# t = np.array([1, 2, 0])
# acc = F.accuracy(y, t)
# print(acc)
max_epoch = 300
batch_size = 30
hidden_size = 10
lr = 1.0
train_set = myPackage.datasets.Spiral(train=True)
test_set = myPackage.datasets.Spiral(train=False)
train_loader = DataLoader(train_set, batch_size)
test_loader = DataLoader(test_set, batch_size, shuffle=False)
model = MLP((hidden_size, 3))
optimizer = optimizers.SGD(lr).setup(model)
train_loss_list = []
test_loss_list = []
train_acc_list = []
test_acc_list = []
for epoch in range(max_epoch):
sum_loss, sum_acc = 0, 0
for x, t in train_loader:
y = model(x)
loss = F.softmax_cross_entropy(y, t)
acc = F.accuracy(y, t)
model.cleargrads()
loss.backward()
sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
import numpy as np
from myPackage import Variable
from myPackage import optimizers
import myPackage.functions as F
from myPackage.models import MLP
np.random.seed(0)
x = np.random.rand(100, 1)
y = np.sin(2 * np.pi * x) + np.random.rand(100, 1)
lr = 0.2
max_iter = 10000
hidden_size = 10
model = MLP((hidden_size, 1))
optimizer = optimizers.SGD(lr)
optimizer.setup(model)
# optimizer = optimizers.SGD(lr).setup(model)
for i in range(max_iter):
y_pred = model(x)
loss = F.mean_squared_error(y, y_pred)
model.cleargrads()
loss.backward()
optimizer.update()
if i % 1000 == 0:
print(loss)