def mainLoop():
n_iter = 0
last_save = 0
min_test_MSE = 1.0
max_iters_after_save = 50
try:
while True:
n_iter += 1
print "Iteration: %5d " % (n_iter),
seg_copy = map(lambda (c, seg): (c, cv.CloneImage(seg)), segments)
seg_copy = map(lambda (c, seg): (c, spoil(seg)), seg_copy)
shuffle(seg_copy)
f = open(train_file, "w")
f.write("%d %d %d\n" % (len(segments), num_input, num_output))
for c, image in seg_copy:
image = adjustSize(image, (segW, segH))
for y in range(image.height):
for x in range(image.width):
n = image[y, x] / 159.375 - 0.8
f.write("%f " % n)
f.write("\n")
n = charset.index(c)
f.write("-1 " * n + "1" + " -1" * (num_output - n - 1) + "\n")
f.close()
train = libfann.training_data()
train.read_train_from_file(train_file)
ann.train_epoch(train)
train.destroy_train()
print "Train MSE: %f " % (ann.get_MSE()),
print "Train bit fail: %5d " % (ann.get_bit_fail()),
ann.test_data(test)
mse = ann.get_MSE()
print "Test MSE: %f " % (mse),
print "Test bit fail: %5d " % (ann.get_bit_fail()),
if mse < min_test_MSE:
min_test_MSE = mse
ann.save(ann_file)
last_save = n_iter
print "saved",
if n_iter - last_save > max_iters_after_save: break
print
except KeyboardInterrupt:
print "Interrupted by user."
def mainLoop():
n_iter = 0
last_save = 0
min_test_MSE = 1.0
max_iters_after_save = 50
try:
while True:
n_iter += 1
print "Iteration: %5d " % (n_iter),
seg_copy = map(lambda (c, seg): (c, cv.CloneImage(seg)), segments)
seg_copy = map(lambda (c, seg): (c, spoil(seg)), seg_copy)
shuffle(seg_copy)
f = open(train_file, "w")
f.write("%d %d %d\n" % (len(segments), num_input, num_output))
for c, image in seg_copy:
image = adjustSize(image, (segW, segH))
for y in range(image.height):
for x in range(image.width):
n = image[y, x] / 159.375 - 0.8
f.write("%f " % n)
f.write("\n")
n = charset.index(c)
f.write("-1 " * n + "1" + " -1" * (num_output - n - 1) + "\n")
f.close()
train = libfann.training_data()
train.read_train_from_file(train_file)
ann.train_epoch(train)
train.destroy_train()
print "Train MSE: %f " % (ann.get_MSE()),
print "Train bit fail: %5d " % (ann.get_bit_fail()),
ann.test_data(test)
mse = ann.get_MSE()
print "Test MSE: %f " % (mse),
print "Test bit fail: %5d " % (ann.get_bit_fail()),
if mse < min_test_MSE:
min_test_MSE = mse
ann.save(ann_file)
last_save = n_iter
print "saved",
if n_iter - last_save > max_iters_after_save: break
print
except KeyboardInterrupt: print "Interrupted by user."
segments = os.listdir(segment_dir)
f = open(train_file, "w")
f.write("%d %d %d\n" % (len(segments), num_input, num_output))
for name in segments:
image = cv.LoadImage(os.path.join(segment_dir, name), cv.CV_LOAD_IMAGE_GRAYSCALE)
image = adjustSize(image, (segW, segH))
for y in range(image.height):
for x in range(image.width):
n = image[y, x] / 159.375 - 0.8
f.write("%f " % n)
f.write("\n")
c = os.path.splitext(name)[0][0]
n = charset.index(c)
f.write("-1 " * n + "1" + " -1" * (num_output - n - 1) + "\n")
f.close()
print "Samples: %d" % len(segments)
print "Input: %d" % num_input
print "Output: %d" % num_output
connection_rate = 1.0
learning_rate = 0.3
num_neurons_hidden = num_input / 3
desired_error = 0.00001
max_iterations = 10000
iterations_between_reports = 20
segments = os.listdir(segment_dir)
f = open(train_file, "w")
f.write("%d %d %d\n" % (len(segments), num_input, num_output))
for name in segments:
image = cv.LoadImage(os.path.join(segment_dir, name),
cv.CV_LOAD_IMAGE_GRAYSCALE)
image = adjustSize(image, (segW, segH))
for y in range(image.height):
for x in range(image.width):
n = image[y, x] / 159.375 - 0.8
f.write("%f " % n)
f.write("\n")
c = os.path.splitext(name)[0][0]
n = charset.index(c)
f.write("-1 " * n + "1" + " -1" * (num_output - n - 1) + "\n")
f.close()
print "Samples: %d" % len(segments)
print "Input: %d" % num_input
print "Output: %d" % num_output
connection_rate = 1.0
learning_rate = 0.3
num_neurons_hidden = num_input / 3
desired_error = 0.00001
max_iterations = 10000
iterations_between_reports = 20
