TheBojda

<?php
// logging the requests for DEBUG
/*
    ob_start();
    echo $_SERVER['REQUEST_URI'];
    $post_data = file_get_contents("php://input");
    print_r($post_data);
    $data = json_decode($post_data);
    var_dump($data);
    echo "\n";

TheBojda

pragma solidity ^0.4.9;

import "./Receiver_Interface.sol";
import "./ERC223_Interface.sol";

 /**
 * ERC223 token by Dexaran
 *
 * https://github.com/Dexaran/ERC223-token-standard
 * source: https://github.com/Dexaran/ERC223-token-standard/blob/Recommended/ERC223_Token.sol

TheBojda

pragma solidity ^0.5.3;

contract EnvientaToken {

  string public constant symbol = "ENV";
  string public constant name = "ENVIENTA token";
  uint8 public constant decimals = 18;
  
  event Transfer(address indexed from, address indexed to, uint256 value);
  event Approval( address indexed owner, address indexed spender, uint256 value);

TheBojda

# TensorFlow CNN model training example 
# based on https://www.tensorflow.org/tutorials/images/cnn

from __future__ import absolute_import, division, print_function, unicode_literals

import tensorflow as tf

from tensorflow.keras import datasets, layers, models
import matplotlib.pyplot as plt

TheBojda

# TensorFlow image classification example
# based on https://www.tensorflow.org/tutorials/keras/classification
# model generation: https://gist.github.com/TheBojda/f297544cc4864b2b10c2aad965339c58

from __future__ import absolute_import, division, print_function, unicode_literals

import tensorflow as tf

from tensorflow.keras import datasets, layers, models
from tensorflow.keras.models import load_model

TheBojda

# Simple neural network with NumPy from 11 lines of code 
# source: https://iamtrask.github.io/2015/07/12/basic-python-network/

X = np.array([ [0,0,1],[0,1,1],[1,0,1],[1,1,1] ])
y = np.array([[0,1,1,0]]).T
syn0 = 2*np.random.random((3,4)) - 1
syn1 = 2*np.random.random((4,1)) - 1
for j in xrange(60000):
    l1 = 1/(1+np.exp(-(np.dot(X,syn0))))
    l2 = 1/(1+np.exp(-(np.dot(l1,syn1))))

TheBojda

# Linear regression using GradientTape
# based on https://sanjayasubedi.com.np/deeplearning/tensorflow-2-linear-regression-from-scratch/

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

class Model:
    def __init__(self):
        self.W = tf.Variable(16.0)

TheBojda

import numpy as np
import tensorflow as tf
from tensorflow_core.python.keras import layers, models

words = ["cat", "dog", "apple", "orange", "car", "airplane", "man", "woman", "drink", "eat", "neural", "network",
         "tensor", "flow"]
dict_len = len(words)
word_index = dict((word, i) for i, word in enumerate(words))

TheBojda

# Word embedding tensorflow example
# based on: https://www.tensorflow.org/tutorials/text/word_embeddings

import io
import tensorflow as tf
from tensorflow_core.python.keras import layers, models, datasets
import tensorflow_datasets as tfds

(train_data, test_data), info = tfds.load(
    'imdb_reviews/subwords8k',

TheBojda

import gym

env = gym.make("Breakout-v0")
observation = env.reset()
for _ in range(10000):
    env.render()
    action = env.action_space.sample()  # your agent here (this takes random actions)
    observation, reward, done, info = env.step(action)

    if done: