def get_bpnn_predict(X, y, param):
model = BPNN(learning_rate=0.05,
num_of_training=100,
input_size=X.shape[1],
hidden_n=2,
parameters=list(param))
model.fit(X, y)
predvalue = model.predict(X)
return np.array(predvalue)
def run_main():
"""
这是主函数
"""
# 导入数据
path = './voice_data.txt'
Data, Label = Load_Voice_Data(path)
# 分割数据集,并对数据集进行标准化
Train_Data, Test_Data, Train_Label, Test_Label = train_test_split(
Data, Label, test_size=1 / 4, random_state=10)
Train_Data = Normalizer().fit_transform(Train_Data)
Test_Data = Normalizer().fit_transform(Test_Data)
# 设置网络参数
input_n = np.shape(Data)[1]
output_n = np.shape(Label)[1]
hidden_n = int(np.sqrt(input_n * output_n))
lambd = 0.001
batch_size = 64
learn_rate = 0.01
epoch = 1000
iteration = 10000
# 训练并测试网络
bpnn = BPNN(input_n, hidden_n, output_n, lambd)
train_loss, test_loss, test_accuracy = bpnn.train_test(
Train_Data, Train_Label, Test_Data, Test_Label, learn_rate, epoch,
iteration, batch_size)
# 解决画图是的中文乱码问题
mpl.rcParams['font.sans-serif'] = [u'simHei']
mpl.rcParams['axes.unicode_minus'] = False
# 结果可视化
col = ['Train_Loss', 'Test_Loss']
epoch = np.arange(epoch)
plt.plot(epoch, train_loss, 'r')
plt.plot(epoch, test_loss, 'b-.')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.grid(True)
plt.legend(labels=col, loc='best')
plt.savefig('./训练与测试损失.jpg')
plt.show()
plt.close()
plt.plot(epoch, test_accuracy, 'r')
plt.xlabel('Epoch')
plt.ylabel('Test Accuracy')
plt.grid(True)
plt.legend(loc='best')
plt.savefig('./测试精度.jpg')
plt.show()
plt.close()
def __init__(self):
print('Creating Neural Network...')
self.bpnn = BPNN(7, 7, 4)
print('Training patterns...')
self.bpnn.train(pat_train, iterations=100000)
print('Training complete!')
super().__init__()
uifile = os.path.join(os.path.dirname(__file__), 'Widget.ui')
self.ui = loadUi(uifile, self)
# list to store which bits are selected
self.bits = [0] * 7
# connect only bit buttons to toggle slot
for elem in self.ui.children():
name = elem.objectName()
if name[:3] == 'bit':
elem.clicked.connect(self.toggle)
# connect check button to check slot
self.ui.buttonCheck.clicked.connect(self.check)
