def test_multinomial_n_samples():
mode_ = mode
if mode == 'FAST_COMPILE':
mode_ = 'FAST_RUN'
if (mode in ['DEBUG_MODE', 'DebugMode', 'FAST_COMPILE'] or
mode == 'Mode' and config.linker in ['py']):
sample_size = (49, 5)
else:
sample_size = (450, 6)
mode_ = theano.compile.mode.get_mode(mode_)
pvals = numpy.asarray(numpy.random.uniform(size=sample_size))
pvals = numpy.apply_along_axis(lambda row: row / numpy.sum(row), 1, pvals)
R = MRG_RandomStreams(234, use_cuda=False)
for n_samples, steps in zip([5, 10, 100, 1000], [20, 10, 1, 1]):
m = R.multinomial(pvals=pvals, n=n_samples, dtype=config.floatX, nstreams=30 * 256)
f = theano.function([], m, mode=mode_)
basic_multinomialtest(f, steps, sample_size, pvals, n_samples, prefix='mrg ')
sys.stdout.flush()
if mode != 'FAST_COMPILE' and cuda_available:
R = MRG_RandomStreams(234, use_cuda=True)
pvals = numpy.asarray(pvals, dtype='float32')
n = R.multinomial(pvals=pvals, n=n_samples, dtype='float32', nstreams=30 * 256)
assert n.dtype == 'float32'
f = theano.function(
[],
theano.sandbox.cuda.basic_ops.gpu_from_host(n),
mode=mode_.including('gpu'))
sys.stdout.flush()
basic_multinomialtest(f, steps, sample_size, pvals, n_samples, prefix='gpu mrg ')
def test_multinomial():
steps = 100
mode_ = mode
if mode == 'FAST_COMPILE':
mode_ = 'FAST_RUN'
if (mode in ['DEBUG_MODE', 'DebugMode', 'FAST_COMPILE'] or
mode == 'Mode' and config.linker in ['py']):
sample_size = (49, 5)
else:
sample_size = (450, 6)
mode_ = theano.compile.mode.get_mode(mode_)
# print ''
# print 'ON CPU:'
pvals = numpy.asarray(numpy.random.uniform(size=sample_size))
pvals = numpy.apply_along_axis(lambda row: row / numpy.sum(row), 1, pvals)
R = MRG_RandomStreams(234, use_cuda=False)
# Note: we specify `nstreams` to avoid a warning.
m = R.multinomial(pvals=pvals, dtype=config.floatX, nstreams=30 * 256)
f = theano.function([], m, mode=mode_)
# theano.printing.debugprint(f)
out = f()
basic_multinomialtest(f, steps, sample_size, pvals, n_samples=1,
prefix='mrg ')
sys.stdout.flush()
if mode != 'FAST_COMPILE' and cuda_available:
# print ''
# print 'ON GPU:'
R = MRG_RandomStreams(234, use_cuda=True)
pvals = numpy.asarray(pvals, dtype='float32')
# We give the number of streams to avoid a warning.
n = R.multinomial(pvals=pvals, dtype='float32', nstreams=30 * 256)
# well, it's really that this test w GPU doesn't make sense otw
assert n.dtype == 'float32'
f = theano.function(
[],
theano.sandbox.cuda.basic_ops.gpu_from_host(n),
mode=mode_.including('gpu'))
# theano.printing.debugprint(f)
gpu_out = f()
sys.stdout.flush()
basic_multinomialtest(f, steps, sample_size, pvals, n_samples=1,
prefix='gpu mrg ')
numpy.testing.assert_array_almost_equal(out, gpu_out, decimal=6)
def prediction(self, h, bias):
srng = RandomStreams(seed=42)
prop, mean_x, mean_y, std_x, std_y, rho, bernoulli = \
self.compute_parameters(h, bias)
mode = T.argmax(srng.multinomial(pvals=prop, dtype=prop.dtype), axis=1)
v = T.arange(0, mean_x.shape[0])
m_x = mean_x[v, mode]
m_y = mean_y[v, mode]
s_x = std_x[v, mode]
s_y = std_y[v, mode]
r = rho[v, mode]
# cov = r * (s_x * s_y)
normal = srng.normal((h.shape[0], 2))
x = normal[:, 0]
y = normal[:, 1]
# x_n = T.shape_padright(s_x * x + cov * y + m_x)
# y_n = T.shape_padright(s_y * y + cov * x + m_y)
x_n = T.shape_padright(m_x + s_x * x)
y_n = T.shape_padright(m_y + s_y * (x * r + y * T.sqrt(1.-r**2)))
uniform = srng.uniform((h.shape[0],))
pin = T.shape_padright(T.cast(bernoulli > uniform, floatX))
return T.concatenate([x_n, y_n, pin], axis=1)
def dropout(X, p_use=1.):
if p_use < 1.:
rs = RandomStreams()
out = rs.multinomial(pvals=[[p_use, 1.-p_use]]*len(X))
print out
else:
return X
def dropout(X, p_use=1.):
if p_use < 1.:
rs = RandomStreams()
out = rs.multinomial(pvals=[[p_use, 1.-p_use]])
print out.flatten()
print dir(out.T)
else:
return X
def test_multinomial():
steps = 100
mode_ = mode
if mode == "FAST_COMPILE":
mode_ = "FAST_RUN"
if mode in ["DEBUG_MODE", "DebugMode", "FAST_COMPILE"]:
sample_size = (49, 5)
else:
sample_size = (450, 6)
mode_ = theano.compile.mode.get_mode(mode_)
print ""
print "ON CPU:"
pvals = numpy.asarray(numpy.random.uniform(size=sample_size))
pvals = numpy.apply_along_axis(lambda row: row / numpy.sum(row), 1, pvals)
R = MRG_RandomStreams(234, use_cuda=False)
# Note: we specify `nstreams` to avoid a warning.
m = R.multinomial(pvals=pvals, dtype=config.floatX, nstreams=30 * 256)
f = theano.function([], m, mode=mode_)
theano.printing.debugprint(f)
out = f()
basic_multinomialtest(f, steps, sample_size, pvals, prefix="mrg ")
sys.stdout.flush()
if mode != "FAST_COMPILE" and cuda_available:
print ""
print "ON GPU:"
R = MRG_RandomStreams(234, use_cuda=True)
pvals = numpy.asarray(pvals, dtype="float32")
# We give the number of streams to avoid a warning.
n = R.multinomial(pvals=pvals, dtype="float32", nstreams=30 * 256)
assert n.dtype == "float32" # well, it's really that this test w GPU doesn't make sense otw
f = theano.function([], theano.sandbox.cuda.basic_ops.gpu_from_host(n), mode=mode_.including("gpu"))
theano.printing.debugprint(f)
gpu_out = f()
sys.stdout.flush()
basic_multinomialtest(f, steps, sample_size, pvals, prefix="gpu mrg ")
numpy.testing.assert_array_almost_equal(out, gpu_out, decimal=6)
def __init__(self, seq_len, emb_size, n_hidden, size_dict, batch_size, lr):
self.seq_len = seq_len
self.batch_size = batch_size
w_emb = shared(np.random.normal(
0, 0.01, size=(size_dict, emb_size)).astype(dtype=floatX))
w_in = shared(np.random.normal(
0, 0.01, size=(emb_size, n_hidden)).astype(dtype=floatX))
b_in = shared(np.random.normal(
0, 0.01, size=(n_hidden,)).astype(dtype=floatX))
# IRNN initialization
# w_hidden = shared(np.eye(n_hidden).astype(dtype=floatX))
w_hidden = shared(np.random.normal(
0, 0.01, size=(n_hidden, n_hidden)).astype(dtype=floatX))
b_hidden = shared(np.random.normal(
0, 0.01, size=(n_hidden,)).astype(dtype=floatX))
w_out = shared(np.random.normal(
0, 0.01, size=(n_hidden, size_dict)).astype(dtype=floatX))
b_out = shared(np.random.normal(
0, 0.01, size=(size_dict,)).astype(dtype=floatX))
self.params = [w_emb, w_in, b_in, w_hidden, b_hidden, w_out, b_out]
x = t.imatrix('x')
y = t.ivector('y')
self.init_state = shared(np.zeros((batch_size, n_hidden), dtype=floatX))
buff = self.init_state
for e in xrange(seq_len):
emb = w_emb[x[:, e]]
emb = emb.reshape((x.shape[0], -1))
buff = relu(t.dot(emb, w_in) + t.dot(buff, w_hidden) + b_hidden)
y_hat = t.nnet.softmax((t.dot(buff, w_out)) + b_out)
cost = t.nnet.categorical_crossentropy(y_hat, y).mean()
params = [w_emb, w_in, w_hidden, b_hidden, w_out, b_out]
grads = t.grad(cost, params)
updates = [(self.init_state, buff)] + \
[(w, w - lr * p) for w, p in zip(params, grads)]
self.fun_cost = function([x, y], cost, updates=updates)
rng = MRG_RandomStreams(42)
next_char = t.argmax(rng.multinomial(pvals=y_hat), axis=1)
self.fun_predict = function([x], next_char)
def test_target_parameter():
srng = MRG_RandomStreams()
pvals = np.array([[.98, .01, .01], [.01, .49, .50]])
def basic_target_parameter_test(x):
f = theano.function([], x)
assert isinstance(f(), np.ndarray)
basic_target_parameter_test(srng.uniform((3, 2), target='cpu'))
basic_target_parameter_test(srng.binomial((3, 2), target='cpu'))
basic_target_parameter_test(srng.multinomial(pvals=pvals.astype('float32'), target='cpu'))
basic_target_parameter_test(srng.choice(p=pvals.astype('float32'), replace=False, target='cpu'))
basic_target_parameter_test(srng.multinomial_wo_replacement(pvals=pvals.astype('float32'), target='cpu'))
def test_undefined_grad_opt():
# Make sure that undefined grad get removed in optimized graph.
random = RandomStreams(np.random.randint(1, 2147462579))
pvals = theano.shared(np.random.rand(10, 20).astype(theano.config.floatX))
pvals = pvals / pvals.sum(axis=1)
pvals = gradient.zero_grad(pvals)
samples = random.multinomial(pvals=pvals, n=1)
samples = theano.tensor.cast(samples, pvals.dtype)
samples = gradient.zero_grad(samples)
cost = theano.tensor.sum(samples + pvals)
grad = theano.tensor.grad(cost, samples)
f = theano.function([], grad)
theano.printing.debugprint(f)
assert not any([isinstance(node.op, gradient.UndefinedGrad) for node in f.maker.fgraph.apply_nodes])
def get_decide_func(self):
"""
Returns a theano function that takes a minibatch
(num_examples, num_features) of contexts and returns
a minibatch (num_examples, num_classes) of one-hot codes
for actions.
"""
X = T.matrix()
y_hat = self.mlp.fprop(X)
theano_rng = MRG_RandomStreams(2013 + 11 + 20)
if self.stochastic:
a = theano_rng.multinomial(pvals=y_hat, dtype='float32')
else:
mx = T.max(y_hat, axis=1).dimshuffle(0, 'x')
a = T.eq(y_hat, mx)
if self.epsilon is not None:
a = theano_rng.multinomial(pvals = (1. - self.epsilon) * a +
self.epsilon * T.ones_like(y_hat) / y_hat.shape[1],
dtype = 'float32')
if self.epsilon_stochastic is not None:
a = theano_rng.multinomial(pvals = (1. - self.epsilon_stochastic) * a +
self.epsilon_stochastic * y_hat,
dtype = 'float32')
print "Compiling classifier agent learning function"
t1 = time.time()
f = function([X], a)
t2 = time.time()
print "...done, took", t2 - t1
return f
def stochastic_pool(neibs, axis, deterministic):
"""
NOTE: assumes that inputs are >= 0
"""
assert axis == 1
# TODO parameterize
epsilon = 1e-6
as_p = neibs / (neibs.sum(axis=axis, keepdims=True) + epsilon)
if deterministic:
mask = as_p
else:
# FIXME save state in network
srng = MRG_RandomStreams()
mask = srng.multinomial(pvals=as_p).astype(fX)
return (neibs * mask).sum(axis=axis)
def softmax_sample_layer(list_of_multinomial_inputs, name, random_state=None):
theano_seed = random_state.randint(-2147462579, 2147462579)
# Super edge case...
if theano_seed == 0:
print("WARNING: prior layer got 0 seed. Reseeding...")
theano_seed = random_state.randint(-2**32, 2**32)
theano_rng = MRG_RandomStreams(seed=theano_seed)
conc_multinomial = concatenate(list_of_multinomial_inputs, name, axis=1)
shape = expression_shape(conc_multinomial)
conc_multinomial /= len(list_of_multinomial_inputs)
tag_expression(conc_multinomial, name, shape)
samp = theano_rng.multinomial(pvals=conc_multinomial,
dtype="int32")
tag_expression(samp, name, (shape[0], shape[1]))
return samp
def test_multinomial():
steps = 100
if (config.mode in ['DEBUG_MODE', 'DebugMode', 'FAST_COMPILE'] or
config.mode == 'Mode' and config.linker in ['py']):
sample_size = (49, 5)
else:
sample_size = (450, 6)
pvals = np.asarray(np.random.uniform(size=sample_size))
pvals = np.apply_along_axis(lambda row: row / np.sum(row), 1, pvals)
R = MRG_RandomStreams(234)
# Note: we specify `nstreams` to avoid a warning.
m = R.multinomial(pvals=pvals, dtype=config.floatX, nstreams=30 * 256)
f = theano.function([], m)
f()
basic_multinomialtest(f, steps, sample_size, pvals, n_samples=1,
prefix='mrg ')
def test_multinomial_n_samples():
if (config.mode in ['DEBUG_MODE', 'DebugMode', 'FAST_COMPILE'] or
config.mode == 'Mode' and config.linker in ['py']):
sample_size = (49, 5)
else:
sample_size = (450, 6)
pvals = np.asarray(np.random.uniform(size=sample_size))
pvals = np.apply_along_axis(lambda row: row / np.sum(row), 1, pvals)
R = MRG_RandomStreams(234)
for n_samples, steps in zip([5, 10, 100, 1000], [20, 10, 1, 1]):
m = R.multinomial(pvals=pvals, n=n_samples,
dtype=config.floatX, nstreams=30 * 256)
f = theano.function([], m)
basic_multinomialtest(f, steps, sample_size, pvals,
n_samples, prefix='mrg ')
sys.stdout.flush()
def get_cost(self, X, Y, **kwargs):
# Dream
theano_rng = MRG_RandomStreams(2012 + 12 + 18)
exp_y = T.nnet.softmax(T.alloc(0., self.batch_size, self.n_classes) + self.gyb)
dy = theano_rng.multinomial(pvals = exp_y, dtype='float32')
dy = block_gradient(dy)
exp_h2 = T.nnet.sigmoid(T.dot(dy, self.gh2w) + self.gh2b)
dh2 = theano_rng.binomial(p = exp_h2, size = exp_h2.shape, dtype='float32')
dh2 = block_gradient(dh2)
exp_h1 = T.nnet.sigmoid(T.dot(dh2, self.gh1w) + self.gh1b)
dh1 = theano_rng.binomial(p = exp_h1, size = exp_h1.shape, dtype='float32')
dh1 = block_gradient(dh1)
exp_v = T.nnet.sigmoid(T.dot(dh1, self.gvw) + self.gvb)
dv = theano_rng.binomial(p = exp_v, size = exp_v.shape, dtype='float32')
dv = block_gradient(dv)
# Explanation of dream
zh1, rh1 = self.infer_h1(dv)
zh2 = T.dot(rh1, self.rh2w) + self.rh2b
rh2 = T.nnet.sigmoid(zh2)
zy = T.dot(rh2, self.ryw) + self.ryb
# Probability of dream
dream_prob = sigmoid_prob(zh1, dh1) + sigmoid_prob(zh2, dh2) + softmax_prob(zy, dy)
# Explanation of reality
zh1, rh1 = self.infer_h1(X)
rh1 = block_gradient(rh1)
zh2 = T.dot(rh1, self.rh2w) + self.rh2b
rh2 = theano_rng.binomial(p = T.nnet.sigmoid(zh2), size = zh2.shape, dtype='float32')
rh2 = block_gradient(rh2)
# Probability of reality
real_prob = softmax_prob(T.alloc(0., self.batch_size, self.n_classes) + self.gyb, Y) + \
sigmoid_prob(T.dot(Y, self.gh2w) + self.gh2b, rh2) + \
sigmoid_prob(T.dot(rh2, self.gh1w) + self.gh1b, rh1) + \
sigmoid_prob(T.dot(rh1, self.gvw) + self.gvb, X)
return - dream_prob - real_prob + .0001 * (
T.sqr(self.gvw).sum() + T.sqr(self.gh1w).sum() + \
T.sqr(self.gh2w).sum()
)
def make_state(self, num_examples, numpy_rng):
""" Returns a shared variable containing an actual state
(not a mean field state) for this variable.
"""
t1 = time.time()
empty_input = self.output_space.get_origin_batch(num_examples)
h_state = sharedX(empty_input)
default_z = T.zeros_like(h_state) + self.b
theano_rng = MRG_RandomStreams(numpy_rng.randint(2 ** 16))
h_exp = T.nnet.softmax(default_z)
h_sample = theano_rng.multinomial(pvals = h_exp, dtype = h_exp.dtype)
p_state = sharedX( self.output_space.get_origin_batch(
num_examples))
t2 = time.time()
f = function([], updates = {
h_state : h_sample
})
t3 = time.time()
f()
t4 = time.time()
print str(self)+'.make_state took',t4-t1
print '\tcompose time:',t2-t1
print '\tcompile time:',t3-t2
print '\texecute time:',t4-t3
h_state.name = 'softmax_sample_shared'
return h_state
def softmax_sample_layer(list_of_multinomial_inputs, graph, name,
random_state=None):
theano_seed = random_state.randint(-2147462579, 2147462579)
# Super edge case...
if theano_seed == 0:
print("WARNING: prior layer got 0 seed. Reseeding...")
theano_seed = random_state.randint(-2**32, 2**32)
theano_rng = MRG_RandomStreams(seed=theano_seed)
conc_multinomial = concatenate(list_of_multinomial_inputs, graph,
name, axis=1)
conc_multinomial /= len(list_of_multinomial_inputs)
samp = theano_rng.multinomial(pvals=conc_multinomial,
dtype="int32")
# We know shape of conc_multinomial == shape of random sample
shape = calc_expected_dims(graph, conc_multinomial)
list_of_random = [samp, ]
list_of_names = [name + "_random", ]
list_of_shapes = [shape, ]
add_random_to_graph(list_of_random, list_of_shapes, list_of_names, graph)
return samp
def __init__(self, seq_len, emb_size, n_hidden, size_dict, lr):
self.seq_len = seq_len
# Parameters
w_emb = shared(np.random.normal(
0, 0.01, size=(size_dict, emb_size)).astype(dtype=floatX))
w_hidden = shared(np.random.normal(
0, 0.01, size=(seq_len * emb_size, n_hidden)).astype(dtype=floatX))
b_hidden = shared(np.random.normal(
0, 0.01, size=(n_hidden,)).astype(dtype=floatX))
w_out = shared(np.random.normal(
0, 0.01, size=(n_hidden, size_dict)).astype(dtype=floatX))
b_out = shared(np.random.normal(
0, 0.01, size=(size_dict,)).astype(dtype=floatX))
# Graph
x = t.imatrix('x')
target = t.ivector('y')
emb = w_emb[x]
buff = relu(t.dot(emb.reshape((x.shape[0], -1)), w_hidden) +
b_hidden)
y_hat = t.nnet.softmax((t.dot(buff, w_out)) + b_out)
cost = t.nnet.categorical_crossentropy(y_hat, target).mean()
params = [w_emb, w_hidden, b_hidden, w_out, b_out]
grads = t.grad(cost, params)
updates = [(w, w - lr * p) for w, p in zip(params, grads)]
self.fun_cost = theano.function([x, target], cost, updates=updates)
# Sampling function
rng = MRG_RandomStreams(42)
next_char = t.argmax(rng.multinomial(pvals=y_hat), axis=1)
self.fun_predict = theano.function([x], next_char)
def lwta(p, block_size):
"""
The hard local winner take all non-linearity from "Compete to Compute"
by Rupesh Srivastava et al
Our implementation differs slightly from theirs--we break ties randomly,
they break them by earliest index. This difference is just due to ease
of implementation in theano.
"""
batch_size = p.shape[0]
num_filters = p.shape[1]
num_blocks = num_filters // block_size
w = p.reshape((batch_size, num_blocks, block_size))
block_max = w.max(axis=2).dimshuffle(0, 1, 'x') * T.ones_like(w)
max_mask = T.cast(w >= block_max, 'float32')
theano_rng = MRG_RandomStreams(20131206 % (2 ** 16))
denom = max_mask.sum(axis=2).dimshuffle(0, 1, 'x')
probs = max_mask / denom
probs = probs.reshape((batch_size * num_blocks, block_size))
max_mask = theano_rng.multinomial(pvals=probs, dtype='float32')
max_mask = max_mask.reshape((batch_size, num_blocks, block_size))
w = w * max_mask
w = w.reshape((p.shape[0], p.shape[1]))
return w
class SLmodel():
#This is the switched conditional linear model for integrating
#action with sensation
def __init__(self, nx, ns, nh, na, npcl, xvar=1.0):
#for this model I assume one linear generative model and a
#combination of nh linear dynamical models
#generative matrix
init_W=np.asarray(np.random.randn(nx,ns)/10.0,dtype='float32')
#observed variable means
init_c=np.asarray(np.zeros(nx),dtype='float32')
#dynamical matrices
init_M=np.asarray((np.tile(np.eye(ns),(1,nh))),dtype='float32') #for state-based predictions
init_C=np.asarray((np.tile(np.zeros((na,ns)),(1,nh))),dtype='float32') #for action-based predictions
#state-variable variances
#(covariance matrix of state variable noise assumed to be diagonal)
init_b=np.asarray(np.ones(ns)*10.0,dtype='float32')
#Switching parameter matrices
init_A=np.asarray(np.zeros((ns,nh)),dtype='float32') #associated with the state
init_B=np.asarray(np.zeros((na,nh)),dtype='float32') #associated with actions
#priors for switching variable
init_ph=np.asarray(np.zeros(nh),dtype='float32')
init_s_now=np.asarray(np.zeros((npcl,ns)),dtype='float32')
init_weights_now=np.asarray(np.ones(npcl)/float(npcl),dtype='float32')
init_s_past=np.asarray(np.zeros((npcl,ns)),dtype='float32')
init_h_past=np.asarray(np.zeros((npcl,nh)),dtype='float32')
init_h_past[:,0]=1.0
init_weights_past=np.asarray(np.ones(npcl)/float(npcl),dtype='float32')
init_a_past=np.asarray(np.zeros((1,na)),dtype='float32')
self.W=theano.shared(init_W)
self.c=theano.shared(init_c)
self.M=theano.shared(init_M)
self.C=theano.shared(init_C)
self.b=theano.shared(init_b)
self.A=theano.shared(init_A)
self.B=theano.shared(init_B)
self.ph=theano.shared(init_ph)
#this is to help vectorize operations
self.sum_mat=T.as_tensor_variable(np.asarray((np.tile(np.eye(ns),nh)).T,dtype='float32'))
self.s_now=theano.shared(init_s_now)
self.weights_now=theano.shared(init_weights_now)
self.s_past=theano.shared(init_s_past)
self.h_past=theano.shared(init_h_past)
self.a_past=theano.shared(init_a_past)
self.weights_past=theano.shared(init_weights_past)
self.xvar=np.asarray(xvar,dtype='float32')
self.nx=nx #dimensionality of observed variables
self.ns=ns #dimensionality of latent variables
self.nh=nh #number of (linear) dynamical modes
self.na=na #dimensionality of action variables
self.npcl=npcl #numer of particles in particle filter
self.theano_rng = RandomStreams()
self.params= [self.W, self.M, self.C, self.b, self.A, self.B, self.c, self.ph]
self.rel_lrates=np.asarray([ 0.1, 1.0, 1.0, 0.01, 10.0, 10.0, 0.1, 1.0] ,dtype='float32')
def sample_proposal_s(self, s, a, h, xpred, sig):
s_pred=self.get_prediction(s, a, h)
n=self.theano_rng.normal(size=T.shape(s))
#This is the proposal distribution that arises when one assumes that W'W=I
mean=2.0*(xpred+s_pred*(self.b**2))*sig
s_prop=mean+n*T.sqrt(sig)
#I compute the term inside the exponent for the pdf of the proposal distrib
prop_term=-T.sum(n**2)/2.0
return T.cast(s_prop,'float32'), T.cast(s_pred,'float32'), T.cast(prop_term,'float32')
#This function is required if we allow multiple generative models
#def get_recon(self, s, h):
#W_vec=T.sum(self.W*h, axis=0)
#W=W.reshape((self.nx, self.ns))
#xr=T.dot(W, s)
#return xr
def calc_h_probs(self, s, a):
#this function takes an np by ns matrix of s samples plus
#an action vector a
#and returns an nh by np set of h probabilities
exp_terms=T.dot(s, self.A)+ T.reshape(T.dot(a, self.B),(1,self.nh)) + T.reshape(self.ph,(1,self.nh))
#re-centering for numerical stability
exp_terms_recentered=exp_terms-T.max(exp_terms,axis=1)
#exponentiation and normalization
rel_probs=T.exp(exp_terms)
probs=rel_probs.T/T.sum(rel_probs, axis=1)
return probs.T
def forward_filter_step(self, a, xp):
#first sample from h given s and a
h_probs = self.calc_h_probs(self.s_now, a)
h_samps=self.theano_rng.multinomial(pvals=h_probs)
#need to sample from the proposal distribution
#these terms are the same for every particle
xpred=T.dot(self.W.T,(xp-self.c))/(2.0*self.xvar**2)
sig=(1.0/(self.b**2+1.0/(2.0*self.xvar**2)))/2.0
#sig=1.0/(self.b**2)
#vectorized version
s_pred=self.get_prediction(self.s_now, a, h_samps)
n=self.theano_rng.normal(size=T.shape(self.s_now))
mean=2.0*(xpred+s_pred*(self.b**2))*sig
#mean=s_pred #trying out using solely predictive proposal distrib
s_samps=mean+n*T.sqrt(sig)
prop_terms=-T.sum(n**2,axis=1)/2.0
updates={}
#now that we have samples from the proposal distribution, we need to reweight them
recons=T.dot(self.W, s_samps.T) + T.reshape(self.c,(self.nx,1))
x_terms=-T.sum((recons-T.reshape(xp,(self.nx,1)))**2,axis=0)/(2.0*self.xvar**2)
s_terms=-T.sum(((s_samps-s_pred)*self.b)**2,axis=1)/2.0
energies=x_terms+s_terms-prop_terms
#to avoid exponentiating large or very small numbers, I
#"re-center" the reweighting factors by adding a constant,
#as this has no impact on the resulting new weights
energies_recentered=energies-T.max(energies)
alpha=T.exp(energies_recentered) #these are the reweighting factors
new_weights_unnorm=self.weights_now*alpha
normalizer=T.sum(new_weights_unnorm)
new_weights=new_weights_unnorm/normalizer #need to normalize new weights
updates[self.h_past]=T.cast(h_samps,'float32')
updates[self.s_past]=T.cast(self.s_now,'float32')
updates[self.a_past]=T.cast(a,'float32')
updates[self.s_now]=T.cast(s_samps,'float32')
updates[self.weights_past]=T.cast(self.weights_now,'float32')
updates[self.weights_now]=T.cast(new_weights,'float32')
#return normalizer, energies_recentered, s_samps, s_pred, T.dot(self.W.T,(xp-self.c)), updates
#return normalizer, energies_recentered, updates
#return h_samps, updates
return updates
def get_prediction(self, s, a, h):
s_dot_M=T.dot(s, self.M) #this is np by nh*ns
a_dot_C=T.dot(a, self.C) #this is 1 by nh*ns
tot=s_dot_M+a_dot_C #should be np by nh*ns
s_pred=T.dot(tot*T.extra_ops.repeat(h,self.ns,axis=1),self.sum_mat) #should be np by ns
return T.cast(s_pred,'float32')
def sample_joint(self, sp):
t2_samp=self.theano_rng.multinomial(pvals=T.reshape(self.weights_now,(1,self.npcl))).T
s2_samp=T.cast(T.sum(self.s_now*T.addbroadcast(t2_samp,1),axis=0),'float32')
h2_samp=T.cast(T.sum(self.h_now*T.addbroadcast(t2_samp,1),axis=0),'float32')
diffs=self.b*(s2_samp-sp)
sqr_term=T.sum(diffs**2,axis=1)
alpha=T.exp(-sqr_term)
probs_unnorm=self.weights_past*alpha
probs=probs_unnorm/T.sum(probs_unnorm)
t1_samp=self.theano_rng.multinomial(pvals=T.reshape(probs,(1,self.npcl))).T
s1_samp=T.cast(T.sum(self.s_past*T.addbroadcast(t1_samp,1),axis=0),'float32')
h1_samp=T.cast(T.sum(self.h_past*T.addbroadcast(t1_samp,1),axis=0),'float32')
return [s1_samp, h1_samp, s2_samp, h2_samp]
def calc_mean_h_energy(self, s, a, h):
#you give this function a set of samples of s, a, and h,
#it gives you the average energy of those samples
exp_terms=T.dot(s, self.A)+ T.reshape(T.dot(a, self.B),(1,self.nh)) + T.reshape(self.ph,(1,self.nh)) #np by nh
energies=T.sum(h*exp_terms,axis=1) - T.log(T.sum(T.exp(exp_terms),axis=1)) #should be np by 1
energy=T.mean(energies)
return energy
def update_params(self, x1, x2, n_samps, lrate):
#this function samples from the joint posterior and performs
# a step of gradient ascent on the log-likelihood
sp=self.get_prediction(self.s_past, self.a_past, self.h_past)
#sp should be np by ns
[s1_samps, h1_samps, s2_samps, h2_samps], updates = theano.scan(fn=self.sample_joint,
outputs_info=[None, None, None, None],
non_sequences=[sp],
n_steps=n_samps)
x1_recons=T.dot(self.W, s1_samps.T) + T.reshape(self.c,(self.nx,1))
x2_recons=T.dot(self.W, s2_samps.T) + T.reshape(self.c,(self.nx,1))
s_pred = self.get_prediction(s1_samps, h1_samps)
hterm1=self.calc_mean_h_energy(s1_samps, h1_samps)
#hterm2=self.calc_mean_h_energy(s2_samps, h2_samps)
sterm=-T.mean(T.sum((self.b*(s2_samps-s_pred))**2,axis=1))/2.0
xterm1=-T.mean(T.sum((x1_recons-T.reshape(x1,(self.nx,1)))**2,axis=0)/(2.0*self.xvar**2))
xterm2=-T.mean(T.sum((x2_recons-T.reshape(x2,(self.nx,1)))**2,axis=0)/(2.0*self.xvar**2))
#energy = hterm1 + xterm1 + hterm2 + xterm2 + sterm -T.sum(T.sum(self.A**2))
energy = hterm1 + xterm1 + xterm2 + sterm
gparams=T.grad(energy, self.params, consider_constant=[s1_samps, s2_samps, h1_samps, h2_samps])
# constructs the update dictionary
for gparam, param, rel_lr in zip(gparams, self.params, self.rel_lrates):
#gnat=T.dot(param, T.dot(param.T,param))
updates[param] = T.cast(param + gparam*lrate*rel_lr,'float32')
#make sure W has unit-length columns
#new_W=updates[self.W]
#updates[self.W]=T.cast(new_W/T.sqrt(T.sum(new_W**2,axis=0)),'float32')
#MIGHT NEED TO NORMALIZE A
return energy, updates
def get_ESS(self):
return 1.0/T.sum(self.weights_now**2)
def resample_step(self):
idx=self.theano_rng.multinomial(pvals=T.reshape(self.weights_now,(1,self.npcl))).T
s_samp=T.sum(self.s_now*T.addbroadcast(idx,1),axis=0)
h_samp=T.sum(self.h_now*T.addbroadcast(idx,1),axis=0)
return T.cast(s_samp,'float32'), T.cast(h_samp,'float32')
def resample(self):
[s_samps, h_samps], updates = theano.scan(fn=self.resample_step,
outputs_info=[None, None],
n_steps=self.npcl)
updates[self.s_now]=T.cast(s_samps,'float32')
updates[self.h_now]=T.cast(h_samps,'float32')
updates[self.weights_now]=T.cast(T.ones_like(self.weights_now)/T.cast(self.npcl,'float32'),'float32') #dtype paranoia
return updates
def simulate_step(self, s, a):
s=T.reshape(s,(1,self.ns))
a=T.reshape(a,(1,self.na))
#get h probabilities
h_probs = self.calc_h_probs(s,a)
h_samp=self.theano_rng.multinomial(pvals=h_probs)
sp=self.get_prediction(s,a,h_samp)
xp=T.dot(self.W, sp.T) + T.reshape(self.c,(self.nx,1))
return T.cast(sp,'float32'), T.cast(xp,'float32'), h_samp
def simulate_forward(self, a, n_steps):
#a should be n_steps by na
s0=T.sum(self.s_now*T.reshape(self.weights_now,(self.npcl,1)),axis=0)
s0=T.reshape(s0,(1,self.ns))
[sp, xp, hs], updates = theano.scan(fn=self.simulate_step,
outputs_info=[s0, None, None],
sequences=[a],
n_steps=n_steps)
return sp, xp, hs, updates
class MultiRBM(RBM):
def __init__(self,
input,
n_vis,
n_hid,
n_cate,
W=None,
vbias=None,
hbias=None):
'''
The input should be a 3D tensor with (n_cat, N_sample, n_vis)
'''
self.input = input
self.n_vis = n_vis
self.n_hid = n_hid
self.n_cate = n_cate
if W is None:
W = theano.shared(np.random.normal(size=(self.n_cate, self.n_vis,
self.n_hid)).astype(
theano.config.floatX),
borrow=True)
if vbias is None:
vbias = theano.shared(np.zeros(shape=(
self.n_cate,
self.n_vis,
)).astype(theano.config.floatX),
borrow=True)
if hbias is None:
hbias = theano.shared(np.zeros(shape=(self.n_hid, )).astype(
theano.config.floatX),
borrow=True)
self.numpy_rng = np.random.RandomState(1234)
self.theano_rng = MRG_RandomStreams(self.numpy_rng.randint(2**30))
self.W = W
self.vbias = vbias
self.hbias = hbias
def free_energy(self, vis):
vW_b = T.batched_dot(vis, self.W) + T.addbroadcast(self.hbias, 1)
visible_term = T.batched_dot(vis, self.vbias)
hidden_term = T.sum(T.log(1 + T.exp(vW_b)), axis=2)
return T.sum(-hidden_term - visible_term, axis=0)
def propup(self, vis):
x = T.batched_dot(vis, self.W) + self.hbias
return [x, T.nnet.sigmoid(x)]
def propdown(self, hid):
x = T.batched_dot(hid, self.W.dimshuffle(0, 2, 1)) + \
self.vbias.dimshuffle((0, 'x', 1))
e_x = T.exp(x - x.max(axis=0, keepdims=True))
out = e_x / e_x.sum(axis=0, keepdims=True)
return [x, out]
def sample_v_given_h(self, hid):
x, out = self.propdown(hid)
v_sample = []
for v in range(self.n_vis):
v_sample += [
self.theano_rng.multinomial(n=1, pvals=out[:, :,
v].T).dimshuffle(
1, 0, 'x')
]
v_sample = T.concate(v_sample, axis=2)
return [x, out, v_sample]
def sample_h_given_v(self, vis):
x, out = self.propup(vis)
h_sample = self.theano_rng.binomial(n=1, p=out, size=out.shape)
return [x, out, h_sample]
def random_multinomial(shape=None, pvals=None, dtype=_FLOATX, seed=None):
if seed is None:
seed = np.random.randint(10e6)
rng = RandomStreams(seed=seed)
return rng.multinomial(size=shape, pvals=pvals, dtype=dtype)
def sample(p, seed=None):
if seed is None:
seed = np.random.randint(10e6)
rng = RandomStreams(seed=seed)
return rng.multinomial(n=1, pvals=p, dtype=theano.config.floatX)
class ImportanceSampler():
'''Implements importance sampling/resampling'''
def __init__(self, ndims, n_particles, true_log_probs, proposal_func=None):
'''
true_log_probs: a function that returns the true relative log probabilities
proposal_func: a function that returns (samples, relative_log_probabilities)
n_particles: the number of particles to use
'''
self.true_log_probs = true_log_probs
self.proposal_func = proposal_func
self.n_particles = n_particles
self.ndims = ndims
init_particles = np.zeros((n_particles, self.ndims))
init_weights = np.ones(n_particles) / float(n_particles)
self.particles = theano.shared(init_particles.astype(np.float32))
self.weights = theano.shared(init_weights.astype(np.float32))
self.theano_rng = RandomStreams()
self.get_ESS = None
self.perform_resampling = None
self.perform_sampling = None
def set_proposal_func(self, proposal_func):
'''You might need to use this if you want to make the proposal
function depend on the current particles'''
self.proposal_func = proposal_func
return
def sample_reweight(self):
'''Samples new particles and reweights them'''
samples, prop_log_probs = self.proposal_func()
true_log_probs = self.true_log_probs(samples)
diffs = true_log_probs - prop_log_probs
weights_unnorm = T.exp(diffs)
weights = weights_unnorm / T.sum(weights_unnorm)
updates = OrderedDict()
updates[self.weights] = T.cast(weights, 'float32')
updates[self.particles] = T.cast(samples, 'float32')
return updates
def compute_ESS(self):
'''Returns the effective sample size'''
return 1.0 / T.sum(self.weights**2)
def resample(self):
'''Resamples using the current weights'''
samps = self.theano_rng.multinomial(pvals=T.extra_ops.repeat(
self.weights.dimshuffle('x', 0), self.n_particles, axis=0))
idxs = T.cast(T.dot(samps, T.arange(self.n_particles)), 'int64')
updates = OrderedDict()
updates[self.particles] = self.particles[idxs]
updates[self.weights] = T.cast(
T.ones_like(self.weights) / float(self.n_particles), 'float32')
return updates
def compile(self):
'''Compiles the ESS, resampling, and sampling functions'''
ess = self.compute_ESS()
self.get_ESS = theano.function([], ess)
resample_updates = self.resample()
self.perform_resampling = theano.function([], updates=resample_updates)
sample_updates = self.sample_reweight()
self.perform_sampling = theano.function([], updates=sample_updates)
return
m1 = numpy.asarray(numpy.random.randint(i32max), dtype="int32")
A2 = numpy.random.randint(0, i32max, (3, 3)).astype('int64')
s2 = numpy.random.randint(0, i32max, 3).astype('int32')
m2 = numpy.asarray(numpy.random.randint(i32max), dtype="int32")
f0.input_storage[0].storage[0] = A1
f0.input_storage[1].storage[0] = s1
f0.input_storage[2].storage[0] = m1
f0.input_storage[3].storage[0] = A2
f0.input_storage[4].storage[0] = s2
f0.input_storage[5].storage[0] = m2
r_a1 = rng_mrg.matVecModM(A1, s1, m1)
r_a2 = rng_mrg.matVecModM(A2, s2, m2)
f0.fn()
r_b = f0.output_storage[0].value
assert numpy.allclose(r_a1, r_b[:3])
assert numpy.allclose(r_a2, r_b[3:])
if __name__ == "__main__":
rng = MRG_RandomStreams(numpy.random.randint(2147462579))
import time
print theano.__file__
pvals = theano.tensor.fmatrix()
for i in range(10):
t0 = time.time()
multinomial = rng.multinomial(pvals=pvals)
print time.time() - t0
def test_undefined_grad():
srng = MRG_RandomStreams(seed=1234)
# checking uniform distribution
low = tensor.scalar()
out = srng.uniform((), low=low)
with pytest.raises(theano.gradient.NullTypeGradError):
theano.grad(out, low)
high = tensor.scalar()
out = srng.uniform((), low=0, high=high)
with pytest.raises(theano.gradient.NullTypeGradError):
theano.grad(out, high)
out = srng.uniform((), low=low, high=high)
with pytest.raises(theano.gradient.NullTypeGradError):
theano.grad(out, (low, high))
# checking binomial distribution
prob = tensor.scalar()
out = srng.binomial((), p=prob)
with pytest.raises(theano.gradient.NullTypeGradError):
theano.grad(out, prob)
# checking multinomial distribution
prob1 = tensor.scalar()
prob2 = tensor.scalar()
p = [theano.tensor.as_tensor_variable([prob1, 0.5, 0.25])]
out = srng.multinomial(size=None, pvals=p, n=4)[0]
with pytest.raises(theano.gradient.NullTypeGradError):
theano.grad(theano.tensor.sum(out), prob1)
p = [theano.tensor.as_tensor_variable([prob1, prob2])]
out = srng.multinomial(size=None, pvals=p, n=4)[0]
with pytest.raises(theano.gradient.NullTypeGradError):
theano.grad(theano.tensor.sum(out), (prob1, prob2))
# checking choice
p = [theano.tensor.as_tensor_variable([prob1, prob2, 0.1, 0.2])]
out = srng.choice(a=None, size=1, p=p, replace=False)[0]
with pytest.raises(theano.gradient.NullTypeGradError):
theano.grad(out[0], (prob1, prob2))
p = [theano.tensor.as_tensor_variable([prob1, prob2])]
out = srng.choice(a=None, size=1, p=p, replace=False)[0]
with pytest.raises(theano.gradient.NullTypeGradError):
theano.grad(out[0], (prob1, prob2))
p = [theano.tensor.as_tensor_variable([prob1, 0.2, 0.3])]
out = srng.choice(a=None, size=1, p=p, replace=False)[0]
with pytest.raises(theano.gradient.NullTypeGradError):
theano.grad(out[0], prob1)
# checking normal distribution
avg = tensor.scalar()
out = srng.normal((), avg=avg)
with pytest.raises(theano.gradient.NullTypeGradError):
theano.grad(out, avg)
std = tensor.scalar()
out = srng.normal((), avg=0, std=std)
with pytest.raises(theano.gradient.NullTypeGradError):
theano.grad(out, std)
out = srng.normal((), avg=avg, std=std)
with pytest.raises(theano.gradient.NullTypeGradError):
theano.grad(out, (avg, std))
# checking truncated normal distribution
avg = tensor.scalar()
out = srng.truncated_normal((), avg=avg)
with pytest.raises(theano.gradient.NullTypeGradError):
theano.grad(out, avg)
std = tensor.scalar()
out = srng.truncated_normal((), avg=0, std=std)
with pytest.raises(theano.gradient.NullTypeGradError):
theano.grad(out, std)
out = srng.truncated_normal((), avg=avg, std=std)
with pytest.raises(theano.gradient.NullTypeGradError):
theano.grad(out, (avg, std))
class VisibleLayer(object):
def __init__(self, v_dim, h_dim, v_type, mrng=None, rng=None, name=''):
self.name = name if name != '' else 'v_layer'
self.v_dim = v_dim
self.h_dim = h_dim
self.v_type = v_type
seed = np.random.randint(1, 2**30)
self._rng = RandomStreams(seed) if rng is None else rng
self._mrng = MRG_RandomStreams(seed) if mrng is None else mrng
self._build_params()
def set_total_count(self, total_count):
if not (self.v_type == InputType.poisson):
raise ValueError(
"The input type should be Poisson to set total count")
self.total_count = total_count
def _build_params(self):
# W to connect with hidden layer
self.params = []
if self.v_type == InputType.poisson:
init_W = np.random.uniform(low=-1 / self.h_dim,
high=1 / self.h_dim,
size=(self.v_dim, self.h_dim))
self.W = init_weight(self.v_dim,
self.h_dim,
value=init_W,
name=self.name + '-W')
else:
self.W = init_weight(self.v_dim, self.h_dim, name=self.name + '-W')
self.b_v = init_bias(self.v_dim, name=self.name + '-b_v')
# Ca binary, gaussian, and categorical
self.params.extend([self.W, self.b_v])
# Truong hop gaussian co them sigma
if self.v_type == InputType.gaussian:
self.sigma_v = T.ones(shape=(self.v_dim, ),
dtype=theano.config.floatX)
self.sigma_v.name = self.name + "-sigma_v"
# Result in a vector of (n, 1)
def v_free_term(self, v):
if self.v_type == InputType.poisson:
return -T.sum(T.gammaln(1 + v), axis=1)
else:
return 0
# Result in a vector of (n, 1)
def v_bias_term(self, v):
# Note that for gaussian case, the v_bias should be negative
if self.v_type == InputType.gaussian:
return -T.sum((v - self.b_v)**2 / (2 * self.sigma_v**2), axis=1)
else:
return T.dot(v, self.b_v)
# Result in a vector of (n, H)
def v_weight_term(self, v):
if self.v_type == InputType.gaussian:
return T.dot((v / (self.sigma_v**2)), self.W)
else:
return T.dot(v, self.W)
# Only support binary, gaussian and categorical
def v_given_h(self, h):
if self.v_type == InputType.binary:
p_v_h = T.nnet.sigmoid(self.b_v + T.dot(h, self.W.T))
return p_v_h
elif self.v_type == InputType.gaussian:
mu_v = self.b_v + T.dot(h, self.W.T)
return mu_v
elif self.v_type == InputType.categorical:
p_v_h = T.nnet.softmax(self.b_v + T.dot(h, self.W.T))
return p_v_h
elif self.v_type == InputType.poisson:
if not hasattr(self, 'total_count') or self.total_count is None:
raise ValueError(
'Total count should be set for constrained Poisson')
unconstrained_lmbd_v = T.exp(self.b_v + T.dot(h, self.W.T))
lmbd_v = unconstrained_lmbd_v * 1.0 / T.sum(unconstrained_lmbd_v, axis=1, keepdims=True) \
* self.total_count
return lmbd_v
# Only support binary, gaussian and categorical
def sample_v_given_h(self, h0_sample):
if self.v_type == InputType.binary:
v1_mean = self.v_given_h(h0_sample)
v1_sample = self._mrng.binomial(size=v1_mean.shape,
n=1,
p=v1_mean,
dtype=theano.config.floatX)
return [v1_mean, v1_sample]
elif self.v_type == InputType.gaussian:
mu_v1 = self.v_given_h(h0_sample) # Note that mu_v1 is returned
v1_sample = self._mrng.normal(size=mu_v1.shape,
avg=mu_v1,
std=self.sigma_v,
dtype=theano.config.floatX)
return [mu_v1, v1_sample]
# Note that there is constraint in the case of Multinomial
elif self.v_type == InputType.categorical:
prob_v1 = self.v_given_h(h0_sample)
v1_sample = self._mrng.multinomial(pvals=prob_v1,
n=1,
dtype=theano.config.floatX)
return [prob_v1, v1_sample]
elif self.v_type == InputType.poisson:
lmbd_v1 = self.v_given_h(h0_sample)
# We have to use RandomStreams, not MRG_RandomStreams
v1_sample = self._rng.poisson(size=lmbd_v1.shape,
lam=lmbd_v1,
dtype=theano.config.floatX)
return [lmbd_v1, v1_sample]
def l1_grad(self, l1):
gW = l1_grad(self.W, l1)
return [gW, 0]
def l2_grad(self, l2):
gW = l2_grad(self.W, l2)
return [gW, 0]
def nll_grad_formula(self, v0, vk, h0, hk):
n_instances = v0.shape[0]
gW = (T.dot(vk.T, hk) - T.dot(v0.T, h0)) / n_instances
if self.v_type == InputType.gaussian:
gb_v = T.mean((vk - v0) / (self.sigma_v**2), axis=0)
grads = [gW, gb_v]
else:
gb_v = T.mean(vk - v0, axis=0)
grads = [gW, gb_v]
return grads
def get_viewed_cost(self, v0, vk_stat):
# Binary cross-entropy
cost = 0
if self.v_type == InputType.binary:
# Clip to avoid log(0)
clip_vk_stat = T.clip(vk_stat, np.float32(0.000001),
np.float32(0.999999))
cost = -T.sum(v0 * T.log(clip_vk_stat) +
(1 - v0) * T.log(1 - clip_vk_stat),
axis=1)
# Sum square error
elif self.v_type == InputType.gaussian:
cost = T.sum((v0 - vk_stat)**2, axis=1)
# Categorical cross-entropy
elif self.v_type == InputType.categorical:
clip_vk_stat = T.clip(vk_stat, np.float32(0.000001),
np.float32(0.999999))
cost = -T.sum(v0 * T.log(clip_vk_stat), axis=1)
elif self.v_type == InputType.poisson:
clip_vk_stat = T.clip(vk_stat, np.float32(0.000001), np.inf)
cost = -T.sum(
-vk_stat + v0 * T.log(clip_vk_stat) - T.gammaln(1 + v0),
axis=1)
return cost
def get_params(self):
return self.params
class RBM(Model):
def __init__(self, v_dim=784, h_dim=500, input_type=InputType.binary,
W=None, b_h=None, b_v=None, sigma=None, input_var=None,
mrng=None, rng=None, name='', **kwargs):
name = 'rbm' if name == '' else name
super(RBM, self).__init__(name=name)
model_file = kwargs.get('model_file')
if model_file is not None:
self.load(model_file)
self._load_params()
else:
# v_dim is the dimensions of visible variable v. v_dim = D
self.v_dim = v_dim
# v_dim is the dimensions of visible variable v. h_dim = H
self.h_dim = h_dim
self.input_type = input_type
seed = np.random.randint(1, 2**30)
self._rng = RandomStreams(seed) if rng is None else rng
self._mrng = MRG_RandomStreams(seed) if mrng is None else mrng
self._build_params(W, b_h, b_v, sigma)
self.input = input_var if input_var is not None else T.matrix('input')
if self.input_type == InputType.poisson or self.input_type == InputType.replicated_softmax:
self.total_count = T.sum(self.input, axis=1, keepdims=True)
def _load_params(self):
[self.W, self.b_h, self.b_v] = self.params
def _build_params(self, W, b_h, b_v, sigma):
self.params = []
self.W = W if W is not None else init_weight(self.v_dim, self.h_dim, name=self.name+'-W')
self.b_h = b_h if b_h is not None else init_bias(self.h_dim, name=self.name+'-b_h')
self.b_v = b_v if b_v is not None else init_bias(self.v_dim, name=self.name+'-b_v')
# sigma_v is not considered to be a param
self.params.extend([self.W, self.b_h, self.b_v])
# Truong hop gaussian co them sigma
self.sigma_v = None
if self.input_type == InputType.gaussian:
print "Your input must be whitened to achieve the desire result."
if sigma is not None:
sigma = np.asarray(sigma)
if sigma.ndim == 0:
print "Sigma is set to {} for all input dimensions.".format(sigma)
self.sigma_v = theano.shared(sigma * np.ones((self.v_dim, ), dtype=theano.config.floatX))
self.sigma_v.name = self.name + "-sigma_v"
else:
assert sigma.ndim == 1 and sigma.shape[0] == self.v_dim, \
"Sigma must be 1D array with the length of {}".format(self.v_dim)
self.sigma_v = theano.shared(sigma)
self.sigma_v.name = self.name + "-sigma_v"
else:
print "Default value of sigma is 1.0 for all input dimensions."
self.sigma_v = theano.shared(np.ones(self.v_dim, dtype=theano.config.floatX))
self.sigma_v.name = self.name + "-sigma_v"
def print_model_info(self):
print "\nInfo of model {}".format(self.name)
print "v_dims: {} | h_dim: {} | input_type: {}".format(self.v_dim, self.h_dim, self.input_type)
def get_save(self):
return [self.name, self.v_dim, self.h_dim, self.input_type,
self._mrng, self._rng, self.params, self.sigma_v]
def set_load(self, saved_data):
[self.name, self.v_dim, self.h_dim, self.input_type,
self._mrng, self._rng, self.params, self.sigma_v] = saved_data
def score(self, v_data):
free_fn = theano.function([self.input], self.free_energy(self.input))
return free_fn(v_data)
def reconstruct(self, v_data):
h = self.h_given_v(self.input)
rv = self.v_given_h(h)
rec_fn = theano.function([self.input], rv)
return rec_fn(v_data)
def reconstruct_from_hidden(self, h_data):
h = self.input.type('hidden')
rv = self.v_given_h(h)
rec_fn = theano.function([h], rv)
return rec_fn(h_data)
def encode(self, v_data):
h_code = self.h_given_v(self.input)
fn = theano.function([self.input], h_code)
return fn(v_data)
# Energy from many v an 1 h
def energy(self, v, h):
v_free = self.v_free_term(v)
v_bias = self.v_bias_term(v)
v_weight = self.v_weight_term(v)
return -(v_free + v_bias + v_weight * h + T.dot(h, self.b_h))
def free_energy(self, v):
v_free = self.v_free_term(v)
v_bias = self.v_bias_term(v)
v_weight = self.v_weight_term(v)
h_term = T.sum(T.log(1 + T.exp(v_weight + self.b_h)), axis=1)
return -(v_bias + v_free + h_term)
def v_weight_term(self, v):
if self.input_type == InputType.gaussian:
return T.dot(v/(self.sigma_v ** 2), self.W)
else:
return T.dot(v, self.W)
def v_bias_term(self, v):
# Note that for gaussian case, the v_bias should be negative
if self.input_type == InputType.gaussian:
return -T.sum((v - self.b_v) ** 2 / (2 * self.sigma_v ** 2), axis=1)
else:
return T.dot(v, self.b_v)
def v_free_term(self, v):
if self.input_type == InputType.poisson:
return -T.sum(T.gammaln(1 + v), axis=1)
else:
return 0
def rv(self, v):
h = self.h_given_v(v)
rv = self.v_given_h(h)
return rv
def h_given_v(self, v):
v_weight = self.v_weight_term(v)
p_h_v = T.nnet.sigmoid(v_weight + self.b_h)
return p_h_v
def v_given_h(self, h):
if self.input_type == InputType.binary:
p_v_h = T.nnet.sigmoid(self.b_v + T.dot(h, self.W.T))
return p_v_h
elif self.input_type == InputType.gaussian:
mu_v = self.b_v + T.dot(h, self.W.T)
return mu_v
elif self.input_type == InputType.categorical or \
self.input_type == InputType.replicated_softmax:
p_v_h = T.nnet.softmax(self.b_v + T.dot(h, self.W.T))
return p_v_h
elif self.input_type == InputType.poisson:
if not hasattr(self, 'total_count') or self.total_count is None:
raise ValueError('Total count should be set for constrained Poisson')
unconstrained_lmbd_v = T.exp(self.b_v + T.dot(h, self.W.T))
lmbd_v = unconstrained_lmbd_v * 1.0 / T.sum(unconstrained_lmbd_v, axis=1, keepdims=True) \
* self.total_count
return lmbd_v
def sample_h_given_v(self, v0_sample):
h1_mean = self.h_given_v(v0_sample)
h1_sample = self._mrng.binomial(size=h1_mean.shape, n=1, p=h1_mean,
dtype=theano.config.floatX)
return [h1_mean, h1_sample]
def sample_v_given_h(self, h0_sample):
if self.input_type == InputType.binary:
v1_mean = self.v_given_h(h0_sample)
v1_sample = self._mrng.binomial(size=v1_mean.shape, n=1, p=v1_mean,
dtype=theano.config.floatX)
return [v1_mean, v1_sample]
elif self.input_type == InputType.gaussian:
mu_v1 = self.v_given_h(h0_sample) # Note that mu_v1 is returned
v1_sample = self._mrng.normal(size=mu_v1.shape, avg=mu_v1, std=self.sigma_v,
dtype=theano.config.floatX)
return [mu_v1, v1_sample]
# Note that there is constraint in the case of Multinomial
elif self.input_type == InputType.categorical:
prob_v1 = self.v_given_h(h0_sample)
# Multinomial with n=1 (It is equal to categorical)
v1_sample = self._mrng.multinomial(pvals=prob_v1, n=1, dtype=theano.config.floatX)
return [prob_v1, v1_sample]
elif self.input_type == InputType.poisson:
lmbd_v1 = self.v_given_h(h0_sample)
# We have to use RandomStreams, not MRG_RandomStreams
v1_sample = self._rng.poisson(size=lmbd_v1.shape, lam=lmbd_v1,
dtype=theano.config.floatX)
return [lmbd_v1, v1_sample]
elif self.input_type == InputType.replicated_softmax:
if not hasattr(self, 'total_count') or self.total_count is None:
raise ValueError('Total count should be set for replicated Softmax')
prob_v1 = self.v_given_h(h0_sample)
# We have to sample the vocabulary distribution given topic D times and sum over D samples
v1_sample = self._mrng.multinomial(pvals=prob_v1, n=self.total_count, ndim=prob_v1.shape[1])
return [prob_v1, v1_sample]
# One step of gibbs sampling
def gibbs_hvh(self, h0_sample):
# Here we use v1_stat to show that it is sufficient statistics of v1
[v1_stat, v1_sample] = self.sample_v_given_h(h0_sample)
[h1_mean, h1_sample] = self.sample_h_given_v(v1_sample)
return [v1_stat, v1_sample, h1_mean, h1_sample]
def gibbs_vhv(self, v0_sample):
[h1_mean, h1_sample] = self.sample_h_given_v(v0_sample)
[v1_stat, v1_sample] = self.sample_v_given_h(h1_sample)
return [h1_mean, h1_sample, v1_stat, v1_sample]
def run_CD_from_h(self, k, data_h):
start_h = T.matrix("start_h")
# [v_stats, v_samples, h_means, h_samples], updates \
outputs, updates \
= theano.scan(fn=self.gibbs_hvh, outputs_info=[None, None, None, start_h],
n_steps=k, name="gibbs_hvh")
# Return the last h_sample after k steps
CD_fn = theano.function([start_h], outputs=outputs[-1], updates=updates)
return CD_fn(data_h)
def run_CD_from_v(self, k, data_v):
start_v = T.matrix("start_v")
# [h_means, h_samples, v_stats, v_samples], updates \
outputs, updates \
= theano.scan(fn=self.gibbs_vhv, outputs_info=[None, None, None, start_v],
n_steps=k, name="gibbs_vhv")
# Return the last v_sample after k steps
CD_fn = theano.function([start_v], outputs=outputs[-1], updates=updates)
return CD_fn(data_v)
# Return visible variables
def _gibbs_vhv_to_v_fn(self, steps, persis_v, is_sample=True, name=''):
[h_means, h_samples, v_stats, v_samples], updates \
= theano.scan(self.gibbs_vhv,
outputs_info=[None, None, None, persis_v],
n_steps=steps, # init_gibbs dung de init
name='gibbs_vhv')
updates.update({persis_v: v_samples[-1]})
if is_sample:
gibbs_fn = theano.function([], v_samples[-1], updates=updates, name=name)
else:
gibbs_fn = theano.function([], v_stats[-1], updates=updates, name=name)
return gibbs_fn
# Also return visible variables
def _gibbs_hvh_to_v_fn(self, steps, persis_h, is_sample=True, name=''):
[v_stats, v_samples, h_means, h_samples], updates \
= theano.scan(self.gibbs_hvh,
outputs_info=[None, None, None, persis_h],
n_steps=steps, # init_gibbs dung de init
name='gibbs_hvh')
updates.update({persis_h: h_samples[-1]})
if is_sample:
gibbs_fn = theano.function([], v_samples[-1], updates=updates, name=name)
else:
gibbs_fn = theano.function([], v_stats[-1], updates=updates, name=name)
return gibbs_fn
def sample_given_data(self, v_data, init_gibbs=1000, betw_gibbs=100, loops=10, is_sample=False):
print "\nSample data from input using model {}".format(self.name)
# Neu kich thuoc input la 1 thi phai chuyen no ve kich thuoc 2
if len(v_data.shape) == 1:
persis_v = theano.shared(np.asarray(v_data.reshape(1, v_data.shape[0]),
dtype=theano.config.floatX))
else:
persis_v = theano.shared(np.asarray(v_data, dtype=theano.config.floatX))
if init_gibbs > 0:
init_sampling_fn = self._gibbs_vhv_to_v_fn(init_gibbs, persis_v,
is_sample=True, name='init_sampling_fn')
else:
init_sampling_fn = None
sample_fn = self._gibbs_vhv_to_v_fn(betw_gibbs, persis_v,
is_sample=is_sample, name='sample_fn')
rvs_data = []
if init_sampling_fn is not None:
init_sampling_fn()
for idx in range(loops):
print "Running sampling loop %d" % idx
rv_data = sample_fn()
rvs_data.append(rv_data)
return np.asarray(rvs_data)
# Sample randomly
# We start from h and run gibbs chain until it reaches equilibrium
def sample(self, init_gibbs=1000, betw_gibbs=100, n_samples=20, loops=10, is_sample=False):
print "\nSample random data using model {}".format(self.name)
persis_h = theano.shared(np.zeros((n_samples, self.h_dim), dtype=theano.config.floatX))
if init_gibbs > 0:
init_sampling_fn = self._gibbs_hvh_to_v_fn(init_gibbs, persis_h,
is_sample=True, name='init_sampling_fn')
else:
init_sampling_fn = None
sample_fn = self._gibbs_hvh_to_v_fn(betw_gibbs, persis_h,
is_sample=is_sample, name='sample_fn')
rvs_data = []
if init_sampling_fn is not None:
init_sampling_fn()
for idx in range(loops):
print "Running sampling loop %d" % idx
rv_data = sample_fn()
rvs_data.append(rv_data)
return np.asarray(rvs_data)
def get_cost_udpates(self, lr, k, persis_h, l1, l2, stable_update, store_grad):
# Run one sample step to get h
h_mean, h_sample = self.sample_h_given_v(self.input)
# Run normal CD
start_h = persis_h if persis_h is not None else h_sample
[v_stats, v_samples, h_means, h_samples], updates \
= theano.scan(fn=self.gibbs_hvh, outputs_info=[None, None, None, start_h],
n_steps=k, name="gibbs_hvh")
vk = v_samples[-1]
v_stat_k = v_stats[-1]
if persis_h is not None:
updates[persis_h] = h_samples[-1]
cost = self.get_viewed_cost(self.input, v_stat_k)
cost = T.mean(cost)
# For stable update, use mean value instead of random sampled value
if stable_update:
print "\nStable update is set to be True"
updates = self.params_updates(self.input, v_stat_k, lr, l1, l2, updates, store_grad)
else:
print "\nStable update is set to be False"
updates = self.params_updates(self.input, vk, lr, l1, l2, updates, store_grad)
# return cost, updates
return cost, updates
def get_viewed_cost(self, v0, vk_stat):
# Binary cross-entropy
cost = 0
if self.input_type == InputType.binary:
clip_vk_stat = T.clip(vk_stat, np.float32(0.000001), np.float32(0.999999))
cost = -T.sum(v0 * T.log(clip_vk_stat) + (1 - v0) * T.log(1 - clip_vk_stat), axis=1)
# Sum square error
elif self.input_type == InputType.gaussian:
cost = T.sum((v0 - vk_stat) ** 2, axis=1)
# Categorical cross-entropy
elif self.input_type == InputType.categorical:
clip_vk_stat = T.clip(vk_stat, np.float32(0.000001), np.float32(0.999999))
cost = -T.sum(v0 * T.log(clip_vk_stat), axis=1)
elif self.input_type == InputType.poisson:
clip_vk_stat = T.clip(vk_stat, np.float32(0.000001), np.inf)
cost = -T.sum(-vk_stat + v0 * T.log(clip_vk_stat) - T.gammaln(1 + v0), axis=1)
if self.input_type == InputType.replicated_softmax:
clip_vk_stat = T.clip(vk_stat, np.float32(0.000001), np.inf)
cost = -T.sum((v0 / self.total_count) * T.log(clip_vk_stat), axis=1)
return cost
def params_updates(self, v0, vk, lr, l1, l2, updates, store_grad):
if updates is None:
updates = OrderedDict()
if store_grad:
self.stored_grads = OrderedDict()
grads = [0 for _ in xrange(len(self.params))]
o_grads = self.nll_grad_formula(v0, vk)
grads = [grads[i] + o_grads[i] for i in xrange(len(self.params))]
if store_grad:
print "\nGradients over negative log-likelihood are stored in original_grads"
o_shared_grads, updates = store_grads_in_update(self.params, o_grads, updates)
self.stored_grads['original_grads'] = o_shared_grads
if l1 is not None:
print "Add L1 regularization ({}) to parameter updates".format(l1)
l1_gW = l1_grad(self.W, l1)
grads[0] = grads[0] + l1_gW
if store_grad:
print "\nGradients over L1 regularization are stored in l1_grads"
l1_shared_grads, updates = store_grads_in_update([self.W], [l1_gW], updates)
self.stored_grads['l1_grads'] = l1_shared_grads
if l2 is not None:
print "Add L2 regularization ({}) to parameter updates".format(l2)
l2_gW = l2_grad(self.W, l2)
grads[0] = grads[0] + l2_gW
if store_grad:
print "\nGradients over L2 regularization are stored in l2_grads"
l2_shared_grads, updates = store_grads_in_update([self.W], [l2_gW], updates)
self.stored_grads['l2_grads'] = l2_shared_grads
if store_grad:
print "\nGradients over total cost are stored in total_grads"
t_shared_grads, updates = store_grads_in_update(self.params, grads, updates)
self.stored_grads['total_grads'] = t_shared_grads
grads = [grad.astype(theano.config.floatX) for grad in grads]
if self.check_learning_algor():
params_updates = self.learning_algor(grads, self.params, lr, **self.learning_config)
updates.update(params_updates)
else:
print "\nSimple SGD is used as training algorithm"
for grad, param in zip(grads, self.params):
updates[param] = param - grad * lr
return updates
def nll_grad_formula(self, v0, vk):
n_instances = v0.shape[0]
h0 = self.h_given_v(v0)
hk = self.h_given_v(vk)
gW = (T.dot(vk.T, hk) - T.dot(v0.T, h0)) / n_instances
gb_h = T.mean(hk - h0, axis=0)
if self.input_type == InputType.gaussian:
gb_v = T.mean((vk - v0) / (self.sigma_v ** 2), axis=0)
ugz_v = (((vk - self.b_v) ** 2 - 2 * vk * T.dot(hk, self.W.T)) - \
((v0 - self.b_v) ** 2 - 2 * v0 * T.dot(h0, self.W.T))) / (self.sigma_v ** 2)
gz_v = T.mean(ugz_v, axis=0)
grads = [gW, gb_h, gb_v, gz_v]
else:
gb_v = T.mean(vk - v0, axis=0)
grads = [gW, gb_h, gb_v]
return grads
def nll_grad_theano(self, v0, vk):
cost = T.mean(self.free_energy(v0)) - T.mean(self.free_energy(vk))
# Note here we have to use consider_constant
grads = T.grad(cost, self.params, consider_constant=[vk])
return grads
def grad_check(self, data_v0, data_vk):
# data_v0 and data_vk is numpy array
# data_vk is computed by calling CD-k
v0 = T.matrix('v0')
vk = T.matrix('vk')
theano_grads = self.nll_grad_theano(v0, vk)
formula_grads = self.nll_grad_formula(v0, vk)
grad_diffs = []
for t_grad, f_grad in zip(theano_grads, formula_grads):
grad_diffs.append(abs(t_grad - f_grad))
grad_test_fn = theano.function([v0, vk], grad_diffs)
diffs_results = grad_test_fn(data_v0, data_vk)
for i in xrange(len(self.params)):
if self.params[i].name is not None:
name = self.params[i].name
else:
name = ""
print ("Max " + name + " diffs: {}").format(np.max(diffs_results[i]))
print ("Min " + name + " diffs: {}").format(np.min(diffs_results[i]))
print ("Average " + name + " diffs: {}").format(np.mean(diffs_results[i]))
print
return diffs_results
def config_train(self, **kwargs):
k = kwargs.get('CD_k')
persis_h_data = kwargs.get('persis_h')
l1 = kwargs.get('L1')
l2 = kwargs.get('L2')
if l1 is None:
print "L1 should be set to enable sparse weight regularization"
if l2 is None:
print "L2 should be set to enable sparse weight regularization"
stable_update = kwargs.get('stable_update')
if stable_update is None:
stable_update = False
store_grad = kwargs.get('store_grad')
if store_grad is None:
store_grad = False
self._build_train(k, persis_h_data, l1, l2, stable_update, store_grad)
# persis_v_data is a numpy array
def _build_train(self, k, persis_h_data, l1, l2, stable_update, store_grad):
print "\nBuild training function of model {}".format(self.name)
if persis_h_data is not None:
persis_h = theano.shared(persis_h_data, borrow=True)
else:
persis_h = None
lr = T.scalar('lr')
cost, updates = self.get_cost_udpates(lr, k, persis_h, l1, l2, stable_update, store_grad)
print "\nBuild computation graph for training function of model {}".format(self.name)
self.train_fn = theano.function([self.input, lr], cost, updates=updates)
rv = self.v_given_h(self.h_given_v(self.input))
test_cost = self.get_viewed_cost(self.input, rv)
test_cost = T.mean(test_cost)
print "\nBuild computation graph for validation function of model {}".format(self.name)
self.valid_fn = theano.function([self.input], test_cost)
class TextDecoder(EncoderDecoderBase):
EVALUATION = 1
SAMPLING = 2
BEAM_SEARCH = 3
def __init__(self, state, rng, parent):
EncoderDecoderBase.__init__(self, state, rng, parent)
self.trng = MRG_RandomStreams(self.seed)
self.init_params()
def init_params(self):
if self.multiplicative_input_from_encoders:
if self.bidirectional_encoder:
self.input_dim = self.qdim * 2
else:
self.input_dim = self.qdim
else:
if self.bidirectional_encoder:
self.input_dim = self.qdim * 4
else:
self.input_dim = self.qdim * 2
if self.use_precomputed_features:
self.input_dim += self.precomputed_features_count
""" Decoder weights """
self.Wd_in = add_to_params(
self.params,
theano.shared(value=NormalInit(self.rng, self.input_dim,
self.mlp_out_dim),
name='Wd_in'))
self.bd_in = add_to_params(
self.params,
theano.shared(value=np.zeros((self.mlp_out_dim, ),
dtype='float32'),
name='bd_in'))
if self.condition_on_previous_speaker_class:
self.Wd_softmax_first = add_to_params(
self.params,
theano.shared(value=NormalInit3D(
self.rng, self.segmentation_token_count, self.mlp_out_dim,
self.segmentation_token_count),
name='Wd_softmax_first'))
self.bd_softmax_first = add_to_params(
self.params,
theano.shared(value=np.zeros((self.segmentation_token_count,
self.segmentation_token_count),
dtype='float32'),
name='bd_softmax__first'))
self.Wd_softmax_second = add_to_params(
self.params,
theano.shared(value=NormalInit3D(
self.rng, self.segmentation_token_count, self.mlp_out_dim,
self.segmentation_token_count),
name='Wd_softmax_second'))
self.bd_softmax_second = add_to_params(
self.params,
theano.shared(value=np.zeros((self.segmentation_token_count,
self.segmentation_token_count),
dtype='float32'),
name='bd_softmax__second'))
self.Wd_softmax_third = add_to_params(
self.params,
theano.shared(value=NormalInit3D(
self.rng, self.segmentation_token_count, self.mlp_out_dim,
self.segmentation_token_count),
name='Wd_softmax_third'))
self.bd_softmax_third = add_to_params(
self.params,
theano.shared(value=np.zeros((self.segmentation_token_count,
self.segmentation_token_count),
dtype='float32'),
name='bd_softmax__third'))
else:
self.Wd_softmax_first = add_to_params(
self.params,
theano.shared(value=NormalInit(self.rng, self.mlp_out_dim,
self.segmentation_token_count),
name='Wd_softmax_first'))
self.bd_softmax_first = add_to_params(
self.params,
theano.shared(value=np.zeros((self.segmentation_token_count, ),
dtype='float32'),
name='bd_softmax__first'))
self.Wd_softmax_second = add_to_params(
self.params,
theano.shared(value=NormalInit(self.rng, self.mlp_out_dim,
self.segmentation_token_count),
name='Wd_softmax_second'))
self.bd_softmax_second = add_to_params(
self.params,
theano.shared(value=np.zeros((self.segmentation_token_count, ),
dtype='float32'),
name='bd_softmax__second'))
self.Wd_softmax_third = add_to_params(
self.params,
theano.shared(value=NormalInit(self.rng, self.mlp_out_dim,
self.segmentation_token_count),
name='Wd_softmax_third'))
self.bd_softmax_third = add_to_params(
self.params,
theano.shared(value=np.zeros((self.segmentation_token_count, ),
dtype='float32'),
name='bd_softmax__third'))
def build_next_probs_predictor(self, inp, x, prev_state):
"""
Return output probabilities given prev_words x, hierarchical pass hs, and previous hd
hs should always be the same (and should not be updated).
"""
return self.build_decoder(inp,
x,
mode=TextDecoder.BEAM_SEARCH,
prev_state=prev_state)
def build_decoder(self, decoder_inp, y=None, y_prev=None, mode=EVALUATION):
# Run the decoder
if self.mlp_activation_function == 'tanh':
hidden_activation = T.tanh(
T.dot(decoder_inp, self.Wd_in) + self.bd_in)
elif self.mlp_activation_function == 'rectifier':
hidden_activation = relu(
T.dot(decoder_inp, self.Wd_in) + self.bd_in)
elif self.mlp_activation_function == 'linear':
hidden_activation = T.dot(decoder_inp, self.Wd_in) + self.bd_in
else:
raise Exception("Invalid activation function specified for MLP!")
if self.condition_on_previous_speaker_class:
first_output = T.nnet.softmax(
T.dot(hidden_activation, self.Wd_softmax_first[y_prev[0]][
0, :, :]) + self.bd_softmax_first[y_prev[0]])
second_output = T.nnet.softmax(
T.dot(hidden_activation, self.Wd_softmax_second[y_prev[0]][
0, :, :]) + self.bd_softmax_second[y_prev[0]])
third_output = T.nnet.softmax(
T.dot(hidden_activation, self.Wd_softmax_third[y_prev[0]][
0, :, :]) + self.bd_softmax_third[y_prev[0]])
outputs = T.concatenate(
[first_output, second_output, third_output])
else:
first_output = T.nnet.softmax(
T.dot(hidden_activation, self.Wd_softmax_first) +
self.bd_softmax_first)
second_output = T.nnet.softmax(
T.dot(hidden_activation, self.Wd_softmax_second) +
self.bd_softmax_second)
third_output = T.nnet.softmax(
T.dot(hidden_activation, self.Wd_softmax_third) +
self.bd_softmax_third)
outputs = T.concatenate(
[first_output, second_output, third_output])
# EVALUATION / BEAM SEARCH: Return outputs
if mode == TextDecoder.EVALUATION:
first_target_outputs = GrabProbs(first_output, y[0])
second_target_outputs = GrabProbs(second_output, y[1])
third_target_outputs = GrabProbs(third_output, y[1])
target_outputs = T.concatenate([
first_target_outputs, second_target_outputs,
third_target_outputs
])
return outputs, target_outputs
elif mode == TextDecoder.BEAM_SEARCH:
return outputs
# SAMPLING : Return a vector with sample
elif mode == TextDecoder.SAMPLING:
first_sample = self.trng.multinomial(pvals=first_output,
dtype='int64').argmax(axis=-1)
second_sample = self.trng.multinomial(
pvals=second_output, dtype='int64').argmax(axis=-1)
third_sample = self.trng.multinomial(pvals=third_output,
dtype='int64').argmax(axis=-1)
return T.concatenate([first_sample, second_sample, third_sample])
def theano_multinomial(n, pvals, seed):
rng = RandomStreams(seed)
return rng.multinomial(n=n, pvals=pvals, dtype='float32')
class OptionCritic_Network():
def __init__(self,
model_network=None,
gamma=0.99,
learning_method="rmsprop",
actor_lr=0.00025,
batch_size=32,
input_size=None,
learning_params=None,
dnn_type=True,
clip_delta=0,
scale=255.,
freeze_interval=100,
grad_clip=0,
termination_reg=0,
num_options=8,
double_q=False,
temp=1,
entropy_reg=0,
BASELINE=False,
**kwargs):
x = T.ftensor4()
next_x = T.ftensor4()
a = T.ivector()
o = T.ivector()
r = T.fvector()
terminal = T.ivector()
self.freeze_interval = freeze_interval
self.theano_rng = MRG_RandomStreams(1000)
self.x_shared = theano.shared(
np.zeros(tuple([batch_size] + input_size[1:]), dtype='float32'))
self.next_x_shared = theano.shared(
np.zeros(tuple([batch_size] + input_size[1:]), dtype='float32'))
self.a_shared = theano.shared(np.zeros((batch_size), dtype='int32'))
self.o_shared = theano.shared(np.zeros((batch_size), dtype='int32'))
self.terminal_shared = theano.shared(
np.zeros((batch_size), dtype='int32'))
self.r_shared = theano.shared(np.zeros((batch_size), dtype='float32'))
state_network = model_network[:-1]
termination_network = copy.deepcopy([model_network[-1]])
termination_network[0]["activation"] = "sigmoid"
print "NUM OPTIONS --->", num_options
termination_network[0]["out_size"] = num_options
option_network = copy.deepcopy([model_network[-1]])
option_network[0]["activation"] = "softmax"
Q_network = copy.deepcopy([model_network[-1]])
Q_network[0]["out_size"] = num_options
self.state_model = Model(state_network,
input_size=input_size,
dnn_type=dnn_type)
self.state_model_prime = Model(state_network,
input_size=input_size,
dnn_type=dnn_type)
output_size = [None, model_network[-2]["out_size"]]
self.Q_model = Model(Q_network,
input_size=output_size,
dnn_type=dnn_type)
self.Q_model_prime = Model(Q_network,
input_size=output_size,
dnn_type=dnn_type)
self.termination_model = Model(termination_network,
input_size=output_size,
dnn_type=dnn_type)
self.options_model = MLP3D(num_options, model_network, temp=temp)
s = self.state_model.apply(x / scale)
next_s = self.state_model.apply(next_x / scale)
next_s_prime = self.state_model_prime.apply(next_x / scale)
termination_probs = self.termination_model.apply(
theano.gradient.disconnected_grad(s))
option_term_prob = termination_probs[T.arange(o.shape[0]), o]
next_termination_probs = self.termination_model.apply(
theano.gradient.disconnected_grad(next_s))
next_option_term_prob = next_termination_probs[T.arange(o.shape[0]), o]
termination_sample = T.gt(option_term_prob,
self.theano_rng.uniform(size=o.shape))
Q = self.Q_model.apply(s)
next_Q = self.Q_model.apply(next_s)
next_Q_prime = theano.gradient.disconnected_grad(
self.Q_model_prime.apply(next_s_prime))
disc_option_term_prob = theano.gradient.disconnected_grad(
next_option_term_prob)
action_probs = self.options_model.apply(s, o)
sampled_actions = T.argmax(self.theano_rng.multinomial(
pvals=action_probs, n=1),
axis=1).astype("int32")
if double_q:
print "TRAINING DOUBLE_Q"
y = r + (1 - terminal) * gamma * (
(1 - disc_option_term_prob) *
next_Q_prime[T.arange(o.shape[0]), o] +
disc_option_term_prob * next_Q_prime[T.arange(next_Q.shape[0]),
T.argmax(next_Q, axis=1)])
else:
y = r + (1 - terminal) * gamma * (
(1 - disc_option_term_prob) *
next_Q_prime[T.arange(o.shape[0]), o] +
disc_option_term_prob * T.max(next_Q_prime, axis=1))
y = theano.gradient.disconnected_grad(y)
option_Q = Q[T.arange(o.shape[0]), o]
td_errors = y - option_Q
if clip_delta > 0:
quadratic_part = T.minimum(abs(td_errors), clip_delta)
linear_part = abs(td_errors) - quadratic_part
td_cost = 0.5 * quadratic_part**2 + clip_delta * linear_part
else:
td_cost = 0.5 * td_errors**2
# critic updates
critic_cost = T.sum(td_cost)
critic_params = self.Q_model.params + self.state_model.params
learning_algo = self.Q_model.get_learning_method(
learning_method, **learning_params)
grads = T.grad(critic_cost, critic_params)
critic_updates = learning_algo.apply(critic_params,
grads,
grad_clip=grad_clip)
# actor updates
actor_params = self.termination_model.params + self.options_model.params
learning_algo = self.termination_model.get_learning_method("sgd",
lr=actor_lr)
disc_Q = theano.gradient.disconnected_grad(option_Q)
disc_V = theano.gradient.disconnected_grad(T.max(Q, axis=1))
term_grad = T.sum(option_term_prob *
(disc_Q - disc_V + termination_reg))
entropy = -T.sum(action_probs * T.log(action_probs))
if not BASELINE:
policy_grad = - \
T.sum(
T.log(action_probs[T.arange(a.shape[0]), a]) * y) - entropy_reg*entropy
else:
policy_grad = - \
T.sum(T.log(action_probs[T.arange(a.shape[0]), a])
* (y-disc_Q)) - entropy_reg*entropy
grads = T.grad(term_grad + policy_grad, actor_params)
actor_updates = learning_algo.apply(actor_params,
grads,
grad_clip=grad_clip)
if self.freeze_interval > 1:
target_updates = OrderedDict()
for t, b in zip(
self.Q_model_prime.params + self.state_model_prime.params,
self.Q_model.params + self.state_model.params):
target_updates[t] = b
self._update_target_params = theano.function(
[], [], updates=target_updates)
self.update_target_params()
print "freeze interval:", self.freeze_interval
else:
print "freeze interval: None"
critic_givens = {
x: self.x_shared,
o: self.o_shared,
r: self.r_shared,
terminal: self.terminal_shared,
next_x: self.next_x_shared
}
actor_givens = {
a: self.a_shared,
r: self.r_shared,
terminal: self.terminal_shared,
o: self.o_shared,
next_x: self.next_x_shared
}
print "compiling...",
self.train_critic = theano.function([], [critic_cost],
updates=critic_updates,
givens=critic_givens)
self.train_actor = theano.function([s], [],
updates=actor_updates,
givens=actor_givens)
self.pred_score = theano.function([],
T.max(Q, axis=1),
givens={x: self.x_shared})
self.sample_termination = theano.function(
[s], [termination_sample, T.argmax(Q, axis=1)],
givens={o: self.o_shared})
self.sample_options = theano.function([s], T.argmax(Q, axis=1))
self.sample_actions = theano.function([s],
sampled_actions,
givens={o: self.o_shared})
self.get_action_dist = theano.function([s, o], action_probs)
self.get_s = theano.function([], s, givens={x: self.x_shared})
print "complete"
def update_target_params(self):
if self.freeze_interval > 1:
self._update_target_params()
return
def predict_move(self, s):
return self.sample_options(s)
def predict_termination(self, s, a):
self.a_shared.set_value(a)
return tuple(self.sample_termination(s))
def get_q_vals(self, x):
self.x_shared.set_value(x)
return self.pred_score()[:, np.newaxis]
def get_state(self, x):
self.x_shared.set_value(x)
return self.get_s()
def get_action(self, s, o):
self.o_shared.set_value(o)
return self.sample_actions(s)
def train_conv_net(self,
train_set_x,
next_x,
options,
r,
terminal,
actions=None,
model=""):
self.next_x_shared.set_value(next_x)
self.o_shared.set_value(options)
self.r_shared.set_value(r)
self.terminal_shared.set_value(terminal)
if model == "critic":
self.x_shared.set_value(train_set_x)
return self.train_critic()
elif model == "actor":
self.a_shared.set_value(actions)
return self.train_actor(train_set_x)
else:
print "WRONG MODEL NAME"
raise NotImplementedError
def save_params(self):
return [
self.state_model.save_params(),
self.Q_model.save_params(),
self.termination_model.save_params(),
self.options_model.save_params()
]
def load_params(self, values):
self.state_model.load_params(values[0])
self.Q_model.load_params(values[1])
self.termination_model.load_params(values[2])
self.options_model.load_params(values[3])
class ParticleFilter():
''' Implements particle filtering and smoothing for Markov Chains
with arbitrary proposal/true distributions '''
def __init__(self,
transition_model,
observation_model,
n_particles,
observation_input=None,
n_history=1):
self.transition_model = transition_model
self.observation_model = observation_model
self.data_dims = observation_model.output_dims
self.state_dims = transition_model.output_dims
self.n_particles = n_particles
self.n_history = n_history
#this is used to keep track of what set of particles corresponds
#to the previous point in time
self.time_counter = theano.shared(0)
self.theano_rng = RandomStreams()
#init_particles=np.zeros((n_history+1, n_particles, self.state_dims)).astype(np.float32)
init_particles = np.random.randn(n_history + 1, n_particles,
self.state_dims).astype(np.float32)
init_weights = (np.ones((n_history + 1, n_particles)) /
float(n_particles)).astype(np.float32)
self.particles = theano.shared(init_particles)
self.weights = theano.shared(init_weights)
self.next_state = self.particles[(self.time_counter + 1) %
(self.n_history + 1)]
self.current_state = self.particles[self.time_counter %
(self.n_history + 1)]
self.previous_state = self.particles[(self.time_counter - 1) %
(self.n_history + 1)]
self.next_weights = self.weights[(self.time_counter + 1) %
(self.n_history + 1)]
self.current_weights = self.weights[self.time_counter %
(self.n_history + 1)]
self.previous_weights = self.weights[(self.time_counter - 1) %
(self.n_history + 1)]
self.proposal_distrib = None
self.true_log_transition_probs = self.transition_model.rel_log_prob
self.true_log_observation_probs = self.observation_model.rel_log_prob
self.perform_inference = None
self.resample = None
self.sample_joint = None
self.observation_input = observation_input
ess = self.compute_ESS()
self.get_ESS = theano.function([], ess)
n_samps = T.lscalar()
n_T = T.lscalar()
data_samples, state_samples, init_state_samples, data_sample_updates = self.sample_future(
n_samps, n_T)
self.sample_from_future = theano.function(
[n_samps, n_T], [data_samples, state_samples, init_state_samples],
updates=data_sample_updates)
self.get_current_particles = theano.function([], self.current_state)
self.get_current_weights = theano.function([], self.current_weights)
def recompile(self):
'''This function compiles each of the theano functions that might
change following a change of the model. '''
samp_updates = self.sample_update(self.observation_input)
self.perform_inference = theano.function([], updates=samp_updates)
res_updates = self.resample_update()
self.resample = theano.function([], updates=res_updates)
nsamps = T.lscalar()
joint_samples, joint_updates = self.sample_from_joint(nsamps)
self.sample_joint = theano.function([nsamps],
joint_samples,
updates=joint_updates)
new_ess, stddevhist, esshist, sr_updates = self.sequential_resample()
self.perform_sequential_resampling = theano.function(
[], [new_ess, stddevhist, esshist], updates=sr_updates)
csamps = self.sample_current(nsamps)
self.sample_current_state = theano.function([nsamps], csamps)
psamps = self.sample_prev(nsamps)
self.sample_previous_state = theano.function([nsamps], psamps)
return
def set_proposal(self, proposal_distrib):
self.proposal_distrib = proposal_distrib
return
def set_true_log_transition_probs(self, true_log_transition_probs):
self.true_log_transition_probs = true_log_transition_probs
return
def set_true_log_observation_probs(self, true_log_observation_probs):
self.true_log_observation_probs = true_log_observation_probs
return
def sample_update(self, data):
proposal_samples, log_proposal_probs = self.proposal_distrib
printing = False
if printing:
log_transition_probs = theano.printing.Print(
'1 log transition probs update')(
self.true_log_transition_probs(self.current_state,
proposal_samples))
log_observation_probs = theano.printing.Print(
'2 log observation probs update')(
self.true_log_observation_probs(proposal_samples,
data.dimshuffle('x', 0)))
log_unnorm_weights = theano.printing.Print(
'3 log unnorm weights update')(log_transition_probs +
log_observation_probs -
log_proposal_probs)
log_unnorm_weights_center = theano.printing.Print(
'4 log unnorm weights center update')(
log_unnorm_weights - T.max(log_unnorm_weights))
unnorm_weights = theano.printing.Print('5 unnorm weights update')(
T.exp(log_unnorm_weights_center) * self.current_weights)
normalizer = theano.printing.Print('6 normalizer update')(
T.sum(unnorm_weights))
else:
log_transition_probs = self.true_log_transition_probs(
self.current_state, proposal_samples)
log_observation_probs = self.true_log_observation_probs(
proposal_samples, data.dimshuffle('x', 0))
log_unnorm_weights = log_transition_probs + log_observation_probs - log_proposal_probs
log_unnorm_weights_center = log_unnorm_weights - T.max(
log_unnorm_weights)
unnorm_weights = T.exp(
log_unnorm_weights_center) * self.current_weights
normalizer = T.sum(unnorm_weights)
weights = unnorm_weights / normalizer
updates = OrderedDict()
updates[self.weights] = T.set_subtensor(self.next_weights, weights)
updates[self.particles] = T.set_subtensor(self.next_state,
proposal_samples)
updates[self.time_counter] = self.time_counter + 1
return updates
def compute_ESS(self):
return 1.0 / T.sum(self.current_weights**2)
def resample_update(self):
#shape: n_particles by n_particles
samps = self.theano_rng.multinomial(pvals=T.extra_ops.repeat(
self.current_weights.dimshuffle('x', 0), self.n_particles, axis=0))
idxs = T.cast(T.dot(samps, T.arange(self.n_particles)), 'int64')
updates = OrderedDict()
updates[self.particles] = T.set_subtensor(self.current_state,
self.current_state[idxs])
updates[self.weights] = T.set_subtensor(
self.current_weights,
T.cast(
T.ones_like(self.current_weights) / float(self.n_particles),
'float32'))
return updates
def sample_step(self, future_samps, t, n_samples):
particles_now = self.particles[(self.time_counter - t) %
(self.n_history + 1)]
weights_now = self.weights[(self.time_counter - t) %
(self.n_history + 1)]
#n_particles by n_samples
rel_log_probs = self.true_log_transition_probs(particles_now,
future_samps,
all_pairs=True)
unnorm_probs = T.exp(rel_log_probs) * weights_now.dimshuffle(0, 'x')
probs = unnorm_probs / T.sum(unnorm_probs, axis=0).dimshuffle('x', 0)
samps = self.theano_rng.multinomial(pvals=probs.T)
idxs = T.cast(T.dot(samps, T.arange(self.n_particles)), 'int64')
output_samples = particles_now[idxs]
return [output_samples, t + 1]
def sample_from_joint(self, n_samples, output_2D=False):
'''Samples from the joint posterior P(s_t-n_history:s_t | observations)
n_samples: the number of samples to draw
Returns an array with shape (n_history+1, n_samples, state_dims),
where array[-1] corresponds to the current time.
'''
samps = self.theano_rng.multinomial(pvals=T.extra_ops.repeat(
self.current_weights.dimshuffle('x', 0), n_samples, axis=0))
idxs = T.cast(T.dot(samps, T.arange(self.n_particles)), 'int64')
samps_t0 = self.current_state[idxs]
t0 = T.as_tensor_variable(1)
[samples, ts], updates = theano.scan(fn=self.sample_step,
outputs_info=[samps_t0, t0],
non_sequences=[n_samples],
n_steps=self.n_history)
#the variable "samples" that results from the scan is time-flipped
#in the sense that samples[0] corresponds to the most recent point
#in time, and higher indices correspond to points in the past.
#I will stick to the convention that for any collection of points in
#time, [-1] will index the most recent time, and [0] will index
#the point farthest in the past. So, the first axis of "samples"
#needs to be flipped.
flip_idxs = T.cast(-T.arange(self.n_history) + self.n_history - 1,
'int64')
samples = T.concatenate(
[samples[flip_idxs],
samps_t0.dimshuffle('x', 0, 1)], axis=0)
if output_2D:
samples = T.reshape(
samples, ((self.n_history + 1) * n_samples, self.state_dims))
return samples, updates
def sample_future(self, n_samples, n_T):
'''Samples from the "future" data distribution:
P(s_t+1,...s_t+n_T, x_t+1,...x_t+n_T | s_t)
n_samples: number of samples to draw
n_T: the number of (future) time points to sample from
Returns three arrays. The first two have shapes
(n_T, n_samples, data_dims) and
(n_T, n_samples, state_dims),
corresponding to samples of future observations and states,
and the third having size (n_samples,state_dims),
corresponding to the "initial" samples taken from the current
state distribution.
'''
samps = self.theano_rng.multinomial(pvals=T.extra_ops.repeat(
self.current_weights.dimshuffle('x', 0), n_samples, axis=0))
idxs = T.cast(T.dot(samps, T.arange(self.n_particles)), 'int64')
samps_t0 = self.current_state[idxs]
def fstep(states):
next_states = self.transition_model.get_samples_noprobs(states)
next_data = self.observation_model.get_samples_noprobs(next_states)
return next_states, next_data
[state_samples,
data_samples], updates = theano.scan(fn=fstep,
outputs_info=[samps_t0, None],
n_steps=n_T)
#data_samples=self.observation_model.get_samples_noprobs(state_samples)
return data_samples, state_samples, samps_t0, updates
def sample_model(self, n_samples, n_T):
'''Samples from the "future" data distribution:
P(s_t+1,...s_t+n_T, x_t+1,...x_t+n_T | s_t)
n_samples: number of samples to draw
n_T: the number of (future) time points to sample from
Returns three arrays. The first two have shapes
(n_T, n_samples, data_dims) and
(n_T, n_samples, state_dims),
corresponding to samples of future observations and states,
and the third having size (n_samples,state_dims),
corresponding to the "initial" samples taken from the current
state distribution.
'''
samps = self.theano_rng.multinomial(pvals=T.extra_ops.repeat(
self.current_weights.dimshuffle('x', 0), n_samples, axis=0))
idxs = T.cast(T.dot(samps, T.arange(self.n_particles)), 'int64')
samps_t0 = self.current_state[idxs]
state_samples, updates = theano.scan(
fn=self.transition_model.get_samples_noprobs,
outputs_info=[samps_t0],
n_steps=n_T)
data_sample = self.observation_model.get_samples_noprobs(
state_samples[-1])
return data_sample, state_samples[-1], state_samples[-2], updates
def sr_step(self, means, weights, stddev, ess, decay):
#Sampling from a mixture of gaussians
msamps = self.theano_rng.multinomial(pvals=T.extra_ops.repeat(
weights.dimshuffle('x', 0), means.shape[0], axis=0))
idxs = T.cast(T.dot(msamps, T.arange(means.shape[0])), 'int64')
sample_means = T.cast(means[idxs], 'float32')
proposal_samples = self.theano_rng.normal(
size=means.shape) * stddev.dimshuffle('x', 0) + sample_means
diffs = proposal_samples.dimshuffle(
0, 'x', 1) - sample_means.dimshuffle('x', 0, 1)
printing = False
if printing:
log_proposal_probs = theano.printing.Print('1 log_proposal_probs')(
T.log(
T.dot(
T.exp(-T.sum(
(1.0 / (2.0 * stddev**2)).dimshuffle('x', 'x', 0) *
diffs**2,
axis=2)), weights)))
log_transition_probs = theano.printing.Print(
'2 log transition probs')(self.true_log_transition_probs(
self.previous_state, proposal_samples, all_pairs=True))
log_transition_probs_2 = theano.printing.Print(
'3 log transition probs 2')(T.log(
T.dot(
T.exp(log_transition_probs).T, self.previous_weights)))
log_observation_probs = theano.printing.Print(
'4 log observation probs')(self.true_log_observation_probs(
proposal_samples,
self.observation_input.dimshuffle('x', 0)))
log_unnorm_weights = theano.printing.Print(
'5 log unnorm weights nomax')(log_transition_probs_2 +
log_observation_probs -
log_proposal_probs)
log_unnorm_weights = theano.printing.Print('6 log unnorm weights')(
log_unnorm_weights - T.max(log_unnorm_weights))
unnorm_weights = theano.printing.Print('7 unnorm weights')(
T.exp(log_unnorm_weights))
normalizer = theano.printing.Print('8 normalizer')(
T.sum(unnorm_weights))
else:
log_proposal_probs = T.log(
T.dot(
T.exp(-T.sum((1.0 /
(2.0 * stddev**2)).dimshuffle('x', 'x', 0) *
diffs**2,
axis=2)), weights))
log_transition_probs = self.true_log_transition_probs(
self.previous_state, proposal_samples, all_pairs=True)
log_transition_probs = T.log(
T.dot(T.exp(log_transition_probs).T, self.previous_weights))
log_observation_probs = self.true_log_observation_probs(
proposal_samples, self.observation_input.dimshuffle('x', 0))
log_unnorm_weights = log_transition_probs + log_observation_probs - log_proposal_probs
log_unnorm_weights = log_unnorm_weights - T.max(log_unnorm_weights)
unnorm_weights = T.exp(log_unnorm_weights)
normalizer = T.sum(unnorm_weights)
new_weights = unnorm_weights / normalizer
new_ess = 1.0 / T.sum(new_weights**2)
sampmean = T.dot(proposal_samples.T, new_weights)
sampvar = T.dot(
((proposal_samples - sampmean.dimshuffle('x', 0))**2).T,
new_weights)
#propmean=T.mean(proposal_samples, axis=0)
#propvar=T.mean((proposal_samples-propmean.dimshuffle('x',0))**2,axis=0)
#new_stddev=stddev*T.clip(T.exp(decay*(1.0-propvar/sampvar)),0.5,2.0)
#new_stddev=T.clip(stddev*T.clip(T.exp(decay*(1.0-stddev**2/sampvar)),0.5,2.0),0.0,4.0)
new_stddev = T.clip(
stddev * T.clip(T.exp(decay *
(1.0 - stddev**2 / sampvar)), 0.5, 1.5), 0.0,
4.0)
return [
proposal_samples, new_weights, new_stddev,
T.cast(new_ess, 'float32')
] #, theano.scan_module.until(new_ess>100)
def sequential_resample(self,
init_stddev=4.0,
max_steps=20,
stddev_decay=0.1):
'''Repeatedly resamples and then samples from a proposal distribution
constructed from the current samples. Should be used when the main
proposal distribution is poor or whenever the ESS is poor.
'''
essT = T.as_tensor_variable(np.asarray(0.0, dtype='float32'))
stddevT = T.as_tensor_variable(
np.asarray(init_stddev * np.ones(self.state_dims),
dtype='float32'))
decayT = T.as_tensor_variable(np.asarray(stddev_decay,
dtype='float32'))
[samphist, weighthist, stddevhist,
esshist], updates = theano.scan(fn=self.sr_step,
outputs_info=[
self.current_state,
self.current_weights, stddevT,
essT
],
non_sequences=decayT,
n_steps=max_steps)
end_samples = samphist[-1]
end_weights = weighthist[-1]
updates[self.particles] = T.set_subtensor(self.current_state,
end_samples)
updates[self.weights] = T.set_subtensor(self.current_weights,
end_weights)
return 1.0 / T.sum(end_weights**2), stddevhist, esshist, updates
def sample_current(self, nsamps):
samps = self.theano_rng.multinomial(pvals=T.extra_ops.repeat(
self.current_weights.dimshuffle('x', 0), nsamps, axis=0))
idxs = T.cast(T.dot(samps, T.arange(self.n_particles)), 'int64')
samples = self.current_state[idxs]
return samples
def sample_prev(self, nsamps):
samps = self.theano_rng.multinomial(pvals=T.extra_ops.repeat(
self.previous_weights.dimshuffle('x', 0), nsamps, axis=0))
idxs = T.cast(T.dot(samps, T.arange(self.n_particles)), 'int64')
samples = self.previous_state[idxs]
return samples
def get_history(self):
'''This function returns a 3-D array containing all the particles
and a 2-D array of weights for the entire memory. The first dimension indexes
time, with the zeroth entry corresponding to the earliest point in
memory.'''
idxs = (T.arange(self.n_history + 1) - self.n_history +
self.time_counter) % (self.n_history + 1)
return self.particles[idxs], self.weights[idxs]
def test_undefined_grad():
srng = MRG_RandomStreams(seed=1234)
# checking uniform distribution
low = tensor.scalar()
out = srng.uniform((), low=low)
assert_raises(theano.gradient.NullTypeGradError, theano.grad, out, low)
high = tensor.scalar()
out = srng.uniform((), low=0, high=high)
assert_raises(theano.gradient.NullTypeGradError, theano.grad, out, high)
out = srng.uniform((), low=low, high=high)
assert_raises(theano.gradient.NullTypeGradError, theano.grad, out,
(low, high))
# checking binomial distribution
prob = tensor.scalar()
out = srng.binomial((), p=prob)
assert_raises(theano.gradient.NullTypeGradError, theano.grad, out, prob)
# checking multinomial distribution
prob1 = tensor.scalar()
prob2 = tensor.scalar()
p = [theano.tensor.as_tensor_variable([prob1, 0.5, 0.25])]
out = srng.multinomial(size=None, pvals=p, n=4)[0]
assert_raises(theano.gradient.NullTypeGradError, theano.grad,
theano.tensor.sum(out), prob1)
p = [theano.tensor.as_tensor_variable([prob1, prob2])]
out = srng.multinomial(size=None, pvals=p, n=4)[0]
assert_raises(theano.gradient.NullTypeGradError, theano.grad,
theano.tensor.sum(out), (prob1, prob2))
# checking choice
p = [theano.tensor.as_tensor_variable([prob1, prob2, 0.1, 0.2])]
out = srng.choice(a=None, size=1, p=p, replace=False)[0]
assert_raises(theano.gradient.NullTypeGradError, theano.grad, out[0],
(prob1, prob2))
p = [theano.tensor.as_tensor_variable([prob1, prob2])]
out = srng.choice(a=None, size=1, p=p, replace=False)[0]
assert_raises(theano.gradient.NullTypeGradError, theano.grad, out[0],
(prob1, prob2))
p = [theano.tensor.as_tensor_variable([prob1, 0.2, 0.3])]
out = srng.choice(a=None, size=1, p=p, replace=False)[0]
assert_raises(theano.gradient.NullTypeGradError, theano.grad, out[0],
prob1)
# checking normal distribution
avg = tensor.scalar()
out = srng.normal((), avg=avg)
assert_raises(theano.gradient.NullTypeGradError, theano.grad, out, avg)
std = tensor.scalar()
out = srng.normal((), avg=0, std=std)
assert_raises(theano.gradient.NullTypeGradError, theano.grad, out, std)
out = srng.normal((), avg=avg, std=std)
assert_raises(theano.gradient.NullTypeGradError, theano.grad, out,
(avg, std))
class MaskGenerator(object):
def __init__(self, input_size, hidden_sizes, l, random_seed=1234):
self._random_seed = random_seed
self._mrng = MRG_RandomStreams(seed=random_seed)
self._rng = RandomStreams(seed=random_seed)
self._hidden_sizes = hidden_sizes
self._input_size = input_size
self._l = l
self.ordering = theano.shared(np.arange(input_size,
dtype=theano.config.floatX),
'ordering',
borrow=False)
# Initial layer connectivity
self.layers_connectivity = [theano.shared((self.ordering + 1).eval(),
'layer_connectivity_input',
borrow=False)]
for i in range(len(self._hidden_sizes)):
lc = theano.shared(np.zeros((self._hidden_sizes[i]),dtype=floatX),
'layer_connectivity_hidden{0}'.format(i),
borrow=False)
self.layers_connectivity += [lc]
self.layers_connectivity += [self.ordering]
## Theano functions
new_ordering = self._rng.shuffle_row_elements(self.ordering)
updates = [(self.ordering, new_ordering),
(self.layers_connectivity[0], new_ordering + 1)]
self.shuffle_ordering = theano.function(name='shuffle_ordering',
inputs=[],
updates=updates)
self.layers_connectivity_updates = []
for i in range(len(self._hidden_sizes)):
lcu = self._get_hidden_layer_connectivity(i)
self.layers_connectivity_updates += [lcu]
hsizes = range(len(self._hidden_sizes))
updates = [(self.layers_connectivity[i+1],
self.layers_connectivity_updates[i]) for i in hsizes]
self.sample_connectivity = theano.function(name='sample_connectivity',
inputs=[],
updates=updates)
# Save random initial state
self._initial_mrng_rstate = copy.deepcopy(self._mrng.rstate)
self._initial_mrng_state_updates = [sup[0].get_value() for sup in
self._mrng.state_updates]
# Ensuring valid initial connectivity
self.sample_connectivity()
def reset(self):
# Set Original ordering
self.ordering.set_value(np.arange(self._input_size,
dtype=theano.config.floatX))
# Reset RandomStreams
self._rng.seed(self._random_seed)
# Initial layer connectivity
self.layers_connectivity[0].set_value((self.ordering + 1).eval())
for i in range(1, len(self.layers_connectivity)-1):
value = np.zeros((self._hidden_sizes[i-1]),
dtype=theano.config.floatX)
self.layers_connectivity[i].set_value(value)
self.layers_connectivity[-1].set_value(self.ordering.get_value())
# Reset MRG_RandomStreams (GPU)
self._mrng.rstate = self._initial_mrng_rstate
states_values = zip(self._mrng.state_updates,
self._initial_mrng_state_updates)
for state, value in states_values:
state[0].set_value(value)
self.sample_connectivity()
def _get_p(self, start_choice):
start_choice_idx = (start_choice-1).astype('int32')
prob = T.nnet.nnet.softmax(self._l * T.arange(start_choice,
self._input_size,
dtype=floatX))[0]
p_vals = T.concatenate([T.zeros((start_choice_idx,)),prob])
p_vals = T.inc_subtensor(p_vals[start_choice_idx], 1.)
return p_vals
def _get_hidden_layer_connectivity(self, layerIdx):
layer_size = self._hidden_sizes[layerIdx]
if layerIdx == 0:
lc = self.layers_connectivity[layerIdx]
p_vals = self._get_p(T.min(lc))
else:
lc = self.layers_connectivity_updates[layerIdx-1]
p_vals = self._get_p(T.min(lc))
return T.sum(
T.cumsum(self._mrng.multinomial(
pvals=T.tile(p_vals[::-1][None, :],(layer_size, 1)),
dtype=floatX), axis=1), axis=1
)
def _get_mask(self, layerIdxIn, layerIdxOut):
return (self.layers_connectivity[layerIdxIn][:, None] <=
self.layers_connectivity[layerIdxOut][None, :]).astype(floatX)
def get_mask_layer_UPDATE(self, layerIdx):
return self._get_mask(layerIdx, layerIdx + 1)
def get_direct_input_mask_layer_UPDATE(self, layerIdx):
return self._get_mask(0, layerIdx)
def get_direct_output_mask_layer_UPDATE(self, layerIdx):
return self._get_mask(layerIdx, -1)
class RNNtsg(model):
'''
The attention-based NMT model for TSG
'''
def __init__(self, config, name=''):
self.config = config
self.name = name
self.creater = LayerFactory()
self.trng = RandomStreams(numpy.random.randint(int(10e6)))
def translate(self, x, T, beam_size=10, return_array=False):
'''
Decode with beam search.
:type x: numpy array
:param x: the indexed source sentence
:type beam_size: int
:param beam_size: beam size
:returns: a numpy array, the indexed translation result
'''
# initialize variables
result = [[]]
loss = [0.]
result_eos = []
loss_eos = []
beam = beam_size
nonterms = [
['S']
] # same length as result, nonterms for each hypothesis # (n_hyps, nonterm for each hyp)
par_state_time = [[0]] # (n_hyps, len(nonterm) for each hyp)
# get encoder states
c, state = self.get_context_and_init(x)
emb_y = numpy.zeros((1, self.config['dim_emb_trg']), dtype='float32')
state_hist = [[
numpy.zeros((1, self.config['dim_rec_enc']), dtype='float32')
]] # (n_hyps, l)
for l in range(x.shape[0] * 3):
cur_nonterm_idx = [
] # length lists, each list is the rule indices for expanding LHS
#print result
for i in range(len(nonterms)):
if len(nonterms[i]) > 0:
potent_rules = T.rule_idx_with_root(
nonterms[i][-1]
) # list of potential rules with the given lhs as root
#print potent_rules + i * self.config['dim_emb_trg']
cur_nonterm_idx += [
r + i * self.config['num_vocab_trg']
for r in potent_rules
]
nonterms[i].pop()
# only take the first k results if we have k < beam_size potential nonterms
if len(cur_nonterm_idx) < beam_size:
beam = len(cur_nonterm_idx)
else:
beam = beam_size
# get word probability
energy, ctx = self.get_probs(numpy.repeat(c, len(result), axis=1),
state, emb_y)
# multiply energy by cur_nonterm_idx mask
energy_mask = numpy.zeros((energy.shape[0] * energy.shape[1]),
dtype='float32')
energy_mask[cur_nonterm_idx] = 1.
energy_mask = energy_mask.reshape(
(energy.shape[0], energy.shape[1]))
energy = energy * energy_mask
probs = tools.softmax(energy)
losses = -numpy.log(probs)
# prevent translation to be too short.
if l < x.shape[0] / 2:
losses[:, self.config['index_eos_trg']] = numpy.inf
# prevent rules that do not have required lhs
#losses[:, not_cur_nonterm_idx] = numpy.inf
for i in range(len(loss)):
losses[i] += loss[i]
# get the n-best partial translations
best_index_flatten = numpy.argpartition(losses.flatten(),
beam)[:beam]
best_index = [(index / self.config['num_vocab_trg'],
index % self.config['num_vocab_trg'])
for index in best_index_flatten]
# save the partial translations in the beam
new_ctx = numpy.zeros((beam, 2 * self.config['dim_rec_enc']),
dtype='float32')
new_y = []
new_state = numpy.zeros((beam, self.config['dim_rec_dec']),
dtype='float32')
new_result = []
new_loss = []
new_nonterms = []
new_par_state_time = []
new_state_hist = []
new_par_state = numpy.zeros((beam, self.config['dim_rec_dec']),
dtype='float32')
#print best_index
#print len(result), len(state_hist), len(par_state_time)
for i in range(beam):
index = best_index[i]
new_result.append(result[index[0]] + [index[1]])
new_loss.append(losses[index[0], index[1]])
new_ctx[i] = ctx[index[0]]
new_y.append(index[1])
new_state[i] = state[index[0]]
par_state_t = par_state_time[index[0]][-1]
new_par_state[i] = state_hist[index[0]][par_state_t]
r = T.get_rule_from_idx(index[1])
if r:
add_nonterms = r.get_expand_tags()[::-1]
else:
add_nonterms = []
new_nonterms.append(nonterms[index[0]] + add_nonterms)
# set the parent of expanded tags to be current
# do not include last par_state_time[] for current hyp
new_par_state_time.append(par_state_time[index[0]][:-1] +
[l + 1] * len(add_nonterms))
new_state_hist.append(state_hist[index[0]] + [state[index[0]]])
# get the next decoder hidden state
new_emby = self.get_trg_embedding(
numpy.asarray(new_y, dtype='int64'))[0]
new_state = self.get_next(new_ctx, new_state, new_par_state,
new_emby)
# remove finished translation from the beam
state = []
emb_y = []
result = []
loss = []
nonterms = []
state_hist = []
par_state_time = []
for i in range(beam):
if len(new_nonterms[i]) == 0:
# par_state_time and nonterms should have same length for each hyp
# par_state_time records parent state timestep for each nonterms that needs to be expanded
assert len(new_par_state_time[i]) == 0
result_eos.append(new_result[i])
#print new_result[i]
loss_eos.append(new_loss[i])
beam -= 1
else:
result.append(new_result[i])
loss.append(new_loss[i])
state.append(new_state[i])
emb_y.append(new_emby[i])
nonterms.append(new_nonterms[i])
state_hist.append(new_state_hist[i])
par_state_time.append(new_par_state_time[i])
#print len(result), len(state_hist), len(par_state_time)
if beam <= 0:
break
state = numpy.asarray(state, dtype='float32')
emb_y = numpy.asarray(emb_y, dtype='float32')
# only used in semi-supervised training
if return_array:
if len(result_eos) > 0:
return result_eos
else:
return [result[-1][:1]]
if len(result_eos) > 0:
# return the best translation
return result_eos[numpy.argmin(loss_eos)]
elif beam_size > 100:
# double the beam size on failure
logging.warning('cannot find translation in beam size %d' %
beam_size)
return []
else:
logging.info('cannot find translation in beam size %d, try %d' %
(beam_size, beam_size * 2))
return self.translate(x, beam_size=beam_size * 2)
def sampling_step(self, state, prev, context, par_state):
'''
Build the computational graph which samples the next word.
:type state: theano variables
:param state: the previous hidden state
:type prev: theano variables
:param prev: the last generated word
:type context: theano variables
:param context: the context vectors.
'''
emb = self.emb_trg.forward(prev)
energy, c = self.decoderGRU.decode_probs(context, state, emb)
probs = tensor.nnet.softmax(energy)
sample = self.trng.multinomial(pvals=probs,
dtype='int64').argmax(axis=-1)
newemb = self.emb_trg.forward(sample)
newstate = self.decoderGRU.decode_next(c, state, newemb, par_state)
return newstate, sample, probs
def decode_sample(self, state_init, c, length, n_samples):
'''
Build the decoder graph for sampling.
:type state_init: theano variables
:param state_init: the initial state of decoder
:type c: theano variables
:param c: the context vectors
:type length: int
:param length: the limitation of sample length
:type n_samples: int
:param n_samples: the number of samples
'''
state = tensor.repeat(state_init, n_samples, axis=0)
sample = tensor.zeros((n_samples, ), dtype='int64')
c = tensor.repeat(c, n_samples, axis=1)
result, updates = theano.scan(self.sampling_step,
outputs_info=[state, sample, None],
non_sequences=[c],
n_steps=length)
samples = result[1]
probs = result[2]
y_idx = tensor.arange(samples.flatten(
).shape[0]) * self.config['num_vocab_trg'] + samples.flatten()
probs = probs.flatten()[y_idx]
probs.reshape(samples.shape)
return samples, probs, updates
def build(self, verbose=False):
'''
Build the computational graph.
:type verbose: bool
:param verbose: only set to True on visualization
'''
config = self.config
#create layers
logging.info('initializing layers...')
self.emb_src = self.creater.createLookupTable(self.name + 'emb_src',
config['num_vocab_src'],
config['dim_emb_src'],
offset=True)
self.emb_trg = self.creater.createLookupTable(self.name + 'emb_trg',
config['num_vocab_trg'],
config['dim_emb_trg'],
offset=True)
self.encoderGRU = self.creater.createGRU(self.name + 'GRU_enc',
config['dim_emb_src'],
config['dim_rec_enc'],
verbose=verbose)
self.encoderGRU_back = self.creater.createGRU(self.name +
'GRU_enc_back',
config['dim_emb_src'],
config['dim_rec_enc'],
verbose=verbose)
self.decoderGRU = self.creater.createGRU_tsg(self.name + 'GRU_dec',
config['dim_emb_trg'],
2 * config['dim_rec_enc'],
config['dim_rec_dec'],
config['num_vocab_trg'],
verbose=verbose)
self.initer = self.creater.createFeedForwardLayer(
self.name + 'initer',
config['dim_rec_enc'],
config['dim_rec_dec'],
offset=True)
# create input variables
self.x = tensor.matrix('x', dtype='int64') # size: (length, batchsize)
self.xmask = tensor.matrix(
'x_mask', dtype='float32') # size: (length, batchsize)
self.y_idx = tensor.matrix('y_idx',
dtype='int64') # size: (length, batchsize)
self.ymask = tensor.matrix(
'y_mask', dtype='float32') # size: (length, batchsize)
#self.y_parent_idx = tensor.matrix('y_parent_idx', dtype='int64') # size: (length, batchsize)
self.y_parent_t = tensor.matrix(
'y_parent_t', dtype='int64') # size: (length, batchsize)
if 'MRT' in config and config['MRT'] is True:
self.MRTLoss = tensor.vector('MRTLoss')
self.inputs = [
self.x, self.xmask, self.y_idx, self.y_parent_t, self.ymask,
self.MRTLoss
]
else:
self.MRTLoss = None
self.inputs = [
self.x, self.xmask, self.y_idx, self.y_parent_t, self.ymask
]
# create computational graph for training
logging.info('building computational graph...')
# ----encoder-----
emb = self.emb_src.forward(
self.x.flatten()) # size: (length, batch_size, dim_emb)
back_emb = self.emb_src.forward(self.x[::-1].flatten())
self.encode_forward = self.encoderGRU.forward(
emb, self.x.shape[0], batch_size=self.x.shape[1],
mask=self.xmask) # size: (length, batch_size, dim)
self.encode_backward = self.encoderGRU_back.forward(
back_emb,
self.x.shape[0],
batch_size=self.x.shape[1],
mask=self.xmask[::-1]) # size: (length, batch_size, dim)
context_forward = self.encode_forward[0]
context_backward = self.encode_backward[0][::-1]
self.context = tensor.concatenate(
(context_forward, context_backward),
axis=2) # size: (length, batch_size, 2*dim)
# ----decoder----
self.init_c = context_backward[0]
self.state_init = self.initer.forward(context_backward[0])
emb = self.emb_trg.forward(
self.y_idx.flatten()) # size: (length, batch_size, dim_emb)
self.decode = self.decoderGRU.forward(
emb,
self.y_idx.shape[0],
self.context,
self.state_init,
self.y_parent_t,
batch_size=self.y_idx.shape[1],
mask=self.ymask,
cmask=self.xmask) # size: (length, batch_size, dim)
energy = self.decode[1]
self.attention = self.decode[2]
self.softmax = tensor.nnet.softmax(energy)
# compute costs and grads
y_idx = tensor.arange(self.y_idx.flatten(
).shape[0]) * self.config['num_vocab_trg'] + self.y_idx.flatten()
cost = self.softmax.flatten()[y_idx]
cost = -tensor.log(cost)
self.cost = cost.reshape(
(self.y_idx.shape[0], self.y_idx.shape[1])) * self.ymask
self.cost_per_sample = self.cost.sum(axis=0)
if 'MRT' in config and config['MRT'] is True:
self.cost_per_sample = self.cost.sum(axis=0)
tmp = self.cost_per_sample
tmp *= config['MRT_alpha']
tmp -= tmp.min()
tmp = tensor.exp(-tmp)
tmp /= tmp.sum()
tmp *= self.MRTLoss
tmp = -tmp.sum()
self.cost = tmp
else:
self.cost = self.cost.sum()
# build sampling graph
self.x_sample = tensor.matrix('x_sample', dtype='int64')
self.n_samples = tensor.scalar('n_samples', dtype='int64')
self.length_sample = tensor.scalar('length', dtype='int64')
emb_sample = self.emb_src.forward(
self.x_sample.flatten()) # (length, batch_size, dim_emb)
back_emb_sample = self.emb_src.forward(self.x_sample[::-1].flatten())
encode_forward_sample = self.encoderGRU.forward(
emb_sample,
self.x_sample.shape[0],
batch_size=self.x_sample.shape[1]) # (length, batch_size, dim)
encode_backward_sample = self.encoderGRU_back.forward(
back_emb_sample,
self.x_sample.shape[0],
batch_size=self.x_sample.shape[1]) # (length, batch_size, dim)
context_sample = tensor.concatenate(
(encode_forward_sample[0], encode_backward_sample[0][::-1]),
axis=2) # (length, batch_size, 2*dim)
state_init_sample = self.initer.forward(
encode_backward_sample[0][::-1][0])
self.state_init_sample = state_init_sample
self.context_sample = context_sample
#self.samples, self.probs_sample, self.updates_sample = self.decode_sample(state_init_sample, context_sample,
# self.length_sample, self.n_samples)
# parameter for decoding
self.y_decode = tensor.vector('y_decode', dtype='int64')
self.context_decode = tensor.tensor3('context_decode', dtype='float32')
self.c_decode = tensor.matrix('c_decode', dtype='float32')
self.state_decode = tensor.matrix('state_decode', dtype='float32')
self.par_state_decode = tensor.matrix('par_state_decode',
dtype='float32')
self.emb_decode = tensor.matrix('emb_decode', dtype='float32')
def encode(self, x):
'''
Encode source sentence to context vector.
'''
if not hasattr(self, "encoder"):
self.encoder = theano.function(inputs=[self.x, self.xmask],
outputs=[self.context])
x = numpy.reshape(x, (x.shape[0], 1))
xmask = numpy.ones(x.shape, dtype='float32')
return self.encoder(x, xmask)
def get_trg_embedding(self, y):
'''
Get the embedding of target sentence.
'''
if not hasattr(self, "get_trg_embeddinger"):
self.get_trg_embeddinger = theano.function(
inputs=[self.y_decode],
outputs=[self.emb_trg.forward(self.y_decode)])
return self.get_trg_embeddinger(y)
def get_init(self, c):
'''
Get the initial decoder hidden state with context vector.
'''
if not hasattr(self, "get_initer"):
self.get_initer = theano.function(
inputs=[self.context],
outputs=[self.initer.forward(context_backward[0])])
return self.get_initer(c)
def get_context_and_init(self, x):
'''
Encode source sentence to context vectors and get the initial decoder hidden state.
'''
if not hasattr(self, "get_context_and_initer"):
self.get_context_and_initer = theano.function(
inputs=[self.x, self.xmask],
outputs=[self.context, self.state_init])
x = numpy.reshape(x, (x.shape[0], 1))
xmask = numpy.ones(x.shape, dtype='float32')
return self.get_context_and_initer(x, xmask)
def get_probs(self, c, state, emb):
'''
Get the probability of the next target word.
'''
if not hasattr(self, "get_probser"):
self.get_probser = theano.function(
inputs=[
self.context_decode, self.state_decode, self.emb_decode
],
outputs=self.decoderGRU.decode_probs(self.context_decode,
self.state_decode,
self.emb_decode))
return self.get_probser(c, state, emb)
def get_next(self, c, state, par_state, emb):
'''
Get the next hidden state.
'''
if not hasattr(self, "get_nexter"):
self.get_nexter = theano.function(
inputs=[
self.c_decode, self.state_decode, self.par_state_decode,
self.emb_decode
],
outputs=self.decoderGRU.decode_next(self.c_decode,
self.state_decode,
self.par_state_decode,
self.emb_decode))
return self.get_nexter(c, state, par_state, emb)
def get_cost(self, x, xmask, y, ymask):
'''
Get the negative log-likelihood of parallel sentences.
'''
if not hasattr(self, "get_coster"):
self.get_coster = theano.function(
inputs=[self.x, self.xmask, self.y, self.ymask],
outputs=[self.cost])
return self.get_coster(x, xmask, y, ymask)
def get_sample(self, x, length, n_samples):
'''
Get sampling results.
'''
if not hasattr(self, "get_sampler"):
self.get_sampler = theano.function(
inputs=[self.x_sample, self.length_sample, self.n_samples],
outputs=[self.samples, self.probs_sample],
updates=self.updates_sample)
return self.get_sampler(x, length, n_samples)
def get_attention(self, x, xmask, y, ymask):
'''
Get the attention weight of parallel sentences.
'''
if not hasattr(self, "get_attentioner"):
self.get_attentioner = theano.function(
inputs=[self.x, self.xmask, self.y, self.ymask],
outputs=[self.attention])
return self.get_attentioner(x, xmask, y, ymask)
def get_layer(self, x, xmask, y, ymask):
'''
Get the hidden states essential for visualization
'''
if not hasattr(self, "get_layerer"):
self.get_layerer = theano.function(
inputs=[self.x, self.xmask, self.y, self.ymask],
outputs=self.encode_forward + self.encode_backward +
tuple(self.decode[0]) + tuple(self.decode[1:]))
layers = self.get_layerer(x, xmask, y, ymask)
enc_names = [
'h', 'gate', 'reset', 'state', 'reseted', 'state_in', 'gate_in',
'reset_in'
]
dec_names = [
'h', 'c', 'att', 'gate_cin', 'gate_preactive', 'gate', 'reset_cin',
'reset_preactive', 'reset', 'state_cin', 'reseted',
'state_preactive', 'state'
]
dec_names += [
'outenergy', 'state_in', 'gate_in', 'reset_in', 'state_in_prev',
'readout', 'maxout', 'outenergy_1', 'outenergy_2'
]
value_name = ['enc_for_' + name for name in enc_names]
value_name += ['enc_back_' + name for name in enc_names]
value_name += ['dec_' + name for name in dec_names]
result = {}
for i in range(len(layers)):
print layers[i].shape
if value_name[i] != '':
result[value_name[i]] = layers[i]
return result
class CRBM:
"""CRBM class.
The class :class:`CRBM` implements functionality for
a *convolutional restricted Boltzmann machine* (cRBM) that
extracts redundant DNA sequence features from a provided set
of sequences.
The model can subsequently be used to study the sequence content
of (e.g. regulatory) sequences, by visualizing the features in terms
of sequence logos or in order to cluster the sequences based
on sequence content.
Parameters
-----------
num_motifs : int
Number of motifs.
motif_length : int
Motif length.
epochs : int
Number of epochs to train (Default: 100).
input_dims :int
Input dimensions aka alphabet size (Default: 4 for DNA).
doublestranded : bool
Single strand or both strands. If set to True,
both strands are scanned. (Default: True).
batchsize : int
Batch size (Default: 20).
learning_rate : float)
Learning rate (Default: 0.1).
momentum : float
Momentum term (Default: 0.95).
pooling : int
Pooling factor (not relevant for
cRBM, but for future work) (Default: 1).
cd_k : int
Number of Gibbs sampling iterations in
each persistent contrastive divergence step (Default: 5).
rho : float
Target frequency of motif occurrences (Default: 0.01).
lambda_rate : float
Sparsity enforcement aka penality term (Default: 0.1).
"""
def __init__(self, num_motifs, motif_length, epochs = 100, input_dims=4, \
doublestranded = True, batchsize = 20, learning_rate = 0.1, \
momentum = 0.95, pooling = 1, cd_k = 5,
rho = 0.01, lambda_rate = 0.1):
# sanity checks:
if num_motifs <= 0:
raise Exception("Number of motifs must be positive.")
if motif_length <= 0:
raise Exception("Motif length must be positive.")
if epochs < 0:
raise Exception("Epochs must be non-negative.")
if input_dims <= 0:
raise Exception("input_dims must be positive.")
elif input_dims != 4:
warnings.warn(
"input_dims != 4 was not comprehensively \
tested yet. Be careful when interpreting the results.",
UserWarning)
if batchsize <= 0:
raise Exception("batchsize must be positive.")
if learning_rate <= 0.0:
raise Exception("learning_rate must be positive.")
if not (momentum >= 0.0 and momentum < 1.):
raise Exception("momentum must be between zero and one.")
if pooling <= 0:
raise Exception("pooling must be positive.")
if cd_k <= 0:
raise Exception("cd_k must be positive.")
if not (rho >= 0.0 and rho < 1.):
raise Exception("rho must be between zero and one.")
if lambda_rate < 0.:
raise Exception("lambda_rate must be non-negative.")
# parameters for the motifs
self.num_motifs = num_motifs
self.motif_length = motif_length
self.input_dims = input_dims
self.doublestranded = doublestranded
self.batchsize = batchsize
self.learning_rate = learning_rate
self.momentum = momentum
self.rho = rho
self.lambda_rate = lambda_rate
self.pooling = pooling
self.cd_k = cd_k
self.epochs = epochs
self.spmethod = 'entropy'
self._gradientSparsityConstraint = \
self._gradientSparsityConstraintEntropy
x = np.random.randn(self.num_motifs, 1, self.input_dims,
self.motif_length).astype(theano.config.floatX)
self.motifs = theano.shared(value=x, name='W', borrow=True)
# determine the parameter rho for the model if not given
if not rho:
rho = 1. / (self.num_motifs * self.motif_length)
if self.doublestranded:
rho = rho / 2.
self.rho = rho
# cRBM parameters (2*x to respect both strands of the DNA)
b = np.zeros((1, self.num_motifs)).astype(theano.config.floatX)
# adapt the bias such that it will initially have rho motif hits in H
# That is, we want to have rho percent of the samples positive
# randn draws from 'standard normal', this is why we have 0 and 1
b = b + scipy.stats.norm.ppf(self.rho, 0, np.sqrt(self.motif_length))
self.bias = theano.shared(value=b, name='bias', borrow=True)
c = np.zeros((1, self.input_dims)).astype(theano.config.floatX)
self.c = theano.shared(value=c, name='c', borrow=True)
# infrastructural parameters
self.theano_rng = RS(seed=int(time.time()))
self.rng_data_permut = theano.tensor.shared_randomstreams.RandomStreams(
)
self.motif_velocity = theano.shared(value=np.zeros(
self.motifs.get_value().shape).astype(theano.config.floatX),
name='velocity_of_W',
borrow=True)
self.bias_velocity = theano.shared(value=np.zeros(b.shape).astype(
theano.config.floatX),
name='velocity_of_bias',
borrow=True)
self.c_velocity = theano.shared(value=np.zeros(c.shape).astype(
theano.config.floatX),
name='velocity_of_c',
borrow=True)
val = np.zeros((self.batchsize, self.num_motifs, 1,
200)).astype(theano.config.floatX)
self.fantasy_h = theano.shared(value=val,
name='fantasy_h',
borrow=True)
if self.doublestranded:
self.fantasy_h_prime = theano.shared(value=\
np.zeros((self.batchsize, self.num_motifs, 1, 200)).astype(theano.config.floatX), \
name='fantasy_h_prime', borrow=True)
self._compileTheanoFunctions()
def saveModel(self, filename):
"""Save the model parameters and additional hyper-parameters.
Parameters
-----------
filename : str
Pickle filename where the model parameters are stored.
"""
numpyParams = (self.motifs.get_value(), self.bias.get_value(),
self.c.get_value())
hyperparams = (self.num_motifs, self.motif_length, self.input_dims,
self.doublestranded, self.batchsize, self.learning_rate,
self.momentum, self.rho, self.lambda_rate, self.pooling,
self.cd_k, self.epochs, self.spmethod)
pickleObject = (numpyParams, hyperparams)
joblib.dump(pickleObject, filename, protocol=2)
@classmethod
def loadModel(cls, filename):
"""Load a model from a given pickle file.
Parameters
-----------
filename : str
Pickle file containing the model parameters.
returns : :class:`CRBM` object
An instance of CRBM with reloaded parameters.
"""
numpyParams, hyperparams = joblib.load(filename)
(num_motifs, motif_length, input_dims, \
doublestranded, batchsize, learning_rate, \
momentum, rho, lambda_rate,
pooling, cd_k,
epochs, spmethod) = hyperparams
obj = cls(num_motifs,
motif_length,
epochs=epochs,
input_dims=input_dims,
doublestranded=doublestranded,
batchsize=batchsize,
learning_rate=learning_rate,
momentum=momentum,
pooling=pooling,
cd_k=cd_k,
rho=rho,
lambda_rate=lambda_rate)
motifs, bias, c = numpyParams
obj.motifs.set_value(motifs)
obj.bias.set_value(bias)
obj.c.set_value(c)
return obj
def _bottomUpActivity(self, data, flip_motif=False):
"""Theano function for computing bottom up activity."""
out = conv(data, self.motifs, filter_flip=flip_motif)
out = out + self.bias.dimshuffle('x', 1, 0, 'x')
return out
def _bottomUpProbability(self, activities):
"""Theano function for computing bottom up Probability."""
pool = self.pooling
x = activities.reshape((activities.shape[0], \
activities.shape[1], activities.shape[2], \
activities.shape[3]//pool, pool))
norm = T.sum(1. + T.exp(x), axis=4, keepdims=True)
x = T.exp(x) / norm
x=x.reshape((activities.shape[0], \
activities.shape[1], activities.shape[2], \
activities.shape[3]))
return x
def _bottomUpSample(self, probs):
"""Theano function for bottom up sampling."""
pool = self.pooling
_probs = probs.reshape((probs.shape[0], probs.shape[1], probs.shape[2],
probs.shape[3] // pool, pool))
_probs_reshape = _probs.reshape(
(_probs.shape[0] * _probs.shape[1] * _probs.shape[2] *
_probs.shape[3], pool))
samples = self.theano_rng.multinomial(pvals=_probs_reshape)
samples = samples.reshape(
(probs.shape[0], probs.shape[1], probs.shape[2], probs.shape[3]))
return T.cast(samples, theano.config.floatX)
def _computeHgivenV(self, data, flip_motif=False):
"""Theano function for complete bottom up pass."""
activity = self._bottomUpActivity(data, flip_motif)
probability = self._bottomUpProbability(activity)
sample = self._bottomUpSample(probability)
return [probability, sample]
def _topDownActivity(self, h, hprime):
"""Theano function for top down activity."""
W = self.motifs.dimshuffle(1, 0, 2, 3)
C = conv(h, W, border_mode='full', filter_flip=True)
out = T.sum(C, axis=1, keepdims=True) # sum over all K
if hprime:
C = conv(hprime, W[:,:,::-1,::-1], \
border_mode='full', filter_flip=True)
out = out + T.sum(C, axis=1, keepdims=True) # sum over all K
c_bc = self.c
c_bc = c_bc.dimshuffle('x', 0, 1, 'x')
activity = out + c_bc
return activity
def _topDownProbability(self, activity, softmaxdown=True):
"""Theano function for top down probability."""
if softmaxdown:
return self._softmax(activity)
else:
return 1. / (1. - T.exp(-activity))
def _topDownSample(self, probability, softmaxdown=True):
"""Theano function for top down sample."""
if softmaxdown:
pV_ = probability.dimshuffle(0, 1, 3, 2).reshape( \
(probability.shape[0]*probability.shape[3],
probability.shape[2]))
V_ = self.theano_rng.multinomial(n=1, pvals=pV_).astype(
theano.config.floatX)
V = V_.reshape((probability.shape[0], 1, probability.shape[3],
probability.shape[2])).dimshuffle(0, 1, 3, 2)
else:
V=self.theano_rng.multinomial(n=1,\
pvals=probability).astype(theano.config.floatX)
return V
def _computeVgivenH(self, H_sample, H_sample_prime, softmaxdown=True):
"""Theano function for complete top down pass."""
activity = self._topDownActivity(H_sample, H_sample_prime)
prob = self._topDownProbability(activity, softmaxdown)
sample = self._topDownSample(prob, softmaxdown)
return [prob, sample]
def _collectVHStatistics(self, prob_of_H, data):
"""Theano function for collecting V*H statistics."""
# reshape input
data = data.dimshuffle(1, 0, 2, 3)
prob_of_H = prob_of_H.dimshuffle(1, 0, 2, 3)
avh = conv(data, prob_of_H, border_mode="valid", filter_flip=False)
avh = avh / T.prod(prob_of_H.shape[1:])
avh = avh.dimshuffle(1, 0, 2, 3).astype(theano.config.floatX)
return avh
def _collectVStatistics(self, data):
"""Theano function for collecting V statistics."""
# reshape input
a = T.mean(data, axis=(0, 1, 3)).astype(theano.config.floatX)
a = a.dimshuffle('x', 0)
a = T.inc_subtensor(a[:, :],
a[:, ::-1]) # match a-t and c-g occurances
return a
def _collectHStatistics(self, data):
"""Theano function for collecting H statistics."""
# reshape input
a = T.mean(data, axis=(0, 2, 3)).astype(theano.config.floatX)
a = a.dimshuffle('x', 0)
return a
def _collectUpdateStatistics(self, prob_of_H, prob_of_H_prime, data):
"""Theano function for collecting the complete update statistics."""
average_VH = self._collectVHStatistics(prob_of_H, data)
average_H = self._collectHStatistics(prob_of_H)
if prob_of_H_prime:
average_VH_prime = self._collectVHStatistics(prob_of_H_prime, data)
average_H_prime = self._collectHStatistics(prob_of_H_prime)
average_VH = (average_VH + average_VH_prime[:, :, ::-1, ::-1]) / 2.
average_H = (average_H + average_H_prime) / 2.
average_V = self._collectVStatistics(data)
return average_VH, average_H, average_V
def _updateWeightsOnMinibatch(self, D, gibbs_chain_length):
"""Theano function that defines an SGD update step with momentum."""
# calculate the data gradient for weights (motifs), bias and c
[prob_of_H_given_data, H_given_data] = self._computeHgivenV(D)
if self.doublestranded:
[prob_of_H_given_data_prime,H_given_data_prime] = \
self._computeHgivenV(D, True)
else:
[prob_of_H_given_data_prime, H_given_data_prime] = [None, None]
# calculate data gradients
G_motif_data, G_bias_data, G_c_data = \
self._collectUpdateStatistics(prob_of_H_given_data, \
prob_of_H_given_data_prime, D)
# calculate model probs
H_given_model = self.fantasy_h
if self.doublestranded:
H_given_model_prime = self.fantasy_h_prime
else:
H_given_model_prime = None
for i in range(gibbs_chain_length):
prob_of_V_given_model, V_given_model = \
self._computeVgivenH(H_given_model, H_given_model_prime)
#sample up
prob_of_H_given_model, H_given_model = \
self._computeHgivenV(V_given_model)
if self.doublestranded:
prob_of_H_given_model_prime, H_given_model_prime = \
self._computeHgivenV(V_given_model, True)
else:
prob_of_H_given_model_prime, H_given_model_prime = None, None
# compute the model gradients
G_motif_model, G_bias_model, G_c_model = \
self._collectUpdateStatistics(prob_of_H_given_model, \
prob_of_H_given_model_prime, V_given_model)
mu = self.momentum
alpha = self.learning_rate
sp = self.lambda_rate
reg_motif, reg_bias = self._gradientSparsityConstraint(D)
vmotifs = mu * self.motif_velocity + \
alpha * (G_motif_data - G_motif_model - sp*reg_motif)
vbias = mu * self.bias_velocity + \
alpha * (G_bias_data - G_bias_model - sp*reg_bias)
vc = mu*self.c_velocity + \
alpha * (G_c_data - G_c_model)
new_motifs = self.motifs + vmotifs
new_bias = self.bias + vbias
new_c = self.c + vc
updates = [(self.motifs, new_motifs), (self.bias, new_bias),
(self.c, new_c), (self.motif_velocity, vmotifs),
(self.bias_velocity, vbias), (self.c_velocity, vc),
(self.fantasy_h, H_given_model)]
if self.doublestranded:
updates.append((self.fantasy_h_prime, H_given_model_prime))
return updates
def _gradientSparsityConstraintEntropy(self, data):
"""Theano function that defines the entropy-based sparsity constraint."""
# get expected[H|V]
[prob_of_H, _] = self._computeHgivenV(data)
q = self.rho
p = T.mean(prob_of_H, axis=(0, 2, 3))
gradKernels = -T.grad(T.mean(q * T.log(p) +
(1 - q) * T.log(1 - p)), self.motifs)
gradBias = -T.grad(T.mean(q * T.log(p) +
(1 - q) * T.log(1 - p)), self.bias)
return gradKernels, gradBias
def _compileTheanoFunctions(self):
"""This methods compiles all theano functions."""
print("Start compiling Theano training function...")
D = T.tensor4('data')
updates = self._updateWeightsOnMinibatch(D, self.cd_k)
self.theano_trainingFct = theano.function([D],
None,
updates=updates,
name='train_CRBM')
#compute mean free energy
mfe_ = self._meanFreeEnergy(D)
#compute number of motif hits
[_, H] = self._computeHgivenV(D)
#H = self.bottomUpProbability(self.bottomUpActivity(D))
nmh_ = T.mean(H) # mean over samples (K x 1 x N_h)
#compute norm of the motif parameters
twn_ = T.sqrt(T.mean(self.motifs**2))
#compute information content
pwm = self._softmax(self.motifs)
entropy = -pwm * T.log2(pwm)
entropy = T.sum(entropy, axis=2) # sum over letters
ic_= T.log2(self.motifs.shape[2]) - \
T.mean(entropy) # log is possible information due to length of sequence
medic_= T.log2(self.motifs.shape[2]) - \
T.mean(T.sort(entropy, axis=2)[:, :, entropy.shape[2] // 2])
self.theano_evaluateData = theano.function([D], [mfe_, nmh_],
name='evaluationData')
W = T.tensor4("W")
self.theano_evaluateParams = theano.function([], [twn_, ic_, medic_],
givens={W: self.motifs},
name='evaluationParams')
fed = self._freeEnergyForData(D)
self.theano_freeEnergy = theano.function([D],
fed,
name='fe_per_datapoint')
fed = self._freeEnergyPerMotif(D)
self.theano_fePerMotif = theano.function([D], fed, name='fe_per_motif')
if self.doublestranded:
self.theano_getHitProbs = theano.function([D], \
self._bottomUpProbability(self._bottomUpActivity(D)))
else:
self.theano_getHitProbs = theano.function([D], \
#self.bottomUpProbability( T.maximum(self.bottomUpActivity(D),
self._bottomUpProbability( self._bottomUpActivity(D) +
self._bottomUpActivity(D, True)))
print("Compilation of Theano training function finished")
def _evaluateData(self, data):
"""Evaluate performance on given numpy array.
This is used to monitor training progress.
"""
return self.theano_evaluateData(data)
def _trainingFct(self, data):
"""Train on mini-batch given numpy array."""
return self.theano_trainingFct(data)
def _evaluateParams(self):
"""Evaluate parameters.
This is used to monitor training progress.
"""
return self.theano_evaluateParams()
def motifHitProbs(self, data):
"""Motif match probabilities.
Parameters
-----------
data : numpy-array
4D numpy array representing a DNA sequence in one-hot encoding.
See :meth:`crbm.sequences.seqToOneHot`.
returns : numpy-array
Per-position motif match probabilities of all motifs as numpy array.
"""
return self.theano_getHitProbs(data)
def freeEnergy(self, data, permotif=False):
"""Free energy determined on the given dataset.
Parameters
-----------
data : numpy-array
4D numpy array representing a DNA sequence in one-hot encoding.
See :meth:`crbm.sequences.seqToOneHot`.
permotif : boolean
Indicates whether the free energy should be computed per motif.
Default: The free energy is computed per sequence, by summing over
the individual motif contributions.
returns : numpy-array
Free energy per sequence.
"""
if permotif:
return self.theano_fePerMotif(data)
else:
return self.theano_freeEnergy(data)
def fit(self, training_data, test_data=None):
"""Fits the cRBM to the provided training sequences.
Parameters
-----------
training_data : numpy-array
4D-Numpy array representing the training sequence in one-hot encoding.
See :meth:`crbm.sequences.seqToOneHot`.
test_data : numpy-array
4D-Numpy array representing the validation sequence in one-hot encoding.
If no test_data is provided, the training progress will be reported
on the training set itself. See :meth:`crbm.sequences.seqToOneHot`.
"""
# assert that pooling can be done without rest to the division
# compute sequence length
nseq=int((training_data.shape[3]-\
self.motif_length + 1)/\
self.pooling)*\
self.pooling+ \
self.motif_length -1
training_data = training_data[:, :, :, :nseq]
if type(test_data) != type(None):
nseq=int((test_data.shape[3]-\
self.motif_length + 1)/\
self.pooling)*\
self.pooling+ \
self.motif_length -1
test_data = test_data[:, :, :, :nseq]
else:
test_data = training_data
print(("BatchSize: " + str(self.batchsize)))
start = time.time()
# compile training function
# now perform training
print("Start training the model...")
starttime = time.time()
for epoch in range(self.epochs):
for [start,end] in self._iterateBatchIndices(\
training_data.shape[0],self.batchsize):
self._trainingFct(training_data[start:end, :, :, :])
meanfe = 0.0
meannmh = 0.0
nb = 0
for [start,end] in self._iterateBatchIndices(\
test_data.shape[0],self.batchsize):
[mfe_,
nmh_] = self._evaluateData(test_data[start:end, :, :, :])
meanfe = meanfe + mfe_
meannmh = meannmh + nmh_
nb = nb + 1
[twn_, ic_, medic_] = self._evaluateParams()
print(("Epoch {:d}: ".format(epoch) + \
"FE={:1.3f} ".format(meanfe/nb) + \
"NumH={:1.4f} ".format(meannmh/nb) + \
"WNorm={:2.2f} ".format(float(twn_)) + \
"IC={:1.3f} medIC={:1.3f}".format(float(ic_), float(medic_))))
# done with training
print(("Training finished after: {:5.2f} seconds!".format(\
time.time()-starttime)))
def _meanFreeEnergy(self, D):
"""Theano function for computing the mean free energy."""
return T.sum(self._freeEnergyForData(D)) / D.shape[0]
def getPFMs(self):
"""Returns the weight matrices converted to *position frequency matrices*.
Parameters
-----------
returns: numpy-array
List of position frequency matrices as numpy arrays.
"""
def softmax_(x):
x_exp = np.exp(x)
y = np.zeros(x.shape)
for i in range(x.shape[1]):
y[:, i] = x_exp[:, i] / np.sum(x_exp[:, i])
return y
return [softmax_(m[0, :, :]) for m in self.motifs.get_value()]
def _freeEnergyForData(self, D):
"""Theano function for computing the free energy (per position)."""
pool = self.pooling
x = self._bottomUpActivity(D)
x = x.reshape(
(x.shape[0], x.shape[1], x.shape[2], x.shape[3] // pool, pool))
free_energy = -T.sum(T.log(1. + T.sum(T.exp(x), axis=4)),
axis=(1, 2, 3))
if self.doublestranded:
x = self._bottomUpActivity(D, True)
x = x.reshape(
(x.shape[0], x.shape[1], x.shape[2], x.shape[3] // pool, pool))
free_energy = free_energy - T.sum(
T.log(1. + T.sum(T.exp(x), axis=4)), axis=(1, 2, 3))
cMod = self.c
cMod = cMod.dimshuffle('x', 0, 1,
'x') # make it 4D and broadcastable there
free_energy = free_energy - T.sum(D * cMod, axis=(1, 2, 3))
return free_energy / D.shape[3]
def _freeEnergyPerMotif(self, D):
"""Theano function for computing the free energy (per motif)."""
pool = self.pooling
x = self._bottomUpActivity(D)
x = x.reshape(
(x.shape[0], x.shape[1], x.shape[2], x.shape[3] // pool, pool))
free_energy = -T.sum(T.log(1. + T.sum(T.exp(x), axis=4)), axis=(2, 3))
if self.doublestranded:
x = self._bottomUpActivity(D, True)
x = x.reshape(
(x.shape[0], x.shape[1], x.shape[2], x.shape[3] // pool, pool))
free_energy = free_energy - T.sum(
T.log(1. + T.sum(T.exp(x), axis=4)), axis=(2, 3))
cMod = self.c
cMod = cMod.dimshuffle('x', 0, 1,
'x') # make it 4D and broadcastable there
free_energy = free_energy - T.sum(D * cMod, axis=(1, 2, 3)).dimshuffle(
0, 'x')
return free_energy
def _softmax(self, x):
"""Softmax operation."""
return T.exp(x) / T.exp(x).sum(axis=2, keepdims=True)
def __repr__(self):
st = "Parameters:\n\n"
st += "Number of motifs: {}\n".format(self.num_motifs)
st += "Motif length: {}\n".format(self.motif_length)
st += "\n"
st += "Hyper-parameters:\n\n"
st += "input dims: {:d}".format(self.input_dims)
st += "doublestranded: {}".format(self.doublestranded)
st += "batchsize: {:d}".format(self.batchsize)
st += "learning rate: {:1.3f}".format(self.learning_rate)
st += "momentum: {:1.3f}".format(self.momentum)
st += "rho: {:1.4f}".format(self.rho)
st += "lambda: {:1.3f}".format(self.lambda_rate)
st += "pooling: {:d}".format(self.pooling)
st += "cd_k: {:d}".format(self.cd_k)
st += "epochs: {:d}".format(self.epochs)
return st
def _iterateBatchIndices(self, totalsize, nbatchsize):
"""Returns indices in batches."""
return [ [i,i+nbatchsize] if i+nbatchsize<=totalsize \
else [i,totalsize] for i in range(totalsize)[0::nbatchsize] ]
m1 = numpy.asarray(numpy.random.randint(i32max), dtype="int32")
A2 = numpy.random.randint(0, i32max, (3, 3)).astype('int64')
s2 = numpy.random.randint(0, i32max, 3).astype('int32')
m2 = numpy.asarray(numpy.random.randint(i32max), dtype="int32")
f0.input_storage[0].storage[0] = A1
f0.input_storage[1].storage[0] = s1
f0.input_storage[2].storage[0] = m1
f0.input_storage[3].storage[0] = A2
f0.input_storage[4].storage[0] = s2
f0.input_storage[5].storage[0] = m2
r_a1 = rng_mrg.matVecModM(A1, s1, m1)
r_a2 = rng_mrg.matVecModM(A2, s2, m2)
f0.fn()
r_b = f0.output_storage[0].value
assert numpy.allclose(r_a1, r_b[:3])
assert numpy.allclose(r_a2, r_b[3:])
if __name__ == "__main__":
rng = MRG_RandomStreams(numpy.random.randint(2147462579))
import time
print theano.__file__
pvals = theano.tensor.fmatrix()
for i in range(10):
t0 = time.time()
multinomial = rng.multinomial(pvals=pvals)
print time.time() - t0
def theano_multinomial(n, pvals, seed):
rng = RandomStreams(seed)
return rng.multinomial(n=n, pvals=pvals, dtype='float32')
class Decoder(EncoderDecoderBase):
NCE = 0
EVALUATION = 1
SAMPLING = 2
BEAM_SEARCH = 3
def __init__(self, state, rng, parent, encoder):
EncoderDecoderBase.__init__(self, state, rng, parent)
# Take as input the encoder instance for the embeddings..
# To modify in the future
self.encoder = encoder
self.trng = MRG_RandomStreams(self.seed)
self.init_params()
def init_params(self):
""" Decoder weights """
self.bd_out = add_to_params(
self.params,
theano.shared(value=np.zeros((self.idim, ), dtype='float32'),
name='bd_out'))
self.Wd_emb = add_to_params(
self.params,
theano.shared(value=NormalInit(self.rng, self.idim, self.rankdim),
name='Wd_emb'))
self.Wd_hh = add_to_params(
self.params,
theano.shared(value=OrthogonalInit(self.rng, self.qdim, self.qdim),
name='Wd_hh'))
self.bd_hh = add_to_params(
self.params,
theano.shared(value=np.zeros((self.qdim, ), dtype='float32'),
name='bd_hh'))
self.Wd_in = add_to_params(
self.params,
theano.shared(value=NormalInit(self.rng, self.rankdim, self.qdim),
name='Wd_in'))
self.Wd_s_0 = add_to_params(
self.params,
theano.shared(value=NormalInit(self.rng, self.sdim, self.qdim),
name='Wd_s_0'))
self.bd_s_0 = add_to_params(
self.params,
theano.shared(value=np.zeros((self.qdim, ), dtype='float32'),
name='bd_s_0'))
if self.decoder_bias_type == 'all':
self.Wd_s_q = add_to_params(
self.params,
theano.shared(value=NormalInit(self.rng, self.sdim, self.qdim),
name='Wd_s_q'))
if self.query_step_type == "gated":
self.Wd_in_r = add_to_params(
self.params,
theano.shared(value=NormalInit(self.rng, self.rankdim,
self.qdim),
name='Wd_in_r'))
self.Wd_in_z = add_to_params(
self.params,
theano.shared(value=NormalInit(self.rng, self.rankdim,
self.qdim),
name='Wd_in_z'))
self.Wd_hh_r = add_to_params(
self.params,
theano.shared(value=OrthogonalInit(self.rng, self.qdim,
self.qdim),
name='Wd_hh_r'))
self.Wd_hh_z = add_to_params(
self.params,
theano.shared(value=OrthogonalInit(self.rng, self.qdim,
self.qdim),
name='Wd_hh_z'))
self.bd_r = add_to_params(
self.params,
theano.shared(value=np.zeros((self.qdim, ), dtype='float32'),
name='bd_r'))
self.bd_z = add_to_params(
self.params,
theano.shared(value=np.zeros((self.qdim, ), dtype='float32'),
name='bd_z'))
if self.decoder_bias_type == 'all':
self.Wd_s_z = add_to_params(
self.params,
theano.shared(value=NormalInit(self.rng, self.sdim,
self.qdim),
name='Wd_s_z'))
self.Wd_s_r = add_to_params(
self.params,
theano.shared(value=NormalInit(self.rng, self.sdim,
self.qdim),
name='Wd_s_r'))
######################
# Output layer weights
######################
out_target_dim = self.qdim
if not self.maxout_out:
out_target_dim = self.rankdim
self.Wd_out = add_to_params(
self.params,
theano.shared(value=NormalInit(self.rng, self.qdim,
out_target_dim),
name='Wd_out'))
# Set up deep output
if self.deep_out:
self.Wd_e_out = add_to_params(
self.params,
theano.shared(value=NormalInit(self.rng, self.rankdim,
out_target_dim),
name='Wd_e_out'))
self.bd_e_out = add_to_params(
self.params,
theano.shared(value=np.zeros((out_target_dim, ),
dtype='float32'),
name='bd_e_out'))
if self.decoder_bias_type != 'first':
self.Wd_s_out = add_to_params(
self.params,
theano.shared(value=NormalInit(self.rng, self.sdim,
out_target_dim),
name='Wd_s_out'))
""" Rank """
if hasattr(self, 'train_rank'):
self.Wr_out = add_to_params(
self.params,
theano.shared(value=NormalInit(self.rng, self.sdim, 1),
name='Wr_out'))
self.br_out = add_to_params(
self.params,
theano.shared(value=np.zeros((1, ), dtype='float32'),
name='br_out'))
def build_rank_layer(self, hs):
return T.dot(hs, self.Wr_out) + self.br_out
def build_output_layer(self, hs, xd, hd):
pre_activ = T.dot(hd, self.Wd_out)
if self.deep_out:
pre_activ += T.dot(xd, self.Wd_e_out) + self.bd_e_out
if self.decoder_bias_type != 'first':
pre_activ += T.dot(hs, self.Wd_s_out)
# ^ if bias all, bias the deep output
if self.maxout_out:
pre_activ = Maxout(2)(pre_activ)
return pre_activ
def build_next_probs_predictor(self, hs, x, prev_hd):
"""
Return output probabilities given prev_words x, hierarchical pass hs, and previous hd
hs should always be the same (and should not be updated).
"""
return self.build_decoder(hs,
x,
mode=Decoder.BEAM_SEARCH,
prev_hd=prev_hd)
def approx_embedder(self, x):
# Here we use the same embeddings learnt in the encoder.. !!!
return self.encoder.approx_embedder(x)
def output_softmax(self, pre_activ):
# returns a (timestep, bs, idim) matrix (huge)
return SoftMax(T.dot(pre_activ, self.Wd_emb.T) + self.bd_out)
def output_nce(self, pre_activ, y, y_hat):
# returns a (timestep, bs, pos + neg) matrix (very small)
target_embedding = self.Wd_emb[y]
# ^ target embedding is (timestep x bs, rankdim)
noise_embedding = self.Wd_emb[y_hat]
# ^ noise embedding is (10, timestep x bs, rankdim)
# pre_activ is (timestep x bs x rankdim)
pos_scores = (target_embedding * pre_activ).sum(2)
neg_scores = (noise_embedding * pre_activ).sum(3)
pos_scores += self.bd_out[y]
neg_scores += self.bd_out[y_hat]
pos_noise = self.parent.t_noise_probs[y] * 10
neg_noise = self.parent.t_noise_probs[y_hat] * 10
pos_scores = -T.log(T.nnet.sigmoid(pos_scores - T.log(pos_noise)))
neg_scores = -T.log(1 - T.nnet.sigmoid(neg_scores -
T.log(neg_noise))).sum(0)
return pos_scores + neg_scores
def build_decoder(self,
hs,
x,
xmask=None,
y=None,
y_neg=None,
mode=EVALUATION,
prev_hd=None,
step_num=None):
# Check parameter consistency
if mode == Decoder.EVALUATION or mode == Decoder.NCE:
assert not prev_hd
assert y
else:
assert not y
assert prev_hd
# if mode == EVALUATION
# xd = (timesteps, batch_size, qdim)
#
# if mode != EVALUATION
# xd = (n_samples, dim)
xd = self.approx_embedder(x)
if not xmask:
xmask = T.neq(x, self.eoq_sym)
# we must zero out the </s> embedding
# i.e. the embedding x_{-1} is the 0 vector
# as well as hd_{-1} which will be reseted in the scan functions
if xd.ndim != 3:
assert mode != Decoder.EVALUATION
xd = (xd.dimshuffle((1, 0)) * xmask).dimshuffle((1, 0))
else:
assert mode == Decoder.EVALUATION or mode == Decoder.NCE
xd = (xd.dimshuffle((2, 0, 1)) * xmask).dimshuffle((1, 2, 0))
# Run the decoder
if mode == Decoder.EVALUATION or mode == Decoder.NCE:
hd_init = T.alloc(np.float32(0), x.shape[1], self.qdim)
else:
hd_init = prev_hd
if self.query_step_type == "gated":
f_dec = self.gated_step
o_dec_info = [hd_init, None, None, None]
else:
f_dec = self.plain_step
o_dec_info = [hd_init]
# If the mode of the decoder is EVALUATION
# then we evaluate by default all the sentence
# xd - i.e. xd.ndim == 3, xd = (timesteps, batch_size, qdim)
if mode == Decoder.EVALUATION or mode == Decoder.NCE:
_res, _ = theano.scan(f_dec,
sequences=[xd, xmask, hs],\
outputs_info=o_dec_info)
# else we evaluate only one step of the recurrence using the
# previous hidden states and the previous computed hierarchical
# states.
else:
_res = f_dec(xd, xmask, hs, prev_hd)
if isinstance(_res, list) or isinstance(_res, tuple):
hd = _res[0]
else:
hd = _res
# if we are using selective bias, we should update our hs
# to the step-selective hs
pre_activ = self.build_output_layer(hs, xd, hd)
# EVALUATION : Return target_probs + all the predicted ranks
# target_probs.ndim == 3
if mode == Decoder.EVALUATION:
target_probs = GrabProbs(self.output_softmax(pre_activ), y)
return target_probs, hd, _res
elif mode == Decoder.NCE:
return self.output_nce(pre_activ, y, y_neg), hd
# BEAM_SEARCH : Return output (the softmax layer) + the new hidden states
elif mode == Decoder.BEAM_SEARCH:
return self.output_softmax(pre_activ), hd
# SAMPLING : Return a vector of n_sample from the output layer
# + log probabilities + the new hidden states
elif mode == Decoder.SAMPLING:
outputs = self.output_softmax(pre_activ)
if outputs.ndim == 1:
outputs = outputs.dimshuffle('x', 0)
sample = self.trng.multinomial(pvals=outputs,
dtype='int64').argmax(axis=-1)
if outputs.ndim == 1:
sample = sample[0]
log_prob = -T.log(T.diag(outputs.T[sample]))
return sample, log_prob, hd
def sampling_step(self, *args):
args = iter(args)
# Arguments that correspond to scan's "sequences" parameteter:
step_num = next(args)
assert step_num.ndim == 0
# Arguments that correspond to scan's "outputs" parameteter:
prev_word = next(args)
assert prev_word.ndim == 1
# skip the previous word log probability
log_prob = next(args)
assert log_prob.ndim == 1
prev_h = next(args)
assert prev_h.ndim == 2
prev_hs = next(args)
assert prev_hs.ndim == 2
prev_hd = next(args)
assert prev_hd.ndim == 2
# When we sample we shall recompute the encoder for one step...
encoder_args = dict(prev_hs=prev_hs, prev_h=prev_h)
h, hs = self.parent.encoder.build_encoder(prev_word, **encoder_args)
assert h.ndim == 2
assert hs.ndim == 2
# ...and decode one step.
sample, log_prob, hd = self.build_decoder(hs,
prev_word,
prev_hd=prev_hd,
step_num=step_num,
mode=Decoder.SAMPLING)
assert sample.ndim == 1
assert log_prob.ndim == 1
assert hd.ndim == 2
return [sample, log_prob, h, hs, hd]
def build_sampler(self, n_samples, n_steps):
# For the naive sampler, the states are:
# 1) a vector [</q>] * n_samples to seed the sampling
# 2) a vector of [ 0. ] * n_samples for the log_probs
# 3) prev_h hidden layers
# 4) prev_hs hidden layers
# 5) prev_hd hidden layers
states = [
T.alloc(np.int64(self.eoq_sym), n_samples),
T.alloc(np.float32(0.), n_samples),
T.alloc(np.float32(0.), n_samples, self.qdim),
T.alloc(np.float32(0.), n_samples, self.sdim),
T.alloc(np.float32(0.), n_samples, self.qdim)
]
outputs, updates = theano.scan(
self.sampling_step,
outputs_info=states,
sequences=[T.arange(n_steps, dtype='int64')],
n_steps=n_steps,
name="sampler_scan")
# Return sample, log_probs and updates (for tnrg multinomial)
return (outputs[0], outputs[1]), updates
def gated_step(self, xd_t, m_t, hs_t, hd_tm1):
if m_t.ndim >= 1:
m_t = m_t.dimshuffle(0, 'x')
hd_tm1 = (m_t) * hd_tm1 + (
1 - m_t) * T.tanh(T.dot(hs_t, self.Wd_s_0) + self.bd_s_0)
# ^ iff x_{t - 1} = </s> (m_t = 0) then x_{t - 1} = 0
# and hd_{t - 1} = tanh(W_s_0 hs_t + bd_s_0) else hd_{t - 1} is left unchanged (m_t = 1)
# In the 'all' decoder bias type each hidden state of the decoder
# RNN receives the hs_t vector as bias without modification
if self.decoder_bias_type == 'all':
rd_t = T.nnet.sigmoid(
T.dot(xd_t, self.Wd_in_r) + T.dot(hd_tm1, self.Wd_hh_r) +
T.dot(hs_t, self.Wd_s_r) + self.bd_r)
zd_t = T.nnet.sigmoid(
T.dot(xd_t, self.Wd_in_z) + T.dot(hd_tm1, self.Wd_hh_z) +
T.dot(hs_t, self.Wd_s_z) + self.bd_z)
hd_tilde = self.query_rec_activation(T.dot(xd_t, self.Wd_in) \
+ T.dot(rd_t * hd_tm1, self.Wd_hh) \
+ T.dot(hs_t, self.Wd_s_q) \
+ self.bd_hh)
hd_t = (np.float32(1.) - zd_t) * hd_tm1 + zd_t * hd_tilde
output = (hd_t, rd_t, zd_t, hd_tilde)
else:
# Do not bias all the decoder (force to store very useful information in the first state)
rd_t = T.nnet.sigmoid(
T.dot(xd_t, self.Wd_in_r) + T.dot(hd_tm1, self.Wd_hh_r) +
self.bd_r)
zd_t = T.nnet.sigmoid(
T.dot(xd_t, self.Wd_in_z) + T.dot(hd_tm1, self.Wd_hh_z) +
self.bd_z)
hd_tilde = self.query_rec_activation(T.dot(xd_t, self.Wd_in) \
+ T.dot(rd_t * hd_tm1, self.Wd_hh) \
+ self.bd_hh)
hd_t = (np.float32(1.) - zd_t) * hd_tm1 + zd_t * hd_tilde
output = (hd_t, rd_t, zd_t, hd_tilde)
return output
def plain_step(self, xd_t, m_t, hs_t, hd_tm1):
if m_t.ndim >= 1:
m_t = m_t.dimshuffle(0, 'x')
# We already assume that xd are zeroed out
hd_tm1 = (m_t) * hd_tm1 + (
1 - m_t) * T.tanh(T.dot(hs_t, self.Wd_s_0) + self.bd_s_0)
# ^ iff x_{t - 1} = </s> (m_t = 0) then x_{t-1} = 0
# and hd_{t - 1} = 0 else hd_{t - 1} is left unchanged (m_t = 1)
if self.decoder_bias_type == 'first':
# Do not bias all the decoder (force to store very useful information in the first state)
hd_t = self.query_rec_activation( T.dot(xd_t, self.Wd_in) \
+ T.dot(hd_tm1, self.Wd_hh) \
+ self.bd_hh )
output = (hd_t, )
elif self.decoder_bias_type == 'all':
hd_t = self.query_rec_activation( T.dot(xd_t, self.Wd_in) \
+ T.dot(hd_tm1, self.Wd_hh) \
+ T.dot(hs_t, self.Wd_s_q) \
+ self.bd_hh )
output = (hd_t, )
return output
class RNNsearch(model):
'''
The attention-based NMT model
'''
def __init__(self, config, name='', fls=None):
self.config = config
self.name = name
self.creater = LayerFactory()
self.fls = fls
#print(self.fls)
self.trng = RandomStreams(numpy.random.randint(int(10e6)))
def sampling_step(self, state, prev, context):
'''
Build the computational graph which samples the next word.
:type state: theano variables
:param state: the previous hidden state
:type prev: theano variables
:param prev: the last generated word
:type context: theano variables
:param context: the context vectors.
'''
emb = self.emb_trg.forward(prev)
energy, c = self.decoderGRU.decode_probs(context, state, emb)
probs = tensor.nnet.softmax(energy)
sample = self.trng.multinomial(pvals=probs,
dtype='int64').argmax(axis=-1)
newemb = self.emb_trg.forward(sample)
newstate = self.decoderGRU.decode_next(c, state, newemb)
return newstate, sample, probs
def decode_sample(self, state_init, c, length, n_samples):
'''
Build the decoder graph for sampling.
:type state_init: theano variables
:param state_init: the initial state of decoder
:type c: theano variables
:param c: the context vectors
:type length: int
:param length: the limitation of sample length
:type n_samples: int
:param n_samples: the number of samples
'''
state = tensor.repeat(state_init, n_samples,
axis=0) # copy state n times
sample = tensor.zeros((n_samples, ), dtype='int64')
c = tensor.repeat(c, n_samples, axis=1)
result, updates = theano.scan(self.sampling_step,
outputs_info=[state, sample, None],
non_sequences=[c],
n_steps=length)
samples = result[1]
probs = result[2]
y_idx = tensor.arange(samples.flatten(
).shape[0]) * self.config['num_vocab_trg'] + samples.flatten()
#probs = probs.flatten()[y_idx]
#probs = probs.reshape(samples.shape)
return samples, probs, updates
def build(self, verbose=False):
'''
Build the computational graph.
:type verbose: bool
:param verbose: only set to True on visualization
'''
config = self.config
# create layers
logging.info('Initializing layers')
self.emb_src = self.creater.createLookupTable(
self.name + 'emb_src',
config['num_vocab_src'],
config['dim_emb_src'],
offset=True) #(input,output)-->[30000,620]
self.emb_trg = self.creater.createLookupTable(
self.name + 'emb_trg',
config['num_vocab_trg'],
config['dim_emb_trg'],
offset=True) #(input,output)-->[30000,620]
self.encoderGRU = self.creater.createGRU(self.name + 'GRU_enc',
config['dim_emb_src'],
config['dim_rec_enc'],
verbose=verbose)
self.encoderGRU_back = self.creater.createGRU(self.name +
'GRU_enc_back',
config['dim_emb_src'],
config['dim_rec_enc'],
verbose=verbose)
self.decoderGRU = self.creater.createGRU_attention(
self.name + 'GRU_dec',
config['dim_emb_trg'],
2 * config['dim_rec_enc'],
config['dim_rec_dec'],
config['num_vocab_trg'],
verbose=verbose)
self.initer = self.creater.createFeedForwardLayer(
self.name + 'initer',
config['dim_rec_enc'],
config['dim_rec_dec'],
offset=True)
if self.fls:
#print("loaded feature")
fl_weight = []
for fl in self.fls:
fl_weight.append(fl.feature_weight)
#logging.info("sen weight")
#print(fl.feature_weight)
fl_weight = numpy.concatenate(fl_weight)
self.feature_weight = theano.shared(fl_weight.astype('float32'),
name="feature_weight")
self.creater.params += [self.feature_weight]
self.feature_weight_dim = self.feature_weight.dimshuffle(
'x', 0) # equal to a.T (m,n)-->(n,m)
# create input variables
self.x = tensor.matrix('x', dtype='int64') # size: (length, batchsize)
self.xmask = tensor.matrix(
'x_mask', dtype='float32') # size: (length, batchsize)
self.y = tensor.matrix('y', dtype='int64') # size: (length, batchsize)
self.ymask = tensor.matrix(
'y_mask', dtype='float32') # size: (length, batchsize)
if 'MRT' in config and config['MRT'] is True:
self.MRTLoss = tensor.vector('MRTLoss')
self.inputs = [
self.x, self.xmask, self.y, self.ymask, self.MRTLoss
]
else:
self.MRTLoss = None
self.inputs = [self.x, self.xmask, self.y, self.ymask]
if config['PR']:
self.ans = tensor.scalar('ans', dtype='int64')
self.features = tensor.matrix('features', dtype='float32')
self.inputs += [self.features, self.ans]
# create computational graph for training
logging.info('Building computational graph')
# ----encoder-----
emb = self.emb_src.forward(
self.x.flatten()) # size: (length, batch_size, dim_emb)
back_emb = self.emb_src.forward(self.x[::-1].flatten())
self.encode_forward = self.encoderGRU.forward(
emb, self.x.shape[0], batch_size=self.x.shape[1],
mask=self.xmask) # size: (length, batch_size, dim)
self.encode_backward = self.encoderGRU_back.forward(
back_emb,
self.x.shape[0],
batch_size=self.x.shape[1],
mask=self.xmask[::-1]) # size: (length, batch_size, dim)
context_forward = self.encode_forward[0] # only hiddens
context_backward = self.encode_backward[0][::-1]
self.context = tensor.concatenate(
(context_forward, context_backward),
axis=2) # size: (length, batch_size, 2*dim)
# ----decoder----
self.init_c = context_backward[0]
self.state_init = self.initer.forward(context_backward[0])
emb = self.emb_trg.forward(
self.y.flatten()) # size: (length, batch_size, dim_emb)
self.decode = self.decoderGRU.forward(
emb,
self.y.shape[0],
self.context,
state_init=self.state_init,
batch_size=self.y.shape[1],
mask=self.ymask,
cmask=self.xmask) # size: (length, batch_size, dim)
energy = self.decode[1]
self.attention = self.decode[2]
self.softmax = tensor.nnet.softmax(energy)
# compute costs and grads
y_idx = tensor.arange(self.y.flatten(
).shape[0]) * self.config['num_vocab_trg'] + self.y.flatten()
cost = self.softmax.flatten()[y_idx]
cost = -tensor.log(cost)
self.cost = cost.reshape(
(self.y.shape[0], self.y.shape[1])) * self.ymask
self.cost_per_sample = self.cost.sum(axis=0)
if 'MRT' in config and config['MRT'] is True:
self.cost_per_sample = self.cost.sum(axis=0)
tmp = self.cost_per_sample
tmp *= config['MRT_alpha']
tmp -= tmp.min()
tmp = tensor.exp(-tmp)
tmp /= tmp.sum()
tmp *= self.MRTLoss
tmp = -tmp.sum()
self.cost = tmp
elif config['PR'] and self.fls:
# calculate p
self.cost_per_sample = self.cost.sum(axis=0)
self.cost_per_sample *= config['alpha_PR']
cost_min = self.cost_per_sample - self.cost_per_sample.min()
probs = tensor.exp(-cost_min)
log_probs = -cost_min - tensor.log(probs.sum())
probs /= probs.sum()
self.probs = log_probs
# calculate q
energy_q = self.features * self.feature_weight_dim
energy_q = energy_q.sum(axis=1)
self.energy_q = energy_q
energy_q_min = energy_q - energy_q.max()
probs_q = tensor.exp(energy_q_min)
log_probs_q = energy_q_min - tensor.log(probs_q.sum())
probs_q /= probs_q.sum()
self.probs_q = log_probs_q
# calculate KL divergence
cost_KL = tensor.exp(log_probs_q) * (log_probs_q - log_probs)
self.cost_KLs = cost_KL
self.cost_KL = cost_KL.sum()
self.cost_NMT = self.cost_per_sample[self.ans]
self.cost = config['lambda_PR'] * self.cost_KL + config[
'lambda_MLE'] * self.cost_NMT
else:
self.cost = self.cost.sum()
# build sampling graph
self.x_sample = tensor.matrix('x_sample', dtype='int64')
self.n_samples = tensor.scalar('n_samples', dtype='int64')
self.length_sample = tensor.scalar('length', dtype='int64')
emb_sample = self.emb_src.forward(
self.x_sample.flatten()) # (length, batch_size, dim_emb)
back_emb_sample = self.emb_src.forward(self.x_sample[::-1].flatten())
encode_forward_sample = self.encoderGRU.forward(
emb_sample,
self.x_sample.shape[0],
batch_size=self.x_sample.shape[1]) # (length, batch_size, dim)
encode_backward_sample = self.encoderGRU_back.forward(
back_emb_sample,
self.x_sample.shape[0],
batch_size=self.x_sample.shape[1]) # (length, batch_size, dim)
context_sample = tensor.concatenate(
(encode_forward_sample[0], encode_backward_sample[0][::-1]),
axis=2) # (length, batch_size, 2*dim)
state_init_sample = self.initer.forward(
encode_backward_sample[0][::-1][0])
self.state_init_sample = state_init_sample
self.context_sample = context_sample
self.samples, self.probs_sample, self.updates_sample = self.decode_sample(
state_init_sample, context_sample, self.length_sample,
self.n_samples)
# parameter for decoding
self.y_decode = tensor.vector('y_decode', dtype='int64')
self.context_decode = tensor.tensor3('context_decode', dtype='float32')
self.c_decode = tensor.matrix('c_decode', dtype='float32')
self.state_decode = tensor.matrix('state_decode', dtype='float32')
self.emb_decode = tensor.matrix('emb_decode', dtype='float32')
def encode(self, x):
'''
Encode source sentence to context vector.
'''
if not hasattr(self, "encoder"):
self.encoder = theano.function(inputs=[self.x, self.xmask],
outputs=[self.context])
x = numpy.reshape(x, (x.shape[0], 1))
xmask = numpy.ones(x.shape, dtype='float32')
return self.encoder(x, xmask)
def get_trg_embedding(self, y):
'''
Get the embedding of target sentence.
'''
if not hasattr(self, "get_trg_embeddinger"):
self.get_trg_embeddinger = theano.function(
inputs=[self.y_decode],
outputs=[self.emb_trg.forward(self.y_decode)])
return self.get_trg_embeddinger(y)
def get_init(self, c):
'''
Get the initial decoder hidden state with context vector.
'''
if not hasattr(self, "get_initer"):
self.get_initer = theano.function(
inputs=[self.context],
outputs=[self.initer.forward(context_backward[0])])
return self.get_initer(c)
def get_context_and_init(self, x):
'''
Encode source sentence to context vectors and get the initial decoder hidden state.
'''
if not hasattr(self, "get_context_and_initer"):
self.get_context_and_initer = theano.function(
inputs=[self.x, self.xmask],
outputs=[self.context, self.state_init])
x = numpy.reshape(x, (x.shape[0], 1))
xmask = numpy.ones(x.shape, dtype='float32')
return self.get_context_and_initer(x, xmask)
def get_probs(self, c, state, emb):
'''
Get the probability of the next target word.
'''
if not hasattr(self, "get_probser"):
self.get_probser = theano.function(inputs = [self.context_decode, \
self.state_decode, \
self.emb_decode], \
outputs = self.decoderGRU.decode_probs(self.context_decode, \
self.state_decode, \
self.emb_decode))
return self.get_probser(c, state, emb)
def get_next(self, c, state, emb):
'''
Get the next hidden state.
'''
if not hasattr(self, "get_nexter"):
self.get_nexter = theano.function(inputs = [self.c_decode, \
self.state_decode, \
self.emb_decode],
outputs = self.decoderGRU.decode_next(self.c_decode, \
self.state_decode, \
self.emb_decode))
return self.get_nexter(c, state, emb)
def get_cost(self, x, xmask, y, ymask):
'''
Get the negative log-likelihood of parallel sentences.
'''
if not hasattr(self, "get_coster"):
self.get_coster = theano.function(
inputs=[self.x, self.xmask, self.y, self.ymask],
outputs=[self.cost])
return self.get_coster(x, xmask, y, ymask)
def get_sample(self, x, length, n_samples):
'''
Get sampling results.
'''
if not hasattr(self, "get_sampler"):
self.get_sampler = theano.function(
inputs=[self.x_sample, self.length_sample, self.n_samples],
outputs=[self.samples, self.probs_sample],
updates=self.updates_sample)
return self.get_sampler(x, length, n_samples)
def get_attention(self, x, xmask, y, ymask):
'''
Get the attention weight of parallel sentences.
'''
if not hasattr(self, "get_attentioner"):
self.get_attentioner = theano.function(
inputs=[self.x, self.xmask, self.y, self.ymask],
outputs=[self.attention])
return self.get_attentioner(x, xmask, y, ymask)
def get_layer(self, x, xmask, y, ymask):
'''
Get the hidden states essential for visualization
'''
if not hasattr(self, "get_layerer"):
self.get_layerer = theano.function(inputs = [self.x, self.xmask, self.y, self.ymask],
outputs = self.encode_forward + \
self.encode_backward + \
tuple(self.decode[0]) + tuple(self.decode[1:]))
layers = self.get_layerer(x, xmask, y, ymask)
enc_names = [
'h', 'gate', 'reset', 'state', 'reseted', 'state_in', 'gate_in',
'reset_in'
]
dec_names = [
'h', 'c', 'att', 'gate_cin', 'gate_preactive', 'gate', 'reset_cin',
'reset_preactive', 'reset', 'state_cin', 'reseted',
'state_preactive', 'state'
]
dec_names += [
'outenergy', 'state_in', 'gate_in', 'reset_in', 'state_in_prev',
'readout', 'maxout', 'outenergy_1', 'outenergy_2'
]
value_name = ['enc_for_' + name for name in enc_names]
value_name += ['enc_back_' + name for name in enc_names]
value_name += ['dec_' + name for name in dec_names]
result = {}
for i in range(len(layers)):
if value_name[i] != '':
result[value_name[i]] = layers[i]
return result
class Categorical(Distribution):
def __init__(self, dim):
self._dim = dim
self._srng = RandomStreams()
@property
def dim(self):
return self._dim
def kl_sym(self, old_dist_info_vars, new_dist_info_vars):
"""
Compute the symbolic KL divergence of two categorical distributions
"""
old_prob_var = old_dist_info_vars["prob"]
new_prob_var = new_dist_info_vars["prob"]
# Assume layout is N * A
return TT.sum(
old_prob_var *
(TT.log(old_prob_var + TINY) - TT.log(new_prob_var + TINY)),
axis=-1)
def kl(self, old_dist_info, new_dist_info):
"""
Compute the KL divergence of two categorical distributions
"""
old_prob = old_dist_info["prob"]
new_prob = new_dist_info["prob"]
return np.sum(old_prob *
(np.log(old_prob + TINY) - np.log(new_prob + TINY)),
axis=-1)
def likelihood_ratio_sym(self, x_var, old_dist_info_vars,
new_dist_info_vars):
old_prob_var = old_dist_info_vars["prob"]
new_prob_var = new_dist_info_vars["prob"]
x_var = TT.cast(x_var, 'float32')
# Assume layout is N * A
return (TT.sum(new_prob_var * x_var, axis=-1) +
TINY) / (TT.sum(old_prob_var * x_var, axis=-1) + TINY)
def entropy(self, info):
probs = info["prob"]
return -np.sum(probs * np.log(probs + TINY), axis=1)
def entropy_sym(self, dist_info_vars):
prob_var = dist_info_vars["prob"]
return -TT.sum(prob_var * TT.log(prob_var + TINY), axis=1)
def log_likelihood_sym(self, x_var, dist_info_vars):
probs = dist_info_vars["prob"]
# Assume layout is N * A
return TT.log(
TT.sum(probs * TT.cast(x_var, 'float32'), axis=-1) + TINY)
def log_likelihood(self, xs, dist_info):
probs = dist_info["prob"]
# Assume layout is N * A
n = probs.shape[0]
return np.log(probs[np.arange(n), from_onehot(np.asarray(xs))] + TINY)
def sample_sym(self, dist_info):
probs = dist_info["prob"]
return self._srng.multinomial(pvals=probs, dtype='uint8')
@property
def dist_info_keys(self):
return ["prob"]
class ParticleFilter():
''' Implements particle filtering and smoothing for Markov Chains
with arbitrary proposal/true distributions '''
def __init__(self, transition_model, observation_model, n_particles, observation_input=None, n_history=1):
self.transition_model=transition_model
self.observation_model=observation_model
self.data_dims=observation_model.output_dims
self.state_dims=transition_model.output_dims
self.n_particles=n_particles
self.n_history=n_history
#this is used to keep track of what set of particles corresponds
#to the previous point in time
self.time_counter=theano.shared(0)
self.theano_rng=RandomStreams()
#init_particles=np.zeros((n_history+1, n_particles, self.state_dims)).astype(np.float32)
init_particles=np.random.randn(n_history+1, n_particles, self.state_dims).astype(np.float32)
init_weights=(np.ones((n_history+1, n_particles))/float(n_particles)).astype(np.float32)
self.particles=theano.shared(init_particles)
self.weights=theano.shared(init_weights)
self.next_state=self.particles[(self.time_counter+1)%(self.n_history+1)]
self.current_state=self.particles[self.time_counter%(self.n_history+1)]
self.previous_state=self.particles[(self.time_counter-1)%(self.n_history+1)]
self.next_weights=self.weights[(self.time_counter+1)%(self.n_history+1)]
self.current_weights=self.weights[self.time_counter%(self.n_history+1)]
self.previous_weights=self.weights[(self.time_counter-1)%(self.n_history+1)]
self.proposal_distrib=None
self.true_log_transition_probs=self.transition_model.rel_log_prob
self.true_log_observation_probs=self.observation_model.rel_log_prob
self.perform_inference=None
self.resample=None
self.sample_joint=None
self.observation_input=observation_input
ess=self.compute_ESS()
self.get_ESS=theano.function([],ess)
n_samps=T.lscalar()
n_T=T.lscalar()
data_samples, state_samples, init_state_samples, data_sample_updates=self.sample_future(n_samps,n_T)
self.sample_from_future=theano.function([n_samps, n_T],[data_samples,state_samples,init_state_samples],updates=data_sample_updates)
self.get_current_particles=theano.function([],self.current_state)
self.get_current_weights=theano.function([],self.current_weights)
def recompile(self):
'''This function compiles each of the theano functions that might
change following a change of the model. '''
samp_updates=self.sample_update(self.observation_input)
self.perform_inference=theano.function([],updates=samp_updates)
res_updates=self.resample_update()
self.resample=theano.function([],updates=res_updates)
nsamps=T.lscalar()
joint_samples, joint_updates=self.sample_from_joint(nsamps)
self.sample_joint=theano.function([nsamps],joint_samples,updates=joint_updates)
new_ess, stddevhist, esshist, sr_updates=self.sequential_resample()
self.perform_sequential_resampling=theano.function([],[new_ess,stddevhist,esshist],updates=sr_updates)
csamps=self.sample_current(nsamps)
self.sample_current_state=theano.function([nsamps],csamps)
psamps=self.sample_prev(nsamps)
self.sample_previous_state=theano.function([nsamps],psamps)
return
def set_proposal(self, proposal_distrib):
self.proposal_distrib=proposal_distrib
return
def set_true_log_transition_probs(self, true_log_transition_probs):
self.true_log_transition_probs=true_log_transition_probs
return
def set_true_log_observation_probs(self, true_log_observation_probs):
self.true_log_observation_probs=true_log_observation_probs
return
def sample_update(self, data):
proposal_samples, log_proposal_probs=self.proposal_distrib
printing=False
if printing:
log_transition_probs=theano.printing.Print('1 log transition probs update')(self.true_log_transition_probs(self.current_state, proposal_samples))
log_observation_probs=theano.printing.Print('2 log observation probs update')(self.true_log_observation_probs(proposal_samples, data.dimshuffle('x',0)))
log_unnorm_weights=theano.printing.Print('3 log unnorm weights update')(log_transition_probs + log_observation_probs - log_proposal_probs)
log_unnorm_weights_center=theano.printing.Print('4 log unnorm weights center update')(log_unnorm_weights-T.max(log_unnorm_weights))
unnorm_weights=theano.printing.Print('5 unnorm weights update')(T.exp(log_unnorm_weights_center)*self.current_weights)
normalizer=theano.printing.Print('6 normalizer update')(T.sum(unnorm_weights))
else:
log_transition_probs=self.true_log_transition_probs(self.current_state, proposal_samples)
log_observation_probs=self.true_log_observation_probs(proposal_samples, data.dimshuffle('x',0))
log_unnorm_weights=log_transition_probs + log_observation_probs - log_proposal_probs
log_unnorm_weights_center=log_unnorm_weights-T.max(log_unnorm_weights)
unnorm_weights=T.exp(log_unnorm_weights_center)*self.current_weights
normalizer=T.sum(unnorm_weights)
weights=unnorm_weights/normalizer
updates=OrderedDict()
updates[self.weights]=T.set_subtensor(self.next_weights, weights)
updates[self.particles]=T.set_subtensor(self.next_state, proposal_samples)
updates[self.time_counter]=self.time_counter+1
return updates
def compute_ESS(self):
return 1.0/T.sum(self.current_weights**2)
def resample_update(self):
#shape: n_particles by n_particles
samps=self.theano_rng.multinomial(pvals=T.extra_ops.repeat(self.current_weights.dimshuffle('x',0),self.n_particles,axis=0))
idxs=T.cast(T.dot(samps, T.arange(self.n_particles)),'int64')
updates=OrderedDict()
updates[self.particles]=T.set_subtensor(self.current_state, self.current_state[idxs])
updates[self.weights]=T.set_subtensor(self.current_weights, T.cast(T.ones_like(self.current_weights)/float(self.n_particles),'float32'))
return updates
def sample_step(self, future_samps, t, n_samples):
particles_now=self.particles[(self.time_counter-t)%(self.n_history+1)]
weights_now=self.weights[(self.time_counter-t)%(self.n_history+1)]
#n_particles by n_samples
rel_log_probs=self.true_log_transition_probs(particles_now, future_samps, all_pairs=True)
unnorm_probs=T.exp(rel_log_probs)*weights_now.dimshuffle(0,'x')
probs=unnorm_probs/T.sum(unnorm_probs, axis=0).dimshuffle('x',0)
samps=self.theano_rng.multinomial(pvals=probs.T)
idxs=T.cast(T.dot(samps, T.arange(self.n_particles)),'int64')
output_samples=particles_now[idxs]
return [output_samples, t+1]
def sample_from_joint(self, n_samples, output_2D=False):
'''Samples from the joint posterior P(s_t-n_history:s_t | observations)
n_samples: the number of samples to draw
Returns an array with shape (n_history+1, n_samples, state_dims),
where array[-1] corresponds to the current time.
'''
samps=self.theano_rng.multinomial(pvals=T.extra_ops.repeat(self.current_weights.dimshuffle('x',0),n_samples,axis=0))
idxs=T.cast(T.dot(samps, T.arange(self.n_particles)),'int64')
samps_t0=self.current_state[idxs]
t0=T.as_tensor_variable(1)
[samples, ts], updates = theano.scan(fn=self.sample_step,
outputs_info=[samps_t0, t0],
non_sequences=[n_samples],
n_steps=self.n_history)
#the variable "samples" that results from the scan is time-flipped
#in the sense that samples[0] corresponds to the most recent point
#in time, and higher indices correspond to points in the past.
#I will stick to the convention that for any collection of points in
#time, [-1] will index the most recent time, and [0] will index
#the point farthest in the past. So, the first axis of "samples"
#needs to be flipped.
flip_idxs=T.cast(-T.arange(self.n_history)+self.n_history-1,'int64')
samples=T.concatenate([samples[flip_idxs], samps_t0.dimshuffle('x',0,1)], axis=0)
if output_2D:
samples=T.reshape(samples, ((self.n_history+1)*n_samples, self.state_dims))
return samples, updates
def sample_future(self, n_samples, n_T):
'''Samples from the "future" data distribution:
P(s_t+1,...s_t+n_T, x_t+1,...x_t+n_T | s_t)
n_samples: number of samples to draw
n_T: the number of (future) time points to sample from
Returns three arrays. The first two have shapes
(n_T, n_samples, data_dims) and
(n_T, n_samples, state_dims),
corresponding to samples of future observations and states,
and the third having size (n_samples,state_dims),
corresponding to the "initial" samples taken from the current
state distribution.
'''
samps=self.theano_rng.multinomial(pvals=T.extra_ops.repeat(self.current_weights.dimshuffle('x',0),n_samples,axis=0))
idxs=T.cast(T.dot(samps, T.arange(self.n_particles)),'int64')
samps_t0=self.current_state[idxs]
def fstep(states):
next_states=self.transition_model.get_samples_noprobs(states)
next_data=self.observation_model.get_samples_noprobs(next_states)
return next_states, next_data
[state_samples, data_samples], updates = theano.scan(fn=fstep,
outputs_info=[samps_t0, None],
n_steps=n_T)
#data_samples=self.observation_model.get_samples_noprobs(state_samples)
return data_samples, state_samples, samps_t0, updates
def sample_model(self, n_samples, n_T):
'''Samples from the "future" data distribution:
P(s_t+1,...s_t+n_T, x_t+1,...x_t+n_T | s_t)
n_samples: number of samples to draw
n_T: the number of (future) time points to sample from
Returns three arrays. The first two have shapes
(n_T, n_samples, data_dims) and
(n_T, n_samples, state_dims),
corresponding to samples of future observations and states,
and the third having size (n_samples,state_dims),
corresponding to the "initial" samples taken from the current
state distribution.
'''
samps=self.theano_rng.multinomial(pvals=T.extra_ops.repeat(self.current_weights.dimshuffle('x',0),n_samples,axis=0))
idxs=T.cast(T.dot(samps, T.arange(self.n_particles)),'int64')
samps_t0=self.current_state[idxs]
state_samples, updates = theano.scan(fn=self.transition_model.get_samples_noprobs,
outputs_info=[samps_t0],
n_steps=n_T)
data_sample=self.observation_model.get_samples_noprobs(state_samples[-1])
return data_sample, state_samples[-1], state_samples[-2], updates
def sr_step(self, means, weights, stddev, ess, decay):
#Sampling from a mixture of gaussians
msamps=self.theano_rng.multinomial(pvals=T.extra_ops.repeat(weights.dimshuffle('x',0),means.shape[0],axis=0))
idxs=T.cast(T.dot(msamps, T.arange(means.shape[0])),'int64')
sample_means=T.cast(means[idxs],'float32')
proposal_samples=self.theano_rng.normal(size=means.shape)*stddev.dimshuffle('x',0)+sample_means
diffs=proposal_samples.dimshuffle(0,'x',1)-sample_means.dimshuffle('x',0,1)
printing=False
if printing:
log_proposal_probs=theano.printing.Print('1 log_proposal_probs')(T.log(T.dot(T.exp(-T.sum((1.0/(2.0*stddev**2)).dimshuffle('x','x',0)*diffs**2,axis=2)),weights)))
log_transition_probs=theano.printing.Print('2 log transition probs')(self.true_log_transition_probs(self.previous_state, proposal_samples, all_pairs=True))
log_transition_probs_2=theano.printing.Print('3 log transition probs 2')(T.log(T.dot(T.exp(log_transition_probs).T,self.previous_weights)))
log_observation_probs=theano.printing.Print('4 log observation probs')(self.true_log_observation_probs(proposal_samples, self.observation_input.dimshuffle('x',0)))
log_unnorm_weights=theano.printing.Print('5 log unnorm weights nomax')(log_transition_probs_2 + log_observation_probs - log_proposal_probs)
log_unnorm_weights=theano.printing.Print('6 log unnorm weights')(log_unnorm_weights-T.max(log_unnorm_weights))
unnorm_weights=theano.printing.Print('7 unnorm weights')(T.exp(log_unnorm_weights))
normalizer=theano.printing.Print('8 normalizer')(T.sum(unnorm_weights))
else:
log_proposal_probs=T.log(T.dot(T.exp(-T.sum((1.0/(2.0*stddev**2)).dimshuffle('x','x',0)*diffs**2,axis=2)),weights))
log_transition_probs=self.true_log_transition_probs(self.previous_state, proposal_samples, all_pairs=True)
log_transition_probs=T.log(T.dot(T.exp(log_transition_probs).T,self.previous_weights))
log_observation_probs=self.true_log_observation_probs(proposal_samples, self.observation_input.dimshuffle('x',0))
log_unnorm_weights=log_transition_probs + log_observation_probs - log_proposal_probs
log_unnorm_weights=log_unnorm_weights-T.max(log_unnorm_weights)
unnorm_weights=T.exp(log_unnorm_weights)
normalizer=T.sum(unnorm_weights)
new_weights=unnorm_weights/normalizer
new_ess=1.0/T.sum(new_weights**2)
sampmean=T.dot(proposal_samples.T, new_weights)
sampvar=T.dot(((proposal_samples-sampmean.dimshuffle('x',0))**2).T,new_weights)
#propmean=T.mean(proposal_samples, axis=0)
#propvar=T.mean((proposal_samples-propmean.dimshuffle('x',0))**2,axis=0)
#new_stddev=stddev*T.clip(T.exp(decay*(1.0-propvar/sampvar)),0.5,2.0)
#new_stddev=T.clip(stddev*T.clip(T.exp(decay*(1.0-stddev**2/sampvar)),0.5,2.0),0.0,4.0)
new_stddev=T.clip(stddev*T.clip(T.exp(decay*(1.0-stddev**2/sampvar)),0.5,1.5),0.0,4.0)
return [proposal_samples, new_weights, new_stddev, T.cast(new_ess,'float32')]#, theano.scan_module.until(new_ess>100)
def sequential_resample(self, init_stddev=4.0, max_steps=20, stddev_decay=0.1):
'''Repeatedly resamples and then samples from a proposal distribution
constructed from the current samples. Should be used when the main
proposal distribution is poor or whenever the ESS is poor.
'''
essT=T.as_tensor_variable(np.asarray(0.0,dtype='float32'))
stddevT=T.as_tensor_variable(np.asarray(init_stddev*np.ones(self.state_dims),dtype='float32'))
decayT=T.as_tensor_variable(np.asarray(stddev_decay,dtype='float32'))
[samphist, weighthist, stddevhist, esshist], updates = theano.scan(fn=self.sr_step,
outputs_info=[self.current_state, self.current_weights, stddevT, essT],
non_sequences=decayT,
n_steps=max_steps)
end_samples=samphist[-1]
end_weights=weighthist[-1]
updates[self.particles]=T.set_subtensor(self.current_state, end_samples)
updates[self.weights]=T.set_subtensor(self.current_weights, end_weights)
return 1.0/T.sum(end_weights**2), stddevhist, esshist, updates
def sample_current(self, nsamps):
samps=self.theano_rng.multinomial(pvals=T.extra_ops.repeat(self.current_weights.dimshuffle('x',0),nsamps,axis=0))
idxs=T.cast(T.dot(samps, T.arange(self.n_particles)),'int64')
samples=self.current_state[idxs]
return samples
def sample_prev(self, nsamps):
samps=self.theano_rng.multinomial(pvals=T.extra_ops.repeat(self.previous_weights.dimshuffle('x',0),nsamps,axis=0))
idxs=T.cast(T.dot(samps, T.arange(self.n_particles)),'int64')
samples=self.previous_state[idxs]
return samples
def get_history(self):
'''This function returns a 3-D array containing all the particles
and a 2-D array of weights for the entire memory. The first dimension indexes
time, with the zeroth entry corresponding to the earliest point in
memory.'''
idxs=(T.arange(self.n_history+1)-self.n_history+self.time_counter)%(self.n_history+1)
return self.particles[idxs], self.weights[idxs]
class SLmodel():
#This version adapts the proposal distribution by keeping a running
#estimate of the exact posterior covariance, parametrized as the
#matrix CC'
def __init__(self, nx, ns, nh, npcl, xvar=1.0):
#for this model I assume one linear generative model and a
#combination of nh linear dynamical models
#generative matrix
init_W = np.asarray(np.random.randn(nx, ns) / 10.0, dtype='float32')
#init_W=np.asarray(np.eye(2),dtype='float32')
#always normalize the columns of W to be unit length
init_W = init_W / np.sqrt(np.sum(init_W**2, axis=0))
#observed variable means
init_c = np.asarray(np.zeros(nx), dtype='float32')
#dynamical matrices
#init_M=np.asarray(np.random.randn(ns,ns*nh)/2.0,dtype='float32')
init_M = np.asarray((np.tile(np.eye(ns), (1, nh))), dtype='float32')
#state-variable variances
#(covariance matrix of state variable noise assumed to be diagonal)
init_b = np.asarray(np.ones(ns) * 10.0, dtype='float32')
#Switching parameter matrix
init_A = np.asarray(np.zeros((ns, nh)), dtype='float32')
#priors for switching variable
init_ph = np.asarray(np.zeros(nh), dtype='float32')
self.W = theano.shared(init_W)
self.c = theano.shared(init_c)
self.M = theano.shared(init_M)
self.b = theano.shared(init_b)
self.A = theano.shared(init_A)
self.ph = theano.shared(init_ph)
#square root of covariance matrix of proposal distribution
#initialized to the true root covariance
init_cov_inv = np.dot(
init_W.T, init_W) / (xvar**2) + np.eye(ns) * np.exp(-init_b)
init_cov = spla.inv(init_cov_inv)
init_C = spla.sqrtm(init_cov)
init_C = np.asarray(np.real(init_C), dtype='float32')
init_s_now = np.asarray(np.zeros((npcl, ns)), dtype='float32')
init_h_now = np.asarray(np.zeros((npcl, nh)), dtype='float32')
init_h_now[:, 0] = 1.0
init_weights_now = np.asarray(np.ones(npcl) / float(npcl),
dtype='float32')
init_s_past = np.asarray(np.zeros((npcl, ns)), dtype='float32')
init_h_past = np.asarray(np.zeros((npcl, nh)), dtype='float32')
init_h_past[:, 0] = 1.0
init_weights_past = np.asarray(np.ones(npcl) / float(npcl),
dtype='float32')
self.C = theano.shared(init_C)
#this is to help vectorize operations
self.sum_mat = T.as_tensor_variable(
np.asarray((np.tile(np.eye(ns), nh)).T, dtype='float32'))
self.s_now = theano.shared(init_s_now)
self.h_now = theano.shared(init_h_now)
self.weights_now = theano.shared(init_weights_now)
self.s_past = theano.shared(init_s_past)
self.h_past = theano.shared(init_h_past)
self.weights_past = theano.shared(init_weights_past)
self.xvar = np.asarray(xvar, dtype='float32')
self.nx = nx #dimensionality of observed variables
self.ns = ns #dimensionality of latent variables
self.nh = nh #number of (linear) dynamical modes
self.npcl = npcl #numer of particles in particle filter
#for ease of use and efficient computation (these are used a lot)
self.CCT = T.dot(self.C, self.C.T)
self.cov_inv = T.dot(
self.W.T, self.W) / (self.xvar**2) + T.eye(ns) * T.exp(-self.b)
self.theano_rng = RandomStreams()
self.params = [self.W, self.M, self.b, self.A, self.c, self.ph]
self.rel_lrates = np.asarray([0.1, 1.0, 1.0, 10.0, 1.0, 1.0],
dtype='float32')
self.meta_params = [self.C]
self.meta_rel_lrates = [1.0]
def sample_proposal_s(self, s, h, xp):
s_pred = self.get_prediction(s, h)
n = self.theano_rng.normal(size=T.shape(s))
mean_term = T.dot(
(xp - self.c), self.W) / (self.xvar**2) + s_pred * T.exp(-self.b)
prop_mean = T.dot(mean_term, self.CCT)
s_prop = prop_mean + T.dot(n, self.C)
#I compute the term inside the exponent for the pdf of the proposal distrib
prop_term = -T.sum(n**2) / 2.0
return T.cast(s_prop, 'float32'), T.cast(s_pred, 'float32'), T.cast(
prop_term, 'float32'), prop_mean
def calc_h_probs(self, s):
#this function takes an np by ns matrix of s samples
#and returns an nh by np set of h probabilities
exp_terms = T.dot(s, self.A) + T.reshape(self.ph, (1, self.nh))
#re-centering for numerical stability
exp_terms_recentered = exp_terms - T.max(exp_terms, axis=1)
#exponentiation and normalization
rel_probs = T.exp(exp_terms)
probs = rel_probs.T / T.sum(rel_probs, axis=1)
return probs.T
def forward_filter_step(self, xp):
#need to sample from the proposal distribution first
s_samps, s_pred, prop_terms, prop_means = self.sample_proposal_s(
self.s_now, self.h_now, xp)
updates = {}
#now that we have samples from the proposal distribution, we need to reweight them
h_probs = self.calc_h_probs(s_samps)
h_samps = self.theano_rng.multinomial(pvals=h_probs)
recons = T.dot(self.W, s_samps.T) + T.reshape(self.c, (self.nx, 1))
x_terms = -T.sum(
(recons - T.reshape(xp, (self.nx, 1)))**2, axis=0) / (2.0 *
self.xvar**2)
s_terms = -T.sum(((s_samps - s_pred) * self.b)**2, axis=1) / 2.0
energies = x_terms + s_terms - prop_terms
#to avoid exponentiating large or very small numbers, I
#"re-center" the reweighting factors by adding a constant,
#as this has no impact on the resulting new weights
energies_recentered = energies - T.max(energies)
alpha = T.exp(energies_recentered) #these are the reweighting factors
new_weights_unnorm = self.weights_now * alpha
normalizer = T.sum(new_weights_unnorm)
new_weights = new_weights_unnorm / normalizer #need to normalize new weights
updates[self.h_past] = T.cast(self.h_now, 'float32')
updates[self.s_past] = T.cast(self.s_now, 'float32')
updates[self.h_now] = T.cast(h_samps, 'float32')
updates[self.s_now] = T.cast(s_samps, 'float32')
updates[self.weights_past] = T.cast(self.weights_now, 'float32')
updates[self.weights_now] = T.cast(new_weights, 'float32')
#return normalizer, energies_recentered, s_samps, s_pred, T.dot(self.W.T,(xp-self.c)), updates
#return normalizer, energies_recentered, updates
#return h_samps, updates
return updates
def proposal_loss(self, C):
#calculates how far off self.CCT is from the true posterior covariance
CCT = T.dot(C, C.T)
prod = T.dot(CCT, self.cov_inv)
diff = prod - T.eye(self.ns)
tot = T.sum(T.sum(diff**2)) #frobenius norm
return tot
def prop_update_step(self, C_now, lr):
loss = self.proposal_loss(C_now)
gr = T.grad(loss, C_now)
return [C_now - lr * gr]
def update_proposal_distrib(self, n_steps, lr):
#does some gradient descent on self.C, so that self.CCT becomes
#closer to the true posterior covariance
C0 = self.C
Cs, updates = theano.scan(fn=self.prop_update_step,
outputs_info=[C0],
non_sequences=[lr],
n_steps=n_steps)
updates[self.C] = Cs[-1]
loss = self.proposal_loss(Cs[-1])
#updates={}
#updates[self.C]=self.prop_update_step(self.C,lr)
#loss=self.proposal_loss(self.C)
return loss, updates
def get_prediction(self, s, h):
s_dot_M = T.dot(s, self.M) #this is np by nh*ns
s_pred = T.dot(s_dot_M * T.extra_ops.repeat(h, self.ns, axis=1),
self.sum_mat) #should be np by ns
return T.cast(s_pred, 'float32')
def sample_joint(self, sp):
t2_samp = self.theano_rng.multinomial(
pvals=T.reshape(self.weights_now, (1, self.npcl))).T
s2_samp = T.cast(
T.sum(self.s_now * T.addbroadcast(t2_samp, 1), axis=0), 'float32')
h2_samp = T.cast(
T.sum(self.h_now * T.addbroadcast(t2_samp, 1), axis=0), 'float32')
diffs = self.b * (s2_samp - sp)
sqr_term = T.sum(diffs**2, axis=1)
alpha = T.exp(-sqr_term)
probs_unnorm = self.weights_past * alpha
probs = probs_unnorm / T.sum(probs_unnorm)
t1_samp = self.theano_rng.multinomial(
pvals=T.reshape(probs, (1, self.npcl))).T
s1_samp = T.cast(
T.sum(self.s_past * T.addbroadcast(t1_samp, 1), axis=0), 'float32')
h1_samp = T.cast(
T.sum(self.h_past * T.addbroadcast(t1_samp, 1), axis=0), 'float32')
return [s1_samp, h1_samp, s2_samp, h2_samp]
def calc_mean_h_energy(self, s, h):
#you give this function a set of samples of s and h,
#it gives you the average energy of those samples
exp_terms = T.dot(s, self.A) + T.reshape(self.ph,
(1, self.nh)) #np by nh
energies = T.sum(h * exp_terms, axis=1) - T.log(
T.sum(T.exp(exp_terms), axis=1)) #should be np by 1
energy = T.mean(energies)
return energy
def update_params(self, x1, x2, n_samps, lrate):
#this function samples from the joint posterior and performs
# a step of gradient ascent on the log-likelihood
sp = self.get_prediction(self.s_past, self.h_past)
#sp should be np by ns
[s1_samps, h1_samps, s2_samps, h2_samps
], updates = theano.scan(fn=self.sample_joint,
outputs_info=[None, None, None, None],
non_sequences=[sp],
n_steps=n_samps)
x1_recons = T.dot(self.W, s1_samps.T) + T.reshape(self.c, (self.nx, 1))
x2_recons = T.dot(self.W, s2_samps.T) + T.reshape(self.c, (self.nx, 1))
s_pred = self.get_prediction(s1_samps, h1_samps)
hterm1 = self.calc_mean_h_energy(s1_samps, h1_samps)
#hterm2=self.calc_mean_h_energy(s2_samps, h2_samps)
sterm = -T.mean(T.sum((self.b * (s2_samps - s_pred))**2, axis=1)) / 2.0
#xterm1=-T.mean(T.sum((x1_recons-T.reshape(x1,(self.nx,1)))**2,axis=0)/(2.0*self.xvar**2))
xterm2 = -T.mean(
T.sum((x2_recons - T.reshape(x2, (self.nx, 1)))**2, axis=0) /
(2.0 * self.xvar**2))
#energy = hterm1 + xterm1 + hterm2 + xterm2 + sterm -T.sum(T.sum(self.A**2))
energy = hterm1 + xterm2 + sterm
gparams = T.grad(
energy,
self.params,
consider_constant=[s1_samps, s2_samps, h1_samps, h2_samps])
# constructs the update dictionary
for gparam, param, rel_lr in zip(gparams, self.params,
self.rel_lrates):
#gnat=T.dot(param, T.dot(param.T,param))
updates[param] = T.cast(param + gparam * lrate * rel_lr, 'float32')
return energy, updates
def get_ESS(self):
return 1.0 / T.sum(self.weights_now**2)
def resample_step(self):
idx = self.theano_rng.multinomial(
pvals=T.reshape(self.weights_now, (1, self.npcl))).T
s_samp = T.sum(self.s_now * T.addbroadcast(idx, 1), axis=0)
h_samp = T.sum(self.h_now * T.addbroadcast(idx, 1), axis=0)
return T.cast(s_samp, 'float32'), T.cast(h_samp, 'float32')
def resample(self):
[s_samps, h_samps], updates = theano.scan(fn=self.resample_step,
outputs_info=[None, None],
n_steps=self.npcl)
updates[self.s_now] = T.cast(s_samps, 'float32')
updates[self.h_now] = T.cast(h_samps, 'float32')
updates[self.weights_now] = T.cast(
T.ones_like(self.weights_now) / T.cast(self.npcl, 'float32'),
'float32') #dtype paranoia
return updates
def simulate_step(self, s):
s = T.reshape(s, (1, self.ns))
#get h probabilities
h_probs = self.calc_h_probs(s)
#h_samp=self.theano_rng.multinomial(pvals=T.reshape(h_probs,(self.nh,1)))
h_samp = self.theano_rng.multinomial(pvals=h_probs)
sp = self.get_prediction(s, h_samp)
xp = T.dot(self.W, sp.T) + T.reshape(self.c, (self.nx, 1))
return T.cast(sp, 'float32'), T.cast(xp, 'float32'), h_samp
def simulate_forward(self, n_steps):
s0 = T.sum(self.s_now * T.reshape(self.weights_now, (self.npcl, 1)),
axis=0)
s0 = T.reshape(s0, (1, self.ns))
[sp, xp, hs], updates = theano.scan(fn=self.simulate_step,
outputs_info=[s0, None, None],
n_steps=n_steps)
return sp, xp, hs, updates
class LatentPolicy(BaseNNModule):
# Policy network takes three inputs and produces a single
# system action embedding. Its use is heavily coupled with decoder.
def __init__(self, latent_size, learn_mode, belief_size, degree_size,
ihidden_size, ohidden_size, tfEncoder, tbEncoder, sfEncoder,
sbEncoder):
# latent variable dimension
self.dl = latent_size
hidden_size = 100
# set default sampling mode: posterior, from all actions
if learn_mode == 'rl': self.setSampleMode('prior', 5)
else: self.setSampleMode('posterior', latent_size)
# random seed
self.srng = RandomStreams(seed=234)
# decoder input embedding
self.Wd1 = theano.shared(0.3 * np.random.uniform(-1.0,1.0,\
(latent_size,hidden_size)).astype(theano.config.floatX))
self.Wd2 = theano.shared(0.3 * np.random.uniform(-1.0,1.0,\
(latent_size,hidden_size)).astype(theano.config.floatX))
self.bd1 = theano.shared(2. * np.ones(
(hidden_size)).astype(theano.config.floatX))
self.Wd3 = theano.shared(0.3 * np.random.uniform(-1.0,1.0,\
(hidden_size*2,ohidden_size)).astype(theano.config.floatX))
# for state construction
# belief to state
self.Ws1 = theano.shared(0.3 * np.random.uniform(-1.0,1.0,\
(belief_size,hidden_size)).astype(theano.config.floatX))
# matching degree to state
self.Ws2 = theano.shared(0.3 * np.random.uniform(-1.0,1.0,\
(degree_size,hidden_size)).astype(theano.config.floatX))
# intent to state
self.Ws3 = theano.shared(0.3 * np.random.uniform(-1.0,1.0,\
(ihidden_size*2,hidden_size)).astype(theano.config.floatX))
# latent policy parameterisation, state -> action
self.Wp1 = theano.shared(0.3 * np.random.uniform(-1.0,1.0,\
(hidden_size,hidden_size)).astype(theano.config.floatX))
self.Wp2 = theano.shared(0.3 * np.random.uniform(-1.0,1.0,\
(hidden_size,latent_size)).astype(theano.config.floatX))
self.bp1 = theano.shared(0.3 * np.random.uniform(-1.0,1.0,\
(hidden_size)).astype(theano.config.floatX))
# prior parameters P(z_t|S_t) and P(R_t|z_t)
self.params = [
self.Wd1, self.Wd2, self.bd1, self.Wd3, self.Ws1, self.Ws2,
self.Ws3, self.Wp1, self.Wp2, self.bp1
]
# approximated posterior parameters Q(z_t|S_t,R_t)
# sentence encoders
self.sfEncoder, self.sbEncoder = sfEncoder, sbEncoder
self.tfEncoder, self.tbEncoder = tfEncoder, tbEncoder
# belief to posterior
self.Wq1 = theano.shared(0.3 * np.random.uniform(-1.0,1.0,\
(belief_size,hidden_size)).astype(theano.config.floatX))
# matching degree to posterior
self.Wq2 = theano.shared(0.3 * np.random.uniform(-1.0,1.0,\
(degree_size,hidden_size)).astype(theano.config.floatX))
# intent to posterior
self.Wq3 = theano.shared(0.3 * np.random.uniform(-1.0,1.0,\
(ihidden_size*2,hidden_size)).astype(theano.config.floatX))
# response to posterior
self.Wq4 = theano.shared(0.3 * np.random.uniform(-1.0,1.0,\
(ihidden_size*2,hidden_size)).astype(theano.config.floatX))
# MLP 2nd layer
self.Wq5 = theano.shared(0.3 * np.random.uniform(-1.0,1.0,\
(hidden_size,latent_size)).astype(theano.config.floatX))
#posterior parameters Q(z_t|S_t,R_t)
self.Qparams = [self.Wq1, self.Wq2, self.Wq3, self.Wq4, self.Wq5]
self.Qparams.extend(self.tfEncoder.params + self.tbEncoder.params +
self.sfEncoder.params + self.sbEncoder.params)
# add posterior also into parameter set
self.params.extend(self.Qparams)
# Reinforce baseline
self.baseline = ReinforceBaseline(belief_size, degree_size,
ihidden_size)
def setSampleMode(self, sample_mode, topN):
self.sample_mode = sample_mode
self.topN = topN
def encode(self,
belief_t,
degree_t,
intent_t,
masked_source_t,
masked_source_len_t,
masked_target_t,
masked_target_len_t,
utt_group_t,
sample_t=None):
# prepare belief state vector
belief_t = G.disconnected_grad(T.concatenate(belief_t, axis=0))
##########################
# prior parameterisarion #
##########################
hidden_t = T.tanh(
T.dot(belief_t, self.Ws1) + T.dot(degree_t, self.Ws2) +
T.dot(intent_t, self.Ws3))
prior_t = T.nnet.softmax(
T.dot(T.tanh(T.dot(hidden_t, self.Wp1) + self.bp1), self.Wp2))
##############################
# posterior parameterisation #
##############################
# response encoding
target_intent_t = bidirectional_encode(self.tfEncoder, self.tbEncoder,
masked_target_t,
masked_target_len_t)
source_intent_t = bidirectional_encode(self.sfEncoder, self.sbEncoder,
masked_source_t,
masked_source_len_t)
# scores before softmax layer
q_logit_t = T.dot(
T.tanh(
T.dot(belief_t, self.Wq1) + T.dot(degree_t, self.Wq2) +
T.dot(source_intent_t, self.Wq3) +
T.dot(target_intent_t, self.Wq4)), self.Wq5)
# sampling from a scaled posterior
if self.sample_mode == 'posterior':
print '\t\tSampling from posterior ...'
posterior_t = T.nnet.softmax(q_logit_t)
z_t = T.switch(
T.lt(utt_group_t, self.dl - 1), utt_group_t,
G.disconnected_grad(
T.argmax(
self.srng.multinomial(pvals=posterior_t,
dtype='float32')[0])))
else:
# choose to use the current sample or ground truth
print '\t\tSampling from prior ...'
z_t = T.switch(T.lt(utt_group_t, self.dl - 1), utt_group_t,
sample_t)
# put sample into decoder to decode
hidden_t = T.nnet.sigmoid(self.Wd2[z_t, :] + self.bd1) * hidden_t
actEmb_t = T.tanh(
T.dot(T.concatenate([T.tanh(self.Wd1[z_t, :]), hidden_t], axis=0),
self.Wd3)).dimshuffle('x', 0)
# return the true posterior
posterior_t = T.nnet.softmax(q_logit_t)
# compute baseline estimate
b_t = self.baseline.encode(belief_t, degree_t, source_intent_t,
target_intent_t)
return actEmb_t, prior_t[0], posterior_t[0], z_t, b_t, posterior_t
def decide(self,
belief_t,
degree_t,
intent_t,
masked_source_t,
masked_target_t,
forced_sample=None):
# prepare belief state vector
belief_t = np.concatenate(belief_t, axis=0)
# sample how many actions
n = 1
# forced sampling
if forced_sample != None:
z_t = [forced_sample]
prob_t = None
# different sampling mode
elif self.sample_mode == 'posterior' and masked_target_t != None:
# training time, sample from posterior
z_t, prob_t = self._sample_from_posterior(belief_t, degree_t,
intent_t,
masked_source_t,
masked_target_t)
elif self.sample_mode == 'prior':
# testing time, sample from prior
z_t, prob_t = self._sample_from_prior(belief_t, degree_t, intent_t)
# state representation
hidden_t = tanh(
np.dot(belief_t, self.Ws1_backup) +
np.dot(degree_t, self.Ws2_backup) +
np.dot(intent_t, self.Ws3_backup))
# put sample into decoder to decode
hidden_t = np.multiply(
sigmoid(self.Wd2_backup[z_t, :] + self.bd1_backup), hidden_t)
hidden_t = np.repeat(hidden_t, n, axis=0)
actEmb_t = tanh(
np.dot(
np.concatenate([tanh(self.Wd1_backup[z_t, :]), hidden_t],
axis=1), self.Wd3_backup))
return actEmb_t, z_t, prob_t
def _sample_from_prior(self, belief_t, degree_t, intent_t):
# prior parameterisarion
hidden_t = tanh(
np.dot(belief_t, self.Ws1_backup) +
np.dot(degree_t, self.Ws2_backup) +
np.dot(intent_t, self.Ws3_backup))
p_logit_t = np.dot(
tanh(np.dot(hidden_t, self.Wp1_backup) + self.bp1_backup),
self.Wp2_backup)
# sampling from prior
sortedIndex = np.argsort(p_logit_t)[::-1][:self.topN]
topN_prior_t = softmax(p_logit_t[sortedIndex])
z_t = sortedIndex[np.argmax(
np.random.multinomial(n=1, pvals=topN_prior_t))]
z_t = np.expand_dims(z_t, axis=0)
# choose the top N samples
print 'Sample : %s' % z_t
print 'Prior dist.: %s' % sortedIndex
print 'probability: %s' % topN_prior_t
print
return z_t, softmax(p_logit_t)
def _sample_from_posterior(self, belief_t, degree_t, intent_t,
masked_source_t, masked_target_t):
# Posterior
# response encoding
target_intent_t = bidirectional_read(self.tfEncoder, self.tbEncoder,
masked_target_t)
source_intent_t = bidirectional_read(self.sfEncoder, self.sbEncoder,
masked_source_t)
# posterior parameterisation
q_logit_t = np.dot(
tanh(
np.dot(belief_t, self.Wq1_backup) +
np.dot(degree_t, self.Wq2_backup) +
np.dot(source_intent_t, self.Wq3_backup) +
np.dot(target_intent_t, self.Wq4_backup)), self.Wq5_backup)
# sampling from a scaled posterior
sortedIndex = np.argsort(q_logit_t)[::-1][:self.topN]
topN_posterior_t = softmax(q_logit_t[sortedIndex])
z_t = sortedIndex[np.argmax(
np.random.multinomial(n=1, pvals=topN_posterior_t))]
#z_t = sortedIndex[0]
z_t = np.expand_dims(z_t, axis=0)
print sortedIndex[:3]
print softmax(q_logit_t)[sortedIndex][:3]
print 'Posterior : %s' % sortedIndex
print 'probability: %s' % topN_posterior_t
return z_t, softmax(q_logit_t)
def loadConverseParams(self):
# decoder
self.Wd1_backup = self.params[0].get_value()
self.Wd2_backup = self.params[1].get_value()
self.bd1_backup = self.params[2].get_value()
self.Wd3_backup = self.params[3].get_value()
# state
self.Ws1_backup = self.params[4].get_value()
self.Ws2_backup = self.params[5].get_value()
self.Ws3_backup = self.params[6].get_value()
# latent policy (conditional prior)
self.Wp1_backup = self.params[7].get_value()
self.Wp2_backup = self.params[8].get_value()
self.bp1_backup = self.params[9].get_value()
# posterior
self.Wq1_backup = self.params[10].get_value()
self.Wq2_backup = self.params[11].get_value()
self.Wq3_backup = self.params[12].get_value()
self.Wq4_backup = self.params[13].get_value()
self.Wq5_backup = self.params[14].get_value()
# posterior sentence encoder
self.tfEncoder.loadConverseParams()
self.tbEncoder.loadConverseParams()
self.sfEncoder.loadConverseParams()
self.sbEncoder.loadConverseParams()
class SCLmodel(): #This class defines the switched constrained linear model, which was #designed to eliminate state-space 'explosions' that can occur when #doing prediction - a serious issue in the basic SL model def __init__(self, nx, ns, nh, npcl, xvar=1.0): #for this model I assume one linear generative model and a #combination of nh linear dynamical models #generative matrix init_W=np.asarray(np.random.randn(nx,ns)/10.0,dtype='float32') #init_W=np.asarray(np.eye(2),dtype='float32') #always normalize the columns of W to be unit length init_W=init_W/np.sqrt(np.sum(init_W**2,axis=0)) #observed variable means init_c=np.asarray(np.zeros(nx),dtype='float32') #dynamical matrices init_M=np.asarray(np.random.randn(nh,ns**2)/2.0,dtype='float32') #state-variable variances #(covariance matrix of state variable noise assumed to be diagonal) init_b=np.asarray(np.ones(ns)*10.0,dtype='float32') #means for switching variable init_mu=np.asarray(np.random.randn(nh,ns)/1.0,dtype='float32') #(natural log of) covariance matrices for switching variable #I assume the covariance matrices to be diagonal, so I #store all the diagonal elements in a ns-by-nh matrix init_A=np.asarray(np.zeros((nh,ns)),dtype='float32') init_s_now=np.asarray(np.zeros((npcl,ns)),dtype='float32') init_h_now=np.asarray(np.zeros((npcl,nh)),dtype='float32') init_h_now[:,0]=1.0 init_weights_now=np.asarray(np.ones(npcl)/float(npcl),dtype='float32') init_s_past=np.asarray(np.zeros((npcl,ns)),dtype='float32') init_h_past=np.asarray(np.zeros((npcl,nh)),dtype='float32') init_h_past[:,0]=1.0 init_weights_past=np.asarray(np.ones(npcl)/float(npcl),dtype='float32') self.W=theano.shared(init_W) self.c=theano.shared(init_c) self.M=theano.shared(init_M) self.b=theano.shared(init_b) self.A=theano.shared(init_A) self.mu=theano.shared(init_mu) #I define thes to avoid repeated computations of the exponential #of the elements of A and of the normalizing constants for each h self.exp_A=T.exp(self.A) self.ln_Z_h=T.reshape(0.5*T.sum(self.A, axis=1), (nh,1)) self.s_now=theano.shared(init_s_now) self.h_now=theano.shared(init_h_now) self.weights_now=theano.shared(init_weights_now) self.s_past=theano.shared(init_s_past) self.h_past=theano.shared(init_h_past) self.weights_past=theano.shared(init_weights_past) self.xvar=np.asarray(xvar,dtype='float32') self.nx=nx #dimensionality of observed variables self.ns=ns #dimensionality of latent variables self.nh=nh #number of (linear) dynamical modes self.npcl=npcl #numer of particles in particle filter self.theano_rng = RandomStreams() self.params= [self.W, self.M, self.b, self.A, self.c, self.mu] self.rel_lrates=np.asarray([ 1.0, 1.0, 0.01, 1.0, 1.0, 10.0] ,dtype='float32') def sample_proposal_s(self, s, h, xpred, sig): s_pred=self.get_prediction(s, h) n=self.theano_rng.normal(size=T.shape(s)) #This is the proposal distribution that arises when one assumes that W'W=I mean=2.0*(xpred+s_pred*(self.b**2))*sig s_prop=mean+n*T.sqrt(sig) #I compute the term inside the exponent for the pdf of the proposal distrib prop_term=-T.sum(n**2)/2.0 return T.cast(s_prop,'float32'), T.cast(s_pred,'float32'), T.cast(prop_term,'float32') #This function is required if we allow multiple generative models #def get_recon(self, s, h): #W_vec=T.sum(self.W*h, axis=0) #W=W.reshape((self.nx, self.ns)) #xr=T.dot(W, s) #return xr def one_h_prob(self, exp_A_i, mu_i, s): #scan function for self.calc_h_probs smi=s-mu_i #should be np by ns smia=smi*T.reshape(exp_A_i,(1,self.ns)) gaussian_term=-T.sum(smia*smi,axis=1) return gaussian_term def calc_h_probs(self, s): #gterms, updates = theano.scan(fn=self.one_h_prob, #outputs_info=[None], #sequences=[self.exp_A, self.mu], #non_sequences=[s], #n_steps=self.nh) #vectorized version t1=T.dot(s*s,self.exp_A.T) t2=-2.0*T.dot(s, (self.exp_A*self.mu).T) t3=T.sum((self.mu*self.mu)*self.exp_A,axis=1) gterms=(t1+t2+t3).T #gterms should be nh by np #need to multiply by relative partition functions exp_terms=gterms+self.ln_Z_h #re-centering for numerical stability exp_terms_recentered=exp_terms-T.max(exp_terms) #exponentiation and normalization rel_probs=T.exp(exp_terms) probs=rel_probs/T.sum(rel_probs, axis=0) return probs def forward_filter_step(self, xp): #need to sample from the proposal distribution first #these terms are the same for every particle xpred=T.dot(self.W.T,(xp-self.c))/(2.0*self.xvar**2) sig=(1.0/(self.b**2+1.0/(2.0*self.xvar**2)))/2.0 [s_samps, s_pred, prop_terms], updates = theano.scan(fn=self.sample_proposal_s, outputs_info=[None, None, None], sequences=[self.s_now, self.h_now], non_sequences=[xpred, sig], n_steps=self.npcl) #now that we have samples from the proposal distribution, we need to reweight them #would use this if we have multiple generative models #recons, updates = theano.scan(fn=get_recon, #outputs_info=[None], #sequences=[s_samps, h_samps], #n_steps=self.npcl) #this loops over every row of A and mu to calculate relative h probabilities #for each particle h_probs = self.calc_h_probs(s_samps) h_samps=self.theano_rng.multinomial(pvals=h_probs.T) recons=T.dot(self.W, s_samps.T) + T.reshape(self.c,(self.nx,1)) x_terms=-T.sum((recons-T.reshape(xp,(self.nx,1)))**2,axis=0)/(2.0*self.xvar**2) s_terms=-T.sum(((s_samps-s_pred)*self.b)**2,axis=1) energies=x_terms+s_terms-prop_terms #to avoid exponentiating large or very small numbers, I #"re-center" the reweighting factors by adding a constant, #as this has no impact on the resulting new weights energies_recentered=energies-T.max(energies) alpha=T.exp(energies_recentered) #these are the reweighting factors new_weights_unnorm=self.weights_now*alpha normalizer=T.sum(new_weights_unnorm) new_weights=new_weights_unnorm/normalizer #need to normalize new weights updates[self.h_past]=T.cast(self.h_now,'float32') updates[self.s_past]=T.cast(self.s_now,'float32') updates[self.h_now]=T.cast(h_samps,'float32') updates[self.s_now]=T.cast(s_samps,'float32') updates[self.weights_past]=T.cast(self.weights_now,'float32') updates[self.weights_now]=T.cast(new_weights,'float32') #return normalizer, energies_recentered, s_samps, s_pred, T.dot(self.W.T,(xp-self.c)), updates #return normalizer, energies_recentered, updates return h_samps, updates def get_prediction(self, s, h): M_vec=T.sum(self.M*T.reshape(h,(self.nh,1)),axis=0) M=M_vec.reshape((self.ns,self.ns)) sp=T.dot(M, s) return T.cast(sp,'float32') def sample_joint(self, sp): t2_samp=self.theano_rng.multinomial(pvals=T.reshape(self.weights_now,(1,self.npcl))).T s2_samp=T.cast(T.sum(self.s_now*T.addbroadcast(t2_samp,1),axis=0),'float32') h2_samp=T.cast(T.sum(self.h_now*T.addbroadcast(t2_samp,1),axis=0),'float32') diffs=self.b*(s2_samp-sp) sqr_term=T.sum(diffs**2,axis=1) alpha=T.exp(-sqr_term) probs_unnorm=self.weights_past*alpha probs=probs_unnorm/T.sum(probs_unnorm) t1_samp=self.theano_rng.multinomial(pvals=T.reshape(probs,(1,self.npcl))).T s1_samp=T.cast(T.sum(self.s_past*T.addbroadcast(t1_samp,1),axis=0),'float32') h1_samp=T.cast(T.sum(self.h_past*T.addbroadcast(t1_samp,1),axis=0),'float32') return [s1_samp, h1_samp, s2_samp, h2_samp] #def sample_posterior(self, n_samps): #sp, updates = theano.scan(fn=self.get_prediction, #outputs_info=[None], #sequences=[self.s_past, self.h_past], #n_steps=self.npcl) ##sp should be np by ns #[s1_samps, h1_samps, s2_samps, h2_samps], updates = theano.scan(fn=self.sample_joint, #outputs_info=[None, None, None, None], #non_sequences=[sp], #n_steps=n_samps) #return [s1_samps, h1_samps, s2_samps, h2_samps] def h_energy_step(self, s, h): #helper function for self.calc_s_energy exp_A_i=T.reshape(T.sum(self.exp_A*T.reshape(h,(self.nh,1)),axis=0),(self.ns,1)) mu_i=T.reshape(T.sum(self.mu*T.reshape(h,(self.nh,1)),axis=0), (self.ns,1)) ln_Z_h_i=T.sum(self.ln_Z_h*T.reshape(h,(self.nh,1))) diff=T.reshape(T.reshape(s,(self.ns,1))-mu_i,(self.ns,1)) diff_dot_exp_A_i=diff*exp_A_i gterm=-T.sum(T.sum(diff_dot_exp_A_i*diff)) energy=gterm+ln_Z_h_i return energy def calc_mean_h_energy(self, s, h, nsamps): #you give this function a set of samples of s and h, #it gives you the average energy of those samples energies, updates = theano.scan(fn=self.h_energy_step, outputs_info=[None], sequences=[s, h], n_steps=nsamps) energy=T.mean(energies) return energy def update_params(self, x1, x2, n_samps, lrate): #this function samples from the joint posterior and performs # a step of gradient ascent on the log-likelihood sp, updates = theano.scan(fn=self.get_prediction, outputs_info=[None], sequences=[self.s_past, self.h_past], n_steps=self.npcl) #sp should be np by ns [s1_samps, h1_samps, s2_samps, h2_samps], updates = theano.scan(fn=self.sample_joint, outputs_info=[None, None, None, None], non_sequences=[sp], n_steps=n_samps) x1_recons=T.dot(self.W, s1_samps.T) + T.reshape(self.c,(self.nx,1)) x2_recons=T.dot(self.W, s2_samps.T) + T.reshape(self.c,(self.nx,1)) s_pred, updates = theano.scan(fn=self.get_prediction, outputs_info=[None], sequences=[s1_samps, h1_samps], n_steps=n_samps) hterm1=self.calc_mean_h_energy(s1_samps, h1_samps, n_samps) hterm2=self.calc_mean_h_energy(s2_samps, h2_samps, n_samps) sterm=-T.mean(T.sum((self.b*(s2_samps-s_pred))**2,axis=1)) xterm1=-T.mean(T.sum((x1_recons-T.reshape(x1,(self.nx,1)))**2,axis=0)/(2.0*self.xvar**2)) xterm2=-T.mean(T.sum((x2_recons-T.reshape(x2,(self.nx,1)))**2,axis=0)/(2.0*self.xvar**2)) energy = hterm1 + xterm1 + hterm2 + xterm2 + sterm gparams=T.grad(energy, self.params, consider_constant=[s1_samps, s2_samps, h1_samps, h2_samps]) # constructs the update dictionary for gparam, param, rel_lr in zip(gparams, self.params, self.rel_lrates): #gnat=T.dot(param, T.dot(param.T,param)) updates[param] = T.cast(param + gparam*lrate*rel_lr,'float32') #make sure W has unit-length columns #new_W=updates[self.W] #updates[self.W]=T.cast(new_W/T.sqrt(T.sum(new_W**2,axis=0)),'float32') #MIGHT NEED TO NORMALIZE A return energy, updates def get_ESS(self): return 1.0/T.sum(self.weights_now**2) def resample_step(self): idx=self.theano_rng.multinomial(pvals=T.reshape(self.weights_now,(1,self.npcl))).T s_samp=T.sum(self.s_now*T.addbroadcast(idx,1),axis=0) h_samp=T.sum(self.h_now*T.addbroadcast(idx,1),axis=0) return T.cast(s_samp,'float32'), T.cast(h_samp,'float32') def resample(self): [s_samps, h_samps], updates = theano.scan(fn=self.resample_step, outputs_info=[None, None], n_steps=self.npcl) updates[self.s_now]=T.cast(s_samps,'float32') updates[self.h_now]=T.cast(h_samps,'float32') updates[self.weights_now]=T.cast(T.ones_like(self.weights_now)/T.cast(self.npcl,'float32'),'float32') #dtype paranoia return updates def simulate_step(self, s): #get h probabilities h_probs = self.calc_h_probs(s) h_samp=self.theano_rng.multinomial(pvals=T.reshape(h_probs,(1,self.nh))) M_vec=T.sum(self.M*T.reshape(h_samp,(self.nh,1)),axis=0) #here I use the 'mean M' by combining the M's according to their probabilities #M_vec=T.sum(self.M*T.reshape(hprobs,(self.nh,1)),axis=0) M=M_vec.reshape((self.ns,self.ns)) sp=T.dot(M, s) xp=T.dot(self.W, sp) + self.c return T.cast(sp,'float32'), T.cast(xp,'float32'), h_samp def simulate_forward(self, n_steps): s0=T.sum(self.s_now*T.reshape(self.weights_now,(self.npcl,1)),axis=0) [sp, xp, hs], updates = theano.scan(fn=self.simulate_step, outputs_info=[s0, None, None], n_steps=n_steps) return sp, xp, hs, updates
class StochasticPoolLayer(layers.Layer):
def __init__(self,
incoming,
ds,
strides=None,
ignore_border=False,
pad=(0, 0),
random_state=42,
**kwargs):
super(StochasticPoolLayer, self).__init__(incoming, **kwargs)
self.ds = ds
self.ignore_border = ignore_border
self.pad = pad
self.st = ds if strides is None else strides
if hasattr(random_state, 'multinomial'):
self.rng = random_state
else:
self.rng = RandomStreams(seed=random_state)
def get_output_shape_for(self, input_shape):
output_shape = list(input_shape) # copy / convert to mutable list
output_shape[2] = pool_output_length(
input_shape[2],
ds=self.ds[0],
st=self.st[0],
ignore_border=self.ignore_border,
pad=self.pad[0],
)
output_shape[3] = pool_output_length(
input_shape[3],
ds=self.ds[1],
st=self.st[1],
ignore_border=self.ignore_border,
pad=self.pad[1],
)
return tuple(output_shape)
def get_output_for(self, input, deterministic=False, **kwargs):
# inspired by:
# https://github.com/lisa-lab/pylearn2/blob/14b2f8bebce7cc938cfa93e640008128e05945c1/pylearn2/expr/stochastic_pool.py#L23
batch, channels, nr, nc = self.input_shape
pr, pc = self.ds
sr, sc = self.st
output_shape = self.get_output_shape()
out_r, out_c = output_shape[2:]
# calculate shape needed for padding
pad_shape = list(output_shape)
pad_shape[2] = (pad_shape[2] - 1) * sr + pr
pad_shape[3] = (pad_shape[3] - 1) * sc + pc
# allocate a new input tensor
padded = T.alloc(0.0, *pad_shape)
# get padding offset
offset_x = (pad_shape[2] - nr) // 2
offset_y = (pad_shape[3] - nc) // 2
padded = T.set_subtensor(
padded[:, :, offset_x:(offset_x + nr), offset_y:(offset_y + nc)],
input)
window = T.alloc(0.0, batch, channels, out_r, out_c, pr, pc)
for row_within_pool in xrange(pr):
row_stop = (output_shape[2] - 1) * sr + row_within_pool + 1
for col_within_pool in xrange(pc):
col_stop = (output_shape[3] - 1) * sc + col_within_pool + 1
# theano dark magic
win_cell = padded[:, :, row_within_pool:row_stop:sr,
col_within_pool:col_stop:sc]
window = T.set_subtensor(
window[:, :, :, :, row_within_pool, col_within_pool],
win_cell)
# sum across pooling regions
norm = window.sum(axis=[4, 5])
norm = T.switch(T.eq(norm, 0.0), 1.0, norm)
norm = window / norm.dimshuffle(0, 1, 2, 3, 'x', 'x')
if deterministic:
res = (window * norm).sum(axis=[4, 5])
else:
prob = self.rng.multinomial(pvals=norm.reshape(
(batch * channels * out_r * out_c, pr * pc)),
dtype=theano.config.floatX)
# double max because of grad problems
res = (window * prob.reshape(
(batch, channels, out_r, out_c, pr, pc))).max(axis=5).max(
axis=4)
return T.cast(res, theano.config.floatX)
class MultiRTRBM(DS_MRTRBM):
"""This Class Implement the Multi-Category Recurrent Temporal RBM """
def __init__(self,
input,
n_visible,
n_hidden,
time,
n_cate,
W=None,
Wt=None,
vbias=None,
hbias=None,
h0=None):
# the input dimeansion should be (n_cate, N_sample * time * n_vis)
self.input = input
self.n_vis = n_visible
self.n_hid = n_hidden
self.time = time
self.n_cate = n_cate
# Define the parameter of the Machine
if W is None:
W = theano.shared(
np.random.normal(size=(self.n_cate, self.n_vis,
self.n_hid)).astype(
theano.config.floatX))
if vbias is None:
vbias = theano.shared(
np.zeros(shape=(self.n_cate, self.time,
self.n_vis)).astype(theano.config.floatX))
if hbias is None:
hbias = theano.shared(
np.zeros(shape=(self.time,
self.n_hid)).astype(theano.config.floatX))
if Wt is None:
Wt = theano.shared(
np.random.normal(size=(self.n_hid, self.n_hid)).astype(
theano.config.floatX))
if h0 is None:
h0 = theano.shared(
np.zeros(shape=(1, 1,
self.n_hid)).astype(theano.config.floatX))
# set parameters
self.W = W
self.Wt = Wt
self.h0 = h0
self.hbias = hbias
self.vbias = vbias
self.params = [self.W, self.Wt, self.h0, self.hbias, self.vbias]
self.numpy_rng = np.random.RandomState(1234)
self.theano_rng = MRG_RandomStreams(self.numpy_rng.randint(2**30))
def h_given_h_lag_vt(self, vt, h_lag, hbias):
if h_lag == self.h0:
x = T.batched_dot(vt, self.W) + T.addbroadcast(
T.dot(h_lag, self.Wt) + hbias.dimshuffle('x', 0), 0, 1)
else:
x = T.batched_dot(vt, self.W) + \
T.dot(h_lag, self.Wt) + hbias.dimshuffle('x', 0)
return [x, T.nnet.sigmoid(x)]
def H_given_h_lag_vt(self, V):
H = [self.h0]
# [x, out], _ = theano.scan(fn=self.h_given_h_lag_vt, sequence=V,
# outputs_info=[None, self.h0],
# n_steps=V.shape[0])
for t in range(self.time):
H += [self.h_given_h_lag_vt(V[t], H[-1], self.hbias[t])[1]]
return T.concatenate(H[1:], axis=2)
def free_energy_given_hid_lag(self, vt, h_lag, hbias, vbias):
if h_lag == self.h0:
wx_b = T.batched_dot(vt, self.W) +\
T.addbroadcast(T.dot(h_lag, self.Wt) + hbias, 0, 1)
vbias_term = T.batched_dot(vt, vbias)
hidden_term = T.sum(T.log(1 + T.exp(wx_b)), axis=2)
else:
wx_b = T.batched_dot(vt, self.W) + T.dot(h_lag, self.Wt) + \
hbias.dimshuffle('x', 0)
vbias_term = T.batched_dot(vt, vbias)
hidden_term = T.sum(T.log(1 + T.exp(wx_b)), axis=2)
return -hidden_term - vbias_term
def free_energy_RTRBM(self, V):
H = self.H_given_h_lag_vt(V)
for t in range(self.time):
if t == 0:
Et = T.sum(self.free_energy_given_hid_lag(
V[t], self.h0, self.hbias[t], self.vbias[:, t, :]),
axis=0)
else:
Et += T.sum(self.free_energy_given_hid_lag(
V[t], H[:, :, t * (self.n_hid):(t + 1) * self.n_hid],
self.hbias[t], self.vbias[:, t, :]),
axis=0)
return Et
def propup_given_h_lag(self, vt, h_lag, hbias):
if h_lag == self.h0:
x = T.batched_dot(vt, self.W) + T.addbroadcast(
T.dot(h_lag, self.Wt) + hbias, 0, 1)
else:
x = T.batched_dot(vt, self.W) + hbias + T.dot(h_lag, self.Wt)
return [x, T.nnet.sigmoid(x)]
def propdown_given_h_lag(self, ht, vbias):
x = T.batched_dot(ht, self.W.dimshuffle(0, 2, 1)) + \
vbias.dimshuffle((0, 'x', 1))
e_x = T.exp(x - x.max(axis=0, keepdims=True))
out = e_x / e_x.sum(axis=0, keepdims=True)
return [x, out]
def sample_vt_given_ht_h_lag(self, ht, vbias):
x, out = self.propdown_given_h_lag(ht, vbias)
v_sample = []
for v in range(self.n_vis):
v_sample += [
self.theano_rng.multinomial(
n=1, pvals=out[:, :, v].T,
dtype=theano.config.floatX).dimshuffle(1, 0, 'x')
]
v_sample = T.concatenate(v_sample, axis=2)
return [x, out, v_sample]
def sample_ht_given_vt_hid_lag(self, vt, h_lag, hbias):
x, out = self.propup_given_h_lag(vt, h_lag, hbias)
h_sample = self.theano_rng.binomial(n=1,
p=out,
size=out.shape,
dtype=theano.config.floatX)
return [x, out, h_sample]
def gibbs_vhv_given_h_lag(self, v0, h_lag, hbias, vbias):
xh, ph, h0 = self.sample_ht_given_vt_hid_lag(v0, h_lag, hbias)
xv, pv, v1 = self.sample_vt_given_ht_h_lag(h0, vbias)
return [xh, ph, h0, xv, pv, v1]
def gibbs_VhV(self, V0):
V = []
H = self.H_given_h_lag_vt(V0)
for t in range(self.time):
if t == 0:
V += [
self.gibbs_vhv_given_h_lag(
V0[t], self.h0, self.hbias[t],
self.vbias[:, t, :])[-1].dimshuffle('x', 0, 1, 2)
]
else:
V += [
self.gibbs_vhv_given_h_lag(
V0[t], H[:, :, t * self.n_hid:(t + 1) * self.n_hid],
self.hbias[t],
self.vbias[:, t, :])[-1].dimshuffle('x', 0, 1, 2)
]
return T.concatenate(V, axis=0)
def get_cost_updates(self, persistant, k=2, lr=0.01, l1=0., l2=0.01):
chain_start = persistant
V_burn_in, updates = theano.scan(fn=self.gibbs_VhV,
outputs_info=[chain_start],
n_steps=k,
name='MultiRTRBM Gibbs Smapler')
chain_end = V_burn_in[-1]
# Contrastive Divergence (Variational method Cost)/ Approxiamted
# likelihood
L1 = T.sum(T.abs_(self.W)) + T.sum(T.abs_(self.Wt))
L2 = T.sum(self.W**2) + T.sum(self.Wt**2)
KL_diff = T.mean(self.free_energy_RTRBM(self.input) -
self.free_energy_RTRBM(chain_end)) +\
T.cast(l1, theano.config.floatX) * L1 + \
T.cast(l2, theano.config.floatX) * L2
self.gparams = T.grad(KL_diff,
self.params,
consider_constant=[chain_end])
for param, gparam in zip(self.params, self.gparams):
if param in [self.W, self.Wt]:
updates[param] = param - 0.0001 * gparam
else:
updates[param] = param - lr * gparam
cost, updates = self.get_pseudo_likelihood_cost(updates)
return cost, updates
def get_pseudo_likelihood_cost(self, updates):
bit_i_idx = theano.shared(value=0, name='bit_i_idx')
xi = T.round(self.input)
fe_xi = self.free_energy_RTRBM(xi)
for k in range(self.n_cate):
xi_flip = T.set_subtensor(xi[:, k, :, bit_i_idx],
1 - xi[:, k, :, bit_i_idx])
# calculate free energy with bit flipped
fe_xi_flip = self.free_energy_RTRBM(xi_flip)
# equivalent to e^(-FE(x_i)) / (e^(-FE(x_i)) + e^(-FE(x_{\i})))
cost = T.mean(self.n_vis * T.log(T.nnet.sigmoid(fe_xi_flip - fe_xi)))
# increment bit_i_idx % number as part of updates
updates[bit_i_idx] = (bit_i_idx + 1) % self.n_vis
return cost, updates
class Network:
def __init__(self, options):
ctx_dim = options['ctx_dim']
dim = options['dim']
dim_word = options['dim_word']
n_words = options['n_words']
self.scale = 0.01
self.Wemb = theano.shared(
(self.scale *
numpy.random.randn(n_words, dim_word)).astype('float32'),
name='Wemb')
self.trng = RandomStreams(1234)
self.use_noise = theano.shared(numpy.float32(0.))
self.FFInit = FFLayer(shape=[ctx_dim, ctx_dim], name='ff_init')
self.FFState = FFLayer(shape=[ctx_dim, dim], name='ff_state')
self.FFMemory = FFLayer(shape=[ctx_dim, dim], name='ff_memory')
self.LSTMLayer = LSTMLayer(shape=[dim_word, dim, ctx_dim],
name='decoder')
self.FFLSTM = FFLayer(shape=[dim, dim_word], name='ff_logit_lstm')
self.FFCtx = FFLayer(shape=[ctx_dim, dim_word], name='ff_logit_ctx')
self.FFLogit = FFLayer(shape=[dim_word, n_words], name='ff_logit')
self.Layers = [
self.FFInit, self.FFState, self.FFMemory, self.LSTMLayer,
self.FFLSTM, self.FFCtx, self.FFLogit
]
self._params = sum([layer.params() for layer in self.Layers],
[self.Wemb])
self.dropOutInit = DropOutLayer(self.use_noise, self.trng)
self.dropOutLSTM = DropOutLayer(self.use_noise, self.trng)
self.dropOutLogit = DropOutLayer(self.use_noise, self.trng)
def params(self):
return self._params
def infer_init(self, ctx_mean):
ctx_mean = self.FFInit(ctx_mean, activation='relu')
ctx_mean = self.dropOutInit(ctx_mean)
init_state = self.FFState(ctx_mean, activation='tanh')
init_memory = self.FFMemory(ctx_mean, activation='tanh')
return init_state, init_memory
def infer_main(self,
ctx,
emb=None,
mask=None,
init_state=None,
init_memory=None,
one_step=False):
output_state = self.LSTMLayer(emb, ctx, init_memory, init_state,
one_step, mask)
output_state_h = self.dropOutLSTM(output_state[0])
logit = self.FFLSTM(output_state_h, activation='linear')
# prev2out
logit += emb
# ctx2out
logit += self.FFCtx(output_state[3], activation='linear')
logit = tensor.tanh(logit)
logit = self.dropOutLogit(logit)
logit = self.FFLogit(logit, activation='linear')
return output_state, logit
def build_training_graph(self, options):
# description string: #words x #samples,
x = tensor.matrix('x', dtype='int64')
mask = tensor.matrix('mask', dtype='float32')
# context: #samples x #annotations x dim
ctx = tensor.tensor3('ctx', dtype='float32')
n_timesteps = x.shape[0]
n_samples = x.shape[1]
# index into the word embedding matrix, shift it forward in time
#n_timesteps == caption length. n_samples = number of captions.
emb = self.Wemb[x.flatten()].reshape(
[n_timesteps, n_samples, options['dim_word']])
emb_shifted = tensor.zeros_like(emb)
emb_shifted = tensor.set_subtensor(emb_shifted[1:], emb[:-1])
emb = emb_shifted
# initial state/cell [top right on page 4]
ctx_mean = ctx.mean(1)
init_state, init_memory = self.infer_init(ctx_mean)
output_state, logit = self.infer_main(ctx=ctx,
emb=emb,
mask=mask,
init_state=init_state,
init_memory=init_memory,
one_step=False)
logit_shp = logit.shape
probs = tensor.nnet.softmax(
logit.reshape([logit_shp[0] * logit_shp[1], logit_shp[2]]))
# Index into the computed probability to give the log likelihood
x_flat = x.flatten()
p_flat = probs.flatten()
cost = -tensor.log(p_flat[tensor.arange(x_flat.shape[0]) *
probs.shape[1] + x_flat] + 1e-8)
cost = cost.reshape([x.shape[0], x.shape[1]])
masked_cost = cost * mask
cost = (masked_cost).sum(0)
alphas = output_state[2]
return self.use_noise, [x, mask, ctx], alphas, cost
def infer(self):
# context: #annotations x dim
ctx = tensor.matrix('ctx_sampler', dtype='float32')
x = tensor.vector('x_sampler', dtype='int64')
# initial state/cell
ctx_mean = ctx.mean(0)
init_state, init_memory = self.infer_init(ctx_mean)
f_init = TFW([ctx], {
'context': ctx,
'state': init_state,
'memory': init_memory
},
name='f_init',
profile=False)
init_state = tensor.matrix('init_state', dtype='float32')
init_memory = tensor.matrix('init_memory', dtype='float32')
# for the first word (which is coded with -1), emb should be all zero
emb = tensor.switch(x[:, None] < 0,
tensor.alloc(0., 1, self.Wemb.shape[1]),
self.Wemb[x])
output_state, logit = self.infer_main(ctx=ctx,
emb=emb,
mask=None,
init_state=init_state,
init_memory=init_memory,
one_step=True)
next_probs = tensor.nnet.softmax(logit)
next_sample = self.trng.multinomial(pvals=next_probs).argmax(1)
next_state, next_memory = output_state[0], output_state[1]
f_next = TFW(
[x, ctx, init_state, init_memory], {
'probs': next_probs,
'sample': next_sample,
'state': next_state,
'memory': next_memory
},
name='f_next',
profile=False)
return f_init, f_next
class EncoderDecoder(object):
def __init__(self, rng, **kwargs):
self.n_in_src = kwargs.get('nembed_src')
self.n_in_trg = kwargs.get('nembed_trg')
self.n_hids_src = kwargs.get('nhids_src')
self.n_hids_trg = kwargs.get('nhids_trg')
self.src_vocab_size = kwargs.get('src_vocab_size')
self.trg_vocab_size = kwargs.get('trg_vocab_size')
self.method = kwargs.get('method')
self.dropout = kwargs.get('dropout')
self.maxout_part = kwargs.get('maxout_part')
self.path = kwargs.get('saveto')
self.clip_c = kwargs.get('clip_c')
self.rng = rng
self.trng = RandomStreams(rng.randint(1e5))
# added by Zhaopeng Tu, 2016-04-29
self.with_coverage = kwargs.get('with_coverage')
self.coverage_dim = kwargs.get('coverage_dim')
self.coverage_type = kwargs.get('coverage_type')
self.max_fertility = kwargs.get('max_fertility')
if self.coverage_type is 'linguistic':
# make sure the dimension of linguistic coverage is always 1
self.coverage_dim = 1
# added by Zhaopeng Tu, 2016-05-30
self.with_context_gate = kwargs.get('with_context_gate')
# added by Zhaopeng Tu, 2017-11-29
self.with_layernorm = kwargs.get('with_layernorm', False)
self.params = []
self.layers = []
self.table_src = LookupTable(self.rng,
self.src_vocab_size,
self.n_in_src,
name='table_src')
self.layers.append(self.table_src)
self.encoder = BidirectionalEncoder(self.rng,
self.n_in_src,
self.n_hids_src,
self.table_src,
name='birnn_encoder')
self.layers.append(self.encoder)
self.table_trg = LookupTable(self.rng,
self.trg_vocab_size,
self.n_in_trg,
name='table_trg')
self.layers.append(self.table_trg)
self.decoder = Decoder(self.rng, self.n_in_trg, self.n_hids_trg, 2*self.n_hids_src, \
maxout_part=self.maxout_part, name='rnn_decoder', \
# added by Zhaopeng Tu, 2016-04-29
with_coverage=self.with_coverage, coverage_dim=self.coverage_dim, coverage_type=self.coverage_type, max_fertility=self.max_fertility, \
# added by Zhaopeng Tu, 2016-05-30
with_context_gate=self.with_context_gate, \
with_layernorm=self.with_layernorm)
self.layers.append(self.decoder)
self.logistic_layer = LogisticRegression(self.rng, self.n_in_trg,
self.trg_vocab_size)
self.layers.append(self.logistic_layer)
# added by Zhaopeng Tu, 2016-07-12
# for reconstruction
self.with_reconstruction = kwargs.get('with_reconstruction')
if self.with_reconstruction:
# added by Zhaopeng Tu, 2016-07-27
self.reconstruction_weight = kwargs.get('reconstruction_weight')
# note the source and target sides are reversed
self.inverse_decoder = Decoder(self.rng, self.n_in_src, 2*self.n_hids_src, self.n_hids_trg, \
maxout_part=self.maxout_part, name='rnn_inverse_decoder', \
with_layernorm=self.with_layernorm)
self.layers.append(self.inverse_decoder)
self.srng = RandomStreams(rng.randint(1e5))
self.inverse_logistic_layer = LogisticRegression(
self.rng,
self.n_in_src,
self.src_vocab_size,
name='inverse_LR')
self.layers.append(self.inverse_logistic_layer)
for layer in self.layers:
self.params.extend(layer.params)
def build_trainer(self, src, src_mask, trg, trg_mask):
annotations = self.encoder.apply(src, src_mask)
# init_context = annotations[0, :, -self.n_hids_src:]
# modification #1
# mean pooling
init_context = (annotations *
src_mask[:, :, None]).sum(0) / src_mask.sum(0)[:, None]
trg_emb = self.table_trg.apply(trg)
trg_emb_shifted = T.zeros_like(trg_emb)
trg_emb_shifted = T.set_subtensor(trg_emb_shifted[1:], trg_emb[:-1])
results = self.decoder.run_pipeline(state_below=trg_emb_shifted,
mask_below=trg_mask,
init_context=init_context,
c=annotations,
c_mask=src_mask)
hiddens, ctxs, readout, alignment = results[:4]
# apply dropout
if self.dropout < 1.0:
logger.info('Apply dropout with p = {}'.format(self.dropout))
readout = Dropout(self.trng, readout, 1, self.dropout)
p_y_given_x = self.logistic_layer.get_probs(readout)
self.cost = self.logistic_layer.cost(p_y_given_x, trg,
trg_mask) / trg.shape[1]
# self.cost = theano.printing.Print('likilihood cost:')(self.cost)
# added by Zhaopeng Tu, 2016-07-12
# for reconstruction
if self.with_reconstruction:
# now hiddens is the annotations
inverse_init_context = (hiddens * trg_mask[:, :, None]
).sum(0) / trg_mask.sum(0)[:, None]
src_emb = self.table_src.apply(src)
src_emb_shifted = T.zeros_like(src_emb)
src_emb_shifted = T.set_subtensor(src_emb_shifted[1:],
src_emb[:-1])
inverse_results = self.inverse_decoder.run_pipeline(
state_below=src_emb_shifted,
mask_below=src_mask,
init_context=inverse_init_context,
c=hiddens,
c_mask=trg_mask)
inverse_hiddens, inverse_ctxs, inverse_readout, inverse_alignment = inverse_results[:
4]
# apply dropout
if self.dropout < 1.0:
# logger.info('Apply dropout with p = {}'.format(self.dropout))
inverse_readout = Dropout(self.srng, inverse_readout, 1,
self.dropout)
p_x_given_y = self.inverse_logistic_layer.get_probs(
inverse_readout)
self.reconstruction_cost = self.inverse_logistic_layer.cost(
p_x_given_y, src, src_mask) / src.shape[1]
# self.reconstruction_cost = theano.printing.Print('reconstructed cost:')(self.reconstruction_cost)
self.cost += self.reconstruction_cost * self.reconstruction_weight
self.L1 = sum(T.sum(abs(param)) for param in self.params)
self.L2 = sum(T.sum(param**2) for param in self.params)
params_regular = self.L1 * 1e-6 + self.L2 * 1e-6
# params_regular = theano.printing.Print('params_regular:')(params_regular)
# train cost
train_cost = self.cost + params_regular
# gradients
grads = T.grad(train_cost, self.params)
# apply gradient clipping here
grads = grad_clip(grads, self.clip_c)
# train function
inps = [src, src_mask, trg, trg_mask]
outs = [train_cost]
if self.with_layernorm:
inps = [src, src_mask, trg, trg_mask]
lr = T.scalar(name='lr')
print 'Building optimizers...',
self.train_fn, self.update_fn = adam(lr, self.params, grads, inps,
outs)
else:
# updates
updates = adadelta(self.params, grads)
# mode=theano.Mode(linker='vm') for ifelse
# Unless linker='vm' or linker='cvm' are used, ifelse will compute both variables and take the same computation time as switch.
self.train_fn = theano.function(inps,
outs,
updates=updates,
name='train_function',
mode=theano.Mode(linker='vm'))
# self.train_fn = theano.function(inps, outs, updates=updates, name='train_function', mode=NanGuardMode(nan_is_error=True, inf_is_error=True, big_is_error=True))
def build_sampler(self):
x = T.lmatrix()
# Build Networks
# src_mask is None
c = self.encoder.apply(x, None)
#init_context = ctx[0, :, -self.n_hids_src:]
# mean pooling
init_context = c.mean(0)
init_state = self.decoder.create_init_state(init_context)
# compile function
print 'Building compile_init_state_and_context function ...'
self.compile_init_and_context = theano.function(
[x], [init_state, c], name='compile_init_and_context')
print 'Done'
y = T.lvector()
cur_state = T.matrix()
# if it is the first word, emb should be a1l zero, and it is indicated by -1
trg_emb = T.switch(y[:, None] < 0, T.alloc(0., 1, self.n_in_trg),
self.table_trg.apply(y))
# added by Zhaopeng Tu, 2016-06-09
if self.with_coverage:
cov_before = T.tensor3()
if self.coverage_type is 'linguistic':
print 'Building compile_fertility ...'
fertility = self.decoder._get_fertility(c)
fertility = T.addbroadcast(fertility, 1)
self.compile_fertility = theano.function(
[c], [fertility], name='compile_fertility')
print 'Done'
else:
fertility = None
else:
cov_before = None
fertility = None
# apply one step
# modified by Zhaopeng Tu, 2016-04-29
results = self.decoder.apply(
state_below=trg_emb,
init_state=cur_state,
c=c,
one_step=True,
# added by Zhaopeng Tu, 2016-04-27
cov_before=cov_before,
fertility=fertility)
next_state, ctxs, alignment = results[:3]
idx = 3
if self.with_coverage:
cov = results[idx]
idx += 1
readout = self.decoder.readout(next_state, ctxs, trg_emb)
# maxout
if self.maxout_part > 1:
readout = self.decoder.one_step_maxout(readout)
# apply dropout
if self.dropout < 1.0:
readout = Dropout(self.trng, readout, 0, self.dropout)
# compute the softmax probability
next_probs = self.logistic_layer.get_probs(readout)
# sample from softmax distribution to get the sample
next_sample = self.trng.multinomial(pvals=next_probs).argmax(1)
# compile function
print 'Building compile_next_state_and_probs function ...'
inps = [y, cur_state, c]
outs = [next_probs, next_state, next_sample, alignment]
# added by Zhaopeng Tu, 2016-04-29
if self.with_coverage:
inps.append(cov_before)
if self.coverage_type is 'linguistic':
inps.append(fertility)
outs.append(cov)
# mode=theano.Mode(linker='vm') for ifelse
# Unless linker='vm' or linker='cvm' are used, ifelse will compute both variables and take the same computation time as switch.
self.compile_next_state_and_probs = theano.function(
inps,
outs,
name='compile_next_state_and_probs',
mode=theano.Mode(linker='vm'))
print 'Done'
# added by Zhaopeng Tu, 2016-07-18
# for reconstruction
if self.with_reconstruction:
# Build Networks
# trg_mask is None
inverse_c = T.tensor3()
# mean pooling
inverse_init_context = inverse_c.mean(0)
inverse_init_state = self.inverse_decoder.create_init_state(
inverse_init_context)
outs = [inverse_init_state]
# compile function
print 'Building compile_inverse_init_state_and_context function ...'
self.compile_inverse_init_and_context = theano.function(
[inverse_c], outs, name='compile_inverse_init_and_context')
print 'Done'
src = T.lvector()
inverse_cur_state = T.matrix()
trg_mask = T.matrix()
# if it is the first word, emb should be all zero, and it is indicated by -1
src_emb = T.switch(src[:, None] < 0, T.alloc(0., 1, self.n_in_src),
self.table_src.apply(src))
# apply one step
# modified by Zhaopeng Tu, 2016-04-29
inverse_results = self.inverse_decoder.apply(
state_below=src_emb,
init_state=inverse_cur_state,
c=inverse_c,
c_mask=trg_mask,
one_step=True)
inverse_next_state, inverse_ctxs, inverse_alignment = inverse_results[:
3]
inverse_readout = self.inverse_decoder.readout(
inverse_next_state, inverse_ctxs, src_emb)
# maxout
if self.maxout_part > 1:
inverse_readout = self.inverse_decoder.one_step_maxout(
inverse_readout)
# apply dropout
if self.dropout < 1.0:
inverse_readout = Dropout(self.srng, inverse_readout, 0,
self.dropout)
# compute the softmax probability
inverse_next_probs, inverse_next_energy = self.inverse_logistic_layer.get_probs(
inverse_readout)
# sample from softmax distribution to get the sample
inverse_next_sample = self.srng.multinomial(
pvals=inverse_next_probs).argmax(1)
# compile function
print 'Building compile_inverse_next_state_and_probs function ...'
inps = [src, trg_mask, inverse_cur_state, inverse_c]
outs = [
inverse_next_probs, inverse_next_state, inverse_next_sample,
inverse_alignment
]
self.compile_inverse_next_state_and_probs = theano.function(
inps, outs, name='compile_inverse_next_state_and_probs')
print 'Done'
def save(self, path=None):
if path is None:
path = self.path
filenpz = open(path, "w")
val = dict([(value.name, value.get_value())
for index, value in enumerate(self.params)])
logger.info("save the model {}".format(path))
numpy.savez(path, **val)
filenpz.close()
def load(self, path=None):
if path is None:
path = self.path
if os.path.isfile(path):
logger.info("load params {}".format(path))
val = numpy.load(path)
for index, param in enumerate(self.params):
logger.info('Loading {} with shape {}'.format(
param.name,
param.get_value(borrow=True).shape))
if param.name not in val.keys():
logger.info('Adding new param {} with shape {}'.format(
param.name,
param.get_value(borrow=True).shape))
continue
if param.get_value().shape != val[param.name].shape:
logger.info("Error: model param != load param shape {} != {}".format(\
param.get_value().shape, val[param.name].shape))
raise Exception("loading params shape mismatch")
else:
param.set_value(val[param.name], borrow=True)
else:
logger.error("file {} does not exist".format(path))
self.save()
def sample(p, seed=None):
if seed is None:
seed = np.random.randint(10e6)
rng = RandomStreams(seed=seed)
return rng.multinomial(n=1, pvals=p, dtype=theano.config.floatX)
class RVal(Elem, TensorWrapped, Masked): # random value
def __init__(self, seed=None, **kw):
super(RVal, self).__init__(**kw)
if seed is None:
seed = np.random.randint(0, 1e6)
self.rng = RandomStreams(seed=seed)
self.value = None
def binomial(self, shape, n=1, p=0.5, ndim=None, dtype="int32"):
if isinstance(shape, Elem):
shape = shape.d
self.value = self.rng.binomial(shape, n, p, ndim, dtype)
return self
def normal(self, shape, avg=0.0, std=1.0, ndim=None, dtype=None):
if isinstance(shape, Elem):
shape = shape.d
self.value = self.rng.normal(shape, avg, std, ndim, dtype)
return self
def multinomial(self,
shape,
n=1,
pvals=None,
without_replacement=False,
ndim=None,
dtype="int32"):
if isinstance(shape, Elem):
shape = shape.d
if without_replacement:
self.value = self.rng.multinomial_wo_replacement(
shape, n, pvals, ndim, dtype)
else:
self.value = self.rng.multinomial(shape, n, pvals, ndim, dtype)
return self
def gumbel(self, shape, eps=1e-10):
if isinstance(shape, Elem):
shape = shape.d
x = self.rng.uniform(shape, 0.0, 1.0)
self.value = -theano.tensor.log(-theano.tensor.log(x + eps) + eps)
return self
@property
def d(self):
return self.value
@property
def v(self):
return self.value.eval()
@property
def allparams(self):
return set()
@property
def allupdates(self):
return {}
@property
def all_extra_outs(self):
return {}
class SCLmodel():
#This class defines the switched constrained linear model, which was
#designed to eliminate state-space 'explosions' that can occur when
#doing prediction - a serious issue in the basic SL model
def __init__(self, nx, ns, nh, npcl, xvar=1.0):
#for this model I assume one linear generative model and a
#combination of nh linear dynamical models
#generative matrix
init_W = np.asarray(np.random.randn(nx, ns) / 10.0, dtype='float32')
#init_W=np.asarray(np.eye(2),dtype='float32')
#always normalize the columns of W to be unit length
init_W = init_W / np.sqrt(np.sum(init_W**2, axis=0))
#observed variable means
init_c = np.asarray(np.zeros(nx), dtype='float32')
#dynamical matrices
init_M = np.asarray(np.random.randn(nh, ns**2) / 2.0, dtype='float32')
#state-variable variances
#(covariance matrix of state variable noise assumed to be diagonal)
init_b = np.asarray(np.ones(ns) * 10.0, dtype='float32')
#means for switching variable
init_mu = np.asarray(np.random.randn(nh, ns) / 1.0, dtype='float32')
#(natural log of) covariance matrices for switching variable
#I assume the covariance matrices to be diagonal, so I
#store all the diagonal elements in a ns-by-nh matrix
init_A = np.asarray(np.zeros((nh, ns)), dtype='float32')
init_s_now = np.asarray(np.zeros((npcl, ns)), dtype='float32')
init_h_now = np.asarray(np.zeros((npcl, nh)), dtype='float32')
init_h_now[:, 0] = 1.0
init_weights_now = np.asarray(np.ones(npcl) / float(npcl),
dtype='float32')
init_s_past = np.asarray(np.zeros((npcl, ns)), dtype='float32')
init_h_past = np.asarray(np.zeros((npcl, nh)), dtype='float32')
init_h_past[:, 0] = 1.0
init_weights_past = np.asarray(np.ones(npcl) / float(npcl),
dtype='float32')
self.W = theano.shared(init_W)
self.c = theano.shared(init_c)
self.M = theano.shared(init_M)
self.b = theano.shared(init_b)
self.A = theano.shared(init_A)
self.mu = theano.shared(init_mu)
#I define thes to avoid repeated computations of the exponential
#of the elements of A and of the normalizing constants for each h
self.exp_A = T.exp(self.A)
self.ln_Z_h = T.reshape(0.5 * T.sum(self.A, axis=1), (nh, 1))
self.s_now = theano.shared(init_s_now)
self.h_now = theano.shared(init_h_now)
self.weights_now = theano.shared(init_weights_now)
self.s_past = theano.shared(init_s_past)
self.h_past = theano.shared(init_h_past)
self.weights_past = theano.shared(init_weights_past)
self.xvar = np.asarray(xvar, dtype='float32')
self.nx = nx #dimensionality of observed variables
self.ns = ns #dimensionality of latent variables
self.nh = nh #number of (linear) dynamical modes
self.npcl = npcl #numer of particles in particle filter
self.theano_rng = RandomStreams()
self.params = [self.W, self.M, self.b, self.A, self.c, self.mu]
self.rel_lrates = np.asarray([1.0, 1.0, 0.01, 1.0, 1.0, 10.0],
dtype='float32')
def sample_proposal_s(self, s, h, xpred, sig):
s_pred = self.get_prediction(s, h)
n = self.theano_rng.normal(size=T.shape(s))
#This is the proposal distribution that arises when one assumes that W'W=I
mean = 2.0 * (xpred + s_pred * (self.b**2)) * sig
s_prop = mean + n * T.sqrt(sig)
#I compute the term inside the exponent for the pdf of the proposal distrib
prop_term = -T.sum(n**2) / 2.0
return T.cast(s_prop, 'float32'), T.cast(s_pred, 'float32'), T.cast(
prop_term, 'float32')
#This function is required if we allow multiple generative models
#def get_recon(self, s, h):
#W_vec=T.sum(self.W*h, axis=0)
#W=W.reshape((self.nx, self.ns))
#xr=T.dot(W, s)
#return xr
def one_h_prob(self, exp_A_i, mu_i, s):
#scan function for self.calc_h_probs
smi = s - mu_i #should be np by ns
smia = smi * T.reshape(exp_A_i, (1, self.ns))
gaussian_term = -T.sum(smia * smi, axis=1)
return gaussian_term
def calc_h_probs(self, s):
#gterms, updates = theano.scan(fn=self.one_h_prob,
#outputs_info=[None],
#sequences=[self.exp_A, self.mu],
#non_sequences=[s],
#n_steps=self.nh)
#vectorized version
t1 = T.dot(s * s, self.exp_A.T)
t2 = -2.0 * T.dot(s, (self.exp_A * self.mu).T)
t3 = T.sum((self.mu * self.mu) * self.exp_A, axis=1)
gterms = (t1 + t2 + t3).T
#gterms should be nh by np
#need to multiply by relative partition functions
exp_terms = gterms + self.ln_Z_h
#re-centering for numerical stability
exp_terms_recentered = exp_terms - T.max(exp_terms)
#exponentiation and normalization
rel_probs = T.exp(exp_terms)
probs = rel_probs / T.sum(rel_probs, axis=0)
return probs
def forward_filter_step(self, xp):
#need to sample from the proposal distribution first
#these terms are the same for every particle
xpred = T.dot(self.W.T, (xp - self.c)) / (2.0 * self.xvar**2)
sig = (1.0 / (self.b**2 + 1.0 / (2.0 * self.xvar**2))) / 2.0
[s_samps, s_pred,
prop_terms], updates = theano.scan(fn=self.sample_proposal_s,
outputs_info=[None, None, None],
sequences=[self.s_now, self.h_now],
non_sequences=[xpred, sig],
n_steps=self.npcl)
#now that we have samples from the proposal distribution, we need to reweight them
#would use this if we have multiple generative models
#recons, updates = theano.scan(fn=get_recon,
#outputs_info=[None],
#sequences=[s_samps, h_samps],
#n_steps=self.npcl)
#this loops over every row of A and mu to calculate relative h probabilities
#for each particle
h_probs = self.calc_h_probs(s_samps)
h_samps = self.theano_rng.multinomial(pvals=h_probs.T)
recons = T.dot(self.W, s_samps.T) + T.reshape(self.c, (self.nx, 1))
x_terms = -T.sum(
(recons - T.reshape(xp, (self.nx, 1)))**2, axis=0) / (2.0 *
self.xvar**2)
s_terms = -T.sum(((s_samps - s_pred) * self.b)**2, axis=1)
energies = x_terms + s_terms - prop_terms
#to avoid exponentiating large or very small numbers, I
#"re-center" the reweighting factors by adding a constant,
#as this has no impact on the resulting new weights
energies_recentered = energies - T.max(energies)
alpha = T.exp(energies_recentered) #these are the reweighting factors
new_weights_unnorm = self.weights_now * alpha
normalizer = T.sum(new_weights_unnorm)
new_weights = new_weights_unnorm / normalizer #need to normalize new weights
updates[self.h_past] = T.cast(self.h_now, 'float32')
updates[self.s_past] = T.cast(self.s_now, 'float32')
updates[self.h_now] = T.cast(h_samps, 'float32')
updates[self.s_now] = T.cast(s_samps, 'float32')
updates[self.weights_past] = T.cast(self.weights_now, 'float32')
updates[self.weights_now] = T.cast(new_weights, 'float32')
#return normalizer, energies_recentered, s_samps, s_pred, T.dot(self.W.T,(xp-self.c)), updates
#return normalizer, energies_recentered, updates
return h_samps, updates
def get_prediction(self, s, h):
M_vec = T.sum(self.M * T.reshape(h, (self.nh, 1)), axis=0)
M = M_vec.reshape((self.ns, self.ns))
sp = T.dot(M, s)
return T.cast(sp, 'float32')
def sample_joint(self, sp):
t2_samp = self.theano_rng.multinomial(
pvals=T.reshape(self.weights_now, (1, self.npcl))).T
s2_samp = T.cast(
T.sum(self.s_now * T.addbroadcast(t2_samp, 1), axis=0), 'float32')
h2_samp = T.cast(
T.sum(self.h_now * T.addbroadcast(t2_samp, 1), axis=0), 'float32')
diffs = self.b * (s2_samp - sp)
sqr_term = T.sum(diffs**2, axis=1)
alpha = T.exp(-sqr_term)
probs_unnorm = self.weights_past * alpha
probs = probs_unnorm / T.sum(probs_unnorm)
t1_samp = self.theano_rng.multinomial(
pvals=T.reshape(probs, (1, self.npcl))).T
s1_samp = T.cast(
T.sum(self.s_past * T.addbroadcast(t1_samp, 1), axis=0), 'float32')
h1_samp = T.cast(
T.sum(self.h_past * T.addbroadcast(t1_samp, 1), axis=0), 'float32')
return [s1_samp, h1_samp, s2_samp, h2_samp]
#def sample_posterior(self, n_samps):
#sp, updates = theano.scan(fn=self.get_prediction,
#outputs_info=[None],
#sequences=[self.s_past, self.h_past],
#n_steps=self.npcl)
##sp should be np by ns
#[s1_samps, h1_samps, s2_samps, h2_samps], updates = theano.scan(fn=self.sample_joint,
#outputs_info=[None, None, None, None],
#non_sequences=[sp],
#n_steps=n_samps)
#return [s1_samps, h1_samps, s2_samps, h2_samps]
def h_energy_step(self, s, h):
#helper function for self.calc_s_energy
exp_A_i = T.reshape(
T.sum(self.exp_A * T.reshape(h, (self.nh, 1)), axis=0),
(self.ns, 1))
mu_i = T.reshape(T.sum(self.mu * T.reshape(h, (self.nh, 1)), axis=0),
(self.ns, 1))
ln_Z_h_i = T.sum(self.ln_Z_h * T.reshape(h, (self.nh, 1)))
diff = T.reshape(T.reshape(s, (self.ns, 1)) - mu_i, (self.ns, 1))
diff_dot_exp_A_i = diff * exp_A_i
gterm = -T.sum(T.sum(diff_dot_exp_A_i * diff))
energy = gterm + ln_Z_h_i
return energy
def calc_mean_h_energy(self, s, h, nsamps):
#you give this function a set of samples of s and h,
#it gives you the average energy of those samples
energies, updates = theano.scan(fn=self.h_energy_step,
outputs_info=[None],
sequences=[s, h],
n_steps=nsamps)
energy = T.mean(energies)
return energy
def update_params(self, x1, x2, n_samps, lrate):
#this function samples from the joint posterior and performs
# a step of gradient ascent on the log-likelihood
sp, updates = theano.scan(fn=self.get_prediction,
outputs_info=[None],
sequences=[self.s_past, self.h_past],
n_steps=self.npcl)
#sp should be np by ns
[s1_samps, h1_samps, s2_samps, h2_samps
], updates = theano.scan(fn=self.sample_joint,
outputs_info=[None, None, None, None],
non_sequences=[sp],
n_steps=n_samps)
x1_recons = T.dot(self.W, s1_samps.T) + T.reshape(self.c, (self.nx, 1))
x2_recons = T.dot(self.W, s2_samps.T) + T.reshape(self.c, (self.nx, 1))
s_pred, updates = theano.scan(fn=self.get_prediction,
outputs_info=[None],
sequences=[s1_samps, h1_samps],
n_steps=n_samps)
hterm1 = self.calc_mean_h_energy(s1_samps, h1_samps, n_samps)
hterm2 = self.calc_mean_h_energy(s2_samps, h2_samps, n_samps)
sterm = -T.mean(T.sum((self.b * (s2_samps - s_pred))**2, axis=1))
xterm1 = -T.mean(
T.sum((x1_recons - T.reshape(x1, (self.nx, 1)))**2, axis=0) /
(2.0 * self.xvar**2))
xterm2 = -T.mean(
T.sum((x2_recons - T.reshape(x2, (self.nx, 1)))**2, axis=0) /
(2.0 * self.xvar**2))
energy = hterm1 + xterm1 + hterm2 + xterm2 + sterm
gparams = T.grad(
energy,
self.params,
consider_constant=[s1_samps, s2_samps, h1_samps, h2_samps])
# constructs the update dictionary
for gparam, param, rel_lr in zip(gparams, self.params,
self.rel_lrates):
#gnat=T.dot(param, T.dot(param.T,param))
updates[param] = T.cast(param + gparam * lrate * rel_lr, 'float32')
#make sure W has unit-length columns
#new_W=updates[self.W]
#updates[self.W]=T.cast(new_W/T.sqrt(T.sum(new_W**2,axis=0)),'float32')
#MIGHT NEED TO NORMALIZE A
return energy, updates
def get_ESS(self):
return 1.0 / T.sum(self.weights_now**2)
def resample_step(self):
idx = self.theano_rng.multinomial(
pvals=T.reshape(self.weights_now, (1, self.npcl))).T
s_samp = T.sum(self.s_now * T.addbroadcast(idx, 1), axis=0)
h_samp = T.sum(self.h_now * T.addbroadcast(idx, 1), axis=0)
return T.cast(s_samp, 'float32'), T.cast(h_samp, 'float32')
def resample(self):
[s_samps, h_samps], updates = theano.scan(fn=self.resample_step,
outputs_info=[None, None],
n_steps=self.npcl)
updates[self.s_now] = T.cast(s_samps, 'float32')
updates[self.h_now] = T.cast(h_samps, 'float32')
updates[self.weights_now] = T.cast(
T.ones_like(self.weights_now) / T.cast(self.npcl, 'float32'),
'float32') #dtype paranoia
return updates
def simulate_step(self, s):
#get h probabilities
h_probs = self.calc_h_probs(s)
h_samp = self.theano_rng.multinomial(
pvals=T.reshape(h_probs, (1, self.nh)))
M_vec = T.sum(self.M * T.reshape(h_samp, (self.nh, 1)), axis=0)
#here I use the 'mean M' by combining the M's according to their probabilities
#M_vec=T.sum(self.M*T.reshape(hprobs,(self.nh,1)),axis=0)
M = M_vec.reshape((self.ns, self.ns))
sp = T.dot(M, s)
xp = T.dot(self.W, sp) + self.c
return T.cast(sp, 'float32'), T.cast(xp, 'float32'), h_samp
def simulate_forward(self, n_steps):
s0 = T.sum(self.s_now * T.reshape(self.weights_now, (self.npcl, 1)),
axis=0)
[sp, xp, hs], updates = theano.scan(fn=self.simulate_step,
outputs_info=[s0, None, None],
n_steps=n_steps)
return sp, xp, hs, updates
class SLmodel():
#This is the switched conditional linear model for integrating
#action with sensation
def __init__(self, nx, ns, nh, na, npcl, xvar=1.0):
#for this model I assume one linear generative model and a
#combination of nh linear dynamical models
#generative matrix
init_W = np.asarray(np.random.randn(nx, ns) / 10.0, dtype='float32')
#observed variable means
init_c = np.asarray(np.zeros(nx), dtype='float32')
#dynamical matrices
init_M = np.asarray((np.tile(np.eye(ns), (1, nh))),
dtype='float32') #for state-based predictions
init_C = np.asarray((np.tile(np.zeros((na, ns)), (1, nh))),
dtype='float32') #for action-based predictions
#state-variable variances
#(covariance matrix of state variable noise assumed to be diagonal)
init_b = np.asarray(np.ones(ns) * 10.0, dtype='float32')
#Switching parameter matrices
init_A = np.asarray(np.zeros((ns, nh)),
dtype='float32') #associated with the state
init_B = np.asarray(np.zeros((na, nh)),
dtype='float32') #associated with actions
#priors for switching variable
init_ph = np.asarray(np.zeros(nh), dtype='float32')
init_s_now = np.asarray(np.zeros((npcl, ns)), dtype='float32')
init_weights_now = np.asarray(np.ones(npcl) / float(npcl),
dtype='float32')
init_s_past = np.asarray(np.zeros((npcl, ns)), dtype='float32')
init_h_past = np.asarray(np.zeros((npcl, nh)), dtype='float32')
init_h_past[:, 0] = 1.0
init_weights_past = np.asarray(np.ones(npcl) / float(npcl),
dtype='float32')
init_a_past = np.asarray(np.zeros((1, na)), dtype='float32')
self.W = theano.shared(init_W)
self.c = theano.shared(init_c)
self.M = theano.shared(init_M)
self.C = theano.shared(init_C)
self.b = theano.shared(init_b)
self.A = theano.shared(init_A)
self.B = theano.shared(init_B)
self.ph = theano.shared(init_ph)
#this is to help vectorize operations
self.sum_mat = T.as_tensor_variable(
np.asarray((np.tile(np.eye(ns), nh)).T, dtype='float32'))
self.s_now = theano.shared(init_s_now)
self.weights_now = theano.shared(init_weights_now)
self.s_past = theano.shared(init_s_past)
self.h_past = theano.shared(init_h_past)
self.a_past = theano.shared(init_a_past)
self.weights_past = theano.shared(init_weights_past)
self.xvar = np.asarray(xvar, dtype='float32')
self.nx = nx #dimensionality of observed variables
self.ns = ns #dimensionality of latent variables
self.nh = nh #number of (linear) dynamical modes
self.na = na #dimensionality of action variables
self.npcl = npcl #numer of particles in particle filter
self.theano_rng = RandomStreams()
self.params = [
self.W, self.M, self.C, self.b, self.A, self.B, self.c, self.ph
]
self.rel_lrates = np.asarray(
[0.1, 1.0, 1.0, 0.01, 10.0, 10.0, 0.1, 1.0], dtype='float32')
def sample_proposal_s(self, s, a, h, xpred, sig):
s_pred = self.get_prediction(s, a, h)
n = self.theano_rng.normal(size=T.shape(s))
#This is the proposal distribution that arises when one assumes that W'W=I
mean = 2.0 * (xpred + s_pred * (self.b**2)) * sig
s_prop = mean + n * T.sqrt(sig)
#I compute the term inside the exponent for the pdf of the proposal distrib
prop_term = -T.sum(n**2) / 2.0
return T.cast(s_prop, 'float32'), T.cast(s_pred, 'float32'), T.cast(
prop_term, 'float32')
#This function is required if we allow multiple generative models
#def get_recon(self, s, h):
#W_vec=T.sum(self.W*h, axis=0)
#W=W.reshape((self.nx, self.ns))
#xr=T.dot(W, s)
#return xr
def calc_h_probs(self, s, a):
#this function takes an np by ns matrix of s samples plus
#an action vector a
#and returns an nh by np set of h probabilities
exp_terms = T.dot(s, self.A) + T.reshape(T.dot(a, self.B),
(1, self.nh)) + T.reshape(
self.ph, (1, self.nh))
#re-centering for numerical stability
exp_terms_recentered = exp_terms - T.max(exp_terms, axis=1)
#exponentiation and normalization
rel_probs = T.exp(exp_terms)
probs = rel_probs.T / T.sum(rel_probs, axis=1)
return probs.T
def forward_filter_step(self, a, xp):
#first sample from h given s and a
h_probs = self.calc_h_probs(self.s_now, a)
h_samps = self.theano_rng.multinomial(pvals=h_probs)
#need to sample from the proposal distribution
#these terms are the same for every particle
xpred = T.dot(self.W.T, (xp - self.c)) / (2.0 * self.xvar**2)
sig = (1.0 / (self.b**2 + 1.0 / (2.0 * self.xvar**2))) / 2.0
#sig=1.0/(self.b**2)
#vectorized version
s_pred = self.get_prediction(self.s_now, a, h_samps)
n = self.theano_rng.normal(size=T.shape(self.s_now))
mean = 2.0 * (xpred + s_pred * (self.b**2)) * sig
#mean=s_pred #trying out using solely predictive proposal distrib
s_samps = mean + n * T.sqrt(sig)
prop_terms = -T.sum(n**2, axis=1) / 2.0
updates = {}
#now that we have samples from the proposal distribution, we need to reweight them
recons = T.dot(self.W, s_samps.T) + T.reshape(self.c, (self.nx, 1))
x_terms = -T.sum(
(recons - T.reshape(xp, (self.nx, 1)))**2, axis=0) / (2.0 *
self.xvar**2)
s_terms = -T.sum(((s_samps - s_pred) * self.b)**2, axis=1) / 2.0
energies = x_terms + s_terms - prop_terms
#to avoid exponentiating large or very small numbers, I
#"re-center" the reweighting factors by adding a constant,
#as this has no impact on the resulting new weights
energies_recentered = energies - T.max(energies)
alpha = T.exp(energies_recentered) #these are the reweighting factors
new_weights_unnorm = self.weights_now * alpha
normalizer = T.sum(new_weights_unnorm)
new_weights = new_weights_unnorm / normalizer #need to normalize new weights
updates[self.h_past] = T.cast(h_samps, 'float32')
updates[self.s_past] = T.cast(self.s_now, 'float32')
updates[self.a_past] = T.cast(a, 'float32')
updates[self.s_now] = T.cast(s_samps, 'float32')
updates[self.weights_past] = T.cast(self.weights_now, 'float32')
updates[self.weights_now] = T.cast(new_weights, 'float32')
#return normalizer, energies_recentered, s_samps, s_pred, T.dot(self.W.T,(xp-self.c)), updates
#return normalizer, energies_recentered, updates
#return h_samps, updates
return updates
def get_prediction(self, s, a, h):
s_dot_M = T.dot(s, self.M) #this is np by nh*ns
a_dot_C = T.dot(a, self.C) #this is 1 by nh*ns
tot = s_dot_M + a_dot_C #should be np by nh*ns
s_pred = T.dot(tot * T.extra_ops.repeat(h, self.ns, axis=1),
self.sum_mat) #should be np by ns
return T.cast(s_pred, 'float32')
def sample_joint(self, sp):
t2_samp = self.theano_rng.multinomial(
pvals=T.reshape(self.weights_now, (1, self.npcl))).T
s2_samp = T.cast(
T.sum(self.s_now * T.addbroadcast(t2_samp, 1), axis=0), 'float32')
h2_samp = T.cast(
T.sum(self.h_now * T.addbroadcast(t2_samp, 1), axis=0), 'float32')
diffs = self.b * (s2_samp - sp)
sqr_term = T.sum(diffs**2, axis=1)
alpha = T.exp(-sqr_term)
probs_unnorm = self.weights_past * alpha
probs = probs_unnorm / T.sum(probs_unnorm)
t1_samp = self.theano_rng.multinomial(
pvals=T.reshape(probs, (1, self.npcl))).T
s1_samp = T.cast(
T.sum(self.s_past * T.addbroadcast(t1_samp, 1), axis=0), 'float32')
h1_samp = T.cast(
T.sum(self.h_past * T.addbroadcast(t1_samp, 1), axis=0), 'float32')
return [s1_samp, h1_samp, s2_samp, h2_samp]
def calc_mean_h_energy(self, s, a, h):
#you give this function a set of samples of s, a, and h,
#it gives you the average energy of those samples
exp_terms = T.dot(s, self.A) + T.reshape(T.dot(a, self.B),
(1, self.nh)) + T.reshape(
self.ph,
(1, self.nh)) #np by nh
energies = T.sum(h * exp_terms, axis=1) - T.log(
T.sum(T.exp(exp_terms), axis=1)) #should be np by 1
energy = T.mean(energies)
return energy
def update_params(self, x1, x2, n_samps, lrate):
#this function samples from the joint posterior and performs
# a step of gradient ascent on the log-likelihood
sp = self.get_prediction(self.s_past, self.a_past, self.h_past)
#sp should be np by ns
[s1_samps, h1_samps, s2_samps, h2_samps
], updates = theano.scan(fn=self.sample_joint,
outputs_info=[None, None, None, None],
non_sequences=[sp],
n_steps=n_samps)
x1_recons = T.dot(self.W, s1_samps.T) + T.reshape(self.c, (self.nx, 1))
x2_recons = T.dot(self.W, s2_samps.T) + T.reshape(self.c, (self.nx, 1))
s_pred = self.get_prediction(s1_samps, h1_samps)
hterm1 = self.calc_mean_h_energy(s1_samps, h1_samps)
#hterm2=self.calc_mean_h_energy(s2_samps, h2_samps)
sterm = -T.mean(T.sum((self.b * (s2_samps - s_pred))**2, axis=1)) / 2.0
xterm1 = -T.mean(
T.sum((x1_recons - T.reshape(x1, (self.nx, 1)))**2, axis=0) /
(2.0 * self.xvar**2))
xterm2 = -T.mean(
T.sum((x2_recons - T.reshape(x2, (self.nx, 1)))**2, axis=0) /
(2.0 * self.xvar**2))
#energy = hterm1 + xterm1 + hterm2 + xterm2 + sterm -T.sum(T.sum(self.A**2))
energy = hterm1 + xterm1 + xterm2 + sterm
gparams = T.grad(
energy,
self.params,
consider_constant=[s1_samps, s2_samps, h1_samps, h2_samps])
# constructs the update dictionary
for gparam, param, rel_lr in zip(gparams, self.params,
self.rel_lrates):
#gnat=T.dot(param, T.dot(param.T,param))
updates[param] = T.cast(param + gparam * lrate * rel_lr, 'float32')
#make sure W has unit-length columns
#new_W=updates[self.W]
#updates[self.W]=T.cast(new_W/T.sqrt(T.sum(new_W**2,axis=0)),'float32')
#MIGHT NEED TO NORMALIZE A
return energy, updates
def get_ESS(self):
return 1.0 / T.sum(self.weights_now**2)
def resample_step(self):
idx = self.theano_rng.multinomial(
pvals=T.reshape(self.weights_now, (1, self.npcl))).T
s_samp = T.sum(self.s_now * T.addbroadcast(idx, 1), axis=0)
h_samp = T.sum(self.h_now * T.addbroadcast(idx, 1), axis=0)
return T.cast(s_samp, 'float32'), T.cast(h_samp, 'float32')
def resample(self):
[s_samps, h_samps], updates = theano.scan(fn=self.resample_step,
outputs_info=[None, None],
n_steps=self.npcl)
updates[self.s_now] = T.cast(s_samps, 'float32')
updates[self.h_now] = T.cast(h_samps, 'float32')
updates[self.weights_now] = T.cast(
T.ones_like(self.weights_now) / T.cast(self.npcl, 'float32'),
'float32') #dtype paranoia
return updates
def simulate_step(self, s, a):
s = T.reshape(s, (1, self.ns))
a = T.reshape(a, (1, self.na))
#get h probabilities
h_probs = self.calc_h_probs(s, a)
h_samp = self.theano_rng.multinomial(pvals=h_probs)
sp = self.get_prediction(s, a, h_samp)
xp = T.dot(self.W, sp.T) + T.reshape(self.c, (self.nx, 1))
return T.cast(sp, 'float32'), T.cast(xp, 'float32'), h_samp
def simulate_forward(self, a, n_steps):
#a should be n_steps by na
s0 = T.sum(self.s_now * T.reshape(self.weights_now, (self.npcl, 1)),
axis=0)
s0 = T.reshape(s0, (1, self.ns))
[sp, xp, hs], updates = theano.scan(fn=self.simulate_step,
outputs_info=[s0, None, None],
sequences=[a],
n_steps=n_steps)
return sp, xp, hs, updates
class SLmodel():
#This is a test of my idea to adapt the proposal distribution by
#maximizing the entropy of the weights
def __init__(self, nx, ns, nh, npcl, xvar=1.0):
#for this model I assume one linear generative model and a
#combination of nh linear dynamical models
#generative matrix
init_W=np.asarray(np.random.randn(nx,ns)/10.0,dtype='float32')
#init_W=np.asarray(np.eye(2),dtype='float32')
#always normalize the columns of W to be unit length
init_W=init_W/np.sqrt(np.sum(init_W**2,axis=0))
#observed variable means
init_c=np.asarray(np.zeros(nx),dtype='float32')
#dynamical matrices
#init_M=np.asarray(np.random.randn(ns,ns*nh)/2.0,dtype='float32')
init_M=np.asarray((np.tile(np.eye(ns),(1,nh))),dtype='float32')
#state-variable variances
#(covariance matrix of state variable noise assumed to be diagonal)
init_b=np.asarray(np.ones(ns)*10.0,dtype='float32')
#Switching parameter matrix
init_A=np.asarray(np.zeros((ns,nh)),dtype='float32')
#priors for switching variable
init_ph=np.asarray(np.zeros(nh),dtype='float32')
#parameters for proposal distribution
init_D=np.asarray(np.eye(ns),dtype='float32')
init_E=np.asarray(np.random.randn(nx,ns)/100.0,dtype='float32')
init_k=np.asarray(np.zeros(ns),dtype='float32')
init_sig=np.asarray(np.ones(ns),dtype='float32')
init_s_now=np.asarray(np.zeros((npcl,ns)),dtype='float32')
init_h_now=np.asarray(np.zeros((npcl,nh)),dtype='float32')
init_h_now[:,0]=1.0
init_weights_now=np.asarray(np.ones(npcl)/float(npcl),dtype='float32')
init_s_past=np.asarray(np.zeros((npcl,ns)),dtype='float32')
init_h_past=np.asarray(np.zeros((npcl,nh)),dtype='float32')
init_h_past[:,0]=1.0
init_weights_past=np.asarray(np.ones(npcl)/float(npcl),dtype='float32')
self.W=theano.shared(init_W)
self.c=theano.shared(init_c)
self.M=theano.shared(init_M)
self.b=theano.shared(init_b)
self.A=theano.shared(init_A)
self.ph=theano.shared(init_ph)
self.D=theano.shared(init_D)
self.E=theano.shared(init_E)
self.k=theano.shared(init_k)
self.sig=theano.shared(init_sig)
#this is to help vectorize operations
self.sum_mat=T.as_tensor_variable(np.asarray((np.tile(np.eye(ns),nh)).T,dtype='float32'))
self.s_now=theano.shared(init_s_now)
self.h_now=theano.shared(init_h_now)
self.weights_now=theano.shared(init_weights_now)
self.s_past=theano.shared(init_s_past)
self.h_past=theano.shared(init_h_past)
self.weights_past=theano.shared(init_weights_past)
self.xvar=np.asarray(xvar,dtype='float32')
self.nx=nx #dimensionality of observed variables
self.ns=ns #dimensionality of latent variables
self.nh=nh #number of (linear) dynamical modes
self.npcl=npcl #numer of particles in particle filter
self.theano_rng = RandomStreams()
self.params= [self.W, self.M, self.b, self.A, self.c, self.ph]
self.rel_lrates=np.asarray([ 0.1, 1.0, 0.01, 10.0, 0.1, 1.0] ,dtype='float32')
self.meta_params= [self.D, self.E, self.k, self.sig]
self.meta_rel_lrates=[ 1.0, 1.0, 1.0, 1.0 ]
def sample_proposal_s(self, s, h, xp):
s_pred=self.get_prediction(s, h)
n=self.theano_rng.normal(size=T.shape(s))
prop_mean=T.dot(s_pred, self.D) + T.reshape(T.dot(xp, self.E),(1,self.ns)) + self.k
s_prop=prop_mean + n*T.reshape(T.exp(self.sig/2.0),(1,self.ns))
#I compute the term inside the exponent for the pdf of the proposal distrib
prop_term=-T.sum(n**2)/2.0
return T.cast(s_prop,'float32'), T.cast(s_pred,'float32'), T.cast(prop_term,'float32'), prop_mean
def calc_h_probs(self, s):
#this function takes an np by ns matrix of s samples
#and returns an nh by np set of h probabilities
exp_terms=T.dot(s, self.A) + T.reshape(self.ph,(1,self.nh))
#re-centering for numerical stability
exp_terms_recentered=exp_terms-T.max(exp_terms,axis=1)
#exponentiation and normalization
rel_probs=T.exp(exp_terms)
probs=rel_probs.T/T.sum(rel_probs, axis=1)
return probs.T
def proposal_loss(self, s_pred, s_samps, xp, weights):
#estimates the KL divergence between the proposal distribution
#and the true posterior (minus one term, which we assume does not
#depend on the proposal distribution).
#prop means should be symblolic variables since we need to
#compute the derivatives of D and E through this function
prop_means=T.dot(s_pred, self.D) + T.reshape(T.dot(xp, self.E),(1,self.ns)) + self.k #np by ns
diffs=(prop_means-s_samps)
scl_diffs=diffs*T.reshape(T.exp(-self.sig),(1,self.ns))
energies=0.5*T.sum(diffs*scl_diffs,axis=1)
tot=T.sum(energies*weights)+0.5*T.sum(self.sig)
return tot
def forward_filter_step(self, xp):
#need to sample from the proposal distribution first
s_samps, s_pred, prop_terms, prop_means = self.sample_proposal_s(self.s_now,self.h_now,xp)
updates={}
#now that we have samples from the proposal distribution, we need to reweight them
h_probs = self.calc_h_probs(s_samps)
h_samps=self.theano_rng.multinomial(pvals=h_probs)
recons=T.dot(self.W, s_samps.T) + T.reshape(self.c,(self.nx,1))
x_terms=-T.sum((recons-T.reshape(xp,(self.nx,1)))**2,axis=0)/(2.0*self.xvar**2)
s_terms=-T.sum(((s_samps-s_pred)*self.b)**2,axis=1)/2.0
energies=x_terms+s_terms-prop_terms
#to avoid exponentiating large or very small numbers, I
#"re-center" the reweighting factors by adding a constant,
#as this has no impact on the resulting new weights
energies_recentered=energies-T.max(energies)
alpha=T.exp(energies_recentered) #these are the reweighting factors
new_weights_unnorm=self.weights_now*alpha
normalizer=T.sum(new_weights_unnorm)
new_weights=new_weights_unnorm/normalizer #need to normalize new weights
#gradient updates for the proposal distribution parameters
lrate=1e-2
loss=self.proposal_loss(s_pred, s_samps, xp, new_weights)
gparams=T.grad(loss, self.meta_params, consider_constant=[s_pred, s_samps, xp, new_weights])
# constructs the update dictionary
for gparam, param, rel_lr in zip(gparams, self.meta_params, self.meta_rel_lrates):
updates[param] = T.cast(param - gparam*lrate*rel_lr,'float32')
updates[self.h_past]=T.cast(self.h_now,'float32')
updates[self.s_past]=T.cast(self.s_now,'float32')
updates[self.h_now]=T.cast(h_samps,'float32')
updates[self.s_now]=T.cast(s_samps,'float32')
updates[self.weights_past]=T.cast(self.weights_now,'float32')
updates[self.weights_now]=T.cast(new_weights,'float32')
#return normalizer, energies_recentered, s_samps, s_pred, T.dot(self.W.T,(xp-self.c)), updates
#return normalizer, energies_recentered, updates
#return h_samps, updates
return updates
def get_prediction(self, s, h):
s_dot_M=T.dot(s, self.M) #this is np by nh*ns
s_pred=T.dot(s_dot_M*T.extra_ops.repeat(h,self.ns,axis=1),self.sum_mat) #should be np by ns
return T.cast(s_pred,'float32')
def sample_joint(self, sp):
t2_samp=self.theano_rng.multinomial(pvals=T.reshape(self.weights_now,(1,self.npcl))).T
s2_samp=T.cast(T.sum(self.s_now*T.addbroadcast(t2_samp,1),axis=0),'float32')
h2_samp=T.cast(T.sum(self.h_now*T.addbroadcast(t2_samp,1),axis=0),'float32')
diffs=self.b*(s2_samp-sp)
sqr_term=T.sum(diffs**2,axis=1)
alpha=T.exp(-sqr_term)
probs_unnorm=self.weights_past*alpha
probs=probs_unnorm/T.sum(probs_unnorm)
t1_samp=self.theano_rng.multinomial(pvals=T.reshape(probs,(1,self.npcl))).T
s1_samp=T.cast(T.sum(self.s_past*T.addbroadcast(t1_samp,1),axis=0),'float32')
h1_samp=T.cast(T.sum(self.h_past*T.addbroadcast(t1_samp,1),axis=0),'float32')
return [s1_samp, h1_samp, s2_samp, h2_samp]
#def sample_posterior(self, n_samps):
#sp, updates = theano.scan(fn=self.get_prediction,
#outputs_info=[None],
#sequences=[self.s_past, self.h_past],
#n_steps=self.npcl)
##sp should be np by ns
#[s1_samps, h1_samps, s2_samps, h2_samps], updates = theano.scan(fn=self.sample_joint,
#outputs_info=[None, None, None, None],
#non_sequences=[sp],
#n_steps=n_samps)
#return [s1_samps, h1_samps, s2_samps, h2_samps]
def h_energy_step(self, s, h):
#helper function for self.calc_mean_h_energy
exp_A_i=T.reshape(T.sum(self.exp_A*T.reshape(h,(self.nh,1)),axis=0),(self.ns,1))
mu_i=T.reshape(T.sum(self.mu*T.reshape(h,(self.nh,1)),axis=0), (self.ns,1))
ln_Z_h_i=T.sum(self.ln_Z_h*T.reshape(h,(self.nh,1)))
ph_i=T.sum(self.ph*T.reshape(h,(self.nh,1)))
diff=T.reshape(T.reshape(s,(self.ns,1))-mu_i,(self.ns,1))
diff_dot_exp_A_i=diff*exp_A_i
gterm=-0.5*T.sum(T.sum(diff_dot_exp_A_i*diff))
energy=gterm+ln_Z_h_i+ph_i
return energy
def calc_mean_h_energy(self, s, h):
#you give this function a set of samples of s and h,
#it gives you the average energy of those samples
exp_terms=T.dot(s, self.A) + T.reshape(self.ph,(1,self.nh)) #np by nh
energies=T.sum(h*exp_terms,axis=1) + T.log(T.sum(T.exp(exp_terms),axis=1)) #should be np by 1
energy=T.mean(energies)
return energy
def update_params(self, x1, x2, n_samps, lrate):
#this function samples from the joint posterior and performs
# a step of gradient ascent on the log-likelihood
sp=self.get_prediction(self.s_past, self.h_past)
#sp should be np by ns
[s1_samps, h1_samps, s2_samps, h2_samps], updates = theano.scan(fn=self.sample_joint,
outputs_info=[None, None, None, None],
non_sequences=[sp],
n_steps=n_samps)
x1_recons=T.dot(self.W, s1_samps.T) + T.reshape(self.c,(self.nx,1))
x2_recons=T.dot(self.W, s2_samps.T) + T.reshape(self.c,(self.nx,1))
s_pred = self.get_prediction(s1_samps, h1_samps)
hterm1=self.calc_mean_h_energy(s1_samps, h1_samps)
#hterm2=self.calc_mean_h_energy(s2_samps, h2_samps)
sterm=-T.mean(T.sum((self.b*(s2_samps-s_pred))**2,axis=1))/2.0
#xterm1=-T.mean(T.sum((x1_recons-T.reshape(x1,(self.nx,1)))**2,axis=0)/(2.0*self.xvar**2))
xterm2=-T.mean(T.sum((x2_recons-T.reshape(x2,(self.nx,1)))**2,axis=0)/(2.0*self.xvar**2))
#energy = hterm1 + xterm1 + hterm2 + xterm2 + sterm -T.sum(T.sum(self.A**2))
energy = hterm1 + xterm2 + sterm
gparams=T.grad(energy, self.params, consider_constant=[s1_samps, s2_samps, h1_samps, h2_samps])
# constructs the update dictionary
for gparam, param, rel_lr in zip(gparams, self.params, self.rel_lrates):
#gnat=T.dot(param, T.dot(param.T,param))
updates[param] = T.cast(param + gparam*lrate*rel_lr,'float32')
#make sure W has unit-length columns
#new_W=updates[self.W]
#updates[self.W]=T.cast(new_W/T.sqrt(T.sum(new_W**2,axis=0)),'float32')
#MIGHT NEED TO NORMALIZE A
return energy, updates
def get_ESS(self):
return 1.0/T.sum(self.weights_now**2)
def resample_step(self):
idx=self.theano_rng.multinomial(pvals=T.reshape(self.weights_now,(1,self.npcl))).T
s_samp=T.sum(self.s_now*T.addbroadcast(idx,1),axis=0)
h_samp=T.sum(self.h_now*T.addbroadcast(idx,1),axis=0)
return T.cast(s_samp,'float32'), T.cast(h_samp,'float32')
def resample(self):
[s_samps, h_samps], updates = theano.scan(fn=self.resample_step,
outputs_info=[None, None],
n_steps=self.npcl)
updates[self.s_now]=T.cast(s_samps,'float32')
updates[self.h_now]=T.cast(h_samps,'float32')
updates[self.weights_now]=T.cast(T.ones_like(self.weights_now)/T.cast(self.npcl,'float32'),'float32') #dtype paranoia
return updates
def simulate_step(self, s):
s=T.reshape(s,(1,self.ns))
#get h probabilities
h_probs = self.calc_h_probs(s)
#h_samp=self.theano_rng.multinomial(pvals=T.reshape(h_probs,(self.nh,1)))
h_samp=self.theano_rng.multinomial(pvals=h_probs)
sp=self.get_prediction(s,h_samp)
xp=T.dot(self.W, sp.T) + T.reshape(self.c,(self.nx,1))
return T.cast(sp,'float32'), T.cast(xp,'float32'), h_samp
def simulate_forward(self, n_steps):
s0=T.sum(self.s_now*T.reshape(self.weights_now,(self.npcl,1)),axis=0)
s0=T.reshape(s0,(1,self.ns))
[sp, xp, hs], updates = theano.scan(fn=self.simulate_step,
outputs_info=[s0, None, None],
n_steps=n_steps)
return sp, xp, hs, updates
class SLmodel():
#This is a test of my idea to adapt the proposal distribution by
#maximizing the entropy of the weights
def __init__(self, nx, ns, nh, npcl, xvar=1.0):
#for this model I assume one linear generative model and a
#combination of nh linear dynamical models
#generative matrix
init_W = np.asarray(np.random.randn(nx, ns) / 10.0, dtype='float32')
#init_W=np.asarray(np.eye(2),dtype='float32')
#always normalize the columns of W to be unit length
init_W = init_W / np.sqrt(np.sum(init_W**2, axis=0))
#observed variable means
init_c = np.asarray(np.zeros(nx), dtype='float32')
#dynamical matrices
#init_M=np.asarray(np.random.randn(ns,ns*nh)/2.0,dtype='float32')
init_M = np.asarray((np.tile(np.eye(ns), (1, nh))), dtype='float32')
#state-variable variances
#(covariance matrix of state variable noise assumed to be diagonal)
init_b = np.asarray(np.ones(ns) * 10.0, dtype='float32')
#Switching parameter matrix
init_A = np.asarray(np.zeros((ns, nh)), dtype='float32')
#priors for switching variable
init_ph = np.asarray(np.zeros(nh), dtype='float32')
#parameters for proposal distribution
init_D = np.asarray(np.eye(ns), dtype='float32')
init_E = np.asarray(np.random.randn(nx, ns) / 100.0, dtype='float32')
init_k = np.asarray(np.zeros(ns), dtype='float32')
init_sig = np.asarray(np.ones(ns), dtype='float32')
init_s_now = np.asarray(np.zeros((npcl, ns)), dtype='float32')
init_h_now = np.asarray(np.zeros((npcl, nh)), dtype='float32')
init_h_now[:, 0] = 1.0
init_weights_now = np.asarray(np.ones(npcl) / float(npcl),
dtype='float32')
init_s_past = np.asarray(np.zeros((npcl, ns)), dtype='float32')
init_h_past = np.asarray(np.zeros((npcl, nh)), dtype='float32')
init_h_past[:, 0] = 1.0
init_weights_past = np.asarray(np.ones(npcl) / float(npcl),
dtype='float32')
self.W = theano.shared(init_W)
self.c = theano.shared(init_c)
self.M = theano.shared(init_M)
self.b = theano.shared(init_b)
self.A = theano.shared(init_A)
self.ph = theano.shared(init_ph)
self.D = theano.shared(init_D)
self.E = theano.shared(init_E)
self.k = theano.shared(init_k)
self.sig = theano.shared(init_sig)
#this is to help vectorize operations
self.sum_mat = T.as_tensor_variable(
np.asarray((np.tile(np.eye(ns), nh)).T, dtype='float32'))
self.s_now = theano.shared(init_s_now)
self.h_now = theano.shared(init_h_now)
self.weights_now = theano.shared(init_weights_now)
self.s_past = theano.shared(init_s_past)
self.h_past = theano.shared(init_h_past)
self.weights_past = theano.shared(init_weights_past)
self.xvar = np.asarray(xvar, dtype='float32')
self.nx = nx #dimensionality of observed variables
self.ns = ns #dimensionality of latent variables
self.nh = nh #number of (linear) dynamical modes
self.npcl = npcl #numer of particles in particle filter
self.theano_rng = RandomStreams()
self.params = [self.W, self.M, self.b, self.A, self.c, self.ph]
self.rel_lrates = np.asarray([0.1, 1.0, 0.01, 10.0, 0.1, 1.0],
dtype='float32')
self.meta_params = [self.D, self.E, self.k, self.sig]
self.meta_rel_lrates = [1.0, 1.0, 1.0, 1.0]
def sample_proposal_s(self, s, h, xp):
s_pred = self.get_prediction(s, h)
n = self.theano_rng.normal(size=T.shape(s))
prop_mean = T.dot(s_pred, self.D) + T.reshape(T.dot(xp, self.E),
(1, self.ns)) + self.k
s_prop = prop_mean + n * T.reshape(T.exp(self.sig / 2.0), (1, self.ns))
#I compute the term inside the exponent for the pdf of the proposal distrib
prop_term = -T.sum(n**2) / 2.0
return T.cast(s_prop, 'float32'), T.cast(s_pred, 'float32'), T.cast(
prop_term, 'float32'), prop_mean
def calc_h_probs(self, s):
#this function takes an np by ns matrix of s samples
#and returns an nh by np set of h probabilities
exp_terms = T.dot(s, self.A) + T.reshape(self.ph, (1, self.nh))
#re-centering for numerical stability
exp_terms_recentered = exp_terms - T.max(exp_terms, axis=1)
#exponentiation and normalization
rel_probs = T.exp(exp_terms)
probs = rel_probs.T / T.sum(rel_probs, axis=1)
return probs.T
def proposal_loss(self, s_pred, s_samps, xp, weights):
#estimates the KL divergence between the proposal distribution
#and the true posterior (minus one term, which we assume does not
#depend on the proposal distribution).
#prop means should be symblolic variables since we need to
#compute the derivatives of D and E through this function
prop_means = T.dot(s_pred, self.D) + T.reshape(T.dot(
xp, self.E), (1, self.ns)) + self.k #np by ns
diffs = (prop_means - s_samps)
scl_diffs = diffs * T.reshape(T.exp(-self.sig), (1, self.ns))
energies = 0.5 * T.sum(diffs * scl_diffs, axis=1)
tot = T.sum(energies * weights) + 0.5 * T.sum(self.sig)
return tot
def forward_filter_step(self, xp):
#need to sample from the proposal distribution first
s_samps, s_pred, prop_terms, prop_means = self.sample_proposal_s(
self.s_now, self.h_now, xp)
updates = {}
#now that we have samples from the proposal distribution, we need to reweight them
h_probs = self.calc_h_probs(s_samps)
h_samps = self.theano_rng.multinomial(pvals=h_probs)
recons = T.dot(self.W, s_samps.T) + T.reshape(self.c, (self.nx, 1))
x_terms = -T.sum(
(recons - T.reshape(xp, (self.nx, 1)))**2, axis=0) / (2.0 *
self.xvar**2)
s_terms = -T.sum(((s_samps - s_pred) * self.b)**2, axis=1) / 2.0
energies = x_terms + s_terms - prop_terms
#to avoid exponentiating large or very small numbers, I
#"re-center" the reweighting factors by adding a constant,
#as this has no impact on the resulting new weights
energies_recentered = energies - T.max(energies)
alpha = T.exp(energies_recentered) #these are the reweighting factors
new_weights_unnorm = self.weights_now * alpha
normalizer = T.sum(new_weights_unnorm)
new_weights = new_weights_unnorm / normalizer #need to normalize new weights
#gradient updates for the proposal distribution parameters
lrate = 1e-2
loss = self.proposal_loss(s_pred, s_samps, xp, new_weights)
gparams = T.grad(loss,
self.meta_params,
consider_constant=[s_pred, s_samps, xp, new_weights])
# constructs the update dictionary
for gparam, param, rel_lr in zip(gparams, self.meta_params,
self.meta_rel_lrates):
updates[param] = T.cast(param - gparam * lrate * rel_lr, 'float32')
updates[self.h_past] = T.cast(self.h_now, 'float32')
updates[self.s_past] = T.cast(self.s_now, 'float32')
updates[self.h_now] = T.cast(h_samps, 'float32')
updates[self.s_now] = T.cast(s_samps, 'float32')
updates[self.weights_past] = T.cast(self.weights_now, 'float32')
updates[self.weights_now] = T.cast(new_weights, 'float32')
#return normalizer, energies_recentered, s_samps, s_pred, T.dot(self.W.T,(xp-self.c)), updates
#return normalizer, energies_recentered, updates
#return h_samps, updates
return updates
def get_prediction(self, s, h):
s_dot_M = T.dot(s, self.M) #this is np by nh*ns
s_pred = T.dot(s_dot_M * T.extra_ops.repeat(h, self.ns, axis=1),
self.sum_mat) #should be np by ns
return T.cast(s_pred, 'float32')
def sample_joint(self, sp):
t2_samp = self.theano_rng.multinomial(
pvals=T.reshape(self.weights_now, (1, self.npcl))).T
s2_samp = T.cast(
T.sum(self.s_now * T.addbroadcast(t2_samp, 1), axis=0), 'float32')
h2_samp = T.cast(
T.sum(self.h_now * T.addbroadcast(t2_samp, 1), axis=0), 'float32')
diffs = self.b * (s2_samp - sp)
sqr_term = T.sum(diffs**2, axis=1)
alpha = T.exp(-sqr_term)
probs_unnorm = self.weights_past * alpha
probs = probs_unnorm / T.sum(probs_unnorm)
t1_samp = self.theano_rng.multinomial(
pvals=T.reshape(probs, (1, self.npcl))).T
s1_samp = T.cast(
T.sum(self.s_past * T.addbroadcast(t1_samp, 1), axis=0), 'float32')
h1_samp = T.cast(
T.sum(self.h_past * T.addbroadcast(t1_samp, 1), axis=0), 'float32')
return [s1_samp, h1_samp, s2_samp, h2_samp]
#def sample_posterior(self, n_samps):
#sp, updates = theano.scan(fn=self.get_prediction,
#outputs_info=[None],
#sequences=[self.s_past, self.h_past],
#n_steps=self.npcl)
##sp should be np by ns
#[s1_samps, h1_samps, s2_samps, h2_samps], updates = theano.scan(fn=self.sample_joint,
#outputs_info=[None, None, None, None],
#non_sequences=[sp],
#n_steps=n_samps)
#return [s1_samps, h1_samps, s2_samps, h2_samps]
def h_energy_step(self, s, h):
#helper function for self.calc_mean_h_energy
exp_A_i = T.reshape(
T.sum(self.exp_A * T.reshape(h, (self.nh, 1)), axis=0),
(self.ns, 1))
mu_i = T.reshape(T.sum(self.mu * T.reshape(h, (self.nh, 1)), axis=0),
(self.ns, 1))
ln_Z_h_i = T.sum(self.ln_Z_h * T.reshape(h, (self.nh, 1)))
ph_i = T.sum(self.ph * T.reshape(h, (self.nh, 1)))
diff = T.reshape(T.reshape(s, (self.ns, 1)) - mu_i, (self.ns, 1))
diff_dot_exp_A_i = diff * exp_A_i
gterm = -0.5 * T.sum(T.sum(diff_dot_exp_A_i * diff))
energy = gterm + ln_Z_h_i + ph_i
return energy
def calc_mean_h_energy(self, s, h):
#you give this function a set of samples of s and h,
#it gives you the average energy of those samples
exp_terms = T.dot(s, self.A) + T.reshape(self.ph,
(1, self.nh)) #np by nh
energies = T.sum(h * exp_terms, axis=1) + T.log(
T.sum(T.exp(exp_terms), axis=1)) #should be np by 1
energy = T.mean(energies)
return energy
def update_params(self, x1, x2, n_samps, lrate):
#this function samples from the joint posterior and performs
# a step of gradient ascent on the log-likelihood
sp = self.get_prediction(self.s_past, self.h_past)
#sp should be np by ns
[s1_samps, h1_samps, s2_samps, h2_samps
], updates = theano.scan(fn=self.sample_joint,
outputs_info=[None, None, None, None],
non_sequences=[sp],
n_steps=n_samps)
x1_recons = T.dot(self.W, s1_samps.T) + T.reshape(self.c, (self.nx, 1))
x2_recons = T.dot(self.W, s2_samps.T) + T.reshape(self.c, (self.nx, 1))
s_pred = self.get_prediction(s1_samps, h1_samps)
hterm1 = self.calc_mean_h_energy(s1_samps, h1_samps)
#hterm2=self.calc_mean_h_energy(s2_samps, h2_samps)
sterm = -T.mean(T.sum((self.b * (s2_samps - s_pred))**2, axis=1)) / 2.0
#xterm1=-T.mean(T.sum((x1_recons-T.reshape(x1,(self.nx,1)))**2,axis=0)/(2.0*self.xvar**2))
xterm2 = -T.mean(
T.sum((x2_recons - T.reshape(x2, (self.nx, 1)))**2, axis=0) /
(2.0 * self.xvar**2))
#energy = hterm1 + xterm1 + hterm2 + xterm2 + sterm -T.sum(T.sum(self.A**2))
energy = hterm1 + xterm2 + sterm
gparams = T.grad(
energy,
self.params,
consider_constant=[s1_samps, s2_samps, h1_samps, h2_samps])
# constructs the update dictionary
for gparam, param, rel_lr in zip(gparams, self.params,
self.rel_lrates):
#gnat=T.dot(param, T.dot(param.T,param))
updates[param] = T.cast(param + gparam * lrate * rel_lr, 'float32')
#make sure W has unit-length columns
#new_W=updates[self.W]
#updates[self.W]=T.cast(new_W/T.sqrt(T.sum(new_W**2,axis=0)),'float32')
#MIGHT NEED TO NORMALIZE A
return energy, updates
def get_ESS(self):
return 1.0 / T.sum(self.weights_now**2)
def resample_step(self):
idx = self.theano_rng.multinomial(
pvals=T.reshape(self.weights_now, (1, self.npcl))).T
s_samp = T.sum(self.s_now * T.addbroadcast(idx, 1), axis=0)
h_samp = T.sum(self.h_now * T.addbroadcast(idx, 1), axis=0)
return T.cast(s_samp, 'float32'), T.cast(h_samp, 'float32')
def resample(self):
[s_samps, h_samps], updates = theano.scan(fn=self.resample_step,
outputs_info=[None, None],
n_steps=self.npcl)
updates[self.s_now] = T.cast(s_samps, 'float32')
updates[self.h_now] = T.cast(h_samps, 'float32')
updates[self.weights_now] = T.cast(
T.ones_like(self.weights_now) / T.cast(self.npcl, 'float32'),
'float32') #dtype paranoia
return updates
def simulate_step(self, s):
s = T.reshape(s, (1, self.ns))
#get h probabilities
h_probs = self.calc_h_probs(s)
#h_samp=self.theano_rng.multinomial(pvals=T.reshape(h_probs,(self.nh,1)))
h_samp = self.theano_rng.multinomial(pvals=h_probs)
sp = self.get_prediction(s, h_samp)
xp = T.dot(self.W, sp.T) + T.reshape(self.c, (self.nx, 1))
return T.cast(sp, 'float32'), T.cast(xp, 'float32'), h_samp
def simulate_forward(self, n_steps):
s0 = T.sum(self.s_now * T.reshape(self.weights_now, (self.npcl, 1)),
axis=0)
s0 = T.reshape(s0, (1, self.ns))
[sp, xp, hs], updates = theano.scan(fn=self.simulate_step,
outputs_info=[s0, None, None],
n_steps=n_steps)
return sp, xp, hs, updates
class EncoderDecoder(object):
def __init__(self, rng, **kwargs):
self.n_in_src = kwargs.pop('nembed_src')
self.n_in_trg = kwargs.pop('nembed_trg')
self.n_hids_src = kwargs.pop('nhids_src')
self.n_hids_trg = kwargs.pop('nhids_trg')
self.src_vocab_size = kwargs.pop('src_vocab_size')
self.trg_vocab_size = kwargs.pop('trg_vocab_size')
self.method = kwargs.pop('method')
self.dropout = kwargs.pop('dropout')
self.maxout_part = kwargs.pop('maxout_part')
self.path = kwargs.pop('saveto')
self.clip_c = kwargs.pop('clip_c')
self.rng = rng
self.trng = RandomStreams(rng.randint(1e5))
# added by Zhaopeng Tu, 2016-06-09
self.with_attention = kwargs.pop('with_attention')
# added by Zhaopeng Tu, 2016-04-29
self.with_coverage = kwargs.pop('with_coverage')
self.coverage_dim = kwargs.pop('coverage_dim')
self.coverage_type = kwargs.pop('coverage_type')
self.max_fertility = kwargs.pop('max_fertility')
if self.coverage_type is 'linguistic':
# make sure the dimension of linguistic coverage is always 1
self.coverage_dim = 1
# added by Zhaopeng Tu, 2016-05-30
self.with_context_gate = kwargs.pop('with_context_gate')
self.params = []
self.layers = []
self.table_src = LookupTable(self.rng,
self.src_vocab_size,
self.n_in_src,
name='table_src')
self.layers.append(self.table_src)
self.encoder = BidirectionalEncoder(self.rng,
self.n_in_src,
self.n_hids_src,
self.table_src,
name='birnn_encoder')
self.layers.append(self.encoder)
# added by Longyue
self.encoder_hist_1 = Encoder(self.rng,
self.n_in_src,
self.n_hids_src,
self.table_src,
name='rnn_encoder_hist_1')
self.layers.append(self.encoder_hist_1)
self.encoder_hist_2 = Encoder(self.rng,
self.n_hids_src,
self.n_hids_src,
self.table_src,
name='rnn_encoder_hist_2')
self.layers.append(self.encoder_hist_2)
self.table_trg = LookupTable(self.rng,
self.trg_vocab_size,
self.n_in_trg,
name='table_trg')
self.layers.append(self.table_trg)
self.decoder = Decoder(self.rng, self.n_in_trg, self.n_hids_trg, 2*self.n_hids_src, self.n_hids_src, \
# added by Zhaopeng Tu, 2016-06-09
with_attention=self.with_attention, \
# added by Zhaopeng Tu, 2016-04-29
with_coverage=self.with_coverage, coverage_dim=self.coverage_dim, coverage_type=self.coverage_type, max_fertility=self.max_fertility, \
# added by Zhaopeng Tu, 2016-05-30
with_context_gate=self.with_context_gate, \
maxout_part=self.maxout_part, name='rnn_decoder')
self.layers.append(self.decoder)
self.logistic_layer = LogisticRegression(self.rng, self.n_in_trg,
self.trg_vocab_size)
self.layers.append(self.logistic_layer)
# added by Zhaopeng Tu, 2016-07-12
# for reconstruction
self.with_reconstruction = kwargs.pop('with_reconstruction')
if self.with_reconstruction:
# added by Zhaopeng Tu, 2016-07-27
self.reconstruction_weight = kwargs.pop('reconstruction_weight')
# note the source and target sides are reversed
self.inverse_decoder = InverseDecoder(self.rng, self.n_in_src, 2*self.n_hids_src, self.n_hids_trg, \
# added by Zhaopeng Tu, 2016-06-09
with_attention=self.with_attention, \
maxout_part=self.maxout_part, name='rnn_inverse_decoder')
self.layers.append(self.inverse_decoder)
self.srng = RandomStreams(rng.randint(1e5))
self.inverse_logistic_layer = LogisticRegression(
self.rng,
self.n_in_src,
self.src_vocab_size,
name='inverse_LR')
self.layers.append(self.inverse_logistic_layer)
for layer in self.layers:
self.params.extend(layer.params)
def build_trainer(self, src, src_mask, src_hist, src_hist_mask, trg,
trg_mask, ite):
# added by Longyue
# checked by Zhaopeng: sentence dim = n_steps, hist_len, batch_size (4, 3, 25)
# hist = (bath_size, sent_num, sent_len) --.T-->
# hist = (sent_len, sent_num, bath_size) --lookup table-->
# (sent_len, sent_num, bath_size, word_emb) --reshape-->
# (sent_len, sent_num*bath_size, word_emb) --word-level rnn-->
# (sent_len, sent_num*bath_size, hidden_size) --reshape-->
# (sent_len, sent_num, bath_size, hidden_size) --[-1]-->
# (sent_num, bath_size, hidden_size) --sent-level rnn-->
# (sent_num, bath_size, hidden_size) --[-1]-->
# (bath_size, hidden_size) = cross-sent context vector
annotations_1 = self.encoder_hist_1.apply_1(src_hist, src_hist_mask)
annotations_1 = annotations_1[-1] # get last hidden states
annotations_2 = self.encoder_hist_2.apply_2(annotations_1)
annotations_3 = annotations_2[-1] # get last hidden states
#modified by Longyue
annotations = self.encoder.apply(src, src_mask, annotations_3)
# init_context = annotations[0, :, -self.n_hids_src:]
# modification #1
# mean pooling
init_context = (annotations *
src_mask[:, :, None]).sum(0) / src_mask.sum(0)[:, None]
#added by Longyue
init_context = concatenate([init_context, annotations_3],
axis=annotations_3.ndim - 1)
trg_emb = self.table_trg.apply(trg)
trg_emb_shifted = T.zeros_like(trg_emb)
trg_emb_shifted = T.set_subtensor(trg_emb_shifted[1:], trg_emb[:-1])
# modified by Longyue
hiddens, readout, alignment = self.decoder.run_pipeline(
state_below=trg_emb_shifted,
mask_below=trg_mask,
init_context=init_context,
c=annotations,
c_mask=src_mask,
hist=annotations_3)
# apply dropout
if self.dropout < 1.0:
logger.info('Apply dropout with p = {}'.format(self.dropout))
readout = Dropout(self.trng, readout, 1, self.dropout)
p_y_given_x = self.logistic_layer.get_probs(readout)
self.cost = self.logistic_layer.cost(p_y_given_x, trg,
trg_mask) / trg.shape[1]
# self.cost = theano.printing.Print('likilihood cost:')(self.cost)
# added by Zhaopeng Tu, 2016-07-12
# for reconstruction
if self.with_reconstruction:
# now hiddens is the annotations
inverse_init_context = (hiddens * trg_mask[:, :, None]
).sum(0) / trg_mask.sum(0)[:, None]
src_emb = self.table_src.apply(src)
src_emb_shifted = T.zeros_like(src_emb)
src_emb_shifted = T.set_subtensor(src_emb_shifted[1:],
src_emb[:-1])
inverse_hiddens, inverse_readout, inverse_alignment = self.inverse_decoder.run_pipeline(
state_below=src_emb_shifted,
mask_below=src_mask,
init_context=inverse_init_context,
c=hiddens,
c_mask=trg_mask)
# apply dropout
if self.dropout < 1.0:
# logger.info('Apply dropout with p = {}'.format(self.dropout))
inverse_readout = Dropout(self.srng, inverse_readout, 1,
self.dropout)
p_x_given_y = self.inverse_logistic_layer.get_probs(
inverse_readout)
self.reconstruction_cost = self.inverse_logistic_layer.cost(
p_x_given_y, src, src_mask) / src.shape[1]
# self.reconstruction_cost = theano.printing.Print('reconstructed cost:')(self.reconstruction_cost)
self.cost += self.reconstruction_cost * self.reconstruction_weight
self.L1 = sum(T.sum(abs(param)) for param in self.params)
self.L2 = sum(T.sum(param**2) for param in self.params)
params_regular = self.L1 * 1e-6 + self.L2 * 1e-6
# params_regular = theano.printing.Print('params_regular:')(params_regular)
# train cost
train_cost = self.cost + params_regular
# gradients
grads = T.grad(train_cost, self.params)
# apply gradient clipping here
grads = grad_clip(grads, self.clip_c)
# updates
updates = adadelta(self.params, grads)
# train function
# modified by Longyue
inps = [src, src_mask, src_hist, src_hist_mask, trg, trg_mask]
self.train_fn = theano.function(inps, [train_cost],
updates=updates,
name='train_function')
# self.train_fn = theano.function(inps, [train_cost], updates=updates, name='train_function', mode=NanGuardMode(nan_is_error=True, inf_is_error=True, big_is_error=True))
def build_sampler(self):
# added by Longyue
x_hist = T.ltensor3()
x_hist_mask = T.tensor3()
annotations_1 = self.encoder_hist_1.apply_1(x_hist, x_hist_mask)
annotations_1 = annotations_1[-1]
annotations_2 = self.encoder_hist_2.apply_2(annotations_1)
annotations_3 = annotations_2[-1]
x = T.lmatrix()
# Build Networks
# src_mask is None
c = self.encoder.apply(x, None, annotations_3)
#init_context = ctx[0, :, -self.n_hids_src:]
# mean pooling
init_context = c.mean(0)
# added by Longyue
init_context = concatenate([init_context, annotations_3],
axis=annotations_3.ndim - 1)
init_state = self.decoder.create_init_state(init_context)
outs = [init_state, c, annotations_3]
if not self.with_attention:
outs.append(init_context)
# compile function
print 'Building compile_init_state_and_context function ...'
self.compile_init_and_context = theano.function(
[x, x_hist, x_hist_mask], outs, name='compile_init_and_context')
print 'Done'
y = T.lvector()
cur_state = T.matrix()
# if it is the first word, emb should be all zero, and it is indicated by -1
trg_emb = T.switch(y[:, None] < 0, T.alloc(0., 1, self.n_in_trg),
self.table_trg.apply(y))
# added by Zhaopeng Tu, 2016-06-09
# for with_attention=False
if self.with_attention and self.with_coverage:
cov_before = T.tensor3()
if self.coverage_type is 'linguistic':
print 'Building compile_fertility ...'
fertility = self.decoder._get_fertility(c)
fertility = T.addbroadcast(fertility, 1)
self.compile_fertility = theano.function(
[c], [fertility], name='compile_fertility')
print 'Done'
else:
fertility = None
else:
cov_before = None
fertility = None
# apply one step
# modified by Zhaopeng Tu, 2016-04-29
# [next_state, ctxs] = self.decoder.apply(state_below=trg_emb,
results = self.decoder.apply(
state_below=trg_emb,
init_state=cur_state,
# added by Zhaopeng Tu, 2016-06-09
init_context=None if self.with_attention else init_context,
c=c if self.with_attention else None,
hist=annotations_3, # added by Longyue
one_step=True,
# added by Zhaopeng Tu, 2016-04-27
cov_before=cov_before,
fertility=fertility)
next_state = results[0]
if self.with_attention:
ctxs, alignment = results[1], results[2]
if self.with_coverage:
cov = results[3]
else:
# if with_attention=False, we always use init_context as the source representation
ctxs = init_context
readout = self.decoder.readout(next_state, ctxs, trg_emb)
# maxout
if self.maxout_part > 1:
readout = self.decoder.one_step_maxout(readout)
# apply dropout
if self.dropout < 1.0:
readout = Dropout(self.trng, readout, 0, self.dropout)
# compute the softmax probability
next_probs = self.logistic_layer.get_probs(readout)
# sample from softmax distribution to get the sample
next_sample = self.trng.multinomial(pvals=next_probs).argmax(1)
# compile function
print 'Building compile_next_state_and_probs function ...'
inps = [y, cur_state]
if self.with_attention:
inps.append(c)
else:
inps.append(init_context)
# added by Longyue
inps.append(annotations_3)
outs = [next_probs, next_state, next_sample]
# added by Zhaopeng Tu, 2016-06-09
if self.with_attention:
outs.append(alignment)
# added by Zhaopeng Tu, 2016-04-29
if self.with_coverage:
inps.append(cov_before)
if self.coverage_type is 'linguistic':
inps.append(fertility)
outs.append(cov)
self.compile_next_state_and_probs = theano.function(
inps, outs, name='compile_next_state_and_probs')
print 'Done'
# added by Zhaopeng Tu, 2016-07-18
# for reconstruction
if self.with_reconstruction:
# Build Networks
# trg_mask is None
inverse_c = T.tensor3()
# mean pooling
inverse_init_context = inverse_c.mean(0)
inverse_init_state = self.inverse_decoder.create_init_state(
inverse_init_context)
outs = [inverse_init_state]
if not self.with_attention:
outs.append(inverse_init_context)
# compile function
print 'Building compile_inverse_init_state_and_context function ...'
self.compile_inverse_init_and_context = theano.function(
[inverse_c], outs, name='compile_inverse_init_and_context')
print 'Done'
src = T.lvector()
inverse_cur_state = T.matrix()
trg_mask = T.matrix()
# if it is the first word, emb should be all zero, and it is indicated by -1
src_emb = T.switch(src[:, None] < 0, T.alloc(0., 1, self.n_in_src),
self.table_src.apply(src))
# apply one step
# modified by Zhaopeng Tu, 2016-04-29
inverse_results = self.inverse_decoder.apply(
state_below=src_emb,
init_state=inverse_cur_state,
# added by Zhaopeng Tu, 2016-06-09
init_context=None
if self.with_attention else inverse_init_context,
c=inverse_c if self.with_attention else None,
c_mask=trg_mask,
one_step=True)
inverse_next_state = inverse_results[0]
if self.with_attention:
inverse_ctxs, inverse_alignment = inverse_results[
1], inverse_results[2]
else:
# if with_attention=False, we always use init_context as the source representation
inverse_ctxs = init_context
inverse_readout = self.inverse_decoder.readout(
inverse_next_state, inverse_ctxs, src_emb)
# maxout
if self.maxout_part > 1:
inverse_readout = self.inverse_decoder.one_step_maxout(
inverse_readout)
# apply dropout
if self.dropout < 1.0:
inverse_readout = Dropout(self.srng, inverse_readout, 0,
self.dropout)
# compute the softmax probability
inverse_next_probs = self.inverse_logistic_layer.get_probs(
inverse_readout)
# sample from softmax distribution to get the sample
inverse_next_sample = self.srng.multinomial(
pvals=inverse_next_probs).argmax(1)
# compile function
print 'Building compile_inverse_next_state_and_probs function ...'
inps = [src, trg_mask, inverse_cur_state]
if self.with_attention:
inps.append(inverse_c)
else:
inps.append(inverse_init_context)
outs = [
inverse_next_probs, inverse_next_state, inverse_next_sample
]
# added by Zhaopeng Tu, 2016-06-09
if self.with_attention:
outs.append(inverse_alignment)
self.compile_inverse_next_state_and_probs = theano.function(
inps, outs, name='compile_inverse_next_state_and_probs')
print 'Done'
def save(self, path=None):
if path is None:
path = self.path
filenpz = open(path, "w")
val = dict([(value.name, value.get_value())
for index, value in enumerate(self.params)])
logger.info("save the model {}".format(path))
numpy.savez(path, **val)
filenpz.close()
def load(self, path=None):
if path is None:
path = self.path
if os.path.isfile(path):
logger.info("load params {}".format(path))
val = numpy.load(path)
for index, param in enumerate(self.params):
logger.info('Loading {} with shape {}'.format(
param.name,
param.get_value(borrow=True).shape))
if param.name not in val.keys():
logger.info('Adding new param {} with shape {}'.format(
param.name,
param.get_value(borrow=True).shape))
continue
if param.get_value().shape != val[param.name].shape:
logger.info("Error: model param != load param shape {} != {}".format(\
param.get_value().shape, val[param.name].shape))
raise Exception("loading params shape mismatch")
else:
param.set_value(val[param.name], borrow=True)
else:
logger.error("file {} does not exist".format(path))
self.save()
class RNNUnidirectionalEncDec(Model):
def __init__(self, hyperparams, encoder_vocab, decoder_vocab):
self.hyperparams = hyperparams
self.encoder_vocab = encoder_vocab
self.decoder_vocab = decoder_vocab
# hyperparams.encoder_vocab_size and hyperparams.decoder_vocab_size setting to max
# TODO: Uncomment this and throw error
#hyperparams.encoder_vocab_size = min(hyperparams.encoder_vocab_size, encoder_vocab.vocab_size)
#hyperparams.decoder_vocab_size = min(hyperparams.decoder_vocab_size, decoder_vocab.vocab_size)
# Preparing and Initializing Network Weights & Biases
self.setup()
# TODO: Loading and storing params
def setup(self):
"""
Setup the shared variables and model components
"""
self._params = OrderedDict()
# Encoder embeddings
self.encoder_embeddings = Embeddings(
'encoder_emb', self.hyperparams.encoder_vocab.vocab_size,
self.hyperparams.encoder_emb_dim)
self._params.update(self.encoder_embeddings.params())
# Decoder embeddings
self.decoder_embeddings = Embeddings(
'decoder_emb',
self.hyperparams.decoder_vocab.vocab_size,
self.hyperparams.decoder_emb_dim,
add_bos=True)
self._params.update(self.decoder_embeddings.params())
################
# Encoder Layer
################
# TODO: make a different class
if self.hyperparams.rnn_cell == 'gru':
from ..nn.layers.gru import GRU as RNN
elif self.hyperparams.rnn_cell == 'lstm':
raise NotImplementedError
else:
logger.error("Invalid RNN Cell Type:" + self.hyperparams.rnn_cell)
self.encoder_rnn_layer_l2r = RNN(
name='encoder' + self.hyperparams.rnn_cell + '0_l2r',
in_dim=self.hyperparams.encoder_emb_dim,
num_units=self.hyperparams.encoder_units)
self._params.update(self.encoder_rnn_layer_l2r.params())
# Transform to prepare init state of decoder
self.decoder_init_transform = Dense(
name='decoder_init_transform',
in_dim=self.hyperparams.encoder_units,
num_units=self.hyperparams.decoder_units,
activation=Activation.tanh)
self._params.update(self.decoder_init_transform.params())
################
# Decoder Layer
###############
# TODO: make a different class
if self.hyperparams.rnn_cell == 'gru':
from ..nn.layers.gru import ConditionalGRU as ConditionalRNN
elif self.hyperparams.rnn_cell == 'lstm':
raise NotImplementedError
else:
logger.error("Invalid RNN Cell Type:" + self.hyperparams.rnn_cell)
self.decoder_rnn_layer = ConditionalRNN(
name='decoder_' + self.hyperparams.rnn_cell + '0',
in_dim=self.hyperparams.decoder_emb_dim,
num_units=self.hyperparams.decoder_units,
context_dim=self.hyperparams.encoder_units)
self._params.update(self.decoder_rnn_layer.params())
# Read out words
self.decoder_state_transform = Dense(
name='decoder_state_transform',
in_dim=self.hyperparams.decoder_units,
num_units=self.hyperparams.decoder_emb_dim,
activation=Activation.linear)
self._params.update(self.decoder_state_transform.params())
self.prev_emb_transform = Dense(
name='prev_emb_transform',
in_dim=self.hyperparams.decoder_emb_dim,
num_units=self.hyperparams.decoder_emb_dim,
activation=Activation.linear)
self._params.update(self.prev_emb_transform.params())
self.encoder_context_transform = Dense(
name='encoder_context_transform',
in_dim=self.hyperparams.encoder_units,
num_units=self.hyperparams.decoder_emb_dim,
activation=Activation.linear)
self._params.update(self.encoder_context_transform.params())
self.word_probs_transform = Dense(
name='word_probs_transform',
in_dim=self.hyperparams.decoder_emb_dim,
num_units=self.decoder_vocab.vocab_size,
activation=Activation.linear)
self._params.update(self.word_probs_transform.params())
# DEBUG
#for k, v in self._params.iteritems():
# print k, v.get_value(), v.get_value().shape
def build(self):
self.trng = RandomStreams(1234)
# dim(x) = (input_time_steps, num_samples)
self.x = T.matrix('x', dtype='int64')
# dim(x_mask) = (input_time_steps, num_samples)
self.x_mask = T.matrix('x_mask', dtype='float32')
# dim(y) = (output_time_steps, num_samples)
self.y = T.matrix('y', dtype='int64')
# dim(y_mask) = (output_time_steps, num_samples)
self.y_mask = T.matrix('y_mask', dtype='float32')
# get source word embeddings
enc_emb = self.encoder_embeddings.Emb[self.x.flatten()]
# dim(x) = timesteps x samples
enc_emb = enc_emb.reshape([
self.x.shape[0], self.x.shape[1], self.hyperparams.encoder_emb_dim
])
# get decoder init state
self.encoder_outputs = self.encoder_rnn_layer_l2r.build(
enc_emb, self.x_mask)[0]
last_encoder_output = self.encoder_outputs[
-1] # This will be the context at every input step
# transform encoder output to get decoder init
self.decoder_init = self.decoder_init_transform.build(
last_encoder_output)
# input
# TODO: remove embedding shifting?
dec_emb = self.decoder_embeddings.Emb[self.y.flatten()]
dec_emb = dec_emb.reshape([
self.y.shape[0], self.y.shape[1], self.hyperparams.decoder_emb_dim
])
# Building the RNN layer
self.decoder_outputs = self.decoder_rnn_layer.build(
x=dec_emb,
x_mask=self.y_mask,
c=last_encoder_output,
h_init=self.decoder_init)[
0] # Only one output, hidden states at every time step
# context with new axis. condition the output on encoder context as well.
# TODO: remove axis adding?
context = last_encoder_output[None, :, :]
# dim(proj_h) = #timesteps x #samples x #num_units
# Computing word probabilities
logit_decoder_rnn = self.decoder_state_transform.build(
self.decoder_outputs
) # dim(logit_rnn) = #timesteps x #samples x #emb_dim
logit_prev_emb = self.prev_emb_transform.build(
dec_emb) # dim(logit_prev) = #timesteps x #samples x #emb_dim
logit_enc_context = self.encoder_context_transform.build(context)
logit = self.word_probs_transform.build(
Activation.tanh(logit_decoder_rnn + logit_prev_emb +
logit_enc_context)
) # dim(logit) = #timesteps x #samples x #vocab_size
# reshaping logit as (#timesteps*#samples) x vocab_size and performing softmax across vocabulary
self.probs = T.nnet.softmax(
logit.reshape([
logit.shape[0] * logit.shape[1], logit.shape[2]
])) #dim(probs) = (#timesteps*#samples) x vocab_size
self.debug = [self.probs.shape, self.y.shape, self.y_mask.shape]
#Building loss function
self.build_loss()
self._outputs = [self.probs]
def build_loss(self):
# TODO: Make it better?
# y[0] is bos, remove it to calculate loss
y_flat = self.y[1:].flatten(
) #x_flat: a linear array with size #timesteps*#samples
y_flat_idx = T.arange(
y_flat.shape[0]) * self.decoder_vocab.vocab_size + y_flat
self._loss = -T.log(self.probs.flatten()[y_flat_idx])
self._loss = self._loss.reshape([self.y.shape[0] - 1, self.y.shape[1]])
self._loss = (self._loss * self.y_mask[1:]).sum(0)
def build_sampler(self, sampling=True):
initializer_input = [self.x, self.x_mask]
initializer_output = [self.encoder_outputs, self.decoder_init]
self.initializer = theano.function(initializer_input,
initializer_output)
sampler_input = [self.y, self.y_mask] + initializer_output
if sampling == True:
# sample a word from the output softmax, instead of selecting argmax
next_token_index = self.trng.multinomial(pvals=self.probs).argmax(
1
) # multinomial will represent 1 hot representation of the selected sample
else:
next_token_index = T.argmax(self.probs, axis=1)
sampler_output = [self.probs, next_token_index, self.decoder_outputs]
self.sampler = theano.function(sampler_input, sampler_output)
def sample(self, batch, num_samples=5):
source, source_mask, target, target_mask = self.prepare_train_input(
batch)
num_samples = np.minimum(1, source.shape[1])
# TODO: replace by random sampling:
source = source[:, 0:num_samples]
source_mask = source_mask[:, 0:num_samples]
target = target[:, 0:num_samples]
target_mask = target_mask[:, 0:num_samples]
samples = []
for sample_index in xrange(num_samples):
hypothesis = self.encode_decode([
source[:, sample_index:sample_index + 1],
source_mask[:, sample_index:sample_index + 1]
])
hypothesis_sent = ' '.join(hypothesis)
source_sent = ' '.join([
self.encoder_vocab.get_token(index)
for index in source[:, sample_index] if
index != self.encoder_vocab.get_index(self.encoder_vocab.eos)
])
#hypothesis_sent = ' '.join([self.decoder_vocab.get_token(index) for index in target[:, sample_index] if index != self.decoder_vocab.get_index(self.decoder_vocab.eos)])
target_sent = ' '.join([
self.decoder_vocab.get_token(index)
for index in target[:, sample_index] if index != -1 and
index != self.decoder_vocab.get_index(self.decoder_vocab.eos)
])
# TODO: change sample to dictionary, and in trainer display all keys and values
samples.append(
OrderedDict({
'SRC': source_sent,
'HYP': hypothesis_sent,
'REF': target_sent
}))
return samples
def encode_decode(self, test_input, max_length=50):
encoding = self.initializer(*test_input)
init_input = [np.array([[-1]]),
np.array([[1.]], dtype='float32')] + encoding
probs, next_token_index, decoder_outputs = self.sampler(*init_input)
hypothesis = []
hyp_length = 0
while (next_token_index[0] != self.decoder_vocab.get_index(
self.decoder_vocab.eos)):
hypothesis.append(self.decoder_vocab.get_token(
next_token_index[0]))
hyp_length += 1
# This is if next_token_index is a scalar, i.e. when only one column is passed as test_input
# [None] adds a new axis
next_input = [
next_token_index[None],
np.array([[1.]], dtype='float32')
] + encoding
probs, next_token_index, decoder_outputs = self.sampler(
*next_input)
if hyp_length == max_length:
break
return hypothesis
def loss(self):
return self._loss.mean()
def log_probs(self):
# TODO: Make it better?
return self._loss
def outputs(self):
return self._outputs
def inputs(self):
return [self.x, self.x_mask, self.y, self.y_mask]
def params(self):
return self._params
def prepare_train_input(self, batch, max_length=None):
# setting maxlen to length of longest sample
max_length_input = max([len(sample[0]) for sample in batch])
max_length_target = max([len(sample[1]) for sample in batch])
# adding end of sentence marker
inp = [[self.encoder_vocab.get_index(token) for token in sample[0]] +
[self.encoder_vocab.get_index(self.encoder_vocab.eos)]
for sample in batch]
target = [[self.decoder_vocab.get_index(token)
for token in sample[1]] +
[self.decoder_vocab.get_index(self.decoder_vocab.eos)]
for sample in batch]
max_length_input += 1
max_length_target += 1
# preparing mask and input
source_mask = np.array(
[[1.] * len(inp_instance[:max_length_input]) + [0.] *
(max_length_input - len(inp_instance)) for inp_instance in inp],
dtype='float32').transpose()
source = np.array([
inp_instance[:max_length_input] + [0.] *
(max_length_input - len(inp_instance)) for inp_instance in inp
],
dtype='int64').transpose()
# taret preparation with -1 (beginning of sentence) row upfront
target_mask = np.array(
[[1.] + [1.] * len(target_instance[:max_length_target]) + [0.] *
(max_length_target - len(target_instance))
for target_instance in target],
dtype='float32').transpose()
target = np.array([[-1] + target_instance[:max_length_target] + [0.] *
(max_length_target - len(target_instance))
for target_instance in target],
dtype='int64').transpose()
return source, source_mask, target, target_mask
def stochastic_max_pool_bc01(bc01,
pool_shape,
pool_stride,
image_shape,
rng=None):
"""
.. todo::
WRITEME properly
Stochastic max pooling for training as defined in:
Stochastic Pooling for Regularization of Deep Convolutional Neural Networks
Matthew D. Zeiler, Rob Fergus
bc01: minibatch in format (batch size, channels, rows, cols),
IMPORTANT: All values should be poitivie
pool_shape: shape of the pool region (rows, cols)
pool_stride: strides between pooling regions (row stride, col stride)
image_shape: avoid doing some of the arithmetic in theano
rng: theano random stream
"""
r, c = image_shape
pr, pc = pool_shape
rs, cs = pool_stride
batch = bc01.shape[0]
channel = bc01.shape[1]
if rng is None:
rng = RandomStreams(2022)
# Compute index in pooled space of last needed pool
# (needed = each input pixel must appear in at least one pool)
def last_pool(im_shp, p_shp, p_strd):
rval = int(numpy.ceil(float(im_shp - p_shp) / p_strd))
assert p_strd * rval + p_shp >= im_shp
assert p_strd * (rval - 1) + p_shp < im_shp
return rval
# Compute starting row of the last pool
last_pool_r = last_pool(image_shape[0], pool_shape[0],
pool_stride[0]) * pool_stride[0]
# Compute number of rows needed in image for all indexes to work out
required_r = last_pool_r + pr
last_pool_c = last_pool(image_shape[1], pool_shape[1],
pool_stride[1]) * pool_stride[1]
required_c = last_pool_c + pc
# final result shape
res_r = int(numpy.floor(last_pool_r / rs)) + 1
res_c = int(numpy.floor(last_pool_c / cs)) + 1
for bc01v in get_debug_values(bc01):
assert not numpy.any(numpy.isinf(bc01v))
assert bc01v.shape[2] == image_shape[0]
assert bc01v.shape[3] == image_shape[1]
# padding
padded = tensor.alloc(0.0, batch, channel, required_r, required_c)
name = bc01.name
if name is None:
name = 'anon_bc01'
bc01 = tensor.set_subtensor(padded[:, :, 0:r, 0:c], bc01)
bc01.name = 'zero_padded_' + name
# unraveling
window = tensor.alloc(0.0, batch, channel, res_r, res_c, pr, pc)
window.name = 'unravlled_winodows_' + name
for row_within_pool in xrange(pool_shape[0]):
row_stop = last_pool_r + row_within_pool + 1
for col_within_pool in xrange(pool_shape[1]):
col_stop = last_pool_c + col_within_pool + 1
win_cell = bc01[:, :, row_within_pool:row_stop:rs,
col_within_pool:col_stop:cs]
window = tensor.set_subtensor(
window[:, :, :, :, row_within_pool, col_within_pool], win_cell)
# find the norm
norm = window.sum(axis=[4, 5])
norm = tensor.switch(tensor.eq(norm, 0.0), 1.0, norm)
norm = window / norm.dimshuffle(0, 1, 2, 3, 'x', 'x')
# get prob
prob = rng.multinomial(pvals=norm.reshape(
(batch * channel * res_r * res_c, pr * pc)),
dtype='float32')
# select
res = (window * prob.reshape(
(batch, channel, res_r, res_c, pr, pc))).max(axis=5).max(axis=4)
res.name = 'pooled_' + name
return tensor.cast(res, theano.config.floatX)
),
dtype = theano.config.floatX
),
name = 'w_01',
borrow = True,
)
b_01 = theano.shared(
value = np.zeros((4,), dtype=theano.config.floatX),
name = 'b_01',
borrow = True,
)
h_0 = T.tanh(T.dot(x, w_00) + b_00)
y_0 = T.dot(h_0, w_01) + b_01
distn_0 = T.nnet.softmax(y_0[:-1])
s_1 = T.nnet.sigmoid(y_0[-1:])
cate_chosen_0 = T.flatten(srng.multinomial(n = 1, pvals = distn_0))
ind_chosen_0 = T.argmax(cate_chosen_0)
w_10 = theano.shared(
value = np.asarray(
rng.uniform(
low = -np.sqrt(6. / 8),
high = np.sqrt(6. / 8),
size = (4, 4)
),
dtype = theano.config.floatX
),
name = 'w_10',
borrow = True,
)
b_10 = theano.shared(
class ImportanceSampler():
'''Implements importance sampling/resampling'''
def __init__(self, ndims, n_particles, true_log_probs, proposal_func=None):
'''
true_log_probs: a function that returns the true relative log probabilities
proposal_func: a function that returns (samples, relative_log_probabilities)
n_particles: the number of particles to use
'''
self.true_log_probs=true_log_probs
self.proposal_func=proposal_func
self.n_particles=n_particles
self.ndims=ndims
init_particles=np.zeros((n_particles, self.ndims))
init_weights=np.ones(n_particles)/float(n_particles)
self.particles=theano.shared(init_particles.astype(np.float32))
self.weights=theano.shared(init_weights.astype(np.float32))
self.theano_rng=RandomStreams()
self.get_ESS=None
self.perform_resampling=None
self.perform_sampling=None
def set_proposal_func(self, proposal_func):
'''You might need to use this if you want to make the proposal
function depend on the current particles'''
self.proposal_func=proposal_func
return
def sample_reweight(self):
'''Samples new particles and reweights them'''
samples, prop_log_probs = self.proposal_func()
true_log_probs=self.true_log_probs(samples)
diffs=true_log_probs-prop_log_probs
weights_unnorm=T.exp(diffs)
weights=weights_unnorm/T.sum(weights_unnorm)
updates=OrderedDict()
updates[self.weights]=T.cast(weights,'float32')
updates[self.particles]=T.cast(samples,'float32')
return updates
def compute_ESS(self):
'''Returns the effective sample size'''
return 1.0/T.sum(self.weights**2)
def resample(self):
'''Resamples using the current weights'''
samps=self.theano_rng.multinomial(pvals=T.extra_ops.repeat(self.weights.dimshuffle('x',0),self.n_particles,axis=0))
idxs=T.cast(T.dot(samps, T.arange(self.n_particles)),'int64')
updates=OrderedDict()
updates[self.particles]=self.particles[idxs]
updates[self.weights]=T.cast(T.ones_like(self.weights)/float(self.n_particles),'float32')
return updates
def compile(self):
'''Compiles the ESS, resampling, and sampling functions'''
ess=self.compute_ESS()
self.get_ESS=theano.function([],ess)
resample_updates=self.resample()
self.perform_resampling=theano.function([],updates=resample_updates)
sample_updates=self.sample_reweight()
self.perform_sampling=theano.function([],updates=sample_updates)
return
