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106 lines
1.8 KiB
Plaintext
106 lines
1.8 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 31,
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"id": "d863e2b2",
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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 matplotlib.pyplot as plt"
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]
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},
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{
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"cell_type": "markdown",
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"id": "eb32e860",
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"metadata": {},
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"source": [
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"relu激活函数"
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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": 32,
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"id": "eead13ab",
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"metadata": {},
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"outputs": [],
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"source": [
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"inputs = [0, -1, 3, 4, 6, 7]"
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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": 33,
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"id": "307e7d14",
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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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"[0, 0, 3, 4, 6, 7]"
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]
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},
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"execution_count": 33,
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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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"source": [
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"outputs = []\n",
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"for i in inputs:\n",
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" if i > 0:\n",
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" outputs.append(i)\n",
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" else:\n",
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" outputs.append(0)\n",
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"outputs"
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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": 34,
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"id": "ef4784b3",
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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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"[0, 0, 3, 4, 6, 7]"
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]
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},
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"execution_count": 34,
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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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"source": [
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"outputs = []\n",
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"for i in inputs:\n",
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" outputs.append(max(0, i))\n",
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"outputs"
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]
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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 (ipykernel)",
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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": "3.7.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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