def backward_V_step(rewards,
is_end,
next_Vpred,
is_tmax,
next_Vref,
*args #you won't dare delete me
):
"""scan inner computation step, going backwards in time
params:
rewards, is_alive, next_Vpred, time_i - sequences
next_Vref - recurrent state value for next turn
returns:
current_Vref - recurrent state value at this turn
current_Vref is computed thus:
Once every n_steps or at session end:
current_Vref = r + gamma*next_Vpred #computation through next predicted state value
Otherwise:
current_Vref = r + gamma*next_Vref #recurrent computation through next Qvalue
"""
propagated_Vref = rewards + gamma_or_gammas * next_Vref # propagates value from actual next action
optimal_Vref = rewards + gamma_or_gammas * next_Vpred # uses agent's prediction for next state
# pick new_Vref if is_Tmax, else propagate existing one
chosen_Vref = T.switch(is_tmax, optimal_Vref, propagated_Vref)
# zero out references if session has ended already
this_Vref = T.switch(is_end, rewards,chosen_Vref)
return this_Vref
def get_gradients(self, model, data, ** kwargs):
cost = self.expr(model=model, data=data, **kwargs)
params = list(model.get_params())
grads = T.grad(cost, params, disconnected_inputs='ignore')
gradients = OrderedDict(izip(params, grads))
if self.gradient_clipping:
norm_gs = 0.
for grad in gradients.values():
norm_gs += (grad ** 2).sum()
not_finite = T.or_(T.isnan(norm_gs), T.isinf(norm_gs))
norm_gs = T.sqrt(norm_gs)
norm_gs = T.switch(T.ge(norm_gs, self.max_magnitude),
self.max_magnitude / norm_gs,
1.)
for param, grad in gradients.items():
gradients[param] = T.switch(not_finite,
.1 * param,
grad * norm_gs)
updates = OrderedDict()
return gradients, updates
def pass_edges(input_idx_t, edge_t, edge_mask_t, counter_t, h_tm1, c_tm1, x):
h_t = h_tm1
c_t = c_tm1
# select the input vector to use for this edge (source)
x_t_i = x[input_idx_t, :]
# zero out the input unless this is a leaf node
x_t_0 = T.switch(T.eq(T.sum(edge_mask_t), 0), x_t_i, x_t_i*0)
# concatenate with the input edge vector
x_t_edge = T.concatenate([x_t_0, edge_t])
# compute attention weights, using a manual softmax
attention_scores = T.dot(self.v_a, T.tanh(T.dot(self.W_h_a, h_tm1))) # (1, n_edges)
# find the max of the unmasked values
max_score = T.max(attention_scores + edge_mask_t * 10000.0) - 10000.0
# exponentiate the differences, masking first to avoid inf, and then to keep only relevant scores
exp_scores = T.exp((attention_scores - max_score) * edge_mask_t) * edge_mask_t
# take the sum, and add one if the mask is all zeros to avoid an inf
exp_scores_sum = T.sum(exp_scores) + T.switch(T.eq(T.sum(edge_mask_t), 0), 1.0, 0.0)
# normalize to compute the weights
weighted_mask = exp_scores / exp_scores_sum
i_t = T.nnet.sigmoid(T.dot(x_t_edge, self.W_x_i) + T.sum(T.dot(self.W_h_i.T, (weighted_mask * h_tm1)).T, axis=0) + self.b_h_i)
f_t = T.nnet.sigmoid(T.dot(x_t_edge, self.W_x_f) + T.sum(T.dot(self.W_h_f.T, (weighted_mask * h_tm1)).T, axis=0) + self.b_h_f)
o_t = T.nnet.sigmoid(T.dot(x_t_edge, self.W_x_o) + T.sum(T.dot(self.W_h_o.T, (weighted_mask * h_tm1)).T, axis=0) + self.b_h_o)
u_t = T.tanh(T.dot(x_t_edge, self.W_x_u) + T.sum(T.dot(self.W_h_u.T, (weighted_mask * h_tm1)).T, axis=0) + self.b_h_u)
c_temp = i_t * u_t + f_t * T.sum((weighted_mask * c_tm1).T, axis=0)
h_temp = o_t * T.tanh(c_temp)
h_t = T.set_subtensor(h_t[:, counter_t], h_temp)
c_t = T.set_subtensor(c_t[:, counter_t], c_temp)
return h_t, c_t
def mindist(translate, min_so_far, ro, rd):
# ro: 3
# transalate: nbatch * 3
# min_so_far: nbatch * width * height
# rd: width * height * 3
ro = ro + translate
# d_o = T.dot(rd, ro) # 640, 480
# d_o = dotty(rd, ro, axis=1)
d_o = T.tensordot(rd, ro, axes=[2,1])
o_o = T.sum(ro**2,axis=1)
b = 2*d_o
c = o_o - 0.001 #FIXME, remove this squaring
inner = b **2 - 4 * c # 640 480
does_not_intersect = inner < 0.0
minus_b = -b
# sqrt_inner = T.sqrt(T.maximum(0.0001, inner))
eps = 1e-9
background_dist = 10.0
sqrt_inner = T.sqrt(T.maximum(eps, inner))
root1 = (minus_b - sqrt_inner)/2.0
root2 = (minus_b + sqrt_inner)/2.0
depth = T.switch(does_not_intersect, background_dist,
T.switch(root1 > 0, root1,
T.switch(root2 > 0, root2, background_dist)))
return T.min([min_so_far, depth], axis=0)
def backward_V_step(rewards,is_alive,next_Vpred,time_i,
next_Vref,
*args):
propagated_Vref = T.switch(is_alive,
rewards + gamma_or_gammas * next_Vref, #assumes optimal next action
0.
)
if n_steps is None:
this_Vref = propagated_Vref
else:
Vref_at_tmax = T.switch(is_alive,
rewards + gamma_or_gammas *next_Vpred,
0.
)
this_Vref = T.switch(T.eq(time_i % n_steps,0), #if Tmax
Vref_at_tmax, #use special case values
propagated_Vref #else use generic ones
)
return this_Vref
def __init__(self, random_state=None, low=0.0, high=1.0):
super(Uniform, self).__init__(low=low, high=high,
random_state=random_state,
optimizer=None)
# pdf
self.pdf_ = T.switch(
T.or_(T.lt(self.X, self.low), T.ge(self.X, self.high)),
0.,
1. / (self.high - self.low)).ravel()
self.make_(self.pdf_, "pdf")
# -log pdf
self.nnlf_ = T.switch(
T.or_(T.lt(self.X, self.low), T.ge(self.X, self.high)),
np.inf,
T.log(self.high - self.low)).ravel()
self.make_(self.nnlf_, "nnlf")
# cdf
self.cdf_ = T.switch(
T.lt(self.X, self.low),
0.,
T.switch(
T.lt(self.X, self.high),
(self.X - self.low) / (self.high - self.low),
1.)).ravel()
self.make_(self.cdf_, "cdf")
# ppf
self.ppf_ = self.p * (self.high - self.low) + self.low
self.make_(self.ppf_, "ppf", args=[self.p])
def s_logprior(self, s_params, strength=10.0):
# -- I don't know what distribution this would be
# but I think it makes a nice shape
s_alpha, s_cond_x, s_cond_y = self.unpack(s_params)
n_alpha_min = self._alpha_from_l(self._lenscales_min)
n_alpha_max = self._alpha_from_l(self._lenscales_max)
#return strength * (alpha - alpha_min) ** 2
log0 = -10000
width = n_alpha_max - n_alpha_min
#alpha_mean = 0.5 * (alpha_max + alpha_min)
energy = strength * 0.5 * (s_alpha - n_alpha_max) ** 2 / width ** 2
lenscale_logprior = TT.switch(s_alpha < n_alpha_min,
log0,
TT.switch(s_alpha < n_alpha_max,
-energy,
log0)).sum()
if self._conditional:
diff_x = s_cond_x
diff_y = s_cond_y - 1
rval = (lenscale_logprior
+ TT.dot(diff_x, diff_x)
+ TT.dot(diff_y, diff_y))
else:
rval = lenscale_logprior
assert rval.ndim == 0
return rval
def T_subspacel1_slow_shrinkage(a,L,lam_sparse,lam_slow,small_value=.001):
amp = T.sqrt(a[::2,:]**2 + a[1::2,:]**2 + small_value)
#damp = amp[:,1:] - amp[:,:-1]
# compose slow shrinkage with subspace l1 shrinkage
# slow shrinkage
div = T.zeros_like(amp)
d1 = amp[:,1:] - amp[:,:-1]
d2 = d1[:,1:] - d1[:,:-1]
div = T.set_subtensor(div[:,1:-1],-d2)
div = T.set_subtensor(div[:,0], -d1[:,0])
div = T.set_subtensor(div[:,-1], d1[:,-1])
slow_amp_shrinkage = 1 - (lam_slow/L)*(div/amp)
slow_amp_value = T.switch(T.gt(slow_amp_shrinkage,0),slow_amp_shrinkage,0)
slow_shrinkage_prox_a = slow_amp_value*a[::2,:]
slow_shrinkage_prox_b = slow_amp_value*a[1::2,:]
# subspace l1 shrinkage
amp_slow_shrinkage_prox = T.sqrt(slow_shrinkage_prox_a**2 + slow_shrinkage_prox_b**2)
#amp_shrinkage = 1. - (lam_slow*lam_sparse/L)*amp_slow_shrinkage_prox
amp_shrinkage = 1. - (lam_sparse/L)/amp_slow_shrinkage_prox
amp_value = T.switch(T.gt(amp_shrinkage,0.),amp_shrinkage,0.)
subspacel1_prox = T.zeros_like(a)
subspacel1_prox = T.set_subtensor(subspacel1_prox[ ::2,:],amp_value*slow_shrinkage_prox_a)
subspacel1_prox = T.set_subtensor(subspacel1_prox[1::2,:],amp_value*slow_shrinkage_prox_b)
return subspacel1_prox
def create_cost_fun (self): # create a cost function that # takes each prediction at every timestep # and guesses next timestep's value: what_to_predict = self.input_mat[:, 1:] # because some sentences are shorter, we # place masks where the sentences end: # (for how long is zero indexed, e.g. an example going from `[2,3)`) # has this value set 0 (here we substract by 1): for_how_long = self.for_how_long - 1 # all sentences start at T=0: starting_when = T.zeros_like(self.for_how_long) self.lstm_cost = masked_loss(self.lstm_predictions, what_to_predict, for_how_long, starting_when).sum() zero_entropy = T.zeros_like(self.entropy) real_entropy = T.switch(self.mask_matrix,self.entropy,zero_entropy) zero_key_entropy = T.zeros_like(self.key_entropy) real_key_entropy = T.switch(self.mask_matrix,self.key_entropy,zero_key_entropy) self.final_cost = masked_loss(self.final_predictions, what_to_predict, for_how_long, starting_when).sum()+self.entropy_reg*real_entropy.sum()+self.key_entropy_reg*real_key_entropy.sum()
def train_fprop(self, X, wts=None, bs=None):
''' Performs forward propagation with for training, which could be different from
the vanilla frprop we would use for testing, due to extra bells and whistles such as
dropout, corruption, etc'''
if wts is None and bs is None:
wts = self.wts_
bs = self.bs_
if 'dropout' in self.loss_terms:
input_p = self.loss_params['input_p']
hidden_p = self.loss_params['hidden_p']
# compute the first activation separately in case we have no hidden
# layer;
act = self.activs[0](
T.dot(self.dropout(X, input_p), wts[0]) + bs[0])
if len(wts) > 1: # len(wts) = 1 corresponds to softmax regression
for i, (w, b, activ) in enumerate(zip(wts[1:], bs[1:], self.activs[1:])):
act = activ(T.dot(self.dropout(act, hidden_p), w) + b)
eps = 1e-6
act = T.switch(act < eps, eps, act)
act = T.switch(act > (1. - eps), (1. - eps), act)
return act
else:
return self.fprop(X, wts, bs)
def T_subspacel1_slow_shrinkage_conv(a, L, lam_sparse, lam_slow, imshp,kshp,featshp,stride=(1,1),small_value=.001):
featshp = (imshp[0],kshp[0],featshp[2],featshp[3]) # num images, features, szy, szx
features = T.reshape(T.transpose(a),featshp,ndim=4)
amp = T.sqrt(features[:,::2,:,:]**2 + features[:,1::2,:,:]**2 + small_value)
#damp = amp[:,1:] - amp[:,:-1]
# compose slow shrinkage with subspace l1 shrinkage
# slow shrinkage
div = T.zeros_like(amp)
d1 = amp[1:,:,:,:] - amp[:-1,:,:,:]
d2 = d1[1:,:,:,:] - d1[:-1,:,:,:]
div = T.set_subtensor(div[1:-1,:,:,:], -d2)
div = T.set_subtensor(div[0,:,:,:], -d1[0,:,:,:])
div = T.set_subtensor(div[-1,:,:,:], d1[-1,:,:,:])
slow_amp_shrinkage = 1 - (lam_slow / L) * (div / amp)
slow_amp_value = T.switch(T.gt(slow_amp_shrinkage, 0), slow_amp_shrinkage, 0)
slow_shrinkage_prox_a = slow_amp_value * features[:, ::2, :,:]
slow_shrinkage_prox_b = slow_amp_value * features[:,1::2, :,:]
# subspace l1 shrinkage
amp_slow_shrinkage_prox = T.sqrt(slow_shrinkage_prox_a ** 2 + slow_shrinkage_prox_b ** 2)
#amp_shrinkage = 1. - (lam_slow*lam_sparse/L)*amp_slow_shrinkage_prox
amp_shrinkage = 1. - (lam_sparse / L) / amp_slow_shrinkage_prox
amp_value = T.switch(T.gt(amp_shrinkage, 0.), amp_shrinkage, 0.)
subspacel1_prox = T.zeros_like(features)
subspacel1_prox = T.set_subtensor(subspacel1_prox[:, ::2, :,:], amp_value * slow_shrinkage_prox_a)
subspacel1_prox = T.set_subtensor(subspacel1_prox[:,1::2, :,:], amp_value * slow_shrinkage_prox_b)
reshape_subspacel1_prox = T.transpose(T.reshape(subspacel1_prox,(featshp[0],featshp[1]*featshp[2]*featshp[3]),ndim=2))
return reshape_subspacel1_prox
def castray(ro, rd, shape_params, nprims, width, height):
tmin = 1.0
tmax = 20.0
precis = 0.002
m = -1.0
# There are a sequence of distances, d1, d2, ..., dn
# then theres the accumulated distances d1, d1+d2, d1+d2+d3....
# What we actually want in the output is the sfor each ray the distance to the surface
# So we want something like 0, 20, 25, 27, 28, 28, 28, 28, 28
# OK
max_num_steps = 25
# distcolors = map(ro + rd * 0, width, height) #FIXME, reshape instead of mul by 0
distcolors = mapedit(ro + rd * 0, shape_params, nprims, width, height)
dists = distcolors
steps = T.switch(dists < precis, T.zeros_like(dists), T.ones_like(dists))
accum_dists = T.reshape(dists, (width, height, 1))
for i in range(max_num_steps - 1):
# distcolors = map(ro + rd * accum_dists, width, height) #FIXME, reshape instead of mul by 0
distcolors = mapedit(ro + rd * accum_dists, shape_params, nprims, width, height) #FIXME, reshape instead of mul by 0
dists = distcolors
steps = steps + T.switch(dists < precis, T.zeros_like(dists), T.ones_like(dists))
accum_dists = accum_dists + T.reshape(dists, (width, height, 1))
last_depth = T.reshape(accum_dists, (width, height))
depthmap = T.switch(last_depth < tmax, last_depth / tmax, T.zeros_like(last_depth))
color = 1.0 - steps / float(max_num_steps)
# Distance marched along ray and delta between last two steps
return depthmap
def mcmc(ll, *frvs):
full_observations = dict(observations)
full_observations.update(dict([(rv, s) for rv, s in zip(free_RVs, frvs)]))
loglik = -full_log_likelihood(full_observations)
proposals = free_RVs_prop
H = tensor.add(*[tensor.sum(tensor.sqr(p)) for p in proposals])/2. + loglik
# -- this should be an inner loop
g = []
g.append(tensor.grad(loglik, frvs))
proposals = [(p - epsilon*gg[0]/2.) for p, gg in zip(proposals, g)]
rvsp = [(rvs + epsilon*rvp) for rvs,rvp in zip(frvs, proposals)]
full_observations = dict(observations)
full_observations.update(dict([(rv, s) for rv, s in zip(free_RVs, rvsp)]))
new_loglik = -full_log_likelihood(full_observations)
gnew = []
gnew.append(tensor.grad(new_loglik, rvsp))
proposals = [(p - epsilon*gn[0]/2.) for p, gn in zip(proposals, gnew)]
# --
Hnew = tensor.add(*[tensor.sum(tensor.sqr(p)) for p in proposals])/2. + new_loglik
dH = Hnew - H
accept = tensor.or_(dH < 0., U < tensor.exp(-dH))
return [tensor.switch(accept, -new_loglik, ll)] + \
[tensor.switch(accept, p, f) for p, f in zip(rvsp, frvs)], \
{}, theano.scan_module.until(accept)
def convert_method(self, method_string):
if method_string == 'sigmoid':
return Tensor.nnet.sigmoid
elif method_string == 'tanh':
return Tensor.tanh
elif method_string == 'scaled_tanh':
return lambda x: 1.7159 * Tensor.tanh(0.66 * x)
elif method_string == 'soft_sigmoid':
return soft_sigmoid
elif method_string == 'relu':
return lambda x: x * (x > 0)
elif method_string == 'relu2':
return lambda x: Tensor.switch(Tensor.lt(x, -1), -1, x) * Tensor.switch(Tensor.gt(x, 1), 1, x) / x
elif method_string == 'leakyrelu':
return lambda x: x * (x > 0) + 0.01 * x * (x < 0)
elif method_string == 'shiftedrelu':
return lambda x: x * (x > -1)
elif method_string == 'hard_sigmoid':
return Tensor.nnet.hard_sigmoid
elif method_string == 'none':
return lambda x: x
else:
raise Exception('method unknown')
def out_shape(imgshape, ds, ignore_border=False):
"""Return the shape of the output from this op, for input of given shape and flags.
:param imgshape: the shape of a tensor of images. The last two elements are interpreted
as the number of rows, and the number of cols.
:type imgshape: tuple, list, or similar of integer or
scalar Theano variable.
:param ds: downsample factor over rows and columns
:type ds: list or tuple of two ints
:param ignore_border: if ds doesn't divide imgshape, do we include an extra row/col of
partial downsampling (False) or ignore it (True).
:type ignore_border: bool
:rtype: list
:returns: the shape of the output from this op, for input of given shape. This will
have the same length as imgshape, but with last two elements reduced as per the
downsampling & ignore_border flags.
"""
if len(imgshape) < 2:
raise TypeError("imgshape must have at least two elements (rows, cols)")
r, c = imgshape[-2:]
rval = list(imgshape[:-2]) + [r // ds[0], c // ds[1]]
if not ignore_border:
if isinstance(r, theano.Variable):
rval[-2] = tensor.switch(r % ds[0], rval[-2] + 1, rval[-2])
elif r % ds[0]:
rval[-2] += 1
if isinstance(c, theano.Variable):
rval[-1] = tensor.switch(c % ds[1], rval[-1] + 1, rval[-1])
elif c % ds[1]:
rval[-1] += 1
return rval
def multiple_switch(*args):
"""
.. todo::
WRITEME properly
Applies a cascade of ifelse. The output will be a Theano expression
which evaluates:
.. code-block:: none
if args0:
then arg1
elif arg2:
then arg3
elif arg4:
then arg5
....
"""
if len(args) == 3:
return T.switch(*args)
else:
return T.switch(args[0],
args[1],
multiple_switch(*args[2:]))
def theano_sentence_prediction(self, Sentence, Chars, WordLengths):
input_lstm_res_f = self.input_lstm_forward_layer.function(Sentence, Chars, WordLengths)
input_lstm_res_b = self.input_lstm_backward_layer.function(Sentence, Chars, WordLengths)
input_combined = T.concatenate((input_lstm_res_f, input_lstm_res_b), axis=1)
#Make pairwise features. This is really just "tensor product with concatenation instead of multiplication". Is there a command for that?
full_matrix, _ = theano.scan(fn=self.__pairwise_features,
outputs_info=None,
sequences=input_combined,
non_sequences=[input_combined, Sentence.shape[0]])
if len(self.lstm_layers) > 0 and self.lstm_layers[0].training:
srng = RandomStreams(seed=12345)
full_matrix = T.switch(srng.binomial(size=(Sentence.shape[0], Sentence.shape[0]+1, self.hidden_dimension*4), p=0.5), full_matrix, 0)
else:
full_matrix = 0.5 * full_matrix
full_matrix = self.transition_layer.function(full_matrix)
for layer in self.lstm_layers:
if layer.training:
print("hah-train")
full_matrix = T.switch(srng.binomial(size=(Sentence.shape[0], Sentence.shape[0]+1, self.hidden_dimension*4), p=0.5), full_matrix, 0)
else:
print("heh-notrain")
full_matrix = 0.5 * full_matrix
full_matrix = layer.function(full_matrix)
final_matrix = self.output_convolution.function(full_matrix)
return T.nnet.softmax(final_matrix)
def hmc_updates(positions, stepsize, avg_acceptance_rate, final_pos, accept,
target_acceptance_rate, stepsize_inc, stepsize_dec,
stepsize_min, stepsize_max, avg_acceptance_slowness):
# broadcast `accept` scalar to tensor with the same dimensions as final_pos.
accept_matrix = accept.dimshuffle(0, *(('x',) * (final_pos.ndim - 1)))
# if accept is True, update to `final_pos` else stay put
new_positions = TT.switch(accept_matrix, final_pos, positions)
## STEPSIZE UPDATES ##
# if acceptance rate is too low, our sampler is too "noisy" and we reduce
# the stepsize. If it is too high, our sampler is too conservative, we can
# get away with a larger stepsize (resulting in better mixing).
_new_stepsize = TT.switch(avg_acceptance_rate > target_acceptance_rate,
stepsize * stepsize_inc, stepsize * stepsize_dec)
# maintain stepsize in [stepsize_min, stepsize_max]
new_stepsize = TT.clip(_new_stepsize, stepsize_min, stepsize_max)
# perform exponential moving average
mean_dtype = theano.scalar.upcast(accept.dtype, avg_acceptance_rate.dtype)
new_acceptance_rate = TT.add(
avg_acceptance_slowness * avg_acceptance_rate,
(1.0 - avg_acceptance_slowness) * accept.mean(dtype=mean_dtype))
return [(positions, new_positions), (stepsize, new_stepsize), (avg_acceptance_rate, new_acceptance_rate)]
def step(self, inputs, inputs_mask, h_tm1, c_tm1, h_mask, *non_sequences):
if self.gate.use_attention:
# attended is the
# src_sequence_length x batch_size x attention_dims
# matrix which we have attention on.
#
# attended_dot_u is the h_t-independent part of the final
# attention vectors, which is precomputed for efficiency.
#
# attention_mask is a binary mask over the valid elements of
# attended, which in practice is the same as the mask passed to
# the encoder that created attended. Size
# src_sequence_length x batch_size
h_t, c_t, attention = self.gate(
inputs, h_tm1 * h_mask.astype(theano.config.floatX), c_tm1,
attended=non_sequences[0],
attended_dot_u=non_sequences[1],
attention_mask=non_sequences[2])
return (T.switch(inputs_mask.dimshuffle(0, 'x'), h_t, h_tm1),
T.switch(inputs_mask.dimshuffle(0, 'x'), c_t, c_tm1),
attention)
else:
h_t, c_t = self.gate(
inputs, h_tm1 * h_mask.astype(theano.config.floatX), c_tm1)
return (T.switch(inputs_mask.dimshuffle(0, 'x'), h_t, h_tm1),
T.switch(inputs_mask.dimshuffle(0, 'x'), c_t, c_tm1))
def cd_updates(self):
"""
Return a dictionary of shared variable updates that implements contrastive divergence
learning by stochastic gradient descent with an annealed learning rate.
"""
ups = {}
if self.persistent_chains:
grads = self.contrastive_grads()
ups.update(dict(self.sampler.updates()))
else:
cd1_sampler, final_p, cd1_updates = self.rbm.CD1_sampler(self.visible_batch,
self.batchsize)
self._last_cd1_sampler = cd1_sampler # hacked in here for the unit test
#ignore the cd1_sampler
grads = self.contrastive_grads(neg_v = final_p)
ups.update(dict(cd1_updates))
# contrastive divergence updates
# TODO: sgd_updates is a particular optization algo (others are possible)
# parametrize so that algo is plugin
# the normalization normVF might be sgd-specific though...
# TODO: when sgd has an annealing schedule, this should
# go through that mechanism.
lr = TT.clip(
self.learn_rate * TT.cast(self.lr_anneal_start / (self.iter+1), floatX),
0.0, #min
self.learn_rate) #max
ups.update(dict(sgd_updates(
self.rbm.params(),
grads,
stepsizes=[a*lr for a in self.learn_rate_multipliers])))
ups[self.iter] = self.iter + 1
# add trainer updates (replace CD update of U)
ups[self.rbm.U], ups[self.normVF] = self.normalize_U(ups[self.rbm.U])
#l1_updates:
if (self.l1_penalty_start > 0) and (self.l1_penalty != 0.0):
ups[self.effective_l1_penalty] = TT.switch(
self.iter >= self.l1_penalty_start,
self.l1_penalty,
0.0)
if getattr(self,'p_lr', None):
ups[self.p_lr] = TT.switch(self.iter > self.p_training_start,
self.p_training_lr,
0)
new_P = ups[self.rbm.P] * self.p_mask
no_pos_P = TT.switch(new_P<0, new_P, 0)
ups[self.rbm.P] = - no_pos_P / no_pos_P.sum(axis=0) #normalize to that columns sum 1
return ups
def __call__(self, input_):
m = input_.mean()
v = input_.std()
new_m = T.switch(T.eq(self.m, 0.),
m,
(np.float32(1.) - self.rate) * self.m + self.rate * m)
new_var = T.switch(T.eq(self.var, 0.),
v,
(np.float32(1.) - self.rate) * self.var + self.rate * v)
updates = [(self.m, new_m), (self.var, new_var)]
input_centered = (
(input_ - new_m) / T.maximum(1., T.sqrt(new_var)))
input_ = T.zeros_like(input_) + input_
outs = OrderedDict(
x=input_,
x_centered=input_centered,
m=new_m,
var=new_var
)
return outs, updates
def compute_updates(self, training_cost, params):
updates = []
grads = T.grad(training_cost, params)
grads = OrderedDict(zip(params, grads))
# Clip stuff
c = numpy.float32(self.cutoff)
clip_grads = []
norm_gs = T.sqrt(sum(T.sum(g ** 2) for p, g in grads.items()))
normalization = T.switch(T.ge(norm_gs, c), c / norm_gs, np.float32(1.))
notfinite = T.or_(T.isnan(norm_gs), T.isinf(norm_gs))
for p, g in grads.items():
clip_grads.append((p, T.switch(notfinite, numpy.float32(.1) * p, g * normalization)))
grads = OrderedDict(clip_grads)
if self.updater == 'adagrad':
updates = Adagrad(grads, self.lr)
elif self.updater == 'sgd':
raise Exception("Sgd not implemented!")
elif self.updater == 'adadelta':
updates = Adadelta(grads)
elif self.updater == 'rmsprop':
updates = RMSProp(grads, self.lr)
elif self.updater == 'adam':
updates = Adam(grads)
else:
raise Exception("Updater not understood!")
return updates
def _activation(self, Y, L, M, W):
"""Returns the activation for a given input.
Derived from the generative model formulation of hierarchical
Poisson mixtures, the formular for the activation in the network
reads as follows:
I_c =
\sum_d \log(W_{cd})y_d + \log(M_{lc}) for labeled data
\sum_d \log(W_{cd})y_d + \log(\sum_k M_{kc}) for unlabeled data
s_c = softmax(I_c)
"""
# first: complete inference to find label
# Input integration:
I = T.tensordot(Y,T.log(W),axes=[1,1])
# recurrent term:
vM = M[L]
L_index = T.eq(L,-1).nonzero()
vM = T.set_subtensor(vM[L_index], T.sum(M, axis=0))
# numeric trick to prevent overflow in the exp-function
max_exponent = 86. - T.ceil(T.log(I.shape[1].astype('float32')))
scale = T.switch(
T.gt(T.max(I, axis=1, keepdims=True), max_exponent),
T.max(I, axis=1, keepdims=True) - max_exponent,
0.)
# numeric approximation to prevent underflow in the exp-function:
# map too low values of I to a fixed minimum value
min_exponent = -87. + T.ceil(T.log(I.shape[1].astype('float32')))
I = T.switch(
T.lt(I-scale, min_exponent),
scale+min_exponent,
I)
# activation: recurrent softmax with overflow protection
s = vM*T.exp(I-scale)/T.sum(vM*T.exp(I-scale), axis=1, keepdims=True)
return s
def adamgc(cost, params, lr=0.0002, b1=0.1, b2=0.001, e=1e-8, max_magnitude=5.0, infDecay=0.1):
updates = []
grads = T.grad(cost, params)
norm = norm_gs(params, grads)
sqrtnorm = T.sqrt(norm)
not_finite = T.or_(T.isnan(sqrtnorm), T.isinf(sqrtnorm))
adj_norm_gs = T.switch(T.ge(sqrtnorm, max_magnitude), max_magnitude / sqrtnorm, 1.)
i = shared(floatX(0.))
i_t = i + 1.
fix1 = 1. - (1. - b1)**i_t
fix2 = 1. - (1. - b2)**i_t
lr_t = lr * (T.sqrt(fix2) / fix1)
for p, g in zip(params, grads):
g = T.switch(not_finite, infDecay * p, g * adj_norm_gs)
m = shared(p.get_value() * 0.)
v = shared(p.get_value() * 0.)
m_t = (b1 * g) + ((1. - b1) * m)
v_t = (b2 * T.sqr(g)) + ((1. - b2) * v)
g_t = m_t / (T.sqrt(v_t) + e)
p_t = p - (lr_t * g_t)
updates.append((m, m_t))
updates.append((v, v_t))
updates.append((p, p_t))
updates.append((i, i_t))
return updates, norm
def _get_targets(y, log_y_hat, y_mask, y_hat_mask):
'''
Returns the target values according to the CTC cost with respect to y_hat.
Note that this is part of the gradient with respect to the softmax output
and not with respect to the input of the original softmax function.
All computations are done in log scale
'''
num_classes = log_y_hat.shape[2] - 1
blanked_y, blanked_y_mask = _add_blanks(
y=y,
blank_symbol=num_classes,
y_mask=y_mask)
log_alpha, log_beta = _log_forward_backward(blanked_y,
log_y_hat, blanked_y_mask,
y_hat_mask, num_classes)
# explicitly not using a mask to prevent inf - inf
y_prob = _class_batch_to_labeling_batch(blanked_y, log_y_hat,
y_hat_mask=None)
marginals = log_alpha + log_beta - y_prob
max_marg = marginals.max(2)
max_marg = T.switch(T.le(max_marg, -np.inf), 0, max_marg)
log_Z = T.log(T.exp(marginals - max_marg[:,:, None]).sum(2))
log_Z = log_Z + max_marg
log_Z = T.switch(T.le(log_Z, -np.inf), 0, log_Z)
targets = _labeling_batch_to_class_batch(blanked_y,
T.exp(marginals -
log_Z[:,:, None]),
num_classes + 1)
return targets
def dlogp(inputs, gradients):
g_logp, = gradients
cov, delta = inputs
g_logp.tag.test_value = floatX(1.)
n, k = delta.shape
chol_cov = cholesky(cov)
diag = tt.nlinalg.diag(chol_cov)
ok = tt.all(diag > 0)
chol_cov = tt.switch(ok, chol_cov, tt.fill(chol_cov, 1))
delta_trans = solve_lower(chol_cov, delta.T).T
inner = n * tt.eye(k) - tt.dot(delta_trans.T, delta_trans)
g_cov = solve_upper(chol_cov.T, inner)
g_cov = solve_upper(chol_cov.T, g_cov.T)
tau_delta = solve_upper(chol_cov.T, delta_trans.T)
g_delta = tau_delta.T
g_cov = tt.switch(ok, g_cov, -np.nan)
g_delta = tt.switch(ok, g_delta, -np.nan)
return [-0.5 * g_cov * g_logp, -g_delta * g_logp]
def __init__(self, rng, f='ReLU', g=lambda x: x, params=None):
if f == 'ReLU':
if hasattr(T.nnet, 'relu'):
self.f = T.nnet.relu
else:
self.f = lambda x: T.switch(x<0,0,x)
self.g = lambda x: x
elif f == 'PReLU':
# Avoids dying ReLU units
if hasattr(T.nnet, 'relu'):
self.f = lambda x: T.nnet.relu(x, alpha=0.01)
else:
self.f = lambda x: T.switch(x<=0,a*x,x)
self.g = lambda x: x
elif f == 'tanh':
self.f = T.tanh
self.g = T.arctanh
elif f == 'sigmoid':
self.f = T.nnet.sigmoid
self.g = lambda x: x
elif f == 'softmax':
self.f = T.nnet.softmax
self.g = lambda x: x
elif f == 'softplus':
self.f = T.nnet.softplus
self.g = lambda x: x
elif f == 'identity':
self.f = lambda x: x
self.g = lambda x: x
else:
self.f = f
self.g = g
self.params = [] if params is None else params
def mixture_model(random_seed=1234):
"""Sample mixture model to use in benchmarks"""
np.random.seed(1234)
size = 1000
w_true = np.array([0.35, 0.4, 0.25])
mu_true = np.array([0., 2., 5.])
sigma = np.array([0.5, 0.5, 1.])
component = np.random.choice(mu_true.size, size=size, p=w_true)
x = np.random.normal(mu_true[component], sigma[component], size=size)
with pm.Model() as model:
w = pm.Dirichlet('w', a=np.ones_like(w_true))
mu = pm.Normal('mu', mu=0., sd=10., shape=w_true.shape)
enforce_order = pm.Potential('enforce_order', tt.switch(mu[0] - mu[1] <= 0, 0., -np.inf) +
tt.switch(mu[1] - mu[2] <= 0, 0., -np.inf))
tau = pm.Gamma('tau', alpha=1., beta=1., shape=w_true.shape)
pm.NormalMixture('x_obs', w=w, mu=mu, tau=tau, observed=x)
# Initialization can be poorly specified, this is a hack to make it work
start = {
'mu': mu_true.copy(),
'tau_log__': np.log(1. / sigma**2),
'w_stickbreaking__': np.array([-0.03, 0.44])
}
return model, start
def theano_digitize(x, bins):
"""
Equivalent to numpy digitize.
Parameters
----------
x : Theano tensor or array_like
The array or matrix to be digitized
bins : array_like
The bins with which x should be digitized
Returns
-------
A Theano tensor
The indices of the bins to which each value in input array belongs.
"""
binned = T.zeros_like(x) + len(bins)
for i in range(len(bins)):
bin=bins[i]
if i == 0:
binned=T.switch(T.lt(x,bin),i,binned)
else:
ineq = T.and_(T.ge(x,bins[i-1]),T.lt(x,bin))
binned=T.switch(ineq,i,binned)
binned=T.switch(T.isnan(x), len(bins), binned)
return binned
def tnormal_icdf(size, avg, std, lbound, ubound, theano_rng, dtype):
"""
Alternative Method:
sample = -Phi_inv(Phi(-lbound)*(1-u) + Phi(-ubound)*u)
"""
def Phi(x):
erfarg = (x - avg) / (std * SQRT2)
rval = 0.5 * (1. + T.erf(erfarg))
return rval.astype(dtype)
def Phi_inv(y, eps=3e-8):
""" eps was calibrated for cublas.erfinv using float32 """
temp = 2. * y - 1.
erfinv_input = T.clip(temp, -1+eps, 1-eps)
rval = avg + std * SQRT2 * T.erfinv(erfinv_input)
return rval.astype(dtype)
# center lower and upper bounds based on mean
u = theano_rng.uniform(size=size, dtype=dtype)
# Inverse CDF method. When method becomes numerically unstable, we simply
# return the bounds based on whether avg < lbound, or ubound < avg.
cdf_range = Phi(ubound) - Phi(lbound)
sample = T.switch(
T.or_(
T.lt(cdf_range, 3e-8),
T.gt(cdf_range, 1-3e-8)),
T.switch(
T.lt(avg, lbound),
lbound,
ubound),
Phi_inv(Phi(lbound) + u * cdf_range))
return sample
def __call__(self, x):
dropped_units = self.rng.binomial(
n=1,
p=self.dropout_rate,
size=x.shape if self.shape is None else self.shape)
return tt.switch(dropped_units, 0, x)
def build(self,
dropout,
char_dim,
char_lstm_dim,
char_bidirect,
word_dim,
word_lstm_dim,
word_bidirect,
lr_method,
pre_emb,
crf,
cap_dim,
training=True,
**kwargs):
"""
Build the network.
"""
# Training parameters
n_words = len(self.id_to_word)
n_chars = len(self.id_to_char)
n_tags = len(self.id_to_tag)
# Number of capitalization features
if cap_dim:
n_cap = 4
# Network variables
is_train = T.iscalar('is_train')
word_ids = T.ivector(name='word_ids')
char_for_ids = T.imatrix(name='char_for_ids')
char_rev_ids = T.imatrix(name='char_rev_ids')
char_pos_ids = T.ivector(name='char_pos_ids')
tag_ids = T.ivector(name='tag_ids')
if cap_dim:
cap_ids = T.ivector(name='cap_ids')
# Sentence length
s_len = (word_ids if word_dim else char_pos_ids).shape[0]
# Final input (all word features)
input_dim = 0
inputs = []
#
# Word inputs
#
if word_dim:
input_dim += word_dim
word_layer = EmbeddingLayer(n_words, word_dim, name='word_layer')
word_input = word_layer.link(word_ids)
inputs.append(word_input)
# Initialize with pretrained embeddings
if pre_emb and training:
new_weights = word_layer.embeddings.get_value()
print 'Loading pretrained embeddings from %s...' % pre_emb
pretrained = {}
emb_invalid = 0
for i, line in enumerate(codecs.open(pre_emb, 'r', 'utf-8')):
line = line.rstrip().split()
if len(line) == word_dim + 1:
pretrained[line[0]] = np.array(
[float(x) for x in line[1:]]).astype(np.float32)
else:
emb_invalid += 1
if emb_invalid > 0:
print 'WARNING: %i invalid lines' % emb_invalid
c_found = 0
c_lower = 0
c_zeros = 0
# Lookup table initialization
for i in xrange(n_words):
word = self.id_to_word[i]
if word in pretrained:
new_weights[i] = pretrained[word]
c_found += 1
elif word.lower() in pretrained:
new_weights[i] = pretrained[word.lower()]
c_lower += 1
elif re.sub('\d', '0', word.lower()) in pretrained:
new_weights[i] = pretrained[re.sub(
'\d', '0', word.lower())]
c_zeros += 1
word_layer.embeddings.set_value(new_weights)
print 'Loaded %i pretrained embeddings.' % len(pretrained)
print(
'%i / %i (%.4f%%) words have been initialized with '
'pretrained embeddings.') % (
c_found + c_lower + c_zeros, n_words, 100. *
(c_found + c_lower + c_zeros) / n_words)
print(
'%i found directly, %i after lowercasing, '
'%i after lowercasing + zero.') % (c_found, c_lower,
c_zeros)
#
# Chars inputs
#
if char_dim:
input_dim += char_lstm_dim
char_layer = EmbeddingLayer(n_chars, char_dim, name='char_layer')
char_lstm_for = LSTM(char_dim,
char_lstm_dim,
with_batch=True,
name='char_lstm_for')
char_lstm_rev = LSTM(char_dim,
char_lstm_dim,
with_batch=True,
name='char_lstm_rev')
char_lstm_for.link(char_layer.link(char_for_ids))
char_lstm_rev.link(char_layer.link(char_rev_ids))
char_for_output = char_lstm_for.h.dimshuffle(
(1, 0, 2))[T.arange(s_len), char_pos_ids]
char_rev_output = char_lstm_rev.h.dimshuffle(
(1, 0, 2))[T.arange(s_len), char_pos_ids]
inputs.append(char_for_output)
if char_bidirect:
inputs.append(char_rev_output)
input_dim += char_lstm_dim
#
# Capitalization feature
#
if cap_dim:
input_dim += cap_dim
cap_layer = EmbeddingLayer(n_cap, cap_dim, name='cap_layer')
inputs.append(cap_layer.link(cap_ids))
# Prepare final input
inputs = T.concatenate(inputs,
axis=1) if len(inputs) != 1 else inputs[0]
#
# Dropout on final input
#
if dropout:
dropout_layer = DropoutLayer(p=dropout)
input_train = dropout_layer.link(inputs)
input_test = (1 - dropout) * inputs
inputs = T.switch(T.neq(is_train, 0), input_train, input_test)
# LSTM for words
word_lstm_for = LSTM(input_dim,
word_lstm_dim,
with_batch=False,
name='word_lstm_for')
word_lstm_rev = LSTM(input_dim,
word_lstm_dim,
with_batch=False,
name='word_lstm_rev')
word_lstm_for.link(inputs)
word_lstm_rev.link(inputs[::-1, :])
word_for_output = word_lstm_for.h
word_rev_output = word_lstm_rev.h[::-1, :]
if word_bidirect:
final_output = T.concatenate([word_for_output, word_rev_output],
axis=1)
tanh_layer = HiddenLayer(2 * word_lstm_dim,
word_lstm_dim,
name='tanh_layer',
activation='tanh')
final_output = tanh_layer.link(final_output)
else:
final_output = word_for_output
# Sentence to Named Entity tags - Score
final_layer = HiddenLayer(word_lstm_dim,
n_tags,
name='final_layer',
activation=(None if crf else 'softmax'))
tags_scores = final_layer.link(final_output)
# No CRF
if not crf:
cost = T.nnet.categorical_crossentropy(tags_scores, tag_ids).mean()
# CRF
else:
transitions = shared((n_tags + 2, n_tags + 2), 'transitions')
small = -1000
b_s = np.array([[small] * n_tags + [0, small]]).astype(np.float32)
e_s = np.array([[small] * n_tags + [small, 0]]).astype(np.float32)
observations = T.concatenate(
[tags_scores, small * T.ones((s_len, 2))], axis=1)
observations = T.concatenate([b_s, observations, e_s], axis=0)
# Score from tags
real_path_score = tags_scores[T.arange(s_len), tag_ids].sum()
# Score from transitions
b_id = theano.shared(value=np.array([n_tags], dtype=np.int32))
e_id = theano.shared(value=np.array([n_tags + 1], dtype=np.int32))
padded_tags_ids = T.concatenate([b_id, tag_ids, e_id], axis=0)
real_path_score += transitions[padded_tags_ids[T.arange(s_len +
1)],
padded_tags_ids[T.arange(s_len + 1)
+ 1]].sum()
all_paths_scores = forward(observations, transitions)
cost = -(real_path_score - all_paths_scores)
# Network parameters
params = []
if word_dim:
self.add_component(word_layer)
params.extend(word_layer.params)
if char_dim:
self.add_component(char_layer)
self.add_component(char_lstm_for)
params.extend(char_layer.params)
params.extend(char_lstm_for.params)
if char_bidirect:
self.add_component(char_lstm_rev)
params.extend(char_lstm_rev.params)
self.add_component(word_lstm_for)
params.extend(word_lstm_for.params)
if word_bidirect:
self.add_component(word_lstm_rev)
params.extend(word_lstm_rev.params)
if cap_dim:
self.add_component(cap_layer)
params.extend(cap_layer.params)
self.add_component(final_layer)
params.extend(final_layer.params)
if crf:
self.add_component(transitions)
params.append(transitions)
if word_bidirect:
self.add_component(tanh_layer)
params.extend(tanh_layer.params)
# Prepare train and eval inputs
eval_inputs = []
if word_dim:
eval_inputs.append(word_ids)
if char_dim:
eval_inputs.append(char_for_ids)
if char_bidirect:
eval_inputs.append(char_rev_ids)
eval_inputs.append(char_pos_ids)
if cap_dim:
eval_inputs.append(cap_ids)
train_inputs = eval_inputs + [tag_ids]
# Parse optimization method parameters
if "-" in lr_method:
lr_method_name = lr_method[:lr_method.find('-')]
lr_method_parameters = {}
for x in lr_method[lr_method.find('-') + 1:].split('-'):
split = x.split('_')
assert len(split) == 2
lr_method_parameters[split[0]] = float(split[1])
else:
lr_method_name = lr_method
lr_method_parameters = {}
# Compile training function
print 'Compiling...'
if training:
updates = Optimization(clip=5.0).get_updates(
lr_method_name, cost, params, **lr_method_parameters)
f_train = theano.function(inputs=train_inputs,
outputs=cost,
updates=updates,
givens=({
is_train: np.cast['int32'](1)
} if dropout else {}))
else:
f_train = None
# Compile evaluation function
if not crf:
f_eval = theano.function(inputs=eval_inputs,
outputs=tags_scores,
givens=({
is_train: np.cast['int32'](0)
} if dropout else {}))
else:
f_eval = theano.function(inputs=eval_inputs,
outputs=forward(
observations,
transitions,
viterbi=True,
return_alpha=False,
return_best_sequence=True),
givens=({
is_train: np.cast['int32'](0)
} if dropout else {}))
return f_train, f_eval
def main(args):
trial = int(args['trial'])
pkl_name = 'vrnn_gmm_%d' % trial
channel_name = 'valid_nll_upper_bound'
data_path = args['data_path']
save_path = args['save_path']
monitoring_freq = int(args['monitoring_freq'])
force_saving_freq = int(args['force_saving_freq'])
reset_freq = int(args['reset_freq'])
epoch = int(args['epoch'])
batch_size = int(args['batch_size'])
m_batch_size = int(args['m_batch_size'])
x_dim = int(args['x_dim'])
z_dim = int(args['z_dim'])
rnn_dim = int(args['rnn_dim'])
k = int(args['num_k'])
lr = float(args['lr'])
debug = int(args['debug'])
print "trial no. %d" % trial
print "batch size %d" % batch_size
print "learning rate %f" % lr
print "saving pkl file '%s'" % pkl_name
print "to the save path '%s'" % save_path
q_z_dim = 500
p_z_dim = 500
p_x_dim = 500
x2s_dim = 500
z2s_dim = 500
target_dim = x_dim * k
file_name = 'blizzard_unseg_tbptt'
normal_params = np.load(data_path + file_name + '_normal.npz')
X_mean = normal_params['X_mean']
X_std = normal_params['X_std']
model = Model()
train_data = Blizzard_tbptt(name='train',
path=data_path,
frame_size=x_dim,
file_name=file_name,
X_mean=X_mean,
X_std=X_std)
valid_data = Blizzard_tbptt(name='valid',
path=data_path,
frame_size=x_dim,
file_name=file_name,
X_mean=X_mean,
X_std=X_std)
x = train_data.theano_vars()
m_x = valid_data.theano_vars()
if debug:
x.tag.test_value = np.zeros((15, batch_size, x_dim),
dtype=theano.config.floatX)
m_x.tag.test_value = np.zeros((15, m_batch_size, x_dim),
dtype=theano.config.floatX)
init_W = InitCell('rand')
init_U = InitCell('ortho')
init_b = InitCell('zeros')
init_b_sig = InitCell('const', mean=0.6)
x_1 = FullyConnectedLayer(name='x_1',
parent=['x_t'],
parent_dim=[x_dim],
nout=x2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
x_2 = FullyConnectedLayer(name='x_2',
parent=['x_1'],
parent_dim=[x2s_dim],
nout=x2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
x_3 = FullyConnectedLayer(name='x_3',
parent=['x_2'],
parent_dim=[x2s_dim],
nout=x2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
x_4 = FullyConnectedLayer(name='x_4',
parent=['x_3'],
parent_dim=[x2s_dim],
nout=x2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
z_1 = FullyConnectedLayer(name='z_1',
parent=['z_t'],
parent_dim=[z_dim],
nout=z2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
z_2 = FullyConnectedLayer(name='z_2',
parent=['z_1'],
parent_dim=[z2s_dim],
nout=z2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
z_3 = FullyConnectedLayer(name='z_3',
parent=['z_2'],
parent_dim=[z2s_dim],
nout=z2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
z_4 = FullyConnectedLayer(name='z_4',
parent=['z_3'],
parent_dim=[z2s_dim],
nout=z2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
rnn = LSTM(name='rnn',
parent=['x_4', 'z_4'],
parent_dim=[x2s_dim, z2s_dim],
nout=rnn_dim,
unit='tanh',
init_W=init_W,
init_U=init_U,
init_b=init_b)
phi_1 = FullyConnectedLayer(name='phi_1',
parent=['x_4', 's_tm1'],
parent_dim=[x2s_dim, rnn_dim],
nout=q_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
phi_2 = FullyConnectedLayer(name='phi_2',
parent=['phi_1'],
parent_dim=[q_z_dim],
nout=q_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
phi_3 = FullyConnectedLayer(name='phi_3',
parent=['phi_2'],
parent_dim=[q_z_dim],
nout=q_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
phi_4 = FullyConnectedLayer(name='phi_4',
parent=['phi_3'],
parent_dim=[q_z_dim],
nout=q_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
phi_mu = FullyConnectedLayer(name='phi_mu',
parent=['phi_4'],
parent_dim=[q_z_dim],
nout=z_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
phi_sig = FullyConnectedLayer(name='phi_sig',
parent=['phi_4'],
parent_dim=[q_z_dim],
nout=z_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
prior_1 = FullyConnectedLayer(name='prior_1',
parent=['s_tm1'],
parent_dim=[rnn_dim],
nout=p_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
prior_2 = FullyConnectedLayer(name='prior_2',
parent=['prior_1'],
parent_dim=[p_z_dim],
nout=p_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
prior_3 = FullyConnectedLayer(name='prior_3',
parent=['prior_2'],
parent_dim=[p_z_dim],
nout=p_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
prior_4 = FullyConnectedLayer(name='prior_4',
parent=['prior_3'],
parent_dim=[p_z_dim],
nout=p_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
prior_mu = FullyConnectedLayer(name='prior_mu',
parent=['prior_4'],
parent_dim=[p_z_dim],
nout=z_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
prior_sig = FullyConnectedLayer(name='prior_sig',
parent=['prior_4'],
parent_dim=[p_z_dim],
nout=z_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
theta_1 = FullyConnectedLayer(name='theta_1',
parent=['z_4', 's_tm1'],
parent_dim=[z2s_dim, rnn_dim],
nout=p_x_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
theta_2 = FullyConnectedLayer(name='theta_2',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=p_x_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
theta_3 = FullyConnectedLayer(name='theta_3',
parent=['theta_2'],
parent_dim=[p_x_dim],
nout=p_x_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
theta_4 = FullyConnectedLayer(name='theta_4',
parent=['theta_3'],
parent_dim=[p_x_dim],
nout=p_x_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
theta_mu = FullyConnectedLayer(name='theta_mu',
parent=['theta_4'],
parent_dim=[p_x_dim],
nout=target_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
theta_sig = FullyConnectedLayer(name='theta_sig',
parent=['theta_4'],
parent_dim=[p_x_dim],
nout=target_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
coeff = FullyConnectedLayer(name='coeff',
parent=['theta_4'],
parent_dim=[p_x_dim],
nout=k,
unit='softmax',
init_W=init_W,
init_b=init_b)
nodes = [
rnn, x_1, x_2, x_3, x_4, z_1, z_2, z_3, z_4, phi_1, phi_2, phi_3,
phi_4, phi_mu, phi_sig, prior_1, prior_2, prior_3, prior_4, prior_mu,
prior_sig, theta_1, theta_2, theta_3, theta_4, theta_mu, theta_sig,
coeff
]
params = OrderedDict()
for node in nodes:
if node.initialize() is not None:
params.update(node.initialize())
params = init_tparams(params)
step_count = sharedX(0, name='step_count')
last_rnn = np.zeros((batch_size, rnn_dim * 2), dtype=theano.config.floatX)
rnn_tm1 = sharedX(last_rnn, name='rnn_tm1')
shared_updates = OrderedDict()
shared_updates[step_count] = step_count + 1
s_0 = T.switch(T.eq(T.mod(step_count, reset_freq), 0),
rnn.get_init_state(batch_size), rnn_tm1)
x_shape = x.shape
x_in = x.reshape((x_shape[0] * x_shape[1], -1))
x_1_in = x_1.fprop([x_in], params)
x_2_in = x_2.fprop([x_1_in], params)
x_3_in = x_3.fprop([x_2_in], params)
x_4_in = x_4.fprop([x_3_in], params)
x_4_in = x_4_in.reshape((x_shape[0], x_shape[1], -1))
def inner_fn(x_t, s_tm1):
phi_1_t = phi_1.fprop([x_t, s_tm1], params)
phi_2_t = phi_2.fprop([phi_1_t], params)
phi_3_t = phi_3.fprop([phi_2_t], params)
phi_4_t = phi_4.fprop([phi_3_t], params)
phi_mu_t = phi_mu.fprop([phi_4_t], params)
phi_sig_t = phi_sig.fprop([phi_4_t], params)
prior_1_t = prior_1.fprop([s_tm1], params)
prior_2_t = prior_2.fprop([prior_1_t], params)
prior_3_t = prior_3.fprop([prior_2_t], params)
prior_4_t = prior_4.fprop([prior_3_t], params)
prior_mu_t = prior_mu.fprop([prior_4_t], params)
prior_sig_t = prior_sig.fprop([prior_4_t], params)
z_t = Gaussian_sample(phi_mu_t, phi_sig_t)
z_1_t = z_1.fprop([z_t], params)
z_2_t = z_2.fprop([z_1_t], params)
z_3_t = z_3.fprop([z_2_t], params)
z_4_t = z_4.fprop([z_3_t], params)
s_t = rnn.fprop([[x_t, z_4_t], [s_tm1]], params)
return s_t, phi_mu_t, phi_sig_t, prior_mu_t, prior_sig_t, z_4_t
((s_temp, phi_mu_temp, phi_sig_temp, prior_mu_temp, prior_sig_temp, z_4_temp), updates) =\
theano.scan(fn=inner_fn,
sequences=[x_4_in],
outputs_info=[s_0, None, None, None, None, None])
for k, v in updates.iteritems():
k.default_update = v
shared_updates[rnn_tm1] = s_temp[-1]
s_temp = concatenate([s_0[None, :, :], s_temp[:-1]], axis=0)
theta_1_temp = theta_1.fprop([z_4_temp, s_temp], params)
theta_2_temp = theta_2.fprop([theta_1_temp], params)
theta_3_temp = theta_3.fprop([theta_2_temp], params)
theta_4_temp = theta_4.fprop([theta_3_temp], params)
theta_mu_temp = theta_mu.fprop([theta_4_temp], params)
theta_sig_temp = theta_sig.fprop([theta_4_temp], params)
coeff_temp = coeff.fprop([theta_4_temp], params)
kl_temp = KLGaussianGaussian(phi_mu_temp, phi_sig_temp, prior_mu_temp,
prior_sig_temp)
x_shape = x.shape
x_in = x.reshape((x_shape[0] * x_shape[1], -1))
theta_mu_in = theta_mu_temp.reshape((x_shape[0] * x_shape[1], -1))
theta_sig_in = theta_sig_temp.reshape((x_shape[0] * x_shape[1], -1))
coeff_in = coeff_temp.reshape((x_shape[0] * x_shape[1], -1))
recon = GMM(x_in, theta_mu_in, theta_sig_in, coeff_in)
recon_term = recon.mean()
kl_term = kl_temp.mean()
nll_upper_bound = recon_term + kl_term
nll_upper_bound.name = 'nll_upper_bound'
m_x_1_temp = x_1.fprop([m_x], params)
m_x_2_temp = x_2.fprop([m_x_1_temp], params)
m_x_3_temp = x_3.fprop([m_x_2_temp], params)
m_x_4_temp = x_4.fprop([m_x_3_temp], params)
m_s_0 = rnn.get_init_state(m_batch_size)
((m_s_temp, m_phi_mu_temp, m_phi_sig_temp, m_prior_mu_temp, m_prior_sig_temp, m_z_4_temp), m_updates) =\
theano.scan(fn=inner_fn,
sequences=[m_x_4_temp],
outputs_info=[m_s_0, None, None, None, None, None])
for k, v in m_updates.iteritems():
k.default_update = v
m_s_temp = concatenate([m_s_0[None, :, :], m_s_temp[:-1]], axis=0)
m_theta_1_temp = theta_1.fprop([m_z_4_temp, m_s_temp], params)
m_theta_2_temp = theta_2.fprop([m_theta_1_temp], params)
m_theta_3_temp = theta_3.fprop([m_theta_2_temp], params)
m_theta_4_temp = theta_4.fprop([m_theta_3_temp], params)
m_theta_mu_temp = theta_mu.fprop([m_theta_4_temp], params)
m_theta_sig_temp = theta_sig.fprop([m_theta_4_temp], params)
m_coeff_temp = coeff.fprop([m_theta_4_temp], params)
m_kl_temp = KLGaussianGaussian(m_phi_mu_temp, m_phi_sig_temp,
m_prior_mu_temp, m_prior_sig_temp)
m_x_shape = m_x.shape
m_x_in = m_x.reshape((m_x_shape[0] * m_x_shape[1], -1))
m_theta_mu_in = m_theta_mu_temp.reshape((m_x_shape[0] * m_x_shape[1], -1))
m_theta_sig_in = m_theta_sig_temp.reshape(
(m_x_shape[0] * m_x_shape[1], -1))
m_coeff_in = m_coeff_temp.reshape((m_x_shape[0] * m_x_shape[1], -1))
m_recon = GMM(m_x_in, m_theta_mu_in, m_theta_sig_in, m_coeff_in)
m_recon_term = m_recon.mean()
m_kl_term = m_kl_temp.mean()
m_nll_upper_bound = m_recon_term + m_kl_term
m_nll_upper_bound.name = 'nll_upper_bound'
m_recon_term.name = 'recon_term'
m_kl_term.name = 'kl_term'
max_x = m_x.max()
mean_x = m_x.mean()
min_x = m_x.min()
max_x.name = 'max_x'
mean_x.name = 'mean_x'
min_x.name = 'min_x'
max_theta_mu = m_theta_mu_in.max()
mean_theta_mu = m_theta_mu_in.mean()
min_theta_mu = m_theta_mu_in.min()
max_theta_mu.name = 'max_theta_mu'
mean_theta_mu.name = 'mean_theta_mu'
min_theta_mu.name = 'min_theta_mu'
max_theta_sig = m_theta_sig_in.max()
mean_theta_sig = m_theta_sig_in.mean()
min_theta_sig = m_theta_sig_in.min()
max_theta_sig.name = 'max_theta_sig'
mean_theta_sig.name = 'mean_theta_sig'
min_theta_sig.name = 'min_theta_sig'
max_phi_sig = m_phi_sig_temp.max()
mean_phi_sig = m_phi_sig_temp.mean()
min_phi_sig = m_phi_sig_temp.min()
max_phi_sig.name = 'max_phi_sig'
mean_phi_sig.name = 'mean_phi_sig'
min_phi_sig.name = 'min_phi_sig'
max_prior_sig = m_prior_sig_temp.max()
mean_prior_sig = m_prior_sig_temp.mean()
min_prior_sig = m_prior_sig_temp.min()
max_prior_sig.name = 'max_prior_sig'
mean_prior_sig.name = 'mean_prior_sig'
min_prior_sig.name = 'min_prior_sig'
model.inputs = [x]
model.params = params
model.nodes = nodes
model.set_updates(shared_updates)
optimizer = Adam(lr=lr)
monitor_fn = theano.function(
inputs=[m_x],
outputs=[
m_nll_upper_bound, m_recon_term, m_kl_term, max_phi_sig,
mean_phi_sig, min_phi_sig, max_prior_sig, mean_prior_sig,
min_prior_sig, max_theta_sig, mean_theta_sig, min_theta_sig, max_x,
mean_x, min_x, max_theta_mu, mean_theta_mu, min_theta_mu
],
on_unused_input='ignore')
extension = [
GradientClipping(batch_size=batch_size, check_nan=1),
EpochCount(epoch),
Monitoring(freq=monitoring_freq,
monitor_fn=monitor_fn,
ddout=[
m_nll_upper_bound, m_recon_term, m_kl_term, max_phi_sig,
mean_phi_sig, min_phi_sig, max_prior_sig,
mean_prior_sig, min_prior_sig, max_theta_sig,
mean_theta_sig, min_theta_sig, max_x, mean_x, min_x,
max_theta_mu, mean_theta_mu, min_theta_mu
],
data=[
Iterator(train_data, m_batch_size, start=0, end=112640),
Iterator(valid_data,
m_batch_size,
start=2040064,
end=2152704)
]),
Picklize(freq=monitoring_freq,
force_save_freq=force_saving_freq,
path=save_path),
EarlyStopping(freq=monitoring_freq,
force_save_freq=force_saving_freq,
path=save_path,
channel=channel_name),
WeightNorm()
]
mainloop = Training(name=pkl_name,
data=Iterator(train_data,
batch_size,
start=0,
end=2040064),
model=model,
optimizer=optimizer,
cost=nll_upper_bound,
outputs=[nll_upper_bound],
extension=extension)
mainloop.run()
def clip_norm(g, c, n):
if c > 0:
g = T.switch(T.ge(n, c), g * c / n, g)
return g
def elu(z): # https://arxiv.org/pdf/1511.07289v1.pdf
return T.switch(T.ge(z, 0), z, T.exp(z) - 1)
def step_rnn(x_t, mask, h_tm1, W, h0):
h_tm1 = T.switch(mask, h0, h_tm1)
return [ACTIVATION[Ah](x_t + h_tm1.dot(W))]
def logpow(x, m):
"""
Calculates log(x**m) since m*log(x) will fail when m, x = 0.
"""
return switch(eq(x, 0) & eq(m, 0), 0, m * log(x))
def apply(self, input_):
res = T.switch(input_ > 0, input_, self.leaky_init * input_)
return T.switch(T.isnan(res), 0, res)
def leakly_relu(x):
return T.switch(T.gt(x, 0.), x, x * np.float32(0.2))
def __init__(self, rng, input, is_train, n_in, n_hidden, n_out, p=0.5, dropout=False, input_p=0.1): #, batch_size=20):
#Need input dropout layer
if input_p!=None:
self.input_layer = drop(input, rng=rng, p=input_p)
self.input_layer = T.switch(T.neq(is_train, 0), self.input_layer, input)
else:
self.input_layer=input
param_to_scale = [] #To scale weights to square length of 15
self.layer_0 = HiddenLayer(
rng=rng,
input=self.input_layer,
n_in=n_in,
n_out=n_hidden[0],
activation=prelu,
is_train=is_train,
p=p,
dropout=dropout
)
self.params = self.layer_0.params
param_to_scale = param_to_scale + [self.layer_0.params[0]]
#Add more layers accordingly
layer_number = 1
if len(n_hidden)>1:
for layer in n_hidden[1:]:
current_hidden_layer = HiddenLayer(
rng=rng,
input=getattr(self, "layer_" + str(layer_number-1)).output,
n_in=n_hidden[layer_number-1],
n_out=n_hidden[layer_number],
activation=prelu,
is_train=is_train,
p=p,
dropout=dropout
)
setattr(self, "layer_" + str(layer_number), current_hidden_layer)
self.params = self.params + getattr(self, "layer_" + str(layer_number)).params
param_to_scale = param_to_scale + [getattr(self, "layer_" + str(layer_number)).params[0]]
layer_number = layer_number + 1
# The logistic regression layer gets as input the hidden units
# of the hidden layer
self.linearRegressionLayer = LinearRegression(
input=getattr(self, "layer_" + str(layer_number-1)).output,
n_in=n_hidden[layer_number-1],
n_out=n_out,
rng=rng #,batch_size=batch_size
)
self.params = self.params + self.linearRegressionLayer.params
#L1 and L2 regularization
self.L1 = (
abs(self.layer_0.W).sum() + abs(self.linearRegressionLayer.W).sum()
)
self.L2_sqr = (
(self.layer_0.W ** 2).sum() + (self.linearRegressionLayer.W ** 2).sum()
)
#
# self.negative_log_likelihood = (
# self.logRegressionLayer.negative_log_likelihood
# )
#
# self.errors = self.logRegressionLayer.errors
# self.pred = self.logRegressionLayer.pred
# self.diff = self.logRegressionLayer.diff
self.param_to_scale = param_to_scale
self.errors = self.linearRegressionLayer.errors
self.loss = self.linearRegressionLayer.loss
self.NRMSE = self.linearRegressionLayer.NRMSE
self.pred = self.linearRegressionLayer.pred
self.input = input #KEEP IN MIND THIS IS DIFFERENT THAN self.input_layer!!!
def build_sampler(tparams, options, trng):
x = tensor.matrix('x', dtype='int64')
xr = x[::-1]
n_timesteps = x.shape[0]
n_samples = x.shape[1]
# word embedding (source), forward and backward
emb = tparams['Wemb'][x.flatten()]
emb = emb.reshape([n_timesteps, n_samples, options['dim_word']])
embr = tparams['Wemb'][xr.flatten()]
embr = embr.reshape([n_timesteps, n_samples, options['dim_word']])
# encoder
proj = get_layer(options['encoder'])[1](tparams,
emb,
options,
prefix='encoder')
projr = get_layer(options['encoder'])[1](tparams,
embr,
options,
prefix='encoder_r')
# concatenate forward and backward rnn hidden states
ctx = concatenate([proj[0], projr[0][::-1]], axis=proj[0].ndim - 1)
# get the input for decoder rnn initializer mlp
ctx_mean = ctx.mean(0)
# ctx_mean = concatenate([proj[0][-1],projr[0][-1]], axis=proj[0].ndim-2)
init_state = get_layer('ff')[1](tparams,
ctx_mean,
options,
prefix='ff_state',
activ='tanh')
print 'Building f_init...',
outs = [init_state, ctx]
f_init = theano.function([x], outs, name='f_init', profile=profile)
print 'Done'
# x: 1 x 1
y = tensor.vector('y_sampler', dtype='int64')
init_state = tensor.matrix('init_state', dtype='float32')
# if it's the first word, emb should be all zero and it is indicated by -1
emb = tensor.switch(y[:, None] < 0,
tensor.alloc(0., 1, tparams['Wemb_dec'].shape[1]),
tparams['Wemb_dec'][y])
# apply one step of conditional gru with attention
proj = get_layer(options['decoder'])[1](tparams,
emb,
options,
prefix='decoder',
mask=None,
context=ctx,
one_step=True,
init_state=init_state)
# get the next hidden state
next_state = proj[0]
# get the weighted averages of context for this target word y
ctxs = proj[1]
logit_lstm = get_layer('ff')[1](tparams,
next_state,
options,
prefix='ff_logit_lstm',
activ='linear')
logit_prev = get_layer('ff')[1](tparams,
emb,
options,
prefix='ff_logit_prev',
activ='linear')
logit_ctx = get_layer('ff')[1](tparams,
ctxs,
options,
prefix='ff_logit_ctx',
activ='linear')
logit = tensor.tanh(logit_lstm + logit_prev + logit_ctx)
logit = get_layer('ff')[1](tparams,
logit,
options,
prefix='ff_logit',
activ='linear')
# compute the softmax probability
next_probs = tensor.nnet.softmax(logit)
# sample from softmax distribution to get the sample
next_sample = trng.multinomial(pvals=next_probs).argmax(1)
# compile a function to do the whole thing above, next word probability,
# sampled word for the next target, next hidden state to be used
print 'Building f_next..',
inps = [y, ctx, init_state]
outs = [next_probs, next_sample, next_state]
f_next = theano.function(inps, outs, name='f_next', profile=profile)
print 'Done'
return f_init, f_next
def apply(self, input_):
input_ = T.switch(T.isnan(input_), 0, input_)
return T.switch(input_ > 0, input_, 0.01 * input_)
def __init__(self, is_train, rng, input=1, n_in=1, n_out = 500,W=None, b=None,
activation=T.tanh, p=0.5):
# type: (object, object, object, object, object, object, object, object, object) -> object
"""
Hidden unit activation is given by: activation(dot(input,W) + b)
:type rng: numpy.random.RandomState
:param rng: a random number generator used to initialize weights
:type is_train: theano.iscalar
:param is_train: indicator pseudo-boolean (int) for switching between training and prediction
:type input: theano.tensor.dmatrix
:param input: a symbolic tensor of shape (n_examples, n_in)
:type n_in: int
:param n_in: dimensionality of input
:type n_out: int
:param n_out: number of hidden units
:type activation: theano.Op or function
:param activation: Non linearity to be applied in the hidden
layer
:type p: float or double
:param p: probability of NOT dropping out a unit
"""
self.input = input
if W is None:
W_values = numpy.asarray(
rng.uniform(
low=-numpy.sqrt(6. / (n_in + n_out)),
high=numpy.sqrt(6. / (n_in + n_out)),
size=(n_in, n_out)
),
dtype=theano.config.floatX
)
if activation == theano.tensor.nnet.sigmoid:
W_values *= 4
W = theano.shared(value=W_values, name='W', borrow=True)
if b is None:
b_values = numpy.zeros((n_out,), dtype=theano.config.floatX)
b = theano.shared(value=b_values, name='b', borrow=True)
self.W = W
self.b = b
lin_output = T.dot(input, self.W) + self.b
output = activation(lin_output)
# multiply output and drop -> in an approximation the scaling effects cancel out
train_output = drop(output,p)
#is_train is a pseudo boolean theano variable for switching between training and prediction
self.output = T.switch(T.neq(is_train, 0), train_output, p*output)
# parameters of the model
self.params = [self.W, self.b]
def relu(x):
return T.switch(T.gt(x, 0.), x, 0.)
def hinge_c(x, y):
return T.switch(T.lt(1 - x * y, 0), 0 * x, 1 - x * y)
def train(
dim_word=100, # word vector dimensionality
dim=1000, # the number of LSTM units
encoder='gru',
decoder='gru_cond',
patience=10, # early stopping patience
max_epochs=5000,
finish_after=10000000, # finish after this many updates
dispFreq=100,
decay_c=0., # L2 regularization penalty
alpha_c=0., # alignment regularization
clip_c=-1., # gradient clipping threshold
lrate=0.01, # learning rate
n_words_src=100000, # source vocabulary size
n_words=100000, # target vocabulary size
maxlen=100, # maximum length of the description
optimizer='rmsprop',
batch_size=16,
valid_batch_size=16,
saveto='model.npz',
validFreq=1000,
saveFreq=1000, # save the parameters after every saveFreq updates
sampleFreq=100, # generate some samples after every sampleFreq
datasets=[
'/data/lisatmp3/chokyun/europarl/europarl-v7.fr-en.en.tok',
'/data/lisatmp3/chokyun/europarl/europarl-v7.fr-en.fr.tok'
],
valid_datasets=[
'../data/dev/newstest2011.en.tok',
'../data/dev/newstest2011.fr.tok'
],
dictionaries=[
'/data/lisatmp3/chokyun/europarl/europarl-v7.fr-en.en.tok.pkl',
'/data/lisatmp3/chokyun/europarl/europarl-v7.fr-en.fr.tok.pkl'
],
use_dropout=False,
reload_=False):
# Model options
model_options = locals().copy()
# load dictionaries and invert them
worddicts = [None] * len(dictionaries)
worddicts_r = [None] * len(dictionaries)
for ii, dd in enumerate(dictionaries):
with open(dd, 'rb') as f:
worddicts[ii] = pkl.load(f)
worddicts_r[ii] = dict()
for kk, vv in worddicts[ii].iteritems():
worddicts_r[ii][vv] = kk
# reload options
if reload_ and os.path.exists(saveto):
with open('%s.pkl' % saveto, 'rb') as f:
models_options = pkl.load(f)
print 'Loading data'
train = TextIterator(datasets[0],
datasets[1],
dictionaries[0],
dictionaries[1],
n_words_source=n_words_src,
n_words_target=n_words,
batch_size=batch_size,
maxlen=maxlen)
valid = TextIterator(valid_datasets[0],
valid_datasets[1],
dictionaries[0],
dictionaries[1],
n_words_source=n_words_src,
n_words_target=n_words,
batch_size=valid_batch_size,
maxlen=maxlen)
print 'Building model'
params = init_params(model_options)
# reload parameters
if reload_ and os.path.exists(saveto):
params = load_params(saveto, params)
tparams = init_tparams(params)
trng, use_noise, \
x, x_mask, y, y_mask, \
opt_ret, \
cost = \
build_model(tparams, model_options)
inps = [x, x_mask, y, y_mask]
print 'Buliding sampler'
f_init, f_next = build_sampler(tparams, model_options, trng)
# before any regularizer
print 'Building f_log_probs...',
f_log_probs = theano.function(inps, cost, profile=profile)
print 'Done'
cost = cost.mean()
# apply L2 regularization on weights
if decay_c > 0.:
decay_c = theano.shared(numpy.float32(decay_c), name='decay_c')
weight_decay = 0.
for kk, vv in tparams.iteritems():
weight_decay += (vv**2).sum()
weight_decay *= decay_c
cost += weight_decay
# regularize the alpha weights
if alpha_c > 0. and not model_options['decoder'].endswith('simple'):
alpha_c = theano.shared(numpy.float32(alpha_c), name='alpha_c')
alpha_reg = alpha_c * (
(tensor.cast(y_mask.sum(0) // x_mask.sum(0), 'float32')[:, None] -
opt_ret['dec_alphas'].sum(0))**2).sum(1).mean()
cost += alpha_reg
# after all regularizers - compile the computational graph for cost
print 'Building f_cost...',
f_cost = theano.function(inps, cost, profile=profile)
print 'Done'
print 'Computing gradient...',
grads = tensor.grad(cost, wrt=itemlist(tparams))
print 'Done'
# apply gradient clipping here
if clip_c > 0.:
g2 = 0.
for g in grads:
g2 += (g**2).sum()
new_grads = []
for g in grads:
new_grads.append(
tensor.switch(g2 > (clip_c**2), g / tensor.sqrt(g2) * clip_c,
g))
grads = new_grads
# compile the optimizer, the actual computational graph is compiled here
lr = tensor.scalar(name='lr')
print 'Building optimizers...',
f_grad_shared, f_update = eval(optimizer)(lr, tparams, grads, inps, cost)
print 'Done'
print 'Optimization'
history_errs = []
# reload history
if reload_ and os.path.exists(saveto):
history_errs = list(numpy.load(saveto)['history_errs'])
best_p = None
bad_count = 0
if validFreq == -1:
validFreq = len(train[0]) / batch_size
if saveFreq == -1:
saveFreq = len(train[0]) / batch_size
if sampleFreq == -1:
sampleFreq = len(train[0]) / batch_size
uidx = 0
estop = False
for eidx in xrange(max_epochs):
n_samples = 0
for x, y in train:
n_samples += len(x)
uidx += 1
use_noise.set_value(1.)
x, x_mask, y, y_mask = prepare_data(x,
y,
maxlen=maxlen,
n_words_src=n_words_src,
n_words=n_words)
if x is None:
print 'Minibatch with zero sample under length ', maxlen
uidx -= 1
continue
ud_start = time.time()
# compute cost, grads and copy grads to shared variables
cost = f_grad_shared(x, x_mask, y, y_mask)
# do the update on parameters
f_update(lrate)
ud = time.time() - ud_start
# check for bad numbers, usually we remove non-finite elements
# and continue training - but not done here
if numpy.isnan(cost) or numpy.isinf(cost):
print 'NaN detected'
return 1., 1., 1.
# verbose
if numpy.mod(uidx, dispFreq) == 0:
print 'Epoch ', eidx, 'Update ', uidx, 'Cost ', cost, 'UD ', ud
# save the best model so far
if numpy.mod(uidx, saveFreq) == 0:
print 'Saving...',
if best_p is not None:
params = best_p
else:
params = unzip(tparams)
numpy.savez(saveto, history_errs=history_errs, **params)
pkl.dump(model_options, open('%s.pkl' % saveto, 'wb'))
print 'Done'
# generate some samples with the model and display them
if numpy.mod(uidx, sampleFreq) == 0:
# FIXME: random selection?
for jj in xrange(numpy.minimum(5, x.shape[1])):
stochastic = True
sample, score = gen_sample(tparams,
f_init,
f_next,
x[:, jj][:, None],
model_options,
trng=trng,
k=1,
maxlen=30,
stochastic=stochastic,
argmax=False)
print 'Source ', jj, ': ',
for vv in x[:, jj]:
if vv == 0:
break
if vv in worddicts_r[0]:
print worddicts_r[0][vv],
else:
print 'UNK',
print
print 'Truth ', jj, ' : ',
for vv in y[:, jj]:
if vv == 0:
break
if vv in worddicts_r[1]:
print worddicts_r[1][vv],
else:
print 'UNK',
print
print 'Sample ', jj, ': ',
if stochastic:
ss = sample
else:
score = score / numpy.array([len(s) for s in sample])
ss = sample[score.argmin()]
for vv in ss:
if vv == 0:
break
if vv in worddicts_r[1]:
print worddicts_r[1][vv],
else:
print 'UNK',
print
# validate model on validation set and early stop if necessary
if numpy.mod(uidx, validFreq) == 0:
use_noise.set_value(0.)
valid_errs = pred_probs(f_log_probs, prepare_data,
model_options, valid)
valid_err = valid_errs.mean()
history_errs.append(valid_err)
if uidx == 0 or valid_err <= numpy.array(history_errs).min():
best_p = unzip(tparams)
bad_counter = 0
if len(history_errs) > patience and valid_err >= \
numpy.array(history_errs)[:-patience].min():
bad_counter += 1
if bad_counter > patience:
print 'Early Stop!'
estop = True
break
if numpy.isnan(valid_err):
ipdb.set_trace()
print 'Valid ', valid_err
# finish after this many updates
if uidx >= finish_after:
print 'Finishing after %d iterations!' % uidx
estop = True
break
print 'Seen %d samples' % n_samples
if estop:
break
if best_p is not None:
zipp(best_p, tparams)
use_noise.set_value(0.)
valid_err = pred_probs(f_log_probs, prepare_data, model_options,
valid).mean()
print 'Valid ', valid_err
params = copy.copy(best_p)
numpy.savez(saveto,
zipped_params=best_p,
history_errs=history_errs,
**params)
return valid_err
def quantization(W,Wacc,method, Wb):
if method == "FPN":
Wb = W
elif method == "LAB":
L = (T.sqrt(Wacc) + 1e-8)
Wb = hard_sigmoid(W)
Wb = round3(Wb)
Wb = T.cast(T.switch(Wb,1.,-1.), theano.config.floatX)
alpha = (T.abs_(L*W).sum()/L.sum()).astype('float32')
Wb = alpha*Wb
elif method=="LATa":
D = (T.sqrt(Wacc) + 1e-8)
b = T.sgn(Wb)
# compute the threshold, converge within 10 iterations
alpha = (T.abs_(b*D*W).sum()/T.abs_(b*D).sum()).astype('float32')
b = T.switch(T.gt(W/alpha, 0.5), 1., T.switch(T.lt(W/alpha, -0.5), -1., 0.) )
def OneStep(alpha, b):
# minimize alpha
alpha_new = (T.abs_(b*D*W).sum()/T.abs_(b*D).sum()).astype('float32')
# minimize b
b_new = T.switch(T.gt(W/alpha_new, 0.5), 1., T.switch(T.lt(W/alpha_new, -0.5), -1., 0.))
delta = T.abs_(alpha_new-alpha)
condition = T.lt(delta, 1e-6)
return [alpha_new, b_new], theano.scan_module.until(condition)
[out1, out2], updates = theano.scan(fn=OneStep ,outputs_info=[alpha, b],n_steps=10)
Wb = out1[-1]*out2[-1]
elif method=="LATe":
D = (T.sqrt(Wacc) + 1e-8)
thres = findalpha(D, W)
alpha = thres*2
Wt = T.switch(T.gt(W, thres), 1., T.switch(T.lt(W, -thres), -1., 0.) )
Wb = alpha*Wt
elif method=="LAT2e":
D = (T.sqrt(Wacc) + 1e-8)
thres1, thres2 = findalpha2(D, W)
alpha1 = thres1*2
Wt1 = T.switch(T.gt(W, thres1), 1., 0.)
alpha2 = thres2*2
Wt2 = T.switch(T.lt(W, -thres2), -1., 0.)
Wb = alpha1*Wt1 + alpha2*Wt2
elif method=="LAT2a":
D = (T.sqrt(Wacc) + 1e-8)
b1 = T.ge(Wb,0)
alpha1 = (T.abs_(b1*D*W).sum()/T.abs_(b1*D).sum()).astype('float32')
b1 = T.switch(T.gt(W/alpha1, 0.5), 1., 0.)
# Wb1 = alpha1*mask1*Wb
b2 = T.lt(Wb,0)
alpha2 = (T.abs_(b2*D*W).sum()/T.abs_(b2*D).sum()).astype('float32')
b2 = T.switch(T.lt(W/alpha2, -0.5), -1., 0.)
def OneStep(alpha1, b1, alpha2, b2):
alpha1_new = (T.abs_(b1*D*W).sum()/T.abs_(b1*D).sum()).astype('float32')
b1_new = T.switch(T.gt(W/alpha1_new, 0.5), 1., 0.)
alpha2_new = (T.abs_(b2*D*W).sum()/T.abs_(b2*D).sum()).astype('float32')
b2_new = T.switch(T.lt(W/alpha2_new, -0.5), -1., 0.)
delta1 = T.abs_(alpha1_new-alpha1)
delta2 = T.abs_(alpha2_new-alpha2)
condition = T.lt(delta1, 1e-6) and T.lt(delta2, 1e-6)
return [alpha1_new, b1_new, alpha2_new, b2_new], theano.scan_module.until(condition)
[out1, out2, out3, out4], updates = theano.scan(fn=OneStep ,outputs_info=[alpha1, b1, alpha2, b2],n_steps=10)
Wb = out1[-1]*out2[-1] + out3[-1]*out4[-1]
elif method=="LAQ_linear":
D = (T.sqrt(Wacc) + 1e-8)
b = T.sgn(Wb)
alpha = (T.abs_(b*D*W).sum()/T.abs_(b*D).sum()).astype('float32')
# b = T.switch(T.gt(W/alpha, 0.5), 1., T.switch(T.lt(W/alpha, -0.5), -1., 0.) )
m = 3 # number of bits
n = 2**(m-1)-1
b = round3(T.clip(W/alpha, -1., 1.)*n)/(n)
def OneStep(alpha, b):
# minimize alpha
alpha_new = (T.abs_(b*D*W).sum()/T.abs_(b*D).sum()).astype('float32')
# minimize b
# b_new = T.switch(T.gt(W/alpha, 0.5), 1., T.switch(T.lt(W/alpha, -0.5), -1., 0.))
b_new = round3(T.clip(W/alpha_new, -1., 1.)*n)/(n)
delta = T.abs_(alpha_new-alpha)
condition = T.lt(delta, 1e-6)
return [alpha_new, b_new], theano.scan_module.until(condition)
[out1, out2], updates = theano.scan(fn=OneStep ,outputs_info=[alpha, b],n_steps=10)
Wb = out1[-1]*out2[-1]
elif method=="LAQ_log":
D = (T.sqrt(Wacc) + 1e-8)
b = T.sgn(Wb)
alpha = (T.abs_(b*D*W).sum()/T.abs_(b*D).sum()).astype('float32')
m = 3 # number of bits
n = 2**(m-1)-1
tmp = T.clip(W/alpha, -1., 1.)
# log2(1/2*(2^(-n)+2^(-(n+1)))) - (-n-(n+1))/2 = 0.0849625
b = T.switch( T.ge(tmp, pow(2, -n)), T.pow(2, round3(T.log2(tmp)-0.0849625)),
T.switch( T.le(tmp, -pow(2,-n)), -T.pow(2, round3(T.log2(-tmp)-0.0849625)), 0.))
b = T.switch(T.ge(b, pow(2, - (n-1))), b, T.switch(T.le(b, -pow(2, -(n-1))), b, T.sgn(b)*pow(2,-(n-1))))
def OneStep(alpha, b):
# minimize alpha
alpha_new = (T.abs_(b*D*W).sum()/T.abs_(b*D).sum()).astype('float32')
# minimize b
tmp_new = T.clip(W/alpha_new, -1., 1.)
b_new = T.switch( T.ge(tmp_new, pow(2, -n)), T.pow(2, round3(T.log2(tmp_new)-0.0849625)),
T.switch( T.le(tmp_new, -pow(2, -n)), -T.pow(2, round3(T.log2(-tmp_new)-0.0849625)), 0.))
b_new = T.switch(T.ge(b_new, pow(2, - (n-1))), b_new,
T.switch(T.le(b_new, -pow(2, -(n-1))), b_new, T.sgn(b_new)*pow(2, -(n-1))))
delta = T.abs_(alpha_new-alpha)
condition = T.lt(delta, 1e-6)
return [alpha_new, b_new], theano.scan_module.until(condition)
[out1, out2], updates = theano.scan(fn=OneStep ,outputs_info=[alpha, b],n_steps=10)
Wb = out1[-1]*out2[-1]
return Wb
def hmc_updates(positions, stepsize, avg_acceptance_rate, final_pos, accept,
target_acceptance_rate, stepsize_inc, stepsize_dec,
stepsize_min, stepsize_max, avg_acceptance_slowness):
"""This function is executed after `n_steps` of HMC sampling
(`hmc_move` function). It creates the updates dictionary used by
the `simulate` function. It takes care of updating: the position
(if the move is accepted), the stepsize (to track a given target
acceptance rate) and the average acceptance rate (computed as a
moving average).
Parameters
----------
positions: shared variable, theano matrix
Shared theano matrix whose rows contain the old position
stepsize: shared variable, theano scalar
Shared theano scalar containing current step size
avg_acceptance_rate: shared variable, theano scalar
Shared theano scalar containing the current average acceptance rate
final_pos: shared variable, theano matrix
Shared theano matrix whose rows contain the new position
accept: theano scalar
Boolean-type variable representing whether or not the proposed HMC move
should be accepted or not.
target_acceptance_rate: float
The stepsize is modified in order to track this target acceptance rate.
stepsize_inc: float
Amount by which to increment stepsize when acceptance rate is too high.
stepsize_dec: float
Amount by which to decrement stepsize when acceptance rate is too low.
stepsize_min: float
Lower-bound on `stepsize`.
stepsize_min: float
Upper-bound on `stepsize`.
avg_acceptance_slowness: float
Average acceptance rate is computed as an exponential moving average.
(1-avg_acceptance_slowness) is the weight given to the newest
observation.
Returns
-------
rval1: dictionary-like
A dictionary of updates to be used by the `HMC_Sampler.simulate`
function. The updates target the position, stepsize and average
acceptance rate.
"""
## POSITION UPDATES ##
# broadcast `accept` scalar to tensor with the same dimensions as
# final_pos.
accept_matrix = accept.dimshuffle(0, *(('x', ) * (final_pos.ndim - 1)))
# if accept is True, update to `final_pos` else stay put
new_positions = TT.switch(accept_matrix, final_pos, positions)
# end-snippet-5 start-snippet-7
## STEPSIZE UPDATES ##
# if acceptance rate is too low, our sampler is too "noisy" and we reduce
# the stepsize. If it is too high, our sampler is too conservative, we can
# get away with a larger stepsize (resulting in better mixing).
_new_stepsize = TT.switch(avg_acceptance_rate > target_acceptance_rate,
stepsize * stepsize_inc, stepsize * stepsize_dec)
# maintain stepsize in [stepsize_min, stepsize_max]
new_stepsize = TT.clip(_new_stepsize, stepsize_min, stepsize_max)
# end-snippet-7 start-snippet-6
## ACCEPT RATE UPDATES ##
# perform exponential moving average
mean_dtype = theano.scalar.upcast(accept.dtype, avg_acceptance_rate.dtype)
new_acceptance_rate = TT.add(avg_acceptance_slowness * avg_acceptance_rate,
(1.0 - avg_acceptance_slowness) *
accept.mean(dtype=mean_dtype))
# end-snippet-6 start-snippet-8
return [(positions, new_positions), (stepsize, new_stepsize),
(avg_acceptance_rate, new_acceptance_rate)]
def relu(x):
# Using T.nnet.relu gives me NaNs. No idea why.
return T.switch(x > lib.floatX(0), x, lib.floatX(0))
def updates(self):
ups = {}
add_updates = lambda b: safe_update(ups, b)
base_lr = numpy.asarray(
self.conf['base_lr_per_example'] / self.conf['batchsize'], floatX)
annealing_coef = clip_ramp(self.iter,
(self.conf['lr_anneal_start'], base_lr),
(self.conf['lr_anneal_end'], 0.0),
dtype=floatX)
ups[self.iter] = self.iter + 1
ups[self.annealing_coef] = annealing_coef
#
# Enforcing Sparsity
#
pos_h = self.rbm.mean_h_given_v(self.visible_batch)
sparsity_cost = 0
KL_eps = 1e-4
if self.conf['sparsity_KL_featuretarget_weight']:
p = self.conf['sparsity_KL_featuretarget_target']
sparsity_cost = sparsity_cost + tensor.mul(
self.conf['sparsity_KL_featuretarget_weight'], -tensor.sum(
p * tensor.log(tensor.mean(pos_h, axis=0) + KL_eps) +
(1 - p) *
tensor.log(1 - tensor.mean(pos_h, axis=0) + KL_eps)))
assert sparsity_cost.ndim == 0
assert sparsity_cost.dtype == 'float32'
if self.conf['sparsity_KL_exampletarget_weight']:
p = self.conf['sparsity_KL_exampletarget_target']
sparsity_cost = sparsity_cost + tensor.mul(
self.conf['sparsity_KL_exampletarget_weight'], -tensor.sum(
p * tensor.log(tensor.mean(pos_h, axis=1) + KL_eps) +
(1 - p) *
tensor.log(1 - tensor.mean(pos_h, axis=1) + KL_eps)))
assert sparsity_cost.ndim == 0
assert sparsity_cost.dtype == 'float32'
#
# Updates related to CD
#
# These updates are for CD-1, PCD/SML, and a stochastic interpolation
# between them.
#
# The idea is to start from negative phase particles neg_v, run them
# through a step of Gibbs, and put them into sampler.particles:
#
# neg_v -> Gibbs -> sampler.particles.
#
# We control the kind of CD by adjusting what neg_v is: either it is the
# visible_batch (for CD-1) or it is the old sampler.particles (PCD). We
# can interpolate between the two algorithms by stochastically choosing
# either the visible_batch[i] or the old particles[i] on a row-by-row
# basis.
#
if self.conf['CD_anneal_start'] < self.conf['CD_anneal_end']:
P_restart = clip_ramp(self.iter,
(self.conf['CD_anneal_start'], 1.0),
(self.conf['CD_anneal_end'], 0.0),
dtype=floatX)
reset_decisions = self.sampler.s_rng.uniform(
size=(self.conf['batchsize'], )) < P_restart
v0_ndim = self.visible_batch.ndim
# broadcast reset_decisions over all but batch idx
neg_v0 = tensor.switch(
reset_decisions.dimshuffle(0, *(['x'] * (v0_ndim - 1))),
self.visible_batch, # reset the chain to data
self.sampler.particles) # continue old chain
else:
neg_v0 = self.sampler.particles
neg_v1 = self.rbm.gibbs_step_for_v(neg_v0, self.sampler.s_rng)
ups[self.sampler.particles] = neg_v1
## N.B. we are manually advancing the sampler, not calling
## Gibbs.updates()
# add_updates(self.sampler.updates())
learn_rates = [
self.annealing_coef * self.lr_dict[p] for p in self.rbm.params()
]
add_updates(
self.rbm.cd_updates(pos_v=self.visible_batch,
neg_v=neg_v1,
lr=learn_rates,
other_cost=sparsity_cost))
#
# Gathering statistics of unit activity
#
neg_h = self.rbm.mean_h_given_v(neg_v1)
self.pos_h_means = sharedX(numpy.zeros(self.rbm.h_shp) + 0.5, 'pos_h')
self.neg_h_means = sharedX(numpy.zeros(self.rbm.h_shp) + 0.5, 'neg_h')
ups[self.
pos_h_means] = 0.1 * pos_h.mean(axis=0) + .9 * self.pos_h_means
ups[self.
neg_h_means] = 0.1 * neg_h.mean(axis=0) + .9 * self.neg_h_means
# Clipping parameters to legal ranges
for clipper in self.clippers:
ups = clipper.filter_update(ups)
return ups
def compute_cost(self,
features,
features_mask,
labels,
labels_mask,
speaker,
start_flag,
batch_size,
raw_audio=None):
if speaker is None:
assert not self.use_speaker
target_features = features[1:]
mask = features_mask[1:]
cell_shape = (mask.shape[0], batch_size, self.rnn_h_dim)
gat_shape = (mask.shape[0], batch_size, 2 * self.rnn_h_dim)
cell_h1 = tensor.zeros(cell_shape, dtype=floatX)
cell_h2 = tensor.zeros(cell_shape, dtype=floatX)
cell_h3 = tensor.zeros(cell_shape, dtype=floatX)
gat_h1 = tensor.zeros(gat_shape, dtype=floatX)
gat_h2 = tensor.zeros(gat_shape, dtype=floatX)
gat_h3 = tensor.zeros(gat_shape, dtype=floatX)
if self.weak_feedback:
input_features = features[:-1]
if self.feedback_noise_level:
noise = self.theano_rng.normal(size=input_features.shape,
avg=0.,
std=1.)
input_features += self.noise_level_var * noise
out_cell_h1, out_gat_h1 = self.out_to_h1.apply(input_features)
to_normalize = [out_cell_h1, out_gat_h1]
out_cell_h1, out_gat_h1 = \
[_apply_norm(x, self.layer_norm) for x in to_normalize]
cell_h1 += out_cell_h1
gat_h1 += out_gat_h1
if self.full_feedback:
assert self.weak_feedback
out_cell_h2, out_gat_h2 = self.out_to_h2.apply(input_features)
out_cell_h3, out_gat_h3 = self.out_to_h3.apply(input_features)
to_normalize = [out_cell_h2, out_gat_h2, out_cell_h3, out_gat_h3]
out_cell_h2, out_gat_h2, out_cell_h3, out_gat_h3 = \
[_apply_norm(x, self.layer_norm) for x in to_normalize]
cell_h2 += out_cell_h2
gat_h2 += out_gat_h2
cell_h3 += out_cell_h3
gat_h3 += out_gat_h3
if self.use_speaker:
speaker = speaker[:, 0]
emb_speaker = self.embed_speaker.apply(speaker)
emb_speaker = tensor.shape_padleft(emb_speaker)
spk_cell_h1, spk_gat_h1 = self.speaker_to_h1.apply(emb_speaker)
spk_cell_h2, spk_gat_h2 = self.speaker_to_h2.apply(emb_speaker)
spk_cell_h3, spk_gat_h3 = self.speaker_to_h3.apply(emb_speaker)
to_normalize = [
spk_cell_h1, spk_gat_h1, spk_cell_h2, spk_gat_h2, spk_cell_h3,
spk_gat_h3
]
spk_cell_h1, spk_gat_h1, spk_cell_h2, spk_gat_h2, \
spk_cell_h3, spk_gat_h3, = \
[_apply_norm(x, self.layer_norm) for x in to_normalize]
cell_h1 = spk_cell_h1 + cell_h1
cell_h2 = spk_cell_h2 + cell_h2
cell_h3 = spk_cell_h3 + cell_h3
gat_h1 = spk_gat_h1 + gat_h1
gat_h2 = spk_gat_h2 + gat_h2
gat_h3 = spk_gat_h3 + gat_h3
initial_h1, last_h1, initial_h2, last_h2, initial_h3, last_h3, \
initial_w, last_w, initial_k, last_k = \
self.initial_states(batch_size)
# If it's a new example, use initial states.
input_h1 = tensor.switch(start_flag, initial_h1, last_h1)
input_h2 = tensor.switch(start_flag, initial_h2, last_h2)
input_h3 = tensor.switch(start_flag, initial_h3, last_h3)
input_w = tensor.switch(start_flag, initial_w, last_w)
input_k = tensor.switch(start_flag, initial_k, last_k)
context_oh = self.encoder.apply(labels) * \
tensor.shape_padright(labels_mask)
u = tensor.shape_padleft(tensor.arange(labels.shape[1], dtype=floatX),
2)
def step(inp_h1_t, gat_h1_t, inp_h2_t, gat_h2_t, inp_h3_t, gat_h3_t,
h1_tm1, h2_tm1, h3_tm1, k_tm1, w_tm1, context_oh):
attinp_h1, attgat_h1 = self.inp_to_h1.apply(w_tm1)
inp_h1_t += attinp_h1
gat_h1_t += attgat_h1
h1_t = self.rnn1.apply(inp_h1_t, gat_h1_t, h1_tm1, iterate=False)
a_t, b_t, k_t = self.h1_to_att.apply(h1_t)
if self.attention_type == "softmax":
a_t = tensor.nnet.softmax(a_t) + self.epsilon
else:
a_t = tensor.exp(a_t) + self.epsilon
b_t = tensor.exp(b_t) + self.epsilon
k_t = k_tm1 + self.attention_alignment * tensor.exp(k_t)
a_t_ = a_t
a_t = tensor.shape_padright(a_t)
b_t = tensor.shape_padright(b_t)
k_t_ = tensor.shape_padright(k_t)
# batch size X att size X len context
if self.attention_type == "softmax":
# numpy.sqrt(1/(2*numpy.pi)) is the weird number
phi_t = 0.3989422917366028 * tensor.sum(
a_t * tensor.sqrt(b_t) * tensor.exp(-0.5 * b_t *
(k_t_ - u)**2),
axis=1)
else:
phi_t = tensor.sum(a_t * tensor.exp(-b_t * (k_t_ - u)**2),
axis=1)
# batch size X len context X num letters
w_t = (tensor.shape_padright(phi_t) * context_oh).sum(axis=1)
attinp_h2, attgat_h2 = self.inp_to_h2.apply(w_t)
attinp_h3, attgat_h3 = self.inp_to_h3.apply(w_t)
inp_h2_t += attinp_h2
gat_h2_t += attgat_h2
inp_h3_t += attinp_h3
gat_h3_t += attgat_h3
h1inp_h2, h1gat_h2 = self.h1_to_h2.apply(h1_t)
h1inp_h3, h1gat_h3 = self.h1_to_h3.apply(h1_t)
to_normalize = [h1inp_h2, h1gat_h2, h1inp_h3, h1gat_h3]
h1inp_h2, h1gat_h2, h1inp_h3, h1gat_h3 = \
[_apply_norm(x, self.layer_norm) for x in to_normalize]
h2_t = self.rnn2.apply(inp_h2_t + h1inp_h2,
gat_h2_t + h1gat_h2,
h2_tm1,
iterate=False)
h2inp_h3, h2gat_h3 = self.h2_to_h3.apply(h2_t)
to_normalize = [h2inp_h3, h2gat_h3]
h2inp_h3, h2gat_h3 = \
[_apply_norm(x, self.layer_norm) for x in to_normalize]
h3_t = self.rnn3.apply(inp_h3_t + h1inp_h3 + h2inp_h3,
gat_h3_t + h1gat_h3 + h2gat_h3,
h3_tm1,
iterate=False)
return h1_t, h2_t, h3_t, k_t, w_t, phi_t, a_t_
(h1, h2, h3, k, w, phi, pi_att), scan_updates = theano.scan(
fn=step,
sequences=[cell_h1, gat_h1, cell_h2, gat_h2, cell_h3, gat_h3],
non_sequences=[context_oh],
outputs_info=[
input_h1, input_h2, input_h3, input_k, input_w, None, None
])
h1_out = self.h1_to_readout.apply(h1)
h2_out = self.h2_to_readout.apply(h2)
h3_out = self.h3_to_readout.apply(h3)
to_normalize = [h1_out, h2_out, h3_out]
h1_out, h2_out, h3_out = \
[_apply_norm(x, self.layer_norm) for x in to_normalize]
readouts = h1_out + h2_out + h3_out
if self.use_speaker:
readouts += self.speaker_to_readout.apply(emb_speaker)
readouts += self.att_to_readout.apply(w)
predicted = self.readout_to_output.apply(readouts)
if self.which_cost == 'MSE':
if self.use_speaker:
predicted += self.speaker_to_output.apply(emb_speaker)
cost = tensor.sum((predicted - target_features)**2, axis=-1)
next_x = predicted
# Dummy value for coeff
coeff = predicted
elif self.which_cost == 'GMM':
mu, sigma, coeff = predicted
if self.use_speaker:
spk_to_out = self.speaker_to_output.apply(emb_speaker)
mu += spk_to_out[0]
sigma += spk_to_out[1]
coeff += spk_to_out[2]
# When training there should not be sampling_bias
sigma = tensor.exp(sigma) + self.epsilon
coeff = tensor.nnet.softmax(coeff.reshape(
(-1, self.k_gmm))).reshape(coeff.shape) + self.epsilon
cost = cost_gmm(target_features, mu, sigma, coeff)
next_x = sample_gmm(mu, sigma, coeff, self.theano_rng)
cost = (cost * mask).sum() / (mask.sum() + 1e-5) + 0. * start_flag
updates = []
updates.append((last_h1, h1[-1]))
updates.append((last_h2, h2[-1]))
updates.append((last_h3, h3[-1]))
updates.append((last_k, k[-1]))
updates.append((last_w, w[-1]))
cost_raw = None
if self.raw_output:
raw_mask = tensor.extra_ops.repeat(features_mask, 80, axis=0)
raw_mask = raw_mask.dimshuffle(1, 0)
# breakpointOp = PdbBreakpoint("Raw mask breakpoint")
# condition = tensor.gt(raw_mask.shape[0], 0)
# raw_mask = breakpointOp(condition, raw_mask)
predicted_transposed = predicted.dimshuffle(1, 0, 2)
last_h0, last_big_h0 = self.sampleRnn.initial_states(batch_size)
raw_audio_reshaped = raw_audio.dimshuffle(1, 0, 2)
raw_audio_reshaped = raw_audio_reshaped.reshape(
(raw_audio_reshaped.shape[0], -1))
cost_raw, ip_cost, all_params, ip_params, other_params, new_h0, new_big_h0 =\
self.sampleRnn.apply(raw_audio_reshaped, predicted_transposed, last_h0, last_big_h0, start_flag, raw_mask)
if self.sampleRnn.N_RNN == 1:
new_h0 = tensor.unbroadcast(new_h0, 1)
new_big_h0 = tensor.unbroadcast(new_big_h0, 1)
updates.append((last_h0, new_h0))
updates.append((last_big_h0, new_big_h0))
# cost = cost + 80.*cost_raw
alpha_ = numpy.float32(0.)
beta_ = numpy.float32(1.)
cost = alpha_ * cost + beta_ * cost_raw
attention_vars = [next_x, k, w, coeff, phi, pi_att]
return cost, scan_updates + updates, attention_vars, cost_raw
def out_shape(imgshape, ds, ignore_border=False, st=None):
"""Return the shape of the output from this op, for input of given
shape and flags.
:param imgshape: the shape of a tensor of images. The last two elements
are interpreted as the number of rows, and the number of cols.
:type imgshape: tuple, list, or similar of integer or
scalar Theano variable.
:param ds: downsample factor over rows and columns
this parameter indicates the size of the pooling region
:type ds: list or tuple of two ints
:param st: the stride size. This is the distance between the pooling
regions. If it's set to None, in which case it equlas ds.
:type st: list or tuple of two ints
:param ignore_border: if ds doesn't divide imgshape, do we include an
extra row/col of partial downsampling (False) or ignore it (True).
:type ignore_border: bool
:rtype: list
:returns: the shape of the output from this op, for input of given
shape. This will have the same length as imgshape, but with last
two elements reduced as per the downsampling & ignore_border flags.
"""
if len(imgshape) < 2:
raise TypeError('imgshape must have at least two elements '
'(rows, cols)')
if st is None:
st = ds
r, c = imgshape[-2:]
if ignore_border:
out_r = (r - ds[0]) // st[0] + 1
out_c = (c - ds[1]) // st[1] + 1
if isinstance(r, theano.Variable):
nr = tensor.maximum(out_r, 0)
else:
nr = numpy.maximum(out_r, 0)
if isinstance(c, theano.Variable):
nc = tensor.maximum(out_c, 0)
else:
nc = numpy.maximum(out_c, 0)
else:
if isinstance(r, theano.Variable):
nr = tensor.switch(
tensor.ge(st[0], ds[0]), (r - 1) // st[0] + 1,
tensor.maximum(0, (r - 1 - ds[0]) // st[0] + 1) + 1)
elif st[0] >= ds[0]:
nr = (r - 1) // st[0] + 1
else:
nr = max(0, (r - 1 - ds[0]) // st[0] + 1) + 1
if isinstance(c, theano.Variable):
nc = tensor.switch(
tensor.ge(st[1], ds[1]), (c - 1) // st[1] + 1,
tensor.maximum(0, (c - 1 - ds[1]) // st[1] + 1) + 1)
elif st[1] >= ds[1]:
nc = (c - 1) // st[1] + 1
else:
nc = max(0, (c - 1 - ds[1]) // st[1] + 1) + 1
rval = list(imgshape[:-2]) + [nr, nc]
return rval
def RMSprop(self,
cost,
params,
full_params,
sampled_params,
sidxs,
epsilon=1e-6):
grads = [T.grad(cost=cost, wrt=param) for param in params]
sgrads = [T.grad(cost=cost, wrt=sparam) for sparam in sampled_params]
updates = OrderedDict()
if self.grad_cap > 0:
norm = T.cast(
T.sqrt(
T.sum([
T.sum([T.sum(g**2) for g in g_list])
for g_list in grads
]) + T.sum([T.sum(g**2) for g in sgrads])),
theano.config.floatX)
grads = [[
T.switch(T.ge(norm, self.grad_cap), g * self.grad_cap / norm,
g) for g in g_list
] for g_list in grads]
sgrads = [
T.switch(T.ge(norm, self.grad_cap), g * self.grad_cap / norm,
g) for g in sgrads
]
for p_list, g_list in zip(params, grads):
for p, g in zip(p_list, g_list):
if self.adapt:
if self.adapt == 'adagrad':
g = self.adagrad(p, g, updates)
if self.adapt == 'rmsprop':
g = self.rmsprop(p, g, updates)
if self.adapt == 'adadelta':
g = self.adadelta(p, g, updates)
if self.adapt == 'adam':
g = self.adam(p, g, updates)
if self.momentum > 0:
velocity = theano.shared(p.get_value(borrow=False) * 0.,
borrow=True)
velocity2 = self.momentum * velocity - np.float32(
self.learning_rate) * (g + self.lmbd * p)
updates[velocity] = velocity2
updates[p] = p + velocity2
else:
updates[p] = p * np.float32(1.0 - self.learning_rate *
self.lmbd) - np.float32(
self.learning_rate) * g
for i in range(len(sgrads)):
g = sgrads[i]
fullP = full_params[i]
sample_idx = sidxs[i]
sparam = sampled_params[i]
if self.adapt:
if self.adapt == 'adagrad':
g = self.adagrad(fullP, g, updates, sample_idx)
if self.adapt == 'rmsprop':
g = self.rmsprop(fullP, g, updates, sample_idx)
if self.adapt == 'adadelta':
g = self.adadelta(fullP, g, updates, sample_idx)
if self.adapt == 'adam':
g = self.adam(fullP, g, updates, sample_idx)
if self.lmbd > 0:
delta = np.float32(
self.learning_rate) * (g + self.lmbd * sparam)
else:
delta = np.float32(self.learning_rate) * g
if self.momentum > 0:
velocity = theano.shared(fullP.get_value(borrow=False) * 0.,
borrow=True)
vs = velocity[sample_idx]
velocity2 = self.momentum * vs - delta
updates[velocity] = T.set_subtensor(vs, velocity2)
updates[fullP] = T.inc_subtensor(sparam, velocity2)
else:
updates[fullP] = T.inc_subtensor(sparam, -delta)
return updates
def clear_nan(x):
return T.switch(T.isnan(x), np.float32(0.0), x)
def leaky(self, X):
return T.switch(T.ge(X, 0), X, self.leak * X)
def build_sampler(tparams, options, trng):
x = tensor.matrix('x', dtype='int64')
n_timesteps = x.shape[0]
n_samples = x.shape[1]
# word embedding (source)
emb = tparams['Wemb'][x.flatten()]
emb = emb.reshape([n_timesteps, n_samples, options['dim_word']])
# encoder
proj = get_layer(options['encoder'])[1](tparams,
emb,
options,
prefix='encoder')
ctx = proj[0][-1]
ctx_mean = ctx
init_state = get_layer('ff')[1](tparams,
ctx_mean,
options,
prefix='ff_state',
activ='tanh')
print 'Building f_init...',
outs = [init_state, ctx]
f_init = theano.function([x], outs, name='f_init', profile=profile)
print 'Done'
# y: 1 x 1
y = tensor.vector('y_sampler', dtype='int64')
init_state = tensor.matrix('init_state', dtype='float32')
# if it's the first word, emb should be all zero
emb = tensor.switch(y[:, None] < 0,
tensor.alloc(0., 1, tparams['Wemb_dec'].shape[1]),
tparams['Wemb_dec'][y])
# apply one step of gru layer
proj = get_layer(options['decoder'])[1](tparams,
emb,
options,
prefix='decoder',
mask=None,
context=ctx,
one_step=True,
init_state=init_state)
next_state = proj
ctxs = ctx
# compute the output probability dist and sample
logit_lstm = get_layer('ff')[1](tparams,
next_state,
options,
prefix='ff_logit_lstm',
activ='linear')
logit_prev = get_layer('ff')[1](tparams,
emb,
options,
prefix='ff_logit_prev',
activ='linear')
logit_ctx = get_layer('ff')[1](tparams,
ctxs,
options,
prefix='ff_logit_ctx',
activ='linear')
logit = tensor.tanh(logit_lstm + logit_prev + logit_ctx)
logit = get_layer('ff')[1](tparams,
logit,
options,
prefix='ff_logit',
activ='linear')
next_probs = tensor.nnet.softmax(logit)
next_sample = trng.multinomial(pvals=next_probs).argmax(1)
# next word probability
print 'Building f_next..',
inps = [y, ctx, init_state]
outs = [next_probs, next_sample, next_state]
f_next = theano.function(inps, outs, name='f_next', profile=profile)
print 'Done'
return f_init, f_next
def in_test_phase(x, alt):
x = T.switch(_LEARNING_PHASE, alt, x)
x._uses_learning_phase = True
return x
def elu(self, X):
return T.switch(T.ge(X, 0), X, self.elu_param * (T.exp(X) - 1))
def switch(condition, then_expression, else_expression):
'''condition: scalar tensor.
'''
return T.switch(condition, then_expression, else_expression)
best_ppl = np.inf
validation_ppl_history = []
gsums = [theano.shared(np.zeros_like(param.get_value(borrow=True))) for param in net.params]
cost = net.cost(y) + L2_REG * net.L2_sqr
gparams = T.grad(cost, net.params)
updates = OrderedDict()
# Compute norm of gradients
norm = T.sqrt(T.sum([T.sum(gparam**2) for gparam in gparams]))
# Adagrad: "Adaptive subgradient methods for online learning and stochastic optimization" (2011)
for gparam, param, gsum in zip(gparams, net.params, gsums):
gparam = T.switch(T.ge(norm, CLIPPING_THRESHOLD), gparam / norm * CLIPPING_THRESHOLD, gparam) # Clipping of gradients
updates[gsum] = gsum + (gparam**2)
updates[param] = param - lr * (gparam / (T.sqrt(updates[gsum] + 1e-6)))
train_model = theano.function(inputs=[x, y, lr], outputs=cost, updates=updates)
validate_model = theano.function(inputs=[x, y], outputs=net.cost(y))
print("Training...")
for epoch in range(starting_epoch, MAX_EPOCHS):
t0 = time()
total_neg_log_likelihood = 0
total_num_output_samples = 0
iteration = 0
for X, Y in get_minibatch(data.TRAIN_FILE, MINIBATCH_SIZE, shuffle=True):
total_neg_log_likelihood += train_model(X, Y, learning_rate)