问题
I was trying to train a simple polynomial linear model with pytorch using Hermite polynomials since they seem to have a better conditioned Hessian.
To do that I decided to use the hermvander since it gives the Vandermonde matrix with each entry being a Hermite term. To do that I just made my feature vectors be the outpute of hermvander:
Kern_train = hermvander(X_train,Degree_mdl)
however, when I proceeded to train I get NaN all the time. I suspected it could have been a step size issue but I decided to use the step size suggested by this question that already has my example working in R, so there is no need to search for a step size I thought. However, when I tried it it does not work.
Anyone has any idea whats going on?
Same error occurs in tensorflow:
import pdb
import numpy as np
from numpy.polynomial.hermite import hermvander
import random
import tensorflow as tf
def get_batch(X,Y,M):
N = len(Y)
valid_indices = np.array( range(N) )
batch_indices = np.random.choice(valid_indices,size=M,replace=False)
batch_xs = X[batch_indices,:]
batch_ys = Y[batch_indices]
return batch_xs, batch_ys
##
D0=1
logging_freq = 100
## SGD params
M = 5
eta = 0.1
#eta = lambda i: eta/(i**0.6)
nb_iter = 500*10
##
lb,ub = 0,1
freq_sin = 4 # 2.3
f_target = lambda x: np.sin(2*np.pi*freq_sin*x)
N_train = 10
X_train = np.linspace(lb,ub,N_train)
Y_train = f_target(X_train).reshape(N_train,1)
x_horizontal = np.linspace(lb,ub,1000).reshape(1000,1)
## degree of mdl
Degree_mdl = N_train-1
## Hermite
Kern_train = hermvander(X_train,Degree_mdl)
print(f'Kern_train.shape={Kern_train.shape}')
Kern_train = Kern_train.reshape(N_train,Kern_train.shape[1])
##
Kern_train_pinv = np.linalg.pinv( Kern_train )
c_pinv = np.dot(Kern_train_pinv, Y_train)
nb_terms = c_pinv.shape[0]
##
condition_number_hessian = np.linalg.cond(Kern_train)
##
graph = tf.Graph()
with graph.as_default():
X = tf.placeholder(tf.float32, [None, nb_terms])
Y = tf.placeholder(tf.float32, [None,1])
w = tf.Variable( tf.zeros([nb_terms,1]) )
#w = tf.Variable( tf.truncated_normal([Degree_mdl,1],mean=0.0,stddev=1.0) )
#w = tf.Variable( 1000*tf.ones([Degree_mdl,1]) )
##
f = tf.matmul(X,w) # [N,1] = [N,D] x [D,1]
#loss = tf.reduce_sum(tf.square(Y - f))
loss = tf.reduce_sum( tf.reduce_mean(tf.square(Y-f), 0))
l2loss_tf = (1/N_train)*2*tf.nn.l2_loss(Y-f)
##
learning_rate = eta
#global_step = tf.Variable(0, trainable=False)
#learning_rate = tf.train.exponential_decay(learning_rate=eta, global_step=global_step,decay_steps=nb_iter/2, decay_rate=1, staircase=True)
train_step = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(loss)
with tf.Session(graph=graph) as sess:
Y_train = Y_train.reshape(N_train,1)
tf.global_variables_initializer().run()
# Train
for i in range(nb_iter):
#if i % (nb_iter/10) == 0:
if i % (nb_iter/10) == 0 or i == 0:
current_loss = sess.run(fetches=loss, feed_dict={X: Kern_train, Y: Y_train})
print(f'tf: i = {i}, current_loss = {current_loss}')
## train
batch_xs, batch_ys = get_batch(Kern_train,Y_train,M)
sess.run(train_step, feed_dict={X: batch_xs, Y: batch_ys})
print(f'condition_number_hessian = {condition_number_hessian}')
print('\a')
Totally self contained code in pytorch:
import numpy as np
from numpy.polynomial.hermite import hermvander
import random
import torch
from torch.autograd import Variable
def vectors_dims_dont_match(Y,Y_):
'''
Checks that vector Y and Y_ have the same dimensions. If they don't
then there might be an error that could be caused due to wrong broadcasting.
'''
DY = tuple( Y.size() )
DY_ = tuple( Y_.size() )
if len(DY) != len(DY_):
return True
for i in range(len(DY)):
if DY[i] != DY_[i]:
return True
return False
def index_batch(X,batch_indices,dtype):
'''
returns the batch indexed/sliced batch
'''
if len(X.shape) == 1: # i.e. dimension (M,) just a vector
batch_xs = torch.FloatTensor(X[batch_indices]).type(dtype)
else:
batch_xs = torch.FloatTensor(X[batch_indices,:]).type(dtype)
return batch_xs
def get_batch2(X,Y,M,dtype):
'''
get batch for pytorch model
'''
# TODO fix and make it nicer, there is pytorch forum question
X,Y = X.data.numpy(), Y.data.numpy()
N = len(Y)
valid_indices = np.array( range(N) )
batch_indices = np.random.choice(valid_indices,size=M,replace=False)
batch_xs = index_batch(X,batch_indices,dtype)
batch_ys = index_batch(Y,batch_indices,dtype)
return Variable(batch_xs, requires_grad=False), Variable(batch_ys, requires_grad=False)
def get_sequential_lifted_mdl(nb_monomials,D_out, bias=False):
return torch.nn.Sequential(torch.nn.Linear(nb_monomials,D_out,bias=bias))
def train_SGD(mdl, M,eta,nb_iter,logging_freq ,dtype, X_train,Y_train):
##
#pdb.set_trace()
N_train,_ = tuple( X_train.size() )
#print(N_train)
for i in range(1,nb_iter+1):
# Forward pass: compute predicted Y using operations on Variables
batch_xs, batch_ys = get_batch2(X_train,Y_train,M,dtype) # [M, D], [M, 1]
## FORWARD PASS
y_pred = mdl.forward(batch_xs)
## Check vectors have same dimension
if vectors_dims_dont_match(batch_ys,y_pred):
raise ValueError('You vectors don\'t have matching dimensions. It will lead to errors.')
## LOSS + Regularization
batch_loss = (1/M)*(y_pred - batch_ys).pow(2).sum()
## BACKARD PASS
batch_loss.backward() # Use autograd to compute the backward pass. Now w will have gradients
## SGD update
for W in mdl.parameters():
delta = eta(i)*W.grad.data
W.data.copy_(W.data - delta)
## train stats
if i % (nb_iter/10) == 0 or i == 0:
#X_train_, Y_train_ = Variable(X_train), Variable(Y_train)
X_train_, Y_train_ = X_train, Y_train
current_train_loss = (1/N_train)*(mdl.forward(X_train_) - Y_train_).pow(2).sum().data.numpy()
print('\n-------------')
print(f'i = {i}, current_train_loss = {current_train_loss}\n')
print(f'eta*W.grad.data = {eta*W.grad.data}')
print(f'W.grad.data = {W.grad.data}')
## Manually zero the gradients after updating weights
mdl.zero_grad()
final_sgd_error = current_train_loss
return final_sgd_error
##
D0=1
logging_freq = 100
#dtype = torch.cuda.FloatTensor
dtype = torch.FloatTensor
## SGD params
M = 5
eta = 0.1
eta = lambda i: eta/(i**0.6)
nb_iter = 500*10
##
lb,ub = 0,1
freq_sin = 4 # 2.3
f_target = lambda x: np.sin(2*np.pi*freq_sin*x)
N_train = 10
X_train = np.linspace(lb,ub,N_train)
Y_train = f_target(X_train).reshape(N_train,1)
x_horizontal = np.linspace(lb,ub,1000).reshape(1000,1)
## degree of mdl
Degree_mdl = N_train-1
## Hermite
Kern_train = hermvander(X_train,Degree_mdl)
Kern_train = Kern_train.reshape(N_train,Kern_train.shape[2])
##
Kern_train_pinv = np.linalg.pinv( Kern_train )
c_pinv = np.dot(Kern_train_pinv, Y_train)
##
condition_number_hessian = np.linalg.cond(Kern_train)
## linear mdl to train with SGD
nb_terms = c_pinv.shape[0]
mdl_sgd = get_sequential_lifted_mdl(nb_monomials=nb_terms,D_out=1, bias=False)
mdl_sgd[0].weight.data.normal_(mean=0,std=0.001)
mdl_sgd[0].weight.data.fill_(0)
## Make polynomial Kernel
Kern_train_pt, Y_train_pt = Variable(torch.FloatTensor(Kern_train).type(dtype), requires_grad=False), Variable(torch.FloatTensor(Y_train).type(dtype), requires_grad=False)
final_sgd_error = train_SGD(mdl_sgd, M,eta,nb_iter,logging_freq ,dtype, Kern_train_pt,Y_train_pt)
## PRINT ERRORS
from plotting_utils import *
train_error_pinv = (1/N_train)*(np.linalg.norm(Y_train-np.dot(Kern_train,c_pinv))**2)
print('\n-----------------')
print(f'N_train={N_train}')
print(f'train_error_pinv = {train_error_pinv}')
print(f'final_sgd_error = {final_sgd_error}')
print(f'condition_number_hessian = {condition_number_hessian}')
print('\a')
回答1:
Maybe it's a bit late, but you might have a look at this https://github.com/Orcuslc/OrthNet
来源：https://stackoverflow.com/questions/47624798/how-does-one-use-hermite-polynomials-with-stochastic-gradient-descent-sgd