aymericdelab

{
  "name": "contract name",
  "description": "contract description",
  "interfaces": ["TZIP-012-2020-11-17"]
}

aymericdelab

{
  "symbol": "NFA",
  "name": "Non Fungible Aymeric",
  "description": "Token representing Aymeric",
  "decimals": "0",
  "isBooleanAmount": true,
  "artifactUri": "link to the tokenised object",
  "thumbnailUri": "logo of theNFT",
  "minter": "aymeric",
  "interfaces": ["TZIP-007-2021-04-17", "TZIP-016-2021-04-17", "TZIP-21"]

aymericdelab

{
  "version": "foo.1.4.2",
  "license": { "name": "ISC" },
  "authors": [ "Seb Mondet <seb@mondet.org>" ],
  "source": {
     "tools": ["SmartPy dev-20201031", "Flextesa 20200921"],
     "location": "https://gitlab.com/smondet/fa2-smartpy/-/blob/c05d8ff0/multi_asset.py"
  },
  "interfaces": [ "TZIP-012" ],
  "errors":[

aymericdelab

{
  "symbol": "AYM5",
  "name": "AymericToken",
  "decimals": "0",
  "description": "token representing aymeric",
  "authors": ["aymeric"],
  "interfaces": ["TZIP-007-2021-04-17", "TZIP-016-2021-04-17"]
}

aymericdelab

#imports
import json
import numpy as np
from tensorflow.contrib import predictor

## transfer the saved model from azure blob storage to your local computer
ds = ws.get_default_datastore()

ds.download(target_path='outputs',
                   prefix='founder-classifier/outputs/model',

aymericdelab

from azureml.train.dnn import TensorFlow

datastore = ws.get_default_datastore()

script_params = {
    '--data-folder': datastore.as_mount(),
    '--batch-size': 32,
    '--learning-rate': 0.001,
    '--prefix': 'founder-classifier',
    '--steps': 1000

aymericdelab

# deployment as an endpoint on a SageMaker instance
predictor = estimator.deploy(initial_instance_count=1, 
                             instance_type='ml.p2.xlarge',
                             endpoint_name='founder-classifier-endpoint')


# image should be an array of dimension [-1,28,28,1] and type float64 as specified in our serving_input_fn
predictor.predict({'x': image})

# don't forget to delete your endpoint when you're finished using it

aymericdelab

from sagemaker import get_execution_role
from sagemaker.tensorflow import TensorFlow

role=get_execution_role()

estimator = TensorFlow(entry_point='aws_entry_point.py',
                             role=role,
                             training_steps=1000, 
                             train_instance_count=1,
                             train_instance_type='ml.p2.xlarge',

aymericdelab

import sagemaker

bucket = sagemaker.Session().default_bucket()

prefix = 'founder-classifier/data'

train_response = sagemaker.Session().upload_data(path='data/train.json', 
                                         bucket=bucket, 
                                         key_prefix=prefix)

aymericdelab

from sklearn.model_selection import train_test_split
import cv2
import json
import numpy as np


def images_to_json(prefix):
    
    founders=[
        {'name': 'Bill Gates',