def train_xy_init_model(num_classes, epochs):
# first get the time
now = datetime.datetime.now()
now_str = now.strftime('%Y-%m-%d_%H-%M-%S')
print("Started at {}".format(now_str))
# then create a new model folder based on name and date
model_name = "XY-Unet-Init"
rootDir = "C:/users/jfauser/IPCAI2019/ModelData/" + model_name + "/"
out_dir = rootDir + now_str + "/"
if not os.path.isdir(out_dir):
os.mkdir(out_dir)
# compile the net architecture
batch_size = 16
input_shape = (512, 512)
model = unet(input_shape + (1, ), num_classes)
# load the two training sets
print("loading data set 1")
if num_classes == 2:
images_1, labels_1 = tools.get_data_set_for_chorda(rootDir +
"dataset1.h5")
else:
images_1, labels_1 = tools.get_data_set(rootDir + "dataset1.h5")
print("loading data set 2")
if num_classes == 2:
images_2, labels_2 = tools.get_data_set_for_chorda(rootDir +
"dataset2.h5")
else:
images_2, labels_2 = tools.get_data_set(rootDir + "dataset2.h5")
# and train
filename_weigths1 = out_dir + "weights1.hdf5"
filename_weigths2 = out_dir + "weights2.hdf5"
print("train model 1")
train_model.train_model([images_1, labels_1], filename_weigths1, model,
batch_size,
epochs) # commences training on the set
del model
print("train model 2")
model = unet(input_shape + (1, ), num_classes)
train_model.train_model([images_2, labels_2], filename_weigths2, model,
batch_size,
epochs) # commences training on the set
del model
return now_str
def get_regularized_constant():
regularization_list = []
data_set = get_data_set(add_extra_feature=True)
for c in (2 ** power for power in xrange(-10, 10)):
print "C: %f" % c
regularization_list.append((cross_validation(c, data_set), c))
print sorted(regularization_list)
return sorted(regularization_list)[0]
def get_regularized_constant(kernel_func=lambda x, y: x.dot(y.T)):
regularization_list = []
data_set = get_data_set()
for c in (10 ** power for power in xrange(0, 1)):
print c
regularization_list.append((cross_validation(c, data_set, kernel_func), c))
print sorted(regularization_list)
return sorted(regularization_list)[0]
def get_regularized_constant():
regularization_list = []
data_set = get_data_set(add_extra_feature=True)
for c in (2**power for power in xrange(-10, 10)):
print "C: %f" % c
regularization_list.append((cross_validation(c, data_set), c))
print sorted(regularization_list)
return sorted(regularization_list)[0]
def get_regularized_constant(kernel_func=lambda x, y: x.dot(y.T)):
regularization_list = []
data_set = get_data_set()
for c in (10**power for power in xrange(0, 1)):
print c
regularization_list.append((cross_validation(c, data_set,
kernel_func), c))
print sorted(regularization_list)
return sorted(regularization_list)[0]
def main():
"""
Best result gives polynominal kernel with d = 1 and C = 100
mean error_rate: 0.00526
Also, polynomal kernel with d = 3 and C = 1000 gives decent result
mean error_rate: 0.06316
Gaussian kernels works pretty bad, i haven't found any (gamma, C) with decent error rate.
to repeat C and kernels analysis, uncomment analyze_C_and_kernels()
"""
# analyze_C_and_kernels()
# time = datetime.datetime.now()
C = 100
print "error_rate mean: %0.5f" % cross_validation(C, get_data_set(), polynominal_kernel(1))
def main():
"""
Best result gives polynominal kernel with d = 1 and C = 100
mean error_rate: 0.00526
Also, polynomal kernel with d = 3 and C = 1000 gives decent result
mean error_rate: 0.06316
Gaussian kernels works pretty bad, i haven't found any (gamma, C) with decent error rate.
to repeat C and kernels analysis, uncomment analyze_C_and_kernels()
"""
# analyze_C_and_kernels()
# time = datetime.datetime.now()
C = 100
print "error_rate mean: %0.5f" % cross_validation(C, get_data_set(),
polynominal_kernel(1))