num_example = dataReader.num_example
num_input = num_feature
num_hidden1 = 64
num_hidden2 = 32
max_epoch = 5
batch_size = 5
learning_rate = 0.02
eps = 0.01
params = CParameters(learning_rate, max_epoch, batch_size, eps,
LossFunctionName.CrossEntropy3, InitialMethod.Xavier,
OptimizerName.SGD)
loss_history = CLossHistory()
net = NeuralNet(params)
fc1 = FcLayer(num_input, num_hidden1, Relu())
net.add_layer(fc1, "fc1")
fc2 = FcLayer(num_hidden1, num_hidden2, Relu())
net.add_layer(fc2, "fc2")
fc3 = FcLayer(num_hidden2, num_output, Softmax())
net.add_layer(fc3, "fc3")
net.train(dataReader, loss_history)
loss_history.ShowLossHistory(params, 0, None, 0, 1)
net.load_parameters()
print("Testing...")
correct, count = net.Test(dataReader)
print(str.format("rate={0} / {1} = {2}", correct, count, correct / count))
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 = HyperParameters(learning_rate,
max_epoch,
batch_size,
eps,
net_type=NetType.Fitting,
init_method=InitialMethod.Xavier)
net = NeuralNet(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)
def model_dropout():
dr = LoadData()
num_input = dr.num_feature
num_hidden1 = 64
num_hidden2 = 64
num_hidden3 = 64
num_hidden4 = 8
num_output = 1
max_epoch = 2000
batch_size = 16
learning_rate = 0.01
eps = 1e-6
params = CParameters(
learning_rate, max_epoch, batch_size, eps,
LossFunctionName.MSE,
InitialMethod.Xavier,
OptimizerName.Momentum,
RegularMethod.L1, 0.001)
net = NeuralNet(params, "HouseSingleDropout64")
fc1 = FcLayer(num_input, num_hidden1, params)
net.add_layer(fc1, "fc1")
r1 = ActivatorLayer(Relu())
net.add_layer(r1, "r1")
#d1 = DropoutLayer(num_hidden1, 0.2)
#net.add_layer(d1, "d1")
fc2 = FcLayer(num_hidden1, num_hidden2, params)
net.add_layer(fc2, "fc2")
r2 = ActivatorLayer(Relu())
net.add_layer(r2, "r2")
#d2 = DropoutLayer(num_hidden2, 0.3)
#net.add_layer(d2, "d2")
fc3 = FcLayer(num_hidden2, num_hidden3, params)
net.add_layer(fc3, "fc3")
r3 = ActivatorLayer(Relu())
net.add_layer(r3, "r3")
#d3 = DropoutLayer(num_hidden3, 0.2)
#net.add_layer(d3, "d3")
fc4 = FcLayer(num_hidden3, num_hidden4, params)
net.add_layer(fc4, "fc4")
r4 = ActivatorLayer(Relu())
net.add_layer(r4, "r4")
#d4 = DropoutLayer(num_hidden4, 0.1)
#net.add_layer(d4, "d4")
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()
ShowResult(net, dr)
