eps=0.0
video_inp = T.matrix("video_inp")
srng = RandomStreams(seed=12345)
disc = Discriminator(memory=1000,time_step=time_step,h_size=500,n_hid=1000,mlp_hid=1000,lr=0.0002,video_size=80*60,video_inp=video_inp,srng=srng)
count_vid = 0
disc_err = 0
fool_err =0
patience = 10
benevolent = 2
punishement = 1.0001
threshold = 2.0
flag = 1
for l in range(0,10000):
for f in onlyfiles:
vr = VR('../data/'+f)
count = vr.count_frame()
vr = VR('../data/'+f)
for j in range(0,count-time_step-1):
input=[]
for i in range(0,time_step):
input.append(numpy.asarray(cv2.resize(vr.return_frame(j+1),(80,60)).flatten()))
new_matrix = (input - numpy.min(input))/(numpy.max(input)-numpy.min(input)*1.0)
input = new_matrix.T
disc_err = disc.get_disc(input)
fool_err = disc.get_gp()
#print disc.train_out()
if flag ==1 and disc_err <threshold:
flag = 0
if flag == 0 and fool_err <threshold:
flag = 1
threshold *= 0.9
lr=0.0002,
video_size=80 * 60,
video_inp=video_inp,
srng=srng)
count_vid = 0
disc_err = 0
fool_err = 0
patience = 10
benevolent = 2
punishement = 1.0001
threshold = 2.0
flag = 1
for l in range(0, 10000):
for f in onlyfiles:
vr = VR('../data/' + f)
count = vr.count_frame()
vr = VR('../data/' + f)
for j in range(0, count - time_step - 1):
input = []
for i in range(0, time_step):
input.append(
numpy.asarray(
cv2.resize(vr.return_frame(j + 1),
(80, 60)).flatten()))
new_matrix = (input - numpy.min(input)) / (numpy.max(input) -
numpy.min(input) * 1.0)
input = new_matrix.T
disc_err = disc.get_disc(input)
fool_err = disc.get_gp()
#print disc.train_out()
if flag == 1 and disc_err < threshold:
