def model(dataReader):
num_input = 2
num_hidden = 3
num_output = 1
max_epoch = 1000
batch_size = 5
learning_rate = 0.1
params = HyperParameters_4_1(learning_rate,
max_epoch,
batch_size,
net_type=NetType.BinaryClassifier,
init_method=InitialMethod.Xavier,
stopper=Stopper(StopCondition.StopLoss, 0.02))
net = NeuralNet_4_1(params, "Arc")
fc1 = FcLayer_1_1(num_input, num_hidden, params)
net.add_layer(fc1, "fc1")
sigmoid1 = ActivationLayer(Sigmoid())
net.add_layer(sigmoid1, "sigmoid1")
fc2 = FcLayer_1_1(num_hidden, num_output, params)
net.add_layer(fc2, "fc2")
logistic = ClassificationLayer(Logistic())
net.add_layer(logistic, "logistic")
net.train(dataReader, checkpoint=10, need_test=True)
return net
def model():
dr = LoadData()
num_input = dr.num_feature
num_hidden1 = 64
num_hidden2 = 64
num_hidden3 = 32
num_hidden4 = 16
num_output = 1
max_epoch = 100
batch_size = 16
learning_rate = 0.1
eps = 1e-3
params = HyperParameters40(learning_rate,
max_epoch,
batch_size,
eps,
net_type=NetType.BinaryClassifier,
init_method=InitialMethod.Xavier)
net = NeuralNet40(params, "Income")
fc1 = FcLayer(num_input, num_hidden1, params)
net.add_layer(fc1, "fc1")
a1 = ActivatorLayer(Relu())
net.add_layer(a1, "relu1")
fc2 = FcLayer(num_hidden1, num_hidden2, params)
net.add_layer(fc2, "fc2")
a2 = ActivatorLayer(Relu())
net.add_layer(a2, "relu2")
fc3 = FcLayer(num_hidden2, num_hidden3, params)
net.add_layer(fc3, "fc3")
a3 = ActivatorLayer(Relu())
net.add_layer(a3, "relu3")
fc4 = FcLayer(num_hidden3, num_hidden4, params)
net.add_layer(fc4, "fc4")
a4 = ActivatorLayer(Relu())
net.add_layer(a4, "relu4")
fc5 = FcLayer(num_hidden4, num_output, params)
net.add_layer(fc5, "fc5")
logistic = ClassificationLayer(Logistic())
net.add_layer(logistic, "logistic")
#net.load_parameters()
net.train(dr, checkpoint=10, need_test=True)
net.ShowLossHistory("epoch")
def model(dr):
num_input = dr.num_feature
num_hidden1 = 32
num_hidden2 = 16
num_hidden3 = 8
num_hidden4 = 4
num_output = 1
max_epoch = 100
batch_size = 16
learning_rate = 0.1
params = HyperParameters_4_0(learning_rate,
max_epoch,
batch_size,
net_type=NetType.BinaryClassifier,
init_method=InitialMethod.MSRA,
stopper=Stopper(StopCondition.StopDiff, 1e-3))
net = NeuralNet_4_0(params, "Income")
fc1 = FcLayer_1_0(num_input, num_hidden1, params)
net.add_layer(fc1, "fc1")
a1 = ActivationLayer(Relu())
net.add_layer(a1, "relu1")
fc2 = FcLayer_1_0(num_hidden1, num_hidden2, params)
net.add_layer(fc2, "fc2")
a2 = ActivationLayer(Relu())
net.add_layer(a2, "relu2")
fc3 = FcLayer_1_0(num_hidden2, num_hidden3, params)
net.add_layer(fc3, "fc3")
a3 = ActivationLayer(Relu())
net.add_layer(a3, "relu3")
fc4 = FcLayer_1_0(num_hidden3, num_hidden4, params)
net.add_layer(fc4, "fc4")
a4 = ActivationLayer(Relu())
net.add_layer(a4, "relu4")
fc5 = FcLayer_1_0(num_hidden4, num_output, params)
net.add_layer(fc5, "fc5")
logistic = ClassificationLayer(Logistic())
net.add_layer(logistic, "logistic")
net.train(dr, checkpoint=1, need_test=True)
return net
def model_sigmoid(num_input, num_hidden, num_output, hp):
net = NeuralNet_4_1(hp, "chinabank_sigmoid")
fc1 = FcLayer_1_1(num_input, num_hidden, hp)
net.add_layer(fc1, "fc1")
s1 = ActivationLayer(Sigmoid())
net.add_layer(s1, "Sigmoid1")
fc2 = FcLayer_1_1(num_hidden, num_output, hp)
net.add_layer(fc2, "fc2")
softmax1 = ClassificationLayer(Softmax())
net.add_layer(softmax1, "softmax1")
net.train(dataReader, checkpoint=50, need_test=True)
net.ShowLossHistory()
ShowResult(net, hp.toString())
ShowData(dataReader)
def model_relu(num_input, num_hidden, num_output, hp):
net = NeuralNet40(hp, "chinabank_relu")
fc1 = FcLayer(num_input, num_hidden, hp)
net.add_layer(fc1, "fc1")
r1 = ActivatorLayer(Relu())
net.add_layer(r1, "Relu1")
fc2 = FcLayer(num_hidden, num_output, hp)
net.add_layer(fc2, "fc2")
softmax1 = ClassificationLayer(Softmax())
net.add_layer(softmax1, "softmax1")
net.train(dataReader, checkpoint=50, need_test=True)
net.ShowLossHistory("epoch")
ShowResult(net, hp.toString())
ShowData(dataReader)
def Net(subfolder,
dataReader,
num_input,
num_hidden,
num_output,
params,
show_history=True):
net = NeuralNet_4_2(params, subfolder)
fc1 = FcLayer_2_0(num_input, num_hidden, params)
net.add_layer(fc1, "fc1")
relu1 = ActivatorLayer(Relu())
net.add_layer(relu1, "relu1")
fc2 = FcLayer_2_0(num_hidden, num_hidden, params)
net.add_layer(fc2, "fc2")
relu2 = ActivatorLayer(Relu())
net.add_layer(relu2, "relu2")
fc3 = FcLayer_2_0(num_hidden, num_hidden, params)
net.add_layer(fc3, "fc3")
relu3 = ActivatorLayer(Relu())
net.add_layer(relu3, "relu3")
fc4 = FcLayer_2_0(num_hidden, num_hidden, params)
net.add_layer(fc4, "fc4")
relu4 = ActivatorLayer(Relu())
net.add_layer(relu4, "relu4")
fc5 = FcLayer_2_0(num_hidden, num_output, params)
net.add_layer(fc5, "fc5")
softmax = ClassificationLayer(Softmax())
net.add_layer(softmax, "softmax")
net.train(dataReader, checkpoint=1, need_test=True)
if show_history:
net.ShowLossHistory(XCoordinate.Iteration)
return net
def Net(dataReader,
num_input,
num_hidden,
num_output,
params,
show_history=True):
net = NeuralNet41(params, "mnist_overfitting")
fc1 = FcLayer(num_input, num_hidden, params)
net.add_layer(fc1, "fc1")
relu1 = ActivatorLayer(Relu())
net.add_layer(relu1, "relu1")
fc2 = FcLayer(num_hidden, num_hidden, params)
net.add_layer(fc2, "fc2")
relu2 = ActivatorLayer(Relu())
net.add_layer(relu2, "relu2")
fc3 = FcLayer(num_hidden, num_hidden, params)
net.add_layer(fc3, "fc3")
relu3 = ActivatorLayer(Relu())
net.add_layer(relu3, "relu3")
fc4 = FcLayer(num_hidden, num_hidden, params)
net.add_layer(fc4, "fc4")
relu4 = ActivatorLayer(Relu())
net.add_layer(relu4, "relu4")
fc5 = FcLayer(num_hidden, num_output, params)
net.add_layer(fc5, "fc5")
softmax = ClassificationLayer(Softmax())
net.add_layer(softmax, "softmax")
net.train(dataReader, checkpoint=1, need_test=True)
if show_history:
net.ShowLossHistory()
return net
max_epoch = 10000
batch_size = 5
learning_rate = 0.1
eps = 1e-3
params = HyperParameters40(learning_rate,
max_epoch,
batch_size,
eps,
net_type=NetType.BinaryClassifier,
init_method=InitialMethod.Xavier)
net = NeuralNet40(params, "Arc")
fc1 = FcLayer(num_input, num_hidden, params)
net.add_layer(fc1, "fc1")
sigmoid1 = ActivatorLayer(Sigmoid())
net.add_layer(sigmoid1, "sigmoid1")
fc2 = FcLayer(num_hidden, num_output, params)
net.add_layer(fc2, "fc2")
logistic = ClassificationLayer(Logistic())
net.add_layer(logistic, "logistic")
#net.load_parameters()
net.train(dataReader, checkpoint=10, need_test=True)
net.ShowLossHistory("epoch")
ShowResult2D(net, dataReader)
fc1 = FcLayer_1_0(num_input, num_hidden1, params)
net.add_layer(fc1, "fc1")
r1 = ActivationLayer(Relu())
net.add_layer(r1, "r1")
fc2 = FcLayer_1_0(num_hidden1, num_hidden2, params)
net.add_layer(fc2, "fc2")
r2 = ActivationLayer(Relu())
net.add_layer(r2, "r2")
fc3 = FcLayer_1_0(num_hidden2, num_hidden3, params)
net.add_layer(fc3, "fc3")
r3 = ActivationLayer(Relu())
net.add_layer(r3, "r3")
fc4 = FcLayer_1_0(num_hidden3, num_hidden4, params)
net.add_layer(fc4, "fc4")
r4 = ActivationLayer(Relu())
net.add_layer(r4, "r4")
fc5 = FcLayer_1_0(num_hidden4, num_output, params)
net.add_layer(fc5, "fc5")
softmax = ClassificationLayer(Softmax())
net.add_layer(softmax, "softmax")
#net.load_parameters()
net.train(dataReader, checkpoint=0.1, need_test=True)
net.ShowLossHistory(xcoord=XCoordinate.Iteration)
def model():
dr = LoadData()
num_input = dr.num_feature
num_hidden1 = 128
num_hidden2 = 64
num_hidden3 = 32
num_hidden4 = 16
num_hidden5 = 8
num_hidden6 = 4
num_output = 1
max_epoch = 100
batch_size = 16
learning_rate = 0.1
params = HyperParameters_4_0(learning_rate,
max_epoch,
batch_size,
net_type=NetType.BinaryClassifier,
init_method=InitialMethod.Xavier,
stopper=Stopper(StopCondition.StopDiff, 1e-3))
net = NeuralNet_4_0(params, "Income")
fc1 = FcLayer_1_0(num_input, num_hidden1, params)
net.add_layer(fc1, "fc1")
a1 = ActivationLayer(Relu())
net.add_layer(a1, "relu1")
fc2 = FcLayer_1_0(num_hidden1, num_hidden2, params)
net.add_layer(fc2, "fc2")
a2 = ActivationLayer(Relu())
net.add_layer(a2, "relu2")
fc3 = FcLayer_1_0(num_hidden2, num_hidden3, params)
net.add_layer(fc3, "fc3")
a3 = ActivationLayer(Relu())
net.add_layer(a3, "relu3")
fc4 = FcLayer_1_0(num_hidden3, num_hidden4, params)
net.add_layer(fc4, "fc4")
a4 = ActivationLayer(Relu())
net.add_layer(a4, "relu4")
fc5 = FcLayer_1_0(num_hidden4, num_hidden5, params)
net.add_layer(fc5, "fc5")
a5 = ActivationLayer(Relu())
net.add_layer(a5, "relu5")
fc6 = FcLayer_1_0(num_hidden5, num_hidden6, params)
net.add_layer(fc6, "fc")
a6 = ActivationLayer(Relu())
net.add_layer(a6, "relu6")
fc7 = FcLayer_1_0(num_hidden6, num_output, params)
net.add_layer(fc7, "fc7")
logistic = ClassificationLayer(Logistic())
net.add_layer(logistic, "logistic")
#net.load_parameters()
net.train(dr, checkpoint=1, need_test=True)
net.ShowLossHistory()