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ksc_spatial_SdA.py
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__author__ = "Zhouhan LIN"
__date__ = "June 2013"
__version__ = "1.0"
import os
import sys
import time
import scipy
import scipy.io as sio
import numpy
import theano
import theano.tensor as T
from scipy.stats import t
from sklearn import svm
from sklearn.metrics import confusion_matrix
from theano.tensor.shared_randomstreams import RandomStreams
from SdA import SdA
from hsi_utils import *
cmap = numpy.asarray( [[0, 0, 0],
[95, 205, 50],
[255, 0, 255],
[215, 115, 0],
[180, 30, 0],
[0, 50, 0],
[75, 0, 0],
[255, 255, 255],
[145, 130, 135],
[255, 255, 170],
[255, 200, 80],
[60, 200, 255],
[10, 65, 125],
[0, 0, 255]], dtype='int32')
# load .mat files
print '... loading data'
hsi_file = u'/home/hantek/data/hsi_data/kennedy/KSC.mat'
gnd_file = u'/home/hantek/data/hsi_data/kennedy/KSC_gt.mat'
data = sio.loadmat(hsi_file)
img = scale_to_unit_interval(data['KSC'].astype(theano.config.floatX))
width = img.shape[0]
height = img.shape[1]
bands = img.shape[2]
data = sio.loadmat(gnd_file)
gnd_img = data['KSC_gt']
gnd_img = gnd_img.astype(numpy.int32)
# PCA transform image, condense its spectral size.
print '... extracting data points'
_, datasets, _, _ = \
prepare_data(hsi_img=img, gnd_img=gnd_img, merge=False,
window_size=7, n_principle=4, batch_size=100)
print '... training'
# begin SdA
finetune_lr=0.008
pretraining_epochs=2000
pretrain_lr=0.02
training_epochs=200000
batch_size=50
hidden_layers_sizes=[310, 310, 310, 310, 310]
corruption_levels = [0., 0., 0., 0., 0.]
print 'finetuning learning rate=', finetune_lr
print 'pretraining learning rate=', pretrain_lr
print 'pretraining epoches=', pretraining_epochs
print 'fine tuning epoches=', training_epochs
print 'batch size=', batch_size
print 'hidden layers sizes=', hidden_layers_sizes
print 'corruption levels=', corruption_levels
# compute number of minibatches for training, validation and testing
n_train_batches = datasets[0][0].get_value(borrow=True).shape[0]
n_train_batches /= batch_size
# numpy random generator
numpy_rng = numpy.random.RandomState(89677)
print '... building the model'
# construct the stacked denoising autoencoder class
sda = SdA(numpy_rng=numpy_rng, n_ins=datasets[0][0].get_value(borrow=True).shape[1],
hidden_layers_sizes=hidden_layers_sizes,
n_outs=gnd_img.max())
################################################################################
# PRETRAINING THE MODEL #
#########################
print '... getting the pretraining functions'
pretraining_fns = sda.pretraining_functions(train_set_x=datasets[0][0],
batch_size=batch_size)
print '... pre-training the model'
start_time = time.clock()
## Pre-train layer-wise
for i in xrange(sda.n_layers):
# go through pretraining epochs
for epoch in xrange(pretraining_epochs):
# go through the training set
c = []
for batch_index in xrange(n_train_batches):
c.append(pretraining_fns[i](index=batch_index,
corruption=corruption_levels[i],
lr=pretrain_lr))
print 'Pre-training layer %i, epoch %d, cost ' % (i, epoch),
print numpy.mean(c)
end_time = time.clock()
print ('The pretraining code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
################################################################################
# FINETUNING THE MODEL #
########################
# get the training, validation and testing function for the model
print '... getting the finetuning functions'
train_fn, validate_model, test_model = sda.build_finetune_functions(
datasets=datasets, batch_size=batch_size,
learning_rate=finetune_lr)
print '... finetunning the model'
validation_frequency = 1000 * n_train_batches
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_params = None
best_validation_loss = numpy.inf
test_score = 0.
start_time = time.clock()
epoch = 0
while (epoch < training_epochs):
epoch = epoch + 1
for minibatch_index in xrange(n_train_batches):
minibatch_avg_cost = train_fn(minibatch_index)
iter = (epoch - 1) * n_train_batches + minibatch_index
if (iter + 1) % validation_frequency == 0:
validation_losses = validate_model()
this_validation_loss = numpy.mean(validation_losses)
print('epoch %i, minibatch %i/%i, validation error %f %%' %
(epoch, minibatch_index + 1, n_train_batches,
this_validation_loss * 100.))
# if we got the best validation score until now
if this_validation_loss < best_validation_loss:
# save best validation score and iteration number
best_validation_loss = this_validation_loss
best_iter = iter
# test it on the test set
test_losses = test_model()
test_score = numpy.mean(test_losses)
print((' epoch %i, minibatch %i/%i, test error of '
'best model %f %%') %
(epoch, minibatch_index + 1, n_train_batches,
test_score * 100.))
end_time = time.clock()
print(('Optimization complete with best validation score of %f %%,'
'with test performance %f %%') %
(best_validation_loss * 100., test_score * 100.))
print ('The training code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
filename = 'ksc_l1sda_pt%d_ft%d_lrp%.4f_f%.4f_bs%d_pca%d' % \
(pretraining_epochs, training_epochs, pretrain_lr, finetune_lr,
batch_size, 4)
print '... saving parameters'
sda.save_params(filename + '_params.pkl')
print '... classifying test set with learnt model:'
pred_func = theano.function(inputs=[sda.x], outputs=sda.logLayer.y_pred)
pred_test = pred_func(datasets[2][0].get_value(borrow=True))
true_test = datasets[2][1].get_value(borrow=True)
true_valid = datasets[1][1].get_value(borrow=True)
true_train = datasets[0][1].get_value(borrow=True)
result_analysis(pred_test, true_train, true_valid, true_test)
print '... classifying the whole image with learnt model:'
print '...... extracting data'
window_size = 7
_, data_spatial, _, _ = \
T_pca_constructor(hsi_img=img, gnd_img=gnd_img, n_principle=4,
window_size=window_size, flag='unsupervised')
start_time = time.clock()
print '...... begin '
y = pred_func(data_spatial) + 1
print '...... done '
end_time = time.clock()
print 'finished, running time:%fs' % (end_time - start_time)
y_rgb = cmap[y, :]
margin = (window_size / 2) * 2 # floor it to a multiple of 2
y_image = y_rgb.reshape(width - margin, height - margin, 3)
scipy.misc.imsave(filename + '_wholeimg.png' , y_image)
print 'Saving classification results'
sio.savemat(filename + 'wholeimg.mat',
{'y': y.reshape(width - margin, height - margin)})
############################################################################
print '... performing Student\'s t-test'
best_c = 10000.
best_g = 10.
svm_classifier = svm.SVC(C=best_c, gamma=best_g, kernel='rbf')
svm_classifier.fit(datasets[0][0].get_value(), datasets[0][1].get_value())
data = [numpy.vstack((datasets[1][0].get_value(),
datasets[2][0].get_value())),
numpy.hstack((datasets[1][1].get_value(),
datasets[2][1].get_value()))]
numpy_rng = numpy.random.RandomState(89677)
num_test = 100
print 'Total number of tests: %d' % num_test
k_sae = []
k_svm = []
for i in xrange(num_test):
[_, _], [_, _], [test_x, test_y], _ = \
train_valid_test(data, ratio=[0, 1, 1], batch_size=1,
random_state=numpy_rng.random_integers(1e10))
test_y = test_y + 1 # fix the label scale problem
pred_y = pred_func(test_x)
cm = confusion_matrix(test_y, pred_y)
pr_a = cm.trace()*1.0 / test_y.size
pr_e = ((cm.sum(axis=0)*1.0/test_y.size) * \
(cm.sum(axis=1)*1.0/test_y.size)).sum()
k_sae.append( (pr_a - pr_e) / (1 - pr_e) )
pred_y = svm_classifier.predict(test_x)
cm = confusion_matrix(test_y, pred_y)
pr_a = cm.trace()*1.0 / test_y.size
pr_e = ((cm.sum(axis=0)*1.0/test_y.size) * \
(cm.sum(axis=1)*1.0/test_y.size)).sum()
k_svm.append( (pr_a - pr_e) / (1 - pr_e) )
std_k_sae = numpy.std(k_sae)
std_k_svm = numpy.std(k_svm)
mean_k_sae = numpy.mean(k_sae)
mean_k_svm = numpy.mean(k_svm)
left = ( (mean_k_sae - mean_k_svm) * numpy.sqrt(num_test*2-2)) \
/ ( numpy.sqrt(2./num_test) * num_test * (std_k_sae**2 + std_k_svm**2) )
rv = t(num_test*2.0 - 2)
right = rv.ppf(0.95)
print '\tstd\t\tmean'
print 'k_sae\t%f\t%f' % (std_k_sae, mean_k_sae)
print 'k_svm\t%f\t%f' % (std_k_svm, mean_k_svm)
if left > right:
print 'left = %f, right = %f, test PASSED.' % (left, right)
else:
print 'left = %f, right = %f, test FAILED.' % (left, right)