def __init__(self, get_optimizer, n_x, n_hidden_q, n_z, n_hidden_p, nonlinear_q='tanh', nonlinear_p='tanh', type_px='bernoulli', type_qz='gaussianmarg', type_pz='gaussianmarg', prior_sd=1, init_sd=1e-2, var_smoothing=0, n_mixture=50):
self.constr = (__name__, inspect.stack()[0][3], locals())
self.n_x = n_x
self.n_hidden_q = n_hidden_q
self.n_z = n_z
self.n_hidden_p = n_hidden_p
self.dropout = False
self.nonlinear_q = nonlinear_q
self.nonlinear_p = nonlinear_p
self.type_px = type_px
self.type_qz = type_qz
self.type_pz = type_pz
self.prior_sd = prior_sd
self.var_smoothing = var_smoothing
self.n_mixture = n_mixture
self.train_residual = False
if os.environ.has_key('train_residual') and bool(int(os.environ['train_residual'])) == True:
self.train_residual = True
color.printBlue('Train residual wrt prior instead of the whole model.')
self.sigma_square = 1
if os.environ.has_key('sigma_square'):
self.sigma_square = float(os.environ['sigma_square'])
color.printBlue('Sigma_square of the prior:', self.sigma_square)
# Init weights
v, w = self.init_w(1e-2)
for i in v: v[i] = shared32(v[i])
for i in w: w[i] = shared32(w[i])
self.v = v
self.w = w
super(GPUVAE_Z_X, self).__init__(get_optimizer)
def __init__(self,
get_optimizer,
n_x,
n_hidden_q,
n_z,
n_hidden_p,
nonlinear_q='tanh',
nonlinear_p='tanh',
type_px='bernoulli',
type_qz='gaussianmarg',
type_pz='gaussianmarg',
prior_sd=1,
init_sd=1e-2,
var_smoothing=0,
n_mixture=50):
self.constr = (__name__, inspect.stack()[0][3], locals())
self.n_x = n_x
self.n_hidden_q = n_hidden_q
self.n_z = n_z
self.n_hidden_p = n_hidden_p
self.dropout = False
self.nonlinear_q = nonlinear_q
self.nonlinear_p = nonlinear_p
self.type_px = type_px
self.type_qz = type_qz
self.type_pz = type_pz
self.prior_sd = prior_sd
self.var_smoothing = var_smoothing
self.n_mixture = n_mixture
self.train_residual = False
if os.environ.has_key('train_residual') and bool(
int(os.environ['train_residual'])) == True:
self.train_residual = True
color.printBlue(
'Train residual wrt prior instead of the whole model.')
self.sigma_square = 1
if os.environ.has_key('sigma_square'):
self.sigma_square = float(os.environ['sigma_square'])
color.printBlue('Sigma_square of the prior:', self.sigma_square)
# Init weights
v, w = self.init_w(1e-2)
for i in v:
v[i] = shared32(v[i])
for i in w:
w[i] = shared32(w[i])
self.v = v
self.w = w
super(GPUVAE_Z_X, self).__init__(get_optimizer)
def installSoftware():
# os.system(clear) #错了姐姐 #我没有！ #好好好
inCtrl=0
while inCtrl==0:
def steam():
print("downloading steam...")
ad="https://media.st.dl.bscstorage.net/client/installer/SteamSetup.exe"
download.downld(ad)
color.printYellow("完成！")
return 0;
#name="SteamSetup.exe" #想不到从ad上直接截下来的方法 好像能用split 害
#download.install(name) #失败
def vscode():
print("downloading vscode for win32-x64...")
ad="https://vscode.cdn.azure.cn/stable/86405ea23e3937316009fc27c9361deee66ffbf5/VSCodeUserSetup-x64-1.40.0.exe"
download.downld(ad)
color.printYellow("完成！")
return 0;
def Github_Desktop():
print("downloading Github_Desktop...")
ad="https://desktop.githubusercontent.com/releases/2.2.3-3e4755f1/GitHubDesktopSetup.exe"
download.downld(ad)
color.printYellow("完成！")
return 0;
def typora():
print("downloading typora...")
#ad="https://www.typora.io/windows/typora-setup-x64.exe"
#download.downld(ad)
#color.printYellow("完成！")
color.printYellow("Sorry，该网站禁止爬虫...")
return 0;
color.printBlue("请选择你要安装的软件")
color.printGreen("a steam\nb vscode \nc Github Desktop \nd typora\n\nq finish")
option2 = {
'a': steam,
'b': vscode,
'c': Github_Desktop,
'd': typora,
'q': finish
}
optionInput = input("")
if optionInput != 'a'and optionInput != 'b'and optionInput != 'c'and optionInput != 'd'and optionInput != 'q' :
color.printYellow("你是猪ma？")
else:
exe2=option2[optionInput]
inCtrl=exe2()
return 0
def main(n_z, n_hidden, dataset, seed, gfx=True, _size=None):
'''Learn a variational auto-encoder with generative model p(x,y,z)=p(y)p(z)p(x|y,z).
x and y are (always) observed.
I.e. this cannot be used for semi-supervised learning
'''
assert (type(n_hidden) == tuple or type(n_hidden) == list)
assert type(n_z) == int
assert isinstance(dataset, basestring)
print 'gpulearn_yz_x', n_z, n_hidden, dataset, seed
comment = ''
if os.environ.has_key('prior') and bool(int(os.environ['prior'])) == True:
comment += 'prior-'
if os.environ.has_key('default') and bool(int(os.environ['default'])) == True:
comment += 'default-'
else:
comment += 'not_default-'
import time
logdir = 'results/gpulearn_yz_x_'+dataset+'_'+str(n_z)+'-'+str(n_hidden)+comment+'-'+str(int(time.time()))+'/'
if not os.path.exists(logdir): os.makedirs(logdir)
print 'logdir:', logdir
np.random.seed(seed)
# Init data
if dataset == 'mnist':
'''
What works well:
100-2-100 (Generated digits stay bit shady)
1000-2-1000 (Needs pretty long training)
'''
import anglepy.data.mnist as mnist
# MNIST
size = 28
train_x, train_y, valid_x, valid_y, test_x, test_y = mnist.load_numpy(size, binarize_y=True)
f_enc, f_dec = lambda x:x, lambda x:x
if os.environ.has_key('prior') and bool(int(os.environ['prior'])) == True:
color.printBlue('Loading prior')
mnist_prior = sio.loadmat('data/mnist_prior/mnist_prior.mat')
train_mean_prior = mnist_prior['z_train']
valid_mean_prior = mnist_prior['z_valid']
else:
train_mean_prior = np.zeros((n_z,train_x.shape[1]))
valid_mean_prior = np.zeros((n_z,valid_x.shape[1]))
x = {'x': train_x[:,:].astype(np.float32), 'mean_prior': train_mean_prior.astype(np.float32), 'y': train_y[:,:].astype(np.float32)}
x_valid = {'x': valid_x.astype(np.float32), 'mean_prior': valid_mean_prior.astype(np.float32),'y': valid_y.astype(np.float32)}
L_valid = 1
dim_input = (size,size)
n_x = size*size
n_y = 10
n_batch = 1000
colorImg = False
bernoulli_x = True
byteToFloat = False
mosaic_w = 5
mosaic_h = 2
type_px = 'bernoulli'
#print 'Network Structure:', n_z,
elif dataset == 'mnist_basic':
# MNIST
size = 28
data_dir = os.environ['ML_DATA_PATH']+'/mnist_variations/'+'mnist_'
tmp = sio.loadmat(data_dir+'train.mat')
#color.printRed(data_dir+'train.mat')
train_x = tmp['x_train'].T
train_y = tmp['t_train'].T.astype(np.int32)
# validation 2000
valid_x = train_x[:,10000:]
valid_y = train_y[10000:]
train_x = train_x[:,:10000]
train_y = train_y[:10000]
tmp = sio.loadmat(data_dir+'test.mat')
test_x = tmp['x_test'].T
test_y = tmp['t_test'].T.astype(np.int32)
print train_x.shape
print train_y.shape
print test_x.shape
print test_y.shape
f_enc, f_dec = pp.Identity()
train_mean_prior = np.zeros((n_z,train_x.shape[1]))
test_mean_prior = np.zeros((n_z,test_x.shape[1]))
valid_mean_prior = np.zeros((n_z,valid_x.shape[1]))
'''
x = {'x': train_x.astype(np.float32), 'y': labelToMat(train_y).astype(np.float32)}
x_train = x
x_valid = {'x': valid_x.astype(np.float32), 'y': labelToMat(valid_y).astype(np.float32)}
x_test = {'x': test_x.astype(np.float32), 'y': labelToMat(test_y).astype(np.float32)}
'''
x = {'x': train_x.astype(np.float32), 'mean_prior': train_mean_prior.astype(np.float32), 'y': labelToMat(train_y).astype(np.float32)}
x_train = x
x_valid = {'x': valid_x.astype(np.float32), 'mean_prior': valid_mean_prior.astype(np.float32), 'y': labelToMat(valid_y).astype(np.float32)}
x_test = {'x': test_x.astype(np.float32), 'mean_prior': test_mean_prior.astype(np.float32), 'y': labelToMat(test_y).astype(np.float32)}
L_valid = 1
dim_input = (size,size)
n_x = size*size
n_y = 10
type_qz = 'gaussianmarg'
type_pz = 'gaussianmarg'
nonlinear = 'softplus'
type_px = 'bernoulli'
n_train = 10000
n_valid = 2000
n_test = 50000
n_batch = 200
colorImg = False
bernoulli_x = True
byteToFloat = False
weight_decay = float(n_batch)/n_train
elif dataset == 'rectangle':
# MNIST
size = 28
data_dir = os.environ['ML_DATA_PATH']+'/mnist_variations/'+'rectangles_'
tmp = sio.loadmat(data_dir+'train.mat')
color.printRed(data_dir+'train.mat')
train_x = tmp['x_train'].T
train_y = tmp['t_train'].T.astype(np.int32)
# validation 2000
valid_x = train_x[:,1000:]
valid_y = train_y[1000:]
train_x = train_x[:,:1000]
train_y = train_y[:1000]
tmp = sio.loadmat(data_dir+'test.mat')
test_x = tmp['x_test'].T
test_y = tmp['t_test'].T.astype(np.int32)
print train_x.shape
print train_y.shape
print test_x.shape
print test_y.shape
f_enc, f_dec = pp.Identity()
train_mean_prior = np.zeros((n_z,train_x.shape[1]))
test_mean_prior = np.zeros((n_z,test_x.shape[1]))
valid_mean_prior = np.zeros((n_z,valid_x.shape[1]))
'''
x = {'x': train_x.astype(np.float32), 'y': labelToMat(train_y).astype(np.float32)}
x_train = x
x_valid = {'x': valid_x.astype(np.float32), 'y': labelToMat(valid_y).astype(np.float32)}
x_test = {'x': test_x.astype(np.float32), 'y': labelToMat(test_y).astype(np.float32)}
'''
x = {'x': train_x.astype(np.float32), 'mean_prior': train_mean_prior.astype(np.float32), 'y': labelToMat(train_y).astype(np.float32)}
x_train = x
x_valid = {'x': valid_x.astype(np.float32), 'mean_prior': valid_mean_prior.astype(np.float32), 'y': labelToMat(valid_y).astype(np.float32)}
x_test = {'x': test_x.astype(np.float32), 'mean_prior': test_mean_prior.astype(np.float32), 'y': labelToMat(test_y).astype(np.float32)}
L_valid = 1
dim_input = (size,size)
n_x = size*size
n_y = 2
type_qz = 'gaussianmarg'
type_pz = 'gaussianmarg'
nonlinear = 'softplus'
type_px = 'bernoulli'
n_train = 1000
n_valid = 200
n_test = 50000
n_batch = 500
colorImg = False
bernoulli_x = True
byteToFloat = False
weight_decay = float(n_batch)/n_train
elif dataset == 'convex':
# MNIST
size = 28
data_dir = os.environ['ML_DATA_PATH']+'/mnist_variations/'+'convex_'
tmp = sio.loadmat(data_dir+'train.mat')
train_x = tmp['x_train'].T
train_y = tmp['t_train'].T.astype(np.int32)
# validation 2000
valid_x = train_x[:,6000:]
valid_y = train_y[6000:]
train_x = train_x[:,:6000]
train_y = train_y[:6000]
tmp = sio.loadmat(data_dir+'test.mat')
test_x = tmp['x_test'].T
test_y = tmp['t_test'].T.astype(np.int32)
print train_x.shape
print train_y.shape
print test_x.shape
print test_y.shape
f_enc, f_dec = pp.Identity()
train_mean_prior = np.zeros((n_z,train_x.shape[1]))
test_mean_prior = np.zeros((n_z,test_x.shape[1]))
valid_mean_prior = np.zeros((n_z,valid_x.shape[1]))
'''
x = {'x': train_x.astype(np.float32), 'y': labelToMat(train_y).astype(np.float32)}
x_train = x
x_valid = {'x': valid_x.astype(np.float32), 'y': labelToMat(valid_y).astype(np.float32)}
x_test = {'x': test_x.astype(np.float32), 'y': labelToMat(test_y).astype(np.float32)}
'''
x = {'x': train_x.astype(np.float32), 'mean_prior': train_mean_prior.astype(np.float32), 'y': labelToMat(train_y).astype(np.float32)}
x_train = x
x_valid = {'x': valid_x.astype(np.float32), 'mean_prior': valid_mean_prior.astype(np.float32), 'y': labelToMat(valid_y).astype(np.float32)}
x_test = {'x': test_x.astype(np.float32), 'mean_prior': test_mean_prior.astype(np.float32), 'y': labelToMat(test_y).astype(np.float32)}
L_valid = 1
dim_input = (size,size)
n_x = size*size
n_y = 2
type_qz = 'gaussianmarg'
type_pz = 'gaussianmarg'
nonlinear = 'softplus'
type_px = 'bernoulli'
n_train = 6000
n_valid = 2000
n_test = 50000
n_batch = 120
colorImg = False
bernoulli_x = True
byteToFloat = False
weight_decay = float(n_batch)/n_train
elif dataset == 'rectangle_image':
# MNIST
size = 28
data_dir = os.environ['ML_DATA_PATH']+'/mnist_variations/'+'rectangles_im_'
tmp = sio.loadmat(data_dir+'train.mat')
train_x = tmp['x_train'].T
train_y = tmp['t_train'].T.astype(np.int32)
# validation 2000
valid_x = train_x[:,10000:]
valid_y = train_y[10000:]
train_x = train_x[:,:10000]
train_y = train_y[:10000]
tmp = sio.loadmat(data_dir+'test.mat')
test_x = tmp['x_test'].T
test_y = tmp['t_test'].T.astype(np.int32)
print train_x.shape
print train_y.shape
print test_x.shape
print test_y.shape
f_enc, f_dec = pp.Identity()
train_mean_prior = np.zeros((n_z,train_x.shape[1]))
test_mean_prior = np.zeros((n_z,test_x.shape[1]))
valid_mean_prior = np.zeros((n_z,valid_x.shape[1]))
'''
x = {'x': train_x.astype(np.float32), 'y': labelToMat(train_y).astype(np.float32)}
x_train = x
x_valid = {'x': valid_x.astype(np.float32), 'y': labelToMat(valid_y).astype(np.float32)}
x_test = {'x': test_x.astype(np.float32), 'y': labelToMat(test_y).astype(np.float32)}
'''
x = {'x': train_x.astype(np.float32), 'mean_prior': train_mean_prior.astype(np.float32), 'y': labelToMat(train_y).astype(np.float32)}
x_train = x
x_valid = {'x': valid_x.astype(np.float32), 'mean_prior': valid_mean_prior.astype(np.float32), 'y': labelToMat(valid_y).astype(np.float32)}
x_test = {'x': test_x.astype(np.float32), 'mean_prior': test_mean_prior.astype(np.float32), 'y': labelToMat(test_y).astype(np.float32)}
L_valid = 1
dim_input = (size,size)
n_x = size*size
n_y = 2
type_qz = 'gaussianmarg'
type_pz = 'gaussianmarg'
nonlinear = 'softplus'
type_px = 'bernoulli'
n_train = 10000
n_valid = 2000
n_test = 50000
n_batch = 200
colorImg = False
bernoulli_x = True
byteToFloat = False
weight_decay = float(n_batch)/n_train
elif dataset == 'mnist_rot':
# MNIST
size = 28
data_dir = os.environ['ML_DATA_PATH']+'/mnist_variations/'+'mnist_all_rotation_normalized_float_'
tmp = sio.loadmat(data_dir+'train.mat')
train_x = tmp['x_train'].T
train_y = tmp['t_train'].T.astype(np.int32)
# validation 2000
valid_x = train_x[:,10000:]
valid_y = train_y[10000:]
train_x = train_x[:,:10000]
train_y = train_y[:10000]
tmp = sio.loadmat(data_dir+'test.mat')
test_x = tmp['x_test'].T
test_y = tmp['t_test'].T.astype(np.int32)
print train_x.shape
print train_y.shape
print test_x.shape
print test_y.shape
f_enc, f_dec = pp.Identity()
train_mean_prior = np.zeros((n_z,train_x.shape[1]))
test_mean_prior = np.zeros((n_z,test_x.shape[1]))
valid_mean_prior = np.zeros((n_z,valid_x.shape[1]))
'''
x = {'x': train_x.astype(np.float32), 'y': labelToMat(train_y).astype(np.float32)}
x_train = x
x_valid = {'x': valid_x.astype(np.float32), 'y': labelToMat(valid_y).astype(np.float32)}
x_test = {'x': test_x.astype(np.float32), 'y': labelToMat(test_y).astype(np.float32)}
'''
x = {'x': train_x.astype(np.float32), 'mean_prior': train_mean_prior.astype(np.float32), 'y': labelToMat(train_y).astype(np.float32)}
x_train = x
x_valid = {'x': valid_x.astype(np.float32), 'mean_prior': valid_mean_prior.astype(np.float32), 'y': labelToMat(valid_y).astype(np.float32)}
x_test = {'x': test_x.astype(np.float32), 'mean_prior': test_mean_prior.astype(np.float32), 'y': labelToMat(test_y).astype(np.float32)}
L_valid = 1
dim_input = (size,size)
n_x = size*size
n_y = 10
type_qz = 'gaussianmarg'
type_pz = 'gaussianmarg'
nonlinear = 'softplus'
type_px = 'bernoulli'
n_train = 10000
n_valid = 2000
n_test = 50000
n_batch = 200
colorImg = False
bernoulli_x = True
byteToFloat = False
weight_decay = float(n_batch)/n_train
elif dataset == 'mnist_back_rand':
# MNIST
size = 28
data_dir = os.environ['ML_DATA_PATH']+'/mnist_variations/'+'mnist_background_random_'
tmp = sio.loadmat(data_dir+'train.mat')
train_x = tmp['x_train'].T
train_y = tmp['t_train'].T.astype(np.int32)
# validation 2000
valid_x = train_x[:,10000:]
valid_y = train_y[10000:]
train_x = train_x[:,:10000]
train_y = train_y[:10000]
tmp = sio.loadmat(data_dir+'test.mat')
test_x = tmp['x_test'].T
test_y = tmp['t_test'].T.astype(np.int32)
print train_x.shape
print train_y.shape
print test_x.shape
print test_y.shape
f_enc, f_dec = pp.Identity()
train_mean_prior = np.zeros((n_z,train_x.shape[1]))
test_mean_prior = np.zeros((n_z,test_x.shape[1]))
valid_mean_prior = np.zeros((n_z,valid_x.shape[1]))
'''
x = {'x': train_x.astype(np.float32), 'y': labelToMat(train_y).astype(np.float32)}
x_train = x
x_valid = {'x': valid_x.astype(np.float32), 'y': labelToMat(valid_y).astype(np.float32)}
x_test = {'x': test_x.astype(np.float32), 'y': labelToMat(test_y).astype(np.float32)}
'''
x = {'x': train_x.astype(np.float32), 'mean_prior': train_mean_prior.astype(np.float32), 'y': labelToMat(train_y).astype(np.float32)}
x_train = x
x_valid = {'x': valid_x.astype(np.float32), 'mean_prior': valid_mean_prior.astype(np.float32), 'y': labelToMat(valid_y).astype(np.float32)}
x_test = {'x': test_x.astype(np.float32), 'mean_prior': test_mean_prior.astype(np.float32), 'y': labelToMat(test_y).astype(np.float32)}
L_valid = 1
dim_input = (size,size)
n_x = size*size
n_y = 10
type_qz = 'gaussianmarg'
type_pz = 'gaussianmarg'
nonlinear = 'softplus'
type_px = 'bernoulli'
n_train = 10000
n_valid = 2000
n_test = 50000
n_batch = 200
colorImg = False
bernoulli_x = True
byteToFloat = False
weight_decay = float(n_batch)/n_train
elif dataset == 'mnist_back_image':
# MNIST
size = 28
data_dir = os.environ['ML_DATA_PATH']+'/mnist_variations/'+'mnist_background_images_'
tmp = sio.loadmat(data_dir+'train.mat')
train_x = tmp['x_train'].T
train_y = tmp['t_train'].T.astype(np.int32)
# validation 2000
valid_x = train_x[:,10000:]
valid_y = train_y[10000:]
train_x = train_x[:,:10000]
train_y = train_y[:10000]
tmp = sio.loadmat(data_dir+'test.mat')
test_x = tmp['x_test'].T
test_y = tmp['t_test'].T.astype(np.int32)
print train_x.shape
print train_y.shape
print test_x.shape
print test_y.shape
f_enc, f_dec = pp.Identity()
train_mean_prior = np.zeros((n_z,train_x.shape[1]))
test_mean_prior = np.zeros((n_z,test_x.shape[1]))
valid_mean_prior = np.zeros((n_z,valid_x.shape[1]))
'''
x = {'x': train_x.astype(np.float32), 'y': labelToMat(train_y).astype(np.float32)}
x_train = x
x_valid = {'x': valid_x.astype(np.float32), 'y': labelToMat(valid_y).astype(np.float32)}
x_test = {'x': test_x.astype(np.float32), 'y': labelToMat(test_y).astype(np.float32)}
'''
x = {'x': train_x.astype(np.float32), 'mean_prior': train_mean_prior.astype(np.float32), 'y': labelToMat(train_y).astype(np.float32)}
x_train = x
x_valid = {'x': valid_x.astype(np.float32), 'mean_prior': valid_mean_prior.astype(np.float32), 'y': labelToMat(valid_y).astype(np.float32)}
x_test = {'x': test_x.astype(np.float32), 'mean_prior': test_mean_prior.astype(np.float32), 'y': labelToMat(test_y).astype(np.float32)}
L_valid = 1
dim_input = (size,size)
n_x = size*size
n_y = 10
type_qz = 'gaussianmarg'
type_pz = 'gaussianmarg'
nonlinear = 'softplus'
type_px = 'bernoulli'
n_train = 10000
n_valid = 2000
n_test = 50000
n_batch = 200
colorImg = False
bernoulli_x = True
byteToFloat = False
weight_decay = float(n_batch)/n_train
elif dataset == 'mnist_back_image_rot':
# MNIST
size = 28
data_dir = os.environ['ML_DATA_PATH']+'/mnist_variations/'+'mnist_all_background_images_rotation_normalized_'
tmp = sio.loadmat(data_dir+'train.mat')
train_x = tmp['x_train'].T
train_y = tmp['t_train'].T.astype(np.int32)
# validation 2000
valid_x = train_x[:,10000:]
valid_y = train_y[10000:]
train_x = train_x[:,:10000]
train_y = train_y[:10000]
tmp = sio.loadmat(data_dir+'test.mat')
test_x = tmp['x_test'].T
test_y = tmp['t_test'].T.astype(np.int32)
print train_x.shape
print train_y.shape
print test_x.shape
print test_y.shape
f_enc, f_dec = pp.Identity()
train_mean_prior = np.zeros((n_z,train_x.shape[1]))
test_mean_prior = np.zeros((n_z,test_x.shape[1]))
valid_mean_prior = np.zeros((n_z,valid_x.shape[1]))
'''
x = {'x': train_x.astype(np.float32), 'y': labelToMat(train_y).astype(np.float32)}
x_train = x
x_valid = {'x': valid_x.astype(np.float32), 'y': labelToMat(valid_y).astype(np.float32)}
x_test = {'x': test_x.astype(np.float32), 'y': labelToMat(test_y).astype(np.float32)}
'''
x = {'x': train_x.astype(np.float32), 'mean_prior': train_mean_prior.astype(np.float32), 'y': labelToMat(train_y).astype(np.float32)}
x_train = x
x_valid = {'x': valid_x.astype(np.float32), 'mean_prior': valid_mean_prior.astype(np.float32), 'y': labelToMat(valid_y).astype(np.float32)}
x_test = {'x': test_x.astype(np.float32), 'mean_prior': test_mean_prior.astype(np.float32), 'y': labelToMat(test_y).astype(np.float32)}
L_valid = 1
dim_input = (size,size)
n_x = size*size
n_y = 10
type_qz = 'gaussianmarg'
type_pz = 'gaussianmarg'
nonlinear = 'softplus'
type_px = 'bernoulli'
n_train = 10000
n_valid = 2000
n_test = 50000
n_batch = 200
colorImg = False
bernoulli_x = True
byteToFloat = False
weight_decay = float(n_batch)/n_train
else:
raise Exception('Unknown dataset.')
# Init model
n_hidden_q = n_hidden
n_hidden_p = n_hidden
from anglepy.models import GPUVAE_YZ_X
if os.environ.has_key('default') and bool(int(os.environ['default'])) == True:
updates = get_adam_optimizer(alpha=3e-4, beta1=0.9, beta2=0.999, weight_decay=0)
else:
updates = get_adam_optimizer(alpha=3e-4, beta1=0.1, beta2=0.001, weight_decay=1000.0/50000.0)
model = GPUVAE_YZ_X(updates, n_x, n_y, n_hidden_q, n_z, n_hidden_p[::-1], 'softplus', 'softplus', type_px=type_px, type_qz='gaussianmarg', type_pz='gaussianmarg', prior_sd=1, uniform_y=True)
if False:
dir = '/home/ubuntu/results/gpulearn_yz_x_svhn_300-(500, 500)-1414094291/'
dir = '/home/ubuntu/results/gpulearn_yz_x_svhn_300-(500, 500)-1414163488/'
w = ndict.loadz(dir+'w_best.ndict.tar.gz')
v = ndict.loadz(dir+'v_best.ndict.tar.gz')
ndict.set_value(model.w, w)
ndict.set_value(model.v, v)
# Some statistics for optimization
ll_valid_stats = [-1e99, 0]
# Fixed sample for visualisation
z_sample = {'z': np.repeat(np.random.standard_normal(size=(n_z, 12)), 12, axis=1).astype(np.float32)}
y_sample = {'y': np.tile(np.random.multinomial(1, [1./n_y]*n_y, size=12).T, (1, 12))}
# Progress hook
def hook(epoch, t, ll):
if epoch%10 != 0:
return
ll_valid, _ = model.est_loglik(x_valid, n_samples=L_valid, n_batch=n_batch, byteToFloat=byteToFloat)
if math.isnan(ll_valid):
print "NaN detected. Reverting to saved best parameters"
ndict.set_value(model.v, ndict.loadz(logdir+'v.ndict.tar.gz'))
ndict.set_value(model.w, ndict.loadz(logdir+'w.ndict.tar.gz'))
return
if ll_valid > ll_valid_stats[0]:
ll_valid_stats[0] = ll_valid
ll_valid_stats[1] = 0
ndict.savez(ndict.get_value(model.v), logdir+'v_best')
ndict.savez(ndict.get_value(model.w), logdir+'w_best')
else:
ll_valid_stats[1] += 1
# Stop when not improving validation set performance in 100 iterations
if False and ll_valid_stats[1] > 1000:
print "Finished"
with open(logdir+'hook.txt', 'a') as f:
print >>f, "Finished"
exit()
# Log
ndict.savez(ndict.get_value(model.v), logdir+'v')
ndict.savez(ndict.get_value(model.w), logdir+'w')
print epoch, t, ll, ll_valid
with open(logdir+'hook.txt', 'a') as f:
print >>f, t, ll, ll_valid
if gfx:
# Graphics
v = {i: model.v[i].get_value() for i in model.v}
w = {i: model.w[i].get_value() for i in model.w}
tail = '-'+str(epoch)+'.png'
image = paramgraphics.mat_to_img(f_dec(v['w0x'][:].T), dim_input, True, colorImg=colorImg)
image.save(logdir+'q_w0x'+tail, 'PNG')
image = paramgraphics.mat_to_img(f_dec(w['out_w'][:]), dim_input, True, colorImg=colorImg)
image.save(logdir+'out_w'+tail, 'PNG')
_x = {'y': np.random.multinomial(1, [1./n_y]*n_y, size=144).T}
_, _, _z_confab = model.gen_xz(_x, {}, n_batch=144)
image = paramgraphics.mat_to_img(f_dec(_z_confab['x']), dim_input, colorImg=colorImg)
image.save(logdir+'samples'+tail, 'PNG')
_, _, _z_confab = model.gen_xz(y_sample, z_sample, n_batch=144)
image = paramgraphics.mat_to_img(f_dec(_z_confab['x']), dim_input, colorImg=colorImg)
image.save(logdir+'samples_fixed'+tail, 'PNG')
if n_z == 2:
import ImageFont
import ImageDraw
n_width = 10
submosaic_offset = 15
submosaic_width = (dim_input[1]*n_width)
submosaic_height = (dim_input[0]*n_width)
mosaic = Image.new("RGB", (submosaic_width*mosaic_w, submosaic_offset+submosaic_height*mosaic_h))
for digit in range(0,n_y):
if digit >= mosaic_h*mosaic_w: continue
_x = {}
n_batch_plot = n_width*n_width
_x['y'] = np.zeros((n_y,n_batch_plot))
_x['y'][digit,:] = 1
_z = {'z':np.zeros((2,n_width**2))}
for i in range(0,n_width):
for j in range(0,n_width):
_z['z'][0,n_width*i+j] = scipy.stats.norm.ppf(float(i)/n_width+0.5/n_width)
_z['z'][1,n_width*i+j] = scipy.stats.norm.ppf(float(j)/n_width+0.5/n_width)
_x, _, _z_confab = model.gen_xz(_x, _z, n_batch=n_batch_plot)
x_samples = _z_confab['x']
image = paramgraphics.mat_to_img(f_dec(x_samples), dim_input, colorImg=colorImg, tile_spacing=(0,0))
#image.save(logdir+'samples_digit_'+str(digit)+'_'+tail, 'PNG')
mosaic_x = (digit%mosaic_w)*submosaic_width
mosaic_y = submosaic_offset+int(digit/mosaic_w)*submosaic_height
mosaic.paste(image, (mosaic_x, mosaic_y))
draw = ImageDraw.Draw(mosaic)
draw.text((1,1),"Epoch #"+str(epoch)+" Loss="+str(int(ll)))
#plt.savefig(logdir+'mosaic'+tail, format='PNG')
mosaic.save(logdir+'mosaic'+tail, 'PNG')
#x_samples = _x['x']
#image = paramgraphics.mat_to_img(f_dec(x_samples), dim_input, colorImg=colorImg)
#image.save(logdir+'samples2'+tail, 'PNG')
# Optimize
dostep = epoch_vae_adam(model, x, n_batch=n_batch, bernoulli_x=bernoulli_x, byteToFloat=byteToFloat)
loop_va(dostep, hook)
pass
# Load MNIST data
dataset = sys.argv[1]
dir = 'models/mnist_yz_x_50-500-500/'
if len(sys.argv) >= 3:
dir = sys.argv[2]
print dir
if dataset == 'mnist':
import anglepy.data.mnist as mnist
_, train_y, _, _, test_x, test_y = mnist.load_numpy(size=28, binarize_y=False)
if os.environ.has_key('prior') and bool(int(os.environ['prior'])) == True:
color.printBlue('Have informative prior')
n_z = 96
mnist_prior = sio.loadmat('data/mnist_prior/mnist_prior.mat')
prior_type = os.environ['prior_type']
color.printBlue('Prior type: '+prior_type)
if prior_type == 'too_strong':
test_mean_prior = mnist_prior['z_test']
elif prior_type == 'naive':
test_mean_prior = np.zeros((n_z,test_x.shape[1]))
elif prior_type == 'train_mean':
train_mean = np.mean(mnist_prior['z_train'], 1)
test_mean_prior = np.tile(train_mean[:,np.newaxis], [1,test_x.shape[1]])
elif prior_type == 'test_mean':
test_mean = np.mean(mnist_prior['z_test'], 1)
test_mean_prior = np.tile(test_mean[:,np.newaxis], [1,test_x.shape[1]])
def main(n_z, n_hidden, dataset, seed, comment, gfx=True):
# Initialize logdir
import time
pre_dir = 'models/gpulearn_z_x_mnist_96-(500, 500)'
if os.environ.has_key('pretrain') and bool(int(os.environ['pretrain'])) == True:
comment+='_pre-train'
if os.environ.has_key('prior') and bool(int(os.environ['prior'])) == True:
comment+='_prior'
pre_dir+='_prior'
if os.environ.has_key('cutoff'):
comment+=('_'+str(int(os.environ['cutoff'])))
if os.environ.has_key('train_residual') and bool(int(os.environ['train_residual'])) == True:
comment+='_train-residual'
pre_dir+='_train-residual'
if os.environ.has_key('sigma_square'):
comment+=('_'+str(float(os.environ['sigma_square'])))
pre_dir+=('_'+str(float(os.environ['sigma_square'])))
pre_dir+='/'
logdir = 'results/gpulearn_z_x_'+dataset+'_'+str(n_z)+'-'+str(n_hidden)+comment+'_'+str(int(time.time()))+'/'
if not os.path.exists(logdir): os.makedirs(logdir)
print 'logdir:', logdir
print 'gpulearn_z_x', n_z, n_hidden, dataset, seed
with open(logdir+'hook.txt', 'a') as f:
print >>f, 'learn_z_x', n_z, n_hidden, dataset, seed
np.random.seed(seed)
gfx_freq = 1
weight_decay = 0
# Init data
if dataset == 'mnist':
import anglepy.data.mnist as mnist
# MNIST
size = 28
train_x, train_y, valid_x, valid_y, test_x, test_y = mnist.load_numpy(size)
f_enc, f_dec = pp.Identity()
if os.environ.has_key('prior') and bool(int(os.environ['prior'])) == True:
color.printBlue('Loading prior')
mnist_prior = sio.loadmat('data/mnist_prior/mnist_prior.mat')
train_mean_prior = mnist_prior['z_train']
test_mean_prior = mnist_prior['z_test']
valid_mean_prior = mnist_prior['z_valid']
else:
train_mean_prior = np.zeros((n_z,train_x.shape[1]))
test_mean_prior = np.zeros((n_z,test_x.shape[1]))
valid_mean_prior = np.zeros((n_z,valid_x.shape[1]))
print '---------------------', type(train_x)
x = {'x': train_x.astype(np.float32), 'mean_prior': train_mean_prior.astype(np.float32)}
x_train = x
x_valid = {'x': valid_x.astype(np.float32), 'mean_prior': valid_mean_prior.astype(np.float32)}
x_test = {'x': test_x.astype(np.float32), 'mean_prior': test_mean_prior.astype(np.float32)}
print '---------------------', type(x_train)
L_valid = 1
dim_input = (size,size)
n_x = size*size
type_qz = 'gaussianmarg'
type_pz = 'gaussianmarg'
nonlinear = 'softplus'
type_px = 'bernoulli'
n_train = 50000
n_test = 10000
n_valid = 10000
n_batch = 1000
colorImg = False
bernoulli_x = True
byteToFloat = False
weight_decay = float(n_batch)/n_train
elif dataset == 'higgs':
size = 28
f_enc, f_dec = pp.Identity()
inputfile = 'data/higgs/HIGGS.csv'
print 'loading file.'
x = np.loadtxt(inputfile, dtype='f4', delimiter=',')
print 'done.'
y = x[:,0].reshape((-1,1))
x = x[:,1:]
x = np.array(x, dtype='float32')
y = np.array(y, dtype='float32')
n_train = 10000000
n_valid = 500000
n_test = 500000
n_batch = 1000
derived_feat = 'all'
if os.environ.has_key('derived_feat'):
derived_feat = os.environ['derived_feat']
color.printBlue(derived_feat)
if derived_feat == 'high':
# Only the 7 high level features.
x = x[:, 21:28]
elif derived_feat == 'low':
# Only the 21 raw features.
x = x[:, 0:21]
else:
pass
train_x = x[0:n_train, :].T
y_train = y[0:n_train, :]
valid_x = x[n_train:n_train+n_valid, :].T
y_valid = y[n_train:n_train+n_valid, :]
test_x = x[n_train+n_valid:n_train+n_valid+n_test, :].T
y_test = y[n_train+n_valid:n_train+n_valid+n_test, :]
n_y = 2
n_x = train_x.shape[0]
train_mean_prior = np.zeros((n_z,train_x.shape[1]))
test_mean_prior = np.zeros((n_z,test_x.shape[1]))
valid_mean_prior = np.zeros((n_z,valid_x.shape[1]))
x = {'x': train_x.astype(np.float32), 'mean_prior': train_mean_prior.astype(np.float32)}
x_train = x
x_valid = {'x': valid_x.astype(np.float32), 'mean_prior': valid_mean_prior.astype(np.float32)}
x_test = {'x': test_x.astype(np.float32), 'mean_prior': test_mean_prior.astype(np.float32)}
type_qz = 'gaussianmarg'
type_pz = 'gaussianmarg'
nonlinear = 'tanh'
if os.environ.has_key('nonlinear'):
nonlinear = os.environ['nonlinear']
color.printBlue(nonlinear)
L_valid = 1
dim_input = (1,size)
type_px = 'gaussian'
colorImg = False
bernoulli_x = False
byteToFloat = False
weight_decay = float(n_batch)/n_train
elif dataset == 'cifar10':
import anglepy.data.cifar10 as cifar10
size = 32
train_x, train_y, test_x, test_y = cifar10.load_numpy()
train_x = train_x.astype(np.float32).T
test_x = test_x.astype(np.float32).T
##
f_enc, f_dec = pp.Identity()
if os.environ.has_key('prior') and bool(int(os.environ['prior'])) == True:
color.printBlue('Loading prior')
cifar_prior = sio.loadmat('data/cifar10_prior/cifar10_prior.mat')
train_mean_prior = cifar_prior['z_train']
test_mean_prior = cifar_prior['z_test']
else:
train_mean_prior = np.zeros((n_z,train_x.shape[1]))
test_mean_prior = np.zeros((n_z,test_x.shape[1]))
x = {'x': train_x.astype(np.float32), 'mean_prior': train_mean_prior.astype(np.float32)}
x_train = x
x_test = {'x': test_x.astype(np.float32), 'mean_prior': test_mean_prior.astype(np.float32)}
x_valid = x_test
L_valid = 1
n_y = 10
dim_input = (size,size)
n_x = x['x'].shape[0]
type_qz = 'gaussianmarg'
type_pz = 'gaussianmarg'
nonlinear = 'softplus'
type_px = 'gaussian'
if os.environ.has_key('type_px'):
type_px = os.environ['type_px']
color.printBlue('Generative type: '+type_px)
n_train = 50000
n_test = 10000
n_batch = 5000
colorImg = True
bernoulli_x = False
byteToFloat = False
#weight_decay = float(n_batch)/n_train
elif dataset == 'cifar10_zca':
import anglepy.data.cifar10 as cifar10
size = 32
train_x, train_y, test_x, test_y = cifar10.load_numpy()
train_x = train_x.astype(np.float32).T
test_x = test_x.astype(np.float32).T
##
f_enc, f_dec = pp.Identity()
zca_mean, zca_w, zca_winv = cifar10.zca(train_x)
train_x = zca_w.dot(train_x-zca_mean)
test_x = zca_w.dot(test_x-zca_mean)
if os.environ.has_key('prior') and bool(int(os.environ['prior'])) == True:
color.printBlue('Loading prior')
cifar_prior = sio.loadmat('data/cifar10_prior/cifar10_prior.mat')
train_mean_prior = cifar_prior['z_train']
test_mean_prior = cifar_prior['z_test']
else:
train_mean_prior = np.zeros((n_z,train_x.shape[1]))
test_mean_prior = np.zeros((n_z,test_x.shape[1]))
x = {'x': train_x.astype(np.float32), 'mean_prior': train_mean_prior.astype(np.float32)}
x_train = x
x_test = {'x': test_x.astype(np.float32), 'mean_prior': test_mean_prior.astype(np.float32)}
x_valid = x_test
L_valid = 1
dim_input = (size,size)
n_y = 10
n_x = x['x'].shape[0]
type_qz = 'gaussianmarg'
type_pz = 'gaussianmarg'
nonlinear = 'softplus'
type_px = 'gaussian'
n_train = 50000
n_test = 10000
n_batch = 5000
colorImg = True
bernoulli_x = False
byteToFloat = False
if os.environ.has_key('type_px'):
type_px = os.environ['type_px']
color.printBlue('Generative type: '+type_px)
nonlinear = 'softplus'
elif dataset == 'mnist_basic':
# MNIST
size = 28
data_dir = os.environ['ML_DATA_PATH']+'/mnist_variations/'+'mnist_'
tmp = sio.loadmat(data_dir+'train.mat')
#color.printRed(data_dir+'train.mat')
train_x = tmp['x_train'].T
train_y = tmp['t_train'].T.astype(np.int32)
# validation 2000
valid_x = train_x[:,10000:]
valid_y = train_y[10000:]
train_x = train_x[:,:10000]
train_y = train_y[:10000]
tmp = sio.loadmat(data_dir+'test.mat')
test_x = tmp['x_test'].T
test_y = tmp['t_test'].T.astype(np.int32)
print train_x.shape
print train_y.shape
print test_x.shape
print test_y.shape
f_enc, f_dec = pp.Identity()
train_mean_prior = np.zeros((n_z,train_x.shape[1]))
test_mean_prior = np.zeros((n_z,test_x.shape[1]))
valid_mean_prior = np.zeros((n_z,valid_x.shape[1]))
'''
x = {'x': train_x.astype(np.float32), 'y': labelToMat(train_y).astype(np.float32)}
x_train = x
x_valid = {'x': valid_x.astype(np.float32), 'y': labelToMat(valid_y).astype(np.float32)}
x_test = {'x': test_x.astype(np.float32), 'y': labelToMat(test_y).astype(np.float32)}
'''
x = {'x': train_x.astype(np.float32), 'mean_prior': train_mean_prior.astype(np.float32)}
x_train = x
x_valid = {'x': valid_x.astype(np.float32), 'mean_prior': valid_mean_prior.astype(np.float32)}
x_test = {'x': test_x.astype(np.float32), 'mean_prior': test_mean_prior.astype(np.float32)}
L_valid = 1
dim_input = (size,size)
n_x = size*size
n_y = 10
type_qz = 'gaussianmarg'
type_pz = 'gaussianmarg'
nonlinear = 'softplus'
type_px = 'bernoulli'
n_train = 10000
n_valid = 2000
n_test = 50000
n_batch = 200
colorImg = False
bernoulli_x = True
byteToFloat = False
weight_decay = float(n_batch)/n_train
elif dataset == 'rectangle':
# MNIST
size = 28
data_dir = os.environ['ML_DATA_PATH']+'/mnist_variations/'+'rectangles_'
tmp = sio.loadmat(data_dir+'train.mat')
color.printRed(data_dir+'train.mat')
train_x = tmp['x_train'].T
train_y = tmp['t_train'].T.astype(np.int32)
# validation 2000
valid_x = train_x[:,1000:]
valid_y = train_y[1000:]
train_x = train_x[:,:1000]
train_y = train_y[:1000]
tmp = sio.loadmat(data_dir+'test.mat')
test_x = tmp['x_test'].T
test_y = tmp['t_test'].T.astype(np.int32)
print train_x.shape
print train_y.shape
print test_x.shape
print test_y.shape
f_enc, f_dec = pp.Identity()
train_mean_prior = np.zeros((n_z,train_x.shape[1]))
test_mean_prior = np.zeros((n_z,test_x.shape[1]))
valid_mean_prior = np.zeros((n_z,valid_x.shape[1]))
'''
x = {'x': train_x.astype(np.float32), 'y': labelToMat(train_y).astype(np.float32)}
x_train = x
x_valid = {'x': valid_x.astype(np.float32), 'y': labelToMat(valid_y).astype(np.float32)}
x_test = {'x': test_x.astype(np.float32), 'y': labelToMat(test_y).astype(np.float32)}
'''
x = {'x': train_x.astype(np.float32), 'mean_prior': train_mean_prior.astype(np.float32)}
x_train = x
x_valid = {'x': valid_x.astype(np.float32), 'mean_prior': valid_mean_prior.astype(np.float32)}
x_test = {'x': test_x.astype(np.float32), 'mean_prior': test_mean_prior.astype(np.float32)}
L_valid = 1
dim_input = (size,size)
n_x = size*size
n_y = 2
type_qz = 'gaussianmarg'
type_pz = 'gaussianmarg'
nonlinear = 'softplus'
type_px = 'bernoulli'
n_train = 1000
n_valid = 200
n_test = 50000
n_batch = 500
colorImg = False
bernoulli_x = True
byteToFloat = False
weight_decay = float(n_batch)/n_train
#print '3', n_x
elif dataset == 'convex':
# MNIST
size = 28
data_dir = os.environ['ML_DATA_PATH']+'/mnist_variations/'+'convex_'
tmp = sio.loadmat(data_dir+'train.mat')
train_x = tmp['x_train'].T
train_y = tmp['t_train'].T.astype(np.int32)
# validation 2000
valid_x = train_x[:,6000:]
valid_y = train_y[6000:]
train_x = train_x[:,:6000]
train_y = train_y[:6000]
tmp = sio.loadmat(data_dir+'test.mat')
test_x = tmp['x_test'].T
test_y = tmp['t_test'].T.astype(np.int32)
print train_x.shape
print train_y.shape
print test_x.shape
print test_y.shape
f_enc, f_dec = pp.Identity()
train_mean_prior = np.zeros((n_z,train_x.shape[1]))
test_mean_prior = np.zeros((n_z,test_x.shape[1]))
valid_mean_prior = np.zeros((n_z,valid_x.shape[1]))
'''
x = {'x': train_x.astype(np.float32), 'y': labelToMat(train_y).astype(np.float32)}
x_train = x
x_valid = {'x': valid_x.astype(np.float32), 'y': labelToMat(valid_y).astype(np.float32)}
x_test = {'x': test_x.astype(np.float32), 'y': labelToMat(test_y).astype(np.float32)}
'''
x = {'x': train_x.astype(np.float32), 'mean_prior': train_mean_prior.astype(np.float32)}
x_train = x
x_valid = {'x': valid_x.astype(np.float32), 'mean_prior': valid_mean_prior.astype(np.float32)}
x_test = {'x': test_x.astype(np.float32), 'mean_prior': test_mean_prior.astype(np.float32)}
L_valid = 1
dim_input = (size,size)
n_x = size*size
n_y = 2
type_qz = 'gaussianmarg'
type_pz = 'gaussianmarg'
nonlinear = 'softplus'
type_px = 'bernoulli'
n_train = 6000
n_valid = 2000
n_test = 50000
n_batch = 120
colorImg = False
bernoulli_x = True
byteToFloat = False
weight_decay = float(n_batch)/n_train
elif dataset == 'rectangle_image':
# MNIST
size = 28
data_dir = os.environ['ML_DATA_PATH']+'/mnist_variations/'+'rectangles_im_'
tmp = sio.loadmat(data_dir+'train.mat')
train_x = tmp['x_train'].T
train_y = tmp['t_train'].T.astype(np.int32)
# validation 2000
valid_x = train_x[:,10000:]
valid_y = train_y[10000:]
train_x = train_x[:,:10000]
train_y = train_y[:10000]
tmp = sio.loadmat(data_dir+'test.mat')
test_x = tmp['x_test'].T
test_y = tmp['t_test'].T.astype(np.int32)
print train_x.shape
print train_y.shape
print test_x.shape
print test_y.shape
f_enc, f_dec = pp.Identity()
train_mean_prior = np.zeros((n_z,train_x.shape[1]))
test_mean_prior = np.zeros((n_z,test_x.shape[1]))
valid_mean_prior = np.zeros((n_z,valid_x.shape[1]))
'''
x = {'x': train_x.astype(np.float32), 'y': labelToMat(train_y).astype(np.float32)}
x_train = x
x_valid = {'x': valid_x.astype(np.float32), 'y': labelToMat(valid_y).astype(np.float32)}
x_test = {'x': test_x.astype(np.float32), 'y': labelToMat(test_y).astype(np.float32)}
'''
x = {'x': train_x.astype(np.float32), 'mean_prior': train_mean_prior.astype(np.float32)}
x_train = x
x_valid = {'x': valid_x.astype(np.float32), 'mean_prior': valid_mean_prior.astype(np.float32)}
x_test = {'x': test_x.astype(np.float32), 'mean_prior': test_mean_prior.astype(np.float32)}
L_valid = 1
dim_input = (size,size)
n_x = size*size
n_y = 2
type_qz = 'gaussianmarg'
type_pz = 'gaussianmarg'
nonlinear = 'softplus'
type_px = 'bernoulli'
n_train = 10000
n_valid = 2000
n_test = 50000
n_batch = 200
colorImg = False
bernoulli_x = True
byteToFloat = False
weight_decay = float(n_batch)/n_train
elif dataset == 'mnist_rot':
# MNIST
size = 28
data_dir = os.environ['ML_DATA_PATH']+'/mnist_variations/'+'mnist_all_rotation_normalized_float_'
tmp = sio.loadmat(data_dir+'train.mat')
train_x = tmp['x_train'].T
train_y = tmp['t_train'].T.astype(np.int32)
# validation 2000
valid_x = train_x[:,10000:]
valid_y = train_y[10000:]
train_x = train_x[:,:10000]
train_y = train_y[:10000]
tmp = sio.loadmat(data_dir+'test.mat')
test_x = tmp['x_test'].T
test_y = tmp['t_test'].T.astype(np.int32)
print train_x.shape
print train_y.shape
print test_x.shape
print test_y.shape
train_mean_prior = np.zeros((n_z,train_x.shape[1]))
test_mean_prior = np.zeros((n_z,test_x.shape[1]))
valid_mean_prior = np.zeros((n_z,valid_x.shape[1]))
f_enc, f_dec = pp.Identity()
x = {'x': train_x.astype(np.float32), 'mean_prior': train_mean_prior.astype(np.float32)}
x_train = x
x_valid = {'x': valid_x.astype(np.float32), 'mean_prior': valid_mean_prior.astype(np.float32)}
x_test = {'x': test_x.astype(np.float32), 'mean_prior': test_mean_prior.astype(np.float32)}
L_valid = 1
dim_input = (size,size)
n_x = size*size
n_y = 10
type_qz = 'gaussianmarg'
type_pz = 'gaussianmarg'
nonlinear = 'softplus'
type_px = 'bernoulli'
n_train = 10000
n_valid = 2000
n_test = 50000
n_batch = 200
colorImg = False
bernoulli_x = True
byteToFloat = False
weight_decay = float(n_batch)/n_train
elif dataset == 'mnist_back_rand':
# MNIST
size = 28
data_dir = os.environ['ML_DATA_PATH']+'/mnist_variations/'+'mnist_background_random_'
tmp = sio.loadmat(data_dir+'train.mat')
train_x = tmp['x_train'].T
train_y = tmp['t_train'].T.astype(np.int32)
# validation 2000
valid_x = train_x[:,10000:]
valid_y = train_y[10000:]
train_x = train_x[:,:10000]
train_y = train_y[:10000]
tmp = sio.loadmat(data_dir+'test.mat')
test_x = tmp['x_test'].T
test_y = tmp['t_test'].T.astype(np.int32)
print train_x.shape
print train_y.shape
print test_x.shape
print test_y.shape
train_mean_prior = np.zeros((n_z,train_x.shape[1]))
test_mean_prior = np.zeros((n_z,test_x.shape[1]))
valid_mean_prior = np.zeros((n_z,valid_x.shape[1]))
f_enc, f_dec = pp.Identity()
x = {'x': train_x.astype(np.float32), 'mean_prior': train_mean_prior.astype(np.float32)}
x_train = x
x_valid = {'x': valid_x.astype(np.float32), 'mean_prior': valid_mean_prior.astype(np.float32)}
x_test = {'x': test_x.astype(np.float32), 'mean_prior': test_mean_prior.astype(np.float32)}
L_valid = 1
dim_input = (size,size)
n_x = size*size
n_y = 10
type_qz = 'gaussianmarg'
type_pz = 'gaussianmarg'
nonlinear = 'softplus'
type_px = 'bernoulli'
n_train = 10000
n_valid = 2000
n_test = 50000
n_batch = 200
colorImg = False
bernoulli_x = True
byteToFloat = False
weight_decay = float(n_batch)/n_train
elif dataset == 'mnist_back_image':
# MNIST
size = 28
data_dir = os.environ['ML_DATA_PATH']+'/mnist_variations/'+'mnist_background_images_'
tmp = sio.loadmat(data_dir+'train.mat')
train_x = tmp['x_train'].T
train_y = tmp['t_train'].T.astype(np.int32)
# validation 2000
valid_x = train_x[:,10000:]
valid_y = train_y[10000:]
train_x = train_x[:,:10000]
train_y = train_y[:10000]
tmp = sio.loadmat(data_dir+'test.mat')
test_x = tmp['x_test'].T
test_y = tmp['t_test'].T.astype(np.int32)
print train_x.shape
print train_y.shape
print test_x.shape
print test_y.shape
train_mean_prior = np.zeros((n_z,train_x.shape[1]))
test_mean_prior = np.zeros((n_z,test_x.shape[1]))
valid_mean_prior = np.zeros((n_z,valid_x.shape[1]))
f_enc, f_dec = pp.Identity()
x = {'x': train_x.astype(np.float32), 'mean_prior': train_mean_prior.astype(np.float32)}
x_train = x
x_valid = {'x': valid_x.astype(np.float32), 'mean_prior': valid_mean_prior.astype(np.float32)}
x_test = {'x': test_x.astype(np.float32), 'mean_prior': test_mean_prior.astype(np.float32)}
L_valid = 1
dim_input = (size,size)
n_x = size*size
n_y = 10
type_qz = 'gaussianmarg'
type_pz = 'gaussianmarg'
nonlinear = 'softplus'
type_px = 'bernoulli'
n_train = 10000
n_valid = 2000
n_test = 50000
n_batch = 200
colorImg = False
bernoulli_x = True
byteToFloat = False
weight_decay = float(n_batch)/n_train
elif dataset == 'mnist_back_image_rot':
# MNIST
size = 28
data_dir = os.environ['ML_DATA_PATH']+'/mnist_variations/'+'mnist_all_background_images_rotation_normalized_'
tmp = sio.loadmat(data_dir+'train.mat')
train_x = tmp['x_train'].T
train_y = tmp['t_train'].T.astype(np.int32)
# validation 2000
valid_x = train_x[:,10000:]
valid_y = train_y[10000:]
train_x = train_x[:,:10000]
train_y = train_y[:10000]
tmp = sio.loadmat(data_dir+'test.mat')
test_x = tmp['x_test'].T
test_y = tmp['t_test'].T.astype(np.int32)
print train_x.shape
print train_y.shape
print test_x.shape
print test_y.shape
train_mean_prior = np.zeros((n_z,train_x.shape[1]))
test_mean_prior = np.zeros((n_z,test_x.shape[1]))
valid_mean_prior = np.zeros((n_z,valid_x.shape[1]))
f_enc, f_dec = pp.Identity()
x = {'x': train_x.astype(np.float32), 'mean_prior': train_mean_prior.astype(np.float32)}
x_train = x
x_valid = {'x': valid_x.astype(np.float32), 'mean_prior': valid_mean_prior.astype(np.float32)}
x_test = {'x': test_x.astype(np.float32), 'mean_prior': test_mean_prior.astype(np.float32)}
L_valid = 1
dim_input = (size,size)
n_x = size*size
n_y = 10
type_qz = 'gaussianmarg'
type_pz = 'gaussianmarg'
nonlinear = 'softplus'
type_px = 'bernoulli'
n_train = 10000
n_valid = 2000
n_test = 50000
n_batch = 200
colorImg = False
bernoulli_x = True
byteToFloat = False
weight_decay = float(n_batch)/n_train
elif dataset == 'mnist_binarized':
#import anglepy.data.mnist_binarized as mnist_binarized
# MNIST
import anglepy.data.mnist as mnist
size = 28
data_dir = '/home/lichongxuan/regbayes2/data/mat_data/'+'binarized_mnist_'
tmp = sio.loadmat(data_dir+'train.mat')
train_x = tmp['x_train'].T
#train_y = tmp['t_train'].T.astype(np.int32)
tmp = sio.loadmat(data_dir+'test.mat')
test_x = tmp['x_test'].T
tmp = sio.loadmat(data_dir+'valid.mat')
#print tmp.keys()
valid_x = tmp['x_valid'].T
#test_y = tmp['t_test'].T.astype(np.int32)
f_enc, f_dec = pp.Identity()
train_mean_prior = np.zeros((n_z,train_x.shape[1]))
test_mean_prior = np.zeros((n_z,test_x.shape[1]))
valid_mean_prior = np.zeros((n_z,valid_x.shape[1]))
train_x = np.hstack((train_x, valid_x)).astype(np.float32)
train_mean_prior = np.hstack((train_mean_prior,valid_mean_prior)).astype(np.float32)
print train_mean_prior.shape
print train_x.shape
x = {'x': train_x.astype(np.float32), 'mean_prior':train_mean_prior.astype(np.float32)}
x_train = x
x_valid = {'x': test_x.astype(np.float32),'mean_prior':test_mean_prior.astype(np.float32)}
x_test = x_valid
L_valid = 1
dim_input = (28,28)
n_x = 28*28
n_y = 10
type_qz = 'gaussianmarg'
type_pz = 'gaussianmarg'
nonlinear = 'softplus'
type_px = 'bernoulli'
n_train = 60000
n_valid = 10000
n_batch = 1000
colorImg = False
bernoulli_x = False
byteToFloat = False
weight_decay = float(n_batch)/n_train
elif dataset == 'mnist_binarized_own':
#import anglepy.data.mnist_binarized as mnist_binarized
# MNIST
import anglepy.data.mnist as mnist
size = 28
data_dir = 'data/mnist_binarized_own/'+'binarized_mnist_'
tmp = sio.loadmat(data_dir+'train.mat')
train_x = tmp['train_x'].T
#train_y = tmp['t_train'].T.astype(np.int32)
tmp = sio.loadmat(data_dir+'test.mat')
test_x = tmp['test_x'].T
tmp = sio.loadmat(data_dir+'valid.mat')
#print tmp.keys()
valid_x = tmp['valid_x'].T
#test_y = tmp['t_test'].T.astype(np.int32)
f_enc, f_dec = pp.Identity()
train_mean_prior = np.zeros((n_z,train_x.shape[1]))
test_mean_prior = np.zeros((n_z,test_x.shape[1]))
valid_mean_prior = np.zeros((n_z,valid_x.shape[1]))
train_x = np.hstack((train_x, valid_x)).astype(np.float32)
train_mean_prior = np.hstack((train_mean_prior,valid_mean_prior)).astype(np.float32)
print train_mean_prior.shape
print train_x.shape
x = {'x': train_x.astype(np.float32), 'mean_prior':train_mean_prior.astype(np.float32)}
x_train = x
x_valid = {'x': test_x.astype(np.float32),'mean_prior':test_mean_prior.astype(np.float32)}
x_test = x_valid
L_valid = 1
dim_input = (28,28)
n_x = 28*28
n_y = 10
type_qz = 'gaussianmarg'
type_pz = 'gaussianmarg'
nonlinear = 'softplus'
type_px = 'bernoulli'
n_train = 60000
n_valid = 10000
n_batch = 1000
colorImg = False
bernoulli_x = False
byteToFloat = False
weight_decay = float(n_batch)/n_train
elif dataset == 'freyface':
# Frey's face
import anglepy.data.freyface as freyface
n_train = 1600
train_x = freyface.load_numpy()
np.random.shuffle(train_x)
x = {'x': train_x.T[:,0:n_train]}
x_valid = {'x': train_x.T[:,n_train:]}
L_valid = 1
dim_input = (28,20)
n_x = 20*28
type_qz = 'gaussianmarg'
type_pz = 'gaussianmarg'
type_px = 'bounded01'
nonlinear = 'tanh' #tanh works better with freyface #'softplus'
n_batch = 100
colorImg = False
bernoulli_x = False
byteToFloat = False
weight_decay = float(n_batch)/n_train
elif dataset == 'freyface_pca':
# Frey's face
import anglepy.data.freyface as freyface
n_train = 1600
train_x = freyface.load_numpy().T
np.random.shuffle(train_x.T)
f_enc, f_dec, _ = pp.PCA(train_x, 0.99)
train_x = f_enc(train_x)
x = {'x': train_x[:,0:n_train].astype(np.float32)}
x_valid = {'x': train_x[:,n_train:].astype(np.float32)}
L_valid = 1
dim_input = (28,20)
n_x = train_x.shape[0]
type_qz = 'gaussianmarg'
type_pz = 'gaussianmarg'
type_px = 'gaussian'
nonlinear = 'softplus'
n_batch = 100
colorImg = False
bernoulli_x = False
byteToFloat = False
elif dataset == 'freyface_bernoulli':
# Frey's face
import anglepy.data.freyface as freyface
n_train = 1600
train_x = freyface.load_numpy().T
np.random.shuffle(train_x.T)
x = {'x': train_x[:,0:n_train].astype(np.float32)}
x_valid = {'x': train_x[:,n_train:].astype(np.float32)}
L_valid = 1
dim_input = (28,20)
n_x = train_x.shape[0]
type_pz = 'gaussianmarg'
type_px = 'bernoulli'
nonlinear = 'softplus'
n_batch = 100
colorImg = False
bernoulli_x = False
byteToFloat = False
elif dataset == 'norb_48_24300_pca':
size = 48
train_x, train_y, test_x, test_y = np.load('data/norb/norb_48_24300.npy')
_x = {'x': train_x, 'y': train_y}
#ndict.shuffleCols(_x)
#train_x = _x['x']
#train_y = _x['y']
#print _x['x'][:,:10000].shape
# Do PCA
print 'pca'
f_enc, f_dec, pca_params = pp.PCA(_x['x'][:,:10000], cutoff=500, toFloat=False)
ndict.savez(pca_params, logdir+'pca_params')
print 'done'
train_mean_prior = np.zeros((n_z,train_x.shape[1]))
test_mean_prior = np.zeros((n_z,test_x.shape[1]))
x = {'x': f_enc(train_x).astype(np.float32), 'mean_prior' : train_mean_prior.astype(np.float32)}
x_valid = {'x': f_enc(test_x).astype(np.float32), 'mean_prior' : test_mean_prior.astype(np.float32)}
x_test = {'x': f_enc(test_x).astype(np.float32), 'mean_prior' : test_mean_prior.astype(np.float32)}
x_train = x
print x['x'].shape
print x['mean_prior'].shape
L_valid = 1
n_y = 5
n_x = x['x'].shape[0]
dim_input = (size,size)
type_qz = 'gaussianmarg'
type_pz = 'gaussianmarg'
type_px = 'gaussian'
nonlinear = 'softplus'
n_batch = 900 #23400/900 = 27
colorImg = False
#binarize = False
bernoulli_x = False
byteToFloat = False
weight_decay= float(n_batch)/train_x.shape[1]
elif dataset == 'norb':
# small NORB dataset
import anglepy.data.norb as norb
size = 48
train_x, train_y, test_x, test_y = norb.load_resized(size, binarize_y=True)
x = {'x': train_x.astype(np.float32)}
x_valid = {'x': test_x.astype(np.float32)}
L_valid = 1
n_x = train_x.shape[0]
dim_input = (size,size)
type_qz = 'gaussianmarg'
type_pz = 'gaussianmarg'
type_px = 'gaussian'
nonlinear = 'softplus'
n_batch = 900 #23400/900 = 27
colorImg = False
#binarize = False
byteToFloat = False
bernoulli_x = False
weight_decay= float(n_batch)/train_x.shape[1]
elif dataset == 'norb_pca':
# small NORB dataset
import anglepy.data.norb as norb
size = 48
train_x, train_y, test_x, test_y = norb.load_resized(size, binarize_y=True)
f_enc, f_dec, _ = pp.PCA(train_x, 0.999)
#f_enc, f_dec, _ = pp.normalize_random(train_x)
train_x = f_enc(train_x)
test_x = f_enc(test_x)
x = {'x': train_x.astype(np.float32)}
x_valid = {'x': test_x.astype(np.float32)}
L_valid = 1
n_x = train_x.shape[0]
dim_input = (size,size)
type_qz = 'gaussianmarg'
type_pz = 'gaussianmarg'
type_px = 'gaussian'
nonlinear = 'softplus'
n_batch = 900 #23400/900 = 27
colorImg = False
#binarize = False
bernoulli_x = False
byteToFloat = False
weight_decay= float(n_batch)/train_x.shape[1]
elif dataset == 'norb_normalized':
# small NORB dataset
import anglepy.data.norb as norb
size = 48
train_x, train_y, test_x, test_y = norb.load_resized(size, binarize_y=True)
#f_enc, f_dec, _ = pp.PCA(train_x, 0.99)
#f_enc, f_dec, _ = pp.normalize_random(train_x)
f_enc, f_dec, _ = pp.normalize(train_x)
train_x = f_enc(train_x)
test_x = f_enc(test_x)
x = {'x': train_x.astype(np.float32)}
x_valid = {'x': test_x.astype(np.float32)}
L_valid = 1
n_x = train_x.shape[0]
dim_input = (size,size)
type_qz = 'gaussianmarg'
type_pz = 'gaussianmarg'
type_px = 'gaussian'
nonlinear = 'softplus'
n_batch = 900 #23400/900 = 27
colorImg = False
#binarize = False
bernoulli_x = False
byteToFloat = False
weight_decay= float(n_batch)/train_x.shape[1]
elif dataset == 'svhn':
# SVHN dataset
#import anglepy.data.svhn as svhn
size = 32
train_x, train_y, test_x, test_y = np.load('data/svhn/svhn.npy')
#extra_x, extra_y = svhn.load_numpy_extra(False, binarize_y=True)
#x = {'x': np.hstack((train_x, extra_x)), 'y':np.hstack((train_y, extra_y))}
#ndict.shuffleCols(x)
x = {'x' : train_x, 'y': train_y}
print 'Performing PCA, can take a few minutes... '
cutoff = 300
if os.environ.has_key('cutoff'):
cutoff = int(os.environ['cutoff'])
color.printBlue('cutoff: '+str(cutoff))
f_enc, f_dec, pca_params = pp.PCA(x['x'][:,:10000], cutoff=cutoff, toFloat=True)
ndict.savez(pca_params, logdir+'pca_params')
print 'Done.'
n_y = 10
if os.environ.has_key('prior') and bool(int(os.environ['prior'])) == True:
color.printBlue('Loading prior')
train_mean_prior, train_y1, test_mean_prior, test_y1 = np.load('data/svhn/svhn_prior.npy')
print np.sum((train_y1 == train_y).astype(np.int32))
print np.sum((test_y1 == test_y).astype(np.int32))
else:
train_mean_prior = np.zeros((n_z,train_x.shape[1]))
test_mean_prior = np.zeros((n_z,test_x.shape[1]))
x = {'x': f_enc(x['x']).astype(np.float32), 'mean_prior':train_mean_prior.astype(np.float32)}
x_train = x
x_test = {'x': f_enc(test_x).astype(np.float32), 'mean_prior':test_mean_prior.astype(np.float32)}
x_valid = x_test
print x_train['x'].shape
print x_test['x'].shape
print train_y.shape
print test_y.shape
print x_train['mean_prior'].shape
print x_test['mean_prior'].shape
L_valid = 1
n_x = x['x'].shape[0]
dim_input = (size,size)
n_batch = 5000
n_train = 604388
n_valid = 26032
n_test = 26032
colorImg = True
bernoulli_x = False
byteToFloat = False
type_qz = 'gaussianmarg'
type_pz = 'gaussianmarg'
type_px = 'gaussian'
nonlinear = 'softplus'
else:
print 'invalid data set'
exit()
#print '2', n_x
# Construct model
from anglepy.models import GPUVAE_Z_X
learning_rate1 = 3e-4
if os.environ.has_key('stepsize'):
learning_rate1 = float(os.environ['stepsize'])
color.printBlue(str(learning_rate1))
if os.environ.has_key('preoption'):
pre = int(os.environ['preoption'])
if pre == 1:
updates = get_adam_optimizer(learning_rate=3e-4, decay1=0.9, decay2=0.999, weight_decay=0)
elif pre ==2:
updates = get_adam_optimizer(learning_rate=3e-4, decay1=0.9, decay2=0.999, weight_decay=weight_decay)
else:
raise Exception('Prepotion unknown')
with open(logdir+'hook.txt', 'a') as f:
print >>f, 'preoption ' + str(pre)
else:
updates = get_adam_optimizer(learning_rate=learning_rate1, weight_decay=weight_decay)
#print '1', n_x
model = GPUVAE_Z_X(updates, n_x, n_hidden, n_z, n_hidden[::-1], nonlinear, nonlinear, type_px, type_qz=type_qz, type_pz=type_pz, prior_sd=100, init_sd=1e-3)
if os.environ.has_key('pretrain') and bool(int(os.environ['pretrain'])) == True:
#dir = '/Users/dpkingma/results/learn_z_x_mnist_binarized_50-(500, 500)_mog_1412689061/'
#dir = '/Users/dpkingma/results/learn_z_x_svhn_bernoulli_300-(1000, 1000)_l1l2_sharing_and_1000HU_1412676966/'
#dir = '/Users/dpkingma/results/learn_z_x_svhn_bernoulli_300-(1000, 1000)_l1l2_sharing_and_1000HU_1412695481/'
#dir = '/Users/dpkingma/results/learn_z_x_mnist_binarized_50-(500, 500)_mog_1412695455/'
#dir = '/Users/dpkingma/results/gpulearn_z_x_svhn_pca_300-(500, 500)__1413904756/'
if len(n_hidden) == 1:
color.printBlue('pre-training-1-layer')
layer_str = '-500'
elif len(n_hidden) == 2:
color.printBlue('pre-training-2-layers')
layer_str = '-(500, 500)'
else:
raise Exception()
pre_str = 'models/gpulearn_z_x_'
if dataset == 'mnist':
#dir = pre_str + 'mnist_'+str(n_z)+layer_str+'_longrun/'
dir = 'models/mnist_z_x_50-500-500_longrun/'
elif dataset == 'mnist_rot':
dir = pre_str + 'mnist_rot_'+str(n_z)+layer_str+'_longrun/'
elif dataset == 'mnist_back_rand':
dir = pre_str + 'mnist_back_rand_'+str(n_z)+layer_str+'_longrun/'
elif dataset == 'mnist_back_image':
dir = pre_str + 'mnist_back_image_'+str(n_z)+layer_str+'_longrun/'
elif dataset == 'mnist_back_image_rot':
dir = pre_str + 'mnist_back_image_rot_'+str(n_z)+layer_str+'_longrun/'
elif dataset == 'rectangle':
dir = pre_str + 'rectangle_'+str(n_z)+layer_str+'_longrun/'
elif dataset == 'rectangle_image':
dir = pre_str + 'rectangle_image_'+str(n_z)+layer_str+'_longrun/'
elif dataset == 'convex':
dir = pre_str + 'convex_'+str(n_z)+layer_str+'_longrun/'
elif dataset == 'mnist_basic':
dir = pre_str + 'mnist_basic_'+str(n_z)+layer_str+'_longrun/'
if dataset == 'svhn':
if (os.environ.has_key('prior') and bool(int(os.environ['prior'])) == True):
print 'prior-------------------'
pre_dir = 'results/gpulearn_z_x_svhn_'+str(n_z)+'-500-500_prior_'+str(cutoff)+'_longrun/'
else:
pre_dir = 'results/gpulearn_z_x_svhn_'+str(n_z)+'-500-500_'+str(cutoff)+'_longrun/'
color.printBlue(pre_dir)
w = ndict.loadz(pre_dir+'w_best.ndict.tar.gz')
v = ndict.loadz(pre_dir+'v_best.ndict.tar.gz')
elif n_z == 50:
print 'n_z = 50', dir
w = ndict.loadz(dir+'w_best.ndict.tar.gz')
v = ndict.loadz(dir+'v_best.ndict.tar.gz')
else:
print 'n_z != 50'
w = ndict.loadz(pre_dir+'w_best.ndict.tar.gz')
v = ndict.loadz(pre_dir+'v_best.ndict.tar.gz')
ndict.set_value2(model.w, w)
ndict.set_value2(model.v, v)
# Some statistics for optimization
ll_valid_stats = [-1e99, 0]
# Progress hook
def hook(epoch, t, ll):
if epoch%10 != 0: return
n_batch_n = n_batch
if n_batch_n > n_valid:
n_batch_n = n_valid
ll_valid, _ = model.est_loglik(x_valid, n_samples=L_valid, n_batch=n_batch_n, byteToFloat=byteToFloat)
ll_test = ll_valid
#if not dataset == 'mnist_binarized':
if not dataset == 'svhn':
ll_test, _ = model.est_loglik(x_test, n_samples=L_valid, n_batch=n_batch, byteToFloat=byteToFloat)
# Log
ndict.savez(ndict.get_value(model.v), logdir+'v')
ndict.savez(ndict.get_value(model.w), logdir+'w')
def infer(data, n_batch=1000):
#print '--', n_batch
size = data['x'].shape[1]
res = np.zeros((sum(n_hidden), size))
res1 = np.zeros((n_z,size))
res2 = np.zeros((n_hidden[-1],size))
res3 = np.zeros((n_z,size))
for i in range(0, size, n_batch):
idx_to = min(size, i+n_batch)
x_batch = ndict.getCols(data, i, idx_to)
# may have bugs
nn_batch = idx_to - i
_x, _z, _z_confab = model.gen_xz(x_batch, {}, nn_batch)
x_samples = _z_confab['x']
for (hi, hidden) in enumerate(_z_confab['hidden']):
res[sum(n_hidden[:hi]):sum(n_hidden[:hi+1]),i:i+nn_batch] = hidden
res1[:,i:i+nn_batch] = _z_confab['mean']
res2[:,i:i+nn_batch] = _z_confab['hidden'][-1]
res3[:,i:i+nn_batch] = _z_confab['logvar']
#print '--'
return res, res1, res2, res3
#print '..', n_batch
#if not dataset == 'mnist_binarized':
if not dataset == 'svhn':
z_test, z_test1, z_test2, vv_test = infer(x_test)
z_train, z_train1, z_train2, vv_train = infer(x_train)
if ll_valid > ll_valid_stats[0]:
ll_valid_stats[0] = ll_valid
ll_valid_stats[1] = 0
ndict.savez(ndict.get_value(model.v), logdir+'v_best')
ndict.savez(ndict.get_value(model.w), logdir+'w_best')
#if not dataset == 'mnist_binarized':
if dataset == 'svhn':
pass
#np.save(logdir+'full_latent', ('z_test': z_test, 'train_y':train_y, 'test_y':test_y, 'z_train': z_train))
#np.save(logdir+'last_latent', ('z_test': z_test2, 'train_y':train_y, 'test_y':test_y, 'z_train': z_train2))
else:
sio.savemat(logdir+'full_latent.mat', {'z_test': z_test, 'train_y':train_y, 'test_y':test_y, 'z_train': z_train})
sio.savemat(logdir+'mean_latent.mat', {'z_test': z_test1, 'train_y':train_y, 'test_y':test_y, 'z_train': z_train1})
sio.savemat(logdir+'last_latent.mat', {'z_test': z_test2, 'train_y':train_y, 'test_y':test_y, 'z_train': z_train2})
else:
ll_valid_stats[1] += 1
# Stop when not improving validation set performance in 100 iterations
if ll_valid_stats[1] > 1000:
print "Finished"
with open(logdir+'hook.txt', 'a') as f:
print >>f, "Finished"
exit()
print epoch, t, ll, ll_valid, ll_test, ll_valid_stats
with open(logdir+'hook.txt', 'a') as f:
print >>f, epoch, t, ll, ll_valid, ll_test, ll_valid_stats
'''
if dataset != 'svhn':
l_t, px_t, pz_t, qz_t = model.test(x_train, n_samples=1, n_batch=n_batch, byteToFloat=byteToFloat)
print 'Elogpx', px_t, 'Elogpz', pz_t, '-Elogqz', qz_t
#sigma_square = float(os.environ['sigma_square'])
print 'var', np.mean(np.exp(vv_train)), 'q', np.mean(np.abs(z_train1)), 'p', np.mean(np.abs(train_mean_prior)), 'd', np.mean(np.abs(z_train1-train_mean_prior))
with open(logdir+'hook.txt', 'a') as f:
print >>f, 'Elogpx', px_t, 'Elogpz', pz_t, '-Elogqz', qz_t
print >>f, 'var', np.mean(np.exp(vv_train)), 'q', np.mean(np.abs(z_train1)), 'p', np.mean(np.abs(train_mean_prior)), 'd', np.mean(np.abs(z_train1-train_mean_prior))
'''
# Graphics
if gfx and epoch%gfx_freq == 0:
#tail = '.png'
tail = '-'+str(epoch)+'.png'
v = {i: model.v[i].get_value() for i in model.v}
w = {i: model.w[i].get_value() for i in model.w}
if 'pca' not in dataset and 'random' not in dataset and 'normalized' not in dataset and 'zca' not in dataset:
if 'w0' in v:
image = paramgraphics.mat_to_img(f_dec(v['w0'][:].T), dim_input, True, colorImg=colorImg)
image.save(logdir+'q_w0'+tail, 'PNG')
image = paramgraphics.mat_to_img(f_dec(w['out_w'][:]), dim_input, True, colorImg=colorImg)
image.save(logdir+'out_w'+tail, 'PNG')
if 'out_unif' in w:
image = paramgraphics.mat_to_img(f_dec(w['out_unif'].reshape((-1,1))), dim_input, True, colorImg=colorImg)
image.save(logdir+'out_unif'+tail, 'PNG')
if n_z == 2:
n_width = 10
import scipy.stats
z = {'z':np.zeros((2,n_width**2))}
for i in range(0,n_width):
for j in range(0,n_width):
z['z'][0,n_width*i+j] = scipy.stats.norm.ppf(float(i)/n_width+0.5/n_width)
z['z'][1,n_width*i+j] = scipy.stats.norm.ppf(float(j)/n_width+0.5/n_width)
x, _, _z = model.gen_xz({}, z, n_width**2)
if dataset == 'mnist':
x = 1 - _z['x']
image = paramgraphics.mat_to_img(f_dec(_z['x']), dim_input)
image.save(logdir+'2dmanifold'+tail, 'PNG')
else:
if 'norb' in dataset or dataset=='svhn':
nn_batch_nn = 64
else:
nn_batch_nn = 144
if not(os.environ.has_key('train_residual') and bool(int(os.environ['train_residual'])) == True) and (os.environ.has_key('prior') and bool(int(os.environ['prior'])) == True):
mp_in = np.random.randint(0,x_train['mean_prior'].shape[1],nn_batch_nn)
m_p = x_train['mean_prior'][:,mp_in]
s_s = 1
if os.environ.has_key('sigma_square'):
s_s = float(os.environ['sigma_square'])
x_samples = model.gen_xz_prior({}, {}, m_p, s_s, n_batch=nn_batch_nn)
x_samples = x_samples['x']
m_p1 = (np.ones((n_z, nn_batch_nn)).T * np.mean(x_train['mean_prior'], axis = 1)).T
x_samples1 = model.gen_xz_prior({}, {}, m_p1.astype(np.float32), s_s, n_batch=nn_batch_nn)
image = paramgraphics.mat_to_img(f_dec(x_samples1['x']), dim_input, colorImg=colorImg)
image.save(logdir+'mean_samples-prior'+tail, 'PNG')
x_samples11 = model.gen_xz_prior11({}, {}, m_p, s_s, n_batch=nn_batch_nn)
image = paramgraphics.mat_to_img(f_dec(x_samples11['x']), dim_input, colorImg=colorImg)
image.save(logdir+'prior-image'+tail, 'PNG')
else:
_x, _, _z_confab = model.gen_xz({}, {}, n_batch=nn_batch_nn)
x_samples = _z_confab['x']
image = paramgraphics.mat_to_img(f_dec(x_samples), dim_input, colorImg=colorImg)
image.save(logdir+'samples-prior'+tail, 'PNG')
#x_samples = _x['x']
#image = paramgraphics.mat_to_img(x_samples, dim_input, colorImg=colorImg)
#image.save(logdir+'samples2'+tail, 'PNG')
else:
# Model with preprocessing
if 'w0' in v:
tmp = f_dec(v['w0'][:].T)
#print dim_input
#print tmp.shape
if 'zca' in dataset or dataset=='svhn':
tmp = zca_dec(zca_mean, zca_winv, tmp)
image = paramgraphics.mat_to_img(tmp, dim_input, True, colorImg=colorImg)
image.save(logdir+'q_w0'+tail, 'PNG')
tmp = f_dec(w['out_w'][:])
if 'zca' in dataset:
tmp = zca_dec(zca_mean, zca_winv, tmp)
image = paramgraphics.mat_to_img(tmp, dim_input, True, colorImg=colorImg)
image.save(logdir+'out_w'+tail, 'PNG')
if dataset == 'svhn':
nn_batch_nn = 64
else:
nn_batch_nn = 144
if not(os.environ.has_key('train_residual') and bool(int(os.environ['train_residual'])) == True) and (os.environ.has_key('prior') and bool(int(os.environ['prior'])) == True):
mp_in = np.random.randint(0,x_train['mean_prior'].shape[1],nn_batch_nn)
m_p = x_train['mean_prior'][:,mp_in]
s_s = 1
if os.environ.has_key('sigma_square'):
s_s = float(os.environ['sigma_square'])
x_samples = model.gen_xz_prior({}, {}, m_p, s_s, n_batch=nn_batch_nn)
x_samples = zca_dec(zca_mean, zca_winv,x_samples['x'])
x_samples = np.minimum(np.maximum(x_samples, 0), 1)
x_samples11 = model.gen_xz_prior11({}, {}, m_p, s_s, n_batch=nn_batch_nn)
x_samples11 = zca_dec(zca_mean,zca_winv,x_samples11['x'])
x_samples11 = np.minimum(np.maximum(x_samples11, 0), 1)
image = paramgraphics.mat_to_img(x_samples11, dim_input, colorImg=colorImg)
image.save(logdir+'prior-image'+tail, 'PNG')
else:
_x, _z, _z_confab = model.gen_xz({}, {}, n_batch=nn_batch_nn)
x_samples = f_dec(_z_confab['x'])
x_samples = np.minimum(np.maximum(x_samples, 0), 1)
image = paramgraphics.mat_to_img(x_samples, dim_input, colorImg=colorImg)
image.save(logdir+'samples'+tail, 'PNG')
'''
def infer(data, n_batch=1000):
#print '--', n_batch
size = data['x'].shape[1]
res = np.zeros((sum(n_hidden), size))
res1 = np.zeros((n_z,size))
res2 = np.zeros((n_hidden[-1],size))
res3 = np.zeros((n_z,size))
for i in range(0, size, n_batch):
idx_to = min(size, i+n_batch)
x_batch = ndict.getCols(data, i, idx_to)
# may have bugs
nn_batch = idx_to - i
_x, _z, _z_confab = model.gen_xz(x_batch, {}, nn_batch)
x_samples = _z_confab['x']
for (hi, hidden) in enumerate(_z_confab['hidden']):
res[sum(n_hidden[:hi]):sum(n_hidden[:hi+1]),i:i+nn_batch] = hidden
res1[:,i:i+nn_batch] = _z_confab['mean']
res2[:,i:i+nn_batch] = _z_confab['hidden'][-1]
res3[:,i:i+nn_batch] = _z_confab['logvar']
#
return res, res1, res2, res3
#print n_batch
#if not dataset == 'mnist_binarized':
z_test, z_test1, z_test2, vv_test = infer(x_test)
z_train, z_train1, z_train2, vv_train = infer(x_train)
l_t, px_t, pz_t, qz_t = model.test(x_train, n_samples=1, n_batch=n_batch, byteToFloat=byteToFloat)
print 'Elogpx', px_t, 'Elogpz', pz_t, '-Elogqz', qz_t
#sigma_square = float(os.environ['sigma_square'])
print 'var', np.mean(np.exp(vv_train)), 'q', np.mean(np.abs(z_train1)), 'p', np.mean(np.abs(train_mean_prior)), 'd', np.mean(np.abs(z_train1-train_mean_prior))
with open(logdir+'hook.txt', 'a') as f:
print >>f, 'Elogpx', px_t, 'Elogpz', pz_t, '-Elogqz', qz_t
print >>f, 'var', np.mean(np.exp(vv_train)), 'q', np.mean(np.abs(z_train1)), 'p', np.mean(np.abs(train_mean_prior)), 'd', np.mean(np.abs(z_train1-train_mean_prior))
#if not dataset == 'mnist_binarized':
sio.savemat(logdir+'full_latent.mat', {'z_test': z_test, 'train_y':train_y, 'test_y':test_y, 'z_train': z_train})
sio.savemat(logdir+'mean_latent.mat', {'z_test': z_test1, 'train_y':train_y, 'test_y':test_y, 'z_train': z_train1})
sio.savemat(logdir+'last_latent.mat', {'z_test': z_test2, 'train_y':train_y, 'test_y':test_y, 'z_train': z_train2})
'''
# Optimize
#SFO
dostep = epoch_vae_adam(model, x, n_batch=n_batch, bernoulli_x=bernoulli_x, byteToFloat=byteToFloat)
loop_va(dostep, hook)
pass
def __init__(self, get_optimizer, n_x, n_y, n_hidden_q, n_z, n_hidden_p, nonlinear_q='tanh', nonlinear_p='tanh', type_px='bernoulli', type_qz='gaussianmarg', type_pz='gaussianmarg', prior_sd=1, init_sd=1e-2, var_smoothing=0, n_mixture=50, c=10, ell=1, average_activation = 0.1, sparsity_weight = 3):
self.constr = (__name__, inspect.stack()[0][3], locals())
self.n_x = n_x
self.n_y = n_y
self.n_hidden_q = n_hidden_q
self.n_z = n_z
self.n_hidden_p = n_hidden_p
self.dropout = False
self.nonlinear_q = nonlinear_q
self.nonlinear_p = nonlinear_p
self.type_px = type_px
self.type_qz = type_qz
self.type_pz = type_pz
self.prior_sd = prior_sd
self.var_smoothing = var_smoothing
self.n_mixture = n_mixture
self.c = c
self.ell = ell
self.average_activation = average_activation
self.sparsity_weight = sparsity_weight
if os.environ.has_key('c'):
self.c = float(os.environ['c'])
if os.environ.has_key('ell'):
self.ell = float(os.environ['ell'])
self.sv = 0
if os.environ.has_key('sv'):
self.sv = int(os.environ['sv'])
color.printBlue('apply supervision from layer ' + str(self.sv+1) + ' to end.')
self.super_to_mean = False
if os.environ.has_key('super_to_mean') and bool(int(os.environ['super_to_mean'])) == True:
self.super_to_mean = True
color.printBlue('apply supervision to z_mean.')
self.train_residual = False
if os.environ.has_key('train_residual') and bool(int(os.environ['train_residual'])) == True:
self.train_residual = True
color.printBlue('Train residual wrt prior instead of the whole model.')
self.Lambda = 0
if os.environ.has_key('Lambda'):
self.Lambda = float(os.environ['Lambda'])
self.sigma_square = 1
if os.environ.has_key('sigma_square'):
self.sigma_square = float(os.environ['sigma_square'])
if os.environ.has_key('dropout'):
self.dropout = bool(int(os.environ['dropout']))
color.printBlue('c = ' + str(self.c) + ' , ell = ' + str(self.ell) + ' , sigma_square = ' + str(self.sigma_square))
# Init weights
v, w = self.init_w(1e-2)
for i in v: v[i] = shared32(v[i])
for i in w: w[i] = shared32(w[i])
if not self.super_to_mean:
W = shared32(np.zeros((sum(n_hidden_q[self.sv:])+1, n_y)))
#print 'apply supervision from', self.sv+1, ' to end.'
else:
W = shared32(np.zeros((n_z+1, n_y)))
#print 'apply supervison to z_mean'
self.v = v
self.v['W'] = W
#print 'dimension of the prediction model: ', self.v['W'].get_value().shape
self.w = w
super(GPUVAE_MM_Z_X, self).__init__(get_optimizer)
# Load MNIST data
dataset = sys.argv[1]
dir = 'models/mnist_yz_x_50-500-500/'
if len(sys.argv) >= 3:
dir = sys.argv[2]
print dir
if dataset == 'mnist':
import anglepy.data.mnist as mnist
_, train_y, _, _, test_x, test_y = mnist.load_numpy(size=28,
binarize_y=False)
if os.environ.has_key('prior') and bool(int(os.environ['prior'])) == True:
color.printBlue('Have informative prior')
n_z = 96
mnist_prior = sio.loadmat('data/mnist_prior/mnist_prior.mat')
prior_type = os.environ['prior_type']
color.printBlue('Prior type: ' + prior_type)
if prior_type == 'too_strong':
test_mean_prior = mnist_prior['z_test']
elif prior_type == 'naive':
test_mean_prior = np.zeros((n_z, test_x.shape[1]))
elif prior_type == 'train_mean':
train_mean = np.mean(mnist_prior['z_train'], 1)
test_mean_prior = np.tile(train_mean[:, np.newaxis],
[1, test_x.shape[1]])
elif prior_type == 'test_mean':
test_mean = np.mean(mnist_prior['z_test'], 1)
test_mean_prior = np.tile(test_mean[:, np.newaxis],
def __init__(self,
get_optimizer,
n_x,
n_y,
n_hidden_q,
n_z,
n_hidden_p,
nonlinear_q='tanh',
nonlinear_p='tanh',
type_px='bernoulli',
type_qz='gaussianmarg',
type_pz='gaussianmarg',
prior_sd=1,
init_sd=1e-2,
var_smoothing=0,
n_mixture=50,
c=10,
ell=1,
average_activation=0.1,
sparsity_weight=3):
self.constr = (__name__, inspect.stack()[0][3], locals())
self.n_x = n_x
self.n_y = n_y
self.n_hidden_q = n_hidden_q
self.n_z = n_z
self.n_hidden_p = n_hidden_p
self.dropout = False
self.nonlinear_q = nonlinear_q
self.nonlinear_p = nonlinear_p
self.type_px = type_px
self.type_qz = type_qz
self.type_pz = type_pz
self.prior_sd = prior_sd
self.var_smoothing = var_smoothing
self.n_mixture = n_mixture
self.c = c
self.ell = ell
self.average_activation = average_activation
self.sparsity_weight = sparsity_weight
if os.environ.has_key('c'):
self.c = float(os.environ['c'])
if os.environ.has_key('ell'):
self.ell = float(os.environ['ell'])
self.sv = 0
if os.environ.has_key('sv'):
self.sv = int(os.environ['sv'])
color.printBlue('apply supervision from layer ' +
str(self.sv + 1) + ' to end.')
self.super_to_mean = False
if os.environ.has_key('super_to_mean') and bool(
int(os.environ['super_to_mean'])) == True:
self.super_to_mean = True
color.printBlue('apply supervision to z_mean.')
self.train_residual = False
if os.environ.has_key('train_residual') and bool(
int(os.environ['train_residual'])) == True:
self.train_residual = True
color.printBlue(
'Train residual wrt prior instead of the whole model.')
self.Lambda = 0
if os.environ.has_key('Lambda'):
self.Lambda = float(os.environ['Lambda'])
self.sigma_square = 1
if os.environ.has_key('sigma_square'):
self.sigma_square = float(os.environ['sigma_square'])
if os.environ.has_key('dropout'):
self.dropout = bool(int(os.environ['dropout']))
color.printBlue('c = ' + str(self.c) + ' , ell = ' + str(self.ell) +
' , sigma_square = ' + str(self.sigma_square))
# Init weights
v, w = self.init_w(1e-2)
for i in v:
v[i] = shared32(v[i])
for i in w:
w[i] = shared32(w[i])
if not self.super_to_mean:
W = shared32(np.zeros((sum(n_hidden_q[self.sv:]) + 1, n_y)))
#print 'apply supervision from', self.sv+1, ' to end.'
else:
W = shared32(np.zeros((n_z + 1, n_y)))
#print 'apply supervison to z_mean'
self.v = v
self.v['W'] = W
#print 'dimension of the prediction model: ', self.v['W'].get_value().shape
self.w = w
super(GPUVAE_MM_Z_X, self).__init__(get_optimizer)
