def fprop(self):
if self.phase == 'train':
# Calculate batch mean
tmp = ca.mean(self.x.out, axis=0, keepdims=True)
# Center input
ca.subtract(self.x.out, tmp, self._tmp_batch_centered)
# Update running mean
tmp *= 1 - self.momentum
self.running_mean *= self.momentum
self.running_mean += tmp
# Calculate batch variance
ca.power(self._tmp_batch_centered, 2, self.out)
ca.mean(self.out, axis=0, keepdims=True,
out=self._tmp_batch_inv_std)
# Calculate 1 / E([x - E(x)]^2)
self._tmp_batch_inv_std += self.eps
ca.sqrt(self._tmp_batch_inv_std, self._tmp_batch_inv_std)
ca.power(self._tmp_batch_inv_std, -1, self._tmp_batch_inv_std)
# Normalize input
ca.multiply(self._tmp_batch_centered, self._tmp_batch_inv_std,
self.out)
# Update running std
self.running_std *= self.momentum
ca.multiply(self._tmp_batch_inv_std, 1-self.momentum, tmp)
self.running_std += tmp
elif self.phase == 'test':
ca.subtract(self.x.out, self.running_mean, self.out)
self.out *= self.running_std
else:
raise ValueError('Invalid phase: %s' % self.phase)
if self.affine:
self.out *= self.gamma.array
self.out += self.beta.array
def fprop(self):
if self.phase == 'train':
# Calculate batch mean
tmp = ca.mean(self.x.out, axis=0, keepdims=True)
# Center input
ca.subtract(self.x.out, tmp, self._tmp_batch_centered)
# Update running mean
tmp *= 1 - self.momentum
self.running_mean *= self.momentum
self.running_mean += tmp
# Calculate batch variance
ca.power(self._tmp_batch_centered, 2, self.out)
ca.mean(self.out,
axis=0,
keepdims=True,
out=self._tmp_batch_inv_std)
# Calculate 1 / E([x - E(x)]^2)
self._tmp_batch_inv_std += self.eps
ca.sqrt(self._tmp_batch_inv_std, self._tmp_batch_inv_std)
ca.power(self._tmp_batch_inv_std, -1, self._tmp_batch_inv_std)
# Normalize input
ca.multiply(self._tmp_batch_centered, self._tmp_batch_inv_std,
self.out)
# Update running std
self.running_std *= self.momentum
ca.multiply(self._tmp_batch_inv_std, 1 - self.momentum, tmp)
self.running_std += tmp
elif self.phase == 'test':
ca.subtract(self.x.out, self.running_mean, self.out)
self.out *= self.running_std
else:
raise ValueError('Invalid phase: %s' % self.phase)
if self.affine:
self.out *= self.gamma.array
self.out += self.beta.array
def step(self, param, mean_square):
grad = param.grad()
# mean_square = decay*mean_square + (1 - decay)*grad
mean_square *= self.decay
tmp = grad**2
tmp *= (1 - self.decay)
mean_square += tmp
# step = -learn_rate*grad/(sqrt(mean_square) + eps)
ca.sqrt(mean_square, tmp)
tmp += self.eps
ca.divide(grad, tmp, tmp)
tmp *= -self.learn_rate
param.step(tmp)
def step(self, param, last_step):
last_step *= self.decay
step = param.grad()
last_step += (1.0 - self.decay) * step**2
scaling = ca.sqrt(last_step) + self.eps
step *= -self.learn_rate
step /= scaling
param.step(step)
def step(self, param, last_step):
last_step *= self.decay
step = param.grad()
last_step += (1.0 - self.decay) * step ** 2
scaling = ca.sqrt(last_step) + self.eps
step *= -self.learn_rate
step /= scaling
param.step(step)
def normalize(matrix,gpuFlag=False):
if gpuFlag==True:
import cudarray as ca
norm=ca.sqrt(ca.sum(ca.power(matrix,2),1,keepdims=True));
matrix_n=matrix/norm
else:
norm=np.sqrt(np.sum(np.square(matrix),1,keepdims=True));
matrix_n=matrix/norm
return matrix_n
def step(self):
for param, rms_grad in zip(self.params, self.steps):
rms_grad *= self.decay
step = param.grad()
if param.penalty is not None:
step -= param.penalty()
rms_grad += (1.0 - self.decay) * step**2
scaling = ca.maximum(ca.sqrt(rms_grad), self.max_scaling_inv)
step_rate = self.learn_rate * param.learn_rate / self.batch_size
param.step(step / scaling * (-step_rate))
def step(self):
for param, rms_grad in zip(self.params, self.steps):
rms_grad *= self.decay
step = param.grad()
if param.penalty is not None:
step -= param.penalty()
rms_grad += (1.0 - self.decay) * step**2
scaling = ca.maximum(ca.sqrt(rms_grad), self.max_scaling_inv)
step_rate = self.learn_rate * param.learn_rate / self.batch_size
param.step(step / scaling * (-step_rate))
def normalize(matrix, gpuFlag=False):
if gpuFlag == True:
import cudarray as ca
norm = ca.sqrt(ca.sum(ca.power(matrix, 2), 1, keepdims=True))
matrix_n = matrix / norm
else:
norm = np.sqrt(np.sum(np.square(matrix), 1, keepdims=True))
matrix_n = matrix / norm
return matrix_n
def fprop(self, x, phase):
n_channels = x.shape[1]
# Calculate local mean
tmp = self.conv_op.fprop(x, self.ca_kernel)
if n_channels > 1:
ca.divide(tmp, n_channels, tmp)
# Center input with local mean
centered = ca.subtract(x, tmp)
# Calculate local standard deviation
tmp = ca.power(centered, 2)
tmp = self.conv_op.fprop(tmp, self.ca_kernel)
if n_channels > 1:
ca.divide(tmp, n_channels, tmp)
ca.sqrt(tmp, tmp)
# Scale centered input with standard deviation
return centered / (tmp + self.eps)
def fprop(self, x):
n_channels = x.shape[1]
# Calculate local mean
tmp = self.conv_op.fprop(x, self.ca_kernel)
if n_channels > 1:
ca.divide(tmp, n_channels, tmp)
# Center input with local mean
centered = ca.subtract(x, tmp)
# Calculate local standard deviation
tmp = ca.power(centered, 2)
tmp = self.conv_op.fprop(tmp, self.ca_kernel)
if n_channels > 1:
ca.divide(tmp, n_channels, tmp)
ca.sqrt(tmp, tmp)
# Scale centered input with standard deviation
return centered / (tmp + self.eps)
def step(self, param, last_step):
last_step *= self.decay
step = param.grad()
penalty = param.penalty()
if penalty is not None:
step -= penalty
last_step += (1.0 - self.decay) * step**2
scaling = ca.sqrt(last_step) + self.eps
step *= -self.learn_rate
step /= scaling
param.step(step)
def step(self, param, last_step):
last_step *= self.decay
step = param.grad()
penalty = param.penalty()
if penalty is not None:
step -= penalty
last_step += (1.0 - self.decay) * step**2
scaling = ca.sqrt(last_step) + self.eps
step *= -self.learn_rate
step /= scaling
param.step(step)
def fprop(self):
if self.phase == 'train':
# Calculate batch mean
tmp = ca.mean(ca.mean(self.x.array, axis=0, keepdims=True),
axis=(2, 3), keepdims=True)
# Center input
ca.subtract(self.x.array, tmp, self._tmp_batch_centered)
# Update running mean
tmp *= 1 - self.momentum
self.running_mean *= self.momentum
self.running_mean += tmp
# Calculate batch variance
ca.power(self._tmp_batch_centered, 2, self.array)
ca.mean(ca.mean(self.array, axis=0, keepdims=True), axis=(2, 3),
keepdims=True, out=self._tmp_batch_inv_std)
# Calculate 1 / E([x - E(x)]^2)
self._tmp_batch_inv_std += self.eps
ca.sqrt(self._tmp_batch_inv_std, self._tmp_batch_inv_std)
ca.power(self._tmp_batch_inv_std, -1, self._tmp_batch_inv_std)
# Normalize input
ca.multiply(self._tmp_batch_centered, self._tmp_batch_inv_std,
self.array)
# Update running std
self.running_std *= self.momentum
ca.multiply(self._tmp_batch_inv_std, 1-self.momentum, tmp)
self.running_std += tmp
if self.noise_std > 0.0:
noise = ca.random.normal(scale=self.noise_std,
size=self.shape)
ca.add(self.array, noise, self.array)
elif self.phase == 'test':
ca.subtract(self.x.array, self.running_mean, self.array)
self.array *= self.running_std
else:
raise ValueError('Invalid phase: %s' % self.phase)
if self.affine:
self.array *= self.gamma.array
self.array += self.beta.array
def step(self, param, state):
m, v, t = state
grad = param.grad()
t += 1
t = int(t)
# m = beta1*m + (1 - beta1)*grad
m *= self.beta1
tmp = (1 - self.beta1)*grad
m += tmp
# v = beta2*v + (1 - beta2)*grad**2
v *= self.beta2
ca.power(grad, 2, tmp)
tmp *= (1 - self.beta2)
v += tmp
# alpha = learn_rate*sqrt(1 - beta2**t)/(1 - beta1**t)
# step = -alpha_t*m/(sqrt(v) + eps)
alpha = self.learn_rate*np.sqrt(1 - self.beta2**t)/(1 - self.beta1**t)
ca.sqrt(v, tmp)
tmp += self.eps
ca.divide(m, tmp, tmp)
tmp *= -alpha
param.step(tmp)
def step(self, param, state):
m, v, t = state
grad = param.grad()
t += 1
t = int(t)
beta1_t = self.beta1 * self.lambd ** (t - 1)
m *= beta1_t
m += (1 - beta1_t) * grad
v *= self.beta2
v += (1 - self.beta2) * grad ** 2
learn_rate = self.learn_rate * (1 - self.beta2 ** t) ** 0.5 / (1 - self.beta1 ** t)
step = m / (ca.sqrt(v) + self.eps)
step *= -learn_rate
param.step(step)
def step(self, param, state):
m, v, t = state
grad = param.grad()
t += 1
t = int(t)
beta1_t = self.beta1 * self.lambd**(t - 1)
m *= beta1_t
m += (1 - beta1_t) * grad
v *= self.beta2
v += (1 - self.beta2) * grad**2
learn_rate = (self.learn_rate * (1 - self.beta2**t)**0.5 /
(1 - self.beta1**t))
step = m / (ca.sqrt(v) + self.eps)
step *= -learn_rate
param.step(step)
def matrix_factorization(R, P, Q, mask, steps=200000000, alpha=0.00005, beta=0.02):
Q = ca.transpose(Q)
for step in xrange(steps):
E = ca.subtract(R, ca.multiply(ca.dot(P,Q), mask))
rmse = ca.sqrt(ca.sum(ca.power(E,2)) / ca.sum(mask))
rmse = np.array(rmse)[0]
print 'step: %i RMSE: %f' % (step, rmse)
if rmse < 0.65:
break
P = ca.add(ca.multiply(P,(1-alpha*beta)),ca.multiply(ca.dot(E,ca.transpose(Q)), 2*alpha))
Q = ca.add(ca.multiply(Q,(1-alpha*beta)),ca.multiply(ca.dot(ca.transpose(P),E),2*alpha))
return P, Q
def step(self, param, state):
m, v, t = state
grad = param.grad()
penalty = param.penalty()
if penalty is not None:
grad -= penalty
t += 1
t = int(t)
beta1_t = self.beta1 * self.lambd**(t - 1)
m *= beta1_t
m += (1 - beta1_t) * grad
v *= self.beta2
v += (1 - self.beta2) * grad**2
learn_rate = (self.learn_rate * (1 - self.beta2**t)**0.5 /
(1 - self.beta1**t))
step = m / (ca.sqrt(v) + self.eps)
step *= -learn_rate
param.step(step)
def matrix_factorization(R,
P,
Q,
mask,
steps=200000000,
alpha=0.00005,
beta=0.02):
Q = ca.transpose(Q)
for step in xrange(steps):
E = ca.subtract(R, ca.multiply(ca.dot(P, Q), mask))
rmse = ca.sqrt(ca.sum(ca.power(E, 2)) / ca.sum(mask))
rmse = np.array(rmse)[0]
print 'step: %i RMSE: %f' % (step, rmse)
if rmse < 0.65:
break
P = ca.add(ca.multiply(P, (1 - alpha * beta)),
ca.multiply(ca.dot(E, ca.transpose(Q)), 2 * alpha))
Q = ca.add(ca.multiply(Q, (1 - alpha * beta)),
ca.multiply(ca.dot(ca.transpose(P), E), 2 * alpha))
return P, Q
