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Kautenja/LogicalCalculator-DeepNet.ipynb

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Solving all binary logic gate functions with one deep net
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LogicalCalculator-DeepNet.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"from pandas import DataFrame"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Truth tables "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def dataframe(truthtable: list, operation: str):\n",
" \"\"\"\n",
" Return a dataframe for the given logic gate truthtable.\n",
" \n",
" Args:\n",
" truthtable: a 2d matix of the truthtable for the logic gate\n",
" operation: the name of the logic gate operation\n",
" \"\"\"\n",
" columns = ['$x$', '$y$', '$f(x,y)$']\n",
" df = DataFrame(truthtable, columns=columns)\n",
" df['$f$'] = operation\n",
" return df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## AND"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>$x$</th>\n",
" <th>$y$</th>\n",
" <th>$f(x,y)$</th>\n",
" <th>$f$</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>AND</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>AND</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>AND</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>AND</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" $x$ $y$ $f(x,y)$ $f$\n",
"0 0 0 0 AND\n",
"1 0 1 0 AND\n",
"2 1 0 0 AND\n",
"3 1 1 1 AND"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"AND = dataframe([[0, 0, 0], [0, 1, 0], [1, 0, 0], [1, 1, 1]], 'AND')\n",
"AND"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## OR"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
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" text-align: left;\n",
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" .dataframe tbody tr th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>$x$</th>\n",
" <th>$y$</th>\n",
" <th>$f(x,y)$</th>\n",
" <th>$f$</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>OR</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
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" <td>1</td>\n",
" <td>OR</td>\n",
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" <tr>\n",
" <th>2</th>\n",
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" <td>0</td>\n",
" <td>1</td>\n",
" <td>OR</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>OR</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" $x$ $y$ $f(x,y)$ $f$\n",
"0 0 0 0 OR\n",
"1 0 1 1 OR\n",
"2 1 0 1 OR\n",
"3 1 1 1 OR"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"OR = dataframe([[0, 0, 0], [0, 1, 1], [1, 0, 1], [1, 1, 1]], 'OR')\n",
"OR"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## NAND"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
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"\n",
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" text-align: left;\n",
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" .dataframe tbody tr th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>$x$</th>\n",
" <th>$y$</th>\n",
" <th>$f(x,y)$</th>\n",
" <th>$f$</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>NAND</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>NAND</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>NAND</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>NAND</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" $x$ $y$ $f(x,y)$ $f$\n",
"0 0 0 1 NAND\n",
"1 0 1 1 NAND\n",
"2 1 0 1 NAND\n",
"3 1 1 0 NAND"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"NAND = dataframe([[0, 0, 1], [0, 1, 1], [1, 0, 1], [1, 1, 0]], 'NAND')\n",
"NAND"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## NOR"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
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"\n",
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" text-align: left;\n",
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"\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>$x$</th>\n",
" <th>$y$</th>\n",
" <th>$f(x,y)$</th>\n",
" <th>$f$</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>NOR</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>NOR</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>NOR</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>NOR</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" $x$ $y$ $f(x,y)$ $f$\n",
"0 0 0 1 NOR\n",
"1 0 1 0 NOR\n",
"2 1 0 0 NOR\n",
"3 1 1 0 NOR"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"NOR = dataframe([[0, 0, 1], [0, 1, 0], [1, 0, 0], [1, 1, 0]], 'NOR')\n",
"NOR"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## XOR"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
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" text-align: left;\n",
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"</style>\n",
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" <thead>\n",
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" <th></th>\n",
" <th>$x$</th>\n",
" <th>$y$</th>\n",
" <th>$f(x,y)$</th>\n",
" <th>$f$</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>XOR</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>XOR</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>XOR</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>XOR</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" $x$ $y$ $f(x,y)$ $f$\n",
"0 0 0 0 XOR\n",
"1 0 1 1 XOR\n",
"2 1 0 1 XOR\n",
"3 1 1 0 XOR"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"XOR = dataframe([[0, 0, 0], [0, 1, 1], [1, 0, 1], [1, 1, 0]], 'XOR')\n",
"XOR"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## XNOR"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>XNOR</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>XNOR</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>XNOR</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" $x$ $y$ $f(x,y)$ $f$\n",
"0 0 0 1 XNOR\n",
"1 0 1 0 XNOR\n",
"2 1 0 0 XNOR\n",
"3 1 1 1 XNOR"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"XNOR = dataframe([[0, 0, 1], [0, 1, 0], [1, 0, 0], [1, 1, 1]], 'XNOR')\n",
"XNOR"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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" <th>$f$_AND</th>\n",
" <th>$f$_NAND</th>\n",
" <th>$f$_NOR</th>\n",
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" <tr>\n",
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" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <th>2</th>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" $x$ $y$ $f(x,y)$ $f$_AND $f$_NAND $f$_NOR $f$_OR $f$_XNOR $f$_XOR\n",
"0 0 0 0 1 0 0 0 0 0\n",
"1 0 1 0 1 0 0 0 0 0\n",
"2 1 0 0 1 0 0 0 0 0\n",
"3 1 1 1 1 0 0 0 0 0\n",
"0 0 0 0 0 0 0 1 0 0\n",
"1 0 1 1 0 0 0 1 0 0\n",
"2 1 0 1 0 0 0 1 0 0\n",
"3 1 1 1 0 0 0 1 0 0\n",
"0 0 0 1 0 1 0 0 0 0\n",
"1 0 1 1 0 1 0 0 0 0\n",
"2 1 0 1 0 1 0 0 0 0\n",
"3 1 1 0 0 1 0 0 0 0\n",
"0 0 0 1 0 0 1 0 0 0\n",
"1 0 1 0 0 0 1 0 0 0\n",
"2 1 0 0 0 0 1 0 0 0\n",
"3 1 1 0 0 0 1 0 0 0\n",
"0 0 0 0 0 0 0 0 0 1\n",
"1 0 1 1 0 0 0 0 0 1\n",
"2 1 0 1 0 0 0 0 0 1\n",
"3 1 1 0 0 0 0 0 0 1\n",
"0 0 0 1 0 0 0 0 1 0\n",
"1 0 1 0 0 0 0 0 1 0\n",
"2 1 0 0 0 0 0 0 1 0\n",
"3 1 1 1 0 0 0 0 1 0"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gates = pd.concat([AND, OR, NAND, NOR, XOR, XNOR])\n",
"gates = pd.get_dummies(gates)\n",
"gates"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"0 0\n",
"1 0\n",
"2 0\n",
"3 1\n",
"0 0\n",
"Name: $f(x,y)$, dtype: int64"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"target_column = '$f(x,y)$'\n",
"target = gates[target_column]\n",
"target.head()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>$x$</th>\n",
" <th>$y$</th>\n",
" <th>$f$_AND</th>\n",
" <th>$f$_NAND</th>\n",
" <th>$f$_NOR</th>\n",
" <th>$f$_OR</th>\n",
" <th>$f$_XNOR</th>\n",
" <th>$f$_XOR</th>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
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" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" $x$ $y$ $f$_AND $f$_NAND $f$_NOR $f$_OR $f$_XNOR $f$_XOR\n",
"0 0 0 1 0 0 0 0 0\n",
"1 0 1 1 0 0 0 0 0\n",
"2 1 0 1 0 0 0 0 0\n",
"3 1 1 1 0 0 0 0 0\n",
"0 0 0 0 0 0 1 0 0\n",
"1 0 1 0 0 0 1 0 0\n",
"2 1 0 0 0 0 1 0 0\n",
"3 1 1 0 0 0 1 0 0\n",
"0 0 0 0 1 0 0 0 0\n",
"1 0 1 0 1 0 0 0 0\n",
"2 1 0 0 1 0 0 0 0\n",
"3 1 1 0 1 0 0 0 0\n",
"0 0 0 0 0 1 0 0 0\n",
"1 0 1 0 0 1 0 0 0\n",
"2 1 0 0 0 1 0 0 0\n",
"3 1 1 0 0 1 0 0 0\n",
"0 0 0 0 0 0 0 0 1\n",
"1 0 1 0 0 0 0 0 1\n",
"2 1 0 0 0 0 0 0 1\n",
"3 1 1 0 0 0 0 0 1\n",
"0 0 0 0 0 0 0 1 0\n",
"1 0 1 0 0 0 0 1 0\n",
"2 1 0 0 0 0 0 1 0\n",
"3 1 1 0 0 0 0 1 0"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"feature_columns = list(gates.columns)\n",
"feature_columns.remove(target_column)\n",
"features = gates[feature_columns]\n",
"features"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Building a Model"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# reproducable random number seeds\n",
"## keras\n",
"from numpy.random import seed\n",
"seed(10)\n",
"## TensorFlow\n",
"from tensorflow import set_random_seed\n",
"set_random_seed(10)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"from keras.models import Sequential\n",
"from keras.layers import Dense"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# build the network in the graph above\n",
"model = Sequential()\n",
"model.add(Dense(16, input_dim=8, activation='relu'))\n",
"model.add(Dense(8, activation='relu'))\n",
"model.add(Dense(1, activation='sigmoid'))"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Training"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/500\n",
"24/24 [==============================] - 0s - loss: 0.7268 - acc: 0.4167\n",
"Epoch 2/500\n",
"24/24 [==============================] - 0s - loss: 0.7247 - acc: 0.4167\n",
"Epoch 3/500\n",
"24/24 [==============================] - 0s - loss: 0.7227 - acc: 0.4167\n",
"Epoch 4/500\n",
"24/24 [==============================] - 0s - loss: 0.7207 - acc: 0.4167\n",
"Epoch 5/500\n",
"24/24 [==============================] - 0s - loss: 0.7188 - acc: 0.4583\n",
"Epoch 6/500\n",
"24/24 [==============================] - 0s - loss: 0.7170 - acc: 0.5417\n",
"Epoch 7/500\n",
"24/24 [==============================] - 0s - loss: 0.7152 - acc: 0.5417\n",
"Epoch 8/500\n",
"24/24 [==============================] - 0s - loss: 0.7135 - acc: 0.5417\n",
"Epoch 9/500\n",
"24/24 [==============================] - 0s - loss: 0.7118 - acc: 0.5417\n",
"Epoch 10/500\n",
"24/24 [==============================] - 0s - loss: 0.7103 - acc: 0.5417\n",
"Epoch 11/500\n",
"24/24 [==============================] - 0s - loss: 0.7088 - acc: 0.5417\n",
"Epoch 12/500\n",
"24/24 [==============================] - 0s - loss: 0.7073 - acc: 0.5417\n",
"Epoch 13/500\n",
"24/24 [==============================] - 0s - loss: 0.7058 - acc: 0.5833\n",
"Epoch 14/500\n",
"24/24 [==============================] - 0s - loss: 0.7044 - acc: 0.5833\n",
"Epoch 15/500\n",
"24/24 [==============================] - 0s - loss: 0.7030 - acc: 0.5833\n",
"Epoch 16/500\n",
"24/24 [==============================] - 0s - loss: 0.7016 - acc: 0.5833\n",
"Epoch 17/500\n",
"24/24 [==============================] - 0s - loss: 0.7004 - acc: 0.5833\n",
"Epoch 18/500\n",
"24/24 [==============================] - 0s - loss: 0.6991 - acc: 0.5833\n",
"Epoch 19/500\n",
"24/24 [==============================] - 0s - loss: 0.6979 - acc: 0.5417\n",
"Epoch 20/500\n",
"24/24 [==============================] - 0s - loss: 0.6967 - acc: 0.5417\n",
"Epoch 21/500\n",
"24/24 [==============================] - 0s - loss: 0.6955 - acc: 0.5417\n",
"Epoch 22/500\n",
"24/24 [==============================] - 0s - loss: 0.6944 - acc: 0.5417\n",
"Epoch 23/500\n",
"24/24 [==============================] - 0s - loss: 0.6933 - acc: 0.5833\n",
"Epoch 24/500\n",
"24/24 [==============================] - 0s - loss: 0.6922 - acc: 0.5833\n",
"Epoch 25/500\n",
"24/24 [==============================] - 0s - loss: 0.6911 - acc: 0.5833\n",
"Epoch 26/500\n",
"24/24 [==============================] - 0s - loss: 0.6901 - acc: 0.5833\n",
"Epoch 27/500\n",
"24/24 [==============================] - 0s - loss: 0.6892 - acc: 0.6250\n",
"Epoch 28/500\n",
"24/24 [==============================] - 0s - loss: 0.6882 - acc: 0.6250\n",
"Epoch 29/500\n",
"24/24 [==============================] - 0s - loss: 0.6873 - acc: 0.6250\n",
"Epoch 30/500\n",
"24/24 [==============================] - 0s - loss: 0.6863 - acc: 0.6250\n",
"Epoch 31/500\n",
"24/24 [==============================] - 0s - loss: 0.6854 - acc: 0.6250\n",
"Epoch 32/500\n",
"24/24 [==============================] - 0s - loss: 0.6846 - acc: 0.6250\n",
"Epoch 33/500\n",
"24/24 [==============================] - 0s - loss: 0.6837 - acc: 0.6250\n",
"Epoch 34/500\n",
"24/24 [==============================] - 0s - loss: 0.6828 - acc: 0.6250\n",
"Epoch 35/500\n",
"24/24 [==============================] - 0s - loss: 0.6819 - acc: 0.6250\n",
"Epoch 36/500\n",
"24/24 [==============================] - 0s - loss: 0.6811 - acc: 0.6250\n",
"Epoch 37/500\n",
"24/24 [==============================] - 0s - loss: 0.6802 - acc: 0.6250\n",
"Epoch 38/500\n",
"24/24 [==============================] - 0s - loss: 0.6794 - acc: 0.6250\n",
"Epoch 39/500\n",
"24/24 [==============================] - 0s - loss: 0.6786 - acc: 0.6250\n",
"Epoch 40/500\n",
"24/24 [==============================] - 0s - loss: 0.6778 - acc: 0.6250\n",
"Epoch 41/500\n",
"24/24 [==============================] - 0s - loss: 0.6770 - acc: 0.6667\n",
"Epoch 42/500\n",
"24/24 [==============================] - 0s - loss: 0.6762 - acc: 0.6667\n",
"Epoch 43/500\n",
"24/24 [==============================] - 0s - loss: 0.6754 - acc: 0.6667\n",
"Epoch 44/500\n",
"24/24 [==============================] - 0s - loss: 0.6746 - acc: 0.6667\n",
"Epoch 45/500\n",
"24/24 [==============================] - 0s - loss: 0.6738 - acc: 0.7083\n",
"Epoch 46/500\n",
"24/24 [==============================] - 0s - loss: 0.6730 - acc: 0.7083\n",
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]
},
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]
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]
},
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]
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"24/24 [==============================] - 0s - loss: 0.1448 - acc: 1.0000\n",
"Epoch 452/500\n",
"24/24 [==============================] - 0s - loss: 0.1436 - acc: 1.0000\n",
"Epoch 453/500\n",
"24/24 [==============================] - 0s - loss: 0.1425 - acc: 1.0000\n",
"Epoch 454/500\n",
"24/24 [==============================] - 0s - loss: 0.1413 - acc: 1.0000\n",
"Epoch 455/500\n",
"24/24 [==============================] - 0s - loss: 0.1402 - acc: 1.0000\n",
"Epoch 456/500\n",
"24/24 [==============================] - 0s - loss: 0.1390 - acc: 1.0000\n",
"Epoch 457/500\n",
"24/24 [==============================] - 0s - loss: 0.1379 - acc: 1.0000\n",
"Epoch 458/500\n",
"24/24 [==============================] - 0s - loss: 0.1368 - acc: 1.0000\n",
"Epoch 459/500\n",
"24/24 [==============================] - 0s - loss: 0.1357 - acc: 1.0000\n",
"Epoch 460/500\n",
"24/24 [==============================] - 0s - loss: 0.1346 - acc: 1.0000\n",
"Epoch 461/500\n",
"24/24 [==============================] - 0s - loss: 0.1335 - acc: 1.0000\n",
"Epoch 462/500\n",
"24/24 [==============================] - 0s - loss: 0.1324 - acc: 1.0000\n",
"Epoch 463/500\n",
"24/24 [==============================] - 0s - loss: 0.1313 - acc: 1.0000\n",
"Epoch 464/500\n",
"24/24 [==============================] - 0s - loss: 0.1302 - acc: 1.0000\n",
"Epoch 465/500\n",
"24/24 [==============================] - 0s - loss: 0.1292 - acc: 1.0000\n",
"Epoch 466/500\n",
"24/24 [==============================] - 0s - loss: 0.1281 - acc: 1.0000\n",
"Epoch 467/500\n",
"24/24 [==============================] - 0s - loss: 0.1271 - acc: 1.0000\n",
"Epoch 468/500\n",
"24/24 [==============================] - 0s - loss: 0.1260 - acc: 1.0000\n",
"Epoch 469/500\n",
"24/24 [==============================] - 0s - loss: 0.1250 - acc: 1.0000\n",
"Epoch 470/500\n",
"24/24 [==============================] - 0s - loss: 0.1239 - acc: 1.0000\n",
"Epoch 471/500\n",
"24/24 [==============================] - 0s - loss: 0.1229 - acc: 1.0000\n",
"Epoch 472/500\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"24/24 [==============================] - 0s - loss: 0.1219 - acc: 1.0000\n",
"Epoch 473/500\n",
"24/24 [==============================] - 0s - loss: 0.1209 - acc: 1.0000\n",
"Epoch 474/500\n",
"24/24 [==============================] - 0s - loss: 0.1200 - acc: 1.0000\n",
"Epoch 475/500\n",
"24/24 [==============================] - 0s - loss: 0.1190 - acc: 1.0000\n",
"Epoch 476/500\n",
"24/24 [==============================] - 0s - loss: 0.1181 - acc: 1.0000\n",
"Epoch 477/500\n",
"24/24 [==============================] - 0s - loss: 0.1171 - acc: 1.0000\n",
"Epoch 478/500\n",
"24/24 [==============================] - 0s - loss: 0.1162 - acc: 1.0000\n",
"Epoch 479/500\n",
"24/24 [==============================] - 0s - loss: 0.1152 - acc: 1.0000\n",
"Epoch 480/500\n",
"24/24 [==============================] - 0s - loss: 0.1143 - acc: 1.0000\n",
"Epoch 481/500\n",
"24/24 [==============================] - 0s - loss: 0.1134 - acc: 1.0000\n",
"Epoch 482/500\n",
"24/24 [==============================] - 0s - loss: 0.1125 - acc: 1.0000\n",
"Epoch 483/500\n",
"24/24 [==============================] - 0s - loss: 0.1116 - acc: 1.0000\n",
"Epoch 484/500\n",
"24/24 [==============================] - 0s - loss: 0.1107 - acc: 1.0000\n",
"Epoch 485/500\n",
"24/24 [==============================] - 0s - loss: 0.1098 - acc: 1.0000\n",
"Epoch 486/500\n",
"24/24 [==============================] - 0s - loss: 0.1089 - acc: 1.0000\n",
"Epoch 487/500\n",
"24/24 [==============================] - 0s - loss: 0.1080 - acc: 1.0000\n",
"Epoch 488/500\n",
"24/24 [==============================] - 0s - loss: 0.1072 - acc: 1.0000\n",
"Epoch 489/500\n",
"24/24 [==============================] - 0s - loss: 0.1063 - acc: 1.0000\n",
"Epoch 490/500\n",
"24/24 [==============================] - 0s - loss: 0.1055 - acc: 1.0000\n",
"Epoch 491/500\n",
"24/24 [==============================] - 0s - loss: 0.1046 - acc: 1.0000\n",
"Epoch 492/500\n",
"24/24 [==============================] - 0s - loss: 0.1038 - acc: 1.0000\n",
"Epoch 493/500\n",
"24/24 [==============================] - 0s - loss: 0.1030 - acc: 1.0000\n",
"Epoch 494/500\n",
"24/24 [==============================] - 0s - loss: 0.1022 - acc: 1.0000\n",
"Epoch 495/500\n",
"24/24 [==============================] - 0s - loss: 0.1013 - acc: 1.0000\n",
"Epoch 496/500\n",
"24/24 [==============================] - 0s - loss: 0.1005 - acc: 1.0000\n",
"Epoch 497/500\n",
"24/24 [==============================] - 0s - loss: 0.0997 - acc: 1.0000\n",
"Epoch 498/500\n",
"24/24 [==============================] - 0s - loss: 0.0989 - acc: 1.0000\n",
"Epoch 499/500\n",
"24/24 [==============================] - 0s - loss: 0.0981 - acc: 1.0000\n",
"Epoch 500/500\n",
"24/24 [==============================] - 0s - loss: 0.0974 - acc: 1.0000\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x124686fd0>"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.fit(features.values, target.values, epochs=500, batch_size=100)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Evaluation"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"24/24 [==============================] - 0s\n"
]
},
{
"data": {
"text/plain": [
"[('loss', 0.096582226455211639), ('acc', 1.0)]"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# evaluate the test score and output some metrics\n",
"scores = model.evaluate(features.values, target.values)\n",
"[(model.metrics_names[i], scores[i]) for i in range(0, len(scores))]"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# the values only get close to 0 and 1, they need rounded\n",
"predictions = [round(prediction[0]) for prediction in model.predict(features.values)]"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 True\n",
"1 True\n",
"2 True\n",
"3 True\n",
"0 True\n",
"1 True\n",
"2 True\n",
"3 True\n",
"0 True\n",
"1 True\n",
"2 True\n",
"3 True\n",
"0 True\n",
"1 True\n",
"2 True\n",
"3 True\n",
"0 True\n",
"1 True\n",
"2 True\n",
"3 True\n",
"0 True\n",
"1 True\n",
"2 True\n",
"3 True\n",
"Name: $f(x,y)$, dtype: bool"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"predictions == target"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"yep it's totally possible"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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