# 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:

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

import numpy as np
from util import get_logger
import logging
 
Forward=1
Backward=-1
import math
import matplotlib.pyplot as plt
 
def plot(_file:str,x:list,y:list,logger:logging.Logger)->None:
    
    fig:plt.Figure=None
    ax:plt.Axes=None
    fig,

import logging
import keras
from consts import stop,nparray,make_net,determe_X_Y,push_obj,predict,push_i,push_str, \
    plot_train,sav_model_wei,sav_model,fit_net,get_mult_class_matr,cr_callback_wi_loss_treshold_and_acc_shure,