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| #2)
#-*- coding: utf-8 -*-
from pybrain.structure import RecurrentNetwork, FullConnection, LinearLayer, SigmoidLayer
from pybrain.datasets import SupervisedDataSet
from pybrain.supervised.trainers import BackpropTrainer
from gensim.models import Word2Vec
import os
word2vec = Word2Vec.load(os.path.join(os.path.dirname(__file__),"my_w2v.model"))
len_all_words=len(word2vec.wv)
target=(0, 1)
matrix_arif=[]
for row_count in range(len_all_words):
row_vector=(word2vec.wv.get_vector(list(word2vec.wv.key_to_index.keys())[row_count]), target) # создаем tuple
matrix_arif.append(row_vector)
def evaluate(net , X_Y_test):
scores = []
res_acc = 0
rows = len(X_Y_test)
wi_y_test = len(X_Y_test[0])
elem_of_out_nn = 0
elem_answer = 0
is_vecs_are_equal = False
out_nn=None
for row in range(rows):
x_test = X_Y_test[row][0]
y_test = X_Y_test[row][1]
out_nn = net.activate(x_test)
for elem in range(wi_y_test):
elem_of_out_nn = out_nn[elem]
elem_answer = y_test[elem]
if elem_of_out_nn > 0.5:
elem_of_out_nn = 1
# print("output vector elem -> ( %f ) " % 1, end=' ')
# print("expected vector elem -> ( %f )" %
elem_answer, end=' ')
else:
elem_of_out_nn = 0
# print("output vector elem -> ( %f ) " % 0, end=' ')
# print("expected vector elem -> ( %f )" %
elem_answer, end=' ')
if elem_of_out_nn == elem_answer:
is_vecs_are_equal = True
else:
is_vecs_are_equal = False
break
if is_vecs_are_equal:
# print("-Vecs are equal-")
scores.append(1)
else:
# print("-Vecs are not equal-")
scores.append(0)
res_acc = sum(scores) / rows * 100
return res_acc
#Define network structure
network = RecurrentNetwork(name="XOR")
inputLayer = LinearLayer(10, name="Input")
hiddenLayer = SigmoidLayer(15, name="Hidden")
outputLayer = LinearLayer(2, name="Output")
network.addInputModule(inputLayer)
network.addModule(hiddenLayer)
network.addOutputModule(outputLayer)
c1 = FullConnection(inputLayer, hiddenLayer, name="Input_to_Hidden")
c2 = FullConnection(hiddenLayer, outputLayer, name="Hidden_to_Output")
c3 = FullConnection(hiddenLayer, hiddenLayer, name="Recurrent_Connection")
network.addConnection(c1)
network.addRecurrentConnection(c3)
network.addConnection(c2)
network.sortModules()
#Add a data set
ds = SupervisedDataSet(10, 2)
height_X_Y=len(matrix_arif)
for input, target in matrix_arif:
ds.addSample(input, target)
#Train the network
trainer = BackpropTrainer(network, ds, momentum=0.99)
max_error = 0.001
error, count = 1, 1000
#Train
while abs(error) >= max_error:
error = trainer.train()
print('err {0:.5f}'.format(error))
# count = count - 1
print("Error: ", error)
print(evaluate(network, matrix_arif)) |
