cur_loss = 0
loss = []
valid_loss = []
threshold, upper, lower = 0.5, 1, 0
Train = DP.get_paths("/home/xvt131/Functions/Adhish_copy/Training-Rand")
Test = DP.get_paths("/home/xvt131/Functions/Adhish_copy/Validating-Rand")
import gc
for epoch in range(num_epochs):
cur_loss = 0
val_loss = 0
confusion_valid = ConfusionMatrix(2)
confusion_train = ConfusionMatrix(2)
for im in Train:
XY, XZ, YZ, Y_train = DP.Patch_triplanar_para(im, PS)
num_samples_train = Y_train.shape[0]
num_batches_train = num_samples_train // batch_size
for i in range(num_batches_train):
idx = range(i * batch_size, (i + 1) * batch_size)
xy_batch = XY[idx]
xz_batch = XZ[idx]
yz_batch = YZ[idx]
target_batch = np.float32(Y_train[idx].reshape(batch_size, 1))
batch_loss = f_train(xy_batch, xz_batch, yz_batch,
target_batch) #this will do the backprop pass
cur_loss += batch_loss[0] / batch_size
for i in range(num_batches_train):
idx = range(i * batch_size, (i + 1) * batch_size)
xy_batch = XY[idx]
