def model():
dataReader = LoadData()
num_input = 1
num_hidden1 = 4
num_output = 1
max_epoch = 10000
batch_size = 10
learning_rate = 0.5
eps = 1e-5
params = HyperParameters40(learning_rate,
max_epoch,
batch_size,
net_type=NetType.Fitting,
init_method=InitialMethod.Xavier)
net = NeuralNet40(params, "Level1_CurveFittingNet")
fc1 = FcLayer(num_input, num_hidden1, params)
net.add_layer(fc1, "fc1")
sigmoid1 = ActivatorLayer(Sigmoid())
net.add_layer(sigmoid1, "sigmoid1")
fc2 = FcLayer(num_hidden1, num_output, params)
net.add_layer(fc2, "fc2")
net.train(dataReader, checkpoint=100, need_test=True)
net.ShowLossHistory("epoch")
ShowResult(net, dataReader)
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_sigmoid(num_input, num_hidden, num_output, hp):
net = NeuralNet40(hp, "chinabank_sigmoid")
fc1 = FcLayer(num_input, num_hidden, hp)
net.add_layer(fc1, "fc1")
s1 = ActivatorLayer(Sigmoid())
net.add_layer(s1, "Sigmoid1")
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)
num_hidden = 2
num_output = 1
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")
def model():
dr = LoadData()
num_input = dr.num_feature
num_hidden1 = 32
num_hidden2 = 16
num_hidden3 = 8
num_hidden4 = 4
num_output = 1
max_epoch = 1000
batch_size = 16
learning_rate = 0.01
eps = 1e-6
params = HyperParameters40(learning_rate,
max_epoch,
batch_size,
eps,
net_type=NetType.Fitting,
init_method=InitialMethod.Xavier)
net = NeuralNet40(params, "HouseSingle")
fc1 = FcLayer(num_input, num_hidden1, params)
net.add_layer(fc1, "fc1")
r1 = ActivatorLayer(Relu())
net.add_layer(r1, "r1")
fc2 = FcLayer(num_hidden1, num_hidden2, params)
net.add_layer(fc2, "fc2")
r2 = ActivatorLayer(Relu())
net.add_layer(r2, "r2")
fc3 = FcLayer(num_hidden2, num_hidden3, params)
net.add_layer(fc3, "fc3")
r3 = ActivatorLayer(Relu())
net.add_layer(r3, "r3")
fc4 = FcLayer(num_hidden3, num_hidden4, params)
net.add_layer(fc4, "fc4")
r4 = ActivatorLayer(Relu())
net.add_layer(r4, "r4")
fc5 = FcLayer(num_hidden4, num_output, params)
net.add_layer(fc5, "fc5")
#ShowResult(net, dr)
#net.load_parameters()
#Inference(net, dr)
#exit()
#ShowResult(net, dr)
net.train(dr, checkpoint=10, need_test=True)
output = net.inference(dr.XTest)
real_output = dr.DeNormalizeY(output)
mse = np.sum((dr.YTestRaw - real_output)**2) / dr.YTest.shape[0] / 10000
print("mse=", mse)
net.ShowLossHistory("epoch")
ShowResult(net, dr)
num_hidden3 = 32
num_hidden4 = 16
num_output = 10
max_epoch = 50
batch_size = 32
learning_rate = 0.01
eps = 1e-3
params = HyperParameters40(learning_rate,
max_epoch,
batch_size,
eps,
net_type=NetType.MultipleClassifier,
init_method=InitialMethod.MSRA)
net = NeuralNet40(params, "Cifar10")
fc1 = FcLayer(num_input, num_hidden1, params)
net.add_layer(fc1, "fc1")
r1 = ActivatorLayer(Relu())
net.add_layer(r1, "r1")
fc2 = FcLayer(num_hidden1, num_hidden2, params)
net.add_layer(fc2, "fc2")
r2 = ActivatorLayer(Relu())
net.add_layer(r2, "r2")
fc3 = FcLayer(num_hidden2, num_hidden3, params)
net.add_layer(fc3, "fc3")
r3 = ActivatorLayer(Relu())
net.add_layer(r3, "r3")