# names of test videos
test_name = ['MP7']
test_num = 1
# define neural network layout
l1 = Layer(4096, 400, 'relu')
l2 = Layer(400, 200, 'relu')
l3 = Layer(200, 100, 'relu')
l4 = Layer(100, 25, 'linear')
layers = [l1, l2, l3, l4]
learning_rate = 0.0002
loss_type = 'mean_square'
opt_type = 'RMSprop'
Q = NeuralNet(layers, learning_rate, loss_type, opt_type)
Q.recover('model/', 'Q_net_all_11_0_1000')
for i in range(test_num):
video = Episode(i, test_num, test_name, feat_path, gt_path)
frame_num = np.shape(video.feat)[0]
summary = np.zeros(frame_num)
Q_value = []
id_curr = 0
while id_curr < frame_num:
action_value = Q.forward([video.feat[id_curr]])
a_index = np.argmax(action_value[0])
id_next = id_curr + a_index + 1
if id_next > frame_num - 1:
break
