forked from glample/tagger
/
optimization.py
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/
optimization.py
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import numpy as np
import theano
import theano.tensor as T
floatX = theano.config.floatX
device = theano.config.device
class Optimization:
def __init__(self, clip=None):
"""
Initialization
"""
self.clip = clip
def get_gradients(self, cost, params):
"""
Compute the gradients, and clip them if required.
"""
if self.clip is None:
return T.grad(cost, params)
else:
assert self.clip > 0
return T.grad(
theano.gradient.grad_clip(cost, -1 * self.clip, self.clip),
params
)
def get_updates(self, method, cost, params, *args, **kwargs):
"""
Compute the updates for different optimizers.
"""
if method == 'sgd':
updates = self.sgd(cost, params, **kwargs)
elif method == 'sgdmomentum':
updates = self.sgdmomentum(cost, params, **kwargs)
elif method == 'adagrad':
updates = self.adagrad(cost, params, **kwargs)
elif method == 'adadelta':
updates = self.adadelta(cost, params, **kwargs)
elif method == 'adam':
updates = self.adam(cost, params, **kwargs)
elif method == 'rmsprop':
updates = self.rmsprop(cost, params, **kwargs)
else:
raise("Not implemented learning method: %s" % method)
return updates
def sgd(self, cost, params, lr=0.01):
"""
Stochatic gradient descent.
"""
lr = theano.shared(np.float32(lr).astype(floatX))
gradients = self.get_gradients(cost, params)
updates = []
for p, g in zip(params, gradients):
updates.append((p, p - lr * g))
return updates
def sgdmomentum(self, cost, params, lr=0.01, momentum=0.9):
"""
Stochatic gradient descent with momentum. Momentum has to be in [0, 1)
"""
# Check that the momentum is a correct value
assert 0 <= momentum < 1
lr = theano.shared(np.float32(lr).astype(floatX))
momentum = theano.shared(np.float32(momentum).astype(floatX))
gradients = self.get_gradients(cost, params)
velocities = [theano.shared(np.zeros_like(param.get_value(borrow=True)).astype(floatX)) for param in params]
updates = []
for param, gradient, velocity in zip(params, gradients, velocities):
new_velocity = momentum * velocity - lr * gradient
updates.append((velocity, new_velocity))
updates.append((param, param + new_velocity))
return updates
def adagrad(self, cost, params, lr=1.0, epsilon=1e-6):
"""
Adagrad. Based on http://www.ark.cs.cmu.edu/cdyer/adagrad.pdf
"""
lr = theano.shared(np.float32(lr).astype(floatX))
epsilon = theano.shared(np.float32(epsilon).astype(floatX))
gradients = self.get_gradients(cost, params)
gsums = [theano.shared(np.zeros_like(param.get_value(borrow=True)).astype(floatX)) for param in params]
updates = []
for param, gradient, gsum in zip(params, gradients, gsums):
new_gsum = gsum + gradient ** 2.
updates.append((gsum, new_gsum))
updates.append((param, param - lr * gradient / (T.sqrt(gsum + epsilon))))
return updates
def adadelta(self, cost, params, rho=0.95, epsilon=1e-6):
"""
Adadelta. Based on:
http://www.matthewzeiler.com/pubs/googleTR2012/googleTR2012.pdf
"""
rho = theano.shared(np.float32(rho).astype(floatX))
epsilon = theano.shared(np.float32(epsilon).astype(floatX))
gradients = self.get_gradients(cost, params)
accu_gradients = [theano.shared(np.zeros_like(param.get_value(borrow=True)).astype(floatX)) for param in params]
accu_deltas = [theano.shared(np.zeros_like(param.get_value(borrow=True)).astype(floatX)) for param in params]
updates = []
for param, gradient, accu_gradient, accu_delta in zip(params, gradients, accu_gradients, accu_deltas):
new_accu_gradient = rho * accu_gradient + (1. - rho) * gradient ** 2.
delta_x = - T.sqrt((accu_delta + epsilon) / (new_accu_gradient + epsilon)) * gradient
new_accu_delta = rho * accu_delta + (1. - rho) * delta_x ** 2.
updates.append((accu_gradient, new_accu_gradient))
updates.append((accu_delta, new_accu_delta))
updates.append((param, param + delta_x))
return updates
def adam(self, cost, params, lr=0.001, beta1=0.9, beta2=0.999, epsilon=1e-8):
"""
Adam. Based on http://arxiv.org/pdf/1412.6980v4.pdf
"""
updates = []
gradients = self.get_gradients(cost, params)
t = theano.shared(np.float32(1.).astype(floatX))
for param, gradient in zip(params, gradients):
value = param.get_value(borrow=True)
m_prev = theano.shared(np.zeros(value.shape, dtype=value.dtype), broadcastable=param.broadcastable)
v_prev = theano.shared(np.zeros(value.shape, dtype=value.dtype), broadcastable=param.broadcastable)
m = beta1 * m_prev + (1. - beta1) * gradient
v = beta2 * v_prev + (1. - beta2) * gradient ** 2.
m_hat = m / (1. - beta1 ** t)
v_hat = v / (1. - beta2 ** t)
theta = param - (lr * m_hat) / (T.sqrt(v_hat) + epsilon)
updates.append((m_prev, m))
updates.append((v_prev, v))
updates.append((param, theta))
updates.append((t, t + 1.))
return updates
def rmsprop(self, cost, params, lr=0.001, rho=0.9, eps=1e-6):
"""
RMSProp.
"""
lr = theano.shared(np.float32(lr).astype(floatX))
gradients = self.get_gradients(cost, params)
accumulators = [theano.shared(np.zeros_like(p.get_value()).astype(np.float32)) for p in params]
updates = []
for param, gradient, accumulator in zip(params, gradients, accumulators):
new_accumulator = rho * accumulator + (1 - rho) * gradient ** 2
updates.append((accumulator, new_accumulator))
new_param = param - lr * gradient / T.sqrt(new_accumulator + eps)
updates.append((param, new_param))
return updates