def test_forward1(self):
x = np.array([[-1, 0, 1, 2], [2, 0, 1, -1]], np.float32)
t = np.array([3, 0]).astype(np.int32)
y = F.softmax_cross_entropy_simple(x, t)
y2 = CF.softmax_cross_entropy(x, t)
res = np.allclose(y.data, y2.data)
self.assertTrue(res)
from dezero import Variable, as_variable
import dezero.functions as F
from dezero.models import MLP
def softmax1d(x):
x = as_variable(x)
y = F.exp(x)
sum_y = F.sum(y)
return y / sum_y
model = MLP((10, 3))
x = Variable(np.array([[0.2, -0.4]]))
y = model(x)
p = softmax1d(y)
print(y)
print(p)
x = np.array([[0.2, -0.4], [0.3, 0.5], [1.3, -3.2], [2.1, 0.3]])
t = np.array([2, 0, 1, 0])
y = model(x)
p = F.softmax_simple(y)
print(y)
print(p)
loss = F.softmax_cross_entropy_simple(y, t)
loss.backward()
print(loss)
def test_backward3(self):
N, CLS_NUM = 100, 10
x = np.random.randn(N, CLS_NUM)
t = np.random.randint(0, CLS_NUM, (N, ))
f = lambda x: F.softmax_cross_entropy_simple(x, t)
self.assertTrue(gradient_check(f, x))
def test_backward1(self):
x = np.array([[-1, 0, 1, 2], [2, 0, 1, -1]], np.float32)
t = np.array([3, 0]).astype(np.int32)
f = lambda x: F.softmax_cross_entropy_simple(x, Variable(t))
self.assertTrue(gradient_check(f, x))
batch_size = 30
hidden_size = 10
lr = 1.0
x,t = dezero.datasets.get_spiral(train=True)
model = MLP((hidden_size,3))
optimizer = optimizers.SGD(lr).setup(model)
data_size = len(x)
max_iter = math.ceil(data_size / batch_size)
for epoch in range(max_epoch):
index = np.random.permutation(data_size)
sum_loss = 0
for i in range(max_iter):
batch_index = index[i*batch_size:(i+1)*batch_size]
batch_x = x[batch_index]
batch_t = t[batch_index]
y = model(batch_x)
loss = F.softmax_cross_entropy_simple(y,batch_t)
model.cleargrads()
loss.backward()
optimizer.update()
sum_loss += float(loss.data) * len(batch_t)
avg_loss = sum_loss / data_size
print('epoch %d, loss %.2f' % (epoch + 1, avg_loss))
