def train(hp, folder):
net = NeuralNet_2_0(hp, folder)
net.train(dataReader, 50, True)
print("Accuracy: ", net.Test(dataReader), "eta: ", hp.eta)
trace = net.GetTrainingHistory()
return trace
def train(hp, folder):
net = NeuralNet_2_0(hp, folder)
net.train(dataReader, 50, True)
trace = net.GetTrainingHistory()
return trace
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)
# net.ShowTrainingHistory()
ShowResult(net, dataReader, hp.toString())
# 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, 3, 1
eta, batch_size, max_epoch = 0.5, 10, 10000
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, "complex_131")
net.train(dataReader, 50, True)
net.ShowTrainingHistory()
ShowResult(net, dataReader, hp.toString())