def binary_cross_entropy(output, target, name=''):
r'''
Computes the binary cross entropy (aka logistic loss) between the ``output`` and ``target``.
Example:
TBA
Args:
output: the computed posterior probability for a variable to be 1 from the network (typ. a ``sigmoid``)
target: ground-truth label, 0 or 1
name (str, optional): the name of the Function instance in the network
Returns:
:class:`~cntk.ops.functions.Function`
'''
from cntk.cntk_py import binary_cross_entropy
dtype = get_data_type(output, target)
output = sanitize_input(output, dtype)
target = sanitize_input(target, dtype)
return binary_cross_entropy(output, target, name)
def binary_cross_entropy(output, target, name=''):
r'''
Computes the binary cross entropy (aka logistic loss) between the ``output`` and ``target``.
Example:
TBA
Args:
output: the computed posterior probability for a variable to be 1 from the network (typ. a ``sigmoid``)
target: ground-truth label, 0 or 1
name (str, optional): the name of the Function instance in the network
Returns:
:class:`~cntk.ops.functions.Function`
'''
from cntk.cntk_py import binary_cross_entropy
dtype = get_data_type(output, target)
output = sanitize_input(output, dtype)
target = sanitize_input(target, dtype)
return binary_cross_entropy(output, target, name)
def train(TrnPath,
SavePath,
savename,
imgX,
imgY,
ans_target,
trn_target,
bs,
epc,
imglist,
use_existing=False):
# Get minibatches of training data and perform model training
minibatch_size = bs
num_epochs = epc
# test = np.zeros((1, 512, 256))
test = np.zeros((1, imgY, imgX))
shape = test.shape
print("test image shape:" + str(test.shape))
x = C.input_variable(shape)
y = C.input_variable(shape)
z = cntk_unet.create_model(x)
#dice_coef = cntk_unet.dice_coefficient(z, y)
# ll = C.Logistic(z, y)
# ce = C.CrossEntropy(z, y)
'''
checkpoint_file = "/home/ys/PycharmProjects/cntk-unet/cntk-unet.dnn"
if use_existing:
z.load_model(checkpoint_file)
'''
# Prepare model and trainer
lr = learning_rate_schedule(0.00005, UnitType.sample)
momentum = C.learners.momentum_as_time_constant_schedule(0)
# loss and metric
ce = binary_cross_entropy(z, y)
pe = binary_cross_entropy(z, y)
trainer = C.Trainer(
z, (ce, pe), C.learners.adam(z.parameters, lr=lr, momentum=momentum))
file_num = len(imglist)
training_errors = []
test_errors = []
sw = time.time()
for e in range(0, num_epochs):
pattern = "[A-Z]"
num = 0
random.shuffle(imglist)
for i in range(0, file_num):
# http://qiita.com/wanwanland/items/ce272419dde2f95cdabc
match = re.search(pattern, imglist[i])
# print("matchgroup:"+match.group())
if match.group() == "A":
ansfile = TrnPath + r"/ans/Amode/" + ans_target + r"/" + imglist[
i]
trnfile = TrnPath + r"/raw/Amode/" + trn_target + r"/" + imglist[
i]
if match.group() == "B":
ansfile = TrnPath + r"/ans/Bmode/" + ans_target + r"/" + imglist[
i]
trnfile = TrnPath + r"/raw/Bmode/" + trn_target + r"/" + imglist[
i]
if match.group() == "Z":
ansfile = TrnPath + r"/ans/Zmode/" + ans_target + r"/" + imglist[
i]
trnfile = TrnPath + r"/raw/Zmode/" + trn_target + r"/" + imglist[
i]
# print(ansfile)
# print(trnfile)
if i % minibatch_size == 0:
training_y = np.array([Img2CntkImg(ansfile, imgX, imgY)])
training_x = np.array([Img2CntkImg(trnfile, imgX, imgY)])
elif i % minibatch_size > 0 and i % minibatch_size < minibatch_size - 1:
training_y = np.append(
training_y,
np.array([Img2CntkImg(ansfile, imgX, imgY, True)]),
axis=0)
training_x = np.append(
training_x,
np.array([Img2CntkImg(trnfile, imgX, imgY, False)]),
axis=0)
elif i % minibatch_size == minibatch_size - 1:
training_y = np.append(
training_y,
np.array([Img2CntkImg(ansfile, imgX, imgY, True)]),
axis=0)
training_x = np.append(
training_x,
np.array([Img2CntkImg(trnfile, imgX, imgY, False)]),
axis=0)
trainer.train_minibatch({x: training_x, y: training_y})
# print("###################")
if i == num_epochs - 1:
# Measure training error
training_errors.append(
trainer.test_minibatch({
x: training_x,
y: training_y
}))
print("Epoch:" + str(e) + " Error:" +
str(np.mean(training_errors)) + " time:" +
str(time.time() - sw))
# print("epoch:" + str(e) + " error:"+str(np.mean(training_errors))+" time:" + str(time.time()-sw))
if e % 50 == 0:
trainer.save_checkpoint(SavePath + r"/" + savename + r"_" +
str(e) + ".dnn")
# print("epoch:" + str(e))
# print("time passed:" + str(time.time() - sw))
training_errors.append(
trainer.test_minibatch({
x: training_x,
y: training_y
}))
print("Epoch:" + str(e) + " Error:" +
str(np.mean(training_errors)) + " time:" +
str(time.time() - sw))
training_errors = []
return trainer
