def fprop(self):
if self.phase == "train":
ca.less(self.dropout, ca.random.uniform(size=self.mask_shape), self._tmp_mask)
ca.multiply(self.x.out, self._tmp_mask, self.out)
elif self.phase == "test":
ca.multiply(self.x.out, 1.0 - self.dropout, self.out)
else:
raise ValueError("Invalid phase: %s" % self.phase)
def fprop(self):
if self.phase == 'train':
ca.less(self.dropout, ca.random.uniform(size=self.mask_shape),
self._tmp_mask)
ca.multiply(self.x.array, self._tmp_mask, self.array)
elif self.phase == 'test':
ca.multiply(self.x.array, 1.0-self.dropout, self.array)
else:
raise ValueError('Invalid phase: %s' % self.phase)
def fprop(self):
if self.phase == 'train':
ca.less(self.dropout, ca.random.uniform(size=self.mask_shape),
self._tmp_mask)
ca.multiply(self.x.out, self._tmp_mask, self.out)
elif self.phase == 'test':
ca.multiply(self.x.out, 1.0 - self.dropout, self.out)
else:
raise ValueError('Invalid phase: %s' % self.phase)
def test_binary_cmp():
a_np = np.random.normal(size=(5, 5))
b_np = np.random.normal(size=(5, 5))
a_ca = ca.array(a_np)
b_ca = ca.array(b_np)
c_np = np.greater(a_np, b_np)
c_ca = ca.greater(a_ca, b_ca)
print(np.allclose(c_np, np.array(c_ca)))
c_np = np.greater(a_np, 0.1)
c_ca = ca.greater(a_ca, 0.1)
print(np.allclose(c_np, np.array(c_ca)))
c_np = np.less(a_np, 0.1)
c_ca = ca.less(a_ca, 0.1)
print(np.allclose(c_np, np.array(c_ca)))
c_np = 0.1 < a_np
c_ca = 0.1 < a_ca
print(np.allclose(c_np, np.array(c_ca)))
def test_binary_cmp():
a_np = np.random.normal(size=(5, 5))
b_np = np.random.normal(size=(5, 5))
a_ca = ca.array(a_np)
b_ca = ca.array(b_np)
c_np = np.greater(a_np, b_np)
c_ca = ca.greater(a_ca, b_ca)
print(np.allclose(c_np, np.array(c_ca)))
c_np = np.greater(a_np, 0.1)
c_ca = ca.greater(a_ca, 0.1)
print(np.allclose(c_np, np.array(c_ca)))
c_np = np.less(a_np, 0.1)
c_ca = ca.less(a_ca, 0.1)
print(np.allclose(c_np, np.array(c_ca)))
c_np = 0.1 < a_np
c_ca = 0.1 < a_ca
print(np.allclose(c_np, np.array(c_ca)))
def bprop(self):
self.x.out_grad = ca.less(self.x.out, 0) * self.a
pos = ca.nnet.relu_d(self.x.out)
self.x.out_grad += pos
self.x.out_grad *= self.out_grad
def bprop(self):
self.x.grad_array = ca.less(self.x.array, 0) * self.a
pos = ca.nnet.relu_d(self.x.array)
self.x.grad_array += pos
self.x.grad_array *= self.grad_array
