def test_ais_with_dbn(self): dbn = DBN(RBM(10, 10)) dbn.add_layer(RBM(10, 20)) hidden_states = utils.binary_numbers(dbn[0].Y.shape[0]) ais = Estimator(dbn) ais_logz = ais.estimate_log_partition_function(100, np.sqrt(np.arange(0, 1, 0.001))) brf_logz = utils.logsumexp(dbn[1]._ulogprob_vis(hidden_states)) brf_probs = [] dbn_probs = [] dbn_bound = [] for i in range(50): sample = np.matrix(np.random.rand(10, 1)) > 0.5 prob, bound = ais.estimate_log_probability(sample, 200) brf_probs.append(utils.logsumexp(dbn[1]._ulogprob_vis(hidden_states) + dbn[0]._clogprob_vis_hid(sample, hidden_states), 1) - brf_logz) dbn_probs.append(prob) dbn_bound.append(bound) self.assertTrue(np.mean(dbn_bound) < np.mean(dbn_probs)) self.assertTrue(np.abs(np.mean(dbn_probs) - np.mean(brf_probs)) < 0.1)
def test_ais_with_semirbm_dbn(self):
dbn = DBN(RBM(5, 5))
dbn.add_layer(SemiRBM(5, 5))
ais = Estimator(dbn)
ais.estimate_log_partition_function(100,
np.arange(0, 1, 1E-3),
layer=0)
ais.estimate_log_partition_function(10, np.arange(0, 1, 1E-3), layer=1)
dbn[0]._brf_logz = utils.logsumexp(dbn[0]._ulogprob_vis(
utils.binary_numbers(dbn[0].X.shape[0])))
dbn[1]._brf_logz = utils.logsumexp(dbn[1]._ulogprob_vis(
utils.binary_numbers(dbn[1].X.shape[0])))
samples = np.concatenate(
[dbn.sample(25, 100, 20),
np.matrix(np.random.rand(5, 25) > 0.5)], 1)
Y = utils.binary_numbers(dbn[0].Y.shape[0])
X = utils.binary_numbers(dbn[0].X.shape[0])
logRy = dbn[1]._ulogprob_vis(Y)
logQy = utils.logsumexp(dbn[0]._ulogprob(X, Y, all_pairs=True), 0)
log_sum = utils.logsumexp(
dbn[0]._clogprob_hid_vis(samples, Y, all_pairs=True) - logQy +
logRy, 1)
logPx = log_sum + dbn[0]._ulogprob_vis(samples) - dbn[1]._brf_logz
logPx_ = ais.estimate_log_probability(samples)[0]
self.assertTrue(np.abs(logPx_.mean() - logPx.mean()) < 0.1)
def test_ais_with_dbn_sanity_check(self): dbn = DBN(RBM(5, 20)) dbn.add_layer(RBM(20, 5)) dbn.add_layer(RBM(5, 20)) dbn[0].W = np.matrix(np.random.randn(5, 20)) dbn[0].b = np.matrix(np.random.rand(5, 1) - 0.5) dbn[0].c = np.matrix(np.random.rand(20, 1) - 0.5) dbn[1].W = dbn[0].W.T dbn[1].b = dbn[0].c dbn[1].c = dbn[0].b dbn[2].W = dbn[0].W dbn[2].b = dbn[0].b dbn[2].c = dbn[0].c samples = dbn.sample(100) ais = Estimator(dbn[0]) rbm_logz = ais.estimate_log_partition_function(100, np.sqrt(np.arange(0, 1, 0.001))) rbm_probs = ais.estimate_log_probability(samples) ais = Estimator(dbn) dbn_logz = ais.estimate_log_partition_function(100, np.sqrt(np.arange(0, 1, 0.001))) dbn_probs = ais.estimate_log_probability(samples) self.assertTrue(abs(dbn_logz - rbm_logz) / rbm_logz < 0.02) self.assertTrue((np.exp(dbn_probs) - np.exp(rbm_probs)).mean() < 0.02)
def test_ais_with_dbn(self):
dbn = DBN(RBM(10, 10))
dbn.add_layer(RBM(10, 20))
hidden_states = utils.binary_numbers(dbn[0].Y.shape[0])
ais = Estimator(dbn)
ais_logz = ais.estimate_log_partition_function(
100, np.sqrt(np.arange(0, 1, 0.001)))
brf_logz = utils.logsumexp(dbn[1]._ulogprob_vis(hidden_states))
brf_probs = []
dbn_probs = []
dbn_bound = []
for i in range(50):
sample = np.matrix(np.random.rand(10, 1)) > 0.5
prob, bound = ais.estimate_log_probability(sample, 200)
brf_probs.append(
utils.logsumexp(
dbn[1]._ulogprob_vis(hidden_states) +
dbn[0]._clogprob_vis_hid(sample, hidden_states), 1) -
brf_logz)
dbn_probs.append(prob)
dbn_bound.append(bound)
self.assertTrue(np.mean(dbn_bound) < np.mean(dbn_probs))
self.assertTrue(np.abs(np.mean(dbn_probs) - np.mean(brf_probs)) < 0.1)
def test_ais_with_dbn_sanity_check(self):
dbn = DBN(RBM(5, 20))
dbn.add_layer(RBM(20, 5))
dbn.add_layer(RBM(5, 20))
dbn[0].W = np.matrix(np.random.randn(5, 20))
dbn[0].b = np.matrix(np.random.rand(5, 1) - 0.5)
dbn[0].c = np.matrix(np.random.rand(20, 1) - 0.5)
dbn[1].W = dbn[0].W.T
dbn[1].b = dbn[0].c
dbn[1].c = dbn[0].b
dbn[2].W = dbn[0].W
dbn[2].b = dbn[0].b
dbn[2].c = dbn[0].c
samples = dbn.sample(100)
ais = Estimator(dbn[0])
rbm_logz = ais.estimate_log_partition_function(
100, np.sqrt(np.arange(0, 1, 0.001)))
rbm_probs = ais.estimate_log_probability(samples)
ais = Estimator(dbn)
dbn_logz = ais.estimate_log_partition_function(
100, np.sqrt(np.arange(0, 1, 0.001)))
dbn_probs = ais.estimate_log_probability(samples)
self.assertTrue(abs(dbn_logz - rbm_logz) / rbm_logz < 0.02)
self.assertTrue((np.exp(dbn_probs) - np.exp(rbm_probs)).mean() < 0.02)
def main(argv):
# load preprocessed data samples
print 'loading data...\t',
data = load('../data/vanhateren.npz')
print '[DONE]'
print
# remove DC component (first component)
data_train = data['train'][1:, :]
data_test = data['test'][1:, :]
# create 1st layer
dbn = DBN(GaussianRBM(num_visibles=data_train.shape[0], num_hiddens=100))
# hyperparameters
dbn[0].learning_rate = 5E-3
dbn[0].weight_decay = 1E-2
dbn[0].momentum = 0.9
dbn[0].sigma = 0.65
dbn[0].cd_steps = 1
dbn[0].persistent = True
# train 1st layer
print 'training...\t',
dbn.train(data_train, num_epochs=100, batch_size=100)
print '[DONE]'
# evaluate 1st layer
print 'evaluating...\t',
logptf = dbn.estimate_log_partition_function(num_ais_samples=100, beta_weights=arange(0, 1, 1E-3))
loglik = dbn.estimate_log_likelihood(data_test)
print '[DONE]'
print
print 'estimated log-partf.:\t', logptf
print 'estimated log-loss:\t', -loglik / data_test.shape[0] / log(2)
print
def test_ais_with_semirbm_dbn(self): dbn = DBN(RBM(5, 5)) dbn.add_layer(SemiRBM(5, 5)) ais = Estimator(dbn) ais.estimate_log_partition_function(100, np.arange(0, 1, 1E-3), layer=0) ais.estimate_log_partition_function(10, np.arange(0, 1, 1E-3), layer=1) dbn[0]._brf_logz = utils.logsumexp(dbn[0]._ulogprob_vis(utils.binary_numbers(dbn[0].X.shape[0]))) dbn[1]._brf_logz = utils.logsumexp(dbn[1]._ulogprob_vis(utils.binary_numbers(dbn[1].X.shape[0]))) samples = np.concatenate([dbn.sample(25, 100, 20), np.matrix(np.random.rand(5, 25) > 0.5)], 1) Y = utils.binary_numbers(dbn[0].Y.shape[0]) X = utils.binary_numbers(dbn[0].X.shape[0]) logRy = dbn[1]._ulogprob_vis(Y) logQy = utils.logsumexp(dbn[0]._ulogprob(X, Y, all_pairs=True), 0) log_sum = utils.logsumexp(dbn[0]._clogprob_hid_vis(samples, Y, all_pairs=True) - logQy + logRy, 1) logPx = log_sum + dbn[0]._ulogprob_vis(samples) - dbn[1]._brf_logz logPx_ = ais.estimate_log_probability(samples)[0] self.assertTrue(np.abs(logPx_.mean() - logPx.mean()) < 0.1)
def main(argv):
num_visibles = 28 * 28
num_hiddens = [1000, 1000]
num_epochs = 50
batch_size = 100
# load data samples
data = load('../data/mnist.npz')['train'] / 255.
# train 1st layer
dbn = DBN(RBM(num_visibles, num_hiddens[0]))
dbn.train(data, num_epochs, batch_size)
# train 2nd layer
dbn.add_layer(RBM(num_hiddens[0], num_hiddens[1]))
dbn.train(data, num_epochs, batch_size, [1E-1, 1E-2])
return 0
def main(argv):
num_visibles = 28 * 28
num_hiddens = [1000, 1000]
num_epochs = 50
batch_size = 100
# load data samples
data = load('../data/mnist.npz')['train'] / 255.
# train 1st layer
dbn = DBN(RBM(num_visibles, num_hiddens[0]))
dbn.train(data, num_epochs, batch_size)
# train 2nd layer
dbn.add_layer(RBM(num_hiddens[0], num_hiddens[1]))
dbn.train(data, num_epochs, batch_size, [1E-1, 1E-2])
return 0
def main(argv):
# load preprocessed data samples
print 'loading data...\t',
data = load('../data/vanhateren.npz')
print '[DONE]'
print
# remove DC component (first component)
data_train = data['train'][1:, :]
data_test = data['test'][1:, :]
# create 1st layer
dbn = DBN(GaussianRBM(num_visibles=data_train.shape[0], num_hiddens=100))
# hyperparameters
dbn[0].learning_rate = 5E-3
dbn[0].weight_decay = 1E-2
dbn[0].momentum = 0.9
dbn[0].sigma = 0.65
dbn[0].cd_steps = 1
dbn[0].persistent = True
# train 1st layer
print 'training...\t',
dbn.train(data_train, num_epochs=100, batch_size=100)
print '[DONE]'
# evaluate 1st layer
print 'evaluating...\t',
logptf = dbn.estimate_log_partition_function(num_ais_samples=100,
beta_weights=arange(
0, 1, 1E-3))
loglik = dbn.estimate_log_likelihood(data_test)
print '[DONE]'
print
print 'estimated log-partf.:\t', logptf
print 'estimated log-loss:\t', -loglik / data_test.shape[0] / log(2)
print
def main(argv):
# load preprocessed data samples
print 'loading data...\t',
#data_train, data_test = genData() #load('../data/vanhateren.npz')
category = "office"
data_train_0,data_train_1,data_train_2, data_test = load_data_BS("..\data\watersurface\orignal_color.pkl")
img = Image.open("..\data\watersurface\hand_segmented_01850.bmp")
x_, y_ = (asarray(img)[:,:,0]).shape
print "Doie : " , (asarray(img)[:,:,0]).shape
groundtruth = (((asarray(img)[:,:,0])/255.0 > 0.5) * 1).flatten()
#groundtruth = (((asarray(img)[:,:])/255.0 > 0.5) * 1).flatten()
print '[DONE]'
print data_test.shape
# remove DC component (first component)
# data_train = data['train'][1:, :]
# data_test = data['test'][1:, :]
# create 1st layer
dbn = DBN(GaussianRBM(num_visibles=data_train_0.shape[0], num_hiddens=8))
dbn1 = DBN(GaussianRBM(num_visibles=data_train_1.shape[0], num_hiddens=8))
dbn2 = DBN(GaussianRBM(num_visibles=data_train_2.shape[0], num_hiddens=8))
dbn[0].learning_rate = 0.001
dbn1[0].learning_rate = 0.001
dbn2[0].learning_rate = 0.001
# train 1st layer
print 'training...\t',
dbn.train(data_train_0, num_epochs=5, batch_size=1,shuffle=False)
dbn1.train(data_train_1, num_epochs=5, batch_size=1,shuffle=False)
dbn2.train(data_train_2, num_epochs=5, batch_size=1,shuffle=False)
print '[DONE]'
data_test_0 = ((data_test.T)[:,::3]).T
data_test_1 = ((data_test.T)[:,1::3]).T
data_test_2 = ((data_test.T)[:,2::3]).T
# global runtime
# sFilename = "data\\weightlogs"
# f = h5.File(sFileName+".hdf5")
# f.create_group(runtime)
# group.create_dataset('parameters',data=par)
#storeWeights(category,"data\\weightlogs", dbn[0].W, dbn[0].vsigma,dbn1[0].W, dbn1[0].vsigma,dbn2[0].W, dbn2[0].vsigma )
Ndat = 25 #data_test_0.shape[1]
Nsteps = 5
# evaluate 1st layer
print 'evaluating 1...\t',
dataout = zeros(x_*y_)
# #datasub = zeros(x_*y_)
for point in xrange(Ndat):
#X = asmatrix(data_test_0[:,point]).T
X = asmatrix(data_test_0[:,-1]).T
#dataout = vstack((dataout,X.flatten()))
#print "testing:", X.shape
for recstep in xrange(Nsteps):
Y = dbn[0].forward(X) # self.activ(1)
X = dbn[0].backward(Y,X)
#print "S hsape:", X.shape
#dataout = vstack((dataout,X.flatten()))
dataout = vstack((dataout,subtract(asarray(X),data_test_0[:,-1],asarray(dbn[0].vsigma),point+1)))
print 'evaluating 2...\t',
dataout1 = zeros(x_*y_)
# #datasub = zeros(x_*y_)
for point in xrange(Ndat):
#X = asmatrix(data_test_1[:,point]).T
X = asmatrix(data_test_1[:,-1]).T
#dataout1 = vstack((dataout1,X.flatten()))
#print "testing:", X.shape
for recstep in xrange(Nsteps):
Y = dbn1[0].forward(X) # self.activ(1)
X = dbn1[0].backward(Y,X)
#print "S hsape:", X.shape
#dataout1 = vstack((dataout1,X.flatten()))
dataout1 = vstack((dataout1,subtract(asarray(X),data_test_1[:,-1],asarray(dbn1[0].vsigma),point+1)))
print 'evaluating 3...\t',
dataout2 = zeros(x_*y_)
# #datasub = zeros(x_*y_)
for point in xrange(Ndat):
#X = asmatrix(data_test_2[:,point]).T
X = asmatrix(data_test_2[:,-1]).T
#dataout2 = vstack((dataout2,X.flatten()))
#print "testing:", X.shape
for recstep in xrange(Nsteps):
Y = dbn2[0].forward(X) # self.activ(1)
X = dbn2[0].backward(Y,X)
#print "S hsape:", X.shape
#dataout2 = vstack((dataout2,X.flatten()))
dataout2 = vstack((dataout2,subtract(asarray(X),data_test_2[:,-1],asarray(dbn2[0].vsigma),point+1)))
# plt.imshow((reshape(data_test[::3,5],(x_,y_))), cmap = cm.Greys_r, interpolation ="nearest")
# plt.axis('off')
# plt.show()
plt.figure(1)
for i in range(Ndat):
plt.subplot(5,5,i+1)
d = multiply(asarray(dataout[i+1,:]),asarray(dataout1[i+1,:]),asarray(dataout2[i+1,:]))
d = mod(d+1,2)
#open_img = ndimage.binary_opening(reshape(d,(x_,y_)))
#close_img = ndimage.binary_closing(open_img)
m_filter = ndimage.median_filter(reshape(d,(x_,y_)),8)
# plt.figure(3)
# plt.imshow(close_img)
# plt.show()
d = m_filter.flatten()
print "Image Example Fmeaure: ",i," : ", f_measure(d,groundtruth) * 100
# d[0::3] = asarray(dataout[i+1,:])
# d[1::3] = asarray(dataout1[i+1,:])
# d[2::3] = asarray(dataout2[i+1,:])
# d[:,:,0] = (reshape(asarray(dataout[i+1,:]),(x_,y_)))
# d[:,:,1] = (reshape(asarray(dataout1[i+1,:]),(x_,y_)))
# d[:,:,2] = (reshape(asarray(dataout2[i+1,:]),(x_,y_)))
#img_s = Image.fromarray(asarray(reshape(d,(x_,y_))*255,dtype="uint8"))
#img_s.save("C:\work\\backgdSubt\dataset\datasets\change detection\\baseline\\baseline\office\grbm" + "\\" + str(i) + ".bmp")
plt.imshow(reshape(d,(x_,y_)), cmap = cm.Greys_r, interpolation ="nearest")
plt.axis('off')
plt.figure(2)
for k in range(8):
plt.subplot(4,2,k+1)
d = zeros((x_*y_*3))
d[0::3] = asarray(dbn[0].W[:,k].flatten())
d[1::3] = asarray(dbn1[0].W[:,k].flatten())
d[2::3] = asarray(dbn2[0].W[:,k].flatten())
plt.imshow(reshape(d,(x_,y_,3)))#, cmap = cm.Greys_r, interpolation ="nearest")
plt.axis('off')
# plt.figure()
# plt.imshow((reshape(dbn[0].vsigma[:19200],(x_,y_))))
# plt.figure(2)
# plt.imshow((reshape(dbn[0].vsigma[19200:19200*2],(x_,y_))))
# plt.figure(3)
# plt.imshow((reshape(dbn[0].vsigma[19200*2:19200*3],(x_,y_))))
plt.figure(3)
print type(dbn[0].vsigma)
plt.imshow(reshape(asarray(dbn[0].vsigma),(x_,y_)))
plt.show()
print dbn[0].vsigma
p.show()
def main(argv):
# load preprocessed data samples
print "loading data...\t",
# data_train, data_test = genData() #load('../data/vanhateren.npz')
data_train_0, data_train_1, data_train_2, data_test = load_data_BS("data/lightswitch/orignal_color.pkl")
print "[DONE]"
# remove DC component (first component)
# data_train = data['train'][1:, :]
# data_test = data['test'][1:, :]
# create 1st layer
dbn = DBN(GaussianRBM(num_visibles=data_train_0.shape[0], num_hiddens=8))
dbn1 = DBN(GaussianRBM(num_visibles=data_train_1.shape[0], num_hiddens=8))
dbn2 = DBN(GaussianRBM(num_visibles=data_train_2.shape[0], num_hiddens=8))
# train 1st layer
print "training...\t",
dbn.train(data_train_0, num_epochs=10, batch_size=1, shuffle=False)
dbn1.train(data_train_1, num_epochs=10, batch_size=1, shuffle=False)
dbn2.train(data_train_2, num_epochs=10, batch_size=1, shuffle=False)
print "[DONE]"
data_test_0 = ((data_test.T)[:, ::3]).T
data_test_1 = ((data_test.T)[:, 1::3]).T
data_test_2 = ((data_test.T)[:, 2::3]).T
Ndat = data_test_0.shape[1]
Nsteps = 5
# evaluate 1st layer
print "evaluating 1...\t",
dataout = zeros(x_ * y_)
# #datasub = zeros(x_*y_)
for point in xrange(Ndat):
X = asmatrix(data_test_0[:, point]).T
# dataout = vstack((dataout,X.flatten()))
# print "testing:", X.shape
for recstep in xrange(Nsteps):
Y = dbn[0].forward(X) # self.activ(1)
X = dbn[0].backward(Y, X)
# print "S hsape:", X.shape
# dataout = vstack((dataout,X.flatten()))
dataout = vstack((dataout, subtract(asarray(X), data_test_0[:, point], asarray(dbn[0].vsigma))))
print "evaluating 2...\t",
dataout1 = zeros(x_ * y_)
# #datasub = zeros(x_*y_)
for point in xrange(Ndat):
X = asmatrix(data_test_1[:, point]).T
# dataout1 = vstack((dataout1,X.flatten()))
# print "testing:", X.shape
for recstep in xrange(Nsteps):
Y = dbn1[0].forward(X) # self.activ(1)
X = dbn1[0].backward(Y, X)
# print "S hsape:", X.shape
# dataout1 = vstack((dataout1,X.flatten()))
dataout1 = vstack((dataout1, subtract(asarray(X), data_test_1[:, point], asarray(dbn1[0].vsigma))))
print "evaluating 3...\t",
dataout2 = zeros(x_ * y_)
# #datasub = zeros(x_*y_)
for point in xrange(Ndat):
X = asmatrix(data_test_2[:, point]).T
# dataout2 = vstack((dataout2,X.flatten()))
# print "testing:", X.shape
for recstep in xrange(Nsteps):
Y = dbn2[0].forward(X) # self.activ(1)
X = dbn2[0].backward(Y, X)
# print "S hsape:", X.shape
# dataout2 = vstack((dataout2,X.flatten()))
dataout2 = vstack((dataout2, subtract(asarray(X), data_test_2[:, point], asarray(dbn2[0].vsigma))))
# plt.imshow((reshape(data_test[::3,5],(x_,y_))), cmap = cm.Greys_r, interpolation ="nearest")
# plt.axis('off')
# plt.show()
plt.figure(1)
for i in range(Ndat):
plt.subplot(5, 5, i + 1)
d = multiply(asarray(dataout[i + 1, :]), asarray(dataout1[i + 1, :]), asarray(dataout2[i + 1, :]))
# d[0::3] = asarray(dataout[i+1,:])
# d[1::3] = asarray(dataout1[i+1,:])
# d[2::3] = asarray(dataout2[i+1,:])
# d[:,:,0] = (reshape(asarray(dataout[i+1,:]),(x_,y_)))
# d[:,:,1] = (reshape(asarray(dataout1[i+1,:]),(x_,y_)))
# d[:,:,2] = (reshape(asarray(dataout2[i+1,:]),(x_,y_)))
plt.imshow(reshape(d, (x_, y_)), cmap=cm.Greys_r, interpolation="nearest")
plt.axis("off")
plt.figure(2)
for k in range(8):
plt.subplot(4, 2, k + 1)
d = zeros((x_ * y_ * 3))
d[0::3] = asarray(dbn[0].W[:, k].flatten())
d[1::3] = asarray(dbn1[0].W[:, k].flatten())
d[2::3] = asarray(dbn2[0].W[:, k].flatten())
plt.imshow(reshape(d, (x_, y_, 3))) # , cmap = cm.Greys_r, interpolation ="nearest")
plt.axis("off")
# plt.figure()
# plt.imshow((reshape(dbn[0].vsigma[:19200],(x_,y_))))
# plt.figure(2)
# plt.imshow((reshape(dbn[0].vsigma[19200:19200*2],(x_,y_))))
# plt.figure(3)
# plt.imshow((reshape(dbn[0].vsigma[19200*2:19200*3],(x_,y_))))
plt.figure(3)
print type(dbn[0].vsigma)
plt.imshow(reshape(asarray(dbn[0].vsigma), (x_, y_)))
plt.show()
print dbn[0].vsigma
p.show()
def main(argv):
# load preprocessed data samples
print 'loading data...\t',
#data_train, data_test = genData() #load('../data/vanhateren.npz')
#data_train_0,data_train_1,data_train_2, data_test = load_data_BS("data/changedetection\\baseline\highway/orignal_color.pkl")
#groundtruth = (((asarray(img)[:,:])/255.0 > 0.5) * 1).flatten()
print '[DONE]'
# remove DC component (first component)
# data_train = data['train'][1:, :]
# data_test = data['test'][1:, :]
# create 1st layer
dbn = DBN(GaussianRBM(num_visibles=x_ * y_, num_hiddens=8))
dbn1 = DBN(GaussianRBM(num_visibles=x_ * y_, num_hiddens=8))
dbn2 = DBN(GaussianRBM(num_visibles=x_ * y_, num_hiddens=8))
f = h5.File("experiments\weightlogs.hdf5", 'r')
category = "office"
dbn[0].W = (f[category])["weights"][:]
dbn1[0].W = (f[category])["weights1"][:]
dbn2[0].W = (f[category])["weights2"][:]
dbn[0].vsigma = (f[category])["sigma"][:]
dbn1[0].vsigma = (f[category])["sigma1"][:]
dbn2[0].vsigma = (f[category])["sigma2"][:]
# dbn[0].learning_rate = 0.001
# dbn1[0].learning_rate = 0.001
# dbn2[0].learning_rate = 0.001
# train 1st layer
# print 'training...\t',
# dbn.train(data_train_0, num_epochs=1, batch_size=1,shuffle=False)
# dbn1.train(data_train_1, num_epochs=1, batch_size=1,shuffle=False)
# dbn2.train(data_train_2, num_epochs=1, batch_size=1,shuffle=False)
# print '[DONE]'
srcDir = "C:\work\\backgdSubt\dataset\datasets\change detection\\baseline\\baseline\highway\input"
targetDir = "C:\work\\backgdSubt\dataset\datasets\change detection\\baseline\\baseline\highway\grbm"
ii = 1
for k in os.listdir(srcDir):
imf = Image.open(srcDir + '/' + k)
print array(imf).shape
data_test = (array(imf)).flatten()
#dataset = np.vstack((dataset,d))
data_test_0 = ((data_test)[::3]).T
data_test_1 = ((data_test)[1::3]).T
data_test_2 = ((data_test)[2::3]).T
Nsteps = 5
# evaluate 1st layer
print 'evaluating 1...\t',
# #datasub = zeros(x_*y_)
#for point in xrange(Ndat):
#X = asmatrix(data_test_0[:,point]).T
X = asmatrix(data_test_0).T
#dataout = vstack((dataout,X.flatten()))
#print "testing:", X.shape
for recstep in xrange(Nsteps):
Y = dbn[0].forward(X) # self.activ(1)
X = dbn[0].backward(Y, X)
#print "S hsape:", X.shape
#dataout = vstack((dataout,X.flatten()))
dataout = subtract(asarray(X), data_test_0, asarray(dbn[0].vsigma), 10)
#X = asmatrix(data_test_1[:,point]).T
X1 = asmatrix(data_test_1).T
#dataout1 = vstack((dataout1,X.flatten()))
#print "testing:", X.shape
for recstep in xrange(Nsteps):
Y1 = dbn1[0].forward(X1) # self.activ(1)
X1 = dbn1[0].backward(Y1, X1)
#print "S hsape:", X.shape
#dataout1 = vstack((dataout1,X.flatten()))
dataout1 = subtract(asarray(X1), data_test_1, asarray(dbn1[0].vsigma),
10)
X2 = asmatrix(data_test_2).T
#dataout2 = vstack((dataout2,X.flatten()))
#print "testing:", X.shape
for recstep in xrange(Nsteps):
Y2 = dbn2[0].forward(X2) # self.activ(1)
X2 = dbn2[0].backward(Y2, X2)
#print "S hsape:", X.shape
#dataout2 = vstack((dataout2,X.flatten()))
# plt.imshow(reshape(X,(x_,y_)))
# plt.show()
dataout2 = subtract(asarray(X2), data_test_2, asarray(dbn2[0].vsigma),
10)
# plt.imshow((reshape(dataout,(x_,y_))), cmap = cm.Greys_r, interpolation ="nearest")
# plt.figure(2)
# plt.imshow((reshape(dataout1,(x_,y_))), cmap = cm.Greys_r, interpolation ="nearest")
# plt.figure(3)
# plt.imshow((reshape(dataout2,(x_,y_))), cmap = cm.Greys_r, interpolation ="nearest")
# plt.axis('off')
# plt.show()
# plt.figure(1)
# for i in range(Ndat):
# plt.subplot(5,5,i+1)
d = multiply(asarray(dataout[:]), asarray(dataout1[:]),
asarray(dataout2[:]))
d = mod(d + 1, 2)
# plt.imshow(reshape(d,(x_,y_)),cmap = cm.Greys_r, interpolation ="nearest")
# print type(d[0])
# #plt.savefig(targetDir + "\\" + str(ii) + ".png")
#print
img_s = Image.fromarray(
asarray(reshape(d, (x_, y_)) * 255, dtype="uint8"))
# plt.figure(2)
# plt.imshow(img_s)#,cmap = cm.Greys_r, interpolation ="nearest")
#plt.show()
img_s.save(targetDir + "\\bin00" + str(ii) + ".bmp")
ii = ii + 1
def main(argv):
# load preprocessed data samples
print 'loading data...\t',
#data_train, data_test = genData() #load('../data/vanhateren.npz')
data_train_0,data_train_1,data_train_2, data_test = load_data_BS("data/changedetection\\baseline\highway\orignal_color.pkl")
img = Image.open("data/changedetection\\baseline\highway/gt001367.png")
print "Doie : " , (asarray(img)[:,:]).shape
groundtruth = (((asarray(img)[:,:])/255.0 > 0.5) * 1).flatten()
#groundtruth = (((asarray(img)[:,:])/255.0 > 0.5) * 1).flatten()
print '[DONE]'
# remove DC component (first component)
# data_train = data['train'][1:, :]
# data_test = data['test'][1:, :]
# create 1st layer
dbn = DBN(GaussianRBM(num_visibles=data_train_0.shape[0], num_hiddens=8))
dbn1 = DBN(GaussianRBM(num_visibles=data_train_1.shape[0], num_hiddens=8))
dbn2 = DBN(GaussianRBM(num_visibles=data_train_2.shape[0], num_hiddens=8))
dbn[0].learning_rate = 0.0001
dbn1[0].learning_rate = 0.0001
dbn2[0].learning_rate = 0.0001
# train 1st layer
print 'training...\t',
dbn.train(data_train_0, num_epochs=25, batch_size=1,shuffle=False)
dbn1.train(data_train_1, num_epochs=25, batch_size=1,shuffle=False)
dbn2.train(data_train_2, num_epochs=25, batch_size=1,shuffle=False)
print '[DONE]'
data_test_0 = ((data_test.T)[:,::3]).T
data_test_1 = ((data_test.T)[:,1::3]).T
data_test_2 = ((data_test.T)[:,2::3]).T
Ndat = 20 #data_test_0.shape[1]
Nsteps = 5
# evaluate 1st layer
print 'evaluating 1...\t',
dataout = zeros(x_*y_)
# #datasub = zeros(x_*y_)
for point in xrange(Ndat):
#X = asmatrix(data_test_0[:,point]).T
X = asmatrix(data_test_0[:,-1]).T
#dataout = vstack((dataout,X.flatten()))
#print "testing:", X.shape
for recstep in xrange(Nsteps):
Y = dbn[0].forward(X) # self.activ(1)
X = dbn[0].backward(Y,X)
#print "S hsape:", X.shape
#dataout = vstack((dataout,X.flatten()))
dataout = vstack((dataout,subtract(asarray(X),data_test_0[:,-1],asarray(dbn[0].vsigma),point+1)))
print 'evaluating 2...\t',
dataout1 = zeros(x_*y_)
# #datasub = zeros(x_*y_)
for point in xrange(Ndat):
#X = asmatrix(data_test_1[:,point]).T
X = asmatrix(data_test_1[:,-1]).T
#dataout1 = vstack((dataout1,X.flatten()))
#print "testing:", X.shape
for recstep in xrange(Nsteps):
Y = dbn1[0].forward(X) # self.activ(1)
X = dbn1[0].backward(Y,X)
#print "S hsape:", X.shape
#dataout1 = vstack((dataout1,X.flatten()))
dataout1 = vstack((dataout1,subtract(asarray(X),data_test_1[:,-1],asarray(dbn1[0].vsigma),point+1)))
print 'evaluating 3...\t',
dataout2 = zeros(x_*y_)
# #datasub = zeros(x_*y_)
for point in xrange(Ndat):
#X = asmatrix(data_test_2[:,point]).T
X = asmatrix(data_test_2[:,-1]).T
#dataout2 = vstack((dataout2,X.flatten()))
#print "testing:", X.shape
for recstep in xrange(Nsteps):
Y = dbn2[0].forward(X) # self.activ(1)
X = dbn2[0].backward(Y,X)
#print "S hsape:", X.shape
#dataout2 = vstack((dataout2,X.flatten()))
dataout2 = vstack((dataout2,subtract(asarray(X),data_test_2[:,-1],asarray(dbn2[0].vsigma),point+1)))
# plt.imshow((reshape(data_test[::3,5],(x_,y_))), cmap = cm.Greys_r, interpolation ="nearest")
# plt.axis('off')
# plt.show()
plt.figure(1)
for i in range(Ndat):
plt.subplot(5,5,i+1)
d = multiply(asarray(dataout[i+1,:]),asarray(dataout1[i+1,:]),asarray(dataout2[i+1,:]))
d = mod(d+1,2)
f_measure(d,groundtruth)
#print "Image Example Fmeaure: ",i," : ", f_measure(d,groundtruth) * 100
# d[0::3] = asarray(dataout[i+1,:])
# d[1::3] = asarray(dataout1[i+1,:])
# d[2::3] = asarray(dataout2[i+1,:])
# d[:,:,0] = (reshape(asarray(dataout[i+1,:]),(x_,y_)))
# d[:,:,1] = (reshape(asarray(dataout1[i+1,:]),(x_,y_)))
# d[:,:,2] = (reshape(asarray(dataout2[i+1,:]),(x_,y_)))
plt.imshow(reshape(d,(x_,y_)), cmap = cm.Greys_r, interpolation ="nearest")
plt.axis('off')
plt.figure(2)
for k in range(8):
plt.subplot(4,2,k+1)
d = zeros((x_*y_*3))
d[0::3] = asarray(dbn[0].W[:,k].flatten())
d[1::3] = asarray(dbn1[0].W[:,k].flatten())
d[2::3] = asarray(dbn2[0].W[:,k].flatten())
plt.imshow(reshape(d,(x_,y_,3)))#, cmap = cm.Greys_r, interpolation ="nearest")
plt.axis('off')
# plt.figure()
# plt.imshow((reshape(dbn[0].vsigma[:19200],(x_,y_))))
# plt.figure(2)
# plt.imshow((reshape(dbn[0].vsigma[19200:19200*2],(x_,y_))))
# plt.figure(3)
# plt.imshow((reshape(dbn[0].vsigma[19200*2:19200*3],(x_,y_))))
plt.figure(3)
print type(dbn[0].vsigma)
plt.imshow(reshape(asarray(dbn[0].vsigma),(x_,y_)))
plt.show()
print dbn[0].vsigma
p.show()
def main(argv):
# load preprocessed data samples
print 'loading data...\t',
#data_train, data_test = genData() #load('../data/vanhateren.npz')
DIR1 = "data/changedetection\\nightvideos\\tramstation"
data_train_0, data_train_1, data_train_2, data_test = load_data_BS(
DIR1 + "/orignal_color.pkl")
img = Image.open(DIR1 + "/gt001330.png")
x_, y_ = (asarray(img)[:, :]).shape
print "Doie : ", x_, y_
groundtruth = (((asarray(img)[:, :]) / 255.0 > 0.5) * 1).flatten()
#groundtruth = (((asarray(img)[:,:])/255.0 > 0.5) * 1).flatten()
print '[DONE]'
print data_test.shape
# remove DC component (first component)
# data_train = data['train'][1:, :]
# data_test = data['test'][1:, :]
# create 1st layer
dbn = DBN(GaussianRBM(num_visibles=data_train_0.shape[0], num_hiddens=8))
dbn1 = DBN(GaussianRBM(num_visibles=data_train_1.shape[0], num_hiddens=8))
dbn2 = DBN(GaussianRBM(num_visibles=data_train_2.shape[0], num_hiddens=8))
dbn[0].learning_rate = 0.001
dbn1[0].learning_rate = 0.001
dbn2[0].learning_rate = 0.001
# train 1st layer
print 'training...\t',
dbn.train(data_train_0, num_epochs=5, batch_size=1, shuffle=False)
dbn1.train(data_train_1, num_epochs=5, batch_size=1, shuffle=False)
dbn2.train(data_train_2, num_epochs=5, batch_size=1, shuffle=False)
print '[DONE]'
data_test_0 = ((data_test.T)[:, ::3]).T
data_test_1 = ((data_test.T)[:, 1::3]).T
data_test_2 = ((data_test.T)[:, 2::3]).T
# global runtime
# sFilename = "data\\weightlogs"
# f = h5.File(sFileName+".hdf5")
# f.create_group(runtime)
# group.create_dataset('parameters',data=par)
#storeWeights(category,"data\\weightlogs", dbn[0].W, dbn[0].vsigma,dbn1[0].W, dbn1[0].vsigma,dbn2[0].W, dbn2[0].vsigma )
coff = 8
Ndat = 20 #data_test_0.shape[1]
Nsteps = 5
# evaluate 1st layer
print 'evaluating 1...\t',
dataout = zeros(x_ * y_)
# #datasub = zeros(x_*y_)
for point in xrange(Ndat):
#X = asmatrix(data_test_0[:,point]).T
X = asmatrix(data_test_0[:, -1]).T
#dataout = vstack((dataout,X.flatten()))
#print "testing:", X.shape
for recstep in xrange(Nsteps):
Y = dbn[0].forward(X) # self.activ(1)
X = dbn[0].backward(Y, X)
#print "S hsape:", X.shape
#dataout = vstack((dataout,X.flatten()))
dataout = vstack((dataout,
subtract(asarray(X), data_test_0[:, -1],
asarray(dbn[0].vsigma), point + 1)))
print 'evaluating 2...\t',
dataout1 = zeros(x_ * y_)
# #datasub = zeros(x_*y_)
for point in xrange(Ndat):
#X = asmatrix(data_test_1[:,point]).T
X = asmatrix(data_test_1[:, -1]).T
#dataout1 = vstack((dataout1,X.flatten()))
#print "testing:", X.shape
for recstep in xrange(Nsteps):
Y = dbn1[0].forward(X) # self.activ(1)
X = dbn1[0].backward(Y, X)
#print "S hsape:", X.shape
#dataout1 = vstack((dataout1,X.flatten()))
dataout1 = vstack((dataout1,
subtract(asarray(X), data_test_1[:, -1],
asarray(dbn1[0].vsigma), point + 1)))
print 'evaluating 3...\t',
dataout2 = zeros(x_ * y_)
# #datasub = zeros(x_*y_)
for point in xrange(Ndat):
#X = asmatrix(data_test_2[:,point]).T
X = asmatrix(data_test_2[:, -1]).T
#dataout2 = vstack((dataout2,X.flatten()))
#print "testing:", X.shape
for recstep in xrange(Nsteps):
Y = dbn2[0].forward(X) # self.activ(1)
X = dbn2[0].backward(Y, X)
#print "S hsape:", X.shape
#dataout2 = vstack((dataout2,X.flatten()))
dataout2 = vstack((dataout2,
subtract(asarray(X), data_test_2[:, -1],
asarray(dbn2[0].vsigma), point + 1)))
# plt.imshow((reshape(data_test[::3,5],(x_,y_))), cmap = cm.Greys_r, interpolation ="nearest")
# plt.axis('off')
# plt.show()
fmeasureV = zeros(Ndat)
plt.figure(1)
for i in range(Ndat):
plt.subplot(5, 5, i + 1)
d = multiply(asarray(dataout[i + 1, :]), asarray(dataout1[i + 1, :]),
asarray(dataout2[i + 1, :]))
d = mod(d + 1, 2)
open_img = ndimage.median_filter(reshape(d, (x_, y_)), coff)
d = open_img.flatten()
fmeasureV[i] = f_measure(d, groundtruth)
print "Image Example Fmeaure: ", i, " : ", fmeasureV[i] * 100
# d[0::3] = asarray(dataout[i+1,:])
# d[1::3] = asarray(dataout1[i+1,:])
# d[2::3] = asarray(dataout2[i+1,:])
# d[:,:,0] = (reshape(asarray(dataout[i+1,:]),(x_,y_)))
# d[:,:,1] = (reshape(asarray(dataout1[i+1,:]),(x_,y_)))
# d[:,:,2] = (reshape(asarray(dataout2[i+1,:]),(x_,y_)))
# img_s = Image.fromarray(asarray(reshape(d,(x_,y_))*255,dtype="uint8"))
# img_s.save("C:\work\\backgdSubt\dataset\datasets\change detection\\baseline\\baseline\office\grbm" + "\\" + str(i) + ".bmp")
plt.imshow(reshape(d, (x_, y_)),
cmap=cm.Greys_r,
interpolation="nearest")
plt.axis('off')
#plt.figure(2)
plt.show()
DIR2 = "C:\work\\backgdSubt\dataset\datasets\change detection\\nightvideos\\tramstation"
srcDir = DIR2 + "\input"
targetDir = DIR2 + "\grbm"
scalevaraince = input("Enter value: ") #argmax(fmeasureV) + 12
ii = 1
for k in os.listdir(srcDir):
if (ii < 500):
ii = ii + 1
continue
if (ii > 1749):
break
imf = Image.open(srcDir + '/' + k)
print array(imf).shape
data_test = (array(imf)).flatten()
#dataset = np.vstack((dataset,d))
data_test_0 = ((data_test)[::3]).T
data_test_1 = ((data_test)[1::3]).T
data_test_2 = ((data_test)[2::3]).T
Nsteps = 5
# evaluate 1st layer
print 'evaluating ', k, "\t"
# #datasub = zeros(x_*y_)
#for point in xrange(Ndat):
#X = asmatrix(data_test_0[:,point]).T
X = asmatrix(data_test_0).T
#dataout = vstack((dataout,X.flatten()))
#print "testing:", X.shape
for recstep in xrange(Nsteps):
Y = dbn[0].forward(X) # self.activ(1)
X = dbn[0].backward(Y, X)
#print "S hsape:", X.shape
#dataout = vstack((dataout,X.flatten()))
dataout = subtract(asarray(X), data_test_0, asarray(dbn[0].vsigma),
scalevaraince + 1)
#X = asmatrix(data_test_1[:,point]).T
X = asmatrix(data_test_1).T
#dataout1 = vstack((dataout1,X.flatten()))
#print "testing:", X.shape
for recstep in xrange(Nsteps):
Y = dbn1[0].forward(X) # self.activ(1)
X = dbn1[0].backward(Y, X)
#print "S hsape:", X.shape
#dataout1 = vstack((dataout1,X.flatten()))
dataout1 = subtract(asarray(X), data_test_1, asarray(dbn1[0].vsigma),
scalevaraince + 1)
X = asmatrix(data_test_2).T
#dataout2 = vstack((dataout2,X.flatten()))
#print "testing:", X.shape
for recstep in xrange(Nsteps):
Y = dbn2[0].forward(X) # self.activ(1)
X = dbn2[0].backward(Y, X)
#print "S hsape:", X.shape
#dataout2 = vstack((dataout2,X.flatten()))
# plt.imshow(reshape(X,(x_,y_)))
# plt.show()
dataout2 = subtract(asarray(X), data_test_2, asarray(dbn2[0].vsigma),
scalevaraince + 1)
# plt.imshow((reshape(data_test[::3,5],(x_,y_))), cmap = cm.Greys_r, interpolation ="nearest")
# plt.axis('off')
# plt.show()
# plt.figure(1)
# for i in range(Ndat):
# plt.subplot(5,5,i+1)
d = multiply(asarray(dataout), asarray(dataout1), asarray(dataout2))
d = mod(d + 1, 2)
open_img = ndimage.median_filter(
asarray(reshape(d, (x_, y_)) * 255, dtype="uint8"), coff)
#d = open_img.flatten()
#print type(d[0])
# #plt.savefig(targetDir + "\\" + str(ii) + ".png")
#print
img_s = Image.fromarray(open_img)
# plt.figure(2)
# plt.imshow(img_s)#,cmap = cm.Greys_r, interpolation ="nearest")
# plt.show()
if (ii < 1000):
img_s.save(targetDir + "\\bin000" + str(ii) + ".bmp")
else:
img_s.save(targetDir + "\\bin00" + str(ii) + ".bmp")
ii = ii + 1
def main(argv):
# load preprocessed data samples
print 'loading data...\t',
data = load('./data/vanhateren.npz')
print '[DONE]'
print
# remove DC component (first component)
data_train = data['train'][1:, :]
data_test = data['test'][1:, :]
# create 1st layer
dbn = DBN(GaussianRBM(num_visibles=data_train.shape[0], num_hiddens=100))
# hyperparameters
dbn[0].learning_rate = 5E-3
dbn[0].weight_decay = 1E-2
dbn[0].momentum = 0.9
dbn[0].sigma = 0.65
dbn[0].cd_steps = 1
dbn[0].persistent = True
# train 1st layer
print 'training...\t',
dbn.train(data_train, num_epochs=100, batch_size=100)
print '[DONE]'
# evaluate 1st layer
print 'evaluating...\t',
logptf = dbn.estimate_log_partition_function(num_ais_samples=100, beta_weights=arange(0, 1, 1E-3))
loglik = dbn.estimate_log_likelihood(data_test)
print '[DONE]'
print
print 'estimated log-partf.:\t', logptf
print 'estimated log-loss:\t', -loglik / data_test.shape[0] / log(2)
print
# create 2nd layer
dbn.add_layer(SemiRBM(num_visibles=100, num_hiddens=100))
# initialize parameters
dbn[1].L = dbn[0].W.T * dbn[0].W
dbn[1].b = dbn[0].W.T * dbn[0].b + dbn[0].c + 0.5 * asmatrix(diag(dbn[1].L)).T
dbn[1].L = dbn[1].L - asmatrix(diag(diag(dbn[1].L)))
# hyperparameters
dbn[1].learning_rate = 5E-3
dbn[1].learning_rate_lateral = 5E-4
dbn[1].weight_decay = 5E-3
dbn[1].weight_decay_lateral = 5E-3
dbn[1].momentum = 0.9
dbn[1].momentum_lateral = 0.9
dbn[1].num_lateral_updates = 20
dbn[1].damping = 0.2
dbn[1].cd_steps = 1
dbn[1].persistent = True
# train 2nd layer
print 'training...\t',
dbn.train(data_train, num_epochs=100, batch_size=100)
print '[DONE]'
# evaluate 2nd layer
print 'evaluating...\t',
logptf = dbn.estimate_log_partition_function(num_ais_samples=100, beta_weights=arange(0, 1, 1E-3))
loglik = dbn.estimate_log_likelihood(data_test, num_samples=100)
print '[DONE]'
print
print 'estimated log-partf.:\t', logptf
print 'estimated log-loss:\t', -loglik / data_test.shape[0] / log(2)
print
# fine-tune with wake-sleep
dbn[0].learning_rate /= 4.
dbn[1].learning_rate /= 4.
print 'fine-tuning...\t',
dbn.train_wake_sleep(data_train, num_epochs=10, batch_size=10)
print '[DONE]'
# reevaluate
print 'evaluating...\t',
logptf = dbn.estimate_log_partition_function(num_ais_samples=100, beta_weights=arange(0, 1, 1E-3))
loglik = dbn.estimate_log_likelihood(data_test, num_samples=100)
print '[DONE]'
print
print 'estimated log-partf.:\t', logptf
print 'estimated log-loss:\t', -loglik / data_test.shape[0] / log(2)
return 0
def main(argv):
# load preprocessed data samples
print 'loading data...\t',
#data_train, data_test = genData() #load('../data/vanhateren.npz')
DIR1 = "../data/changedetection/camerajitter/traffic"
data_train_0,data_train_1,data_train_2, data_test = load_data_BS(DIR1 + "/orignal_color.pkl")
img = Image.open(DIR1 + "/gt001537.png")
x_, y_ = (asarray(img)[:,:]).shape #determine procesing image size
print "Doie : " , x_,y_
groundtruth = (((asarray(img)[:,:])/255.0 > 0.5) * 1).flatten()
#groundtruth = (((asarray(img)[:,:])/255.0 > 0.5) * 1).flatten()
print '[DONE]'
# remove DC component (first component)
# data_train = data['train'][1:, :]
# data_test = data['test'][1:, :]
# create 1st layer
dbn = DBN(GaussianRBM(num_visibles=data_train_0.shape[0], num_hiddens=20))
dbn1 = DBN(GaussianRBM(num_visibles=data_train_1.shape[0], num_hiddens=20))
dbn2 = DBN(GaussianRBM(num_visibles=data_train_2.shape[0], num_hiddens=20))
dbn[0].learning_rate = 0.001
dbn1[0].learning_rate = 0.001
dbn2[0].learning_rate = 0.001
# train 1st layer
print 'training...\t',
dbn.train(data_train_0, num_epochs=1, batch_size=1,shuffle=False)
dbn1.train(data_train_1, num_epochs=1, batch_size=1,shuffle=False)
dbn2.train(data_train_2, num_epochs=1, batch_size=1,shuffle=False)
print '[DONE]'
data_test_0 = ((data_test.T)[:,::3]).T
data_test_1 = ((data_test.T)[:,1::3]).T
data_test_2 = ((data_test.T)[:,2::3]).T
# global runtime
# sFilename = "data\\weightlogs"
# f = h5.File(sFileName+".hdf5")
# f.create_group(runtime)
# group.create_dataset('parameters',data=par)
#storeWeights(category,"data\\weightlogs", dbn[0].W, dbn[0].vsigma,dbn1[0].W, dbn1[0].vsigma,dbn2[0].W, dbn2[0].vsigma )
Ndat = 20 #data_test_0.shape[1]
Nsteps = 5
# evaluate 1st layer
print 'evaluating 1...\t',
dataout = zeros(x_*y_)
# #datasub = zeros(x_*y_)
for point in xrange(Ndat):
#X = asmatrix(data_test_0[:,point]).T
X = asmatrix(data_test_0[:,-1]).T
#dataout = vstack((dataout,X.flatten()))
#print "testing:", X.shape
for recstep in xrange(Nsteps):
Y = dbn[0].forward(X) # self.activ(1)
X = dbn[0].backward(Y,X)
#print "S hsape:", X.shape
#dataout = vstack((dataout,X.flatten()))
dataout = vstack((dataout,subtract(asarray(X),data_test_0[:,-1],asarray(dbn[0].vsigma),point+1)))
print 'evaluating 2...\t',
dataout1 = zeros(x_*y_)
# #datasub = zeros(x_*y_)
for point in xrange(Ndat):
#X = asmatrix(data_test_1[:,point]).T
X = asmatrix(data_test_1[:,-1]).T
#dataout1 = vstack((dataout1,X.flatten()))
#print "testing:", X.shape
for recstep in xrange(Nsteps):
Y = dbn1[0].forward(X) # self.activ(1)
X = dbn1[0].backward(Y,X)
#print "S hsape:", X.shape
#dataout1 = vstack((dataout1,X.flatten()))
dataout1 = vstack((dataout1,subtract(asarray(X),data_test_1[:,-1],asarray(dbn1[0].vsigma),point+1)))
print 'evaluating 3...\t',
dataout2 = zeros(x_*y_)
# #datasub = zeros(x_*y_)
for point in xrange(Ndat):
#X = asmatrix(data_test_2[:,point]).T
X = asmatrix(data_test_2[:,-1]).T
#dataout2 = vstack((dataout2,X.flatten()))
#print "testing:", X.shape
for recstep in xrange(Nsteps):
Y = dbn2[0].forward(X) # self.activ(1)
X = dbn2[0].backward(Y,X)
#print "S hsape:", X.shape
#dataout2 = vstack((dataout2,X.flatten()))
dataout2 = vstack((dataout2,subtract(asarray(X),data_test_2[:,-1],asarray(dbn2[0].vsigma),point+1)))
# plt.imshow((reshape(data_test[::3,5],(x_,y_))), cmap = cm.Greys_r, interpolation ="nearest")
# plt.axis('off')
# plt.show()
fmeasureV = zeros(Ndat)
plt.figure(2)
for i in range(Ndat):
plt.subplot(5,5,i+1)
d = multiply(asarray(dataout[i+1,:]),asarray(dataout1[i+1,:]),asarray(dataout2[i+1,:]))
d = mod(d+1,2)
fmeasureV[i] = f_measure(d,groundtruth)
print "Image Example Fmeaure: ",i," : ", fmeasureV[i] * 100
# d[0::3] = asarray(dataout[i+1,:])
# d[1::3] = asarray(dataout1[i+1,:])
# d[2::3] = asarray(dataout2[i+1,:])
# d[:,:,0] = (reshape(asarray(dataout[i+1,:]),(x_,y_)))
# d[:,:,1] = (reshape(asarray(dataout1[i+1,:]),(x_,y_)))
# d[:,:,2] = (reshape(asarray(dataout2[i+1,:]),(x_,y_)))
# img_s = Image.fromarray(asarray(reshape(d,(x_,y_))*255,dtype="uint8"))
# img_s.save("C:\work\\backgdSubt\dataset\datasets\change detection\\baseline\\baseline\office\grbm" + "\\" + str(i) + ".bmp")
plt.imshow(reshape(d,(x_,y_)), cmap = cm.Greys_r, interpolation ="nearest")
plt.axis('off')
#plt.figure(2)
plt.show()
DIR2 = "../data/changedetection/camerajitter/traffic"
srcDir = DIR2 + "/input"
targetDir = DIR2 + "/grbm"
scalevaraince = input("Enter value: ") #argmax(fmeasureV) + 12
ii = 1
for k in os.listdir(srcDir):
imf = Image.open(srcDir + '/' + k)
print array(imf).shape
data_test = (array(imf)).flatten()
#dataset = np.vstack((dataset,d))
data_test_0 = ((data_test)[::3]).T
data_test_1 = ((data_test)[1::3]).T
data_test_2 = ((data_test)[2::3]).T
Nsteps = 5
# evaluate 1st layer
print 'evaluating 1...\t',
# #datasub = zeros(x_*y_)
#for point in xrange(Ndat):
#X = asmatrix(data_test_0[:,point]).T
X = asmatrix(data_test_0).T
#dataout = vstack((dataout,X.flatten()))
#print "testing:", X.shape
for recstep in xrange(Nsteps):
Y = dbn[0].forward(X) # self.activ(1)
X = dbn[0].backward(Y,X)
#print "S hsape:", X.shape
#dataout = vstack((dataout,X.flatten()))
dataout = subtract(asarray(X),data_test_0,asarray(dbn[0].vsigma),scalevaraince + 1 )
#X = asmatrix(data_test_1[:,point]).T
X = asmatrix(data_test_1).T
#dataout1 = vstack((dataout1,X.flatten()))
#print "testing:", X.shape
for recstep in xrange(Nsteps):
Y = dbn1[0].forward(X) # self.activ(1)
X = dbn1[0].backward(Y,X)
#print "S hsape:", X.shape
#dataout1 = vstack((dataout1,X.flatten()))
dataout1 = subtract(asarray(X),data_test_1,asarray(dbn1[0].vsigma),scalevaraince + 1)
X = asmatrix(data_test_2).T
#dataout2 = vstack((dataout2,X.flatten()))
#print "testing:", X.shape
for recstep in xrange(Nsteps):
Y = dbn2[0].forward(X) # self.activ(1)
X = dbn2[0].backward(Y,X)
#print "S hsape:", X.shape
#dataout2 = vstack((dataout2,X.flatten()))
# plt.imshow(reshape(X,(x_,y_)))
# plt.show()
dataout2 = subtract(asarray(X),data_test_2,asarray(dbn2[0].vsigma),scalevaraince + 1)
# plt.imshow((reshape(data_test[::3,5],(x_,y_))), cmap = cm.Greys_r, interpolation ="nearest")
# plt.axis('off')
# plt.show()
# plt.figure(1)
# for i in range(Ndat):
# plt.subplot(5,5,i+1)
d = multiply(asarray(dataout),asarray(dataout1),asarray(dataout2))
d = mod(d+1,2)
#print type(d[0])
# #plt.savefig(targetDir + "\\" + str(ii) + ".png")
#print
img_s = Image.fromarray(asarray(reshape(d,(x_,y_))*255,dtype="uint8"))
# plt.figure(2)
# plt.imshow(img_s)#,cmap = cm.Greys_r, interpolation ="nearest")
# plt.show()
if (ii <1000):
img_s.save(targetDir + "\\bin000" + str(ii) + ".bmp")
else:
img_s.save(targetDir + "\\bin00" + str(ii) + ".bmp")
ii = ii +1
def main(argv):
# load preprocessed data samples
print 'loading data...\t',
#data_train, data_test = genData() #load('../data/vanhateren.npz')
data_train_0, data_train_1, data_train_2, data_test = load_data_BS(
"./data/gf_dborignal_color.pkl")
img = Image.open("./data/gf_db/mensa_seq0_1.1/seq0_0000_0.jpg")
print "Doie : ", (asarray(img)[:, :]).shape
groundtruth = (((asarray(img)[:, :]) / 255.0 > 0.5) * 1).flatten()
#groundtruth = (((asarray(img)[:,:])/255.0 > 0.5) * 1).flatten()
print '[DONE]'
# remove DC component (first component)
# data_train = data['train'][1:, :]
# data_test = data['test'][1:, :]
# create 1st layer
dbn = DBN(GaussianRBM(num_visibles=data_train_0.shape[0], num_hiddens=8))
dbn1 = DBN(GaussianRBM(num_visibles=data_train_1.shape[0], num_hiddens=8))
dbn2 = DBN(GaussianRBM(num_visibles=data_train_2.shape[0], num_hiddens=8))
# train 1st layer
print 'training...\t',
dbn.train(data_train_0, num_epochs=100, batch_size=1, shuffle=False)
dbn1.train(data_train_1, num_epochs=100, batch_size=1, shuffle=False)
dbn2.train(data_train_2, num_epochs=100, batch_size=1, shuffle=False)
print '[DONE]'
data_test_0 = ((data_test.T)[:, ::3]).T
data_test_1 = ((data_test.T)[:, 1::3]).T
data_test_2 = ((data_test.T)[:, 2::3]).T
Ndat = 25 #data_test_0.shape[1]
Nsteps = 5
# evaluate 1st layer
print 'evaluating 1...\t',
dataout = zeros(x_ * y_)
# #datasub = zeros(x_*y_)
for point in xrange(Ndat):
#X = asmatrix(data_test_0[:,point]).T
X = asmatrix(data_test_0[:, -1]).T
#dataout = vstack((dataout,X.flatten()))
#print "testing:", X.shape
for recstep in xrange(Nsteps):
Y = dbn[0].forward(X) # self.activ(1)
X = dbn[0].backward(Y, X)
#print "S hsape:", X.shape
#dataout = vstack((dataout,X.flatten()))
dataout = vstack((dataout,
subtract(asarray(X), data_test_0[:, -1],
asarray(dbn[0].vsigma), point + 1)))
print 'evaluating 2...\t',
dataout1 = zeros(x_ * y_)
# #datasub = zeros(x_*y_)
for point in xrange(Ndat):
#X = asmatrix(data_test_1[:,point]).T
X = asmatrix(data_test_1[:, -1]).T
#dataout1 = vstack((dataout1,X.flatten()))
#print "testing:", X.shape
for recstep in xrange(Nsteps):
Y = dbn1[0].forward(X) # self.activ(1)
X = dbn1[0].backward(Y, X)
#print "S hsape:", X.shape
#dataout1 = vstack((dataout1,X.flatten()))
dataout1 = vstack((dataout1,
subtract(asarray(X), data_test_1[:, -1],
asarray(dbn1[0].vsigma), point + 1)))
print 'evaluating 3...\t',
dataout2 = zeros(x_ * y_)
# #datasub = zeros(x_*y_)
for point in xrange(Ndat):
#X = asmatrix(data_test_2[:,point]).T
X = asmatrix(data_test_2[:, -1]).T
#dataout2 = vstack((dataout2,X.flatten()))
#print "testing:", X.shape
for recstep in xrange(Nsteps):
Y = dbn2[0].forward(X) # self.activ(1)
X = dbn2[0].backward(Y, X)
#print "S hsape:", X.shape
#dataout2 = vstack((dataout2,X.flatten()))
dataout2 = vstack((dataout2,
subtract(asarray(X), data_test_2[:, -1],
asarray(dbn2[0].vsigma), point + 1)))
# plt.imshow((reshape(data_test[::3,5],(x_,y_))), cmap = cm.Greys_r, interpolation ="nearest")
# plt.axis('off')
# plt.show()
plt.figure(1)
for i in range(Ndat):
plt.subplot(5, 5, i + 1)
d = multiply(asarray(dataout[i + 1, :]), asarray(dataout1[i + 1, :]),
asarray(dataout2[i + 1, :]))
d = mod(d + 1, 2)
print "Image Example Fmeaure: ", i, " : ", f_measure(d,
groundtruth) * 100
# d[0::3] = asarray(dataout[i+1,:])
# d[1::3] = asarray(dataout1[i+1,:])
# d[2::3] = asarray(dataout2[i+1,:])
# d[:,:,0] = (reshape(asarray(dataout[i+1,:]),(x_,y_)))
# d[:,:,1] = (reshape(asarray(dataout1[i+1,:]),(x_,y_)))
# d[:,:,2] = (reshape(asarray(dataout2[i+1,:]),(x_,y_)))
plt.imshow(reshape(d, (x_, y_)),
cmap=cm.Greys_r,
interpolation="nearest")
plt.axis('off')
plt.figure(2)
for k in range(8):
plt.subplot(4, 2, k + 1)
d = zeros((x_ * y_ * 3))
d[0::3] = asarray(dbn[0].W[:, k].flatten())
d[1::3] = asarray(dbn1[0].W[:, k].flatten())
d[2::3] = asarray(dbn2[0].W[:, k].flatten())
plt.imshow(reshape(
d, (x_, y_, 3))) #, cmap = cm.Greys_r, interpolation ="nearest")
plt.axis('off')
# plt.figure()
# plt.imshow((reshape(dbn[0].vsigma[:19200],(x_,y_))))
# plt.figure(2)
# plt.imshow((reshape(dbn[0].vsigma[19200:19200*2],(x_,y_))))
# plt.figure(3)
# plt.imshow((reshape(dbn[0].vsigma[19200*2:19200*3],(x_,y_))))
plt.figure(3)
print type(dbn[0].vsigma)
plt.imshow(reshape(asarray(dbn[0].vsigma), (x_, y_)))
plt.show()
print dbn[0].vsigma
p.show()
def main(argv):
# load preprocessed data samples
print 'loading data...\t',
#data_train, data_test = genData() #load('../data/vanhateren.npz')
data_train, data_test = load_data_BS("data/wavingtree/imagedata_blr_01.pkl")
print '[DONE]'
# remove DC component (first component)
# data_train = data['train'][1:, :]
# data_test = data['test'][1:, :]
# create 1st layer
dbn = DBN(GaussianRBM(num_visibles=data_train.shape[0], num_hiddens=8))
# hyperparameters
dbn[0].learning_rate = 0.001
#dbn[0].weight_decay = 1E-2
dbn[0].momentum = 0.9
dbn[0].sigma = 0.07
dbn[0].cd_steps = 15
# dbn[0].persistent = Trueahsanrafique
# train 1st layer
print 'training...\t',
dbn.train(data_train, num_epochs=20, batch_size=1,shuffle=False)
print '[DONE]'
Ndat = data_test.shape[1]
Nsteps = 1
# evaluate 1st layer
print 'evaluating...\t',
dataout = zeros(120*160)
#datasub = zeros(120*160)
for point in xrange(Ndat):
X = asmatrix(data_test[:,point]).T
dataout = vstack((dataout,X.flatten()))
#print "testing:", X.shape
for recstep in xrange(Nsteps):
Y = dbn[0].forward(X) # self.activ(1)
X = dbn[0].backward(Y,X)
#print "S hsape:", X.shape
dataout = vstack((dataout,X.flatten()))
dataout = vstack((dataout,subtract(asarray(X),data_test[:,point],asarray(dbn[0].vsigma))))
# plt.imshow((reshape(data_test[:,point],(120,160))), cmap = cm.Greys_r, interpolation ="nearest")
# plt.axis('off')
# plt.figure(2)
for i in range(10*3):
plt.subplot(10,3,i+1)
plt.imshow((reshape(dataout[i+1,:],(120,160))), cmap = cm.Greys_r, interpolation ="nearest")
plt.axis('off')
plt.figure(2)
for k in range(8):
plt.subplot(4,2,k+1)
plt.imshow((reshape(dbn[0].W[:,k],(120,160))), cmap = cm.Greys_r, interpolation ="nearest")
plt.axis('off')
plt.figure(3)
plt.imshow((reshape(dbn[0].vsigma,(120,160))), cmap = cm.Greys_r, interpolation ="nearest")
plt.show()
# p.figure(1)
# p.plot(data_train[0,:],data_train[1,:], 'b.')
# p.axis([0.0, 1.0, 0.0, 1.0])
# p.figure(2)
# p.plot(data_test[0,:],data_test[1,:], 'b.')
# p.axis([0.0, 1.0, 0.0, 1.0])
# p.figure(3)
# p.plot(datout[0,:],datout[1,:], 'b.')
# p.axis([0.0, 1.0, 0.0, 1.0])
# p.figure(4)
# p.plot(datout[0,:],datout[1,:], 'b.')
#p.axis([0.0, 1.0, 0.0, 1.0])
#p.figure(4)
#p.hist(datout)
print dbn[0].vsigma
p.show()
def main(argv):
# load preprocessed data samples
print 'loading data...\t',
#data_train, data_test = genData() #load('../data/vanhateren.npz')
# data_train_0,data_train_1,data_train_2, data_test = load_data_BS("data/changedetection\intermitentmotion\streetlight/orignal_color.pkl")
img = Image.open("data/changedetection\ptz\continuous/gt000962.png")
print "Doie : ", (asarray(img)[:, :]).shape
groundtruth = (((asarray(img)[:, :]) / 255.0 > 0.5) * 1).flatten()
# #groundtruth = (((asarray(img)[:,:])/255.0 > 0.5) * 1).flatten()
# print '[DONE]'
batches = 3
# remove DC component (first component)
# data_train = data['train'][1:, :]
# data_test = data['test'][1:, :]
# create 1st layer
dbn = DBN(GaussianRBM(num_visibles=x_ * y_, num_hiddens=15))
dbn1 = DBN(GaussianRBM(num_visibles=x_ * y_, num_hiddens=15))
dbn2 = DBN(GaussianRBM(num_visibles=x_ * y_, num_hiddens=15))
dbn[0].learning_rate = 0.0001
dbn1[0].learning_rate = 0.0001
dbn2[0].learning_rate = 0.0001
# train 1st layer
print 'training...\t',
for k in range(2):
print "epoc :", k
for i in range(batches):
global batch_no
batch_no = i
os.path.walk(
'C:\work\\backgdSubt\dataset\changedetection\ptz\continuous\\orignal',
load_batch, 0)
global train_set_x0
global train_set_x1
global train_set_x2
dbn.train(train_set_x0.T,
num_epochs=1,
batch_size=1,
shuffle=False)
dbn1.train(train_set_x1.T,
num_epochs=1,
batch_size=1,
shuffle=False)
dbn2.train(train_set_x2.T,
num_epochs=1,
batch_size=1,
shuffle=False)
print '[DONE]'
os.path.walk(
'C:\work\\backgdSubt\dataset\changedetection\ptz\continuous\\test',
load_test_batch, 0)
data_test_0 = ((data_test.T)[:, ::3]).T
data_test_1 = ((data_test.T)[:, 1::3]).T
data_test_2 = ((data_test.T)[:, 2::3]).T
Ndat = 25 #data_test_0.shape[1]
Nsteps = 5
# evaluate 1st layer
print 'evaluating 1...\t',
dataout = zeros(x_ * y_)
# #datasub = zeros(x_*y_)
for point in xrange(Ndat):
#X = asmatrix(data_test_0[:,point]).T
X = asmatrix(data_test_0[:, -1]).T
#dataout = vstack((dataout,X.flatten()))
#print "testing:", X.shape
for recstep in xrange(Nsteps):
Y = dbn[0].forward(X) # self.activ(1)
X = dbn[0].backward(Y, X)
#print "S hsape:", X.shape
#dataout = vstack((dataout,X.flatten()))
dataout = vstack((dataout,
subtract(asarray(X), data_test_0[:, -1],
asarray(dbn[0].vsigma), point + 1)))
print 'evaluating 2...\t',
dataout1 = zeros(x_ * y_)
# #datasub = zeros(x_*y_)
for point in xrange(Ndat):
#X = asmatrix(data_test_1[:,point]).T
X = asmatrix(data_test_1[:, -1]).T
#dataout1 = vstack((dataout1,X.flatten()))
#print "testing:", X.shape
for recstep in xrange(Nsteps):
Y = dbn1[0].forward(X) # self.activ(1)
X = dbn1[0].backward(Y, X)
#print "S hsape:", X.shape
#dataout1 = vstack((dataout1,X.flatten()))
dataout1 = vstack((dataout1,
subtract(asarray(X), data_test_1[:, -1],
asarray(dbn1[0].vsigma), point + 1)))
print 'evaluating 3...\t',
dataout2 = zeros(x_ * y_)
# #datasub = zeros(x_*y_)
for point in xrange(Ndat):
#X = asmatrix(data_test_2[:,point]).T
X = asmatrix(data_test_2[:, -1]).T
#dataout2 = vstack((dataout2,X.flatten()))
#print "testing:", X.shape
for recstep in xrange(Nsteps):
Y = dbn2[0].forward(X) # self.activ(1)
X = dbn2[0].backward(Y, X)
#print "S hsape:", X.shape
#dataout2 = vstack((dataout2,X.flatten()))
dataout2 = vstack((dataout2,
subtract(asarray(X), data_test_2[:, -1],
asarray(dbn2[0].vsigma), point + 1)))
# plt.imshow((reshape(data_test[::3,5],(x_,y_))), cmap = cm.Greys_r, interpolation ="nearest")
# plt.axis('off')
# plt.show()
plt.figure(1)
for i in range(Ndat):
plt.subplot(5, 5, i + 1)
d = multiply(asarray(dataout[i + 1, :]), asarray(dataout1[i + 1, :]),
asarray(dataout2[i + 1, :]))
d = mod(d + 1, 2)
#print "Image Example Fmeaure: ",i," : ", f_measure(d,groundtruth) * 100
# d[0::3] = asarray(dataout[i+1,:])
# d[1::3] = asarray(dataout1[i+1,:])
# d[2::3] = asarray(dataout2[i+1,:])
# d[:,:,0] = (reshape(asarray(dataout[i+1,:]),(x_,y_)))
# d[:,:,1] = (reshape(asarray(dataout1[i+1,:]),(x_,y_)))
# d[:,:,2] = (reshape(asarray(dataout2[i+1,:]),(x_,y_)))
plt.imshow(reshape(d, (x_, y_)),
cmap=cm.Greys_r,
interpolation="nearest")
plt.axis('off')
plt.figure(2)
for k in range(15):
plt.subplot(5, 3, k + 1)
d = zeros((x_ * y_ * 3))
d[0::3] = asarray(dbn[0].W[:, k].flatten())
d[1::3] = asarray(dbn1[0].W[:, k].flatten())
d[2::3] = asarray(dbn2[0].W[:, k].flatten())
plt.imshow(reshape(d[0::3], (x_, y_)),
cmap=cm.Greys_r,
interpolation="nearest")
plt.axis('off')
# plt.figure()
# plt.imshow((reshape(dbn[0].vsigma[:19200],(x_,y_))))
# plt.figure(2)
# plt.imshow((reshape(dbn[0].vsigma[19200:19200*2],(x_,y_))))
# plt.figure(3)
# plt.imshow((reshape(dbn[0].vsigma[19200*2:19200*3],(x_,y_))))
plt.figure(3)
print type(dbn[0].vsigma)
plt.imshow(reshape(asarray(dbn[0].vsigma), (x_, y_)))
plt.show()
print dbn[0].vsigma
p.show()