losses[:] = 100.
y_true = []
y_pred = []
test_loss = 0
accuracies = []
valid_batch_ctr = 0
for data, seg, labels in data_gen_validation:
# loss, acc = val_fn(data, convert_seg_map_for_crossentropy(seg, range(4)).astype(np.float32))
seg_flat = seg.flatten().astype(np.int32)
w = class_frequencies2[seg_flat]
loss, acc = val_fn(data, seg_flat, w) #, class_weights[seg_flat]
test_loss += loss
accuracies.append(acc)
valid_batch_ctr += 1
y_true.append(convert_seg_flat_to_binary_label_indicator_array(seg_flat))
y_pred.append(get_class_probas(data))
if valid_batch_ctr > n_test_batches:
break
test_loss /= n_test_batches
print "test loss: ", test_loss
print "test acc: ", np.mean(accuracies), "\n"
y_true = np.concatenate(y_true, axis=0)
y_pred = np.concatenate(y_pred, axis=0)
scores = roc_auc_score(y_true, y_pred, None)
auc_all.append(scores)
all_validation_losses.append(test_loss)
all_validation_accuracies.append(np.mean(accuracies))
auc_scores = np.concatenate(auc_all, axis=0).reshape(-1, 5)
printLosses(all_training_losses, all_training_accuracies, all_validation_losses, all_validation_accuracies, os.path.join(results_dir, "%s.png" % EXPERIMENT_NAME), n_feedbacks_per_epoch, auc_scores=auc_scores, auc_labels=["bg", "brain", "edema", "ce_tumor", "necrosis"], ylim_score=(0,0.75))
# learning_rate *= 0.62
losses[:] = 100.
y_true = []
y_pred = []
test_loss = 0
accuracies = []
valid_batch_ctr = 0
for data, seg, labels in data_gen_validation:
# loss, acc = val_fn(data, convert_seg_map_for_crossentropy(seg, range(4)).astype(np.float32))
seg_flat = seg.flatten().astype(np.int32)
w = class_frequencies2[seg_flat]
loss, acc = val_fn(data, seg_flat, w) #, class_weights[seg_flat]
test_loss += loss
accuracies.append(acc)
valid_batch_ctr += 1
y_true.append(convert_seg_flat_to_binary_label_indicator_array(seg_flat[seg_flat!=0]-1, len(class_frequencies2)-1))
y_pred.append(get_class_probas(data)[seg_flat!=0, :][:, 1:])
if valid_batch_ctr > n_test_batches:
break
test_loss /= n_test_batches
print "test loss: ", test_loss
print "test acc: ", np.mean(accuracies), "\n"
y_true = np.concatenate(y_true, axis=0)
y_pred = np.concatenate(y_pred, axis=0)
scores = roc_auc_score(y_true, y_pred, None)
del y_pred, y_true
auc_all.append(scores)
all_validation_losses.append(test_loss)
all_validation_accuracies.append(np.mean(accuracies))
auc_scores = np.concatenate(auc_all, axis=0).reshape(-1, len(class_frequencies2)-1)
printLosses(all_training_losses, all_training_accuracies, all_validation_losses, all_validation_accuracies, os.path.join(results_dir, "%s.png" % EXPERIMENT_NAME), n_feedbacks_per_epoch, auc_scores=auc_scores, auc_labels=["brain", "1", "2", "3", "4"], ylim_score=(0,0.08))
losses[:] = train_loss
y_true = []
y_pred = []
test_loss = 0
accuracies = []
valid_batch_ctr = 0
for data, seg, labels in data_gen_validation:
# loss, acc = val_fn(data, convert_seg_map_for_crossentropy(seg, range(4)).astype(np.float32))
seg_flat = seg.flatten().astype(np.int32)
w = class_frequencies2[seg_flat]
loss, acc = val_fn(data, seg_flat, w) #, class_weights[seg_flat]
test_loss += loss
accuracies.append(acc)
valid_batch_ctr += 1
y_true.append(convert_seg_flat_to_binary_label_indicator_array(seg_flat))
y_pred.append(get_class_probas(data))
if valid_batch_ctr > n_test_batches:
break
test_loss /= n_test_batches
print "test loss: ", test_loss
print "test acc: ", np.mean(accuracies), "\n"
y_true = np.concatenate(y_true, axis=0)
y_pred = np.concatenate(y_pred, axis=0)
scores = roc_auc_score(y_true, y_pred, None)
auc_all.append(scores)
all_validation_losses.append(test_loss)
all_validation_accuracies.append(np.mean(accuracies))
auc_scores = np.concatenate(auc_all, axis=0).reshape(-1, 5)
printLosses(all_training_losses, all_training_accuracies, all_validation_losses, all_validation_accuracies, os.path.join(results_dir, "%s.png" % EXPERIMENT_NAME), n_feedbacks_per_epoch, auc_scores=auc_scores, auc_labels=["bg", "brain", "edema", "ce_tumor", "necrosis"], ylim_score=(0,1.5))
learning_rate *= 0.62
break
train_loss /= n_batches_per_epoch
y_true = []
y_pred = []
valid_loss = 0
accuracies = []
valid_batch_ctr = 0
for data, gt in data_gen_validation:
# loss, acc = val_fn(data, convert_seg_map_for_crossentropy(seg, range(4)).astype(np.float32))
acc_marker, loss = val_marker(data, gt[:, 1].astype(np.int32))
valid_loss += loss
accuracies.append(acc_marker)
valid_batch_ctr += 1
y_true.append(convert_seg_flat_to_binary_label_indicator_array(gt[:, 1].astype(np.int32), num_classes))
y_pred.append(get_class_probas_markers(data)[0])
if valid_batch_ctr > n_validation_batches:
break
valid_loss /= n_validation_batches
print "test loss: ", valid_loss
print "test acc: ", np.mean(accuracies), "\n"
y_true = np.concatenate(y_true, axis=0)
y_pred = np.concatenate(y_pred, axis=0)
scores = roc_auc_score(y_true, y_pred, None)
del y_pred, y_true
auc_all.append(scores)
all_validation_losses.append(valid_loss)
all_validation_accuracies.append(np.mean(accuracies))
auc_scores = np.concatenate(auc_all, axis=0).reshape(-1, num_classes)
printLosses(all_training_losses, all_training_accuracies, all_validation_losses, all_validation_accuracies, os.path.join(results_dir, "%s.png" % EXPERIMENT_NAME), n_feedbacks_per_epoch, auc_scores=auc_scores, auc_labels=["0", "1"], ylim_score=None)
losses[:] = 100.
y_true = []
y_pred = []
test_loss = 0
accuracies = []
valid_batch_ctr = 0
for data, seg, labels in data_gen_validation:
# loss, acc = val_fn(data, convert_seg_map_for_crossentropy(seg, range(4)).astype(np.float32))
seg_flat = seg.flatten().astype(np.int32)
w = class_frequencies2[seg_flat]
loss, acc = val_fn(data, seg_flat, w) #, class_weights[seg_flat]
test_loss += loss
accuracies.append(acc)
valid_batch_ctr += 1
y_true.append(convert_seg_flat_to_binary_label_indicator_array(seg_flat[seg_flat!=0]-1, len(class_frequencies2)-1))
y_pred.append(get_class_probas(data)[seg_flat!=0, :][:, 1:])
if valid_batch_ctr > n_test_batches:
break
test_loss /= n_test_batches
print "test loss: ", test_loss
print "test acc: ", np.mean(accuracies), "\n"
y_true = np.concatenate(y_true, axis=0)
y_pred = np.concatenate(y_pred, axis=0)
scores = roc_auc_score(y_true, y_pred, None)
del y_pred, y_true
auc_all.append(scores)
all_validation_losses.append(test_loss)
all_validation_accuracies.append(np.mean(accuracies))
auc_scores = np.concatenate(auc_all, axis=0).reshape(-1, len(class_frequencies2)-1)
printLosses(all_training_losses, all_training_accuracies, all_validation_losses, all_validation_accuracies, os.path.join(results_dir, "%s.png" % EXPERIMENT_NAME), n_feedbacks_per_epoch, auc_scores=auc_scores, auc_labels=["brain", "1", "2", "3", "4"], ylim_score=(0,0.08))
y_true = []
y_pred = []
test_loss = 0
accuracies = []
valid_batch_ctr = 0
for data, seg, labels in data_gen_validation:
# loss, acc = val_fn(data, convert_seg_map_for_crossentropy(seg, range(4)).astype(np.float32))
seg_flat = seg.flatten().astype(np.int32)
w = class_frequencies2[seg_flat]
loss, acc = val_fn(data, seg_flat, w) #, class_weights[seg_flat]
test_loss += loss
accuracies.append(acc)
valid_batch_ctr += 1
y_true.append(
convert_seg_flat_to_binary_label_indicator_array(seg_flat))
y_pred.append(get_class_probas(data))
if valid_batch_ctr > n_test_batches:
break
test_loss /= n_test_batches
print "test loss: ", test_loss
print "test acc: ", np.mean(accuracies), "\n"
y_true = np.concatenate(y_true, axis=0)
y_pred = np.concatenate(y_pred, axis=0)
scores = roc_auc_score(y_true, y_pred, None)
auc_all.append(scores)
all_validation_losses.append(test_loss)
all_validation_accuracies.append(np.mean(accuracies))
auc_scores = np.concatenate(auc_all, axis=0).reshape(-1, 5)
printLosses(all_training_losses,
all_training_accuracies,
print "training loss average on epoch: ", train_loss
y_true = []
y_pred = []
test_loss = 0
accuracies = []
valid_batch_ctr = 0
for data, seg, labels in data_gen_validation:
# loss, acc = val_fn(data, convert_seg_map_for_crossentropy(seg, range(4)).astype(np.float32))
seg_flat = seg.flatten().astype(np.int32)
w = class_weights[seg_flat]
loss, acc = val_fn(data, seg_flat, w) #, class_weights[seg_flat]
test_loss += loss
accuracies.append(acc)
valid_batch_ctr += 1
y_true.append(convert_seg_flat_to_binary_label_indicator_array(seg_flat[seg_flat!=0]-1, num_classes-1))
y_pred.append(get_class_probas(data)[seg_flat!=0, :][:, 1:])
if valid_batch_ctr > n_test_batches:
break
test_loss /= n_test_batches
print "test loss: ", test_loss
print "test acc: ", np.mean(accuracies), "\n"
y_true = np.concatenate(y_true, axis=0)
y_pred = np.concatenate(y_pred, axis=0)
scores = roc_auc_score(y_true, y_pred, None)
del y_pred, y_true
auc_all.append(scores)
all_validation_losses.append(test_loss)
all_validation_accuracies.append(np.mean(accuracies))
auc_scores = np.concatenate(auc_all, axis=0).reshape((-1, num_classes-1))
printLosses(all_training_losses, all_training_accuracies, all_validation_losses, all_validation_accuracies, os.path.join(results_dir, "%s.png" % EXPERIMENT_NAME), n_feedbacks_per_epoch, auc_scores=auc_scores, auc_labels=["brain", "1", "2", "3", "4"], ylim_score=(0,0.08))
