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main_test_ExpHM_FASHIONMNIST.py
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# test Exp-HM on MNIST
#from __future__ import division
import numpy
import helmholtz_machine
import classification as cl
import shutil
import os
workdir="/home/yifeng/research/deep/github/xdgm/"
os.chdir(workdir)
dir_data="./data/FASHIONMNIST/"
parent_dir_save="./results/HM/"
prefix="HM_FASHIONMNIST"
# load data
train_set_x=numpy.loadtxt(dir_data+"fashion-mnist_train.csv", dtype=int, delimiter=",",skiprows=1)
train_set_y=train_set_x[:,0]
train_set_x=train_set_x[:,1:]
train_set_x=train_set_x.transpose()
test_set_x=numpy.loadtxt(dir_data+"fashion-mnist_test.csv", dtype=int, delimiter=",",skiprows=1)
test_set_y=test_set_x[:,0]
test_set_x=test_set_x[:,1:]
test_set_x=test_set_x.transpose()
print(train_set_x.shape)
print(train_set_y.shape)
print(test_set_x.shape)
print(test_set_y.shape)
# limit the number of training set
#train_set_x=train_set_x[:,0:10000]
#train_set_y=train_set_y[0:10000]
num_train=train_set_x.shape[1]
num_test=test_set_x.shape[1]
# convert train_set_y to binary codes
train_set_y01,z_unique=cl.membership_vector_to_indicator_matrix(z=train_set_y, z_unique=range(10))
train_set_y01=train_set_y01.transpose()
test_set_y01,_=cl.membership_vector_to_indicator_matrix(z=test_set_y, z_unique=range(10))
test_set_y01=test_set_y01.transpose()
num_feat=train_set_x.shape[0]
visible_type="Bernoulli"
hidden_type="Bernoulli"
hidden_type_fixed_param=0
rng=numpy.random.RandomState(100)
M=num_feat
normalization_method="None"
if visible_type=="Bernoulli":
# normalization method
normalization_method="scale"
# parameter setting
learn_rate_a_pretrain=0.1
learn_rate_b_pretrain=[0.1,0.1,0.1] # can be a list
learn_rate_W_pretrain=[0.1,0.1,0.1] # can be a list
learn_rate_a_train=0.02
learn_rate_b_train=[0.02,0.02,0.02] # can be a list
learn_rate_W_train=[0.02,0.02,0.02] # can be a list
change_rate_pretrain=0.95
change_rate_train=0.95
adjust_change_rate_at_pretrain=[6000,12000,15000]
adjust_coef_pretrain=1.02
adjust_change_rate_at_train=[6000,12000,15000]
adjust_coef_train=1.02
reg_lambda_a=0#0.5
reg_alpha_a=1
reg_lambda_b=0#0.5
reg_alpha_b=1
reg_lambda_W=0
reg_alpha_W=1
K=[500,500,1000]
batch_size=100
pcdk=20
NS=100
maxiter_pretrain=18000
maxiter_train=18000
change_every_many_iters=120
init_chain_time=100
visible_type_fixed_param=0
reinit_a_use_data_stat=True
elif visible_type=="Poisson":
# normalization method
normalization_method="samecount"
count=1000
# parameter setting
# for unnormalized data
# for Bernoulli hidden type, use 0.00001
# for Binomial hidden type, use 0.0000001
if hidden_type=="Bernoulli":
K=[500,500]
sumout="auto"
learn_rate_a_pretrain=0.001
learn_rate_b_pretrain=[0.001,0.01]
learn_rate_W_pretrain=[0.001,0.01]
learn_rate_a_train=0.0003
learn_rate_b_train=[0.0003,0.003] # can be a list
learn_rate_W_train=[0.0003,0.003] # can be a list
reg_lambda_a=0
reg_alpha_a=1
reg_lambda_b=0
reg_alpha_b=1
reg_lambda_W=0
reg_alpha_W=1
if hidden_type=="Binomial":
K=250 # for Binomial K=20, for Bernoulli K=200
sumout="auto"
learn_rate_a=0.001
learn_rate_b=0.001
learn_rate_W=0.001
reg_lambda_a=0
reg_alpha_a=1
reg_lambda_b=0
reg_alpha_b=1
reg_lambda_W=0
reg_alpha_W=1
batch_size=100
NMF=100
pcdk=20
NS=100
maxiter_pretrain=6600
maxiter_train=4400
change_rate_pretrain=0.9
change_rate_train=0.9
change_every_many_iters=110
init_chain_time=100
visible_type_fixed_param=0
reinit_a_use_data_stat=True
elif visible_type=="Gaussian":
# normalization method
normalization_method="tfidf"
# parameter setting
K=500
learn_rate_a=[1,10]
learn_rate_b=0.01
learn_rate_W=0.1
reg_lambda_a=[0,0]
reg_alpha_a=[0,0]
reg_lambda_b=0
reg_alpha_b=0
reg_lambda_W=0
reg_alpha_W=0
batch_size=100
NMF=100
pcdk=20
NS=100
maxiter_pretrain=300
maxiter_train=300
change_rate=0.8
change_every_many_iters=20
init_chain_time=10
visible_type_fixed_param=0
reinit_a_use_data_stat=True
# normalization method
if normalization_method=="binary":
# discret data
threshold=0
ind=train_set_x<=threshold
train_set_x[ind]=0
train_set_x[numpy.logical_not(ind)]=1
ind=test_set_x<=threshold
test_set_x[ind]=0
test_set_x[numpy.logical_not(ind)]=1
if normalization_method=="scale":
train_set_x=train_set_x/255
test_set_x=test_set_x/255
# creat the object
model_hm=helmholtz_machine.helmholtz_machine(features=None, M=M, K=K, visible_type=visible_type, visible_type_fixed_param=visible_type_fixed_param, tol_poisson_max=8, rng=rng)
# create a folder to save the results
dir_save=model_hm.make_dir_save(parent_dir_save, prefix, learn_rate_a_pretrain, learn_rate_b_pretrain, learn_rate_W_pretrain, maxiter_pretrain+maxiter_train, normalization_method)
# make a copy of this script in dir_save
shutil.copy(workdir+"main_test_ExpHM_FASHIONMNIST.py", dir_save)
shutil.copy(workdir+"restricted_boltzmann_machine.py", dir_save)
shutil.copy(workdir+"helmholtz_machine.py", dir_save)
# pretrain
model_hm.pretrain(X=train_set_x, batch_size=batch_size, pcdk=pcdk, NS=NS, maxiter=maxiter_pretrain, learn_rate_a=learn_rate_a_pretrain, learn_rate_b=learn_rate_b_pretrain, learn_rate_W=learn_rate_W_pretrain, change_rate=change_rate_pretrain, adjust_change_rate_at=adjust_change_rate_at_pretrain, adjust_coef=adjust_coef_pretrain, change_every_many_iters=change_every_many_iters, init_chain_time=init_chain_time, train_subset_size_for_compute_error=100, valid_subset_size_for_compute_error=100, track_reconstruct_error=True, track_free_energy=True, reinit_a_use_data_stat=reinit_a_use_data_stat, if_plot_error_free_energy=True, dir_save=dir_save, prefix="HM_pretrain", figwidth=5, figheight=3)
# sampling
sampling_time=10
sampling_NS=100
for s in range(sampling_time):
Xg,XMg,Hg,HMr=model_hm.sample_xh_sleep(sampling_NS)
# plot sampled data
sample_set_x_3way=numpy.reshape(XMg,newshape=(28,28,100))
print(s)
cl.plot_image_subplots(dir_save+"/fig_"+prefix+"_pretrain_generated_samples_randinit_"+str(s)+".pdf", data=sample_set_x_3way, figwidth=6, figheight=6, colormap=None, num_col=10, wspace=0.01, hspace=0.001)
sampling_time=10
sampling_NS=100
sampling_num_iter=1000
Xg,XMg=model_hm.generate_x(NS=sampling_NS, num_iter=10*sampling_num_iter, init=True)
for s in range(sampling_time):
Xg,XMg=model_hm.generate_x(num_iter=sampling_num_iter, init=False)
# plot sampled data
sample_set_x_3way=numpy.reshape(XMg,newshape=(28,28,100))
print(s)
cl.plot_image_subplots(dir_save+"/fig_"+prefix+"_Gibbs_pretrain_generated_samples_randinit_"+str(s)+".pdf", data=sample_set_x_3way, figwidth=6, figheight=6, colormap=None, num_col=10, wspace=0.01, hspace=0.001)
# train
model_hm.train(X=train_set_x, X_validate=None, batch_size=batch_size, maxiter=maxiter_train, learn_rate_a=learn_rate_a_train, learn_rate_b=learn_rate_b_train, learn_rate_W=learn_rate_W_train, change_rate=change_rate_train, adjust_change_rate_at=adjust_change_rate_at_train, adjust_coef=adjust_coef_train, change_every_many_iters=change_every_many_iters, init_chain_time=init_chain_time, train_subset_size_for_compute_error=100, valid_subset_size_for_compute_error=100, track_reconstruct_error=True, track_free_energy=True, if_plot_error_free_energy=True, dir_save=dir_save, prefix="HM_train", figwidth=5, figheight=3)
# sampling
sampling_time=10
sampling_NS=100
for s in range(sampling_time):
Xg,XMg,Hg,HMr=model_hm.sample_xh_sleep(sampling_NS)
# plot sampled data
sample_set_x_3way=numpy.reshape(XMg,newshape=(28,28,100))
print(s)
cl.plot_image_subplots(dir_save+"/fig_"+prefix+"_finetune_generated_samples_randinit_"+str(s)+".pdf", data=sample_set_x_3way, figwidth=6, figheight=6, colormap=None, num_col=10, wspace=0.01, hspace=0.001)
sampling_time=10
sampling_NS=100
sampling_num_iter=1000
Xg,XMg=model_hm.generate_x(NS=sampling_NS, num_iter=10*sampling_num_iter, init=True)
for s in range(sampling_time):
Xg,XMg=model_hm.generate_x(num_iter=sampling_num_iter, init=False)
# plot sampled data
sample_set_x_3way=numpy.reshape(XMg,newshape=(28,28,100))
print(s)
cl.plot_image_subplots(dir_save+"/fig_"+prefix+"_Gibbs_finetune_generated_samples_randinit_"+str(s)+".pdf", data=sample_set_x_3way, figwidth=6, figheight=6, colormap=None, num_col=10, wspace=0.01, hspace=0.001)