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)
xy_batch = XY[idx]
xz_batch = XZ[idx]
yz_batch = YZ[idx]
targets_batch = Y_test[idx]
net_out = f_eval(xy_batch, xz_batch, yz_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
np.savez('Evaluation_Params.npz', *lasagne.layers.get_all_param_values(output))
X, Y = E.Evaluate2("/home/xvt131/Running/train4", DP.Tri_Image_Load, PS,
f_eval)
print "Mean Tibia Dice Score:", np.mean(X)
print "Mean Femur Dice Score:", np.mean(Y)
