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import * as functions from "firebase-functions"; | |
admin.initializeApp(functions.config().firebase); | |
// ... | |
exports.getUser = functions.https.onRequest((req, res) => { | |
// ... | |
// note, req.query.uid might be null! If we use a router, the function will never get matched unless the route is matched correctly | |
const uid = req.query.uid; | |
// ... | |
}); |
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import * as express from "express"; | |
import * as admin from "firebase-admin"; | |
import * as functions from "firebase-functions"; | |
admin.initializeApp(functions.config().firebase); | |
const app = express(); | |
// https://expressjs.com/en/advanced/best-practice-security.html#at-a-minimum-disable-x-powered-by-header | |
app.disable("x-powered-by"); |
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import * as express from "express"; | |
// This is the router which will be imported in our | |
// api hub (the index.ts which will be sent to Firebase Functions). | |
export let userRouter = express.Router(); | |
// Now that we have a router, we can define routes which this router | |
// will handle. Please look into the Express documentation for more info. | |
userRouter.get("/:uid", async function getUser(req: express.Request, res: express.Response) { | |
// ... |
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import * as express from "express"; | |
import * as admin from "firebase-admin"; | |
import * as functions from "firebase-functions"; | |
import * as usersApi from "./api/users"; | |
admin.initializeApp(functions.config().firebase); | |
const app = express(); | |
// https://expressjs.com/en/advanced/best-practice-security.html#at-a-minimum-disable-x-powered-by-header | |
app.disable("x-powered-by"); |
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In this chapter we'll write a computer program implementing a neural network that learns to recognize | |
handwritten digits. The program is just 74 lines long, and uses no special neural network libraries. But | |
this short program can recognize digits with an accuracy over 96 percent, without human intervention. | |
Furthermore, in later chapters we'll develop ideas which can improve accuracy to over 99 percent. In fact, | |
the best commercial neural networks are now so good that they are used by banks to process cheques, and by | |
post offices to recognize addresses. |
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In this chapter we'll write a computer program implementing a neural network that learns to recognize | |
handwritten digits. The program is just 74 lines long, and uses no special neural network libraries. But | |
this short program can recognize digits with an accuracy over 96 percent, without human intervention. | |
Furthermore, in later chapters we'll develop ideas which can improve accuracy to over 99 percent. In fact, | |
the best commercial neural networks are now so good that they are used by banks to process cheques, and by | |
post offices to recognize addresses. |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
In this chapter we'll write a computer program implementing a neural network that learns to recognize | |
handwritten digits. The program is just 74 lines long, and uses no special neural network libraries. But | |
this short program can recognize digits with an accuracy over 96 percent, without human intervention. | |
Furthermore, in later chapters we'll develop ideas which can improve accuracy to over 99 percent. In fact, | |
the best commercial neural networks are now so good that they are used by banks to process cheques, and by | |
post offices to recognize addresses. |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
In this chapter we'll write a computer program implementing a neural network that learns to recognize | |
handwritten digits. The program is just 74 lines long, and uses no special neural network libraries. But | |
this short program can recognize digits with an accuracy over 96 percent, without human intervention. | |
Furthermore, in later chapters we'll develop ideas which can improve accuracy to over 99 percent. In fact, | |
the best commercial neural networks are now so good that they are used by banks to process cheques, and by | |
post offices to recognize addresses. |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
In this chapter we'll write a computer program implementing a neural network that learns to recognize | |
handwritten digits. The program is just 74 lines long, and uses no special neural network libraries. But | |
this short program can recognize digits with an accuracy over 96 percent, without human intervention. | |
Furthermore, in later chapters we'll develop ideas which can improve accuracy to over 99 percent. In fact, | |
the best commercial neural networks are now so good that they are used by banks to process cheques, and by | |
post offices to recognize addresses. |
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""" | |
Module of SLIM models of LSTM RNNs. Uses the layer LSTM in Keras2.1 to generate | |
inherited layers of reduced gated and cell parameters. | |
LSTM1 | |
LSTM2 | |
LSTM3 | |
LSTM4 | |
LSTM5 | |
LSTM6 | |
There are more parameter-reduced models as appeared in arXiv.org: |
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