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train.py
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train.py
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"""
Training script for MNIST/TFD.
Yujia Li, 01/2015
"""
import argparse
import cPickle as pickle
import pynn.nn as nn
import pynn.layer as ly
import pynn.loss as ls
import pynn.learner as learner
import core.generative as gen
import gnumpy as gnp
import numpy as np
import time
import dataio.mnist as mnistio
import dataio.tfd as tfd
import eval_mmd_generative_model as ev
# You may want to change this
OUTPUT_BASE_DIR = 'output'
def write_config(file_name, config):
"""
file_name: output config file name
config: dict containing all the configs.
"""
with open(file_name, 'w') as f:
for k, v in sorted(config.items(), key=lambda t: t[0]):
f.write(str(k) + '=' + str(v) + '\n')
def cat_list(lst):
return '_'.join([str(v) for v in lst])
def load_tfd_fold(fold=0):
"""
Return train, val, test data for the particular fold.
"""
# note that the training set used here is the 'unlabeled' set in TFD
x_train, _, _ = tfd.load_proper_fold(fold, 'unlabeled', scale=True)
x_val, _, _ = tfd.load_proper_fold(fold, 'val', scale=True)
x_test, _, _ = tfd.load_proper_fold(fold, 'test', scale=True)
imsz = np.prod(x_train.shape[1:])
return x_train.reshape(x_train.shape[0], imsz), \
x_val.reshape(x_val.shape[0], imsz), \
x_test.reshape(x_test.shape[0], imsz)
def load_tfd_all_folds(set_name='val', n_folds=5):
x = []
for i_fold in range(n_folds):
#xx, _, _ = tfd.load_fold(i_fold, set_name, scale=True)
xx, _, _ = tfd.load_proper_fold(i_fold, set_name, scale=True)
x.append(xx.reshape(xx.shape[0], np.prod(xx.shape[1:])))
return x
def mnist_mmd_input_space(n_hids=[10,64,256,256,1024], sigma=[2,5,10,20,40,80], learn_rate=2, momentum=0.9):
"""
n_hids: number of hidden units on all layers (top-down) in the generative network.
sigma: a list of scales used for the kernel
learn_rate, momentum: parameters for the learning process
return: KDE log_likelihood on validation set.
"""
gnp.seed_rand(8)
x_train, x_val, x_test = mnistio.load_data()
print ''
print 'Training data: %d x %d' % x_train.shape
in_dim = n_hids[0]
out_dim = x_train.shape[1]
net = gen.StochasticGenerativeNet(in_dim, out_dim)
for i in range(1, len(n_hids)):
net.add_layer(n_hids[i], nonlin_type=ly.NONLIN_NAME_RELU, dropout=0)
net.add_layer(0, nonlin_type=ly.NONLIN_NAME_SIGMOID, dropout=0)
# place holder loss
net.set_loss(ls.LOSS_NAME_MMDGEN, loss_after_nonlin=True, sigma=80, loss_weight=1000)
print ''
print '========'
print 'Training'
print '========'
print ''
print net
print ''
mmd_learner = gen.StochasticGenerativeNetLearner(net)
mmd_learner.load_data(x_train)
output_base = OUTPUT_BASE_DIR + '/mnist/input_space'
#sigma = [2,5,10,20,40,80]
sigma_weights = [1,1,1,1,1,1]
#learn_rate = 1
#momentum = 0.9
minibatch_size = 1000
n_sample_update_iters = 1
max_iters = 40000
i_checkpoint = 2000
output_dir = output_base + '/nhids_%s_sigma_%s_lr_%s_m_%s' % (
'_'.join([str(nh) for nh in n_hids]), '_'.join([str(s) for s in sigma]), str(learn_rate), str(momentum))
print ''
print '>>>> output_dir = %s' % output_dir
print ''
mmd_learner.set_output_dir(output_dir)
#net.set_loss(ls.LOSS_NAME_MMDGEN_MULTISCALE, loss_after_nonlin=True, sigma=sigma, scale_weight=sigma_weights, loss_weight=1000)
net.set_loss(ls.LOSS_NAME_MMDGEN_SQRT_GAUSSIAN, loss_after_nonlin=True, sigma=sigma, scale_weight=sigma_weights, loss_weight=1000)
print '**********************************'
print net.loss
print '**********************************'
print ''
def f_checkpoint(i_iter, w):
mmd_learner.save_checkpoint('%d' % i_iter)
mmd_learner.train_sgd(minibatch_size=minibatch_size, n_samples_per_update=minibatch_size,
n_sample_update_iters=n_sample_update_iters, learn_rate=learn_rate, momentum=momentum,
weight_decay=0, learn_rate_schedule={10000:learn_rate/10.0},
momentum_schedule={10000:1-(1-momentum)/10.0},
learn_rate_drop_iters=0, decrease_type='linear', adagrad_start_iter=0,
max_iters=max_iters, iprint=100, i_exe=i_checkpoint, f_exe=f_checkpoint)
mmd_learner.save_model()
print ''
print '===================='
print 'Evaluating the model'
print '===================='
print ''
log_prob, std, sigma = ev.kde_eval_mnist(net, x_val, verbose=False)
test_log_prob, test_std, _ = ev.kde_eval_mnist(net, x_test, sigma_range=[sigma], verbose=False)
print 'Validation: %.2f (%.2f)' % (log_prob, std)
print 'Test : %.2f (%.2f)' % (test_log_prob, test_std)
print ''
write_config(output_dir + '/params_and_results.cfg', { 'n_hids': n_hids,
'sigma': sigma, 'sigma_weights': sigma_weights, 'learn_rate': learn_rate,
'momentum': momentum, 'minibatch_size': minibatch_size,
'n_sample_update_iters': n_sample_update_iters, 'max_iters': max_iters,
'i_checkpoint': i_checkpoint, 'val_log_prob': log_prob, 'val_std': std,
'test_log_prob': test_log_prob, 'test_std': test_std })
print '>>>> output_dir = %s' % output_dir
print ''
return log_prob
def mnist_mmd_code_space(
ae_n_hids=[1024, 32],
ae_dropout=[0.2, 0.5],
ae_learn_rate=1e-1,
ae_momentum=0.9,
mmd_n_hids=[10, 64, 256, 256, 1024],
mmd_sigma=1,
mmd_learn_rate=2,
mmd_momentum=0.9):
"""
ae_n_hids: #hid for the encoder, bottom-up
ae_dropout: the amount of dropout for each layer in the encoder, same order
ae_learn_rate, ae_momentum: .
mmd_n_hids: #hid for the generative net, top-down
mmd_sigma: scale of the kernel
mmd_learn_rate, mmd_momentum: .
Return KDE log_likelihood on the validation set.
"""
gnp.seed_rand(8)
x_train, x_val, x_test = mnistio.load_data()
common_output_base = OUTPUT_BASE_DIR + '/mnist/code_space'
output_base = common_output_base + '/aeh_%s_dr_%s_aelr_%s_aem_%s_nh_%s_s_%s_lr_%s_m_%s' % (
cat_list(ae_n_hids), cat_list(ae_dropout), str(ae_learn_rate), str(ae_momentum),
cat_list(mmd_n_hids), str(mmd_sigma), str(mmd_learn_rate), str(mmd_momentum))
#######################
# Auto-encoder training
#######################
n_dims = x_train.shape[1]
h_dim = ae_n_hids[-1]
encoder = nn.NeuralNet(n_dims, h_dim)
for i in range(len(ae_n_hids) - 1):
encoder.add_layer(ae_n_hids[i], nonlin_type=ly.NONLIN_NAME_SIGMOID, dropout=ae_dropout[i])
encoder.add_layer(0, nonlin_type=ly.NONLIN_NAME_SIGMOID, dropout=ae_dropout[-1])
decoder = nn.NeuralNet(h_dim, n_dims)
for i in range(len(ae_n_hids) - 1)[::-1]:
decoder.add_layer(ae_n_hids[i], nonlin_type=ly.NONLIN_NAME_SIGMOID, dropout=0)
decoder.add_layer(0, nonlin_type=ly.NONLIN_NAME_SIGMOID, dropout=0)
decoder.set_loss(ls.LOSS_NAME_BINARY_CROSSENTROPY, loss_weight=1)
autoenc = nn.AutoEncoder(encoder=encoder, decoder=decoder)
print ''
print autoenc
print ''
learn_rate = ae_learn_rate
final_momentum = ae_momentum
max_iters = 3000
nn_pretrainer = learner.AutoEncoderPretrainer(autoenc)
nn_pretrainer.load_data(x_train)
nn_pretrainer.pretrain_network(learn_rate=1e-1, momentum=0.5, weight_decay=0, minibatch_size=100,
max_grad_norm=10, max_iters=max_iters, iprint=100)
nn_learner = learner.Learner(autoenc)
nn_learner.set_output_dir(output_base + '/ae')
nn_learner.load_data(x_train, x_train)
def f_checkpoint(i_iter, w):
nn_learner.save_checkpoint('%d' % i_iter)
nn_learner.train_sgd(learn_rate=learn_rate, momentum=0, weight_decay=0, minibatch_size=100,
learn_rate_schedule=None, momentum_schedule={50:0.5, 200:final_momentum},
max_grad_norm=10, learn_rate_drop_iters=0, decrease_type='linear', adagrad_start_iter=0,
max_iters=max_iters, iprint=100, i_exe=2000, f_exe=f_checkpoint)
nn_learner.save_checkpoint('best')
##################
# Training MMD net
##################
n_hids = mmd_n_hids
in_dim = n_hids[0]
out_dim = autoenc.encoder.out_dim
net = gen.StochasticGenerativeNetWithAutoencoder(in_dim, out_dim, autoenc)
for i in range(1, len(n_hids)):
net.add_layer(n_hids[i], nonlin_type=ly.NONLIN_NAME_RELU, dropout=0)
net.add_layer(0, nonlin_type=ly.NONLIN_NAME_SIGMOID, dropout=0)
print ''
print '========'
print 'Training'
print '========'
print ''
print net
print ''
mmd_learner = gen.StochasticGenerativeNetLearner(net)
mmd_learner.load_data(x_train)
sigma = [mmd_sigma]
sigma_weights = [1]
learn_rate = mmd_learn_rate
momentum = mmd_momentum
minibatch_size = 1000
n_sample_update_iters = 1
max_iters = 40000
i_checkpoint = 2000
mmd_learner.set_output_dir(output_base + '/mmd')
#net.set_loss(ls.LOSS_NAME_MMDGEN_MULTISCALE, loss_after_nonlin=True, sigma=sigma, scale_weight=sigma_weights, loss_weight=1000)
net.set_loss(ls.LOSS_NAME_MMDGEN_SQRT_GAUSSIAN, loss_after_nonlin=True, sigma=sigma, scale_weight=sigma_weights, loss_weight=1000)
print '**********************************'
print net.loss
print '**********************************'
print ''
def f_checkpoint(i_iter, w):
mmd_learner.save_checkpoint('%d' % i_iter)
mmd_learner.train_sgd(minibatch_size=minibatch_size, n_samples_per_update=minibatch_size,
n_sample_update_iters=n_sample_update_iters, learn_rate=learn_rate, momentum=momentum,
weight_decay=0, learn_rate_schedule={10000:learn_rate/10.0},
momentum_schedule={10000:1-(1-momentum)/10.0},
learn_rate_drop_iters=0, decrease_type='linear', adagrad_start_iter=0,
max_iters=max_iters, iprint=100, i_exe=i_checkpoint, f_exe=f_checkpoint)
mmd_learner.save_model()
# Evaluation
print ''
print '===================='
print 'Evaluating the model'
print '===================='
print ''
log_prob, std, sigma = ev.kde_eval_mnist(net, x_val, verbose=False)
test_log_prob, test_std, _ = ev.kde_eval_mnist(net, x_test, sigma_range=[sigma], verbose=False)
print 'Validation: %.2f (%.2f)' % (log_prob, std)
print 'Test : %.2f (%.2f)' % (test_log_prob, test_std)
print ''
write_config(output_base + '/params_and_results.cfg', {
'ae_n_hids' : ae_n_hids, 'ae_dropout' : ae_dropout, 'ae_learn_rate' : ae_learn_rate,
'ae_momentum' : ae_momentum, 'mmd_n_hids': mmd_n_hids,
'mmd_sigma': mmd_sigma, 'mmd_sigma_weights': sigma_weights, 'mmd_learn_rate': mmd_learn_rate,
'mmd_momentum': mmd_momentum, 'mmd_minibatch_size': minibatch_size,
'mmd_n_sample_update_iters': n_sample_update_iters, 'mmd_max_iters': max_iters,
'mmd_i_checkpoint': i_checkpoint, 'val_log_prob': log_prob, 'val_std': std,
'test_log_prob': test_log_prob, 'test_std': test_std })
print '>>>> output_dir = %s' % output_base
print ''
return log_prob
def tfd_mmd_input_space(n_hids=[10,64,256,256,1024], sigma=[5,10,20,40,80,160], learn_rate=2, momentum=0.9):
"""
return validation log prob.
"""
gnp.seed_rand(8)
# train on only one fold - that's enough as the training set is the same across folds
x_train, x_val, x_test = load_tfd_fold(0)
print ''
print 'Training data: %d x %d' % x_train.shape
in_dim = n_hids[0]
out_dim = x_train.shape[1]
net = gen.StochasticGenerativeNet(in_dim, out_dim)
for i in range(1, len(n_hids)):
net.add_layer(n_hids[i], nonlin_type=ly.NONLIN_NAME_RELU, dropout=0)
net.add_layer(0, nonlin_type=ly.NONLIN_NAME_SIGMOID, dropout=0)
# place holder loss
net.set_loss(ls.LOSS_NAME_MMDGEN, loss_after_nonlin=True, sigma=80, loss_weight=1000)
print ''
print '========'
print 'Training'
print '========'
print ''
print net
print ''
mmd_learner = gen.StochasticGenerativeNetLearner(net)
mmd_learner.load_data(x_train)
output_base = OUTPUT_BASE_DIR + '/tfd/input_space'
#sigma = [2,5,10,20,40,80]
sigma_weights = [1,1,1,1,1,1]
#learn_rate = 1
#momentum = 0.9
minibatch_size = 1000
n_sample_update_iters = 1
max_iters = 48000
i_checkpoint = 2000
output_dir = output_base + '/nhids_%s_sigma_%s_lr_%s_m_%s' % (
'_'.join([str(nh) for nh in n_hids]), '_'.join([str(s) for s in sigma]), str(learn_rate), str(momentum))
print ''
print '>>>> output_dir = %s' % output_dir
print ''
mmd_learner.set_output_dir(output_dir)
#net.set_loss(ls.LOSS_NAME_MMDGEN_MULTISCALE, loss_after_nonlin=True, sigma=sigma, scale_weight=sigma_weights, loss_weight=1000)
net.set_loss(ls.LOSS_NAME_MMDGEN_SQRT_GAUSSIAN, loss_after_nonlin=True, sigma=sigma, scale_weight=sigma_weights, loss_weight=1000)
print '**********************************'
print net.loss
print '**********************************'
print ''
def f_checkpoint(i_iter, w):
mmd_learner.save_checkpoint('%d' % i_iter)
mmd_learner.train_sgd(minibatch_size=minibatch_size, n_samples_per_update=minibatch_size,
n_sample_update_iters=n_sample_update_iters, learn_rate=learn_rate, momentum=momentum,
weight_decay=0, learn_rate_schedule={10000:learn_rate/10.0},
momentum_schedule={10000:1-(1-momentum)/10.0},
learn_rate_drop_iters=0, decrease_type='linear', adagrad_start_iter=0,
max_iters=max_iters, iprint=100, i_exe=i_checkpoint, f_exe=f_checkpoint)
mmd_learner.save_model()
print ''
print '===================='
print 'Evaluating the model'
print '===================='
print ''
x_val = load_tfd_all_folds('val')
x_test = load_tfd_all_folds('test')
log_prob, std, sigma = ev.kde_eval_tfd(net, x_val, verbose=False)
test_log_prob, test_std, _ = ev.kde_eval_tfd(net, x_test, sigma_range=[sigma], verbose=False)
print 'Validation: %.2f (%.2f)' % (log_prob, std)
print 'Test : %.2f (%.2f)' % (test_log_prob, test_std)
print ''
write_config(output_dir + '/params_and_results.cfg', { 'n_hids': n_hids,
'sigma': sigma, 'sigma_weights': sigma_weights, 'learn_rate': learn_rate,
'momentum': momentum, 'minibatch_size': minibatch_size,
'n_sample_update_iters': n_sample_update_iters, 'max_iters': max_iters,
'i_checkpoint': i_checkpoint, 'val_log_prob': log_prob, 'val_std': std,
'test_log_prob': test_log_prob, 'test_std': test_std })
print '>>>> output_dir = %s' % output_dir
print ''
return log_prob
def tfd_mmd_code_space(
ae_n_hids=[512, 512, 128],
ae_dropout=[0.1, 0.1, 0.1],
ae_learn_rate=1e-1,
ae_momentum=0,
mmd_n_hids=[10, 64, 256, 256, 1024],
mmd_sigma=[1,2,5,10,20,40],
mmd_learn_rate=1e-1,
mmd_momentum=0.9):
"""
ae_n_hids: #hid for the encoder, bottom-up
ae_dropout: the amount of dropout for each layer in the encoder, same order
ae_learn_rate, ae_momentum: .
mmd_n_hids: #hid for the generative net, top-down
mmd_sigma: scale of the kernel
mmd_learn_rate, mmd_momentum: .
Return KDE log_likelihood on the validation set.
"""
gnp.seed_rand(8)
x_train, x_val, x_test = load_tfd_fold(0)
common_output_base = OUTPUT_BASE_DIR + '/tfd/code_space'
output_base = common_output_base + '/aeh_%s_dr_%s_aelr_%s_aem_%s_nh_%s_s_%s_lr_%s_m_%s' % (
cat_list(ae_n_hids), cat_list(ae_dropout), str(ae_learn_rate), str(ae_momentum),
cat_list(mmd_n_hids), cat_list(mmd_sigma), str(mmd_learn_rate), str(mmd_momentum))
#######################
# Auto-encoder training
#######################
n_dims = x_train.shape[1]
h_dim = ae_n_hids[-1]
encoder = nn.NeuralNet(n_dims, h_dim)
for i in range(len(ae_n_hids) - 1):
encoder.add_layer(ae_n_hids[i], nonlin_type=ly.NONLIN_NAME_SIGMOID, dropout=ae_dropout[i])
encoder.add_layer(0, nonlin_type=ly.NONLIN_NAME_SIGMOID, dropout=ae_dropout[-1])
decoder = nn.NeuralNet(h_dim, n_dims)
for i in range(len(ae_n_hids) - 1)[::-1]:
decoder.add_layer(ae_n_hids[i], nonlin_type=ly.NONLIN_NAME_SIGMOID, dropout=0)
decoder.add_layer(0, nonlin_type=ly.NONLIN_NAME_SIGMOID, dropout=0)
decoder.set_loss(ls.LOSS_NAME_BINARY_CROSSENTROPY, loss_weight=1)
autoenc = nn.AutoEncoder(encoder=encoder, decoder=decoder)
print ''
print autoenc
print ''
learn_rate = ae_learn_rate
final_momentum = ae_momentum
max_iters = 15000
#max_iters = 200
nn_pretrainer = learner.AutoEncoderPretrainer(autoenc)
nn_pretrainer.load_data(x_train)
nn_pretrainer.pretrain_network(learn_rate=1e-1, momentum=0.5, weight_decay=0, minibatch_size=100,
max_grad_norm=10, max_iters=max_iters, iprint=100)
nn_learner = learner.Learner(autoenc)
nn_learner.set_output_dir(output_base + '/ae')
nn_learner.load_data(x_train, x_train)
def f_checkpoint(i_iter, w):
nn_learner.save_checkpoint('%d' % i_iter)
nn_learner.train_sgd(learn_rate=learn_rate, momentum=0, weight_decay=0, minibatch_size=100,
learn_rate_schedule=None, momentum_schedule={50:0.5, 200:final_momentum},
max_grad_norm=10, learn_rate_drop_iters=0, decrease_type='linear', adagrad_start_iter=0,
max_iters=max_iters, iprint=100, i_exe=2000, f_exe=f_checkpoint)
nn_learner.save_checkpoint('best')
##################
# Training MMD net
##################
n_hids = mmd_n_hids
in_dim = n_hids[0]
out_dim = autoenc.encoder.out_dim
net = gen.StochasticGenerativeNetWithAutoencoder(in_dim, out_dim, autoenc)
for i in range(1, len(n_hids)):
net.add_layer(n_hids[i], nonlin_type=ly.NONLIN_NAME_RELU, dropout=0)
net.add_layer(0, nonlin_type=ly.NONLIN_NAME_SIGMOID, dropout=0)
print ''
print '========'
print 'Training'
print '========'
print ''
print net
print ''
mmd_learner = gen.StochasticGenerativeNetLearner(net)
mmd_learner.load_data(x_train)
sigma = mmd_sigma
sigma_weights = [1] * len(sigma)
learn_rate = mmd_learn_rate
momentum = mmd_momentum
minibatch_size = 1000
n_sample_update_iters = 1
max_iters = 48000
#max_iters = 200
i_checkpoint = 2000
mmd_learner.set_output_dir(output_base + '/mmd')
#net.set_loss(ls.LOSS_NAME_MMDGEN_MULTISCALE, loss_after_nonlin=True, sigma=sigma, scale_weight=sigma_weights, loss_weight=1000)
net.set_loss(ls.LOSS_NAME_MMDGEN_SQRT_GAUSSIAN, loss_after_nonlin=True, sigma=sigma, scale_weight=sigma_weights, loss_weight=1000)
print '**********************************'
print net.loss
print '**********************************'
print ''
def f_checkpoint(i_iter, w):
mmd_learner.save_checkpoint('%d' % i_iter)
mmd_learner.train_sgd(minibatch_size=minibatch_size, n_samples_per_update=minibatch_size,
n_sample_update_iters=n_sample_update_iters, learn_rate=learn_rate, momentum=momentum,
weight_decay=0, learn_rate_schedule={10000:learn_rate/10.0},
momentum_schedule={10000:1-(1-momentum)/10.0},
learn_rate_drop_iters=0, decrease_type='linear', adagrad_start_iter=0,
max_iters=max_iters, iprint=100, i_exe=i_checkpoint, f_exe=f_checkpoint)
mmd_learner.save_model()
# Evaluation
print ''
print '===================='
print 'Evaluating the model'
print '===================='
print ''
x_val = load_tfd_all_folds('val')
x_test = load_tfd_all_folds('test')
log_prob, std, sigma = ev.kde_eval_tfd(net, x_val, verbose=False)
test_log_prob, test_std, _ = ev.kde_eval_tfd(net, x_test, sigma_range=[sigma], verbose=False)
print 'Validation: %.2f (%.2f)' % (log_prob, std)
print 'Test : %.2f (%.2f)' % (test_log_prob, test_std)
print ''
write_config(output_base + '/params_and_results.cfg', {
'ae_n_hids' : ae_n_hids, 'ae_dropout' : ae_dropout, 'ae_learn_rate' : ae_learn_rate,
'ae_momentum' : ae_momentum, 'mmd_n_hids': mmd_n_hids,
'mmd_sigma': mmd_sigma, 'mmd_sigma_weights': sigma_weights, 'mmd_learn_rate': mmd_learn_rate,
'mmd_momentum': mmd_momentum, 'mmd_minibatch_size': minibatch_size,
'mmd_n_sample_update_iters': n_sample_update_iters, 'mmd_max_iters': max_iters,
'mmd_i_checkpoint': i_checkpoint, 'val_log_prob': log_prob, 'val_std': std,
'test_log_prob': test_log_prob, 'test_std': test_std })
print '>>>> output_dir = %s' % output_base
print ''
return log_prob
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Model parameter tuning')
parser.add_argument('-m', '--mode', choices=['mnistinput', 'mnistcode', 'tfdinput', 'tfdcode'])
args = parser.parse_args()
print ''
print '************************'
print 'Testing %s' % args.mode
print '************************'
print ''
if args.mode == 'mnistinput':
mnist_mmd_input_space()
elif args.mode == 'mnistcode':
mnist_mmd_code_space()
elif args.mode == 'tfdinput':
tfd_mmd_input_space()
elif args.mode == 'tfdcode':
tfd_mmd_code_space()