def WalkThroughAllOptimizers(option):
dataReader = DataReader(x_data_name, y_data_name)
XData, YData = dataReader.ReadData()
X = dataReader.NormalizeX()
Y = dataReader.NormalizeY()
n_input, n_output = dataReader.num_feature, 1
n_hidden = option[2]
eta, batch_size, max_epoch = option[1], 10, 10000
eps = 0.001
params = CParameters(n_input, n_hidden, n_output, eta, max_epoch,
batch_size, eps, InitialMethod.Xavier, option[0])
loss_history = CLossHistory()
net = TwoLayerNet(NetType.Fitting)
wbs = net.train(dataReader, params, loss_history)
trace = loss_history.GetMinimalLossData()
print(trace.toString())
title = loss_history.ShowLossHistory(params)
print("wait for 10 seconds...")
ShowResult(net, X, Y, title, trace.wb1, trace.wb2)
def train(ne, batch, eta):
dataReader = DataReader(x_data_name, y_data_name)
XData, YData = dataReader.ReadData()
X = dataReader.NormalizeX(passthrough=True)
Y = dataReader.NormalizeY()
n_input, n_hidden, n_output = 1, ne, 1
eta, batch_size, max_epoch = eta, batch, 10000
eps = 0.001
params = CParameters(n_input, n_hidden, n_output, eta, max_epoch,
batch_size, eps, LossFunctionName.MSE,
InitialMethod.Xavier)
loss_history = CLossHistory(params)
net = TwoLayerFittingNet()
wb1, wb2 = net.train(dataReader, params, loss_history)
return loss_history
def train(init_method):
dataReader = DataReader(x_data_name, y_data_name)
XData, YData = dataReader.ReadData()
X = dataReader.NormalizeX(passthrough=True)
Y = dataReader.NormalizeY()
n_input, n_hidden, n_output = 1, 4, 1
eta, batch_size, max_epoch = 0.5, 10, 30000
eps = 0.001
params = CParameters(n_input, n_hidden, n_output, eta, max_epoch,
batch_size, eps, init_method, OptimizerName.SGD)
loss_history = CLossHistory()
net = TwoLayerNet(NetType.Fitting)
net.train(dataReader, params, loss_history)
trace = loss_history.GetMinimalLossData()
print(trace.toString())
title = loss_history.ShowLossHistory(params)
ShowResult(net, X, YData, title, trace.wb1, trace.wb2)
def WalkThroughAllOptimizers(option):
dataReader = DataReader(x_data_name, y_data_name)
XData,YData = dataReader.ReadData()
X = dataReader.NormalizeX()
Y = dataReader.ToOneHot()
n_input, n_output = dataReader.num_feature, dataReader.num_category
n_hidden = 8
eta, batch_size, max_epoch = option[1], 10, 10000
eps = 0.06
params = HyperParameters41(n_input, n_output, n_hidden,
eta, max_epoch, batch_size, eps,
LossFunctionName.CrossEntropy3,
InitialMethod.Xavier,
option[0])
loss_history = CLossHistory()
net = TwoLayerClassificationNet()
#ShowData(XData, YData)
net.train(dataReader, params, loss_history)
trace = loss_history.GetMinimalLossData()
print(trace.toString())
title = loss_history.ShowLossHistory(params)
print("wait for 10 seconds...")
wbs_min = WeightsBias30(params)
wbs_min.W1 = trace.dict_weights["W1"]
wbs_min.W2 = trace.dict_weights["W2"]
wbs_min.B1 = trace.dict_weights["B1"]
wbs_min.B2 = trace.dict_weights["B2"]
ShowAreaResult(X, wbs_min, net, title)
ShowData(X, YData)
def WalkThroughAllOptimizers(option):
dataReader = DataReader(x_data_name, y_data_name)
XData,YData = dataReader.ReadData()
X = dataReader.NormalizeX()
Y = dataReader.NormalizeY()
n_input, n_output = dataReader.num_feature, 1
n_hidden = 4
eta, batch_size, max_epoch = option[1], 10, 10000
eps = 0.001
params = CParameters(n_input, n_output, n_hidden,
eta, max_epoch, batch_size, eps,
LossFunctionName.MSE,
InitialMethod.Xavier,
option[0])
loss_history = CLossHistory()
net = TwoLayerFittingNet()
#ShowData(XData, YData)
wbs = net.train(dataReader, params, loss_history)
trace = loss_history.GetMinimalLossData()
print(trace.toString())
title = loss_history.ShowLossHistory(params)
print("wait for 10 seconds...")
wbs_min = WeightsBias(params)
wbs_min.W1 = trace.dict_weights["W1"]
wbs_min.W2 = trace.dict_weights["W2"]
wbs_min.B1 = trace.dict_weights["B1"]
wbs_min.B2 = trace.dict_weights["B2"]
ShowResult(X, Y, net, wbs_min, title)
def Train():
dataReader = DataReader(x_data_name, y_data_name)
dataReader.ReadData()
dataReader.NormalizeX()
dataReader.NormalizeY()
n_input, n_hidden, n_output = 1, 3, 1
eta, batch_size, max_epoch = 0.5, 10, 50000
eps = 0.001
params = CParameters(n_input, n_hidden, n_output, eta, max_epoch, batch_size, eps)
# SGD, MiniBatch, FullBatch
loss_history = CLossHistory()
net = TwoLayerFittingNet()
wb1, wb2 = net.train(dataReader, params, loss_history)
trace = loss_history.GetMinimalLossData()
print(trace.toString())
title = loss_history.ShowLossHistory(params)
ShowResult(net, dataReader.X, dataReader.Y, title, trace.wb1, trace.wb2)
trace.wb1.Save("wb1")
trace.wb2.Save("wb2")
plt.plot(X[0, :], Y[0, :], '.', c='b')
# create and draw visualized validation data
TX = np.linspace(0, 1, 100).reshape(1, 100)
dict_cache = net.ForwardCalculationBatch(TX, wb1, wb2)
TY = dict_cache["Output"]
plt.plot(TX, TY, 'x', c='r')
plt.title(title)
plt.show()
#end def
if __name__ == '__main__':
dataReader = DataReader(x_data_name, y_data_name)
dataReader.ReadData()
dataReader.NormalizeX()
dataReader.NormalizeY()
n_input, n_hidden, n_output = 1, 3, 1
eta, batch_size, max_epoch = 0.5, 10, 50000
eps = 0.001
params = CParameters(n_input, n_hidden, n_output, eta, max_epoch,
batch_size, eps)
# SGD, MiniBatch, FullBatch
loss_history = CLossHistory()
net = TwoLayerFittingNet()
wb1, wb2 = net.train(dataReader, params, loss_history)
trace = loss_history.GetMinimalLossData()
def ShowResult(net, X, Y, title, wb1, wb2):
# draw train data
plt.plot(X[0,:], Y[0,:], '.', c='b')
# create and draw visualized validation data
TX = np.linspace(0,1,100).reshape(1,100)
dict_cache = net.ForwardCalculationBatch(TX, wb1, wb2)
TY = dict_cache["Output"]
plt.plot(TX, TY, 'x', c='r')
plt.title(title)
plt.show()
if __name__ == '__main__':
dataReader = DataReader(x_data_name, y_data_name)
XData,YData = dataReader.ReadData()
X = dataReader.NormalizeX(passthrough=True)
Y = dataReader.NormalizeY()
# 为了说明问题，我们用2个隐层单元和20批大小来做试验
n_input, n_hidden, n_output = 1, 4, 1
eta, batch_size, max_epoch = 0.1, 1, 10000
eps = 0.001
params = CParameters(n_input, n_hidden, n_output,
eta, max_epoch, batch_size, eps,
LossFunctionName.MSE,
InitialMethod.Xavier)
loss_history = CLossHistory(params)
# 试验时，先注释掉try_2，运行try_1；然后注释掉try_1，运行try_2
#lr_Searcher = try_1()