Tensor.py

import tensorflow as tf
from tensorflow import keras
from tensorflow.python.keras.layers import Dense, Activation
from tensorflow.python.keras.models import Sequential
import numpy as np
from time import sleep

class Rede_neural(object):
    def __init__(self,wids=None):
        types=""
        if wids == None:
            types="m"
        else:
            types="zeros"
        self.model = Sequential()
        self.model.add(Dense(64, input_dim=16,kernel_initializer=types,bias_initializer=types))
        self.model.add(Activation('hard_sigmoid'))
        self.model.add(Dense(4,kernel_initializer=types,bias_initializer=types))
        self.model.add(Activation('softmax'))
        if wids == None:
            self.wids = self.model.get_weights()
        else:
            self.setWid(wids)
        
    def getWid(self):
        return self.wids

    def setWid(self,wid):
        self.wids = wid
        self.model.set_weights(wid)

    def predict(self,X):
        X = np.matrix(X.flatten())
        Y= self.model.predict(X)
        return (Y.argmax())

# def mutacao(wid):
#     aux  = wid
#     while (True):
#         num = np.random.randint(len(aux))
#         if (not hasattr(aux[num], "__len__")):
#             aux[num] = np.random.uniform(-1,1)
#             break
#         aux = aux[num]
#     return wid
# y = []
# for x in range(16):
#     if x==0:
#         y.append(0.0625)
#         continue
#     y.append(0.0625 +y[-1]);
# y= np.matrix(y)
# #print(y)
# model = Sequential()
# model.add(Dense(32, input_dim=16,kernel_initializer='random_uniform',bias_initializer='random_uniform'))
# model.add(Activation('hard_sigmoid'))
# model.add(Dense(4,kernel_initializer='random_uniform',bias_initializer='random_uniform'))
# model.add(Activation('softmax'))
# wid = model.get_weights()
# x = model.predict(y)
# while True:
#     print(model.predict(y))
#     wid = mutacao(wid);
#     model.set_weights(wid)
#     sleep(1)
#print (aux)
#print (wid)