def vtovMBall(self, VsampM):
"""
computes visible unit outputs given visible unit inputs (single MCMC iteration)
multiple paralle MCMC iterations using rows of the input matrix
args:
VsampM (T.matrix): rows of this matrix are visible unit inputs
return:
ahtovMBres (T.matrix): rows of this matrix are visible unit outputs after a single MCMC iteration
"""
#v to h part
aVomg = T.matrix(name="Vomg", dtype=theano.config.floatX)
avtohMBres = T.matrix(name ="vtohMBres", dtype=theano.config.floatX)
aT_HP = T.matrix(name="T_HP", dtype=theano.config.floatX)
aVomg = T.dot(T.mul(T.fill(VsampM, T.exp(-self.T_z)), VsampM), self.T_omega)
aT_Hp = T.nnet.ultra_fast_sigmoid(T.fill(aVomg, self.T_b) + aVomg)
avtohMBres = self.T_rng.binomial(size = aT_Hp.shape, p=aT_Hp, dtype=theano.config.floatX)
#h to v part:
aT_omgH = T.matrix(name="T_omgH", dtype=theano.config.floatX)
aT_means = T.matrix(name="T_means", dtype=theano.config.floatX)
ahtovMBres = T.matrix(name="htovMBres", dtype=theano.config.floatX)
aT_omgH = T.transpose(T.dot(self.T_omega, T.transpose(avtohMBres)))
aT_means = T.fill(aT_omgH, self.T_a) + aT_omgH
ahtovMBres = self.T_rng.normal(size=aT_means.shape, avg=aT_means, std=T.fill(aT_means,T.sqrt(T.exp(self.T_z))), dtype=theano.config.floatX)
return [ahtovMBres, avtohMBres, aT_Hp, aT_means]
def test_exp_over_1_plus_exp(self):
m = self.get_mode(excluding=['local_elemwise_fusion'])
x = T.dvector()
# tests exp_over_1_plus_exp
f = theano.function([x], T.exp(x)/(1+T.exp(x)), mode=m)
theano.printing.debugprint(f)
assert [node.op for node in f.maker.env.toposort()] == [sigmoid]
# tests inv_1_plus_exp
f = theano.function([x], T.fill(x,1.0) / (1+T.exp(-x)), mode=m)
theano.printing.debugprint(f)
assert [node.op for node in f.maker.env.toposort()] == [sigmoid]
# tests inv_1_plus_exp with neg
f = theano.function([x], T.fill(x,-1.0) / (1+T.exp(-x)), mode=m)
assert [node.op for node in f.maker.env.toposort()] == [sigmoid,
theano.tensor.inplace.neg_inplace]
# tests double inv_1_plus_exp with neg
# (-1)(exp(x)) / (1+exp(x))(1+exp(-x))
# = (-1)/(1+exp(-x)) * exp(x)/(1+exp(x))
# = - (sigm(x) * sigm(x))
f = theano.function([x], (T.fill(x,-1.0)*T.exp(x)) / ((1+T.exp(x))*(1+T.exp(-x))), mode=m)
theano.printing.debugprint(f)
assert [node.op for node in f.maker.env.toposort()] == [sigmoid,
T.mul, theano.tensor.inplace.neg_inplace]
def _gen_exprs(self, inpt):
"""Return the exprssions of the recognition model."""
P = self.parameters.gen
n_layers = len(self.n_hiddens_gen)
hidden_to_hiddens = [
getattr(P, 'hidden_to_hidden_%i' % i) for i in range(n_layers - 1)
]
hidden_biases = [
getattr(P, 'hidden_bias_%i' % i) for i in range(n_layers)
]
initial_hidden_means = [
getattr(P, 'initial_hidden_means_%i' % i) for i in range(n_layers)
]
initial_hidden_vars = [
getattr(P, 'initial_hidden_vars_%i' % i)**2 + 1e-4
for i in range(n_layers)
]
recurrents = [getattr(P, 'recurrent_%i' % i) for i in range(n_layers)]
p_dropout_inpt = T.zeros_like(inpt[:, :, :self.n_latent])
p_dropout_inpt = T.fill(p_dropout_inpt, self.p_dropout_inpt)
p_dropout_shortcut = T.zeros_like(inpt[:, :, self.n_latent:])
p_dropout_shortcut = T.fill(p_dropout_shortcut, self.p_dropout_inpt)
p_dropout_inpt = T.concatenate([p_dropout_inpt, p_dropout_shortcut],
axis=2)
p_dropouts = [p_dropout_inpt] + self.p_dropout_hiddens
if self.p_dropout_hidden_to_out is None:
p_dropouts.append(self.p_dropout_hiddens[-1])
else:
p_dropouts.append(self.p_dropout_hidden_to_out)
exprs = vprnn.exprs(inpt,
T.zeros_like(inpt),
P.in_to_hidden,
hidden_to_hiddens,
P.hidden_to_out,
hidden_biases, [1 for _ in hidden_biases],
initial_hidden_means,
initial_hidden_vars,
recurrents,
P.out_bias,
1,
self.gen_transfers,
self.assumptions.statify_visible,
p_dropouts=p_dropouts)
return exprs
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 _step2(ctx_, state_, hs_, Cs_):
hs, Cs = [], []
token_idxs = tensor.cast(state_.argmax(axis=-1), "int32")
msk_ = tensor.fill(
(tensor.zeros_like(token_idxs, dtype="float32")), 1)
msk_ = msk_.dimshuffle('x', 0)
state_below0 = self.de_lookuptable[token_idxs].reshape(
(1, encoderInputs.shape[1], self.de_hidden_size))
for i, lstm in enumerate(self.decoder_lstm_layers):
h, C = lstm.forward(state_below0, msk_, hs_[i],
Cs_[i]) #mind msk
hs += h[-1],
Cs += C[-1],
state_below0 = h
hs, Cs = tensor.as_tensor_variable(hs), tensor.as_tensor_variable(
Cs)
state_below0 = state_below0.reshape(
(encoderInputs.shape[1], self.de_hidden_size))
state_below0 = tensor.concatenate([ctx_, state_below0], axis=1)
newpred = tensor.dot(state_below0, self.linear)
state_below = tensor.nnet.softmax(newpred)
return state_below, hs, Cs
def likelihoodFunc(v,size,graphMatrix,value):
v=tt.fill(tt.ones(size),v)#create a vector of the null bernouli probs
penalty=tt.dot(graphMatrix,value) #matrix multipl. to get how many correlated features are "on"
penalty=5-4*tt.exp(-penalty)
v=v*penalty
ll=tt.sum(tt.log(tt.pow(v, value)))
return ll
def _step2(ctx_, state_, hs_, Cs_):
#print ctx_.shape, state_.shape, hs_.shape, Cs_.shape
hs, Cs = [], []
token_idxs = T.cast(state_.argmax(axis=-1), "int32")
msk_ = T.fill((T.zeros_like(token_idxs, dtype="float32")), 1)
msk_ = msk_.dimshuffle('x', 0)
state_below0 = self.de_lookuptable[token_idxs].reshape(
(1, ctx_.shape[0], self.de_hidden_size))
for i, lstm in enumerate(self.decoder_lstm_layers):
h, C = lstm.forward(state_below0, msk_, hs_[i],
Cs_[i]) #mind msk
hs += h[-1],
Cs += C[-1],
state_below0 = h
hs, Cs = T.as_tensor_variable(hs), T.as_tensor_variable(Cs)
state_below0 = state_below0.reshape(
(ctx_.shape[0], self.de_hidden_size))
state_below0 = T.concatenate([ctx_, state_below0], axis=1)
newpred = T.dot(state_below0,
self.linear) + self.linear_bias[None, :]
state_below = T.nnet.softmax(newpred)
extra_p = T.zeros_like(hs[:, :, 0])
state_below = T.concatenate([state_below, extra_p.T], axis=1)
return state_below, hs, Cs
def _step2(ctx_, state_, hs_, Cs_):
### ctx_: b x h
### state_ : b x h
### hs_ : 1 x b x h the first dimension is the number of the decoder layers
### Cs_ : 1 x b x h the first dimension is the number of the decoder layers
hs, Cs = [], []
token_idxs = tensor.cast(state_.argmax(axis=-1), "int32")
msk_ = tensor.fill(
(tensor.zeros_like(token_idxs, dtype="float32")), 1)
msk_ = msk_.dimshuffle('x', 0)
state_below0 = self.de_lookuptable[token_idxs].reshape(
(1, ctx_.shape[0], self.de_hidden_size))
for i, lstm in enumerate(self.decoder_lstm_layers):
h, C = lstm.forward(state_below0, msk_, hs_[i],
Cs_[i]) #mind msk
hs += h[-1],
Cs += C[-1],
state_below0 = h
hs, Cs = tensor.as_tensor_variable(hs), tensor.as_tensor_variable(
Cs)
state_below0 = state_below0.reshape(
(ctx_.shape[0], self.de_hidden_size))
state_below0 = tensor.concatenate([ctx_, state_below0], axis=1)
newpred = tensor.dot(state_below0,
self.linear) + self.linear_bias[None, :]
state_below = tensor.nnet.softmax(newpred)
##### the beging symbole probablity is 0
extra_p = tensor.zeros_like(hs[:, :, 0])
state_below = tensor.concatenate([state_below, extra_p.T], axis=1)
return state_below, hs, Cs
def output_probabilistic(self, m_x, v_x):
m_linear = T.dot(m_x, self.m_W[ 0, :, : ]) + T.tile(self.m_b[ 0, :, : ], [ m_x.shape[ 0 ], 1 ])
v_linear = T.dot(m_x**2, self.v_W[ 0, :, : ]) + T.dot(v_x, self.m_W[ 0, :, : ]**2) + T.dot(v_x, self.v_W[ 0, :, : ]) + \
T.tile(self.v_b[ 0, :, : ], [ m_x.shape[ 0 ], 1 ])
if not self.output_layer:
# We compute the mean and variance after the ReLU activation
alpha = m_linear / T.sqrt(v_linear)
gamma = Network_layer.gamma(-alpha)
gamma_robust = -alpha - 1.0 / alpha + 2.0 / alpha**3
gamma_final = T.switch(T.lt(-alpha, T.fill(alpha, 30)), gamma, gamma_robust)
v_aux = m_linear + T.sqrt(v_linear) * gamma_final
m_a = Network_layer.n_cdf(alpha) * v_aux
v_a = m_a * v_aux * Network_layer.n_cdf(-alpha) + Network_layer.n_cdf(alpha) * v_linear * (1 - gamma_final * (gamma_final + alpha))
return (m_a, v_a)
else:
return (m_linear, v_linear)
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 _FindB_best(lPLcl, lPprev, dVLcl):
srtLcl = tensor.argsort(-lPLcl)
srtLcl = srtLcl[:beam_size]
deltaVec = tensor.fill( lPLcl[srtLcl], numpy_floatX(-10000.))
deltaVec = tensor.set_subtensor(deltaVec[0], lPprev)
lProbBest = ifelse(tensor.eq( dVLcl, tensor.zeros_like(dVLcl)), lPLcl[srtLcl] + lPprev, deltaVec)
xWIdxBest = ifelse(tensor.eq( dVLcl, tensor.zeros_like(dVLcl)), srtLcl, tensor.zeros_like(srtLcl))
return lProbBest, xWIdxBest
def transform_targets(targets):
"""Transform targets into a format suitable for passing to cost()."""
reshaped = T.shape_padleft(targets)
blanks = T.fill(reshaped, _BLANK)
result = T.concatenate([blanks, reshaped]).dimshuffle(1, 0, 2).reshape((2*targets.shape[0], targets.shape[1]))
result = T.concatenate([result, T.shape_padleft(result[0])])
return result
def energyFnMB(self, VM, HM):
"""
evaluates the energy functions of the RBM given row vector(s) of v and h
args:
VM (T.matrix): rows of visible layer values
HM (T.matrix): rows of hidden layer values
return:
a row Theano vector, elements being E(v_row, h_row)
"""
T_bh = T.dot(HM, self.T_b)
T_omghv = T.transpose(T.sum(T.mul(T.dot(T.mul(T.fill(VM, T.exp(-self.T_z)), VM), self.T_omega), HM), axis=1,acc_dtype=theano.config.floatX))
T_Vsqr = T.mul(VM-T.fill(VM, self.T_a),VM-T.fill(VM, self.T_a))
T_VsqrOmg = T.transpose(T.sum(T.mul(T.fill(T_Vsqr,np.float32(0.5)*T.exp(-self.T_z)),T_Vsqr),axis=1, acc_dtype=theano.config.floatX))
return -T_VsqrOmg + T_omghv + T_bh
def my_crf_accuracy(energies):
assert energies.ndim == 4
def inner_function(energies_one_step, prior_pi, prior_pointer):
"""
:param energies_one_step: [batch_size, t, t]
:param prior_pi: [batch_size, t]
:param prior_pointer: [batch_size, t]
:return:
"""
prior_pi_shuffled = prior_pi.dimshuffle(0, 1, 'x')
pi_t = T.max(prior_pi_shuffled + energies_one_step, axis=1)
pointer_t = T.argmax(prior_pi_shuffled + energies_one_step, axis=1)
return [pi_t, pointer_t]
def back_pointer(pointer, pointer_tp1):
"""
:param pointer: [batch, t]
:param point_tp1: [batch,]
:return:
"""
return pointer[T.arange(pointer.shape[0]), pointer_tp1]
# Input should be provided as (n_batch, n_time_steps, num_labels, num_labels)
# but scan requires the iterable dimension to be first
# So, we need to dimshuffle to (n_time_steps, n_batch, num_labels, num_labels)
energies_shuffled = energies.dimshuffle(1, 0, 2, 3)
# pi at time 0 is the last rwo at time 0. but we need to remove the last column which is the pad symbol.
pi_time0 = energies_shuffled[0, :, -1, :-1]
# the last row and column is the tag for pad symbol. reduce these two dimensions by 1 to remove that.
# now the shape of energies_shuffled is [n_time_steps, b_batch, t, t] where t = num_labels - 1.
energies_shuffled = energies_shuffled[:, :, :-1, :-1]
initials = [pi_time0, T.cast(T.fill(pi_time0, -1), 'int64')]
[pis, pointers], _ = theano.scan(fn=inner_function,
outputs_info=initials,
sequences=[energies_shuffled[1:]])
pi_n = pis[-1]
pointer_n = T.argmax(pi_n, axis=1)
back_pointers, _ = theano.scan(fn=back_pointer,
outputs_info=pointer_n,
sequences=[pointers],
go_backwards=True)
# prediction shape [batch_size, length]
prediction_revered = T.concatenate(
[pointer_n.dimshuffle(0, 'x'),
back_pointers.dimshuffle(1, 0)], axis=1)
prediction = prediction_revered[:,
T.arange(prediction_revered.shape[1] -
1, -1, -1)]
return prediction
def __init__(self, mean, var, rng=None):
self.mean = mean
# This allows to use var with shape (1, 1, n)
self.var = T.fill(mean, var)
self.stt = T.concatenate((mean, self.var), -1)
self.maximum = self.mean
super(DiagGauss, self).__init__(rng)
def chain_crf_loss(energies, targets, masks):
"""
compute minus log likelihood of chain crf as chain crf loss.
:param energies: Theano 4D tensor
energies of each step. the shape is [batch_size, n_time_steps, num_labels, num_labels],
where the pad label index is at last.
:param targets: Theano 2D tensor
targets in the shape [batch_size, n_time_steps]
:param masks: Theano 2D tensor
masks in the shape [batch_size, n_time_steps]
:return: Theano 1D tensor
an expression for minus log likelihood loss.
"""
assert energies.ndim == 4
assert targets.ndim == 2
assert masks.ndim == 2
def inner_function(energies_one_step, targets_one_step, mask_one_step, prior_partition, prev_label, tg_energy):
"""
:param energies_one_step: [batch_size, t, t]
:param targets_one_step: [batch_size]
:param prior_partition: [batch_size, t]
:param prev_label: [batch_size]
:param tg_energy: [batch_size]
:return:
"""
partition_shuffled = prior_partition.dimshuffle(0, 1, 'x')
partition_t = T.switch(mask_one_step.dimshuffle(0, 'x'),
theano_logsumexp(energies_one_step + partition_shuffled, axis=1),
prior_partition)
return [partition_t, targets_one_step,
tg_energy + energies_one_step[T.arange(energies_one_step.shape[0]), prev_label, targets_one_step]]
# Input should be provided as (n_batch, n_time_steps, num_labels, num_labels)
# but scan requires the iterable dimension to be first
# So, we need to dimshuffle to (n_time_steps, n_batch, num_labels, num_labels)
energies_shuffled = energies.dimshuffle(1, 0, 2, 3)
targets_shuffled = targets.dimshuffle(1, 0)
masks_shuffled = masks.dimshuffle(1, 0)
# initials should be energies_shuffles[0, :, -1, :]
init_label = T.cast(T.fill(energies[:, 0, 0, 0], -1), 'int32')
energy_time0 = energies_shuffled[0]
target_time0 = targets_shuffled[0]
initials = [energies_shuffled[0, :, -1, :], target_time0,
energy_time0[T.arange(energy_time0.shape[0]), init_label, target_time0]]
[partitions, _, target_energies], _ = theano.scan(fn=inner_function, outputs_info=initials,
sequences=[energies_shuffled[1:], targets_shuffled[1:],
masks_shuffled[1:]])
partition = partitions[-1]
target_energy = target_energies[-1]
loss = theano_logsumexp(partition, axis=1) - target_energy
return loss
def __init__(self, mean, var, rng=None):
self.mean = mean
# This allows to use var with shape (1, 1, n)
self.var = T.fill(mean, var)
self.stt = T.concatenate((mean, self.var), -1)
self.maximum = self.mean
super(DiagGauss, self).__init__(rng)
def transform_targets(targets):
"""Transform targets into a format suitable for passing to cost()."""
reshaped = T.shape_padleft(targets)
blanks = T.fill(reshaped, _BLANK)
result = T.concatenate([blanks, reshaped]).dimshuffle(1, 0, 2).reshape(
(2 * targets.shape[0], targets.shape[1]))
result = T.concatenate([result, T.shape_padleft(result[0])])
return result
def _FindB_best(lPLcl, lPprev, dVLcl):
srtLcl = tensor.argsort(-lPLcl)
srtLcl = srtLcl[:beam_size]
deltaVec = tensor.fill(lPLcl[srtLcl], numpy_floatX(-10000.))
deltaVec = tensor.set_subtensor(deltaVec[0], lPprev)
lProbBest = ifelse(tensor.eq(dVLcl, tensor.zeros_like(dVLcl)),
lPLcl[srtLcl] + lPprev, deltaVec)
xWIdxBest = ifelse(tensor.eq(dVLcl, tensor.zeros_like(dVLcl)),
srtLcl, tensor.zeros_like(srtLcl))
return lProbBest, xWIdxBest
def MRR_loss(y_true, y_pred):
'''
Training data have to be Xloop, Xtap, target = utils.MakeTrainingDataRank(Loop, Tap)
Batch size have to be 40
'''
comp = T.zeros_like(y_true)
comp = T.fill(comp, T.mean(y_pred[T.argmax(y_true)]))
Rank = T.sum(T.gt(comp, y_pred))
#T.dot(y_pred[39].T, T.ones_like(y_true).T)
return Rank + T.mean(y_true) * 0 + T.mean(y_pred) * 0
def output_deterministic(self, output_previous):
# We add an additional input with value 1
output_previous_with_bias = \
T.concatenate([ output_previous, T.alloc(1, 1) ], 0) / \
T.sqrt(self.n_inputs)
# We compute the mean and variance after the linear operation
a = T.dot(self.w, output_previous_with_bias)
if (self.non_linear):
# We compute the ReLU activation
a = T.switch(T.lt(a, T.fill(a, 0)), T.fill(a, 0), a)
return a
def vtohMB(self, VsampM):
"""
computes hidden unit outputs given visible unit outputs ("half" a MCMC iteration)
computes in parallel given input rows of visible units
args:
VsampM (T.matrix): rows of visible unit outputs
returns:
a T.matrix, rows of hidden unit outputs
"""
Vomg = T.matrix(name="Vomg", dtype=theano.config.floatX)
vtohMBres = T.matrix(name ="vtohMBres", dtype=theano.config.floatX)
T_HP = T.matrix(name="T_HP", dtype=theano.config.floatX)
Vomg = T.dot(T.mul(T.fill(VsampM, T.exp(-self.T_z)), VsampM), self.T_omega)
T_Hp = T.nnet.ultra_fast_sigmoid(T.fill(Vomg, self.T_b) + Vomg)
vtohMBres = self.T_rng.binomial(size = T_Hp.shape, p=T_Hp, dtype=theano.config.floatX)
return vtohMBres
def _gen_exprs(self, inpt):
"""Return the exprssions of the recognition model."""
P = self.parameters.gen
n_layers = len(self.n_hiddens_gen)
hidden_to_hiddens = [getattr(P, 'hidden_to_hidden_%i' % i)
for i in range(n_layers - 1)]
hidden_biases = [getattr(P, 'hidden_bias_%i' % i)
for i in range(n_layers)]
initial_hidden_means = [getattr(P, 'initial_hidden_means_%i' % i)
for i in range(n_layers)]
initial_hidden_vars = [getattr(P, 'initial_hidden_vars_%i' % i)
for i in range(n_layers)]
recurrents = [getattr(P, 'recurrent_%i' % i)
for i in range(n_layers)]
shortcut_size = self.n_hiddens_recog[-1]
p_dropout_inpt = T.zeros_like(inpt[:, :, :self.n_latent])
p_dropout_inpt = T.fill(p_dropout_inpt, self.p_dropout_inpt)
p_dropout_shortcut = T.zeros_like(inpt[:, :, self.n_latent:])
p_dropout_shortcut = T.fill(p_dropout_shortcut, self.p_dropout_inpt)
p_dropout_inpt = T.concatenate([p_dropout_inpt, p_dropout_shortcut],
axis=2)
p_dropouts = [p_dropout_inpt] + self.p_dropout_hiddens
if self.p_dropout_hidden_to_out is None:
p_dropouts.append(self.p_dropout_hiddens[-1])
else:
p_dropouts.append(self.p_dropout_hidden_to_out)
exprs = vprnn.exprs(
inpt, T.zeros_like(inpt), P.in_to_hidden, hidden_to_hiddens, P.hidden_to_out,
hidden_biases, [1 for _ in hidden_biases],
initial_hidden_means, initial_hidden_vars,
recurrents,
P.out_bias, 1, self.gen_transfers, self.assumptions.statify_visible,
p_dropouts=p_dropouts)
return exprs
def htovMB(self, HsampM):
"""
computes visible unit outputs given hidden unit inputs ("half" a MCMC iteration)
computes in parallel given input rows of hidden units
args:
HsampM (T.matrix): rows of hidden unit inputs
returns:
a T.matrix, rows of visible unit outputs
"""
T_omgH = T.matrix(name="T_omgH", dtype=theano.config.floatX)
T_means = T.matrix(name="T_means", dtype=theano.config.floatX)
htovMBres = T.matrix(name="htovMBres", dtype=theano.config.floatX)
T_omgH = T.transpose(T.dot(self.T_omega, T.transpose(HsampM)))
T_means = T.fill(T_omgH, self.T_a) + T_omgH
htovMBres = self.T_rng.normal(size=T_means.shape, avg=T_means, std=T.fill(T_means,T.sqrt(T.exp(self.T_z))), dtype=theano.config.floatX)
return htovMBres
def _log_likelihood(self, x_vars, means):
"""
This function computes the symbolic log-likelihood for a diagonal gaussian defined by the given
means and a fixed sigma.
:param x_vars:
:param means:
:return:
"""
std = T.fill(T.zeros_like(means), self.policy.sigma)
zs = (x_vars - means) / std
return -T.sum(T.log(std), axis=-1)\
-0.5 * T.sum(T.square(zs), axis=-1)\
-0.5 * means.shape[-1] * np.log(2 * np.pi)
def CalculateCosineS(self, options, ctx=None, proj_h=None, h_mask=None):
r = options['r']
fill_matrix = tensor.ones_like(h_mask) - h_mask
norm_ctx = ctx.norm(2, 2)
norm_proj = (proj_h + fill_matrix[:, :, None]).norm(2, 2) * h_mask
mul_cp = (ctx * proj_h).sum(2)
cos_cp = mul_cp / (norm_ctx * norm_proj + fill_matrix)
r_ = tensor.zeros_like(cos_cp)
r_ = tensor.fill(r_, r)
exp_cp = tensor.exp(cos_cp * r_) * h_mask
p = exp_cp / (exp_cp.sum(0)[None, :] +
tensor.min(fill_matrix, axis=0)[None, :])
return p
def _r_loss(self, preds, y):
"""
:param preds: (n_batch, T) this variable stores the predictions for one path of size T
:param y: (n_batch, ) this variable stores the targets
:return:
"""
# y_rep: (n_batch, T, )
y_rep = T.stack([
T.fill(T.zeros((self.policy.n_steps)), y[b])
for b in xrange(self.policy.n_batch)
],
axis=0)
#return T.nnet.binary_crossentropy(probs[:, :, 1], y_rep).mean(axis=[0,1])
return T.neq(preds, y_rep).mean(axis=[0, 1])
def CalculateCosine(self, options, ctx=None, proj_h=None, ctx_mask=None):
r = options['r']
fill_matrix = tensor.ones_like(ctx_mask) - ctx_mask
norm_ctx = (ctx + fill_matrix[:, :, None]).norm(2, 2) * ctx_mask
norm_proj = proj_h.norm(2, 1)
mul_cp = (ctx * proj_h[None, :, :]).sum(2)
cos_cp = mul_cp / (norm_ctx * norm_proj[None, :] + fill_matrix)
r_ = tensor.zeros_like(cos_cp)
r_ = tensor.fill(r_, r)
exp_cp = tensor.exp(cos_cp * r_) * ctx_mask
p = exp_cp / (exp_cp.sum(0)[None, :] +
tensor.min(fill_matrix, axis=0)[None, :])
prob_max = p.argmax(0)
return p, prob_max
def test_1msigmoid(self):
if not register_local_1msigmoid:
return
m = self.get_mode()
x = T.fmatrix()
# tests exp_over_1_plus_exp
f = theano.function([x], 1 - T.exp(x) / (1 + T.exp(x)), mode=m)
assert [node.op for node in f.maker.fgraph.toposort()] == [tensor.neg, sigmoid_inplace]
# tests inv_1_plus_exp
f = theano.function([x], 1 - T.fill(x, 1.0) / (1 + T.exp(-x)), mode=m)
assert [node.op for node in f.maker.fgraph.toposort()] == [tensor.neg, sigmoid_inplace]
def get_pi_from_v(self, Q):
if self.v_to_pi == 'greedy' or self.v_to_pi == 'e-greedy':
greedy_actions = T.argmax(Q, axis=-1)
greedy_pi = T.extra_ops.to_one_hot(greedy_actions,
nb_class=5,
dtype='int32')
if self.v_to_pi == 'greedy':
return greedy_pi
else:
return T.fill(
Q, self.epsilon / 5) + (1 - self.epsilon) * greedy_pi
elif self.v_to_pi == 'softmax':
return T.nnet.softmax(Q)
else:
raise Exception()
def test_1msigmoid(self):
if not register_local_1msigmoid:
return
m = self.get_mode()
x = T.fmatrix()
# tests exp_over_1_plus_exp
f = theano.function([x], 1 - T.exp(x) / (1 + T.exp(x)), mode=m)
assert [node.op for node in f.maker.fgraph.toposort()] == [
tensor.neg, sigmoid_inplace]
# tests inv_1_plus_exp
f = theano.function([x], 1 - T.fill(x, 1.0) / (1 + T.exp(-x)), mode=m)
assert [node.op for node in f.maker.fgraph.toposort()] == [tensor.neg,
sigmoid_inplace]
def CalculateCosine_webS(self,
options,
ctx=None,
proj_h=None,
mask_x=None):
r = options['r']
norm_ctx = ctx.norm(2, 2)
norm_proj = proj_h.norm(2, 2)
mul_cp = (ctx * proj_h).sum(2)
cos_cp = mul_cp / (norm_ctx * norm_proj + 0.0001)
r_ = tensor.zeros_like(cos_cp)
r_ = tensor.fill(r_, r)
exp_cp = tensor.exp(cos_cp * r_)
exp_cp_ = exp_cp * (mask_x.reshape(
[mask_x.shape[0], ctx.shape[0], ctx.shape[1]]).max(0))
p = exp_cp_ / (exp_cp_.sum(0)[None, :] + 0.0001)
return p
def _step2(diag_, state_, hs_, Cs_):
hs, Cs = [], []
token_idxs = tensor.cast(state_.argmax(axis=-1), "int32")
msk_ = tensor.fill(
(tensor.zeros_like(token_idxs, dtype="float32")), 1)
msk_ = msk_.dimshuffle('x', 0)
state_below0 = self.de_lookuptable[token_idxs].reshape(
(1, encoderInputs.shape[1], self.de_hidden_size))
for i, lstm in enumerate(self.decoder_lstm_layers):
h, C = lstm.forward(state_below0, msk_, hs_[i],
Cs_[i]) #mind msk
hs += h[-1],
Cs += C[-1],
state_below0 = h
hs, Cs = tensor.as_tensor_variable(hs), tensor.as_tensor_variable(
Cs)
state_below0 = state_below0.reshape(
(encoderInputs.shape[1], self.de_hidden_size))
attn_index = tensor.nonzero(diag_, True)
attn_value = tensor.nonzero_values(diag_)
en_context = Encoder_shuffle[:, attn_index[0], :]
attn_context = Encoder_shuffle_re[:, attn_index[0], :]
attn_weight = tensor.batched_dot(attn_context, state_below0)
attn_weight = tensor.nnet.softmax(attn_weight)
#attn_weight *= (encoderMask.dimshuffle(1,0))
attn_weight *= (attn_value.dimshuffle('x', 0))
##attn_weight = attn_weight/(tensor.sum(attn_weight, axis=1).dimshuffle(0,'x'))
####### ctx_ : (b, h)
ctx_ = tensor.sum(en_context * attn_weight[:, :, None], axis=1)
state_below0 = tensor.concatenate([ctx_, state_below0], axis=1)
newpred = tensor.dot(state_below0,
self.linear) + self.linear_bias[None, :]
state_below = tensor.nnet.softmax(newpred)
##### the beging symbole probablity is 0
extra_p = tensor.zeros_like(hs[:, :, 0])
state_below = tensor.concatenate([state_below, extra_p.T], axis=1)
return state_below, hs, Cs
def crf_loss(energies, targets, masks):
assert energies.ndim == 4
assert targets.ndim == 2
assert masks.ndim == 2
def inner_function(energies_one_step, targets_one_step, mask_one_step,
prior_partition, prev_label, tg_energy):
partition_shuffled = prior_partition.dimshuffle(0, 1, 'x')
partition_t = T.switch(
mask_one_step.dimshuffle(0, 'x'),
theano_logsumexp(energies_one_step + partition_shuffled, axis=1),
prior_partition)
return [
partition_t, targets_one_step,
tg_energy + energies_one_step[T.arange(energies_one_step.shape[0]),
prev_label, targets_one_step]
]
# Input should be provided as (n_batch, n_time_steps, num_labels, num_labels)
# but scan requires the iterable dimension to be first
# So, we need to dimshuffle to (n_time_steps, n_batch, num_labels, num_labels)
energies_shuffled = energies.dimshuffle(1, 0, 2, 3)
targets_shuffled = targets.dimshuffle(1, 0)
masks_shuffled = masks.dimshuffle(1, 0)
# initials should be energies_shuffles[0, :, -1, :]
init_label = T.cast(T.fill(energies[:, 0, 0, 0], -1), 'int32')
energy_time0 = energies_shuffled[0]
target_time0 = targets_shuffled[0]
initials = [
energies_shuffled[0, :, -1, :], target_time0,
energy_time0[T.arange(energy_time0.shape[0]), init_label, target_time0]
]
[partitions, _,
target_energies], _ = theano.scan(fn=inner_function,
outputs_info=initials,
sequences=[
energies_shuffled[1:],
targets_shuffled[1:],
masks_shuffled[1:]
])
partition = partitions[-1]
target_energy = target_energies[-1]
loss = theano_logsumexp(partition, axis=1) - target_energy
return loss
def __init__(self, input, input_sm, vocab_size, emb_dim, local_context_size, global_context_size):
# initialize W_emb
global rng
global init_range
if pretrain_file:
linear_W_emb = load_pretrain_emb(pretrain_file)
print "* Using pretrained linear_W_emb ..."
assert(len(linear_W_emb) == vocab_size)
else:
linear_W_emb = np.asarray(rng.uniform(
low=-init_range, high=init_range, size=(vocab_size, emb_dim)), dtype=theano.config.floatX)
# shared variables
self.W_emb = theano.shared(value=linear_W_emb, name='W_emb')
# stack vectors
input = T.cast(input, 'int32')
# output is a matrix where each row correponds to a context_size embedding vector, and row number equals to batch size
# output dimensions: batch_size * ((context_size + 1) * emb_dim)
output_local = self.W_emb[input[:, :local_context_size].flatten()].reshape(
(input.shape[0], local_context_size * emb_dim)) # self.W_emb.shape[1]
# define symbolic functions for calculating the mean of sentences
W = T.matrix('W')
eos_vector = T.vector('eos_vector')
eos_vector = T.fill(T.zeros_like(input[0,local_context_size:]), io_vocab.VocabConstants.EOS_INDEX)
def weighted_sentence(sentence, W, eos_vector):
sent_len = T.sum(T.neq(sentence, eos_vector))
return T.mean(W[sentence[:sent_len]], axis=0)
output_global, updates = theano.scan(fn=weighted_sentence,
outputs_info=None,
sequences=input[:, local_context_size:],
non_sequences=[self.W_emb, eos_vector])
# concatenate local output and global output to form the output matrix
self.output = T.concatenate([output_local, output_global], axis=1)
# params is the word embedding matrix
self.params = [self.W_emb]
def test_1msigmoid(self):
if not register_local_1msigmoid:
return
m = theano.config.mode
if m == 'FAST_COMPILE':
m = 'FAST_RUN'
x = T.fmatrix()
# tests exp_over_1_plus_exp
f = theano.function([x], 1 - T.exp(x)/(1+T.exp(x)), mode=m)
theano.printing.debugprint(f)
assert [node.op for node in f.maker.env.toposort()] == [tensor.neg, sigmoid_inplace]
# tests inv_1_plus_exp
f = theano.function([x], 1 - T.fill(x,1.0) / (1+T.exp(-x)), mode=m)
theano.printing.debugprint(f)
assert [node.op for node in f.maker.env.toposort()] == [tensor.neg,
sigmoid_inplace]
def crf_loss(energies, targets, masks):
assert energies.ndim == 4
assert targets.ndim == 2
assert masks.ndim == 2
def inner_function(energies_one_step, targets_one_step, mask_one_step,
prior_partition, prev_label, tg_energy):
partition_shuffled = prior_partition.dimshuffle(0, 1, 'x')
partition_t = T.switch(
mask_one_step.dimshuffle(0, 'x'),
theano_logsumexp(energies_one_step + partition_shuffled, axis=1),
prior_partition)
return [
partition_t, targets_one_step,
tg_energy + energies_one_step[T.arange(energies_one_step.shape[0]),
prev_label, targets_one_step]
]
energies_shuffled = energies.dimshuffle(1, 0, 2, 3)
targets_shuffled = targets.dimshuffle(1, 0)
masks_shuffled = masks.dimshuffle(1, 0)
init_label = T.cast(T.fill(energies[:, 0, 0, 0], -1), 'int32')
energy_time0 = energies_shuffled[0]
target_time0 = targets_shuffled[0]
initials = [
energies_shuffled[0, :, -1, :], target_time0,
energy_time0[T.arange(energy_time0.shape[0]), init_label, target_time0]
]
[partitions, _,
target_energies], _ = theano.scan(fn=inner_function,
outputs_info=initials,
sequences=[
energies_shuffled[1:],
targets_shuffled[1:],
masks_shuffled[1:]
])
partition = partitions[-1]
target_energy = target_energies[-1]
loss = theano_logsumexp(partition, axis=1) - target_energy
return loss
def test_1msigmoid(self):
if not register_local_1msigmoid:
return
m = self.get_mode()
x = tt.fmatrix()
# tests exp_over_1_plus_exp
f = theano.function([x], 1 - tt.exp(x) / (1 + tt.exp(x)), mode=m)
assert check_stack_trace(f, ops_to_check=[tt.neg, sigmoid_inplace])
assert [node.op for node in f.maker.fgraph.toposort()] == [
tt.neg,
sigmoid_inplace,
]
# tests inv_1_plus_exp
f = theano.function([x], 1 - tt.fill(x, 1.0) / (1 + tt.exp(-x)), mode=m)
assert check_stack_trace(f, ops_to_check=[tt.neg, sigmoid_inplace])
assert [node.op for node in f.maker.fgraph.toposort()] == [
tt.neg,
sigmoid_inplace,
]
def output_probabilistic(self, m_w_previous, v_w_previous):
# We add an additional deterministic input with mean 1 and variance 0
m_w_previous_with_bias = \
T.concatenate([ m_w_previous, T.alloc(1, 1) ], 0)
v_w_previous_with_bias = \
T.concatenate([ v_w_previous, T.alloc(0, 1) ], 0)
# We compute the mean and variance after the linear operation
m_linear = T.dot(self.m_w, m_w_previous_with_bias) / T.sqrt(self.n_inputs)
v_linear = (T.dot(self.v_w, v_w_previous_with_bias) + \
T.dot(self.m_w**2, v_w_previous_with_bias) + \
T.dot(self.v_w, m_w_previous_with_bias**2)) / self.n_inputs
if (self.non_linear):
# We compute the mean and variance after the ReLU activation
alpha = m_linear / T.sqrt(v_linear)
gamma = Network_layer.gamma(-alpha)
gamma_robust = -alpha - 1.0 / alpha + 2.0 / alpha**3
gamma_final = T.switch(T.lt(-alpha, T.fill(alpha, 30)), gamma, gamma_robust)
v_aux = m_linear + T.sqrt(v_linear) * gamma_final
m_a = Network_layer.n_cdf(alpha) * v_aux
v_a = m_a * v_aux * Network_layer.n_cdf(-alpha) + \
Network_layer.n_cdf(alpha) * v_linear * \
(1 - gamma_final * (gamma_final + alpha))
return (m_a, v_a)
else:
return (m_linear, v_linear)
def _collect_samples(self, y):
"""
This function collect N samples of size T using the current policy.
:param y:
:return: locations (n_batch, N, T, 2), probabilities (n_batch, N, T, n_classes),
rewards (n_batch, N, T, ) and returns (n_batch, N, T, )
"""
means = []
locs = []
probs = []
returns = []
preds = []
# Reshape target labels to match the classification outputs along each path of length T
y_rep = T.stack([
T.fill(T.zeros((self.policy.n_steps)), y[b])
for b in xrange(self.policy.n_batch)
],
axis=0)
for _ in xrange(self.policy.N):
loc_means_t, locs_t, _, x_ts, p_ts = self.policy.step_forward()
locs.append(locs_t)
means.append(loc_means_t)
probs.append(p_ts)
pred = np.argmax(p_ts, axis=2)
preds.append(pred)
rewards = self._acc_score(pred, y_rep)
returns.append(cumsum(rewards, axis=1))
locs = T.stack(locs).dimshuffle(1, 0, *range(2, T.stack(locs).ndim))
means = T.stack(means).dimshuffle(1, 0, *range(2, T.stack(means).ndim))
preds = T.stack(preds).dimshuffle(1, 0, *range(2, T.stack(preds).ndim))
returns = T.stack(returns).dimshuffle(1, 0,
*range(2,
T.stack(returns).ndim))
return locs, means, preds, returns
def myMask(input_t, mask, binarize=False, clip=True):
""" Same of myMaskArr but with theano thensors.
It performs the following :
1) masks *input_t* with *mask*, takes only value bigger than `settings.THRESHOLD`
# no more 2) divides each entry to the maximum value in the resulting tensor
3) if *binarize* is True, sets 1.0 - EPS(0) in the maximum value of each column
and EPS(0) in any other position of the column
This uses EPS(0) to avoid nans in cross-entropy loss function.
If *clip* is True, then the returned tensor will be clipped between EPS(0) and
1.0 - EPS(0)
RETURNS :
theano tensor
"""
assert (input_t.ndim == 4 and mask.ndim == 4 and binarize) or (not binarize),\
"input and mask MUST be 4D tensors to the end of binarization"
masked = mask * input_t
# masked = masked * (masked > settings.THRESHOLD)
# masked = abs(input_t * mask)
# # normalized = masked
# normalized = masked / masked.max()
if binarize:
binarized = T.fill(masked, EPS(0))
max_rows = masked.argmax(axis=2)
max_cols = T.arange(masked.shape[3])
normalized = T.set_subtensor(binarized[0, 0, max_rows, max_cols],
1.0 - EPS(0))
returned = normalized
elif clip:
returned = masked.clip(EPS(0), 1 - EPS(0))
else:
returned = masked
return returned
def test_exp_over_1_plus_exp(self):
m = self.get_mode(excluding=["local_elemwise_fusion"])
x = tt.vector()
data = np.random.rand(54).astype(config.floatX)
backup = config.warn__identify_1pexp_bug
config.warn__identify_1pexp_bug = False
try:
# tests exp_over_1_plus_exp
f = theano.function([x], tt.exp(x) / (1 + tt.exp(x)), mode=m)
assert [node.op for node in f.maker.fgraph.toposort()] == [sigmoid]
f(data)
f = theano.function([x], tt.exp(x) / (2 + tt.exp(x)), mode=m)
assert [node.op for node in f.maker.fgraph.toposort()] != [sigmoid]
f(data)
f = theano.function([x], tt.exp(x) / (1 - tt.exp(x)), mode=m)
assert [node.op for node in f.maker.fgraph.toposort()] != [sigmoid]
f(data)
f = theano.function([x], tt.exp(x + 1) / (1 + tt.exp(x)), mode=m)
assert [node.op for node in f.maker.fgraph.toposort()] != [sigmoid]
f(data)
# tests inv_1_plus_exp
f = theano.function([x], tt.fill(x, 1.0) / (1 + tt.exp(-x)), mode=m)
# todo: solve issue #4589 first
# assert check_stack_trace(f, ops_to_check=sigmoid)
assert [node.op for node in f.maker.fgraph.toposort()] == [sigmoid]
f(data)
f = theano.function([x], tt.fill(x, 1.0) / (2 + tt.exp(-x)), mode=m)
assert [node.op for node in f.maker.fgraph.toposort()] != [sigmoid]
f(data)
f = theano.function([x], tt.fill(x, 1.0) / (1 - tt.exp(-x)), mode=m)
assert [node.op for node in f.maker.fgraph.toposort()] != [sigmoid]
f(data)
f = theano.function([x], tt.fill(x, 1.1) / (1 + tt.exp(-x)), mode=m)
assert [node.op for node in f.maker.fgraph.toposort()] != [sigmoid]
f(data)
# tests inv_1_plus_exp with neg
f = theano.function([x], tt.fill(x, -1.0) / (1 + tt.exp(-x)), mode=m)
# todo: solve issue #4589 first
# assert check_stack_trace(
# f, ops_to_check=[sigmoid, neg_inplace])
assert [node.op for node in f.maker.fgraph.toposort()] == [
sigmoid,
neg_inplace,
]
f(data)
f = theano.function([x], tt.fill(x, -1.0) / (1 - tt.exp(-x)), mode=m)
assert [node.op for node in f.maker.fgraph.toposort()] != [
sigmoid,
neg_inplace,
]
f(data)
f = theano.function([x], tt.fill(x, -1.0) / (2 + tt.exp(-x)), mode=m)
assert [node.op for node in f.maker.fgraph.toposort()] != [
sigmoid,
neg_inplace,
]
f(data)
f = theano.function([x], tt.fill(x, -1.1) / (1 + tt.exp(-x)), mode=m)
assert [node.op for node in f.maker.fgraph.toposort()] != [
sigmoid,
neg_inplace,
]
f(data)
# tests double inv_1_plus_exp with neg
# (-1)(exp(x)) / (1+exp(x))(1+exp(-x))
# = (-1)/(1+exp(-x)) * exp(x)/(1+exp(x))
# = - (sigm(x) * sigm(x))
f = theano.function(
[x],
(tt.fill(x, -1.0) * tt.exp(x)) / ((1 + tt.exp(x)) * (1 + tt.exp(-x))),
mode=m,
)
# todo: solve issue #4589 first
# assert check_stack_trace(f, ops_to_check=[sigmoid, tt.mul])
assert [node.op for node in f.maker.fgraph.toposort()] == [sigmoid, tt.mul]
f(data)
f = theano.function(
[x],
(tt.fill(x, -1.1) * tt.exp(x)) / ((1 + tt.exp(x)) * (1 + tt.exp(-x))),
mode=m,
)
assert [node.op for node in f.maker.fgraph.toposort()] != [
sigmoid,
tt.mul,
neg_inplace,
]
f(data)
f = theano.function(
[x],
(tt.fill(x, -1.0) * tt.exp(x)) / ((2 + tt.exp(x)) * (1 + tt.exp(-x))),
mode=m,
)
assert [node.op for node in f.maker.fgraph.toposort()] != [
sigmoid,
tt.mul,
neg_inplace,
]
f(data)
f = theano.function(
[x],
(tt.fill(x, -1.0) * tt.exp(x)) / ((1 + tt.exp(x)) * (2 + tt.exp(-x))),
mode=m,
)
assert [node.op for node in f.maker.fgraph.toposort()] != [
sigmoid,
tt.mul,
neg_inplace,
]
f(data)
f = theano.function(
[x],
(tt.fill(x, -1.0) * tt.exp(x)) / ((1 + tt.exp(x)) * (1 + tt.exp(x))),
mode=m,
)
assert [node.op for node in f.maker.fgraph.toposort()] != [
sigmoid,
tt.mul,
neg_inplace,
]
f(data)
f = theano.function(
[x],
(tt.fill(x, -1.0) * tt.exp(x)) / ((1 + tt.exp(x)) * (2 + tt.exp(-x))),
mode=m,
)
assert [node.op for node in f.maker.fgraph.toposort()] != [
sigmoid,
tt.mul,
neg_inplace,
]
f(data)
finally:
# Restore config option.
config.warn__identify_1pexp_bug = backup
def MvNormalLogp():
"""Compute the log pdf of a multivariate normal distribution.
This should be used in MvNormal.logp once Theano#5908 is released.
Parameters
----------
cov : tt.matrix
The covariance matrix.
delta : tt.matrix
Array of deviations from the mean.
"""
cov = tt.matrix('cov')
cov.tag.test_value = floatX(np.eye(3))
delta = tt.matrix('delta')
delta.tag.test_value = floatX(np.zeros((2, 3)))
solve_lower = tt.slinalg.Solve(A_structure='lower_triangular')
solve_upper = tt.slinalg.Solve(A_structure='upper_triangular')
cholesky = Cholesky(lower=True, on_error='nan')
n, k = delta.shape
n, k = f(n), f(k)
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
result = n * k * tt.log(f(2) * np.pi)
result += f(2) * n * tt.sum(tt.log(diag))
result += (delta_trans ** f(2)).sum()
result = f(-.5) * result
logp = tt.switch(ok, result, -np.inf)
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]
return theano.OpFromGraph(
[cov, delta], [logp], grad_overrides=dlogp, inline=True)
def chain_crf_accuracy(energies, targets):
"""
decode crf and compute accuracy
:param energies: Theano 4D tensor
energies of each step. the shape is [batch_size, n_time_steps, num_labels, num_labels],
where the pad label index is at last.
:param targets: Theano 2D tensor
targets in the shape [batch_size, n_time_steps]
:return: Theano 1D tensor
an expression for minus log likelihood loss.
"""
assert energies.ndim == 4
assert targets.ndim == 2
def inner_function(energies_one_step, prior_pi, prior_pointer):
"""
:param energies_one_step: [batch_size, t, t]
:param prior_pi: [batch_size, t]
:param prior_pointer: [batch_size, t]
:return:
"""
prior_pi_shuffled = prior_pi.dimshuffle(0, 1, 'x')
pi_t = T.max(prior_pi_shuffled + energies_one_step, axis=1)
pointer_t = T.argmax(prior_pi_shuffled + energies_one_step, axis=1)
return [pi_t, pointer_t]
def back_pointer(pointer, pointer_tp1):
"""
:param pointer: [batch, t]
:param point_tp1: [batch,]
:return:
"""
return pointer[T.arange(pointer.shape[0]), pointer_tp1]
# Input should be provided as (n_batch, n_time_steps, num_labels, num_labels)
# but scan requires the iterable dimension to be first
# So, we need to dimshuffle to (n_time_steps, n_batch, num_labels, num_labels)
energies_shuffled = energies.dimshuffle(1, 0, 2, 3)
# pi at time 0 is the last rwo at time 0. but we need to remove the last column which is the pad symbol.
pi_time0 = energies_shuffled[0, :, -1, :-1]
# the last row and column is the tag for pad symbol. reduce these two dimensions by 1 to remove that.
# now the shape of energies_shuffled is [n_time_steps, b_batch, t, t] where t = num_labels - 1.
energies_shuffled = energies_shuffled[:, :, :-1, :-1]
initials = [pi_time0, T.cast(T.fill(pi_time0, -1), 'int64')]
[pis, pointers], _ = theano.scan(fn=inner_function, outputs_info=initials, sequences=[energies_shuffled[1:]])
pi_n = pis[-1]
pointer_n = T.argmax(pi_n, axis=1)
back_pointers, _ = theano.scan(fn=back_pointer, outputs_info=pointer_n, sequences=[pointers], go_backwards=True)
# prediction shape [batch_size, length]
prediction_revered = T.concatenate([pointer_n.dimshuffle(0, 'x'), back_pointers.dimshuffle(1, 0)], axis=1)
prediction = prediction_revered[:, T.arange(prediction_revered.shape[1] - 1, -1, -1)]
return prediction, T.eq(prediction, targets)
def _InitializeModelThatPredictsCharsMultiSoftmax(self,learning_rate, num_softmaxes=5):
image_input = T.tensor4('image_input')
print ("num_of_softmax: " + str(num_softmaxes))
#prediction_layer = self._BuildModelToPredictFirstChar(image_input)
prediction_layer = self._BuildModelToPredictCharsMultiSoftmax(
image_input, num_softmaxes=num_softmaxes)
target_chars_input = T.imatrix('target_chars_input')
target_chars = target_chars_input[:, :num_softmaxes].reshape(shape=(-1,))
# Create a loss expression for training, Using cross-entropy loss.
prediction = lasagne.layers.get_output(prediction_layer)
l_loss = lasagne.objectives.categorical_crossentropy(prediction, target_chars)
loss = l_loss.mean()
# Create update expressions for training, i.e., how to modify the
# parameters at each training step. Here, we'll use Stochastic Gradient
# Descent (SGD) with Nesterov momentum.
params = lasagne.layers.get_all_params(prediction_layer, trainable=True)
updates = lasagne.updates.nesterov_momentum(
loss, params, learning_rate, momentum=0.9)
#updates = lasagne.updates.adagrad(loss, params, learning_rate=0.0001)
# Create a loss expression for validation/testing. The crucial difference
# here is that we do a deterministic forward pass through the network,
# disabling dropout layers.
test_prediction = lasagne.layers.get_output(prediction_layer, deterministic=True)
test_loss = lasagne.objectives.categorical_crossentropy(test_prediction,
target_chars)
test_loss = test_loss.mean()
predicted_chars = T.argmax(test_prediction, axis=1)
correctly_predicted_chars = T.eq(predicted_chars, target_chars)
# An expression for the classification accuracy:
test_acc = T.mean(correctly_predicted_chars,
dtype=theano.config.floatX)
predicted_chars = predicted_chars.reshape(shape=(-1, num_softmaxes))
correctly_predicted_chars = correctly_predicted_chars.reshape(shape=(-1, num_softmaxes))
num_chars_matched = T.sum(correctly_predicted_chars, axis=1, dtype=theano.config.floatX)
seq_test_acc = T.mean(T.eq(num_chars_matched, T.fill(num_chars_matched, num_softmaxes)),
dtype=theano.config.floatX)
test_prediction = test_prediction.reshape(shape=(-1, num_softmaxes, len(self.CHARS)))
# Compile a function performing a training step on a mini-batch (by giving
# the updates dictionary) and returning the corresponding training loss:
train_fn = theano.function(
[image_input, target_chars_input],
loss,
updates=updates,
allow_input_downcast=True)
# Compile a second function computing the prediction, validation loss and accuracy:
test_fn = theano.function([image_input, target_chars_input],
[test_loss, test_acc, seq_test_acc],
allow_input_downcast=True)
# Compile a third function computing the prediction.
inference_fn = theano.function([image_input],
[predicted_chars, test_prediction],
allow_input_downcast=True)
return prediction_layer, train_fn, test_fn, inference_fn
def _InitializeModelThatPredictsAllChars(
self, learning_rate, bidirectional_rnn=False, use_mask_input=False,
lstm_layer_units=256):
image_input = T.tensor4('image_input')
num_rnn_steps = self.num_rnn_steps
target_chars_input = T.imatrix('target_chars')
target_chars = target_chars_input[:, :num_rnn_steps]
target_chars = target_chars.reshape(shape=(-1,))
mask_input_input = None
mask_input = None
if use_mask_input:
mask_input_input = T.imatrix('mask_input')
mask_input = mask_input_input[:, :num_rnn_steps]
#mask_input = mask_input.reshape(shape=(-1,))
prediction_layer, l_cnn, l_lstm = self._BuildModelToPredictAllChars(
image_input, num_rnn_steps=num_rnn_steps, mask_input=mask_input,
bidirectional_rnn=bidirectional_rnn, lstm_layer_units=lstm_layer_units)
# Create a loss expression for training, Using cross-entropy loss.
#prediction = lasagne.layers.get_output(prediction_layer)
prediction, l_cnn, l_lstm = tuple(
lasagne.layers.get_output([prediction_layer, l_cnn, l_lstm]))
l_loss = lasagne.objectives.categorical_crossentropy(prediction, target_chars)
if use_mask_input:
l_loss = l_loss.reshape(shape=(-1, num_rnn_steps))
l_loss *= mask_input
loss = l_loss.sum() / mask_input.sum()
else:
loss = l_loss.mean()
# Create update expressions for training, i.e., how to modify the
# parameters at each training step. Here, we'll use Stochastic Gradient
# Descent (SGD) with Nesterov momentum.
params = lasagne.layers.get_all_params(prediction_layer, trainable=True)
updates = lasagne.updates.nesterov_momentum(
loss, params, learning_rate, momentum=0.9)
#updates = lasagne.updates.adagrad(loss, params, learning_rate=0.001)
grads = theano.grad(loss, params)
# Create a loss expression for validation/testing. The crucial difference
# here is that we do a deterministic forward pass through the network,
# disabling dropout layers.
test_prediction = lasagne.layers.get_output(prediction_layer, deterministic=True)
test_loss = lasagne.objectives.categorical_crossentropy(test_prediction,
target_chars)
test_loss = test_loss.mean()
predicted_chars = T.argmax(test_prediction, axis=1)
correctly_predicted_chars = T.eq(predicted_chars, target_chars)
# An expression for the classification accuracy:
test_acc = T.mean(correctly_predicted_chars,
dtype=theano.config.floatX)
predicted_chars = predicted_chars.reshape(shape=(-1, num_rnn_steps))
correctly_predicted_chars = correctly_predicted_chars.reshape(shape=(-1, num_rnn_steps))
num_chars_matched = T.sum(correctly_predicted_chars, axis=1, dtype=theano.config.floatX)
seq_test_acc = T.mean(T.eq(num_chars_matched, T.fill(num_chars_matched, num_rnn_steps)),
dtype=theano.config.floatX)
test_prediction = test_prediction.reshape(shape=(-1, num_rnn_steps, len(self.CHARS)))
mask_input_vec = [mask_input_input] if use_mask_input else []
# Compile a function performing a training step on a mini-batch (by giving
# the updates dictionary) and returning the corresponding training loss:
train_fn = theano.function(
[image_input, target_chars_input] + mask_input_vec,
loss,
updates=updates,
allow_input_downcast=True)
# Compile a second function computing the prediction, validation loss and accuracy:
test_fn = theano.function([image_input, target_chars_input] + mask_input_vec,
[test_loss, test_acc, seq_test_acc],
allow_input_downcast=True)
# Compile a third function computing the prediction.
inference_fn = theano.function([image_input] + mask_input_vec,
[predicted_chars, test_prediction],
allow_input_downcast=True)
return prediction_layer, train_fn, test_fn, inference_fn
def __init__(self, voca_size, hidden_size, lstm_layers_num, learning_rate=0.2):
self.voca_size = voca_size
self.hidden_size = hidden_size
self.lstm_layers_num = lstm_layers_num
self.learning_rate = learning_rate
self._train = None
self._utter = None
self.params = []
self.encoder_lstm_layers = []
self.decoder_lstm_layers = []
self.hos = []
self.Cos = []
encoderInputs, encoderMask = tensor.imatrices(2)
decoderInputs, decoderMask, decoderTarget = tensor.imatrices(3)
self.lookuptable = theano.shared(
name="Encoder LookUpTable",
value=utils.init_norm(self.voca_size, self.hidden_size),
borrow=True
)
self.linear = theano.shared(
name="Linear",
value=utils.init_norm(self.hidden_size, self.voca_size),
borrow=True
)
self.params += [self.lookuptable, self.linear] #concatenate
#(max_sent_size, batch_size, hidden_size)
state_below = self.lookuptable[encoderInputs.flatten()].reshape((encoderInputs.shape[0], encoderInputs.shape[1], self.hidden_size))
for _ in range(self.lstm_layers_num):
enclstm = LSTM(self.hidden_size)
self.encoder_lstm_layers += enclstm, #append
self.params += enclstm.params #concatenate
hs, Cs = enclstm.forward(state_below, encoderMask)
self.hos += hs[-1],
self.Cos += Cs[-1],
state_below = hs
state_below = self.lookuptable[decoderInputs.flatten()].reshape((decoderInputs.shape[0], decoderInputs.shape[1], self.hidden_size))
for i in range(self.lstm_layers_num):
declstm = LSTM(self.hidden_size)
self.decoder_lstm_layers += declstm, #append
self.params += declstm.params #concatenate
ho, Co = self.hos[i], self.Cos[i]
state_below, Cs = declstm.forward(state_below, decoderMask, ho, Co)
decoder_lstm_outputs = state_below
ei, em, di, dm, dt = tensor.imatrices(5) #place holders
#####################################################
#####################################################
linear_outputs = tensor.dot(decoder_lstm_outputs, self.linear)
softmax_outputs, updates = theano.scan(
fn=lambda x: tensor.nnet.softmax(x),
sequences=[linear_outputs],
)
def _NLL(pred, y, m):
return -m * tensor.log(pred[tensor.arange(decoderInputs.shape[1]), y])
costs, updates = theano.scan(fn=_NLL, sequences=[softmax_outputs, decoderTarget, decoderMask])
loss = costs.sum() / decoderMask.sum()
gparams = [tensor.grad(loss, param) for param in self.params]
updates = [(param, param - self.learning_rate*gparam) for param, gparam in zip(self.params, gparams)]
self._train = theano.function(
inputs=[ei, em, di, dm, dt],
outputs=[loss, costs],
updates=updates,
givens={encoderInputs:ei, encoderMask:em, decoderInputs:di, decoderMask:dm, decoderTarget:dt}
)
#####################################################
#####################################################
hs0, Cs0 = tensor.as_tensor_variable(self.hos, name="hs0"), tensor.as_tensor_variable(self.Cos, name="Cs0")
token_idxs = tensor.fill( tensor.zeros_like(decoderInputs, dtype="int32"), utils.idx_start)
msk = tensor.fill( (tensor.zeros_like(decoderInputs, dtype="int32")), 1)
def _step(token_idxs, hs_, Cs_):
hs, Cs = [], []
state_below = self.lookuptable[token_idxs].reshape((decoderInputs.shape[0], decoderInputs.shape[1], self.hidden_size))
for i, lstm in enumerate(self.decoder_lstm_layers):
h, C = lstm.forward(state_below, msk, hs_[i], Cs_[i]) #mind msk
hs += h[-1],
Cs += C[-1],
state_below = h
hs, Cs = tensor.as_tensor_variable(hs), tensor.as_tensor_variable(Cs)
next_token_idx = tensor.cast( tensor.dot(state_below, self.linear).argmax(axis=-1), "int32" )
return next_token_idx, hs, Cs
outputs, updates = theano.scan(
fn=_step,
outputs_info=[token_idxs, hs0, Cs0],
n_steps=utils.max_sent_size
)
listof_token_idx = outputs[0]
self._utter = theano.function(
inputs=[ei, em, di],
outputs=listof_token_idx,
givens={encoderInputs:ei, encoderMask:em, decoderInputs:di}
#givens={encoderInputs:ei, encoderMask:em}
)
def test_exp_over_1_plus_exp(self):
m = self.get_mode(excluding=['local_elemwise_fusion'])
x = T.vector()
data = numpy.random.rand(54).astype(config.floatX)
backup = config.warn.identify_1pexp_bug
config.warn.identify_1pexp_bug = False
try:
# tests exp_over_1_plus_exp
f = theano.function([x], T.exp(x) / (1 + T.exp(x)), mode=m)
assert [node.op for node in f.maker.fgraph.toposort()] == [sigmoid]
f(data)
f = theano.function([x], T.exp(x) / (2 + T.exp(x)), mode=m)
assert [node.op for node in f.maker.fgraph.toposort()] != [sigmoid]
f(data)
f = theano.function([x], T.exp(x) / (1 - T.exp(x)), mode=m)
assert [node.op for node in f.maker.fgraph.toposort()] != [sigmoid]
f(data)
f = theano.function([x], T.exp(x + 1) / (1 + T.exp(x)), mode=m)
assert [node.op for node in f.maker.fgraph.toposort()] != [sigmoid]
f(data)
# tests inv_1_plus_exp
f = theano.function([x], T.fill(x, 1.0) / (1 + T.exp(-x)), mode=m)
assert [node.op for node in f.maker.fgraph.toposort()] == [sigmoid]
f(data)
f = theano.function([x], T.fill(x, 1.0) / (2 + T.exp(-x)), mode=m)
assert [node.op for node in f.maker.fgraph.toposort()] != [sigmoid]
f(data)
f = theano.function([x], T.fill(x, 1.0) / (1 - T.exp(-x)), mode=m)
assert [node.op for node in f.maker.fgraph.toposort()] != [sigmoid]
f(data)
f = theano.function([x], T.fill(x, 1.1) / (1 + T.exp(-x)), mode=m)
assert [node.op for node in f.maker.fgraph.toposort()] != [sigmoid]
f(data)
# tests inv_1_plus_exp with neg
f = theano.function([x], T.fill(x, -1.0) / (1 + T.exp(-x)), mode=m)
assert [node.op for node in f.maker.fgraph.toposort()] == [sigmoid,
theano.tensor.inplace.neg_inplace]
f(data)
f = theano.function([x], T.fill(x, -1.0) / (1 - T.exp(-x)), mode=m)
assert [node.op for node in f.maker.fgraph.toposort()] != [sigmoid,
theano.tensor.inplace.neg_inplace]
f(data)
f = theano.function([x], T.fill(x, -1.0) / (2 + T.exp(-x)), mode=m)
assert [node.op for node in f.maker.fgraph.toposort()] != [sigmoid,
theano.tensor.inplace.neg_inplace]
f(data)
f = theano.function([x], T.fill(x, -1.1) / (1 + T.exp(-x)), mode=m)
assert [node.op for node in f.maker.fgraph.toposort()] != [sigmoid,
theano.tensor.inplace.neg_inplace]
f(data)
# tests double inv_1_plus_exp with neg
# (-1)(exp(x)) / (1+exp(x))(1+exp(-x))
# = (-1)/(1+exp(-x)) * exp(x)/(1+exp(x))
# = - (sigm(x) * sigm(x))
f = theano.function([x], (T.fill(x, -1.0) * T.exp(x)) /
((1 + T.exp(x)) * (1 + T.exp(-x))), mode=m)
assert [node.op for node in f.maker.fgraph.toposort()] == [sigmoid,
T.mul]
f(data)
f = theano.function([x], (T.fill(x, -1.1) * T.exp(x)) /
((1 + T.exp(x)) * (1 + T.exp(-x))), mode=m)
assert [node.op for node in f.maker.fgraph.toposort()] != [sigmoid,
T.mul, theano.tensor.inplace.neg_inplace]
f(data)
f = theano.function([x], (T.fill(x, -1.0) * T.exp(x)) /
((2 + T.exp(x)) * (1 + T.exp(-x))), mode=m)
assert [node.op for node in f.maker.fgraph.toposort()] != [sigmoid,
T.mul, theano.tensor.inplace.neg_inplace]
f(data)
f = theano.function([x], (T.fill(x, -1.0) * T.exp(x)) /
((1 + T.exp(x)) * (2 + T.exp(-x))), mode=m)
assert [node.op for node in f.maker.fgraph.toposort()] != [sigmoid,
T.mul, theano.tensor.inplace.neg_inplace]
f(data)
f = theano.function([x], (T.fill(x, -1.0) * T.exp(x)) /
((1 + T.exp(x)) * (1 + T.exp(x))), mode=m)
assert [node.op for node in f.maker.fgraph.toposort()] != [sigmoid,
T.mul, theano.tensor.inplace.neg_inplace]
f(data)
f = theano.function([x], (T.fill(x, -1.0) * T.exp(x)) /
((1 + T.exp(x)) * (2 + T.exp(-x))), mode=m)
assert [node.op for node in f.maker.fgraph.toposort()] != [sigmoid,
T.mul, theano.tensor.inplace.neg_inplace]
f(data)
finally:
# Restore config option.
config.warn.identify_1pexp_bug = backup
