def train_car():
car_idx = np.where(classes == "car")[0]
data_train = load_data("train", independent=False)
car_images = np.array(
[i for i, y in enumerate(data_train.Y) if np.any(y == car_idx)])
n_states_per_label = np.ones(22, dtype=np.int)
n_states_per_label[car_idx] = 6
X, Y, file_names, images, all_superpixels = zip(
*[(data_train.X[i], data_train.Y[i], data_train.file_names[i],
data_train.images[i], data_train.superpixels[i])
for i in car_images])
problem = GraphCRF(n_states=22, inference_method='ad3', n_features=21 * 6)
ssvm = learners.SubgradientStructuredSVM(problem,
verbose=2,
C=.001,
max_iter=5000,
n_jobs=-1,
show_loss_every=10,
learning_rate=0.0001,
decay_exponent=0.5)
ssvm.fit(X, Y)
Y_pred = ssvm.predict(X)
plot_results(images,
file_names,
Y,
Y_pred,
all_superpixels,
folder="cars_only")
data_val = load_data("val", independent=False)
car_images_val = np.array(
[i for i, y in enumerate(data_val.Y) if np.any(y == car_idx)])
X_val, Y_val, file_names_val, images_val, all_superpixels_val = \
zip(*[(data_val.X[i], data_val.Y[i], data_val.file_names[i],
data_val.images[i], data_val.superpixels[i]) for i in
car_images_val])
Y_pred_val = ssvm.predict(X_val)
plot_results(images_val,
file_names_val,
Y_val,
Y_pred_val,
all_superpixels_val,
folder="cars_only_val")
# C=10
## train:
#0.92743060939680566V
#> ssvm.score(X_val, Y_val)
#0.52921719955898561
# test 0.61693548387096775
tracer()
