num_example = dataReader.num_example
num_input = num_feature
num_hidden1 = 64
num_hidden2 = 32
max_epoch = 1
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 net():
num_output = 10
dr = ReadData()
max_epoch = 1
batch_size = 50
eta = 0.001
eps = 0.01
params = CParameters(eta, max_epoch, batch_size, eps,
LossFunctionName.CrossEntropy3, InitialMethod.Xavier,
OptimizerName.Adam)
loss_history = CLossHistory()
net = NeuralNet(params)
c1 = ConvLayer((3, 32, 32), (32, 3, 3), (1, 1), Relu(), params)
net.add_layer(c1, "c1")
p1 = PoolingLayer(c1.output_shape, (
2,
2,
), 2, PoolingTypes.MAX)
net.add_layer(p1, "p1")
c2 = ConvLayer(p1.output_shape, (64, 3, 3), (1, 1), Relu(), params)
net.add_layer(c2, "c2")
p2 = PoolingLayer(c2.output_shape, (
2,
2,
), 2, PoolingTypes.MAX)
net.add_layer(p2, "p2")
f1 = FcLayer(p2.output_size, 512, Relu(), params)
net.add_layer(f1, "f1")
f2 = FcLayer(f1.output_size, 10, Softmax(), params)
net.add_layer(f2, "f2")
net.train(dr, loss_history)
loss_history.ShowLossHistory(params)
return dr
if __name__ == '__main__':
dataReader = LoadData()
num_input = 1
num_hidden1 = 4
num_output = 1
max_epoch = 10000
batch_size = 10
learning_rate = 0.1
eps = 0.001
params = CParameters(learning_rate, max_epoch, batch_size, eps,
LossFunctionName.MSE,
InitialMethod.Xavier,
OptimizerName.Momentum)
net = NeuralNet(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()
ShowResult(net, dataReader, params.toString())
def Net(dataReader, num_input, num_hidden, num_output, params):
net = NeuralNet(params)
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 = ActivatorLayer(Softmax())
net.add_layer(softmax, "softmax")
net.train(dataReader, checkpoint=1, need_test=True)
net.ShowLossHistory()
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 = HyperParameters(learning_rate,
max_epoch,
batch_size,
eps,
net_type=NetType.BinaryClassifier,
init_method=InitialMethod.Xavier)
net = NeuralNet(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():
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 = HyperParameters(
learning_rate, max_epoch, batch_size,
net_type=NetType.Fitting,
init_method=InitialMethod.Xavier)
net = NeuralNet(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.load_parameters()
#ShowResult(net, dataReader, params.toString())
#ShowResult2(net, dataReader)
net.train(dataReader, checkpoint=100, need_test=True)
net.ShowLossHistory("epoch")
#ShowResult(net, dataReader, params.toString())
ShowResult(net, dataReader)
num_input = dr.num_feature
num_hidden1 = 16
num_hidden2 = 8
num_output = 1
max_epoch = 10000
batch_size = 32
learning_rate = 0.1
eps = 0.001
params = CParameters(learning_rate, max_epoch, batch_size, eps,
LossFunctionName.MSE, InitialMethod.MSRA,
OptimizerName.SGD)
net = NeuralNet(params, "PM25")
fc1 = FcLayer(num_input, num_hidden1, params)
net.add_layer(fc1, "fc1")
sigmoid1 = ActivatorLayer(Relu())
net.add_layer(sigmoid1, "sigmoid1")
fc2 = FcLayer(num_hidden1, num_hidden2, params)
net.add_layer(fc2, "fc2")
sigmoid2 = ActivatorLayer(Relu())
net.add_layer(sigmoid2, "sigmoid2")
fc3 = FcLayer(num_hidden2, num_output, params)
net.add_layer(fc3, "fc3")
net.load_parameters()
