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Table of Contents Introduction I/O Examples credits Algorithm Console Introduction This algorithm is a direct implementation of Google's InceptionNet , which was trained on the ImageNet 2015 dataset . It is implemented using Google's Tensorflow  python bindings, the InceptionNet model architecture is as shown below: I/O Input source: String source - (required) - the source image to classify, this must be in one of the following formats: data connector URI, web http/https resource file http://.. or https://... , or a base64 encoded JPEG String. Output { "tags":[ { "confidence": Double, "class": String }, { "confidence": Double, "class": String }, { "confidence": Double, "class": String }, { "confidence": Double, "class": String }, { "confidence":Double, "class": String } ] } tags - The top 5 classes that the model predicts are relevant to the image. className - The classname for the class. confidence - The confidence that this label is relevant. Note: The first call to this algorithm will take a bit longer than sequential calls to due algorithm initialization. All following calls will be significantly faster. Examples Example 1 ![car]( input "" output { "tags":[ { "class":"convertible", "confidence":0.3217212557792664 }, { "class":"sports car, sport car", "confidence":0.18678018450737005 }, { "class":"racer, race car, racing car", "confidence":0.09395135194063188 }, { "class":"car wheel", "confidence":0.08665172755718234 }, { "class":"grille, radiator grille", "confidence":0.07477507740259172 } ] } Example 2 ![dog]( input "" output { "tags":[ { "class": "Samoyed, Samoyede", "confidence": 0.9004066586494446 }, { "class": "keeshond", "confidence": 0.004059927538037299 }, { "class": "Pomeranian", "confidence": 0.0024697233457118277 }, { "class": "Eskimo dog, husky", "confidence": 0.0013620780082419515 }, { "class": "Loafer", "confidence": 0.0011537882965058086 } ] } Credits For more info please check out Going Deeper With Convolutions by Christian Szegedy, Wei Liu et al All sample images retrived from on May 26, 2016.

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curl -X POST -d '{{input | formatInput:"curl"}}' -H 'Content-Type: application/json' -H 'Authorization: Simple YOUR_API_KEY'
View cURL Docs
algo auth
algo run algo://deeplearning/InceptionNet/1.0.4 -d '{{input | formatInput:"cli"}}'
View CLI Docs
import (
  algorithmia ""

input := {{input | formatInput:"go"}}

var client = algorithmia.NewClient("YOUR_API_KEY", "")
algo, _ := client.Algo("algo://deeplearning/InceptionNet/1.0.4")
resp, _ := algo.Pipe(input)
response := resp.(*algorithmia.AlgoResponse)
View Go Docs
import com.algorithmia.*;
import com.algorithmia.algo.*;

String input = "{{input | formatInput:"java"}}";
AlgorithmiaClient client = Algorithmia.client("YOUR_API_KEY");
Algorithm algo = client.algo("algo://deeplearning/InceptionNet/1.0.4");
AlgoResponse result = algo.pipeJson(input);
View Java Docs
import com.algorithmia._
import com.algorithmia.algo._

val input = {{input | formatInput:"scala"}}
val client = Algorithmia.client("YOUR_API_KEY")
val algo = client.algo("algo://deeplearning/InceptionNet/1.0.4")
val result = algo.pipeJson(input)
View Scala Docs
var input = {{input | formatInput:"javascript"}};
           .then(function(output) {
View Javascript Docs
using Algorithmia;

var input = "{{input | formatInput:"cs"}}";
var client = new Client("YOUR_API_KEY");
var algorithm = client.algo("algo://deeplearning/InceptionNet/1.0.4");
var response = algorithm.pipe<object>(input);
View .NET/C# Docs
var input = {{input | formatInput:"javascript"}};
           .then(function(response) {
View NodeJS Docs
import Algorithmia

input = {{input | formatInput:"python"}}
client = Algorithmia.client('YOUR_API_KEY')
algo = client.algo('deeplearning/InceptionNet/1.0.4')
print algo.pipe(input)
View Python Docs

input <- {{input | formatInput:"r"}}
client <- getAlgorithmiaClient("YOUR_API_KEY")
algo <- client$algo("deeplearning/InceptionNet/1.0.4")
result <- algo$pipe(input)$result
View R Docs
require 'algorithmia'

input = {{input | formatInput:"ruby"}}
client = Algorithmia.client('YOUR_API_KEY')
algo = client.algo('deeplearning/InceptionNet/1.0.4')
puts algo.pipe(input).result
View Ruby Docs
use algorithmia::Algorithmia;

let input = {{input | formatInput:"rust"}};
let client = Algorithmia::client("YOUR_API_KEY");
let algo = client.algo("deeplearning/InceptionNet/1.0.4");
let response = algo.pipe(input);
View Rust Docs
import Algorithmia

let input = "{{input | formatInput:"swift"}}";
let client = Algorithmia.client(simpleKey: "YOUR_API_KEY")
let algo = client.algo(algoUri: "deeplearning/InceptionNet/1.0.4") { resp, error in
View Swift Docs
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