BEAT - amohammadi/mnist

This algorithm is a sequential one. The platform will call its process() method once per data incoming on its inputs.

Endpoint Groups 2

Algorithms have at least one input and one output. All algorithm endpoints are organized in groups. Groups are used by the platform to indicate which inputs and outputs are synchronized together. The first group is automatically synchronized with the channel defined by the block in which the algorithm is deployed.

Group: main

Endpoint Name	Data Format	Nature
image	system/array_2d_uint8/1	Input
label	system/uint64/1	Output

Group: model

Endpoint Name	Data Format	Nature
model	system/text/1	Input

import pickle
import base64
import numpy as np
from sklearn.utils import check_array

def reshape(X):
    X = check_array(X, allow_nd=True)
    return X.reshape((X.shape[0], -1))

def load_model(text):
    print("Loading model")
    return pickle.loads(base64.b64decode(text))

class Algorithm:
    def prepare(self, data_loaders):

# Loads the model at the beginning
        loader = data_loaders.loaderOf("model")
        for i in range(loader.count()):
            view = loader.view("model", i)
            data, _, _ = view[0]
            self.estimator = load_model(data["model"].text)
            if i == 1:
                raise RuntimeError("Something is wrong!")
        return True

def process(self, inputs, data_loaders, outputs):

# N.B.: this will be called for every unit in `image'
        X = reshape([inputs["image"].data.value])
        out = self.estimator.predict(X)[0]

# Writes the output
        outputs["label"].write({"value": np.cast["uint64"](out)})
        return True

xxxxxxxxxx
 
import pickle
import base64
import numpy as np
from sklearn.utils import check_array
​
​
def reshape(X):
    X = check_array(X, allow_nd=True)
    return X.reshape((X.shape[0], -1))
​
​
def load_model(text):
    print("Loading model")
    return pickle.loads(base64.b64decode(text))
​
​
class Algorithm:
    def prepare(self, data_loaders):
​
        # Loads the model at the beginning
        loader = data_loaders.loaderOf("model")
        for i in range(loader.count()):
            view = loader.view("model", i)
            data, _, _ = view[0]
            self.estimator = load_model(data["model"].text)
            if i == 1:
                raise RuntimeError("Something is wrong!")
        return True
​
    def process(self, inputs, data_loaders, outputs):
​
        # N.B.: this will be called for every unit in `image'
        X = reshape([inputs["image"].data.value])
        out = self.estimator.predict(X)[0]
​
        # Writes the output
        outputs["label"].write({"value": np.cast["uint64"](out)})
        return True
​

The code for this algorithm in Python
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This algorithm contains a logistic regression model trained on the MNIST database. It takes as input images of digits and outputs the classification label of images.

Experiments

Attestation:

Privacy:

Status:

			Updated	Name	Databases/Protocols	Analyzers
				amohammadi/amohammadi/mnist_simple/1/mnist1	mnist/5@idiap	amohammadi/accuracy_analyzer/1

Python (2.1.0) 1

This table shows the number of times this algorithm has been successfully run using the given environment. Note this does not provide sufficient information to evaluate if the algorithm will run when submitted to different conditions.

algorithms amohammadi mnist_tester 1

Endpoint Groups 2

Group: main

Group: model

Experiments

algorithms

amohammadi

mnist_tester

1