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81 lines
16 KiB
Plaintext
81 lines
16 KiB
Plaintext
5 years ago
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n",
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"\n",
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"x = np.arange(-4,4)\n",
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"def sigmoid(x):\n",
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" e = np.exp(-x)\n",
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" return np.divide(1,e + e)\n",
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"df = pd.DataFrame({\"x\":x,\"tanh\":np.tanh(x),\"sigmoid\":sigmoid( np.tanh(x))})"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<matplotlib.axes._subplots.AxesSubplot at 0x7ff349080d30>"
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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}
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],
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"source": [
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"df[['tanh','sigmoid']].plot()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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