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)
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 = get_output(layer,inputs)
#y = T.clip(y, 0.00001, 0.99999) # stability
###########################
# loss and grad
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)
logpyx = - cc(y,target_var).mean()
kl = (logqw - logpw).mean()
ds = T.cast(dataset_size,floatX)
loss = - (logpyx - kl/ds)
params = lasagne.layers.get_all_params([h_layer,layer])
grads = T.grad(loss, params)
###########################
# extra monitoring
nll_grads = flatten_list(T.grad(-logpyx, params, disconnected_inputs='warn')).norm(2)
prior_grads = flatten_list(T.grad(-logpw.mean() / ds, params, disconnected_inputs='warn')).norm(2)
entropy_grads = flatten_list(T.grad(logqw.mean() / ds, params, disconnected_inputs='warn')).norm(2)
outputs = [loss, -logpyx, -logpw / ds, logqw / ds,
nll_grads, prior_grads, entropy_grads,
logdets] # logdets is "legacy"
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)