val_pos_memmap = memmap("../data/%s_val_pos.memmap" % memmap_name, dtype=np.float32, mode="r", shape=memmap_properties["val_pos_shape"])
val_neg_memmap = memmap("../data/%s_val_neg.memmap" % memmap_name, dtype=np.float32, mode="r", shape=memmap_properties["val_neg_shape"])
all_training_losses = []
all_validation_losses = []
all_validation_accuracies = []
all_training_accs = []
n_epochs = 20
for epoch in range(n_epochs):
print "epoch: ", epoch
train_loss = 0
train_acc_tmp = 0
train_loss_tmp = 0
batch_ctr = 0
for data, seg, labels in threaded_generator(memmapGeneratorDataAugm(train_neg_memmap, train_pos_memmap, BATCH_SIZE, n_pos_train, n_neg_train)):
if batch_ctr != 0 and batch_ctr % int(np.floor(n_batches_per_epoch/10.)) == 0:
print "number of batches: ", batch_ctr, "/", n_batches_per_epoch
print "training_loss since last update: ", train_loss_tmp/np.floor(n_batches_per_epoch/10.), " train accuracy: ", train_acc_tmp/np.floor(n_batches_per_epoch/10.)
all_training_losses.append(train_loss_tmp/np.floor(n_batches_per_epoch/10.))
all_training_accs.append(train_acc_tmp/np.floor(n_batches_per_epoch/10.))
train_loss_tmp = 0
train_acc_tmp = 0
printLosses(all_training_losses, all_training_accs, all_validation_losses, all_validation_accuracies, "../results/%s.png" % EXPERIMENT_NAME, 10)
loss, acc = train_fn(data, labels)
train_loss += loss
train_loss_tmp += loss
train_acc_tmp += acc
batch_ctr += 1
if batch_ctr > n_batches_per_epoch:
break
val_pos_memmap = memmap("../data/patchClassification_val_pos.memmap", dtype=np.float32, mode="r+", shape=memmap_properties["val_pos_shape"])
val_neg_memmap = memmap("../data/patchClassification_val_neg.memmap", dtype=np.float32, mode="r+", shape=memmap_properties["val_neg_shape"])
all_training_losses = []
all_validation_losses = []
all_validation_accuracies = []
all_training_accs = []
n_epochs = 20
for epoch in range(n_epochs):
print "epoch: ", epoch
train_loss = 0
train_acc_tmp = 0
train_loss_tmp = 0
batch_ctr = 0
for data, seg, labels in threaded_generator(memmapGeneratorDataAugm(train_neg_memmap, train_pos_memmap, BATCH_SIZE, n_pos_train, n_neg_train)):
if batch_ctr != 0 and batch_ctr % int(np.floor(n_batches_per_epoch/10.)) == 0:
print "number of batches: ", batch_ctr, "/", n_batches_per_epoch
print "training_loss since last update: ", train_loss_tmp/np.floor(n_batches_per_epoch/10.), " train accuracy: ", train_acc_tmp/np.floor(n_batches_per_epoch/10.)
all_training_losses.append(train_loss_tmp/np.floor(n_batches_per_epoch/10.))
all_training_accs.append(train_acc_tmp/np.floor(n_batches_per_epoch/10.))
train_loss_tmp = 0
train_acc_tmp = 0
printLosses(all_training_losses, all_training_accs, all_validation_losses, all_validation_accuracies, "../results/%s.png" % EXPERIMENT_NAME, 10)
loss, acc = train_fn(data, labels)
train_loss += loss
train_loss_tmp += loss
train_acc_tmp += acc
batch_ctr += 1
if batch_ctr > n_batches_per_epoch:
break
shape=memmap_properties["val_neg_shape"])
all_training_losses = []
all_validation_losses = []
all_validation_accuracies = []
all_training_accs = []
n_epochs = 100
for epoch in range(n_epochs):
print "epoch: ", epoch
train_loss = 0
train_acc_tmp = 0
train_loss_tmp = 0
batch_ctr = 0
for data, seg, labels in threaded_generator(
memmapGeneratorDataAugm_t1km_flair_adc_cbv(train_pos_memmap,
train_neg_memmap,
BATCH_SIZE, n_pos_train,
n_pos_train)):
if batch_ctr != 0 and batch_ctr % int(
np.floor(n_batches_per_epoch / 10.)) == 0:
print "number of batches: ", batch_ctr, "/", n_batches_per_epoch
print "training_loss since last update: ", train_loss_tmp / np.floor(
n_batches_per_epoch /
10.), " train accuracy: ", train_acc_tmp / np.floor(
n_batches_per_epoch / 10.)
all_training_losses.append(train_loss_tmp /
np.floor(n_batches_per_epoch / 10.))
all_training_accs.append(train_acc_tmp /
np.floor(n_batches_per_epoch / 10.))
train_loss_tmp = 0
train_acc_tmp = 0