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 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

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:

TheBojda

# Deep Q-learning CartPole example
# based on https://github.com/gsurma/cartpole.git & https://gym.openai.com/evaluations/eval_OeUSZwUcR2qSAqMmOE1UIw/

import gym
import random
import numpy as np
from os import path
from collections import deque
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

TheBojda

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

text = "Hello World!"

input_text = text[:-1]
target_text = text[1:]
print(input_text)
print(target_text)

TheBojda

# MINST GAN Example
# based on https://www.tensorflow.org/tutorials/generative/dcgan

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

BUFFER_SIZE = 60000
BATCH_SIZE = 256