# evaluate_.py
import numpy as np
 
def evaluate(model, X_test: list, Y_test: list, verbose=0):
    """
    Оценка набора в процентах

import keras
from keras.datasets import cifar10 # subroutines for fetching the CIFAR-10 dataset
from keras.models import Model # basic class for specifying and training a neural network

#-*-coding: cp1251-*-
"""
   Автоэнкодер более глубокий dense
"""
import sys
import numpy as np
import sys
import

# app.py
import struct as st
 
# D - data, I - instruciya.
(Dmatrix2D, Istop)=range(2)
 
 
def make_bin_string(string:str)->tuple:

#-*-coding: cp1251-*-

from pybrain.structure import RecurrentNetwork, FullConnection, LinearLayer, SigmoidLayer
from pybrain.datasets import SupervisedDataSet
from pybrain.supervised.trainers import BackpropTrainer

#1)
from pybrain.tools.shortcuts import buildNetwork
from pybrain.structure import TanhLayer, SoftmaxLayer
net = buildNetwork(2, 3, 2, hiddenclass=TanhLayer, outclass=SoftmaxLayer)
"""
 Сеть рандомна инициализирована, проверим ее
"""
print('act', net.activate((2,

from pybrain.tools.shortcuts import buildNetwork
from pybrain.structure import SigmoidLayer
from pybrain.datasets import SupervisedDataSet