batch_size = 5
learning_rate = 0.05
stop_eps = 0.2
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, Sigmoid())
net.add_layer(fc1, "fc1")
fc2 = FcLayer(num_hidden1, num_hidden2, Tanh())
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)
print("Testing...")
correct, count = net.Test(dataReader)
print(str.format("rate={0} / {1} = {2}", correct, count, correct / count))
net.load_parameters()
print("Testing...")
correct, count = net.Test(dataReader)
print(str.format("rate={0} / {1} = {2}", correct, count, correct / count))
