def test_forward3(self):
np.random.seed(0)
x = np.random.rand(10, 10, 10).astype('f')
y2 = CF.softmax(x, axis=1)
y = F.softmax_simple(Variable(x))
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_forward1(self):
x = np.array([[0, 1, 2], [0, 2, 4]], np.float32)
y2 = CF.softmax(x, axis=1)
y = F.softmax_simple(Variable(x))
res = np.allclose(y.data, y2.data)
self.assertTrue(res)
def test_backward3(self):
np.random.seed(0)
x_data = np.random.rand(10, 10, 10)
f = lambda x: F.softmax_simple(x, axis=1)
self.assertTrue(gradient_check(f, x_data))
def test_backward1(self):
x_data = np.array([[0, 1, 2], [0, 2, 4]])
f = lambda x: F.softmax_simple(x, axis=1)
self.assertTrue(gradient_check(f, x_data))
