技术信息：
操作系统：Mac OS X 10.9.5
IDE：Eclipse Mars.1发行版（4.5.1），包含PyDev和Anaconda解释器（语法版本3.4）
GPU：NVIDIA GeForce GT 650M
Libs：numpy，aeosa，Sphinx-1.3.1，Theano 0.7，nltk-3.1
我的背景：我对theano和numpy都很陌生，并没有参加机器学习或离散数学的正式课程。
我目前使用的自然语言处理的递归神经网络取自这里：
https://github.com/dennybritz/rnn-tutorial-gru-lstm/blob/master/gru_theano.py
对此文件所做的唯一更改是使用字符串theano.config.floatX替换对'float32'的引用。
我还使用了存储库中包含的utils.py和train.py模块，只做了很小的改动。
我计划合并的adam优化器代替示例存储库中实现的sgd / rms代码，可在此处找到：https://gist.github.com/skaae/ae7225263ca8806868cb
此处转载（再次引用.config.floatX替换为硬编码的'float32'）：
（theano为th，theano.shared为thsh，theano.tensor为T，numpy为np }）
def adam(loss, all_params, learning_rate=0.001, b1=0.9, b2=0.999, e=1e-8, gamma=1-1e-8):
"""
ADAM update rules
Default values are taken from [Kingma2014]
References:
[Kingma2014] Kingma, Diederik, and Jimmy Ba.
"Adam: A Method for Stochastic Optimization."
arXiv preprint arXiv:1412.6980 (2014).
http://arxiv.org/pdf/1412.6980v4.pdf
"""
updates = []
all_grads = th.grad(loss, all_params)
alpha = learning_rate
t = thsh(np.float32(1))
b1_t = b1*gamma**(t-1) #(Decay the first moment running average coefficient)
for theta_previous, g in zip(all_params, all_grads):
m_previous = thsh(np.zeros(theta_previous.get_value().shape.astype('float32')))
v_previous = thsh(np.zeros(theta_previous.get_value().shape.astype('float32')))
m = b1_t*m_previous + (1 - b1_t)*g # (Update biased first moment estimate)
v = b2*v_previous + (1 - b2)*g**2 # (Update biased second raw moment estimate)
m_hat = m / (1-b1**t) # (Compute bias-corrected first moment estimate)
v_hat = v / (1-b2**t) # (Compute bias-corrected second raw moment estimate)
theta = theta_previous - (alpha * m_hat) / (T.sqrt(v_hat) + e) #(Update parameters)
updates.append((m_previous, m))
updates.append((v_previous, v))
updates.append((theta_previous, theta) )
updates.append((t, t + 1.))
return updates
我的问题是：
如何修改GRUTheano模块以使用上面的Adam方法代替内置的sgd / rmsprop函数？
看起来关键的变化将是GRUTheano的第99-126行：
# SGD parameters
learning_rate = T.scalar('learning_rate')
decay = T.scalar('decay')
# rmsprop cache updates
mE = decay * self.mE + (1 - decay) * dE ** 2
mU = decay * self.mU + (1 - decay) * dU ** 2
mW = decay * self.mW + (1 - decay) * dW ** 2
mV = decay * self.mV + (1 - decay) * dV ** 2
mb = decay * self.mb + (1 - decay) * db ** 2
mc = decay * self.mc + (1 - decay) * dc ** 2
self.sgd_step = theano.function(
[x, y, learning_rate, theano.Param(decay, default=0.9)],
[],
updates=[(E, E - learning_rate * dE / T.sqrt(mE + 1e-6)),
(U, U - learning_rate * dU / T.sqrt(mU + 1e-6)),
(W, W - learning_rate * dW / T.sqrt(mW + 1e-6)),
(V, V - learning_rate * dV / T.sqrt(mV + 1e-6)),
(b, b - learning_rate * db / T.sqrt(mb + 1e-6)),
(c, c - learning_rate * dc / T.sqrt(mc + 1e-6)),
(self.mE, mE),
(self.mU, mU),
(self.mW, mW),
(self.mV, mV),
(self.mb, mb),
(self.mc, mc)
])
答案 0 :(得分：0)
我尚未测试此代码，但您唯一需要更改的是告诉 更新 使用adam（..）而不是已提供的更新在这里，所以这样的东西应该工作（完整的代码看起来像这样（我们需要摆脱rmsprop东西））：
import numpy as np
import theano as theano
import theano.tensor as T
from theano.gradient import grad_clip
import time
import operator
class GRUTheano(object):
def __init__(self, word_dim, hidden_dim=128, bptt_truncate=-1):
# Assign instance variables
self.word_dim = word_dim
self.hidden_dim = hidden_dim
self.bptt_truncate = bptt_truncate
# Initialize the network parameters
E = np.random.uniform(-np.sqrt(1./word_dim), np.sqrt(1./word_dim), (hidden_dim, word_dim))
U = np.random.uniform(-np.sqrt(1./hidden_dim), np.sqrt(1./hidden_dim), (6, hidden_dim, hidden_dim))
W = np.random.uniform(-np.sqrt(1./hidden_dim), np.sqrt(1./hidden_dim), (6, hidden_dim, hidden_dim))
V = np.random.uniform(-np.sqrt(1./hidden_dim), np.sqrt(1./hidden_dim), (word_dim, hidden_dim))
b = np.zeros((6, hidden_dim))
c = np.zeros(word_dim)
# Theano: Created shared variables
self.E = theano.shared(name='E', value=E.astype(theano.config.floatX))
self.U = theano.shared(name='U', value=U.astype(theano.config.floatX))
self.W = theano.shared(name='W', value=W.astype(theano.config.floatX))
self.V = theano.shared(name='V', value=V.astype(theano.config.floatX))
self.b = theano.shared(name='b', value=b.astype(theano.config.floatX))
self.c = theano.shared(name='c', value=c.astype(theano.config.floatX))
# We store the Theano graph here
self.theano = {}
self.__theano_build__()
def __theano_build__(self):
E, V, U, W, b, c = self.E, self.V, self.U, self.W, self.b, self.c
x = T.ivector('x')
y = T.ivector('y')
def forward_prop_step(x_t, s_t1_prev, s_t2_prev):
# This is how we calculated the hidden state in a simple RNN. No longer!
# s_t = T.tanh(U[:,x_t] + W.dot(s_t1_prev))
# Word embedding layer
x_e = E[:,x_t]
# GRU Layer 1
z_t1 = T.nnet.hard_sigmoid(U[0].dot(x_e) + W[0].dot(s_t1_prev) + b[0])
r_t1 = T.nnet.hard_sigmoid(U[1].dot(x_e) + W[1].dot(s_t1_prev) + b[1])
c_t1 = T.tanh(U[2].dot(x_e) + W[2].dot(s_t1_prev * r_t1) + b[2])
s_t1 = (T.ones_like(z_t1) - z_t1) * c_t1 + z_t1 * s_t1_prev
# GRU Layer 2
z_t2 = T.nnet.hard_sigmoid(U[3].dot(s_t1) + W[3].dot(s_t2_prev) + b[3])
r_t2 = T.nnet.hard_sigmoid(U[4].dot(s_t1) + W[4].dot(s_t2_prev) + b[4])
c_t2 = T.tanh(U[5].dot(s_t1) + W[5].dot(s_t2_prev * r_t2) + b[5])
s_t2 = (T.ones_like(z_t2) - z_t2) * c_t2 + z_t2 * s_t2_prev
# Final output calculation
# Theano's softmax returns a matrix with one row, we only need the row
o_t = T.nnet.softmax(V.dot(s_t2) + c)[0]
return [o_t, s_t1, s_t2]
[o, s, s2], updates = theano.scan(
forward_prop_step,
sequences=x,
truncate_gradient=self.bptt_truncate,
outputs_info=[None,
dict(initial=T.zeros(self.hidden_dim)),
dict(initial=T.zeros(self.hidden_dim))])
prediction = T.argmax(o, axis=1)
o_error = T.sum(T.nnet.categorical_crossentropy(o, y))
# Total cost (could add regularization here)
cost = o_error
# Gradients
dE = T.grad(cost, E)
dU = T.grad(cost, U)
dW = T.grad(cost, W)
db = T.grad(cost, b)
dV = T.grad(cost, V)
dc = T.grad(cost, c)
# Assign functions
self.predict = theano.function([x], o)
self.predict_class = theano.function([x], prediction)
self.ce_error = theano.function([x, y], cost)
self.bptt = theano.function([x, y], [dE, dU, dW, db, dV, dc])
self.params = [self.E, self.U, self.W, self.V, self.b, self.c]
updates=adam(cost, self.params)
self.sgd_step = theano.function(
inputs=[x, y],
outputs=[],
updates=updates
)
def calculate_total_loss(self, X, Y):
return np.sum([self.ce_error(x,y) for x,y in zip(X,Y)])
def calculate_loss(self, X, Y):
# Divide calculate_loss by the number of words
num_words = np.sum([len(y) for y in Y])
return self.calculate_total_loss(X,Y)/float(num_words)
def adam(loss, all_params, learning_rate=0.001, b1=0.9, b2=0.999, e=1e-8,
gamma=1-1e-8):
"""
ADAM update rules
Default values are taken from [Kingma2014]
References:
[Kingma2014] Kingma, Diederik, and Jimmy Ba.
"Adam: A Method for Stochastic Optimization."
arXiv preprint arXiv:1412.6980 (2014).
http://arxiv.org/pdf/1412.6980v4.pdf
"""
updates = []
all_grads = theano.grad(loss, all_params)
alpha = learning_rate
t = theano.shared(np.float32(1))
b1_t = b1*gamma**(t-1) #(Decay the first moment running average coefficient)
for theta_previous, g in zip(all_params, all_grads):
m_previous = theano.shared(np.zeros(theta_previous.get_value().shape,
dtype=theano.config.floatX))
v_previous = theano.shared(np.zeros(theta_previous.get_value().shape,
dtype=theano.config.floatX))
m = b1_t*m_previous + (1 - b1_t)*g # (Update biased first moment estimate)
v = b2*v_previous + (1 - b2)*g**2 # (Update biased second raw moment estimate)
m_hat = m / (1-b1**t) # (Compute bias-corrected first moment estimate)
v_hat = v / (1-b2**t) # (Compute bias-corrected second raw moment estimate)
theta = theta_previous - (alpha * m_hat) / (T.sqrt(v_hat) + e) #(Update parameters)
updates.append((m_previous, m))
updates.append((v_previous, v))
updates.append((theta_previous, theta) )
updates.append((t, t + 1.))
return updates