKpmZICaWN"
+ },
+ "outputs": [],
+ "source": [
+ "from __future__ import absolute_import, division, print_function, unicode_literals\n",
+ "\n",
+ "\n",
+ "# Import TensorFlow Datasets\n",
+ "import tensorflow as tf\n",
+ "import tensorflow_datasets as tfds\n",
+ "tfds.disable_progress_bar()\n",
+ "\n",
+ "# Helper libraries\n",
+ "import math\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "590z76KRGtKk"
+ },
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "logger = tf.get_logger()\n",
+ "logger.setLevel(logging.ERROR)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "ktyvYGhTDDDY"
+ },
+ "source": [
+ "## Import the MNIST dataset"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "DdJ28n_1DDDa"
+ },
+ "source": [
+ "This guide uses the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset—often used as the \"Hello, World\" of machine learning programs for computer vision. The MNIST dataset contains images of handwritten digits (0, 1, 2, etc)\n",
+ "\n",
+ "\n",
+ "We will use 60,000 images to train the network and 10,000 images to evaluate how accurately the network learned to classify images. You can access the MNIST directly from TensorFlow, using the [Datasets](https://www.tensorflow.org/datasets) API:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "8BFIbPwFDDDc"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[1mDownloading and preparing dataset mnist/3.0.1 (download: 11.06 MiB, generated: 21.00 MiB, total: 32.06 MiB) to /home/jovyan/tensorflow_datasets/mnist/3.0.1...\u001b[0m\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "WARNING:absl:Dataset mnist is hosted on GCS. It will automatically be downloaded to your\n",
+ "local data directory. If you'd instead prefer to read directly from our public\n",
+ "GCS bucket (recommended if you're running on GCP), you can instead pass\n",
+ "`try_gcs=True` to `tfds.load` or set `data_dir=gs://tfds-data/datasets`.\n",
+ "\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[1mDataset mnist downloaded and prepared to /home/jovyan/tensorflow_datasets/mnist/3.0.1. Subsequent calls will reuse this data.\u001b[0m\n"
+ ]
+ }
+ ],
+ "source": [
+ "dataset, metadata = tfds.load('mnist', as_supervised=True, with_info=True)\n",
+ "train_dataset, test_dataset = dataset['train'], dataset['test']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "jkxXuk1mDDDj"
+ },
+ "source": [
+ "Loading the dataset returns metadata as well as a *training dataset* and *test dataset*.\n",
+ "\n",
+ "* The model is trained using `train_dataset`.\n",
+ "* The model is tested against `test_dataset`.\n",
+ "\n",
+ "The images are 28 $\\times$ 28 arrays, with pixel values in the range `[0, 255]`. The *labels* are an array of integers, in the range `[0, 9]`. These correspond to the handwritten numbers. \n",
+ "\n",
+ "\n",
+ "Each image is mapped to a single label. Since the *class names* are not included with the dataset, store them here to use later when plotting the images:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "XkVkGFjaDDDl"
+ },
+ "outputs": [],
+ "source": [
+ "class_names = ['Zero', 'One', 'Two', 'Three', 'Four', 'Five',\n",
+ " 'Six', 'Seven', 'Eight', 'Nine']\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "fi4_Zd7lDDDr"
+ },
+ "source": [
+ "### Explore the data\n",
+ "\n",
+ "Let's explore the format of the dataset before training the model. The following shows there are 60,000 images in the training set, and 10000 images in the test set:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "7qFC11SPDDDu"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Number of training examples: 60000\n",
+ "Number of test examples: 10000\n"
+ ]
+ }
+ ],
+ "source": [
+ "num_train_examples = metadata.splits['train'].num_examples\n",
+ "num_test_examples = metadata.splits['test'].num_examples\n",
+ "print(\"Number of training examples: {}\".format(num_train_examples))\n",
+ "print(\"Number of test examples: {}\".format(num_test_examples))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "wO9b_JabDDD1"
+ },
+ "source": [
+ "Let's plot an image to see what it looks like."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "YghUhL-FDDD2"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Take a single image, and remove the color dimension by reshaping\n",
+ "for image, label in test_dataset.take(1):\n",
+ " break\n",
+ "image = image.numpy().reshape((28,28))\n",
+ "\n",
+ "# Plot the image - voila an example of a handwritten digit\n",
+ "plt.figure()\n",
+ "plt.imshow(image, cmap=plt.cm.binary)\n",
+ "plt.colorbar()\n",
+ "plt.grid(False)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "iL7MpNrWDDD8"
+ },
+ "source": [
+ "Display the first 25 images from the *test set* and display the class name below each image. Verify that the data is in the correct format and we're ready to build and train the KNN classifier."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "X9_2qg5QDDD9"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x720 with 25 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(10,10))\n",
+ "i = 0\n",
+ "for (image, label) in test_dataset.take(25):\n",
+ " image = image.numpy().reshape((28,28))\n",
+ " plt.subplot(5,5,i+1)\n",
+ " plt.xticks([])\n",
+ " plt.yticks([])\n",
+ " plt.grid(False)\n",
+ " plt.imshow(image, cmap=plt.cm.binary)\n",
+ " plt.xlabel(class_names[label])\n",
+ " i += 1\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "yR0EdgrLCaWR"
+ },
+ "source": [
+ "## Import the Fashion MNIST dataset"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "DLdCchMdCaWQ"
+ },
+ "source": [
+ "If numbers are not your thing then use the [Fashion MNIST](https://github.com/zalandoresearch/fashion-mnist) dataset, which contains 70,000 grayscale images in 10 categories. The images show individual articles of clothing at low resolution (28 $\\times$ 28 pixels), as seen here:\n",
+ "\n",
+ "<table>\n",
+ " <tr><td>\n",
+ " <img src=\"https://tensorflow.org/images/fashion-mnist-sprite.png\"\n",
+ " alt=\"Fashion MNIST sprite\" width=\"600\">\n",
+ " </td></tr>\n",
+ " <tr><td align=\"center\">\n",
+ " <b>Figure 1.</b> <a href=\"https://github.com/zalandoresearch/fashion-mnist\">Fashion-MNIST samples</a> (by Zalando, MIT License).<br/> \n",
+ " </td></tr>\n",
+ "</table>\n",
+ "\n",
+ "You may use Fashion MNIST for variety, and because it's a slightly more challenging problem than regular MNIST. Both datasets are relatively small and are used to verify that an algorithm works as expected. They're good starting points to test and debug code.\n",
+ "\n",
+ "We will use 60,000 images to train the network and 10,000 images to evaluate how accurately the network learned to classify images. You can access the Fashion MNIST directly from TensorFlow, using the [Datasets](https://www.tensorflow.org/datasets) API:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "7MqDQO0KCaWS"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[1mDownloading and preparing dataset fashion_mnist/3.0.1 (download: 29.45 MiB, generated: 36.42 MiB, total: 65.87 MiB) to /home/jovyan/tensorflow_datasets/fashion_mnist/3.0.1...\u001b[0m\n",
+ "Shuffling and writing examples to /home/jovyan/tensorflow_datasets/fashion_mnist/3.0.1.incomplete9MU5LV/fashion_mnist-train.tfrecord\n",
+ "Shuffling and writing examples to /home/jovyan/tensorflow_datasets/fashion_mnist/3.0.1.incomplete9MU5LV/fashion_mnist-test.tfrecord\n",
+ "\u001b[1mDataset fashion_mnist downloaded and prepared to /home/jovyan/tensorflow_datasets/fashion_mnist/3.0.1. Subsequent calls will reuse this data.\u001b[0m\n"
+ ]
+ }
+ ],
+ "source": [
+ "dataset, metadata = tfds.load('fashion_mnist', as_supervised=True, with_info=True)\n",
+ "train_dataset, test_dataset = dataset['train'], dataset['test']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "t9FDsUlxCaWW"
+ },
+ "source": [
+ "Loading the dataset returns metadata as well as a *training dataset* and *test dataset*.\n",
+ "\n",
+ "* The model is trained using `train_dataset`.\n",
+ "* The model is tested against `test_dataset`.\n",
+ "\n",
+ "The images are 28 $\\times$ 28 arrays, with pixel values in the range `[0, 255]`. The *labels* are an array of integers, in the range `[0, 9]`. These correspond to the *class* of clothing the image represents:\n",
+ "\n",
+ "<table>\n",
+ " <tr>\n",
+ " <th>Label</th>\n",
+ " <th>Class</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>0</td>\n",
+ " <td>T-shirt/top</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>Trouser</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>Pullover</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>Dress</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>4</td>\n",
+ " <td>Coat</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>5</td>\n",
+ " <td>Sandal</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>6</td>\n",
+ " <td>Shirt</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>7</td>\n",
+ " <td>Sneaker</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>8</td>\n",
+ " <td>Bag</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>9</td>\n",
+ " <td>Ankle boot</td>\n",
+ " </tr>\n",
+ "</table>\n",
+ "\n",
+ "Each image is mapped to a single label. Since the *class names* are not included with the dataset, store them here to use later when plotting the images:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "IjnLH5S2CaWx"
+ },
+ "outputs": [],
+ "source": [
+ "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
+ " 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "Brm0b_KACaWX"
+ },
+ "source": [
+ "### Explore the data\n",
+ "\n",
+ "Let's explore the format of the dataset before training the model. The following shows there are 60,000 images in the training set, and 10000 images in the test set:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "MaOTZxFzi48X"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Number of training examples: 60000\n",
+ "Number of test examples: 10000\n"
+ ]
+ }
+ ],
+ "source": [
+ "num_train_examples = metadata.splits['train'].num_examples\n",
+ "num_test_examples = metadata.splits['test'].num_examples\n",
+ "print(\"Number of training examples: {}\".format(num_train_examples))\n",
+ "print(\"Number of test examples: {}\".format(num_test_examples))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "lIQbEiJGXM-q"
+ },
+ "source": [
+ "Let's plot an image to see what it looks like."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "oSzE9l7PjHx0"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Take a single image, and remove the color dimension by reshaping\n",
+ "for image, label in test_dataset.take(1):\n",
+ " break\n",
+ "image = image.numpy().reshape((28,28))\n",
+ "\n",
+ "# Plot the image - voila a piece of fashion clothing\n",
+ "plt.figure()\n",
+ "plt.imshow(image, cmap=plt.cm.binary)\n",
+ "plt.colorbar()\n",
+ "plt.grid(False)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "Ee638AlnCaWz"
+ },
+ "source": [
+ "Display the first 25 images from the *training set* and display the class name below each image. Verify that the data is in the correct format and we're ready to build and train the network."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "oZTImqg_CaW1"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x720 with 25 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(10,10))\n",
+ "i = 0\n",
+ "for (image, label) in test_dataset.take(25):\n",
+ " image = image.numpy().reshape((28,28))\n",
+ " plt.subplot(5,5,i+1)\n",
+ " plt.xticks([])\n",
+ " plt.yticks([])\n",
+ " plt.grid(False)\n",
+ " plt.imshow(image, cmap=plt.cm.binary)\n",
+ " plt.xlabel(class_names[label])\n",
+ " i += 1\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "KTWFLcVQDDEr"
+ },
+ "source": [
+ "Decide whether you want to work with the traditional or Fashion MNIST dataset, then extract 5000 training examples and \n",
+ "500 test examples."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "tZKtLTf_DDEu"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Shape of image training data : (5000, 784)\n",
+ "Shape of training data labels : (5000,)\n"
+ ]
+ }
+ ],
+ "source": [
+ "i=0\n",
+ "for (image, label) in train_dataset.take(5000):\n",
+ " if i==0:\n",
+ " X_train = image.numpy().reshape((1,28*28))\n",
+ " y_train = np.array([label])\n",
+ " else:\n",
+ " X_train = np.concatenate([X_train, image.numpy().reshape((1,28*28))], axis=0)\n",
+ " y_train = np.concatenate([y_train, np.array([label])], axis=0)\n",
+ " i+=1\n",
+ "print(\"Shape of image training data : \", X_train.shape)\n",
+ "print(\"Shape of training data labels : \", y_train.shape)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "Rwb7aQgqDDEy"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Shape of image test data : (500, 784)\n",
+ "Shape of test data labels : (500,)\n"
+ ]
+ }
+ ],
+ "source": [
+ "j=0\n",
+ "for (image, label) in test_dataset.take(500):\n",
+ " if j==0:\n",
+ " X_test = image.numpy().reshape((1,28*28))\n",
+ " y_test = np.array([label])\n",
+ " else:\n",
+ " X_test = np.concatenate([X_test, image.numpy().reshape((1,28*28))], axis=0)\n",
+ " y_test = np.concatenate([y_test, np.array([label])], axis=0)\n",
+ " j+=1\n",
+ "print(\"Shape of image test data : \", X_test.shape)\n",
+ "print(\"Shape of test data labels : \", y_test.shape)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "-KtnHECKZni_"
+ },
+ "source": [
+ "# Exercises\n",
+ "\n",
+ "1. Apply Nearest Neighbour with L1 distance to this subset of the dataset and determine the accuracy on the \n",
+ "test dataset and plot the confusion matrix.\n",
+ "\n",
+ "2. Apply K-Nearest Neighbour with $k=5$ and L2 distance to this subset of the dataset and determine the accuracy on the test dataset and plot the confusion matrix.\n",
+ "\n",
+ "3. Determine by means of 5-fold cross-validation the best value of $k$ in the set $\\{1,4,5,10,12,18,20\\}$.\n",
+ "\n",
+ "4. Scale the pixel values to the interval $[0, 1]$ and compute the test accuracy for the best value of k determined in exercise 3.\n",
+ "\n",
+ "5. Implement the cosine distance measure in the k-nearest neighbour classifier. The cosine distance between two vectors $a$ and $b$ can be computed by\n",
+ "\n",
+ "```python\n",
+ "from numpy.linalg import norm\n",
+ "from numpy import dot\n",
+ "\n",
+ "dists[a,b] = 1 - dot(a, b)/(norm(a)*norm(b))\n",
+ "```\n",
+ "\n",
+ "\n",
+ "`\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "collapsed_sections": [],
+ "name": "Exercises_Block_1.ipynb",
+ "private_outputs": true,
+ "provenance": []
+ },
+ "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": "3.7.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/Block 1/Jupyter Notebook Block 1 - Introduction to Image Classification.ipynb b/notebooks/Block 1/Jupyter Notebook Block 1 - Introduction to Image Classification.ipynb
new file mode 100644
index 0000000..be71545
--- /dev/null
+++ b/notebooks/Block 1/Jupyter Notebook Block 1 - Introduction to Image Classification.ipynb
@@ -0,0 +1,2206 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "df630e55-57dd-470a-849b-f1bc90ac719e"
+ }
+ },
+ "source": [
+ "# 1. Importing and Visualization of CIFAR-10 Dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "nbpresent": {
+ "id": "409a1ab7-fe1d-4430-b904-7694020a6223"
+ }
+ },
+ "outputs": [
+ {
+ "ename": "FileNotFoundError",
+ "evalue": "[Errno 2] No such file or directory: './Daten/data_batch_1'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-1-d20dcc958bb0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mdict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'bytes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mdata_batch_1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0munpickle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"./Daten/data_batch_1\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 10\u001b[0m \u001b[0mdata_batch_2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0munpickle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"./Daten/data_batch_2\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0mdata_batch_3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0munpickle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"./Daten/data_batch_3\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m<ipython-input-1-d20dcc958bb0>\u001b[0m in \u001b[0;36munpickle\u001b[0;34m(file)\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0munpickle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfo\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0mdict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'bytes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: './Daten/data_batch_1'"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "\n",
+ "# function to import CIFAR-10 data set\n",
+ "def unpickle(file):\n",
+ " import pickle\n",
+ " with open(file, 'rb') as fo:\n",
+ " dict = pickle.load(fo, encoding='bytes')\n",
+ " return dict\n",
+ "data_batch_1 = unpickle(\"./Daten/data_batch_1\")\n",
+ "data_batch_2 = unpickle(\"./Daten/data_batch_2\")\n",
+ "data_batch_3 = unpickle(\"./Daten/data_batch_3\")\n",
+ "data_batch_4 = unpickle(\"./Daten/data_batch_4\")\n",
+ "data_batch_5 = unpickle(\"./Daten/data_batch_5\")\n",
+ "test_batch = unpickle(\"./Daten/test_batch\")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "ce2be501-0be3-4750-8207-dfc00d7db01a"
+ }
+ },
+ "source": [
+ "What is the data structure of e.g. data_batch_1 ?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 66,
+ "metadata": {
+ "nbpresent": {
+ "id": "f77bd9ec-de3b-4c56-b08d-4a65f0780408"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "dict"
+ ]
+ },
+ "execution_count": 66,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "type(data_batch_1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "09a5b60b-dcbb-4f97-ab57-e4611c253e2e"
+ }
+ },
+ "source": [
+ "What are the keys of e.g. data_batch_1 ?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 67,
+ "metadata": {
+ "nbpresent": {
+ "id": "c874a7c9-de0c-4ccd-a0f1-8f8a3265a0b6"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "dict_keys([b'batch_label', b'labels', b'data', b'filenames'])"
+ ]
+ },
+ "execution_count": 67,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data_batch_1.keys()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "a7f910e7-0b11-453b-84d5-df6ac88ac6dd"
+ }
+ },
+ "source": [
+ "What is the data structure of data_batch_1[b'data'] ?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 68,
+ "metadata": {
+ "nbpresent": {
+ "id": "fe299a35-c930-4078-97b7-c9b67f42ec42"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "numpy.ndarray"
+ ]
+ },
+ "execution_count": 68,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "type(data_batch_1[b'data'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "3f978c4f-50d0-4f00-9f19-bf8744e505a3"
+ }
+ },
+ "source": [
+ "What is the data structure of data_batch_1[b'labels'] ?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 69,
+ "metadata": {
+ "nbpresent": {
+ "id": "46a97575-36c0-4920-a8dc-762e94239b7e"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "list"
+ ]
+ },
+ "execution_count": 69,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "type(data_batch_1[b'labels'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "2fd19982-c318-4303-8042-7a5a6998d175"
+ }
+ },
+ "source": [
+ "What is the shape of data_batch_1[b'data'] ?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 70,
+ "metadata": {
+ "nbpresent": {
+ "id": "b012720d-81f8-455d-8ce7-bfca64a842c8"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(10000, 3072)"
+ ]
+ },
+ "execution_count": 70,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data_batch_1[b'data'].shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "378aeda2-a547-435e-b28b-09ceb0074a53"
+ }
+ },
+ "source": [
+ "What is the size of data_batch_1[b'labels'] ?\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 71,
+ "metadata": {
+ "nbpresent": {
+ "id": "49c776cb-c8aa-461b-a0da-4f4d38342e2e"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "10000"
+ ]
+ },
+ "execution_count": 71,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(data_batch_1[b'labels'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "02ca495c-e6d2-48d4-9bc2-2295272d5f6f"
+ }
+ },
+ "source": [
+ "What are the first 10 elements of data_batch_1[b'labels'] ?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 73,
+ "metadata": {
+ "nbpresent": {
+ "id": "438920f4-774e-4e94-9b7c-30a2106d163c"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[6, 9, 9, 4, 1, 1, 2, 7, 8, 3]"
+ ]
+ },
+ "execution_count": 73,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data_batch_1[b'labels'][:10]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "1e599fcd-a46c-4750-94f8-1cf4ad8fb342"
+ }
+ },
+ "source": [
+ "What is the data type of data_batch_1[b'data'] ?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 74,
+ "metadata": {
+ "nbpresent": {
+ "id": "7617a699-c3d5-434f-97a5-3443489ac9db"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "dtype('uint8')"
+ ]
+ },
+ "execution_count": 74,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data_batch_1[b'data'].dtype"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "067d850d-0411-4af6-8714-a79b310ca8c1"
+ }
+ },
+ "source": [
+ "Let us concatenate the batch training data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "nbpresent": {
+ "id": "942f351b-b771-4375-8df2-eec28391a576"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "X_train=np.concatenate([data_batch_1[b'data'], \n",
+ " data_batch_2[b'data'], \n",
+ " data_batch_3[b'data'], \n",
+ " data_batch_4[b'data'], \n",
+ " data_batch_5[b'data']], \n",
+ " axis = 0)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "f4c3aa97-0d97-4e9c-a6b3-f2d2b1f08632"
+ }
+ },
+ "source": [
+ "What is the shape of X_train ?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "nbpresent": {
+ "id": "a0eb7a33-19c9-46e4-b471-6f7904389177"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(50000, 3072)"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "X_train.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "b289f0b9-b3ab-480b-9ae6-76a893980efe"
+ }
+ },
+ "source": [
+ "Let us concatenate the training labels"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "nbpresent": {
+ "id": "9b85b9a0-5f2b-4c68-a74f-82f1ec212215"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "y_train=np.concatenate([data_batch_1[b'labels'] , \n",
+ " data_batch_2[b'labels'],\n",
+ " data_batch_3[b'labels'],\n",
+ " data_batch_4[b'labels'],\n",
+ " data_batch_5[b'labels']], \n",
+ " axis = 0)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "81d3c33f-544e-44c8-8260-e89143cc6ef1"
+ }
+ },
+ "source": [
+ "What is the shape of Y_train ?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "nbpresent": {
+ "id": "d699e7a7-efc0-421f-bd8d-2d2b34a09516"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(50000,)"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "y_train.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "d9967582-1305-4b95-948b-e75c46fc49bb"
+ }
+ },
+ "source": [
+ "Let us define the test data as X_test"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "nbpresent": {
+ "id": "5c85918c-f89e-4156-8cdd-ca38d14afbb9"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(10000, 3072)"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "X_test=test_batch[b'data']\n",
+ "X_test.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "48754d72-9acd-49cf-b209-737c45047284"
+ }
+ },
+ "source": [
+ "Let us cast the test labels as ndarray"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "nbpresent": {
+ "id": "5f913d95-aa49-4727-8c6f-5630cbf59741"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(10000,)"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "y_test=np.array(test_batch[b'labels']) \n",
+ "y_test.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "d0a61e44-a2a9-4dff-9849-5f6a0e232290"
+ }
+ },
+ "source": [
+ "Let us visualize an image. \n",
+ "\n",
+ "By default, NumPy arrays are created in row major order. \n",
+ "Spatially this means, that if we have a two-dimensional \n",
+ "array of data, the items in each row of the array are stored \n",
+ "in adjacent memory locations. In the case of a three-dimensional \n",
+ "array of data, the items along `axis=2` are stored in adjacent order. \n",
+ "\n",
+ "Since the first $32$ entries of the array $X\\_train[0]$ are the \n",
+ "red channel values of the first row of the image, etc., we need to \n",
+ "pass the tuple $(3,32,32)$ to reshape. \n",
+ "\n",
+ "By default, NumPy arrays are created in row major order, that is, when \n",
+ "reshaping the array, higher order dimensions are traversed first \n",
+ "(e.g. `axis=2` before advancing on `axis=1`.)\n",
+ "\n",
+ "\n",
+ "`plt.imshow` needs for each inner list the values representing \n",
+ "a pixel. Here, with an RGB image, there are 3 values. We thus \n",
+ "need to transpose the array : the RGB values need to be located \n",
+ "along `axis=2`. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 76,
+ "metadata": {
+ "nbpresent": {
+ "id": "d817d603-7d37-4ff2-b3d1-e95875b48f8f"
+ },
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.image.AxesImage at 0x11196ceb8>"
+ ]
+ },
+ "execution_count": 76,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "%matplotlib inline\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "plt.imshow(X_train[20].reshape((3,32,32)).transpose((1,2,0)).astype('uint8'))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "38cf20c0-9404-4f32-91ed-a00e910832f8"
+ }
+ },
+ "source": [
+ "We visualize some examples from the dataset.\n",
+ "We show a few examples of training images from each class."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "nbpresent": {
+ "id": "ba3743b9-ea50-4201-ad99-5fa47e8b82fb"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 70 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "%matplotlib inline\n",
+ "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
+ "num_classes = len(classes)\n",
+ "samples_per_class = 7\n",
+ "\n",
+ "\n",
+ "\n",
+ "for y, cls in enumerate(classes):\n",
+ " idxs = np.flatnonzero(y_train == y)\n",
+ " idxs = np.random.choice(idxs, samples_per_class, replace=False)\n",
+ " for i, idx in enumerate(idxs):\n",
+ " plt_idx = i * num_classes + y + 1\n",
+ " plt.subplot(samples_per_class, num_classes, plt_idx)\n",
+ " plt.imshow(X_train[idx].reshape((3,32,32)).transpose((1,2,0)).astype('uint8'))\n",
+ " plt.axis('off')\n",
+ " if i == 0:\n",
+ " plt.title(cls)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "ab168c02-9867-455d-815d-c2de707e2f87"
+ }
+ },
+ "source": [
+ "# 2. K-Nearest-Neighbour Classifier"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "36557d31-2ba4-416c-8ead-a92fb7446e85"
+ }
+ },
+ "source": [
+ " We subsample the data for more efficient code execution in this exercise."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "nbpresent": {
+ "id": "26316896-3b01-455b-9a0a-87278f088d83"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "num_training = 5000\n",
+ "mask = range(num_training)\n",
+ "X_train = X_train[mask]\n",
+ "y_train = y_train[mask]\n",
+ "\n",
+ "num_test = 500\n",
+ "mask = range(num_test)\n",
+ "X_test = X_test[mask]\n",
+ "y_test = y_test[mask]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "27db2b6f-c417-4d15-bff4-8c00d58cb808"
+ }
+ },
+ "source": [
+ "We define Class KNearestNeighbor."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "nbpresent": {
+ "id": "497fbf77-9a17-4b35-a0d8-375972850902"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "class KNearestNeighbor():\n",
+ " \"\"\" a kNN classifier with L2 distance \"\"\"\n",
+ "\n",
+ " def __init__(self):\n",
+ " pass\n",
+ "\n",
+ " def train(self, X, y):\n",
+ " \"\"\"\n",
+ " Train the classifier. For k-nearest neighbors this is just \n",
+ " memorizing the training data.\n",
+ "\n",
+ " Inputs:\n",
+ " - X: A numpy array of shape (num_train, D) containing the training data\n",
+ " consisting of num_train samples each of dimension D.\n",
+ " - y: A numpy array of shape (N,) containing the training labels, where\n",
+ " y[i] is the label for X[i].\n",
+ " \"\"\"\n",
+ " self.X_train = X.astype('float')\n",
+ " self.y_train = y\n",
+ " \n",
+ " def predict(self, X, k=1, num_loops=0):\n",
+ " \"\"\"\n",
+ " Predict labels for test data using this classifier.\n",
+ "\n",
+ " Inputs:\n",
+ " - X: A numpy array of shape (num_test, D) containing test data consisting\n",
+ " of num_test samples each of dimension D.\n",
+ " - k: The number of nearest neighbors that vote for the predicted labels.\n",
+ " - num_loops: Determines which implementation to use to compute distances\n",
+ " between training points and testing points.\n",
+ "\n",
+ " Returns:\n",
+ " - y: A numpy array of shape (num_test,) containing predicted labels for the\n",
+ " test data, where y[i] is the predicted label for the test point X[i]. \n",
+ " \"\"\"\n",
+ " if num_loops == 0:\n",
+ " dists = self.compute_distances_no_loops(X)\n",
+ " elif num_loops == 1:\n",
+ " dists = self.compute_distances_one_loop(X)\n",
+ " elif num_loops == 2:\n",
+ " dists = self.compute_distances_two_loops(X)\n",
+ " else:\n",
+ " raise ValueError('Invalid value %d for num_loops' % num_loops)\n",
+ "\n",
+ " return self.predict_labels(dists, k=k)\n",
+ "\n",
+ " def compute_distances_two_loops(self, X):\n",
+ " \"\"\"\n",
+ " Compute the distance between each test point in X and each \n",
+ " training point in self.X_train using a nested loop over both \n",
+ " the training data and the test data.\n",
+ "\n",
+ " Inputs:\n",
+ " - X: A numpy array of shape (num_test, D) containing test data.\n",
+ "\n",
+ " Returns:\n",
+ " - dists: A numpy array of shape (num_test, num_train) where \n",
+ " dists[i, j] is the Euclidean distance between the ith test \n",
+ " point and the jth training point.\n",
+ " \"\"\"\n",
+ " num_test = X.shape[0]\n",
+ " num_train = self.X_train.shape[0]\n",
+ " dists = np.zeros((num_test, num_train))\n",
+ " X = X.astype('float')\n",
+ " for i in range(num_test):\n",
+ " for j in range(num_train):\n",
+ " dists[i, j] = np.sqrt(np.sum(np.square(self.X_train[j,:] - X[i,:])))\n",
+ " \n",
+ " return dists\n",
+ "\n",
+ " def compute_distances_one_loop(self, X):\n",
+ " \"\"\"\n",
+ " Compute the distance between each test point in X and each training point\n",
+ " in self.X_train using a single loop over the test data.\n",
+ "\n",
+ " Input / Output: Same as compute_distances_two_loops\n",
+ " \"\"\"\n",
+ " num_test = X.shape[0]\n",
+ " num_train = self.X_train.shape[0]\n",
+ " dists = np.zeros((num_test, num_train))\n",
+ " X = X.astype('float')\n",
+ " for i in range(num_test):\n",
+ " dists[i, :] = np.sqrt(np.sum(np.square(self.X_train - X[i,:]), axis = 1))\n",
+ " \n",
+ " \n",
+ " return dists\n",
+ "\n",
+ " def compute_distances_no_loops(self, X):\n",
+ " \"\"\"\n",
+ " Compute the distance between each test point in X and each training point\n",
+ " in self.X_train using no explicit loops.\n",
+ "\n",
+ " Input / Output: Same as compute_distances_two_loops\n",
+ " \"\"\"\n",
+ " num_test = X.shape[0]\n",
+ " num_train = self.X_train.shape[0]\n",
+ " dists = np.zeros((num_test, num_train)) \n",
+ " X=X.astype('float')\n",
+ " \n",
+ " # Most \"elegant\" solution leads however to memory issues\n",
+ " # dists = np.sqrt(np.square((self.X_train[:, np.newaxis, :] - X)).sum(axis=2)).T\n",
+ " # split (p-q)^2 to p^2 + q^2 - 2pq\n",
+ " dists = np.sqrt((X**2).sum(axis=1)[:, np.newaxis] + (self.X_train**2).sum(axis=1) - 2 * X.dot(self.X_train.T))\n",
+ " \n",
+ " \n",
+ " \n",
+ " return dists\n",
+ "\n",
+ " def predict_labels(self, dists, k=1):\n",
+ " \"\"\"\n",
+ " Given a matrix of distances between test points and training points,\n",
+ " predict a label for each test point.\n",
+ "\n",
+ " Inputs:\n",
+ " - dists: A numpy array of shape (num_test, num_train) where dists[i, j]\n",
+ " gives the distance betwen the ith test point and the jth training point.\n",
+ "\n",
+ " Returns:\n",
+ " - y: A numpy array of shape (num_test,) containing predicted labels for the\n",
+ " test data, where y[i] is the predicted label for the test point X[i]. \n",
+ " \"\"\"\n",
+ " num_test = dists.shape[0]\n",
+ " y_pred = np.zeros(num_test, dtype='float64')\n",
+ " for i in range(num_test):\n",
+ " # A list of length k storing the labels of the k nearest neighbors to\n",
+ " # the ith test point.\n",
+ " closest_y = []\n",
+ " # get the k indices with smallest distances\n",
+ " min_indices = np.argsort(dists[i,:])[:k] \n",
+ " closest_y = np.bincount(self.y_train[min_indices])\n",
+ " # predict the label of the nearest example\n",
+ " y_pred[i] = np.argmax(closest_y) \n",
+ "\n",
+ " return y_pred"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "91c8998c-f531-4774-98ca-6c9631050fd3"
+ }
+ },
+ "source": [
+ "Create an instance nn from the class KNearestNeighbor"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "nbpresent": {
+ "id": "215be79c-8fe0-4e10-9587-6bea172bb33a"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "classifier = KNearestNeighbor()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "2f886096-8250-4739-8645-37950f408d41"
+ }
+ },
+ "source": [
+ "We call the method `train` of the `KNearestNeighbor` class."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {
+ "nbpresent": {
+ "id": "de24c3a8-0860-446e-b974-3e0c334feced"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "classifier.train(X_train, y_train)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "d058a8de-3c50-4514-8405-5aff67b26398"
+ }
+ },
+ "source": [
+ "We test our implementation with two_loops"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {
+ "nbpresent": {
+ "id": "d87bb3a8-6338-4957-ac73-4c81b87821eb"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(500, 5000)"
+ ]
+ },
+ "execution_count": 28,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dists = classifier.compute_distances_two_loops(X_test)\n",
+ "dists.shape "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "c1277b26-a267-4dec-ab9d-e44d31cdaa3e"
+ }
+ },
+ "source": [
+ "We can visualize the distance matrix: each row is a single test example and its distances to training examples"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {
+ "nbpresent": {
+ "id": "ae3a05a2-a3e6-4e65-a59f-0204411f57f9"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.imshow(dists, interpolation='none')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "7855beeb-d7e2-4ea7-994f-1adfa5a2c886"
+ }
+ },
+ "source": [
+ "Let us now predict labels and run the code below: We use $k = 1$ (which is Nearest Neighbor)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {
+ "nbpresent": {
+ "id": "219d7522-e633-4136-aa98-9abe80ca7bf3"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "y_test_pred = classifier.predict_labels(dists, k=1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "f083926f-4bd0-488f-8ba9-e77dc946fac8"
+ }
+ },
+ "source": [
+ "We compute and print the fraction of correctly predicted examples."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {
+ "nbpresent": {
+ "id": "f1ac90b4-5005-4940-9663-0bfd9574dc8c"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Got 137 / 500 correct => accuracy: 0.274000\n"
+ ]
+ }
+ ],
+ "source": [
+ "num_correct = np.sum(y_test_pred == y_test)\n",
+ "accuracy = float(num_correct) / num_test\n",
+ "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "7a33b48c-c106-4903-ba68-769ce91ccb8b"
+ }
+ },
+ "source": [
+ " Let us now predict labels and run the code below: We use k = 10"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {
+ "nbpresent": {
+ "id": "7a4433f3-d7d4-4b7c-bd21-6f6d5272c837"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "y_test_pred = classifier.predict_labels(dists, k=10)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "8cede653-c157-4396-a534-b4a8741251e2"
+ }
+ },
+ "source": [
+ "We compute and print the fraction of correctly predicted examples."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {
+ "nbpresent": {
+ "id": "445220c9-4974-41a0-a36c-a309d395490b"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Got 141 / 500 correct => accuracy: 0.282000\n"
+ ]
+ }
+ ],
+ "source": [
+ "num_correct = np.sum(y_test_pred == y_test)\n",
+ "accuracy = float(num_correct) / len(y_test_pred)\n",
+ "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Confusion Matrix"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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OuQXR9qUOgYiISPzKBs5wzm0zsxRglpm9F37sLufcxNLuSB0CERGRWPk8hsA554Bt4dWU8OJi2ZfGEIiIiMQxM0syswXAOuBD59wX4YcGm9m3ZjbMzFJL2o86BCIiIrFyeZ4vZjbAzOYXWgYUieBcrnPuRKAx0N7MjgfuBY4B2gG1gbtL+lZ0ykBERCTAnHOjgFGleN5mM5sOnOOcezy8OdvMRgN3lvR6VQhERERilZfn/RKFmdU1s4PCX1cGugFLzKxBeJsBvYHvSvpWVCEQERGJXw2AsWaWRP4/8sc75yab2SdmVhcwYAFwfUk7UodAREQkVj7PQ+Cc+xZoXcz2M/Z3XzplICIiIqoQiIiIxEz3MhAREZFEUiEqBNVqVOXex+/iiKMPwznH4DuG8N2Xi/yOBUBqaiUmTB5DpdRKJCcnMfWdDxn66DN+xyqiV/9enH352TgHK5as4Kk7nyQnO8fvWBHdz+7C0KEPkxQK8eLo1xjy2Ai/IxXR+PDG/HXk/ZH1Bk3rM+bxcbz5wls+pvrdyGeHcO45Z7B+/Ubatevud5y9BP09EsT2u3LI9Rx/Rhu2bszike75V5u1Pq8j5916MYcc2YjHe93PLwuX+Zzyd0Fsw1JThSC+3PbwzXw+fS6Xnd6XP3b7Myt+/NnvSBHZ2bu4rHd/zul8Med0voTTz+xE67Yn+B0rovYhdejRrwe3nX8bN3UbSFJSiM49OvsdKyIUCjH8qcFc0OMqWrbqSp8+vWnR4ii/YxWxatkqrut+A9d1v4Ebzh1I9s5sZr0/2+9YES+/NJHevfv6HWOfgv4eCWL7fT5xBiP6/rPItswfVvLc9U+QPnexT6n2LYhtWBF51iEIT6X4ilf7L62q1atyYocTePe1qQDsztnNtqztPqcqasf2nQAkpySTnJxM/tTUwRFKTqJSWiVCSSFSK6eyae0mvyNFtG/XmvT0FSxf/gs5OTmMHz+Jnj2C+y+M1qe2JvPn1azLWOd3lIjZs+eyadOWkp/ooyC/R4LYfulzF7Njy7Yi29amZ7Bu2WqfEkUXxDYstXKYqbC8eNYhcM7lAoeaWSWvjlEaDZvWZ/PGzTww7G7GfjCKex+7k7TKaX5G2ksoFOK9GRP4+ocZzPr0cxZ8udDvSBGb1m7krVFv8eLnoxk3/yW2Z+3g6/997XesiIaN6rNyVWZkfVXGaho2rO9joui69jydTyZN9ztG3Anye0QkUXh9ymAZMNvM/mpmtxcs+3py4fma127P3NfT9ktSUhLNWzbnzXHv0Lf7AHbu+I2rb7q8TPZdVvLy8jj39EvocPxZtGpzPM1bHOl3pIiqNavSoVsH/typP33bXU1alVS6XNjF71hxKTklmVPOPpmZk2f6HSXuBPk9IhWczzMVliWvOwTpwOTwcaoXWorlnBvlnGvrnGt7SNWGZRJg3er1rF+9nkVf5583mz5lBs1bNi+TfZe1rKytzJk1jy5ndvI7SsSJp57I2pVrydqURe7uXD57fw4tTmrhd6yIzIw1NGn8++9K40YNyMxc42OifWvftR0/LvyJXzds9jtK3Arie0QkUXh6lYFz7iEv918am9b/ytrMdTQ9ogm/pK+k7altWLF0hd+xImrXqcXunN1kZW0lNS2V07p0ZORTL/odK2J9xnqOaXM0qWmpZP+WTatOrfjp2x/9jhUxb/4CjjzyMJo1a0JGxhouvbQXf7x6oN+xinVGr646XRCDoL9HpILzeabCsuRphyA8j/JfgOOAyIn7WKZUPBBD/zqcB5++n5SUZDJ+Wc3g2/9VnoePqt4hdRn6zCCSkpIIhYzJb0/j42nBKSkvXbCU2VNn8+TUJ8nNzWPZ9+m8/+r7fseKyM3N5ZZbH2DqlFdJCoUYM/YNFi1a6nesvaRVTuOkzm0Yds+TfkfZy5gxwzmtc0fq1KnF0h/nMGjQMMaNHe93rIigv0eC2H7XDP8/jup4LNVqVecfc55h6rAJbN+yjUse7Ee12jW4/sW7yVj8MyOufsTXnAWC2IYVkXk5WtfMpgFvkH/bxeuBvsB651yJ92U+uVHX4AwjLkbGzg1+R4iqVbVD/Y4Q1XtrgjMwsTin1zvO7wgl+nxT8Do+hdVJ2+fZwUDY+NtWvyNE1a9eB78jRDV63Rd+RyjR9h0rzOtj7HzrUc8/qypfeI/n3wd4PzFRHefcC2Z2i3NuBjDDzOZ5fEwREZHyoVMGpVYwnd1qMzsfyARqe3xMERER2U9edwgGmVlN4A7gaaAGcKvHxxQRESkfmrq41C4hf5zCd865rkA34EKPjykiIiL7yesKwQnOuchF1865TWbW2uNjioiIlA9VCEq/fzOrVbBiZrWpIHdYFBERiSdefzg/Acwxswnh9UuAwR4fU0REpHwE6EZbB8rrmQrHmdl8oGAiooucc4u8PKaIiIjsP8/L9+EOgDoBIiKSeDSGQERERBKJBviJiIjEShUCERERSSSqEIiIiMQqge5loAqBiIiIqEIgIiISM40hEBERkUSiCoGIiEisEmimQlUIRERERBUCERGRmCXQGILAdggOTqrqd4So5m1b6neEqL7uVNvvCFE13ZDid4SoWiTXKvlJPvsmuZLfEeJa9u4cvyNENXrdF35HiCro7Sf7L7AdAhERkcBThUBEREQ0MZGIiIgkFFUIREREYuTydNmhiIiIJBBVCERERGKVQIMKVSEQERERVQhERERipqsMREREJJGoQiAiIhIrXWUgIiIiiUQVAhERkVjpKgMRERFJJKoQiIiIxEoVAhEREUkkqhCIiIjEyukqAxEREUkgqhCIiIjESmMIREREJJGoQiAiIhIrzVQYX3r178WIj0bw7w9HcOfTd5GSmuJ3pCK6n92F77+byZJFs/jLXQP9jkPo4LrUGPwkNUeMpeaIMaT1+AMAlS+/hlpjJlLzqeep+dTzpJzUweek+UY+O4QVK+Yzb94HfkeJuHLI9fxz/iju++DxyLbW53Xk/mmPM3zZazRtebiP6Ypq2Kg+b08ex+y5U5n1xRQG3HC135GKSE2txDsfvsr7Myfy0Wdvcfs9N/odaS9Bew/vKYjvkcKC3n4VRcJ3CGofUoce/Xpw2/m3cVO3gSQlhejco7PfsSJCoRDDnxrMBT2uomWrrvTp05sWLY7yNZPLzWX7iyPYMrAvW+68gbTzLySpyaEA7Jw0gS23/Jktt/yZnC+/8DVngZdfmkjv3n39jlHE5xNnMKLvP4tsy/xhJc9d/wTpcxf7lKp4ubtz+dv9j9Kp/Xmcc+al9L/2SpoffYTfsSKys3dxWe/+nNP5Ys7pfAmnn9mJ1m1P8DtWRBDfw3sK4nukQDy0X1Quz/slCjNLM7O5ZvaNmX1vZg+Ftx9mZl+Y2U9m9oaZVSrpW0n4DgFAKDmJSmmVCCWFSK2cyqa1m/yOFNG+XWvS01ewfPkv5OTkMH78JHr26O5rJvfrJnLTf8xf2bmT3JU/E6pT19dM0cyePZdNm7b4HaOI9LmL2bFlW5Fta9MzWLdstU+J9m3t2vV8+80iALZt287SH9Jp0PAQn1MVtWP7TgCSU5JJTk7GBehSryC+h/cUxPdIgXhov6jynPdLdNnAGc65VsCJwDlm1hH4FzDMOXck8CvQv6QdeTKGwMyeBvb5XTjn/s+L4xZn09qNvDXqLV78fDS7ftvF1zO/5uv/fV1ehy9Rw0b1WbkqM7K+KmM17du19jFRUaF69Uk64ih2/7CI5BbHk3b+haR27c7un35gxwsjcNu3lbwTiRtNmjai5QnH8uX8b/yOUkQoFGLK9DdodlhTxr3wOgu+XOh3pIigv4eDTu13YFx+77jgD3FKeHHAGcAV4e1jgQeBkdH25VWFYD7wJZAGtAF+DC8nAvssW5jZADObb2bzf972S5kEqVqzKh26deDPnfrTt93VpFVJpcuFXcpk3wkvrTLV732YHc89jdu5g9/em8TmAVew5Zb+5P26kSr9da4vkVStWoUxLz3N/fc8wrat2/2OU0ReXh7nnn4JHY4/i1Ztjqd5iyP9jiQCgMvL83wp/NkYXgYUzmBmSWa2AFgHfAikA5udc7vDT1kFNCrpe/GkQ+CcG+ucGwucAHRxzj3tnHsaOJP8TsG+XjfKOdfWOdf20GpNyyTLiaeeyNqVa8nalEXu7lw+e38OLU5qUSb7LguZGWto0rhhZL1xowZkZq7xMVFYUhLV732Y7E8/Ytec/wHgNv+af82tc2R/MJnk5sf4HFLKSnJyMqNffpqJ499lyrvT/I6zT1lZW5kzax5dzuzkd5SIwL6H44Tar2SFPxvDy6g9Hs91zp0INAbaAzH9cfZ6DEEtoEah9WrhbeVmfcZ6jmlzNKlpqQC06tSKlT+tLM8IUc2bv4AjjzyMZs2akJKSwqWX9uLdyf7/Qa72f3eTu/Jnfps0PrLNatWOfF3p5NPI/Xm5H9HEA0+NeISlP6QzcsRov6PspXadWtSoUR2A1LRUTuvSkfSlwfndC+p7OF7Effv5P4Ygwjm3GZgOnAwcZGYFwwIaAxklvd7reQgeBb42s+mAAZ3JP49RbpYuWMrsqbN5cuqT5Obmsez7dN5/9f3yjBBVbm4ut9z6AFOnvEpSKMSYsW+waNFSXzMlH9uS1DO6s3t5OjWfeh6AHeOeI/X0s0g67Ehwjrx1a9g24vES9lQ+xowZzmmdO1KnTi2W/jiHQYOGMW7s+JJf6KFrhv8fR3U8lmq1qvOPOc8wddgEtm/ZxiUP9qNa7Rpc/+LdZCz+mRFXP+JrToAOHU+iz+W9+f67JUyfNQmAwQ8P5aNpM3xOlq/eIXUZ+swgkpKSCIWMyW9P4+NpM/2OFRHE9/CegvgeKRAP7RdkZlYXyHHObTazykA38gcUTgcuBl4H+gKTStyXV6N1zSwEdASWAQUXrH/hnCtVLahH0wuCM4y4GO+tCc7AxOKs7R7sc6xNP/7Z7whR9asXjDkWonl9U7B/B6skp/odIarMbcG52qg4qcnBmi9lT9m7c/yOUKLduzLM62NsH3SV559VVR94eZ/fh5mdQP6gwSTyq/7jnXMPm9nh5HcGagNfA1c557KjHcezCoFzLs/MRjjnWlOKnomIiIjsH+fct8Bel2U455aRP56g1LweQ/Cxmf3BzDzvpYmIiJS7AI0hOFBedwiuAyYA2WaWZWZbzSzL42OKiIjIfvJ0UKFzrrqX+xcREfFVAt3+2KuZCo9xzi0xszbFPe6c+8qL44qIiEhsvKoQ3A4MAJ6g6BTGxu9TKoqIiMQ33f44OudcwbSK5wFTgC3AZuCd8DYREREJEK8nJhoLZAHDw+tXAOOASz0+roiIiPdKuD1xPPG6Q3C8c+7YQuvTzWyRx8cUERGR/eR1h+ArM+vonPscwMw6kH8nRBERkfiXQGMIvLrKYCH5gwdTgM/M7Jfw+qHAEi+OKSIiIrHzqkJwgUf7FRERCQyneQiic84F+841IiIiUoTXYwhEREQSl8YQiIiISCJ1CLy+uZGIiIjEAVUIREREYpVAExOpQiAiIiKqEIiIiMQsgcYQBLZD8M02Xbl4IKbPb+x3hKg61k71O0JUO8j1O0KJqiQHuw1bVTvU7whRZW7b5HcEkUAJbIdAREQk6FwCVQg0hkBERERUIRAREYmZKgQiIiKSSFQhEBERiVUC3dxIFQIRERFRhUBERCRmGkMgIiIiiUQVAhERkVipQiAiIiKJRBUCERGRGDmnCoGIiIgkEFUIREREYqUxBCIiIpJIVCEQERGJlSoEIiIikkhUIRAREYmRS6AKgToEIiIisUqgDoFOGYiIiEjiVwhSUysxYfIYKqVWIjk5ianvfMjQR5/xO1YR3c/uwtChD5MUCvHi6NcY8tgIX/OcNPRaGnRrTfaGLD7seg8AHZ69mepHNAAgpWYVcrbs4KNu9/kZE4DGhzfmryPvj6w3aFqfMY+P480X3vIxFfQbciOtzjiJrI1b+Fv32wGoWrMa1//7Ng5uXI8Nq9YxcuBQdmRt9zUnxMd7pFf/Xpx9+dk4ByuWrOCpO58kJzvH71gRQXsP72nks0M495wzWL9+I+3adfc7zl6C3n5RJc7djxO/QpCdvYvLevfnnM4Xc07nSzj9zE60bnuC3+6pzEsAACAASURBVLEiQqEQw58azAU9rqJlq6706dObFi2O8jXTz+P/x6wrhhTZ9sX1T/NRt/v4qNt9ZEyZR8bUeT6lK2rVslVc1/0Grut+AzecO5DsndnMen+237GYPXE6Q/sOKrLtvBt6s/izhdzb9WYWf7aQ82680Kd0RQX9PVL7kDr06NeD286/jZu6DSQpKUTnHp39jhURxPfwnl5+aSK9e/f1O0ax4qH9KoqE7xAA7Ni+E4DklGSSk5MDNdVk+3atSU9fwfLlv5CTk8P48ZPo2cPfHvyGz5ew69dt+3y8cY8OrHz7s3JMVDqtT21N5s+rWZexzu8oLJ27mO1birZh627tmD3xUwBmT/yUNt3a+ZCseEF+jwCEkpOolFaJUFKI1MqpbFq7ye9IEUF8D+9p9uy5bNq0xe8YxYqH9ovG5TnPl/LiaYfAzDqVZpvXQqEQ782YwNc/zGDWp5+z4MuF5R1hnxo2qs/KVZmR9VUZq2nYsL6PiaI7uOMx/LZhC9uWr/U7yl669jydTyZN9zvGPtWoexBb1m8GYMv6zdSoe5DPiX4X5PfIprUbeWvUW7z4+WjGzX+J7Vk7+Pp/X/sdKyLe3sNBo/YLDq8rBE+Xcpun8vLyOPf0S+hw/Fm0anM8zVscWd4REkaT3iez8q05fsfYS3JKMqecfTIzJ8/0O0qpBelf4UF+j1StWZUO3Trw50796dvuatKqpNLlwi5+xxLJl+e8X8qJJx0CMzvZzO4A6prZ7YWWB4GkKK8bYGbzzWz+tuyyLwlmZW1lzqx5dDmz3IsU+5SZsYYmjRtG1hs3akBm5hofE+2bJYVodF47Vr3zud9R9tK+azt+XPgTv27Y7HeUfcpav5ma4apAzboHsXVD8Eq4QXyPnHjqiaxduZasTVnk7s7ls/fn0OKkFn7Hioin93AQqf2Cw6sKQSWgGvlXMVQvtGQBF+/rRc65Uc65ts65ttVSa5dJkNp1alGjRnUAUtNSOa1LR9KXLi+TfZeFefMXcOSRh9GsWRNSUlK49NJevDt5mt+xilWv8/Fs/SmTnauDc/62wBm9ugb6dAHA1x/Np9PFXQDodHEXvv4wGAMzg/4eWZ+xnmPaHE1qWioArTq1YuVPK31O9bt4eg8HUdy3X145LOXEk8sOnXMzgBlmNsY597MXxyiteofUZegzg0hKSiIUMia/PY2PpwWnrJybm8sttz7A1CmvkhQKMWbsGyxatNTXTO2fGUjdU1qQWrs65335NIsen8iK12bQpNfJrHw7eKcL0iqncVLnNgy750m/o0RcN/xWju54HNVqVefxOf9h0rA3mDryLW4YcQenXXomGzPWM3LgUL9jAsF/jyxdsJTZU2fz5NQnyc3NY9n36bz/6vt+x4oI4nt4T2PGDOe0zh2pU6cWS3+cw6BBwxg3drzfsYD4aL+Kwrw8j2lmdYG/AMcBaQXbnXNnlPTaprVbBucEazEytwXvX8mFvV6ni98RohqZtN7vCFEdmlzD7wgl+ijrB78jRNWq2qF+R4jqvTXBGZhYnNTkFL8jRJW9OzjzQOzL7l0Z5vUxfr2ki+efVbUmfOr59wHeDyp8BVgCHAY8BKwAglEnFRERkQivOwR1nHMvADnOuRnOuT8BJVYHRERE4oLGEJRaQU1ptZmdD2QCZTNaUERERMqM1x2CQWZWE7iD/PkHagC3enxMERGRcpFItz/2+pTBJeQPXPzOOdcV6AYEYwJ3ERERifC6Q3CCcy4yU4xzbhPQ2uNjioiIlA+fxxCYWRMzm25mi8zsezO7Jbz9QTPLMLMF4eW8kr4Vr08ZhMyslnPu13DA2uVwTBERkYpiN3CHc+4rM6sOfGlmH4YfG+ace7y0O/L6w/kJYI6ZTQivXwIM9viYIiIi5cKV41UAxR7fudXA6vDXW81sMdAoln15esrAOTcOuAhYG14ucs695OUxRUREEknh+/yElwH7eF4z8k/LfxHedJOZfWtmL5pZrZKO43n53jm3CFjk9XFERETKXTlUCJxzo4BR0Z5jZtWA/wK3OueyzGwk8A/Ahf//BPCnaPvQ+XwREZEY+X3KAMDMUsjvDLzinHsTwDm3ttDjzwGTS9qP11cZiIiIiEfMzIAXgMXOuaGFtjco9LQLge9K2pcqBCIiIrHyv0LQCfgjsNDMFoS33QdcbmYnkn/KYAVwXUk7UodAREQkTjnnZgHF3Q1x6v7uSx0CERGRGAVhDEFZ0RgCERERUYVAREQkVqoQiIiISEJRhUBERCRGqhCIiIhIQglshSAtKdXvCFGlJqf4HSGqRZX8ThDdjNXf+x0hqr816OJ3hBJlbtvkd4So6lSq4XeEuNao6sF+R4hq2ZbVfkcIBlfcFX/xSRUCERERCW6FQEREJOg0hkBEREQSiioEIiIiMXJ5GkMgIiIiCUQVAhERkRhpDIGIiIgkFFUIREREYuQ0D4GIiIgkElUIREREYpRIYwjUIRAREYmRLjsUERGRhKIKgYiISIyc8ztB2VGFQERERFQhEBERiZXGEIiIiEhCUYVAREQkRqoQiIiISEJRhUBERCRGusogDoVCId7+5BX+88owv6MUMfLZIaxYMZ958z7wO0pEj8eu5Y4vn+H6aY9GtnW542Kue/+fDJj6CFe+dA/V6h3kY8Kiup/dhe+/m8mSRbP4y10D/Y4DqA3L2pR5Exk/fRyvfzSGVz54we84ewl6+0Fw/wZCfLRfRVBhOgR9B1xO+tLlfsfYy8svTaR3775+xyjimwn/45W+Q4ps++w/U/jPOfcy6rz7+PHjr+l8y0U+pSsqFAox/KnBXNDjKlq26kqfPr1p0eIov2OpDT0w4A83c9lZ13Bl9/5+RykiXtovqH8D46X99sXlmedLeakQHYJDGtSjS7dOTHj5bb+j7GX27Lls2rTF7xhF/DJ3CTs3byuybde2nZGvU6qkBqZO1r5da9LTV7B8+S/k5OQwfvwkevbo7ncstWEFEg/tF+S/gfHQfhWFZ2MIzCwJGOecu9KrY5TW/YPvYMhDw6lararfUeJa17su4YSLTiN76w7GXTbY7zgANGxUn5WrMiPrqzJW075dax8TRac2jI1zjmdeH4Zzjv++NIk3X37H70gR8dB+Qf4bGA/tF41uf1wKzrlc4FAzq+TVMUqjS7dT2bh+E99/u8TPGAlh+mMTeOrk/2Ph25/Rru/ZfseJS2rD2PTreQNXnP0nbrryDvr0u4g2HVv5HSlu6G+glJbXpwyWAbPN7K9mdnvBsq8nm9kAM5tvZvO3/La+TAKc1KEVZ57TmU++fIdhzw2m46nteOyZh8tk3xXVwrdn0+Lcdn7HACAzYw1NGjeMrDdu1IDMzDU+JiodteH+Wb9mAwC/btjMJ+/N5LjWx/qc6HdBb7+g/w0MevuVxOV5v5QXrzsE6cDk8HGqF1qK5Zwb5Zxr65xrWzOtbpkEeGLQCDq3Op8zTurJbdfez+ez5nHXjX8rk31XJLWbHRL5+uizT2JD+mof0/xu3vwFHHnkYTRr1oSUlBQuvbQX706e5nesYqkNY5NWJY0qVatEvj759PakL1nmc6rfBb39gv43MOjtV5F4Og+Bc+4hADOr4pzb4eWx4tWYMcM5rXNH6tSpxdIf5zBo0DDGjR3va6aLhg/k0JNbUKVWdW79/Gk+HTaRo7qeSJ3DG+DyHFsyNjDlvhd9zVggNzeXW259gKlTXiUpFGLM2DdYtGip37HUhmWozsG1GTr6EQCSkpN5781pfDb9C59T/S7o7Rd08d5+eQk0hsCchyOdzexk4AWgmnOuqZm1Aq5zzt1Y0mub120bjCHY+5CxfYPfEaK6u24nvyNE9fDqT/2OENXfGnTxO0KJgt6GLWs38ztCVAs3rfA7QlSH12zgd4Solm0JRoUrmt27Mjz/tF7a4hzPP6uaL36/XHodXs9U+CTQHXgHwDn3jZl19viYIiIi5UJXGewH59zKPTblen1MERER2T9eVwhWmtkpgDOzFOAWYLHHxxQRESkXiXS3Q687BNcDTwGNgAxgGqCJqkVEJCEEZMLRMuH1VQYbAN9nKhQREZHoPB1DYGbNzexjM/suvH6CmT3g5TFFRETKi25uVHrPAfcCOQDOuW+Byzw+poiIiOwnr8cQVHHOzTUr0sPZ7fExRUREykUiTUzkdYVgg5kdATgAM7sYCP5sFiIiIhWM1xWCgcAo4BgzywCWo0GGIiKSIBJpYiJPOgR73NFwKjCd/GrEduAPwFAvjisiIiKxKbFDYPkDAK4EDnfOPWxmTYH6zrm5UV5WcEfDo4F2wCTAgD8C0V4nIiISNyraPATPAHnAGcDDwFbgv+R/0Ber0F0OZwJtnHNbw+sPAlMOLLKIiIiUtdJ0CDo459qY2dcAzrlfzaxSKfd/CLCr0Pqu8DYREZG4l0hXGZSmQ5BjZkn8fqVAXfIrBqUxDphrZm+F13sDY/Y3pIiIiHirNB2C4cBbQD0zGwxcDJRqtkHn3GAzew84Lbypn3Pu65iSioiIBEyFusrAOfeKmX0JnEn+wMDezrlS37HQOfcV8FXsEUVERMRrJU5MFL6qYAfwLvAOsD28TUREpEJzzvslGjNrYmbTzWyRmX1vZreEt9c2sw/N7Mfw/2uV9L2U5pTBFPLHDxiQBhwG/AAcV4rXioiIiHd2A3c4574ys+rAl2b2IXAN8LFz7lEzuwe4B7g72o5Kc8qgZeF1M2sD3BhrchERkUTh91UGzrnVhG8J4JzbamaLgUZAL6BL+GljgU850A5BMQf/ysw67O/r9tdvudleH+KAZO/O8TtCXGtYrbbfEaKqV463HI1Vu7rN/Y4QVcbODX5HiGtVk1L9jiABYWYDgAGFNo1yzo0q5nnNgNbAF8Ah4c4CwBpKccl/aWYqLDwNcQhoA2SW9DoREZFEVx5XGYQ//PfqABRmZtXInzTwVudcVuG7DDvnnJmVOKdiae52WL3Qkkr+mIJepXidiIiIeMzMUsjvDLzinHszvHmtmTUIP94AWFfSfqJWCMITElV3zt15gHlFREQSjt9jCML3G3oBWOycK3zjwHeAvsCj4f9PKmlf++wQmFmyc263mXU6wLwiIiLijU7k3zhwoZktCG+7j/yOwHgz6w/8DFxa0o6iVQjmkj9eYIGZvQNMIP/2xQAUKkuIiIhUSH7f7NA5N4v8aQGKc+b+7Ks0VxmkARvJv9thwXwEDlCHQEREKjS/TxmUpWgdgnrhKwy+4/eOQAG/O0UiIiJShqJ1CJKAahRfilCHQEREKryKcnOj1c65h8stiYiIiPgmWocgcbo9IiIiHsjzO0AZijYx0X6NThQREZH4tc8KgXNuU3kGERERiTcugYrppZm6WERERBLcft/tUERERPLlJdA1d6oQiIiIiCoEIiIiscrTGAIRERFJJKoQiIiIxEhXGYiIiEhCSfgKQWpqJSZMHkOl1EokJycx9Z0PGfroM37HKqL72V0YOvRhkkIhXhz9GkMeG+Frnh6PXUvzM1qzfWMWz559DwBd7riYo7udhMtzbN+YxaQ7nmXbus2+5oTg/ny7PH4th555Ijs3ZjH+rHsBaHvbRbS4ogs7N24FYO6/xvPL9G/8jBlRrUZV7n38Lo44+jCccwy+YwjffbnI71hAcH/GhQXtPbynKfMmsn3bDvJy88jNzeXK7v39jlRE0NsvmkSaqTDhOwTZ2bu4rHd/dmzfSXJyMv99byzTP5rF1/O/9TsaAKFQiOFPDeac8y5n1arVfD5nKu9OnsbixT/6lumbCf9j3tgP6T30+si2z/4zhU+fmAhA+2u60/mWi5h6/4t+RYwI6s/3hwkz+W7Mh5zx5HVFtn/7/Pt885+pPqXat9sevpnPp8/l/gEPkpySTFrlVL8jRQT1Z1wgiO/h4gz4w81s3rTF7xh7iZf2qwgqxCmDHdt3ApCckkxycjLOBefC0fbtWpOevoLly38hJyeH8eMn0bNHd18z/TJ3CTs3byuybde2nZGvU6qkQoDaMIg/39Vf/ED2Hm0YVFWrV+XEDifw7mv5HZXdObvZlrXd51RFBfFnXCCI7+F4Eu/t5zDPl/JSIToEoVCI92ZM4OsfZjDr089Z8OVCvyNFNGxUn5WrMiPrqzJW07BhfR8T7VvXuy7hljnDadn7FD4dOtHvOBFB/vnu6fi+3bhk2iN0efxaKtWs4nccABo2rc/mjZt5YNjdjP1gFPc+didpldP8jlVEkH/G8fAeds7xzOvDeOWDF7joqp5+xykiHtqvovC0Q2BmC83s2z2W/5nZMDOr4+WxC8vLy+Pc0y+hw/Fn0arN8TRvcWR5HTqhTH9sAk+d/H8sfPsz2vU92+84EfHy8/3+pY949dTbmdD9fnas28wpf73S70gAJCUl0bxlc94c9w59uw9g547fuPqmy/2OVUS8/IyDql/PG7ji7D9x05V30KffRbTp2MrvSAkjrxyW8uJ1heA9YApwZXh5F5gPrAHG7PlkMxtgZvPNbP627LK/t1JW1lbmzJpHlzM7lfm+Y5WZsYYmjRtG1hs3akBm5hofE5Vs4duzaXFuO79j7CWIP9/Cdm7IwuU5cI7Fr06n3omH+x0JgHWr17N+9XoWfb0YgOlTZtC8ZXOfUxUviD/jeHgPr1+zAYBfN2zmk/dmclzrY31O9Lt4aL+KwusOwVnOuXudcwvDy/3A6c65fwHN9nyyc26Uc66tc65ttdTaZRKgdp1a1KhRHYDUtFRO69KR9KXLy2TfZWHe/AUceeRhNGvWhJSUFC69tBfvTp7md6y91G52SOTro88+iQ3pq31M87ug/3wLq1LvoMjXh53Tlk0/rPIxze82rf+VtZnraHpEEwDantqGFUtX+BuqkKD/jIP+Hk6rkkaVqlUiX598envSlyzzOdXvgt5+JUmkCoHXVxkkmVl759xcADNrBySFH9vt8bEBqHdIXYY+M4ikpCRCIWPy29P4eNrM8jh0qeTm5nLLrQ8wdcqrJIVCjBn7BosWLfU100XDB3LoyS2oUqs6t37+NJ8Om8hRXU+kzuENcHmOLRkbmHKf/1cYQHB/vmf+eyANO7YgrXY1rpo7nPlP/JeGJ7egznGHgnNsXbWBmfcEow0Bhv51OA8+fT8pKclk/LKawbf/y+9IEUH9GRcI4nu4sDoH12bo6EcASEpO5r03p/HZ9C98TvW7oLdfSRJpYiLzcrRuuAPwIlANMCAL6A8sAs53zo3f12ub1m4ZnGHExcjcVvanNMrS3xp08TtCVM9vDcYlY/tyX9Xgn2Mdm5fhd4SoMnZu8DtCVEF/D7es3czvCFEt3LTC7wgl2r0rw/NP6ymHXO75Z9X5a18rl16HpxUC59w8oKWZ1QyvF74Idp+dARERkXiQlzgFAs+vMqhpZkOBj4GPzeyJgs6BiIiIBIfXgwpfBLYCl4aXLGC0x8cUEREpF3mY50t58XpQ4RHOuT8UWn/IzBZ4fEwRERHZT15XCHaa2akFK2bWCdgZ5fkiIiJxw5XDUl68rhBcD4wrNG7gV6Cvx8cUERGR/eRJh8DMbi+0Og6oGv56O3AWEOxrzkREREpBtz8uWfXw/48G2gGTyJ+H4CpgrkfHFBERkRh50iFwzj0EYGYzgTbOua3h9QfJv7eBiIhI3MuzxJmIwOtBhYcAuwqt7wpvExERkQDxelDhOGCumb0VXu9NMXc5FBERiUeBnmN/P3k9dfFgM3sPOC28qZ9z7msvjykiIiL7z+sKAc65r4CvvD6OiIhIeUukqwy8HkMgIiIiccDzCoGIiEii0t0ORUREJKGoQiAiIhKj8rwboddUIRARERFVCERERGKleQhEREQkoQYVBrZDsGN3tt8R4toVVTf4HSGqf63f6neEqA5ODX6/f97GpX5HiOr6hqf6HSGqZ7fN8jtCVEuzMvyOIBVMYDsEIiIiQaeJiURERCShqEIgIiISo+CfXCw9VQhEREREFQIREZFYJdJVBqoQiIiIiCoEIiIisdJVBiIiIpJQ1CEQERGJUV45LCUxsxfNbJ2ZfVdo24NmlmFmC8LLeSXtRx0CERGR+DYGOKeY7cOccyeGl6kl7URjCERERGLkAnCVgXNuppk1O9D9qEIgIiISYGY2wMzmF1oGlPKlN5nZt+FTCrVKerI6BCIiIjEqjzEEzrlRzrm2hZZRpYg2EjgCOBFYDTxR0gvUIRAREUkwzrm1zrlc51we8BzQvqTXaAyBiIhIjII6D4GZNXDOrQ6vXgh8F+35oA6BiIhIXDOz14AuwMFmtgr4O9DFzE4k//5LK4DrStqPOgQiIiIxCsLdDp1zlxez+YX93Y/GEIiIiIgqBCIiIrHS3Q7jSMNG9Xl78jhmz53KrC+mMOCGq/2OtJfuZ3fh++9msmTRLP5y10C/45Bcvy4NRw+h6bujaPLOKGpe1RuAOnf+maaTn6fJWyOpP/xvhKpX9TlpvpHPDmHFivnMm/eB31EiThp6LRcsfIZu0x+NbOvw7M2c9eEjnPXhI5w790nO+vARHxMWFbTfwSuHXM8/54/ivg8ej2xrfV5H7p/2OMOXvUbTlof7mG5vQWu/PQXxPVJY0NsvmiBMXVxWEr5DkLs7l7/d/yid2p/HOWdeSv9rr6T50Uf4HSsiFAox/KnBXNDjKlq26kqfPr1p0eIoXzO53blsHDKKX3oMYNVlt1Dzih6kHNGUHZ99xS+9BrDywhvIWZFBrWsv8zVngZdfmkjv3n39jlHEz+P/x6wrhhTZ9sX1T/NRt/v4qNt9ZEyZR8bUeT6lKyqIv4OfT5zBiL7/LLIt84eVPHf9E6TPXexTquIFsf32FMT3SIF4aL+KIuE7BGvXrufbbxYBsG3bdpb+kE6Dhof4nOp37du1Jj19BcuX/0JOTg7jx0+iZ4/uvmbK3bCJ7MU/AeB27GTXspUk1zuYnZ99Bbn5/dXfvllMcv2D/YwZMXv2XDZt2uJ3jCI2fL6EXb9u2+fjjXt0YOXbn5Vjon0L4u9g+tzF7NhStP3WpmewbtnqfbzCP0Fsvz0F8T1SIB7aLxpVCOJUk6aNaHnCsXw5/xu/o0Q0bFSflasyI+urMlbTsGF9HxMVldzwEFJbHMFv3y4psr3GRd3Z/r9g/As33hzc8Rh+27CFbcvX+h0FCP7vYNCp/Q6M2i84PBtUaGZb2fuKjC3AfOAO59wyr45dnKpVqzDmpae5/55H2LZ1e3keOm5ZlTTqP/VXNvzzWdz2HZHtta67HJeby7Z3P/ExXfxq0vtkVr41x+8YIlIGgnDZYVnxskLwJHAX0AhoDNwJvAq8DrxY3AsK38Dht11lV95KTk5m9MtPM3H8u0x5d1qZ7bcsZGasoUnjhpH1xo0akJm5xsdEYclJNHjyr2yb/AnbP5od2Vy9dzeqnt6etX/5l4/h4pclhWh0XjtWvfO531EiAvs7GCfUfgdG7RccXnYIejrn/uOc2+qcywrfjKG7c+4NoNi7LhW+gUNapZplFuSpEY+w9Id0Ro4YXWb7LCvz5i/gyCMPo1mzJqSkpHDppb14d7L/nZZ6/7idXctWsnnsm5FtVU5tS63+l5A58EHcb9k+potf9Tofz9afMtm5epPfUSKC+jsYL9R+Bybe2y/PvF/Ki5fzEOwws0uBieH1i4Hfwl+XW5WlQ8eT6HN5b77/bgnTZ00CYPDDQ/lo2ozyihBVbm4ut9z6AFOnvEpSKMSYsW+waNFSXzOltTmOGr3OIvuHZTR58xkANj45moPvuxFLSaHRC/mjv3/7ZgnrHxruZ1QAxowZzmmdO1KnTi2W/jiHQYOGMW7seF8ztX9mIHVPaUFq7eqc9+XTLHp8Iitem0GTXiez8u1gnS4I4u/gNcP/j6M6Hku1WtX5x5xnmDpsAtu3bOOSB/tRrXYNrn/xbjIW/8yIq/2/dDOI7benIL5HCsRD+1UU5pw3n81mdjjwFHByeNMc4DYgAzjJOTcr2usPrtE80KdmNv8W7HEIS4483u8IUbVa8YPfEaIaW7OT3xFKdNnGT/2OENX1DU/1O0JUz2ZG/RPku9TkFL8jRJW9O8fvCCXavSvD839fP3roVZ5/Vt3z88vlUifwrEIQHjTYYx8PB/udKCIiUsF4NobAzBqb2Vtmti68/NfMGnt1PBERkfLmymEpL14OKhwNvAM0DC/vhreJiIhIwHjZIajrnBvtnNsdXsYAdT08noiISLnKw3m+lBcvOwQbzewqM0sKL1cBGz08noiIiMTIyw7Bn4BLgTXAavIvO+zn4fFERETKVSLdy8CTqwzMLAl4xDnX04v9i4iISNnypEPgnMs1s0PNrJJzbpcXxxAREfFboCfM2U9ezlS4DJhtZu8AkVl8nHNDPTymiIiIxKDMxxCY2UvhL3sCk8PHqF5oERERSQgaQxDdSWbWEPgFeNqD/YuIiEgZ86JD8CzwMXAYML/QdiP/dMvhHhxTRESk3JXn3Qi9VuYdAufccGC4mY10zt1Q1vsXEREJivKcOMhrns1DoM6AiIhI/PDyKgMREZGEljj1AW9nKhQREZE4oQqBiIhIjMrzskCvqUIgIiIiqhCIiIjESlcZiIiISEIJbIXgqOqN/I4Q1bzflvodIapHdgR7lug6acHOtyMU/NlGGlar7XeEqN7JWuR3hKhSk1P8jhBVo6oH+x0hqoztG/yOEAiJUx9QhUBEREQIcIVAREQk6HSVgYiIiCQUVQhERERipKsMREREJKGoQiAiIhKjxKkPqEIgIiIiqEIgIiISM11lICIiIglFFQIREZEYuQQaRaAKgYiIiKhCICIiEiuNIRAREZGEogqBiIhIjBJppkJ1CERERGKUON0BnTIQERERVCEQERGJWSKdMlCFQERERFQhEBERiVUiXXZYIToE1WpU5d7H7+KIow/DOcfgO4bw3ZeL/I4V0f3sLgwd+jBJoRAvjn6NIY+N8DVPvyE30uqMk8jauIW/ozL4mwAAIABJREFUdb8dgKo1q3H9v2/j4Mb12LBqHSMHDmVH1nZfcwKkplZiwuQxVEqtRHJyElPf+ZChjz7jdyxOeeJaGp11Ir9tyOLdM++NbD+mXzeOvqYbLjePVR8v4KvBr/uYMl9Q27BA0PONfHYI555zBuvXb6Rdu+5+x9mnUCjEmx+9xNrV67juytv8jlNEvLRhoqsQpwxue/hmPp8+l8tO78sfu/2ZFT/+7HekiFAoxPCnBnNBj6to2aorffr0pkWLo3zNNHvidIb2HVRk23k39GbxZwu5t+vNLP5sIefdeKFP6YrKzt7FZb37c07nizmn8yWcfmYnWrc9we9Y/DR+Jh9f+ViRbYec0oIm3U/i3W738c4Z97Do2ak+pSsqqG1YIOj5Xn5pIr179/U7Ron6Dric9KXL/Y5RrHhpw+K4cvivJGb2opmtM7PvCm2rbWYfmtmP4f/XKmk/Cd8hqFq9Kid2OIF3X8v/47s7ZzfbAvAv2wLt27UmPX0Fy5f/Qk5ODuPHT6Ln/7d333FS1Pcfx1+fu6NJUUHaUX4IiNGoqIBgEMSCHTUxYvwl0RgjsSSWRH8xwdiCXVFRLJgooDERrDSFqAGEoJQISBEUQWniSe/C3ef3x8ydd+cV7tjZmTveTx77YGZ2d77vm53d/e53vjPfPvHWkBdPX8jWjVuKLDumdxemvjwRgKkvT+TY3l1iSFaybVu3A5BVI4usrCzc4+/k89UHi9i5oeg2PPSSU5k3eDR53+wGYMfaTXFEK1ESt2FhSc43dep01q3bGHeMMjVt3oRevbsz8oXX445SoqqwDRNuKHBGsWU3A++4+yHAO+F8mSKvEJjZ/5jZqeF0HTOrH3WZhWW3bsaGtRu45eE/MGz8EP74wI3UrlM7nRHKlN2iGctXrCqYX7FyNdnZzWJMVLIGjQ9gY84GADbmbKBB4wNiTvStjIwM3pw0kg8XTWLKxPeZPeujuCOVqEHbZjQ57lDOHH07p73cn0Yd28YdqUDSt2HS8yVd/7t+z/13DCIvLzkVqeoiLw238rj7ZGBdscXnAcPC6WHA+eWtJ9IKgZldAbwMPB0uagmUWkU1s35mNtPMZq7Zuqq0h1VIZmYmHY7swKvDR3Hp6f3Yvm0Hl/zm4pSse1+WpF9oeXl5nHnihXQ94lQ6HnsEHQ5rH3ekEllmBrUOqMebfW5n1oB/0POp38QdqUDSt2HS8yVZr94nsDZnHfPnfhx3FKmkwt+N4a3fHjytqbuvDqe/BJqW94SoWwiuAboDmwDc/ROgSWkPdvch7t7Z3Ts3rZudkgBfrc4hZ3UOCz5cCMC/x06iw5EdUrLuVFi18ktatfz2b23ZojmrVn0ZY6KSbcrZwP5hq8D+jQ9g89fJa97btGkz06bMoNcp3eOOUqJtq9fz+ZszAFg7+zPIc2o1TGuDWbmSvg2Tni+JOnXtyCln9OTdWaN4+Jm76HZCFx544s64Y1Ub6ehDUPi7MbwNqVDG4Bdcub/ioq4Q7HT3b/JnzCyLNF/pcV3Oetas+orW7VoB0PmEY1m2eFk6I5RpxszZtG9/MG3atKJGjRr07Xseo8dMiDvWd3z49ky6/7gXAN1/3IsP/zUj3kChho0OpEGD4Eu1Vu1a9OjVLbEdp5aPn0mzHxwOQP22zciomcXOdZtjTpX8bZj0fEn30IDB9Ox4Nid3OpcbrujP+1NmcNPVt8YdS6K3xsyaA4T/f1XeE6I+7XCSmf0JqGNmvYGrgdERl/kdA/88iNsf60+NGlms/GI1d/3uvnRHKFVubi7XXX8L48a+SGZGBkOHvcSCBYtjzfTrQddzaLfvU+/A+jw47WneePglxj35GlcN/j09+p7C2pU5PHnNwFgz5mvStDEDnxhAZmYmGRnGmNcn8M6EyXHHosfga2h6/GHUbliPC2YOYs6Dr/DpPyfxg4f60eede8jblcvU658uf0VpkNRtmC/p+YYOHUSPnt1o1OhAFn8yjQEDHmb4sBFxx6pSqvI2TPB1CEYBlwL3hv+/Ud4TLMpjwWaWAVwOnAYYMB74q+9Boce3OCk5B6lLMCMn3i/t8lySfXzcEcr09qZFcUco04A6HeOOUK5bts+JO0KVtnZH/K0zZWlR96C4I5Rp5dav445Qrq3bllnUZVza5oLIv6uGLXulzL/DzP4B9AIOAtYAtxH01xsBtAY+B/q6e/GOh0VE2kLg7nnAM8AzZtYQaLknlQEREZGqIC8BX2nuXlpP+VMqsp6ozzKYaGYNwsrALIKKwcNRlikiIiIVF3Wnwv3dfRPwI2C4u3elgjUWERGRpPI03NIl6gpBVti7sS8wJuKyREREpJKiPsvgDoKOhFPcfYaZtQU+ibhMERGRtMhL75n0kYqsQmBmmUArdy8YhcTdPwMuiKpMERERqZzIDhm4ey6gawSLiEi1lYTRDlMl6kMGU83sceAloGCIQXf/b8TlioiIRC7BFyaqsKgrBEeH/xe+cLYDJ0dcroiIiFRA1BcmOinK9YuIiMSpOnUqjPrCRPub2cBCQzY+ZGb7R1mmiIiIVFzU1yF4FthMcB2CvgTDID8XcZkiIiJpoU6Fe66duxc+zfAOM5sdcZkiIiJSQVG3EGw3sxPyZ8ysO7A94jJFRETSIi8Nt3SJuoXgKmBYoX4D6wnGZRYREZEEibpCsBC4H2gHHABsBM4H5kZcroiISOQ8AcMfp0rUFYI3gA3Af4GVEZclIiIilRR1haClu58RcRkiIiKx0HUI9tx/zOzIiMsQERGRvRRJC4GZfURwieIs4DIz+wzYCRjghUdALE2nGk2iiJYyM1gcd4QyDV81Le4IVdqAzI/jjlCubbt3xh2hTBt2bC3/QVKq+QtHxB2hTHWye8QdIRE0lkH5zolovSIiIhKBSCoE7v55FOsVERFJknReSTBqUfchEBERkSog6rMMREREqi2dZSAiIiLViloIREREKqk6XalQLQQiIiKiFgIREZHK0nUIRERERKcdioiISPWiFgIREZFK0mmHIiIiUq2ohUBERKSSdNqhiIiIVCtqIRAREakk9SEQERGRakUtBCIiIpWk6xCIiIhItaIWAhERkUrK01kGIiIiUp1UywrBT++/kntmDuFP4x8sWHbMWd3oP+FBBn32D1of2TbGdN91+mm9mD9vMh8vmML/3XRN3HG+Q/n2XkZGBq+/+3ee/vvDcUf5juwWzXh9zHCmTh/HlA/G0u+qS+KO9B1Jf42Tmi83N5cf/+Iarr7pNgBWrPqSi6+4njP7/pLf//kedu3aFXPCQFK3357wNNzSpVpWCN5/eRKDL72nyLJVi5bzzJUPsWT6wphSlSwjI4NBj97FOX1+xpEdT+Kii87nsMMOiTtWAeVLjUv7XcySxUvjjlGi3N253Nr/XrofdxZnnNKXy6/4KR0ObRd3rAJJf42TnO+FkW/Qtk3rgvmHn3yWn190Pm+OeJYG9evxypjxMaYLJHn77WuqZYVgyfSFbNu4pciyNUtW8tVnq2NKVLrjuhzDkiXLWLr0C3bt2sWIEW9wbp/T445VQPn2XtPmTejVuzsjX3g97iglWrMmh7lzFgCwZctWFi9aQvPspjGn+lbSX+Ok5vvyqxwm/2c6F4RZ3J0PZs3htF49ADjvrFN5d/K0OCMCyd1+eyoPj/yWLpFXCMysppkdZWZHmlnNqMurarJbNGP5ilUF8ytWriY7u1mMiYpSvr3X/67fc/8dg8jLS37no1atW3DkUYcza+acuKMUSPprnNR89z36NL+7+nLMgo/5DRs3Ub9eXbKyMgFo2vggvspZG2dEILnbb18UaYXAzM4GlgCDgMeBT83szDIe38/MZprZzPmbl0QZTSQtevU+gbU565g/9+O4o5Srbt39GPr8Y/S/+W62bN4adxzZCxOnfkDDAw/g+99T03vUqlMLQdSnHT4EnOTunwKYWTtgLPBmSQ929yHAEIDftLko+T+nUmDVyi9p1TK7YL5li+asWvVljImKUr6906lrR045oycnntqdWrVrUq9ePR544k5uuvrWuKMVkZWVxXMvPMbLI0YzdvSEuOMUkfTXOIn5Ppy7gIlT3ue9aTPY+c0utm7dxr2PPMXmLVvZvTuXrKxM1uR8TZPGjWLNCcncfvuqqA8ZbM6vDIQ+AzZHXGaVMmPmbNq3P5g2bVpRo0YN+vY9j9FjkvOBrHx756EBg+nZ8WxO7nQuN1zRn/enzEhcZQDg0cF3s3jREp4c/FzcUb4j6a9xEvPdcNVlvPP6C0x4ZRgP3HEzx3XqyH23/4Hjjj2KCRPfA+CNcW9zco/jY80Jydx+FeHukd/SJeoWgplmNg4YQXD2xIXADDP7EYC7vxpFob8YdC2HdDucegfW5y/TnmDcwyPZunELF95+GfUaNuDKZ//AyoWfM/iSu6MovkJyc3O57vpbGDf2RTIzMhg67CUWLFgcd6wCylf9de3WiYsuPp/58z7m31PeAOCuOwfy9oRJMScLJP01Tnq+wm646pfcdNu9PDZkOId1aMePzjkt7khVavtVdxZl7cPMyvq54e7+y9LuTPohg6dWTYk7gkSo7f7N445QrnU7N8UdoUwbdqgfwt7Yvuq9uCOUqU52j7gjlGv3Nyst6jKOyz4x8u+q6asmRf53QMQtBO5+WZTrFxERiVN1GtwokgqBmf2fu99vZo9RwoWW3P3aKMoVERGRyomqhSD/coAzI1q/iIhI7NLZ6S9qkVQI3H10+P+wKNYvIiIiqRVpHwIz6wDcCLQpXJa7nxxluSIiIumQzgsHlcbMlhGc0p8L7Hb3zpVZT9SnHY4EngL+ShBUREREUu8kd/96b1YQdYVgt7s/GXEZIiIisahOfQgiuVKhmTU0s4bAaDO72sya5y8Ll4uIiEhqODDBzGaZWb/KriSqFoJZBAHzL6ZwE0VPP2wbUbkiIiJpk44+BOGXfOEv+iHh2D/5TnD3lWbWBPiXmX3s7pMrWk5UZxkcDGBmfYG33H2Tmf0ZOBb4SxRlioiIVEeFB/4r5f6V4f9fmdlrwHFAhSsEUQ9udEtYGTgBOJmgc6H6FIiISLXgafhXFjOra2b186eB04B5lflboq4Q5J9ZcDbwjLuPBWpGXKaIiMi+oikwxczmANOBse7+VmVWFPVZBivN7GmgN3CfmdUi+kqIiIhIWuTFfJaBu38GdEzFuqL+cu4LjAdOd/cNQEOCDoYiIiKSIFGPdrgNeLXQ/GpgdZRlioiIpEt1Gu1QzfciIiISeR8CERGRaivuPgSppBYCERERUQuBiIhIZVWnPgSJrRB8nrc17ghVWpfGHeKOUKa565fGHaFMh9ZpFneEcr25Mdn9c7PrJXvYkm27d8YdoUyLjrs27ghlOrHJ9+OOICmW2AqBiIhI0qkPgYiIiFQraiEQERGpJPUhEBERER0yEBERkepFLQQiIiKVVJ0OGaiFQERERNRCICIiUlnueXFHSBm1EIiIiIhaCERERCorT30IREREpDpRC4GIiEglua5DICIiItWJWghEREQqSX0IREREpFpRC4GIiEglqQ+BiIiIVCtqIRAREakkjXYoIiIi1YpaCERERCqpOo12uE9UCM67/DxOu/g03GHZx8t49MZH2LVzV9yxCpx+Wi8GDryTzIwMnn3uH9z/wOC4IxVRr0Fd/vjgTbQ79GDcnbt+fz/zZi2IO1aBJ5+6nzPPOJmcnLV06XJ63HG+I+n7HyR7H6xVqyYjxwylZq2aZGVlMm7Uvxh47xNxxyqQ3aIZTzx9P42bHIS7M3zoSwx5cnismWo0P4gWD/6OrIMOAHfW/3M8a4eOosGZ3Wly3f9Sq30rlvzwd+z46NNYcwK0bNuSPz/Zv2C+eetmDH1wOK/+7bUYU+2bqv0hg4ZNG9Hnsj7ccPYN/Kb3NWRmZtCzT8+4YxXIyMhg0KN3cU6fn3Fkx5O46KLzOeywQ+KOVcQNd/6W9/89nZ+ceCk/7/0rln3yedyRinjh+Zc5//xL445RoqTvf5D8fXDnzm/4yfmXc0bPH3NGzws58ZTuHNP5qLhjFcjdncut/e+l+3FnccYpfbn8ip/S4dB2sWby3bl8efff+PT0q/nsghtp+POzqdW+FTsXf84XV93NtunzY81X2IrPVvDr06/i16dfxVVnXsPO7TuZ8tbUuGPtMXeP/JYu1b5CAJCRlUnN2jXJyMygVp1arFuzLu5IBY7rcgxLlixj6dIv2LVrFyNGvMG5fZLzK7du/boc3fUoRv9jHAC7d+1my6atMacqaurU6axbtzHuGKVK8v4Hyd8HAbZt3Q5AVo0ssrKyEnWq15o1OcydE7SYbdmylcWLltA8u2msmXbnrGfH/CUA5G3dzs5Pl5PVrBE7l6zgm6UrY81WlmNOOIZVn6/mq5VfxR1lnxRZhcDMDi5hWZeoyivNujVreW3Iazz7/nMMn/k8Wzdt48P3Pkx3jFJlt2jG8hWrCuZXrFxNdnazGBMVld26GRvWbuCWh//AsPFD+OMDN1K7Tu24Y1UZSd//IPn7IAStGG9OGsmHiyYxZeL7zJ71UdyRStSqdQuOPOpwZs2cE3eUAjVaNKH299uyffaiuKOU66RzT+TdN/4dd4wKycMjv6VLlC0Er5hZi/wZMzsReLasJ5hZPzObaWYzP9/yRUpC1N2/Ll17d+VX3S/n0i6XUHu/WvT6Ya+UrHtfkJmZSYcjO/Dq8FFceno/tm/bwSW/uTjuWFWG9r/UyMvL48wTL6TrEafS8dgj6HBY+7gjfUfduvsx9PnH6H/z3WzZnIxWtIz9atP6iT/x5V+eIW/L9rjjlCmrRhY/OO14Jo+ZHHeUCtEhgz3za+B1M2tmZmcBg4CzynqCuw9x987u3vl/6rVOSYijTziaNcvXsGndJnJ35/Kft6ZxWKfDUrLuVFi18ktatcwumG/ZojmrVn0ZY6KivlqdQ87qHBZ8uBCAf4+dRIcjO8ScqupI+v4Hyd8HC9u0aTPTpsyg1ynd445SRFZWFs+98BgvjxjN2NET4o4TyMqk1RN/YsOoiWwaPy3uNOU67qQufPLRp6z/ekPcUfZZkVUI3H0GcC0wAbgdONXdl0dVXmlyVubwvWMPpVbtWgB07N6R5Z+mPUapZsycTfv2B9OmTStq1KhB377nMXpMQj5QgHU561mz6itat2sFQOcTjmXZ4mXxhqpCkr7/QfL3wYaNDqRBg/oA1Kpdix69urFk8dKYUxX16OC7WbxoCU8Ofi7uKAVa3HsdO5csZ+3fXo87yh45+byTqtzhAgguTBT1LV1SftqhmY2GIgc99gM2An8zM9z93FSXWZbFsxczddxUHhn3CLm5eXw2fwlvvfhWOiOUKTc3l+uuv4VxY18kMyODocNeYsGCxXHHKmLgnwdx+2P9qVEji5VfrOau390Xd6Qihg4dRI+e3WjU6EAWfzKNAQMeZviwEXHHApK//0Hy98EmTRsz8IkBZGZmkpFhjHl9Au9MSE6zctdunbjo4vOZP+9j/j3lDQDuunMgb0+YFFum/TofzoE/OpkdHy+l3ZhBAKx5cDhWswbZt/2azIb70+Zvt7F9wVI+/8WtseXMV7tObTr1PJaHb34k7ij7NEv18Ymwr0Cp3H2P3iV9Wp+TnG7EJXjzy2R1DCuuS+NkN+vPXZ+sX3jFnXzQEXFHKFfS98Hseg3jjlCmbbt3xh2hTBMPSvZ7+Lpvvok7QrneWTHBoi7jwHrtI/+uWr/l08j/DoighSD/Cz88y2C1u+8I5+sA8Z6LIyIiIiWKslPhSCCv0HxuuExERKRa0GmHeybL3QvalMLpmhGWJyIiIpUUZYUgx8wKOhCa2XnA1xGWJyIiklbV6ToEUQ5udCXwdzN7HDBgOXBJhOWJiIhIJUVWIXD3JUA3M6sXzm+JqiwREZE4pPM6AVGLrEJgZrcWmwfA3e+MqkwRERGpnCgPGRS+mHdt4BxgYYTliYiIpJWn8SyAqEV5yOChwvNm9iAwPqryREREpPKibCEobj+gZRrLExERiZT6EOwBM/uIb8c0yAQaA+o/ICIikkBRthCcU2h6N7DG3XdHWJ6IiEhapfM6AVGLpEJgZpnAeHf/XhTrFxERkdSKpELg7rlmtsjMWrv7F1GUISIiEjedZbBnDgTmm9l0Cp2C6O7nlv4UERERiUOUFYL8aw/kM+C+CMsTERFJK/Uh2MN1u/ukwgvMrE6E5YmIiKRVEioEZnYG8CjBGX1/dfd7K7OelFcIzOwq4GqgrZnNLXRXfWBqqssTERHZV4Wd+AcDvYEVwAwzG+XuCyq6rihaCF4E3gTuAW4utHyzu6+LoDwREZFYxN8+wHHAp+7+GYCZ/RM4D4i/QuDuG4GNwMWpXreIiIgU0QJYXmh+BdC1MitK56WLK2T0F2Msleszs37uPiSV60ylpOeD5GdUvr2jfHsv6RlTme+dVKykmKRvv5Ls/mZlSr+rSmJm/YB+hRYNiWI7ZaR6hQnWr/yHxCrp+SD5GZVv7yjf3kt6RuWrgtx9iLt3LnQrXBlYCbQqNN8yXFZh+1KFQEREpLqZARxiZgebWU3gJ8CoyqwosYcMREREpGzuvtvMfgOMJzjt8Fl3n1+Zde1LFYKkH5dKej5Ifkbl2zvKt/eSnlH5qiF3HweM29v1WBIuqiAiIiLxUh8CERERqV4VAjObaGad485RFZlZGzObV8Lyv5rZ4Xvw/F+Y2ePRpKscM+tlZj9Ic5m3m9mN6SyzMpKY08yuNbOFZvb3BGQp8f1QlZjZMjM7qITl55rZzSU9J8XlH2BmV6doXb3MbEwq1iWlq1YVgurKzGLr6+HuvyrpEpjh5TKTrheQ1gpBKsT5esfsaqC3u/80f0FV3BZJz+zuoyp7rfsKOoDgNS0i6dtnX1YlKwRh7f1jM/t7+IviZTPbr9hjnjSzmWY238zuKLR8mZndYWb/NbOPzOx74fK6ZvasmU03sw/N7LyIsl9iZnPNbI6ZPW9mfczsg7DMt82safi428P7pwLPR5GlBFnFt2nhVhcz22JmD5nZHOB4M7vMzBaHQ1x3T1PGPdqGZtYGuBK4wcxmm1mPCPP0D7fDFODQcFk7M3vLzGaZ2XuF9rPGZvaKmc0Ib93D5ZG/3qXkPNrM3g+352tmdmC4vEu4bLaZPRD1r2UzewpoC7xpZhsLb4vw/f5umOcdM2sdPqddmP0jMxtgZltSHCvTzJ4JP0MmmFmdMrbXRDN7xMxmAteZ2YVmNi/cRyeHj8kMt+WM8Pm/TlXQ8PNrbFjePDO7KLzrtyV81hW05pnZUDN7KvysXGxm55RaSMXdC7QL96EZ4ftgFLDAirXAmNmNZnZ7ON0+fB/PCbO3K/a3dgnf60WWSwq4e5W7AW0ILiHdPZx/FrgRmAh0Dpc1DP/PDJcfFc4vA34bTl9NMDIUwN3Az8LpA4DFQN0U5/5+uN6D8jMCB/Jt585fAQ+F07cDs4A6CdqmDvQNp5sDXwCNgZoEA1c9noacFd2GN0acpxPwEbAf0AD4NNxu7wCHhI/pCrwbTr8InBBOtwYWpuP1LiPnXODE8DF3Ao+E0/OA48Ppe4F5aXhtlwEHFd8WwGjg0nD6l8Dr4fQY4OJw+kpgSwqztAF2A0eH8yOAn5WxvSYCTxR6/kdAi3D6gPD/fsAt4XQtYCZwcIryXgA8U2h+f0r/rPtF/nsVGAq8RfDj8BCCy97WTuE2nBdO9wK25v+9he8L528Ebg+nPwB+GE7XDvfZXuHr/YNw32gd9f64L96qZAtBaLm754+e+AJwQrH7+5rZf4EPCb5ECh8HfzX8fxbBjglwGnCzmc0meHPXJvjATqWTgZHu/jWAB4M9tQTGm9lHwE1h1nyj3H17ijOUpbxtmgu8Ek53BSa6e467fwO8lKaMFd2GUesBvObu29x9E8EFQWoTfHCNDPenpwkqUACnAo+Hy0cBDcysXnhflK93STnrEnxZ5Q9TPgzoaWYHAPXdfVq4/MWIMpWl8LY4vlCG5/l2vzweGBlOR5FxqbvPDqdnAe0oYXsVenzh98BUYKiZXUHwowSCz5hLwtf+A6ARwZdwKnwE9Daz+8yshwdjykDJn3XFjXD3PHf/BPgM+F6KMhU33d2XlvUAM6tPUJF6DcDdd7j7tvDuwwhOS+zj7l9ElHGfVpWP5RQ/X7Jg3swOJqhxdnH39WY2lOBDOt/O8P9cvt0GBlzg7ouiiVuqx4CB7j7KzHoR/DrKtzXNWUrdpqEd7p6brjAVUNY2jEMGsMHdjy7lvm7uvqPwQjOD9L/eSZaEbbGz0HQuQcthWQoyu/uVZtYVOBuYZWadCD5jfuvu41Md1N0Xm9mxwFnAADPLH2qgpM+67zy9nPlUKfya7qboIevalG91+LhjgFUpzCWhqtxC0NrMjg+n/xeYUui+BgQ730YLjsmfuQfrG09wvM0AzOyYVIYNvQtcaGaNwjIaEjTt5V93+tIIyqyIsrZpcR8AJ5pZIzOrAVwYebpARbbhZqB+xHkmA+eHx5frA32AbcBSM7swzGhm1jF8/ATgt/lPNrOSKg3pyrkVWG/f9q/4OTDJ3TcAm8MvNAguhRqn/xTK8FPgvXD6fYKmckhPxo2UsL1KeqCZtXP3D9z9ViCH4Frz44GrwvcLZtbBzOqmIpiZZQPb3P0F4AHg2Ao8/UIzywiPybcFUvWjqKz33xqgSfj5UQs4B8DdNwMrzOx8ADOrZd/2D9tAUMG6J6z4S4pV5QrBIuAaM1tIcAz5yfw73H0OwaGCjwmaEqeWuIai/gLUAOaa2fxwPqU8uJzkXcAkCzrmDST4NTvSzGYBX6e6zAoqdZsW5+6rCbJPI9i+C9MRsILbcDTwQ4uwU6G7/5egqXgO8CbBdcUh+OK6PMw4n2B8coBrgc5hp7IFBMe+I1dGzkuBB8xsLnA0wXFxgMuBZ8Lm7boEX4Zx+S1wWZjx58B14fLrgd8Pz7GzAAAC6ElEQVSFy9uTnoylba/iHgg78s0jqNDMAf5KMEb9f8PlT5O6Vtojgenh63UbMKACz/0CmE6wX1xZvPWqstx9LTA1/FsfKHbfLoJtNx34F8Fndb6fA9eG2/g/QLNCz1tDUHkYXKjCKilSJa9UaEEP8jHufkTMUUSqJTOr5+5bwumbgebufl05T0ur8Jfjdnd3M/sJQQfDSM4Oqq7Cw6lj3P3luLNI/KpyHwIRic7ZZvZHgs+Izwl6pidNJ4IOmkbQnPzLmPOIVGlVsoVAREREUqsq9yEQERGRFFGFQERERFQhEBEREVUIRCJnZrnhqY/zzGykFRt3o4LrGmpmPw6nyxyJ0io52qOVMkqeiFRvqhCIRG+7ux8dnib7DcWuPWCVHP3NSxmJspBeVMHRHkUkHqoQiKTXe0D78Nd74dHfShwJL7zK4eNmtsjM3gaa5K/Iio5EeYYFI8PNsWBEwDYUG+3RSh9psZEFo/nNN7O/ElxiV0T2MboOgUiahC0BZxKMLgfB5WWPcPelZtYP2OjuXcJLuU41swkE120/lGBwrqYEV7p7tth6GwPPAD3DdTV093UWDCm8xd0fDB/3IvCwu0+xYAjh8QQDxtwGTHH3O83sbIKrFIrIPkYVApHo1QkvKQtBC8HfCJryC4/+dhpwVH7/AILxGQ4hGE3vH+GgUqvM7N0S1t8NmJy/rnAEyJKcChweXMcH+HakxZ7Aj8LnjjWz9ZX8O0WkClOFQCR624uPfGjfHd2wxJHwzOysFOYoa6RFEdnHqQ+BSDKUNhLeZOCisI9Bc+CkEp77PtDTgmG/80eAhO+ONlfaSIuTCUa3xMzOJBjYSkT2MaoQiCRDaSPhvQZ8Et43nGB0ySLcPQfoB7wajq74UnhX8dEeSxtp8Q6CCsV8gkMHX0T0N4pIgmksAxEREVELgYiIiKhCICIiIqhCICIiIqhCICIiIqhCICIiIqhCICIiIqhCICIiIqhCICIiIsD/A0UlE8KQsRakAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "<Figure size 648x648 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# utility function for plotting confusion matrix\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "from sklearn.metrics import confusion_matrix\n",
+ "\n",
+ "def plot_confmat(y_true, y_pred):\n",
+ " \"\"\"\n",
+ " Plot the confusion matrix and save to user_files dir\n",
+ " \"\"\"\n",
+ " conf_matrix = confusion_matrix(y_true, y_pred)\n",
+ " fig = plt.figure(figsize=(9,9))\n",
+ " ax = fig.add_subplot(111)\n",
+ " sns.heatmap(conf_matrix,\n",
+ " annot=True,\n",
+ " fmt='.0f')\n",
+ " plt.title('Confusion matrix')\n",
+ " ax.set_xticklabels( classes)\n",
+ " ax.set_yticklabels( classes)\n",
+ " plt.ylabel('True')\n",
+ " plt.xlabel('Predicted')\n",
+ " \n",
+ "plot_confmat(y_test, y_test_pred) "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "df615e0b-aeeb-4074-abef-075af4118640"
+ }
+ },
+ "source": [
+ "## Algebra and Performance of Distance Matrix Computation\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "04f92811-3067-4a08-8227-ed55c42fed50"
+ }
+ },
+ "source": [
+ "To ensure that our vectorized implementation is correct, we make sure that it\n",
+ "agrees with the naive implementation. There are many ways to decide whether\n",
+ "two matrices are similar; one of the simplest is the Frobenius norm. In case\n",
+ "you haven't seen it before, the Frobenius norm of two matrices is the square\n",
+ "root of the squared sum of differences of all elements; in other words, reshape\n",
+ "the matrices into vectors and compute the Euclidean distance between them."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {
+ "nbpresent": {
+ "id": "edecc2dc-bbf4-47bb-8902-6910fef3eae0"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Difference was: 0.000000\n",
+ "Good! The distance matrices are the same\n",
+ "Difference was: 0.000000\n",
+ "Good! The distance matrices are the same\n"
+ ]
+ }
+ ],
+ "source": [
+ "dists_two = classifier.compute_distances_two_loops(X_test)\n",
+ "dists_one = classifier.compute_distances_one_loop(X_test)\n",
+ "dists_zero = classifier.compute_distances_no_loops(X_test)\n",
+ "\n",
+ "\n",
+ "difference_two_2_one = np.linalg.norm(dists_two - dists_one, ord='fro')\n",
+ "print('Difference was: %f' % (difference_two_2_one, ))\n",
+ "if difference_two_2_one < 0.001:\n",
+ " print('Good! The distance matrices are the same')\n",
+ "else:\n",
+ " print('Uh-oh! The distance matrices are different')\n",
+ "\n",
+ "difference_one_2_zero = np.linalg.norm(dists_one - dists_zero, ord='fro')\n",
+ "print('Difference was: %f' % (difference_one_2_zero, ))\n",
+ "if difference_one_2_zero < 0.001:\n",
+ " print('Good! The distance matrices are the same')\n",
+ "else:\n",
+ " print('Uh-oh! The distance matrices are different')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "94c6dacb-929f-4378-b80f-4859256bd7f4"
+ }
+ },
+ "source": [
+ "Let's compare how fast the implementations are"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {
+ "nbpresent": {
+ "id": "1d3c6b0c-9a33-4f71-b283-0b1eb8061e77"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Two loop version took 48.112765 seconds\n",
+ "One loop version took 60.490100 seconds\n",
+ "No loop version took 0.274836 seconds\n"
+ ]
+ }
+ ],
+ "source": [
+ "def time_function(f, *args):\n",
+ " \"\"\"\n",
+ " Call a function f with args and return the time (in seconds) that it took to execute.\n",
+ " \"\"\"\n",
+ " import time\n",
+ " tic = time.time()\n",
+ " f(*args)\n",
+ " toc = time.time()\n",
+ " return toc - tic\n",
+ "\n",
+ "two_loop_time = time_function(classifier.compute_distances_two_loops, X_test)\n",
+ "print('Two loop version took %f seconds' % two_loop_time)\n",
+ "\n",
+ "one_loop_time = time_function(classifier.compute_distances_one_loop, X_test)\n",
+ "print('One loop version took %f seconds' % one_loop_time)\n",
+ "\n",
+ "no_loop_time = time_function(classifier.compute_distances_no_loops, X_test)\n",
+ "print('No loop version took %f seconds' % no_loop_time)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "e55a0c49-3d30-47b3-bbfc-2ba53025a0eb"
+ }
+ },
+ "source": [
+ "# 3. k-fold cross validation\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 77,
+ "metadata": {
+ "nbpresent": {
+ "id": "48a7d639-21bd-4b58-892d-c54a818111aa"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "num_folds = 5\n",
+ "\n",
+ "k_choices = [1, 3, 5, 7, 9, 10, 12, 15, 18, 20, 50, 100]\n",
+ "\n",
+ "X_train_folds = []\n",
+ "y_train_folds = []"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "8b1aa44f-7099-4511-8b20-168c0f37edb9"
+ }
+ },
+ "source": [
+ "Split up the training data into folds. After splitting, `X_train_folds` and \n",
+ "`y_train_folds` should each be lists of length `num_folds`, where \n",
+ "`y_train_folds[i]` is the label vector for the points in `X_train_folds[i]`. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 78,
+ "metadata": {
+ "nbpresent": {
+ "id": "ee7f2e26-fa37-45b0-af4c-c225369eedc2"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "num_train = X_train.shape[0]\n",
+ "fold_size = np.ceil(num_train/num_folds).astype('int')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "235c4927-a8f9-475f-83c4-54fb4b1de699"
+ }
+ },
+ "source": [
+ "In the case of `num_train = 5000` and 5 folds, we obtain \n",
+ "`X_train_folds = np.split(X_train, [1000, 2000, 3000, 4000])`\n",
+ "`y_train_folds = np.split(y_train, [1000, 2000, 3000, 4000])`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 79,
+ "metadata": {
+ "nbpresent": {
+ "id": "dd9d3e91-fb0d-4ea1-8e37-6282e1eea5f5"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "X_train_folds = np.split(X_train, [(i + 1)*fold_size for i in np.arange(num_folds)])\n",
+ "y_train_folds = np.split(y_train, [(i + 1)*fold_size for i in np.arange(num_folds)])\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 83,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(1000, 3072)"
+ ]
+ },
+ "execution_count": 83,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "X_train_folds[1].shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "99d20b22-bc30-49c6-85a1-86f153b21fe0"
+ }
+ },
+ "source": [
+ "A dictionary holding the accuracies for different values of $k$ that we find\n",
+ "when running cross-validation. After running cross-validation,\n",
+ "`k_to_accuracies[k]` should be a list of length `num_folds` giving the different\n",
+ "accuracy values that we found when using that value of $k$."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {
+ "nbpresent": {
+ "id": "a14b3164-b63a-49eb-980e-57c74b2304db"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "k_to_accuracies = {}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "369cc408-fb92-4899-9e37-92c02a9ef4c1"
+ }
+ },
+ "source": [
+ "We perform $k$-fold cross validation to find the best value of $k$. For each \n",
+ "possible value of $k$, run the $k$-nearest-neighbor algorithm `num_folds` times, \n",
+ "where in each case you use all but one of the folds as training data and the \n",
+ "last fold as a validation set. Store the accuracies for all fold and all \n",
+ "values of k in the `k_to_accuracies` dictionary. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 85,
+ "metadata": {
+ "nbpresent": {
+ "id": "6c869757-5e74-48cc-b7ef-14246b832a99"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "for k in k_choices:\n",
+ " \n",
+ " k_to_accuracies[k] = []\n",
+ " classifier = KNearestNeighbor()\n",
+ " for i in range(num_folds):\n",
+ " X_cv_training = np.concatenate([x for k, x in enumerate(X_train_folds) if k!=i], axis=0)\n",
+ " y_cv_training = np.concatenate([x for k, x in enumerate(y_train_folds) if k!=i], axis=0)\n",
+ " classifier.train(X_cv_training, y_cv_training)\n",
+ " dists = classifier.compute_distances_no_loops(X_train_folds[i])\n",
+ " y_test_pred = classifier.predict_labels(dists, k=k)\n",
+ " k_to_accuracies[k].append(np.mean(y_train_folds[i] == y_test_pred))\n",
+ " \n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "c10d6b24-607c-470b-bffd-614c8fa0be2c"
+ }
+ },
+ "source": [
+ "We print out the computed accuracies."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {
+ "nbpresent": {
+ "id": "d7c42393-850e-4329-91db-5c052fe247e0"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "k = 1, accuracy = 0.263000\n",
+ "k = 1, accuracy = 0.257000\n",
+ "k = 1, accuracy = 0.264000\n",
+ "k = 1, accuracy = 0.278000\n",
+ "k = 1, accuracy = 0.266000\n",
+ "k = 3, accuracy = 0.239000\n",
+ "k = 3, accuracy = 0.249000\n",
+ "k = 3, accuracy = 0.240000\n",
+ "k = 3, accuracy = 0.266000\n",
+ "k = 3, accuracy = 0.254000\n",
+ "k = 5, accuracy = 0.248000\n",
+ "k = 5, accuracy = 0.266000\n",
+ "k = 5, accuracy = 0.280000\n",
+ "k = 5, accuracy = 0.292000\n",
+ "k = 5, accuracy = 0.280000\n",
+ "k = 7, accuracy = 0.261000\n",
+ "k = 7, accuracy = 0.279000\n",
+ "k = 7, accuracy = 0.268000\n",
+ "k = 7, accuracy = 0.288000\n",
+ "k = 7, accuracy = 0.276000\n",
+ "k = 9, accuracy = 0.259000\n",
+ "k = 9, accuracy = 0.283000\n",
+ "k = 9, accuracy = 0.270000\n",
+ "k = 9, accuracy = 0.285000\n",
+ "k = 9, accuracy = 0.285000\n",
+ "k = 10, accuracy = 0.265000\n",
+ "k = 10, accuracy = 0.296000\n",
+ "k = 10, accuracy = 0.276000\n",
+ "k = 10, accuracy = 0.284000\n",
+ "k = 10, accuracy = 0.280000\n",
+ "k = 12, accuracy = 0.260000\n",
+ "k = 12, accuracy = 0.295000\n",
+ "k = 12, accuracy = 0.279000\n",
+ "k = 12, accuracy = 0.283000\n",
+ "k = 12, accuracy = 0.280000\n",
+ "k = 15, accuracy = 0.252000\n",
+ "k = 15, accuracy = 0.289000\n",
+ "k = 15, accuracy = 0.278000\n",
+ "k = 15, accuracy = 0.282000\n",
+ "k = 15, accuracy = 0.274000\n",
+ "k = 18, accuracy = 0.266000\n",
+ "k = 18, accuracy = 0.275000\n",
+ "k = 18, accuracy = 0.281000\n",
+ "k = 18, accuracy = 0.284000\n",
+ "k = 18, accuracy = 0.282000\n",
+ "k = 20, accuracy = 0.270000\n",
+ "k = 20, accuracy = 0.279000\n",
+ "k = 20, accuracy = 0.279000\n",
+ "k = 20, accuracy = 0.282000\n",
+ "k = 20, accuracy = 0.285000\n",
+ "k = 50, accuracy = 0.271000\n",
+ "k = 50, accuracy = 0.288000\n",
+ "k = 50, accuracy = 0.278000\n",
+ "k = 50, accuracy = 0.269000\n",
+ "k = 50, accuracy = 0.266000\n",
+ "k = 100, accuracy = 0.256000\n",
+ "k = 100, accuracy = 0.270000\n",
+ "k = 100, accuracy = 0.263000\n",
+ "k = 100, accuracy = 0.256000\n",
+ "k = 100, accuracy = 0.263000\n"
+ ]
+ }
+ ],
+ "source": [
+ "for k in sorted(k_to_accuracies):\n",
+ " for accuracy in k_to_accuracies[k]:\n",
+ " print('k = %d, accuracy = %f' % (k, accuracy))\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We plot the raw observations."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {
+ "nbpresent": {
+ "id": "e81573f1-9d05-44e2-a581-ffa01100b7af"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "for k in k_choices:\n",
+ " accuracies = k_to_accuracies[k]\n",
+ " plt.scatter([k] * len(accuracies), accuracies)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "21f79bed-12f0-4e15-abdd-1105b4467cf0"
+ }
+ },
+ "source": [
+ " We plot the trend line with error bars that correspond to standard deviation."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {
+ "nbpresent": {
+ "id": "c9af79e8-2cfa-42ed-84fe-efbdadcf65fd"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "accuracies_mean = np.array([np.mean(v) for k,v in sorted(k_to_accuracies.items())])\n",
+ "accuracies_std = np.array([np.std(v) for k,v in sorted(k_to_accuracies.items())])\n",
+ "plt.errorbar(k_choices, accuracies_mean, yerr=accuracies_std)\n",
+ "plt.title('Cross-validation on k')\n",
+ "plt.xlabel('k')\n",
+ "plt.ylabel('Cross-validation accuracy')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "301c698f-4817-4bc5-8e35-ee37caebacba"
+ }
+ },
+ "source": [
+ " # K-Nearest Neighbor with L1 distance"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 86,
+ "metadata": {
+ "nbpresent": {
+ "id": "ce60718f-a584-4026-8071-292b5943eca4"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "class KNearestNeighbor_L1(KNearestNeighbor):\n",
+ " \"\"\" a kNN classifier with L1 distance \"\"\"\n",
+ "\n",
+ " def __init__(self):\n",
+ " super().__init__()\n",
+ " \n",
+ "\n",
+ " def compute_distances_one_loop(self, X):\n",
+ " \"\"\"\n",
+ " We overwrite the compute_distance_one_loop method of the parent class \n",
+ " KNearestNeighbor. \n",
+ " Compute the distance between each test point in X and each training point\n",
+ " in self.X_train using one loop and the L1 distance measure.\n",
+ "\n",
+ " Input / Output: Same as compute_distances_two_loops\n",
+ " \"\"\"\n",
+ " num_test = X.shape[0]\n",
+ " num_train = self.X_train.shape[0]\n",
+ " dists = np.zeros((num_test, num_train))\n",
+ " X = X.astype('float')\n",
+ " for i in range(num_test):\n",
+ " dists[i, :] = (np.sum(np.abs(self.X_train - X[i,:]), axis = 1))\n",
+ " \n",
+ " \n",
+ " return dists\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "d5745a61-1071-4704-8b71-6c0d175de9fc"
+ }
+ },
+ "source": [
+ "We create an instance nn form the class `KNearestNeighbor_L1`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 87,
+ "metadata": {
+ "nbpresent": {
+ "id": "235c3d13-a428-4dae-a286-6ea912f8a0b2"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "classifier = KNearestNeighbor_L1()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "94df5594-5eff-4354-bc83-889aca850336"
+ }
+ },
+ "source": [
+ "Call the method train of the `KNearestNeighbor` class"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 88,
+ "metadata": {
+ "nbpresent": {
+ "id": "627b4ca8-b0df-473d-8e53-3bcc2e31acd8"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "classifier.train(X_train, y_train)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "b96d32ad-0526-4a52-a91e-dffb4a9e634a"
+ }
+ },
+ "source": [
+ "We test our implementation with one loop."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 89,
+ "metadata": {
+ "nbpresent": {
+ "id": "f6ecd69e-e8b4-44a5-8ec1-8aeb47fbc5b5"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(500, 5000)"
+ ]
+ },
+ "execution_count": 89,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dists = classifier.compute_distances_one_loop(X_test)\n",
+ "dists.shape "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "cd4c75ed-d9f1-4f3f-8990-2259f4f2f0d5"
+ }
+ },
+ "source": [
+ " Let us now predict labels and run the code below: We use $k = 10$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 90,
+ "metadata": {
+ "nbpresent": {
+ "id": "606e2720-6672-45f3-ae46-761df5c2066d"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "y_test_pred = classifier.predict_labels(dists, k=10)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "3408b28c-0781-4186-b1cc-f8d0040ecf8f"
+ }
+ },
+ "source": [
+ "We compute and print the fraction of correctly predicted examples."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 91,
+ "metadata": {
+ "nbpresent": {
+ "id": "1919eb5a-988f-4bee-a646-d110372bbca6"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Got 139 / 500 correct => accuracy: 0.278000\n"
+ ]
+ }
+ ],
+ "source": [
+ "num_correct = np.sum(y_test_pred == y_test)\n",
+ "accuracy = float(num_correct) / len(y_test_pred)\n",
+ "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy)) "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The confusion matrix looks as follows:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 92,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 648x648 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# utility function for plotting confusion matrix\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "from sklearn.metrics import confusion_matrix\n",
+ "\n",
+ "def plot_confmat(y_true, y_pred):\n",
+ " \"\"\"\n",
+ " Plot the confusion matrix and save to user_files dir\n",
+ " \"\"\"\n",
+ " conf_matrix = confusion_matrix(y_true, y_pred)\n",
+ " fig = plt.figure(figsize=(9,9))\n",
+ " ax = fig.add_subplot(111)\n",
+ " sns.heatmap(conf_matrix,\n",
+ " annot=True,\n",
+ " fmt='.0f')\n",
+ " plt.title('Confusion matrix')\n",
+ " ax.set_xticklabels( classes)\n",
+ " ax.set_yticklabels( classes)\n",
+ " plt.ylabel('True')\n",
+ " plt.xlabel('Predicted')\n",
+ " \n",
+ "plot_confmat(y_test, y_test_pred) "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "09892b80-b73f-41f3-8671-04ebc8f58ece"
+ }
+ },
+ "source": [
+ "# k-fold cross validation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 93,
+ "metadata": {
+ "nbpresent": {
+ "id": "4d4d5599-4959-4aa9-8250-7ccd99c0eef6"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "num_folds = 5\n",
+ "\n",
+ "k_choices = [1, 3, 5, 7, 9, 10, 12, 15, 18, 20, 50, 100]\n",
+ "\n",
+ "X_train_folds = []\n",
+ "y_train_folds = []"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "eeb5aeda-0ce5-4581-9fbe-e7605376384a"
+ }
+ },
+ "source": [
+ "We Split up the training data into folds. After splitting, `X_train_folds` and \n",
+ "`y_train_folds` should each be lists of length `num_folds`, where \n",
+ "`y_train_folds[i]` is the label vector for the points in `X_train_folds[i]` "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 94,
+ "metadata": {
+ "nbpresent": {
+ "id": "50f9138b-3378-411f-96a1-5e3fe013e396"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "num_train = X_train.shape[0]\n",
+ "fold_size = np.ceil(num_train/num_folds).astype('int')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "267d72f8-6485-4abd-a1d7-d26b4be5a6cc"
+ }
+ },
+ "source": [
+ " In the case of `num_train = 5000` and 5 folds, we obtain \n",
+ "`X_train_folds = np.split(X_train, [1000, 2000, 3000, 4000])`\n",
+ "`y_train_folds = np.split(y_train, [1000, 2000, 3000, 4000])`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 95,
+ "metadata": {
+ "nbpresent": {
+ "id": "ca3a1d8c-4b8a-42d6-94e7-793e87cebdea"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "X_train_folds = np.split(X_train, [(i + 1)*fold_size for i in np.arange(num_folds)])\n",
+ "y_train_folds = np.split(y_train, [(i + 1)*fold_size for i in np.arange(num_folds)])\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "e1be1d21-0776-4587-9b88-d38e804eab71"
+ }
+ },
+ "source": [
+ "A dictionary holding the accuracies for different values of $k$ that we find\n",
+ "when running cross-validation. After running cross-validation,\n",
+ "`k_to_accuracies[k]` should be a list of length num_folds giving the different\n",
+ "accuracy values that we found when using that value of $k$."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 96,
+ "metadata": {
+ "nbpresent": {
+ "id": "05e1ac10-1a25-4740-a21b-8b067116fd69"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "k_to_accuracies = {}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "f97b560b-929b-4a1f-90ee-3cf17ecef7e6"
+ }
+ },
+ "source": [
+ "We perform $k$-fold cross validation to find the best value of $k$. For each \n",
+ "possible value of $k$, run the $k$-nearest-neighbor algorithm `num_folds` times, \n",
+ "where in each case you use all but one of the folds as training data and the \n",
+ "last fold as a validation set. Store the accuracies for all fold and all \n",
+ "values of $k$ in the `k_to_accuracies` dictionary. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 102,
+ "metadata": {
+ "nbpresent": {
+ "id": "bc2a21b5-4387-4bfc-8851-abcf62acaafc"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "for k in k_choices:\n",
+ " \n",
+ " k_to_accuracies[k] = []\n",
+ " classifier = KNearestNeighbor_L1()\n",
+ " for i in range(num_folds):\n",
+ " X_cv_training = np.concatenate([x for k, x in enumerate(X_train_folds) if k!=i], axis=0)\n",
+ " y_cv_training = np.concatenate([x for k, x in enumerate(y_train_folds) if k!=i], axis=0)\n",
+ " classifier.train(X_cv_training, y_cv_training)\n",
+ " dists = classifier.compute_distances_one_loop(X_train_folds[i])\n",
+ " y_test_pred = classifier.predict_labels(dists, k=k)\n",
+ " k_to_accuracies[k].append(np.mean(y_train_folds[i] == y_test_pred))\n",
+ " \n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "c24db8cd-04a8-45a6-b15e-24194bb42248"
+ }
+ },
+ "source": [
+ "We print out the computed accuracies."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 103,
+ "metadata": {
+ "nbpresent": {
+ "id": "972c66f2-03ea-4de0-8ac6-a564c3365f50"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "k = 1, accuracy = 0.291000\n",
+ "k = 1, accuracy = 0.313000\n",
+ "k = 1, accuracy = 0.294000\n",
+ "k = 1, accuracy = 0.275000\n",
+ "k = 1, accuracy = 0.308000\n",
+ "k = 3, accuracy = 0.269000\n",
+ "k = 3, accuracy = 0.299000\n",
+ "k = 3, accuracy = 0.290000\n",
+ "k = 3, accuracy = 0.278000\n",
+ "k = 3, accuracy = 0.296000\n",
+ "k = 5, accuracy = 0.275000\n",
+ "k = 5, accuracy = 0.311000\n",
+ "k = 5, accuracy = 0.301000\n",
+ "k = 5, accuracy = 0.314000\n",
+ "k = 5, accuracy = 0.309000\n",
+ "k = 7, accuracy = 0.280000\n",
+ "k = 7, accuracy = 0.329000\n",
+ "k = 7, accuracy = 0.313000\n",
+ "k = 7, accuracy = 0.320000\n",
+ "k = 7, accuracy = 0.313000\n",
+ "k = 9, accuracy = 0.291000\n",
+ "k = 9, accuracy = 0.314000\n",
+ "k = 9, accuracy = 0.310000\n",
+ "k = 9, accuracy = 0.322000\n",
+ "k = 9, accuracy = 0.315000\n",
+ "k = 10, accuracy = 0.289000\n",
+ "k = 10, accuracy = 0.312000\n",
+ "k = 10, accuracy = 0.320000\n",
+ "k = 10, accuracy = 0.323000\n",
+ "k = 10, accuracy = 0.313000\n",
+ "k = 12, accuracy = 0.295000\n",
+ "k = 12, accuracy = 0.320000\n",
+ "k = 12, accuracy = 0.324000\n",
+ "k = 12, accuracy = 0.332000\n",
+ "k = 12, accuracy = 0.318000\n",
+ "k = 15, accuracy = 0.287000\n",
+ "k = 15, accuracy = 0.324000\n",
+ "k = 15, accuracy = 0.317000\n",
+ "k = 15, accuracy = 0.319000\n",
+ "k = 15, accuracy = 0.321000\n",
+ "k = 18, accuracy = 0.289000\n",
+ "k = 18, accuracy = 0.321000\n",
+ "k = 18, accuracy = 0.307000\n",
+ "k = 18, accuracy = 0.319000\n",
+ "k = 18, accuracy = 0.306000\n",
+ "k = 20, accuracy = 0.287000\n",
+ "k = 20, accuracy = 0.327000\n",
+ "k = 20, accuracy = 0.309000\n",
+ "k = 20, accuracy = 0.307000\n",
+ "k = 20, accuracy = 0.306000\n",
+ "k = 50, accuracy = 0.285000\n",
+ "k = 50, accuracy = 0.301000\n",
+ "k = 50, accuracy = 0.294000\n",
+ "k = 50, accuracy = 0.290000\n",
+ "k = 50, accuracy = 0.293000\n",
+ "k = 100, accuracy = 0.283000\n",
+ "k = 100, accuracy = 0.285000\n",
+ "k = 100, accuracy = 0.279000\n",
+ "k = 100, accuracy = 0.285000\n",
+ "k = 100, accuracy = 0.277000\n"
+ ]
+ }
+ ],
+ "source": [
+ "for k in sorted(k_to_accuracies):\n",
+ " for accuracy in k_to_accuracies[k]:\n",
+ " print('k = %d, accuracy = %f' % (k, accuracy))\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "57f5291f-1e32-456f-b84f-76eba7b40d44"
+ }
+ },
+ "source": [
+ "We plot the raw observations."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 104,
+ "metadata": {
+ "nbpresent": {
+ "id": "a028040f-a7a6-4b61-904d-48090dcbbe8d"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "for k in k_choices:\n",
+ " accuracies = k_to_accuracies[k]\n",
+ " plt.scatter([k] * len(accuracies), accuracies)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "nbpresent": {
+ "id": "6a867f1e-9207-4d0d-adf9-7884532ed06e"
+ }
+ },
+ "source": [
+ "We plot the trend line with error bars that correspond to standard deviation."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 106,
+ "metadata": {
+ "nbpresent": {
+ "id": "caf9f446-5155-42db-a69b-46f1d7f06322"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "accuracies_mean = np.array([np.mean(v) for k,v in sorted(k_to_accuracies.items())])\n",
+ "accuracies_std = np.array([np.std(v) for k,v in sorted(k_to_accuracies.items())])\n",
+ "plt.errorbar(k_choices, accuracies_mean, yerr=accuracies_std)\n",
+ "plt.title('Cross-validation on k')\n",
+ "plt.xlabel('k')\n",
+ "plt.ylabel('Cross-validation accuracy')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "\n",
+ "$"
+ ]
+ }
+ ],
+ "metadata": {
+ "anaconda-cloud": {},
+ "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": "3.7.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/Block 1/Solutions to Exercises Block 1 - Introduction to Image Classification.ipynb b/notebooks/Block 1/Solutions to Exercises Block 1 - Introduction to Image Classification.ipynb
new file mode 100644
index 0000000..eb0ab38
--- /dev/null
+++ b/notebooks/Block 1/Solutions to Exercises Block 1 - Introduction to Image Classification.ipynb
@@ -0,0 +1,1451 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "jYysdyb-CaWM"
+ },
+ "source": [
+ "# Exercise 1 - K-Nearest Neighbor Classifier for MNIST"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "FbVhjPpzn6BM"
+ },
+ "source": [
+ "In this exercise, we'll apply KNN Classifiers to the MNIST dataset. The aim of the exercise is to get acquainted with the MNIST dataset. \n",
+ "\n",
+ "This guide uses [tf.keras](https://www.tensorflow.org/guide/keras), a high-level API to build and train models in TensorFlow."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "H0tMfX2vR0uD"
+ },
+ "source": [
+ "## Install and import dependencies\n",
+ "\n",
+ "We'll need [TensorFlow Datasets](https://www.tensorflow.org/datasets/), an API that simplifies downloading and accessing datasets, and provides several sample datasets to work with. We're also using a few helper libraries."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "P7mUJVqcINSM"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Collecting tensorflow_datasets\n",
+ " Downloading tensorflow_datasets-2.1.0-py3-none-any.whl (3.1 MB)\n",
+ "\u001b[K |████████████████████████████████| 3.1 MB 6.1 MB/s eta 0:00:01\n",
+ "\u001b[?25hRequirement already satisfied, skipping upgrade: tensorflow-metadata in /Users/mirkobirbaumer/Dropbox/Statistics/Deep_Learning_Master_HSLU/tensorflow/lib/python3.6/site-packages (from tensorflow_datasets) (0.21.1)\n",
+ "Requirement already satisfied, skipping upgrade: promise in /Users/mirkobirbaumer/Dropbox/Statistics/Deep_Learning_Master_HSLU/tensorflow/lib/python3.6/site-packages (from tensorflow_datasets) (2.3)\n",
+ "Requirement already satisfied, skipping upgrade: wrapt in /Users/mirkobirbaumer/Dropbox/Statistics/Deep_Learning_Master_HSLU/tensorflow/lib/python3.6/site-packages (from tensorflow_datasets) (1.12.0)\n",
+ "Requirement already satisfied, skipping upgrade: tqdm in /Users/mirkobirbaumer/Dropbox/Statistics/Deep_Learning_Master_HSLU/tensorflow/lib/python3.6/site-packages (from tensorflow_datasets) (4.43.0)\n",
+ "Requirement already satisfied, skipping upgrade: future in /Users/mirkobirbaumer/Dropbox/Statistics/Deep_Learning_Master_HSLU/tensorflow/lib/python3.6/site-packages (from tensorflow_datasets) (0.18.2)\n",
+ "Requirement already satisfied, skipping upgrade: dill in /Users/mirkobirbaumer/Dropbox/Statistics/Deep_Learning_Master_HSLU/tensorflow/lib/python3.6/site-packages (from tensorflow_datasets) (0.3.1.1)\n",
+ "Requirement already satisfied, skipping upgrade: requests>=2.19.0 in /Users/mirkobirbaumer/Dropbox/Statistics/Deep_Learning_Master_HSLU/tensorflow/lib/python3.6/site-packages (from tensorflow_datasets) (2.23.0)\n",
+ "Requirement already satisfied, skipping upgrade: numpy in /Users/mirkobirbaumer/Dropbox/Statistics/Deep_Learning_Master_HSLU/tensorflow/lib/python3.6/site-packages (from tensorflow_datasets) (1.18.1)\n",
+ "Requirement already satisfied, skipping upgrade: protobuf>=3.6.1 in /Users/mirkobirbaumer/Dropbox/Statistics/Deep_Learning_Master_HSLU/tensorflow/lib/python3.6/site-packages (from tensorflow_datasets) (3.11.3)\n",
+ "Requirement already satisfied, skipping upgrade: termcolor in /Users/mirkobirbaumer/Dropbox/Statistics/Deep_Learning_Master_HSLU/tensorflow/lib/python3.6/site-packages (from tensorflow_datasets) (1.1.0)\n",
+ "Requirement already satisfied, skipping upgrade: six in /Users/mirkobirbaumer/.pyenv/versions/3.6.8/lib/python3.6/site-packages (from tensorflow_datasets) (1.14.0)\n",
+ "Requirement already satisfied, skipping upgrade: attrs>=18.1.0 in /Users/mirkobirbaumer/Dropbox/Statistics/Deep_Learning_Master_HSLU/tensorflow/lib/python3.6/site-packages (from tensorflow_datasets) (19.3.0)\n",
+ "Requirement already satisfied, skipping upgrade: absl-py in /Users/mirkobirbaumer/Dropbox/Statistics/Deep_Learning_Master_HSLU/tensorflow/lib/python3.6/site-packages (from tensorflow_datasets) (0.9.0)\n",
+ "Requirement already satisfied, skipping upgrade: googleapis-common-protos in /Users/mirkobirbaumer/Dropbox/Statistics/Deep_Learning_Master_HSLU/tensorflow/lib/python3.6/site-packages (from tensorflow-metadata->tensorflow_datasets) (1.51.0)\n",
+ "Requirement already satisfied, skipping upgrade: certifi>=2017.4.17 in /Users/mirkobirbaumer/Dropbox/Statistics/Deep_Learning_Master_HSLU/tensorflow/lib/python3.6/site-packages (from requests>=2.19.0->tensorflow_datasets) (2019.11.28)\n",
+ "Requirement already satisfied, skipping upgrade: idna<3,>=2.5 in /Users/mirkobirbaumer/Dropbox/Statistics/Deep_Learning_Master_HSLU/tensorflow/lib/python3.6/site-packages (from requests>=2.19.0->tensorflow_datasets) (2.9)\n",
+ "Requirement already satisfied, skipping upgrade: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /Users/mirkobirbaumer/Dropbox/Statistics/Deep_Learning_Master_HSLU/tensorflow/lib/python3.6/site-packages (from requests>=2.19.0->tensorflow_datasets) (1.25.8)\n",
+ "Requirement already satisfied, skipping upgrade: chardet<4,>=3.0.2 in /Users/mirkobirbaumer/Dropbox/Statistics/Deep_Learning_Master_HSLU/tensorflow/lib/python3.6/site-packages (from requests>=2.19.0->tensorflow_datasets) (3.0.4)\n",
+ "Requirement already satisfied, skipping upgrade: setuptools in /Users/mirkobirbaumer/Dropbox/Statistics/Deep_Learning_Master_HSLU/tensorflow/lib/python3.6/site-packages (from protobuf>=3.6.1->tensorflow_datasets) (45.2.0)\n",
+ "Installing collected packages: tensorflow-datasets\n",
+ " Attempting uninstall: tensorflow-datasets\n",
+ " Found existing installation: tensorflow-datasets 2.0.0\n",
+ " Uninstalling tensorflow-datasets-2.0.0:\n",
+ " Successfully uninstalled tensorflow-datasets-2.0.0\n",
+ "Successfully installed tensorflow-datasets-2.1.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "!pip install -U tensorflow_datasets"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "dzLKpmZICaWN"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2.1.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Import TensorFlow\n",
+ "\n",
+ "# FOR COLAB USERS:\n",
+ "# If you run this notebook in Colab, then execute the following line (uncomment it)\n",
+ "# %tensorflow_version 2.x\n",
+ "\n",
+ "\n",
+ "# If you run this noteook in your tensorflow 2.x environment, then \n",
+ "# verify you have version > 2.0\n",
+ "\n",
+ "import tensorflow as tf\n",
+ "print(tf.__version__)\n",
+ "\n",
+ "# Now you should get version 2.x\n",
+ "# If you still get version 1.x, then execute (uncomment) the following lines and run the cell\n",
+ "\n",
+ "#!pip uninstall tensorflow\n",
+ "#!pip install --upgrade pip\n",
+ "#!pip install --upgrade tensorflow\n",
+ "#!python3 -c \"import tensorflow as tf;print(tf.reduce_sum(tf.random.normal([1000, 1000])))\"\n",
+ "#try:\n",
+ "# import tensorflow as tf\n",
+ "#except Exception:\n",
+ "# pass\n",
+ "#print(tf.__version__)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from __future__ import absolute_import, division, print_function, unicode_literals\n",
+ "\n",
+ "\n",
+ "# Import TensorFlow Datasets\n",
+ "import tensorflow as tf\n",
+ "import tensorflow_datasets as tfds\n",
+ "tfds.disable_progress_bar()\n",
+ "\n",
+ "# Helper libraries\n",
+ "import math\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "590z76KRGtKk"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Python 3.6.8\r\n"
+ ]
+ }
+ ],
+ "source": [
+ "import logging\n",
+ "logger = tf.get_logger()\n",
+ "logger.setLevel(logging.ERROR)\n",
+ "!python -V"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Import the MNIST dataset"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This guide uses the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset—often used as the \"Hello, World\" of machine learning programs for computer vision. The MNIST dataset contains images of handwritten digits (0, 1, 2, etc)\n",
+ "\n",
+ "\n",
+ "We will use 60,000 images to train the network and 10,000 images to evaluate how accurately the network learned to classify images. You can access the MNIST directly from TensorFlow, using the [Datasets](https://www.tensorflow.org/datasets) API:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dataset, metadata = tfds.load('mnist', as_supervised=True, with_info=True)\n",
+ "train_dataset, test_dataset = dataset['train'], dataset['test']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Loading the dataset returns metadata as well as a *training dataset* and *test dataset*.\n",
+ "\n",
+ "* The model is trained using `train_dataset`.\n",
+ "* The model is tested against `test_dataset`.\n",
+ "\n",
+ "The images are 28 $\\times$ 28 arrays, with pixel values in the range `[0, 255]`. The *labels* are an array of integers, in the range `[0, 9]`. These correspond to the handwritten numbers. \n",
+ "\n",
+ "\n",
+ "Each image is mapped to a single label. Since the *class names* are not included with the dataset, store them here to use later when plotting the images:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class_names = ['Zero', 'One', 'Two', 'Three', 'Four', 'Five',\n",
+ " 'Six', 'Seven', 'Eight', 'Nine']\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Explore the data\n",
+ "\n",
+ "Let's explore the format of the dataset before training the model. The following shows there are 60,000 images in the training set, and 10000 images in the test set:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Number of training examples: 60000\n",
+ "Number of test examples: 10000\n"
+ ]
+ }
+ ],
+ "source": [
+ "num_train_examples = metadata.splits['train'].num_examples\n",
+ "num_test_examples = metadata.splits['test'].num_examples\n",
+ "print(\"Number of training examples: {}\".format(num_train_examples))\n",
+ "print(\"Number of test examples: {}\".format(num_test_examples))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's plot an image to see what it looks like."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Take a single image, and remove the color dimension by reshaping\n",
+ "for image, label in test_dataset.take(1):\n",
+ " break\n",
+ "image = image.numpy().reshape((28,28))\n",
+ "\n",
+ "# Plot the image - voila an example of a handwritten digit\n",
+ "plt.figure()\n",
+ "plt.imshow(image, cmap=plt.cm.binary)\n",
+ "plt.colorbar()\n",
+ "plt.grid(False)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Display the first 25 images from the *test set* and display the class name below each image. Verify that the data is in the correct format and we're ready to build and train the network."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x720 with 25 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(10,10))\n",
+ "i = 0\n",
+ "for (image, label) in test_dataset.take(25):\n",
+ " image = image.numpy().reshape((28,28))\n",
+ " plt.subplot(5,5,i+1)\n",
+ " plt.xticks([])\n",
+ " plt.yticks([])\n",
+ " plt.grid(False)\n",
+ " plt.imshow(image, cmap=plt.cm.binary)\n",
+ " plt.xlabel(class_names[label])\n",
+ " i += 1\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "yR0EdgrLCaWR"
+ },
+ "source": [
+ "## Import the Fashion MNIST dataset"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "DLdCchMdCaWQ"
+ },
+ "source": [
+ "If numbers are not your thing then use the [Fashion MNIST](https://github.com/zalandoresearch/fashion-mnist) dataset, which contains 70,000 grayscale images in 10 categories. The images show individual articles of clothing at low resolution (28 $\\times$ 28 pixels), as seen here:\n",
+ "\n",
+ "<table>\n",
+ " <tr><td>\n",
+ " <img src=\"https://tensorflow.org/images/fashion-mnist-sprite.png\"\n",
+ " alt=\"Fashion MNIST sprite\" width=\"600\">\n",
+ " </td></tr>\n",
+ " <tr><td align=\"center\">\n",
+ " <b>Figure 1.</b> <a href=\"https://github.com/zalandoresearch/fashion-mnist\">Fashion-MNIST samples</a> (by Zalando, MIT License).<br/> \n",
+ " </td></tr>\n",
+ "</table>\n",
+ "\n",
+ "You may use Fashion MNIST for variety, and because it's a slightly more challenging problem than regular MNIST. Both datasets are relatively small and are used to verify that an algorithm works as expected. They're good starting points to test and debug code.\n",
+ "\n",
+ "We will use 60,000 images to train the network and 10,000 images to evaluate how accurately the network learned to classify images. You can access the Fashion MNIST directly from TensorFlow, using the [Datasets](https://www.tensorflow.org/datasets) API:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "7MqDQO0KCaWS"
+ },
+ "outputs": [],
+ "source": [
+ "dataset, metadata = tfds.load('fashion_mnist', as_supervised=True, with_info=True)\n",
+ "train_dataset, test_dataset = dataset['train'], dataset['test']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "t9FDsUlxCaWW"
+ },
+ "source": [
+ "Loading the dataset returns metadata as well as a *training dataset* and *test dataset*.\n",
+ "\n",
+ "* The model is trained using `train_dataset`.\n",
+ "* The model is tested against `test_dataset`.\n",
+ "\n",
+ "The images are 28 $\\times$ 28 arrays, with pixel values in the range `[0, 255]`. The *labels* are an array of integers, in the range `[0, 9]`. These correspond to the *class* of clothing the image represents:\n",
+ "\n",
+ "<table>\n",
+ " <tr>\n",
+ " <th>Label</th>\n",
+ " <th>Class</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>0</td>\n",
+ " <td>T-shirt/top</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>Trouser</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>Pullover</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>Dress</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>4</td>\n",
+ " <td>Coat</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>5</td>\n",
+ " <td>Sandal</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>6</td>\n",
+ " <td>Shirt</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>7</td>\n",
+ " <td>Sneaker</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>8</td>\n",
+ " <td>Bag</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>9</td>\n",
+ " <td>Ankle boot</td>\n",
+ " </tr>\n",
+ "</table>\n",
+ "\n",
+ "Each image is mapped to a single label. Since the *class names* are not included with the dataset, store them here to use later when plotting the images:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "IjnLH5S2CaWx"
+ },
+ "outputs": [],
+ "source": [
+ "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
+ " 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "Brm0b_KACaWX"
+ },
+ "source": [
+ "### Explore the data\n",
+ "\n",
+ "Let's explore the format of the dataset before training the model. The following shows there are 60,000 images in the training set, and 10000 images in the test set:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "MaOTZxFzi48X"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Number of training examples: 60000\n",
+ "Number of test examples: 10000\n"
+ ]
+ }
+ ],
+ "source": [
+ "num_train_examples = metadata.splits['train'].num_examples\n",
+ "num_test_examples = metadata.splits['test'].num_examples\n",
+ "print(\"Number of training examples: {}\".format(num_train_examples))\n",
+ "print(\"Number of test examples: {}\".format(num_test_examples))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "lIQbEiJGXM-q"
+ },
+ "source": [
+ "Let's plot an image to see what it looks like."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "oSzE9l7PjHx0"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Take a single image, and remove the color dimension by reshaping\n",
+ "for image, label in test_dataset.take(1):\n",
+ " break\n",
+ "image = image.numpy().reshape((28,28))\n",
+ "\n",
+ "# Plot the image - voila a piece of fashion clothing\n",
+ "plt.figure()\n",
+ "plt.imshow(image, cmap=plt.cm.binary)\n",
+ "plt.colorbar()\n",
+ "plt.grid(False)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "Ee638AlnCaWz"
+ },
+ "source": [
+ "Display the first 25 images from the *test set* and display the class name below each image. Verify that the data is in the correct format and we're ready to build and train the network."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "oZTImqg_CaW1"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x720 with 25 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(10,10))\n",
+ "i = 0\n",
+ "for (image, label) in test_dataset.take(25):\n",
+ " image = image.numpy().reshape((28,28))\n",
+ " plt.subplot(5,5,i+1)\n",
+ " plt.xticks([])\n",
+ " plt.yticks([])\n",
+ " plt.grid(False)\n",
+ " plt.imshow(image, cmap=plt.cm.binary)\n",
+ " plt.xlabel(class_names[label])\n",
+ " i += 1\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Decide whether you want to work with the traditional or Fashin MNIST dataset, then extract 5000 training examples and \n",
+ "500 test examples."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Shape of image training data : (5000, 784)\n",
+ "Shape of training data labels : (5000,)\n"
+ ]
+ }
+ ],
+ "source": [
+ "i=0\n",
+ "for (image, label) in train_dataset.take(5000):\n",
+ " if i==0:\n",
+ " X_train = image.numpy().reshape((1,28*28))\n",
+ " y_train = np.array([label])\n",
+ " else:\n",
+ " X_train = np.concatenate([X_train, image.numpy().reshape((1,28*28))], axis=0)\n",
+ " y_train = np.concatenate([y_train, np.array([label])], axis=0)\n",
+ " i+=1\n",
+ "print(\"Shape of image training data : \", X_train.shape)\n",
+ "print(\"Shape of training data labels : \", y_train.shape)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Shape of image test data : (500, 784)\n",
+ "Shape of test data labels : (500,)\n"
+ ]
+ }
+ ],
+ "source": [
+ "j=0\n",
+ "for (image, label) in test_dataset.take(500):\n",
+ " if j==0:\n",
+ " X_test = image.numpy().reshape((1,28*28))\n",
+ " y_test = np.array([label])\n",
+ " else:\n",
+ " X_test = np.concatenate([X_test, image.numpy().reshape((1,28*28))], axis=0)\n",
+ " y_test = np.concatenate([y_test, np.array([label])], axis=0)\n",
+ " j+=1\n",
+ "print(\"Shape of image test data : \", X_test.shape)\n",
+ "print(\"Shape of test data labels : \", y_test.shape)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "-KtnHECKZni_"
+ },
+ "source": [
+ "# Exercises\n",
+ "\n",
+ "1. Apply Nearest Neighbour with L1 distance to this subset of the dataset and determine the accuracy on the \n",
+ "test dataset and plot the confusion matrix.\n",
+ "\n",
+ "2. Apply K-Nearest Neighbour with $k=5$ and L2 distance to this subset of the dataset and determine the accuracy on the test dataset and plot the confusion matrix.\n",
+ "\n",
+ "3. Determine by means of 5-fold cross-validation the best value of $k$ in the set $\\{1,4,5,10,12,18,20\\}$.\n",
+ "\n",
+ "4. Scale the pixel values to the interval $[0, 1]$ and compute the test accuracy for the best value of k determined in exercise 3.\n",
+ "\n",
+ "5. Implement the Cosine distance measure in the k-nearest neighbour classifier. The cosine distance between two vectors $a$ and $b$ can be computed by\n",
+ "\n",
+ "```python\n",
+ "from numpy.linalg import norm\n",
+ "from numpy import dot\n",
+ "\n",
+ "dists[a,b] = 1 - dot(a, b)/(norm(a)*norm(b))\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Solution 1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class KNearestNeighbor():\n",
+ " \"\"\" a kNN classifier with L2 distance \"\"\"\n",
+ "\n",
+ " def __init__(self):\n",
+ " pass\n",
+ "\n",
+ " def train(self, X, y):\n",
+ " \"\"\"\n",
+ " Train the classifier. For k-nearest neighbors this is just \n",
+ " memorizing the training data.\n",
+ "\n",
+ " Inputs:\n",
+ " - X: A numpy array of shape (num_train, D) containing the training data\n",
+ " consisting of num_train samples each of dimension D.\n",
+ " - y: A numpy array of shape (N,) containing the training labels, where\n",
+ " y[i] is the label for X[i].\n",
+ " \"\"\"\n",
+ " self.X_train = X.astype('float')\n",
+ " self.y_train = y\n",
+ " \n",
+ " def predict(self, X, k=1, num_loops=0):\n",
+ " \"\"\"\n",
+ " Predict labels for test data using this classifier.\n",
+ "\n",
+ " Inputs:\n",
+ " - X: A numpy array of shape (num_test, D) containing test data consisting\n",
+ " of num_test samples each of dimension D.\n",
+ " - k: The number of nearest neighbors that vote for the predicted labels.\n",
+ " - num_loops: Determines which implementation to use to compute distances\n",
+ " between training points and testing points.\n",
+ "\n",
+ " Returns:\n",
+ " - y: A numpy array of shape (num_test,) containing predicted labels for the\n",
+ " test data, where y[i] is the predicted label for the test point X[i]. \n",
+ " \"\"\"\n",
+ " if num_loops == 0:\n",
+ " dists = self.compute_distances_no_loops(X)\n",
+ " elif num_loops == 1:\n",
+ " dists = self.compute_distances_one_loop(X)\n",
+ " elif num_loops == 2:\n",
+ " dists = self.compute_distances_two_loops(X)\n",
+ " else:\n",
+ " raise ValueError('Invalid value %d for num_loops' % num_loops)\n",
+ "\n",
+ " return self.predict_labels(dists, k=k)\n",
+ "\n",
+ " def compute_distances_two_loops(self, X):\n",
+ " \"\"\"\n",
+ " Compute the distance between each test point in X and each \n",
+ " training point in self.X_train using a nested loop over both \n",
+ " the training data and the test data.\n",
+ "\n",
+ " Inputs:\n",
+ " - X: A numpy array of shape (num_test, D) containing test data.\n",
+ "\n",
+ " Returns:\n",
+ " - dists: A numpy array of shape (num_test, num_train) where \n",
+ " dists[i, j] is the Euclidean distance between the ith test \n",
+ " point and the jth training point.\n",
+ " \"\"\"\n",
+ " num_test = X.shape[0]\n",
+ " num_train = self.X_train.shape[0]\n",
+ " dists = np.zeros((num_test, num_train))\n",
+ " X = X.astype('float')\n",
+ " for i in range(num_test):\n",
+ " for j in range(num_train):\n",
+ " dists[i, j] = np.sqrt(np.sum(np.square(self.X_train[j,:] - X[i,:])))\n",
+ " \n",
+ " return dists\n",
+ "\n",
+ " def compute_distances_one_loop(self, X):\n",
+ " \"\"\"\n",
+ " Compute the distance between each test point in X and each training point\n",
+ " in self.X_train using a single loop over the test data.\n",
+ "\n",
+ " Input / Output: Same as compute_distances_two_loops\n",
+ " \"\"\"\n",
+ " num_test = X.shape[0]\n",
+ " num_train = self.X_train.shape[0]\n",
+ " dists = np.zeros((num_test, num_train))\n",
+ " X = X.astype('float')\n",
+ " for i in range(num_test):\n",
+ " dists[i, :] = np.sqrt(np.sum(np.square(self.X_train - X[i,:]), axis = 1))\n",
+ " \n",
+ " \n",
+ " return dists\n",
+ "\n",
+ " def compute_distances_no_loops(self, X):\n",
+ " \"\"\"\n",
+ " Compute the distance between each test point in X and each training point\n",
+ " in self.X_train using no explicit loops.\n",
+ "\n",
+ " Input / Output: Same as compute_distances_two_loops\n",
+ " \"\"\"\n",
+ " num_test = X.shape[0]\n",
+ " num_train = self.X_train.shape[0]\n",
+ " dists = np.zeros((num_test, num_train)) \n",
+ " X=X.astype('float')\n",
+ " \n",
+ " # Most \"elegant\" solution leads however to memory issues\n",
+ " # dists = np.sqrt(np.square((self.X_train[:, np.newaxis, :] - X)).sum(axis=2)).T\n",
+ " # split (p-q)^2 to p^2 + q^2 - 2pq\n",
+ " dists = np.sqrt((X**2).sum(axis=1)[:, np.newaxis] + (self.X_train**2).sum(axis=1) - 2 * X.dot(self.X_train.T))\n",
+ " \n",
+ " \n",
+ " \n",
+ " return dists\n",
+ "\n",
+ " def predict_labels(self, dists, k=1):\n",
+ " \"\"\"\n",
+ " Given a matrix of distances between test points and training points,\n",
+ " predict a label for each test point.\n",
+ "\n",
+ " Inputs:\n",
+ " - dists: A numpy array of shape (num_test, num_train) where dists[i, j]\n",
+ " gives the distance betwen the ith test point and the jth training point.\n",
+ "\n",
+ " Returns:\n",
+ " - y: A numpy array of shape (num_test,) containing predicted labels for the\n",
+ " test data, where y[i] is the predicted label for the test point X[i]. \n",
+ " \"\"\"\n",
+ " num_test = dists.shape[0]\n",
+ " y_pred = np.zeros(num_test, dtype='float64')\n",
+ " for i in range(num_test):\n",
+ " # A list of length k storing the labels of the k nearest neighbors to\n",
+ " # the ith test point.\n",
+ " closest_y = []\n",
+ " # get the k indices with smallest distances\n",
+ " min_indices = np.argsort(dists[i,:])[:k] \n",
+ " closest_y = np.bincount(self.y_train[min_indices])\n",
+ " # predict the label of the nearest example\n",
+ " y_pred[i] = np.argmax(closest_y) \n",
+ "\n",
+ " return y_pred\n",
+ "\n",
+ "class KNearestNeighbor_L1(KNearestNeighbor):\n",
+ " \"\"\" a kNN classifier with L1 distance \"\"\"\n",
+ "\n",
+ " def __init__(self):\n",
+ " super().__init__()\n",
+ " \n",
+ "\n",
+ " def compute_distances_one_loop(self, X):\n",
+ " \"\"\"\n",
+ " We overwrite the compute_distance_one_loop method of the parent class \n",
+ " KNearestNeighbor. \n",
+ " Compute the distance between each test point in X and each training point\n",
+ " in self.X_train using one loop and the L1 distance measure.\n",
+ "\n",
+ " Input / Output: Same as compute_distances_two_loops\n",
+ " \"\"\"\n",
+ " num_test = X.shape[0]\n",
+ " num_train = self.X_train.shape[0]\n",
+ " dists = np.zeros((num_test, num_train))\n",
+ " X = X.astype('float')\n",
+ " for i in range(num_test):\n",
+ " dists[i, :] = (np.sum(np.abs(self.X_train - X[i,:]), axis = 1))\n",
+ " \n",
+ " \n",
+ " return dists"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "classifier = KNearestNeighbor_L1()\n",
+ "classifier.train(X_train, y_train)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(500, 5000)"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dists = classifier.compute_distances_no_loops(X_test)\n",
+ "dists.shape "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "y_test_pred = classifier.predict_labels(dists, k=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Got 399 / 500 correct => accuracy: 0.798000\n"
+ ]
+ }
+ ],
+ "source": [
+ "num_test = X_test.shape[0]\n",
+ "\n",
+ "num_correct = np.sum(y_test_pred == y_test)\n",
+ "accuracy = float(num_correct) / num_test\n",
+ "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 648x648 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# utility function for plotting confusion matrix\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "from sklearn.metrics import confusion_matrix\n",
+ "\n",
+ "def plot_confmat(y_true, y_pred):\n",
+ " \"\"\"\n",
+ " Plot the confusion matrix and save to user_files dir\n",
+ " \"\"\"\n",
+ " conf_matrix = confusion_matrix(y_true, y_pred)\n",
+ " fig = plt.figure(figsize=(9,9))\n",
+ " ax = fig.add_subplot(111)\n",
+ " sns.heatmap(conf_matrix,\n",
+ " annot=True,\n",
+ " fmt='.0f')\n",
+ " plt.title('Confusion matrix')\n",
+ " ax.set_xticklabels(class_names)\n",
+ " ax.set_yticklabels(class_names)\n",
+ " plt.ylabel('True')\n",
+ " plt.xlabel('Predicted')\n",
+ " \n",
+ "plot_confmat(y_test, y_test_pred) "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Solution 2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "classifier = KNearestNeighbor()\n",
+ "classifier.train(X_train, y_train)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(500, 5000)"
+ ]
+ },
+ "execution_count": 31,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dists = classifier.compute_distances_no_loops(X_test)\n",
+ "dists.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "y_test_pred = classifier.predict_labels(dists, k=5)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Got 400 / 500 correct => accuracy: 0.800000\n"
+ ]
+ }
+ ],
+ "source": [
+ "num_test = X_test.shape[0]\n",
+ "\n",
+ "num_correct = np.sum(y_test_pred == y_test)\n",
+ "accuracy = float(num_correct) / num_test\n",
+ "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 648x648 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_confmat(y_test, y_test_pred) "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Solution 3"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "num_folds = 5\n",
+ "\n",
+ "k_choices = [1, 4, 5, 10, 12, 18, 20]\n",
+ "\n",
+ "X_train_folds = []\n",
+ "y_train_folds = []"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "num_train = X_train.shape[0]\n",
+ "fold_size = np.ceil(num_train/num_folds).astype('int')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "X_train_folds = np.split(X_train, [(i + 1)*fold_size for i in np.arange(num_folds)])\n",
+ "y_train_folds = np.split(y_train, [(i + 1)*fold_size for i in np.arange(num_folds)])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "k_to_accuracies = {}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for k in k_choices:\n",
+ " \n",
+ " k_to_accuracies[k] = []\n",
+ " classifier = KNearestNeighbor()\n",
+ " for i in range(num_folds):\n",
+ " X_cv_training = np.concatenate([x for k, x in enumerate(X_train_folds) if k!=i], axis=0)\n",
+ " y_cv_training = np.concatenate([x for k, x in enumerate(y_train_folds) if k!=i], axis=0)\n",
+ " classifier.train(X_cv_training, y_cv_training)\n",
+ " dists = classifier.compute_distances_no_loops(X_train_folds[i])\n",
+ " y_test_pred = classifier.predict_labels(dists, k=k)\n",
+ " k_to_accuracies[k].append(np.mean(y_train_folds[i] == y_test_pred))\n",
+ " \n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "k = 1, accuracy = 0.809000\n",
+ "k = 1, accuracy = 0.809000\n",
+ "k = 1, accuracy = 0.812000\n",
+ "k = 1, accuracy = 0.780000\n",
+ "k = 1, accuracy = 0.790000\n",
+ "k = 4, accuracy = 0.811000\n",
+ "k = 4, accuracy = 0.834000\n",
+ "k = 4, accuracy = 0.842000\n",
+ "k = 4, accuracy = 0.806000\n",
+ "k = 4, accuracy = 0.799000\n",
+ "k = 5, accuracy = 0.804000\n",
+ "k = 5, accuracy = 0.828000\n",
+ "k = 5, accuracy = 0.839000\n",
+ "k = 5, accuracy = 0.807000\n",
+ "k = 5, accuracy = 0.805000\n",
+ "k = 10, accuracy = 0.810000\n",
+ "k = 10, accuracy = 0.827000\n",
+ "k = 10, accuracy = 0.832000\n",
+ "k = 10, accuracy = 0.805000\n",
+ "k = 10, accuracy = 0.803000\n",
+ "k = 12, accuracy = 0.808000\n",
+ "k = 12, accuracy = 0.818000\n",
+ "k = 12, accuracy = 0.833000\n",
+ "k = 12, accuracy = 0.802000\n",
+ "k = 12, accuracy = 0.802000\n",
+ "k = 18, accuracy = 0.804000\n",
+ "k = 18, accuracy = 0.810000\n",
+ "k = 18, accuracy = 0.832000\n",
+ "k = 18, accuracy = 0.798000\n",
+ "k = 18, accuracy = 0.791000\n",
+ "k = 20, accuracy = 0.799000\n",
+ "k = 20, accuracy = 0.806000\n",
+ "k = 20, accuracy = 0.822000\n",
+ "k = 20, accuracy = 0.797000\n",
+ "k = 20, accuracy = 0.787000\n"
+ ]
+ }
+ ],
+ "source": [
+ "for k in sorted(k_to_accuracies):\n",
+ " for accuracy in k_to_accuracies[k]:\n",
+ " print('k = %d, accuracy = %f' % (k, accuracy))\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# plot the raw observations\n",
+ "for k in k_choices:\n",
+ " accuracies = k_to_accuracies[k]\n",
+ " plt.scatter([k] * len(accuracies), accuracies)\n",
+ "\n",
+ "# plot the trend line with error bars that correspond \n",
+ "# to standard deviation\n",
+ "accuracies_mean = np.array([np.mean(v) for k,v in \n",
+ " sorted(k_to_accuracies.items())])\n",
+ "accuracies_std = np.array([np.std(v) for k,v in sorted(k_to_accuracies.items())])\n",
+ "plt.errorbar(k_choices, accuracies_mean, yerr=accuracies_std)\n",
+ "plt.title('Cross-validation on k')\n",
+ "plt.xlabel('k')\n",
+ "plt.ylabel('Cross-validation accuracy')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Solution 4"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Got 399 / 500 correct => accuracy: 0.798000\n"
+ ]
+ }
+ ],
+ "source": [
+ "X_train =X_train/255\n",
+ "X_test = X_test/255\n",
+ "\n",
+ "classifier = KNearestNeighbor()\n",
+ "classifier.train(X_train, y_train)\n",
+ "\n",
+ "dists = classifier.compute_distances_no_loops(X_test)\n",
+ "dists.shape\n",
+ "\n",
+ "y_test_pred = classifier.predict_labels(dists, k=1)\n",
+ "\n",
+ "\n",
+ "num_test = X_test.shape[0]\n",
+ "\n",
+ "num_correct = np.sum(y_test_pred == y_test)\n",
+ "accuracy = float(num_correct) / num_test\n",
+ "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Solution 5"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from numpy.linalg import norm\n",
+ "from numpy import dot\n",
+ "\n",
+ "class KNearestNeighbor_cosine():\n",
+ " \"\"\" a kNN classifier with L2 distance \"\"\"\n",
+ "\n",
+ " def __init__(self):\n",
+ " pass\n",
+ "\n",
+ " def train(self, X, y):\n",
+ " \"\"\"\n",
+ " Train the classifier. For k-nearest neighbors this is just \n",
+ " memorizing the training data.\n",
+ "\n",
+ " Inputs:\n",
+ " - X: A numpy array of shape (num_train, D) containing the training data\n",
+ " consisting of num_train samples each of dimension D.\n",
+ " - y: A numpy array of shape (N,) containing the training labels, where\n",
+ " y[i] is the label for X[i].\n",
+ " \"\"\"\n",
+ " self.X_train = X.astype('float')\n",
+ " self.y_train = y\n",
+ " \n",
+ " def predict(self, X, k=1):\n",
+ " \"\"\"\n",
+ " Predict labels for test data using this classifier.\n",
+ "\n",
+ " Inputs:\n",
+ " - X: A numpy array of shape (num_test, D) containing test data consisting\n",
+ " of num_test samples each of dimension D.\n",
+ " - k: The number of nearest neighbors that vote for the predicted labels.\n",
+ " - num_loops: Determines which implementation to use to compute distances\n",
+ " between training points and testing points.\n",
+ "\n",
+ " Returns:\n",
+ " - y: A numpy array of shape (num_test,) containing predicted labels for the\n",
+ " test data, where y[i] is the predicted label for the test point X[i]. \n",
+ " \"\"\"\n",
+ " \n",
+ " dists = self.compute_distances_two_loops(X)\n",
+ " \n",
+ "\n",
+ " return self.predict_labels(dists, k=k)\n",
+ "\n",
+ " def compute_distances_two_loops(self, X):\n",
+ " \"\"\"\n",
+ " Compute the distance between each test point in X and each \n",
+ " training point in self.X_train using a nested loop over both \n",
+ " the training data and the test data.\n",
+ "\n",
+ " Inputs:\n",
+ " - X: A numpy array of shape (num_test, D) containing test data.\n",
+ "\n",
+ " Returns:\n",
+ " - dists: A numpy array of shape (num_test, num_train) where \n",
+ " dists[i, j] is the Euclidean distance between the ith test \n",
+ " point and the jth training point.\n",
+ " \"\"\"\n",
+ " num_test = X.shape[0]\n",
+ " num_train = self.X_train.shape[0]\n",
+ " dists = np.zeros((num_test, num_train))\n",
+ " X = X.astype('float')\n",
+ " for i in range(num_test):\n",
+ " for j in range(num_train):\n",
+ " dists[i, j] = 1 - dot(self.X_train[j,:] , X[i,:])/(norm(self.X_train[j,:])*norm(self.X_train[j,:]))\n",
+ " \n",
+ " return dists\n",
+ "\n",
+ "\n",
+ "\n",
+ " def predict_labels(self, dists, k=1):\n",
+ " \"\"\"\n",
+ " Given a matrix of distances between test points and training points,\n",
+ " predict a label for each test point.\n",
+ "\n",
+ " Inputs:\n",
+ " - dists: A numpy array of shape (num_test, num_train) where dists[i, j]\n",
+ " gives the distance betwen the ith test point and the jth training point.\n",
+ "\n",
+ " Returns:\n",
+ " - y: A numpy array of shape (num_test,) containing predicted labels for the\n",
+ " test data, where y[i] is the predicted label for the test point X[i]. \n",
+ " \"\"\"\n",
+ " num_test = dists.shape[0]\n",
+ " y_pred = np.zeros(num_test, dtype='float64')\n",
+ " for i in range(num_test):\n",
+ " # A list of length k storing the labels of the k nearest neighbors to\n",
+ " # the ith test point.\n",
+ " closest_y = []\n",
+ " # get the k indices with smallest distances\n",
+ " min_indices = np.argsort(dists[i,:])[:k] \n",
+ " closest_y = np.bincount(self.y_train[min_indices])\n",
+ " # predict the label of the nearest example\n",
+ " y_pred[i] = np.argmax(closest_y) \n",
+ "\n",
+ " return y_pred\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "classifier = KNearestNeighbor_cosine()\n",
+ "classifier.train(X_train, y_train)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(500, 5000)"
+ ]
+ },
+ "execution_count": 46,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dists = classifier.compute_distances_two_loops(X_test)\n",
+ "dists.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "y_test_pred = classifier.predict_labels(dists, k=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Got 192 / 500 correct => accuracy: 0.384000\n"
+ ]
+ }
+ ],
+ "source": [
+ "num_test = X_test.shape[0]\n",
+ "\n",
+ "num_correct = np.sum(y_test_pred == y_test)\n",
+ "accuracy = float(num_correct) / num_test\n",
+ "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
+ ]
+ }
+ ],
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "collapsed_sections": [],
+ "name": "Classifying Images of Clothing",
+ "private_outputs": true,
+ "provenance": [],
+ "toc_visible": true,
+ "version": "0.3.2"
+ },
+ "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": 1
+}
--
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