def load_data():
train_data_name = "../data/ch08.train.npz"
test_data_name = "../data/ch08.test.npz"
dataReader = DataReader_2_0(train_data_name, test_data_name)
dataReader.ReadData()
dataReader.GenerateValidationSet()
x_train, y_train, x_val, y_val = dataReader.XTrain, dataReader.YTrain, dataReader.XDev, dataReader.YDev
x_test, y_test = dataReader.XTest, dataReader.YTest
return x_train, y_train, x_val, y_val, x_test, y_test
def load_data():
train_data_name = "../data/ch11.train.npz"
test_data_name = "../data/ch11.test.npz"
dataReader = DataReader_2_0(train_data_name, test_data_name)
dataReader.ReadData()
dataReader.NormalizeY(NetType.MultipleClassifier, base=1)
dataReader.NormalizeX()
dataReader.Shuffle()
dataReader.GenerateValidationSet()
x_train, y_train = dataReader.XTrain, dataReader.YTrain
x_test, y_test = dataReader.XTest, dataReader.YTest
x_val, y_val = dataReader.XDev, dataReader.YDev
return x_train, y_train, x_test, y_test, x_val, y_val
def ShowResult(net, dataReader, title):
# draw train data
X, Y = dataReader.XTrain, dataReader.YTrain
plt.plot(X[:, 0], Y[:, 0], '.', c='b')
# create and draw visualized validation data
TX = np.linspace(0, 1, 100).reshape(100, 1)
TY = net.inference(TX)
plt.plot(TX, TY, 'x', c='r')
plt.title(title)
plt.show()
#end def
if __name__ == '__main__':
dataReader = DataReader_2_0(train_data_name, test_data_name)
dataReader.ReadData()
dataReader.GenerateValidationSet()
n_input, n_hidden, n_output = 1, 2, 1
eta, batch_size, max_epoch = 0.05, 10, 5000
eps = 0.001
hp = HyperParameters_2_0(n_input, n_hidden, n_output, eta, max_epoch,
batch_size, eps, NetType.Fitting,
InitialMethod.Xavier)
net = NeuralNet_2_0(hp, "sin_121")
# 加载已经训练好的权重
net.LoadResult()
# net.train(dataReader, 50, True)