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data-maker/Untitled.ipynb

81 lines
16 KiB
Plaintext

{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"x = np.arange(-4,4)\n",
"def sigmoid(x):\n",
" e = np.exp(-x)\n",
" return np.divide(1,e + e)\n",
"df = pd.DataFrame({\"x\":x,\"tanh\":np.tanh(x),\"sigmoid\":sigmoid( np.tanh(x))})"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7ff349080d30>"
]
},
"execution_count": 10,
"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": [
"df[['tanh','sigmoid']].plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}