def train(reader, title):
draw_source_data(reader, title, show=True)
# net train
num_input = reader.XTrain.shape[1]
num_output = 1
hp = HyperParameters_1_1(num_input, num_output, eta=0.5, max_epoch=5000, batch_size=1, eps=2e-3,
net_type=NetType.BinaryClassifier)
net = NeuralNet_1_2(hp)
net.train(reader, checkpoint=1)
# test
print(Test(net, reader))
# visualize
draw_source_data(reader, title, show=False)
draw_split_line(net)
plt.show()
show=True,
isPredicate=True)
# 主程序
if __name__ == '__main__':
num_category = 3
reader = DataReader_1_3(file_name)
reader.ReadData()
reader.ToOneHot(num_category, base=1)
# show raw data before normalization
ShowData(reader.XRaw, reader.YTrain)
reader.NormalizeX()
num_input = 2
params = HyperParameters_1_1(num_input,
num_category,
eta=0.1,
max_epoch=100,
batch_size=10,
eps=1e-3,
net_type=NetType.MultipleClassifier)
net = NeuralNet_1_2(params)
net.train(reader, checkpoint=1)
xt_raw = np.array([5, 1, 7, 6, 5, 6, 2, 7]).reshape(4, 2)
xt = reader.NormalizePredicateData(xt_raw)
output = net.inference(xt)
print(output)
ShowResult(reader.XTrain, reader.YTrain, xt, output)
plt.plot(X[:,0], Y[:,0], '.', c='b')
# create and draw visualized validation data
TX1 = np.linspace(0,1,100).reshape(100,1)
TX2 = np.hstack((TX1, TX1[:,]**2))
TX3 = np.hstack((TX2, TX1[:,]**3))
TX4 = np.hstack((TX3, TX1[:,]**4))
TX5 = np.hstack((TX4, TX1[:,]**5))
TX6 = np.hstack((TX5, TX1[:,]**6))
TX7 = np.hstack((TX6, TX1[:,]**7))
TX8 = np.hstack((TX7, TX1[:,]**8))
TY = net.inference(TX8)
plt.plot(TX1, TY, 'x', c='r')
plt.title(title)
plt.show()
#end def
if __name__ == '__main__':
dataReader = DataReaderEx(file_name)
dataReader.ReadData()
dataReader.Add()
print(dataReader.XTrain.shape)
# net
num_input = 8
num_output = 1
hp = HyperParameters_1_1(num_input, num_output, eta=0.2, max_epoch=50000, batch_size=10, eps=1e-3, net_type=NetType.Fitting)
#params = HyperParameters(eta=0.2, max_epoch=1000000, batch_size=10, eps=1e-3, net_type=NetType.Fitting)
net = NeuralNet_1_2(hp)
net.train(dataReader, checkpoint=500)
ShowResult(net, dataReader, "Polynomial")