def _get_hyper_net(self):
# inition random noise
ep = self.srng.normal(size=(self.wd1, self.num_params), dtype=floatX)
logdets_layers = []
h_net = lasagne.layers.InputLayer([None, self.num_params])
# mean and variation of the initial noise
layer_temp = LinearFlowLayer(h_net)
h_net = IndexLayer(layer_temp, 0)
logdets_layers.append(IndexLayer(layer_temp, 1))
if self.flow == 'RealNVP':
if self.coupling:
layer_temp = CoupledDenseLayer(h_net, 200)
h_net = IndexLayer(layer_temp, 0)
logdets_layers.append(IndexLayer(layer_temp, 1))
for c in range(self.coupling - 1):
h_net = PermuteLayer(h_net, self.num_params)
layer_temp = CoupledDenseLayer(h_net, 200)
h_net = IndexLayer(layer_temp, 0)
logdets_layers.append(IndexLayer(layer_temp, 1))
elif self.flow == 'IAF':
layer_temp = IAFDenseLayer(h_net,
200,
1,
L=self.coupling,
cond_bias=False)
layer = IndexLayer(layer_temp, 0)
logdets_layers.append(IndexLayer(layer_temp, 1))
else:
assert False
self.h_net = h_net
self.weights = lasagne.layers.get_output(h_net, ep)
self.logdets = sum([get_output(ld, ep) for ld in logdets_layers])
def _get_hyper_net(self):
# inition random noise
ep = self.srng.normal(size=(self.wd1, self.num_params), dtype=floatX)
logdets_layers = []
h_net = lasagne.layers.InputLayer([None, self.num_params])
# mean and variation of the initial noise
layer_temp = LinearFlowLayer(h_net)
h_net = IndexLayer(layer_temp, 0)
logdets_layers.append(IndexLayer(layer_temp, 1))
if self.coupling:
# add more to introduce more correlation if needed
layer_temp = CoupledDenseLayer(h_net, 200)
h_net = IndexLayer(layer_temp, 0)
logdets_layers.append(IndexLayer(layer_temp, 1))
h_net = PermuteLayer(h_net, self.num_params)
layer_temp = CoupledDenseLayer(h_net, 200)
h_net = IndexLayer(layer_temp, 0)
logdets_layers.append(IndexLayer(layer_temp, 1))
self.h_net = h_net
self.weights = lasagne.layers.get_output(h_net, ep)
self.logdets = sum([get_output(ld, ep) for ld in logdets_layers])
def _get_flow_r(self):
self.z_T_f = self.weights
# TODO:
logdets_layers = []
flow_r = lasagne.layers.InputLayer([None, self.num_params])
if 1: # we always use RNVP for this!
if self.coupling:
layer_temp = CoupledDenseLayer(flow_r, 200)
flow_r = IndexLayer(layer_temp, 0)
logdets_layers.append(IndexLayer(layer_temp, 1))
for c in range(self.coupling - 1):
flow_r = PermuteLayer(flow_r, self.num_params)
layer_temp = CoupledDenseLayer(flow_r, 200)
flow_r = IndexLayer(layer_temp, 0)
logdets_layers.append(IndexLayer(layer_temp, 1))
else:
assert False
self.flow_r = flow_r
self.z_T_b = lasagne.layers.get_output(self.flow_r, self.z_T_f)
# split z_T_b into the different layers:
self.z_T_bs = []
t = 0
for ws in self.weight_shapes:
self.z_T_bs.append(self.z_T_b[:, t:t + ws[0]])
t += ws[0]
# TODO
self.logdets_z_T_b = sum(
[get_output(ld, self.ep) for ld in logdets_layers])
def _get_hyper_net(self):
# inition random noise
ep = self.srng.normal(size=(1, self.num_params), dtype=floatX)
logdets_layers = []
h_net = lasagne.layers.InputLayer([1, self.num_params])
# mean and variation of the initial noise
layer_temp = LinearFlowLayer(h_net)
h_net = IndexLayer(layer_temp, 0)
logdets_layers.append(IndexLayer(layer_temp, 1))
# split the noise: hnet1 for filters, hnet2 for WN params (DK)
h_net = SplitLayer(h_net, self.num_params - self.num_classes, 1)
h_net1 = IndexLayer(h_net, 0, (1, self.num_params - self.num_classes))
# TODO: full h_net2
h_net2 = IndexLayer(h_net, 1)
h_net1 = lasagne.layers.ReshapeLayer(h_net1,
(self.n_kernels,) + \
(np.prod(self.kernel_shape),))
if self.flow == 'RealNVP':
if self.coupling:
layer_temp = CoupledDenseLayer(h_net1, 100)
h_net1 = IndexLayer(layer_temp, 0)
logdets_layers.append(IndexLayer(layer_temp, 1))
for c in range(self.coupling - 1):
h_net1 = PermuteLayer(h_net1, self.num_params)
layer_temp = CoupledDenseLayer(h_net1, 100)
h_net1 = IndexLayer(layer_temp, 0)
logdets_layers.append(IndexLayer(layer_temp, 1))
elif self.flow == 'IAF':
layer_temp = IAFDenseLayer(h_net1,
200,
1,
L=self.coupling,
cond_bias=False)
layer = IndexLayer(layer_temp, 0)
logdets_layers.append(IndexLayer(layer_temp, 1))
else:
assert False
self.kernel_weights = lasagne.layers.get_output(h_net1, ep)
h_net1 = lasagne.layers.ReshapeLayer(h_net1,
(1, self.n_kernels * \
np.prod(self.kernel_shape) ) )
h_net = lasagne.layers.ConcatLayer([h_net1, h_net2], 1)
self.h_net = h_net
self.weights = lasagne.layers.get_output(h_net, ep)
self.logdets = sum([get_output(ld, ep) for ld in logdets_layers])
def _get_hyper_net(self):
# inition random noise
if self.noise_distribution == 'spherical_gaussian':
self.ep = self.srng.normal(size=(self.wd1,
self.num_params),dtype=floatX)
elif self.noise_distribution == 'exponential_MoG':
self.ep = self.srng.normal(size=(self.wd1, self.num_params), dtype=floatX)
self.ep += 2 * self.srng.binomial(size=(self.wd1, self.num_params), dtype=floatX) - 1
logdets_layers = []
h_net = lasagne.layers.InputLayer([None,self.num_params])
# mean and variation of the initial noise
layer_temp = LinearFlowLayer(h_net)
h_net = IndexLayer(layer_temp,0)
logdets_layers.append(IndexLayer(layer_temp,1))
if self.coupling:
layer_temp = CoupledDenseLayer(h_net,200)
h_net = IndexLayer(layer_temp,0)
logdets_layers.append(IndexLayer(layer_temp,1))
for c in range(self.coupling-1):
h_net = PermuteLayer(h_net,self.num_params)
layer_temp = CoupledDenseLayer(h_net,200)
h_net = IndexLayer(layer_temp,0)
logdets_layers.append(IndexLayer(layer_temp,1))
self.h_net = h_net
self.logits = lasagne.layers.get_output(h_net,self.ep)
self.drop_probs = T.nnet.sigmoid(self.logits)
self.logdets = sum([lasagne.layers.get_output(ld,self.ep) for ld in logdets_layers])
# TODO: test this!
self.logdets += T.log(T.grad(T.sum(self.drop_probs), self.logits)).sum()
self.logqw = - self.logdets
# TODO: we should multiply this by #units if we don't output them independently...
self.logpw = (self.alpha-1)*T.log(self.drop_probs).sum() + (self.beta-1)*T.log(1 - self.drop_probs).sum() # - np.log(self.denom) #<--- this term is constant
# we'll compute the whole KL term right here...
self.kl = (self.logqw - self.logpw).mean()
def _get_hyper_net(self):
# inition random noise
ep = self.srng.normal(size=(1, self.num_params), dtype=floatX)
logdets_layers = []
h_net = lasagne.layers.InputLayer([1, self.num_params])
# mean and variation of the initial noise
layer_temp = LinearFlowLayer(h_net)
h_net = IndexLayer(layer_temp, 0)
logdets_layers.append(IndexLayer(layer_temp, 1))
h_net = SplitLayer(h_net, self.num_params - self.num_classes, 1)
h_net1 = IndexLayer(h_net, 0, (1, self.num_params - self.num_classes))
h_net2 = IndexLayer(h_net, 1)
h_net1 = lasagne.layers.ReshapeLayer(h_net1,
(self.n_kernels,) + \
(np.prod(self.kernel_shape),))
if self.coupling:
# add more to introduce more correlation if needed
layer_temp = CoupledDenseLayer(h_net1, 100)
h_net1 = IndexLayer(layer_temp, 0)
logdets_layers.append(IndexLayer(layer_temp, 1))
h_net1 = PermuteLayer(h_net1, self.num_params)
layer_temp = CoupledDenseLayer(h_net1, 100)
h_net1 = IndexLayer(layer_temp, 0)
logdets_layers.append(IndexLayer(layer_temp, 1))
self.kernel_weights = lasagne.layers.get_output(h_net1, ep)
h_net1 = lasagne.layers.ReshapeLayer(h_net1,
(1, self.n_kernels * \
np.prod(self.kernel_shape) ) )
h_net = lasagne.layers.ConcatLayer([h_net1, h_net2], 1)
self.h_net = h_net
self.weights = lasagne.layers.get_output(h_net, ep)
self.logdets = sum([get_output(ld, ep) for ld in logdets_layers])
# hypernet graph
ep = srng.normal(size=(wd1,num_params), dtype=floatX)
logdets_layers = []
h_layer = lasagne.layers.InputLayer([None,num_params])
layer_temp = LinearFlowLayer(h_layer)
h_layer = IndexLayer(layer_temp,0)
logdets_layers.append(IndexLayer(layer_temp,1))
if coupling == 'conv':
if fix_sigma: assert False # not implemented
layer_temp = CoupledConv1DLayer(h_layer,16,5)
h_layer = IndexLayer(layer_temp,0)
logdets_layers.append(IndexLayer(layer_temp,1))
h_layer = PermuteLayer(h_layer,num_params)
layer_temp = CoupledConv1DLayer(h_layer,16,5)
h_layer = IndexLayer(layer_temp,0)
logdets_layers.append(IndexLayer(layer_temp,1))
elif coupling == 'dense':
layer_temp = CoupledDenseLayer(h_layer, num_params, fix_sigma=fix_sigma)
h_layer = IndexLayer(layer_temp,0)
logdets_layers.append(IndexLayer(layer_temp,1))
h_layer = PermuteLayer(h_layer,num_params)
layer_temp = CoupledDenseLayer(h_layer, num_params, fix_sigma=fix_sigma)
h_layer = IndexLayer(layer_temp,0)
logdets_layers.append(IndexLayer(layer_temp,1))
def main():
"""
MNIST example
weight norm reparameterized MLP with prior on rescaling parameters
"""
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--perdatapoint', action='store_true')
parser.add_argument('--coupling', action='store_true')
parser.add_argument('--size', default=10000, type=int)
parser.add_argument('--lrdecay', action='store_true')
parser.add_argument('--lr0', default=0.1, type=float)
parser.add_argument('--lbda', default=0.01, type=float)
parser.add_argument('--bs', default=50, type=int)
args = parser.parse_args()
print args
perdatapoint = args.perdatapoint
coupling = 1 #args.coupling
lr0 = args.lr0
lrdecay = args.lrdecay
lbda = np.cast[floatX](args.lbda)
bs = args.bs
size = max(10, min(50000, args.size))
clip_grad = 100
max_norm = 100
# load dataset
filename = '/data/lisa/data/mnist.pkl.gz'
train_x, train_y, valid_x, valid_y, test_x, test_y = load_mnist(filename)
input_var = T.matrix('input_var')
target_var = T.matrix('target_var')
dataset_size = T.scalar('dataset_size')
lr = T.scalar('lr')
# 784 -> 20 -> 10
weight_shapes = [(784, 200), (200, 10)]
num_params = sum(ws[1] for ws in weight_shapes)
if perdatapoint:
wd1 = input_var.shape[0]
else:
wd1 = 1
# stochastic hypernet
ep = srng.normal(std=0.01, size=(wd1, num_params), dtype=floatX)
logdets_layers = []
h_layer = lasagne.layers.InputLayer([None, num_params])
layer_temp = LinearFlowLayer(h_layer)
h_layer = IndexLayer(layer_temp, 0)
logdets_layers.append(IndexLayer(layer_temp, 1))
if coupling:
layer_temp = CoupledDenseLayer(h_layer, 200)
h_layer = IndexLayer(layer_temp, 0)
logdets_layers.append(IndexLayer(layer_temp, 1))
h_layer = PermuteLayer(h_layer, num_params)
layer_temp = CoupledDenseLayer(h_layer, 200)
h_layer = IndexLayer(layer_temp, 0)
logdets_layers.append(IndexLayer(layer_temp, 1))
weights = lasagne.layers.get_output(h_layer, ep)
# primary net
t = np.cast['int32'](0)
layer = lasagne.layers.InputLayer([None, 784])
inputs = {layer: input_var}
for ws in weight_shapes:
num_param = ws[1]
w_layer = lasagne.layers.InputLayer((None, ws[1]))
weight = weights[:, t:t + num_param].reshape((wd1, ws[1]))
inputs[w_layer] = weight
layer = stochasticDenseLayer2([layer, w_layer], ws[1])
print layer.output_shape
t += num_param
layer.nonlinearity = nonlinearities.softmax
y = T.clip(get_output(layer, inputs), 0.001, 0.999) # stability
# loss terms
logdets = sum([get_output(logdet, ep) for logdet in logdets_layers])
logqw = -(0.5 *
(ep**2).sum(1) + 0.5 * T.log(2 * np.pi) * num_params + logdets)
#logpw = log_normal(weights,0.,-T.log(lbda)).sum(1)
logpw = log_stdnormal(weights).sum(1)
kl = (logqw - logpw).mean()
logpyx = -cc(y, target_var).mean()
loss = -(logpyx - kl / T.cast(dataset_size, floatX))
params = lasagne.layers.get_all_params([h_layer, layer])
grads = T.grad(loss, params)
mgrads = lasagne.updates.total_norm_constraint(grads, max_norm=max_norm)
cgrads = [T.clip(g, -clip_grad, clip_grad) for g in mgrads]
updates = lasagne.updates.adam(cgrads, params, learning_rate=lr)
train = theano.function([input_var, target_var, dataset_size, lr],
loss,
updates=updates)
predict = theano.function([input_var], y.argmax(1))
records = train_model(train, predict, train_x[:size], train_y[:size],
valid_x, valid_y, lr0, lrdecay, bs)
def main():
"""
MNIST example
"""
import argparse
parser = argparse.ArgumentParser()
parser = argparse.ArgumentParser()
parser.add_argument('--perdatapoint', action='store_true')
parser.add_argument('--coupling', action='store_true')
parser.add_argument('--size', default=10000, type=int)
parser.add_argument('--lrdecay', action='store_true')
parser.add_argument('--lr0', default=0.1, type=float)
parser.add_argument('--lbda', default=10, type=float)
parser.add_argument('--bs', default=50, type=int)
args = parser.parse_args()
print args
perdatapoint = args.perdatapoint
coupling = args.coupling
size = max(10, min(50000, args.size))
clip_grad = 10
max_norm = 1000
# load dataset
filename = '/data/lisa/data/mnist.pkl.gz'
train_x, train_y, valid_x, valid_y, test_x, test_y = load_mnist(filename)
input_var = T.matrix('input_var')
target_var = T.matrix('target_var')
dataset_size = T.scalar('dataset_size')
lr = T.scalar('lr')
# 784 -> 20 -> 10
weight_shapes = [(784, 20), (20, 20), (20, 10)]
num_params = sum(np.prod(ws) for ws in weight_shapes)
if perdatapoint:
wd1 = input_var.shape[0]
else:
wd1 = 1
# stochastic hypernet
ep = srng.normal(size=(wd1, num_params), dtype=floatX)
logdets_layers = []
h_layer = lasagne.layers.InputLayer([None, num_params])
layer_temp = LinearFlowLayer(h_layer)
h_layer = IndexLayer(layer_temp, 0)
logdets_layers.append(IndexLayer(layer_temp, 1))
if coupling:
layer_temp = CoupledConv1DLayer(h_layer, 16, 5)
h_layer = IndexLayer(layer_temp, 0)
logdets_layers.append(IndexLayer(layer_temp, 1))
h_layer = PermuteLayer(h_layer, num_params)
layer_temp = CoupledConv1DLayer(h_layer, 16, 5)
h_layer = IndexLayer(layer_temp, 0)
logdets_layers.append(IndexLayer(layer_temp, 1))
weights = lasagne.layers.get_output(h_layer, ep)
# primary net
t = np.cast['int32'](0)
layer = lasagne.layers.InputLayer([None, 784])
inputs = {layer: input_var}
for ws in weight_shapes:
num_param = np.prod(ws)
print t, t + num_param
w_layer = lasagne.layers.InputLayer((None, ) + ws)
weight = weights[:, t:t + num_param].reshape((wd1, ) + ws)
inputs[w_layer] = weight
layer = stochasticDenseLayer([layer, w_layer], ws[1])
t += num_param
layer.nonlinearity = nonlinearities.softmax
y = T.clip(get_output(layer, inputs), 0.001, 0.999) # stability
# loss terms
logdets = sum([get_output(logdet, ep) for logdet in logdets_layers])
logqw = -(0.5 *
(ep**2).sum(1) + 0.5 * T.log(2 * np.pi) * num_params + logdets)
logpw = log_stdnormal(weights).sum(1)
kl = (logqw - logpw).mean()
logpyx = -cc(y, target_var).mean()
loss = -(logpyx - kl / T.cast(dataset_size, floatX))
params = lasagne.layers.get_all_params([h_layer, layer])
grads = T.grad(loss, params)
mgrads = lasagne.updates.total_norm_constraint(grads, max_norm=max_norm)
cgrads = [T.clip(g, -clip_grad, clip_grad) for g in mgrads]
updates = lasagne.updates.nesterov_momentum(cgrads,
params,
learning_rate=lr)
train = theano.function([input_var, target_var, dataset_size, lr],
loss,
updates=updates)
predict = theano.function([input_var], y.argmax(1))
records = train_model(train, predict, train_x[:size], train_y[:size],
valid_x, valid_y)
output_probs = theano.function([input_var], y)
MCt = np.zeros((100, 1000, 10))
MCv = np.zeros((100, 1000, 10))
for i in range(100):
MCt[i] = output_probs(train_x[:1000])
MCv[i] = output_probs(valid_x[:1000])
tr = np.equal(MCt.mean(0).argmax(-1), train_y[:1000].argmax(-1)).mean()
va = np.equal(MCv.mean(0).argmax(-1), valid_y[:1000].argmax(-1)).mean()
print "train perf=", tr
print "valid perf=", va
for ii in range(15):
print np.round(MCt[ii][0] * 1000)
def main():
"""
MNIST example
weight norm reparameterized MLP with prior on rescaling parameters
"""
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--coupling', action='store_true')
parser.add_argument('--size', default=10000, type=int)
parser.add_argument('--lrdecay', action='store_true')
parser.add_argument('--lr0', default=0.1, type=float)
parser.add_argument('--lbda', default=0.01, type=float)
parser.add_argument('--bs', default=50, type=int)
args = parser.parse_args()
print args
coupling = args.coupling
lr0 = args.lr0
lrdecay = args.lrdecay
lbda = np.cast[floatX](args.lbda)
bs = args.bs
size = max(10, min(50000, args.size))
clip_grad = 5
max_norm = 10
# load dataset
filename = '/data/lisa/data/mnist.pkl.gz'
train_x, train_y, valid_x, valid_y, test_x, test_y = load_mnist(filename)
train_x = train_x.reshape(50000, 1, 28, 28)
valid_x = valid_x.reshape(10000, 1, 28, 28)
test_x = test_x.reshape(10000, 1, 28, 28)
input_var = T.tensor4('input_var')
target_var = T.matrix('target_var')
dataset_size = T.scalar('dataset_size')
lr = T.scalar('lr')
# 784 -> 20 -> 10
weight_shapes = [
(16, 1, 5, 5), # -> (None, 16, 14, 14)
(16, 16, 5, 5), # -> (None, 16, 7, 7)
(16, 16, 5, 5)
] # -> (None, 16, 4, 4)
num_params = sum(np.prod(ws) for ws in weight_shapes) + 10
wd1 = 1
# stochastic hypernet
ep = srng.normal(std=0.01, size=(wd1, num_params), dtype=floatX)
logdets_layers = []
h_layer = lasagne.layers.InputLayer([None, num_params])
layer_temp = LinearFlowLayer(h_layer)
h_layer = IndexLayer(layer_temp, 0)
logdets_layers.append(IndexLayer(layer_temp, 1))
if coupling:
layer_temp = CoupledDenseLayer(h_layer, 200)
h_layer = IndexLayer(layer_temp, 0)
logdets_layers.append(IndexLayer(layer_temp, 1))
h_layer = PermuteLayer(h_layer, num_params)
layer_temp = CoupledDenseLayer(h_layer, 200)
h_layer = IndexLayer(layer_temp, 0)
logdets_layers.append(IndexLayer(layer_temp, 1))
weights = lasagne.layers.get_output(h_layer, ep)
# primary net
t = np.cast['int32'](0)
layer = lasagne.layers.InputLayer([None, 1, 28, 28])
inputs = {layer: input_var}
for ws in weight_shapes:
num_param = np.prod(ws)
weight = weights[:, t:t + num_param].reshape(ws)
num_filters = ws[0]
filter_size = ws[2]
stride = 2
pad = 'same'
layer = stochasticConv2DLayer([layer, weight], num_filters,
filter_size, stride, pad)
print layer.output_shape
t += num_param
w_layer = lasagne.layers.InputLayer((None, 10))
weight = weights[:, t:t + 10].reshape((wd1, 10))
inputs[w_layer] = weight
layer = stochasticDenseLayer2([layer, w_layer],
10,
nonlinearity=nonlinearities.softmax)
y = T.clip(get_output(layer, inputs), 0.001, 0.999)
# loss terms
logdets = sum([get_output(logdet, ep) for logdet in logdets_layers])
logqw = -(0.5 *
(ep**2).sum(1) + 0.5 * T.log(2 * np.pi) * num_params + logdets)
logpw = log_normal(weights, 0., -T.log(lbda)).sum(1)
#logpw = log_stdnormal(weights).sum(1)
kl = (logqw - logpw).mean()
logpyx = -cc(y, target_var).mean()
loss = -(logpyx - kl / T.cast(dataset_size, floatX))
params = lasagne.layers.get_all_params([layer])[1:] # excluding rand state
grads = T.grad(loss, params)
mgrads = lasagne.updates.total_norm_constraint(grads, max_norm=max_norm)
cgrads = [T.clip(g, -clip_grad, clip_grad) for g in mgrads]
updates = lasagne.updates.adam(cgrads, params, learning_rate=lr)
train = theano.function([input_var, target_var, dataset_size, lr],
loss,
updates=updates)
predict = theano.function([input_var], y.argmax(1))
records = train_model(train, predict, train_x[:size], train_y[:size],
valid_x, valid_y, lr0, lrdecay, bs)
output_probs = theano.function([input_var], y)
MCt = np.zeros((100, 1000, 10))
MCv = np.zeros((100, 1000, 10))
for i in range(100):
MCt[i] = output_probs(train_x[:1000])
MCv[i] = output_probs(valid_x[:1000])
tr = np.equal(MCt.mean(0).argmax(-1), train_y[:1000].argmax(-1)).mean()
va = np.equal(MCv.mean(0).argmax(-1), valid_y[:1000].argmax(-1)).mean()
print "train perf=", tr
print "valid perf=", va
for ii in range(15):
print np.round(MCt[ii][0] * 1000)
