def main():
learningRate = float(sys.argv[1]) if len(sys.argv) >= 2 else 0.0001
maxIterations = int(sys.argv[2]) if len(sys.argv) >= 3 else 300
# load data
data = scipy.io.loadmat('data/usps_resampled.mat')
train_patterns = data['train_patterns']
train_labels = data['train_labels']
test_patterns = data['test_patterns']
test_labels = data['test_labels']
# initialize and train RBM
rbm = RBM(192, train_patterns, learningRate=learningRate, verbose=True)
iterationsCompleted = rbm.train(convThreshold=0.03,
maxIterations=maxIterations)
print 'Autoencoding. . . '
hidden_patterns = rbm.translate(train_patterns)
ae_patterns = rbm.invert(hidden_patterns)
print 'Finished.'
while True:
while True:
try:
sampleImage = raw_input("Pick a sample image from [0-" +
str(train_patterns.shape[1] - 1) +
"] (q to quit): ")
if sampleImage == 'q':
y = raw_input("Save this classifier (y)? ")
fn = 'data/RBM_' + str(
(learningRate, 192, iterationsCompleted))
if y in ['y', '']:
f = open(fn, 'w')
pickle.dump(TrainedRBM(rbm.W, rbm.b_h, rbm.b_v), f)
print "RBM saved as " + fn
sys.exit(0)
sampleImage = int(sampleImage)
if sampleImage not in range(train_patterns.shape[1]):
raise ValueError
except ValueError:
continue
except EOFError:
sys.exit(0)
except KeyboardInterrupt:
sys.exit(0)
break
# show example image
plt.matshow(train_patterns[:, sampleImage].reshape(16, 16))
plt.matshow(ae_patterns[:, sampleImage].reshape(16, 16))
plt.show()
def main():
learningRate = float(sys.argv[1]) if len(sys.argv) >= 2 else 0.0001
maxIterations = int(sys.argv[2]) if len(sys.argv) >= 3 else 300
# load data
data = scipy.io.loadmat('data/usps_resampled.mat')
train_patterns = data['train_patterns']
train_labels = data['train_labels']
test_patterns = data['test_patterns']
test_labels = data['test_labels']
# initialize and train RBM
rbm = RBM(192, train_patterns, learningRate=learningRate, verbose=True)
rbm.train(convThreshold=0.1, maxIterations=maxIterations)
print 'Autoencoding. . . '
hidden_patterns = rbm.translate(train_patterns)
ae_patterns = rbm.invert(hidden_patterns)
print 'Finished.'
W = rbm.W.get_value()
while True:
while True:
try:
detectorNum = raw_input("Pick a detector image from [0-" +
str(W.shape[0] - 1) +
"] (q to quit): ")
if detectorNum == 'q':
sys.exit(0)
detectorNum = int(detectorNum)
if detectorNum not in range(W.shape[0]):
raise ValueError
except ValueError:
continue
except EOFError:
sys.exit(0)
except KeyboardInterrupt:
sys.exit(0)
break
# show example image
plt.matshow(W[detectorNum, :].reshape(16, 16))
plt.show()
