learning_rate=0.001,
momentum=0.8)
f_eval = theano.function([input_var, input2_var, input3_var], eval_out)
f_train = theano.function([input_var, input2_var, input3_var, target_var],
[cost],
updates=updates)
import Evaluation as E
with np.load('/home/xvt131/Functions/epoch_31_params.npz') as f:
param_values = [f['arr_%d' % i] for i in range(len(f.files))]
lasagne.layers.set_all_param_values(output, param_values)
Tibial_Score, Femoral_Score = E.Evaluate1("/home/xvt131/Running/train2",
DP.image_load, 9, 5, f_eval)
#E.Segment("/home/xvt131/Running/evaluation", DP.image_load_eval, 9, 5 ,f_eval)
#for param in DP.get_paths('/home/xvt131/Functions/params_101'):
# with np.load(param) as f:
# param_values = [f['arr_%d' % i] for i in range(len(f.files))]
# lasagne.layers.set_all_param_values(output, param_values)
# Tibial_Score, Femoral_Score = E.Evaluate1("/home/xvt131/Running/testing", DP.image_load, 9, 5 ,f_eval)
# print param
# print "Mean Tibia Dice Score:" , np.mean(Tibial_Score)
# print "Mean Femur Dice Score:", np.mean(Femoral_Score)
print "Mean Tibia Dice Score:", np.mean(Tibial_Score)
print "Mean Femur Dice Score:", np.mean(Femoral_Score)
loss += [cur_loss / batch_size]
for i in range(num_batches_train):
idx = range(i * batch_size, (i + 1) * batch_size)
x_batch = X_train[idx]
pos_batch = X_pos[idx]
Bigger_batch = X_Bigger[idx]
targets_batch = Y_train[idx]
net_out = f_eval(x_batch, pos_batch, Bigger_batch)
preds = np.argmax(net_out, axis=-1)
confusion_train.batch_add(targets_batch, preds)
for img in Test:
Tibia, Femur, Time = E.Evaluate1("/home/xvt131/Running/vtest",
DP.image_load, 9, 5, f_eval)
print "Mean Tibia Dice Score:", np.mean(Tibia)
print "Mean Femur Dice Score:", np.mean(Femur)
# [X_test, X_Bigs], X_post, Y_test = DP.voxel_samples(im, [9, 5])
# num_samples_valid = Y_test.shape[0]
# num_batches_valid = num_samples_valid // batch_size
# for i in range(num_batches_valid):
# idx = range(i*batch_size, (i+1)*batch_size)
# x_batch = X_test[idx]
# post_batch = X_post[idx]
# big_batch = X_Bigs[idx]
# targets_batch = Y_test[idx]
# net_out = f_eval(x_batch, post_batch, big_batch)
# preds = np.argmax(net_out, axis=-1)
x_batch = X_test[idx]
post_batch = X_post[idx]
big_batch = X_Bigs[idx]
targets_batch = Y_test[idx]
net_out = f_eval(x_batch, post_batch, big_batch)
preds = np.argmax(net_out, axis=-1)
confusion_valid.batch_add(targets_batch, preds)
train_acc_cur = confusion_train.accuracy()
valid_acc_cur = confusion_valid.accuracy()
if (epoch) % 5 == 0:
np.savez('/home/xvt131/Functions/epoch_%d_params.npz' % (epoch + 1),
*lasagne.layers.get_all_param_values(output))
print confusion_train
print "Epoch %i : Train Loss %e , Train acc %f, Valid acc %f " % (
epoch + 1, loss[-1], train_acc_cur, valid_acc_cur)
import Evaluation as E
np.savez('Evaluation_Params.npz', *lasagne.layers.get_all_param_values(output))
X, Y = E.Evaluate1("/home/xvt131/Running/validating", DP.image_load, 9, 5,
f_eval)
print "Mean Tibia Dice Score:", np.mean(X)
print "Mean Femur Dice Score:", np.mean(Y)
for img in Test:
[X_test, X_Bigs], X_post, Y_test = DP.voxel_samples3(im, [15, 9])
num_samples_valid = Y_test.shape[0]
num_batches_valid = num_samples_valid // batch_size
for i in range(num_batches_valid):
idx = range(i * batch_size, (i + 1) * batch_size)
x_batch = X_test[idx]
post_batch = X_post[idx]
big_batch = X_Bigs[idx]
targets_batch = Y_test[idx]
net_out = f_eval(x_batch, post_batch, big_batch)
preds = np.argmax(net_out, axis=-1)
confusion_valid.batch_add(targets_batch, preds)
train_acc_cur = confusion_train.accuracy()
valid_acc_cur = confusion_valid.accuracy()
print confusion_train
print "Epoch %i : Train Loss %e , Train acc %f, Valid acc %f " % (
epoch + 1, loss[-1], train_acc_cur, valid_acc_cur)
import Evaluation as E
X, Y, Z, Pre, Lab = E.Evaluate1("/home/xvt131/Running/train4", DP.image_load,
15, 9, f_eval)
print "Mean Tibia Dice Score:", np.mean(X)
print "Mean Femur Dice Score:", np.mean(Y)
