def rmsprop(lr, tparams, grads, x, mask, y, cost):
"""
A variant of SGD that scales the step size by running average of the
recent step norms.
Parameters
----------
lr : Theano SharedVariable
Initial learning rate
tpramas: Theano SharedVariable
Model parameters
grads: Theano variable
Gradients of cost w.r.t to parameres
x: Theano variable
Model inputs
mask: Theano variable
Sequence mask
y: Theano variable
Targets
cost: Theano variable
Objective fucntion to minimize
Notes
-----
For more information, see [Hint2014]_.
.. [Hint2014] Geoff Hinton, *Neural Networks for Machine Learning*,
lecture 6a,
http://cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf
"""
zipped_grads = [
theano.shared(p.get_value() * utils.numpy_floatX(0.0), name="%s_grad" % k) for k, p in tparams.iteritems()
]
running_grads = [
theano.shared(p.get_value() * utils.numpy_floatX(0.0), name="%s_rgrad" % k) for k, p in tparams.iteritems()
]
running_grads2 = [
theano.shared(p.get_value() * utils.numpy_floatX(0.0), name="%s_rgrad2" % k) for k, p in tparams.iteritems()
]
zgup = [(zg, g) for zg, g in zip(zipped_grads, grads)]
rgup = [(rg, 0.95 * rg + 0.05 * g) for rg, g in zip(running_grads, grads)]
rg2up = [(rg2, 0.95 * rg2 + 0.05 * (g ** 2)) for rg2, g in zip(running_grads2, grads)]
f_grad_shared = theano.function([x, mask, y], cost, updates=zgup + rgup + rg2up, name="rmsprop_f_grad_shared")
updir = [
theano.shared(p.get_value() * utils.numpy_floatX(0.0), name="%s_updir" % k) for k, p in tparams.iteritems()
]
updir_new = [
(ud, 0.9 * ud - 1e-4 * zg / tensor.sqrt(rg2 - rg ** 2 + 1e-4))
for ud, zg, rg, rg2 in zip(updir, zipped_grads, running_grads, running_grads2)
]
param_up = [(p, p + udn[1]) for p, udn in zip(tparams.values(), updir_new)]
f_update = theano.function(
[lr], [], updates=updir_new + param_up, on_unused_input="ignore", name="rmsprop_f_update"
)
return f_grad_shared, f_update
def adadelta(lr, tparams, grads, x, mask, y, cost):
"""
An adaptive learning rate optimizer
Parameters
----------
lr : Theano SharedVariable
Initial learning rate
tpramas: Theano SharedVariable
Model parameters
grads: Theano variable
Gradients of cost w.r.t to parameres
x: Theano variable
Model inputs
mask: Theano variable
Sequence mask
y: Theano variable
Targets
cost: Theano variable
Objective fucntion to minimize
Notes
-----
For more information, see [ADADELTA]_.
.. [ADADELTA] Matthew D. Zeiler, *ADADELTA: An Adaptive Learning
Rate Method*, arXiv:1212.5701.
"""
zipped_grads = [theano.shared(p.get_value() * utils.numpy_floatX(0.),
name='%s_grad' % k)
for k, p in tparams.items()]
running_up2 = [theano.shared(p.get_value() * utils.numpy_floatX(0.),
name='%s_rup2' % k)
for k, p in tparams.items()]
running_grads2 = [theano.shared(p.get_value() * utils.numpy_floatX(0.),
name='%s_rgrad2' % k)
for k, p in tparams.items()]
zgup = [(zg, g) for zg, g in zip(zipped_grads, grads)]
rg2up = [(rg2, 0.95 * rg2 + 0.05 * (g ** 2))
for rg2, g in zip(running_grads2, grads)]
f_grad_shared = theano.function([x, mask, y], cost, updates=zgup + rg2up,
name='adadelta_f_grad_shared')
updir = [-T.sqrt(ru2 + 1e-6) / T.sqrt(rg2 + 1e-6) * zg
for zg, ru2, rg2 in zip(zipped_grads,
running_up2,
running_grads2)]
ru2up = [(ru2, 0.95 * ru2 + 0.05 * (ud ** 2))
for ru2, ud in zip(running_up2, updir)]
param_up = [(p, p + ud) for p, ud in zip(tparams.values(), updir)]
f_update = theano.function([lr], [], updates=ru2up + param_up,
on_unused_input='ignore',
name='adadelta_f_update')
return f_grad_shared, f_update
def encoder(tparams,
state_below,
mask,
seq_output=False,
prefix='lstm_encoder'):
""" state_below: size of n_steps * n_samples * n_x
"""
n_steps = state_below.shape[0]
n_samples = state_below.shape[1]
n_h = tparams[_p(prefix, 'U')].shape[0]
def _slice(_x, n, dim):
if _x.ndim == 3:
return _x[:, :, n * dim:(n + 1) * dim]
return _x[:, n * dim:(n + 1) * dim]
state_below_ = tensor.dot(state_below, tparams[_p(prefix, 'W')]) + \
tparams[_p(prefix, 'b')]
def _step(m_, x_, h_, c_, U):
preact = tensor.dot(h_, U)
preact += x_
i = tensor.nnet.sigmoid(_slice(preact, 0, n_h))
f = tensor.nnet.sigmoid(_slice(preact, 1, n_h))
o = tensor.nnet.sigmoid(_slice(preact, 2, n_h))
c = tensor.tanh(_slice(preact, 3, n_h))
c = f * c_ + i * c
c = m_[:, None] * c + (1. - m_)[:, None] * c_
h = o * tensor.tanh(c)
h = m_[:, None] * h + (1. - m_)[:, None] * h_
return h, c
seqs = [mask, state_below_]
rval, updates = theano.scan(_step,
sequences=seqs,
outputs_info=[
tensor.alloc(numpy_floatX(0.), n_samples,
n_h),
tensor.alloc(numpy_floatX(0.), n_samples,
n_h)
],
non_sequences=[tparams[_p(prefix, 'U')]],
name=_p(prefix, '_layers'),
n_steps=n_steps,
strict=True)
h_rval = rval[0]
if seq_output:
return h_rval
else:
# size of n_samples * n_h
return h_rval[-1]
def encoder(tparams, state_below, mask, seq_output=False, prefix='lstm_encoder'):
""" state_below: size of n_steps * n_samples * n_x
"""
n_steps = state_below.shape[0]
n_samples = state_below.shape[1]
n_h = tparams[_p(prefix,'U')].shape[0]
def _slice(_x, n, dim):
if _x.ndim == 3:
return _x[:, :, n*dim:(n+1)*dim]
return _x[:, n*dim:(n+1)*dim]
state_below_ = tensor.dot(state_below, tparams[_p(prefix, 'W')]) + \
tparams[_p(prefix, 'b')]
def _step(m_, x_, h_, c_, U):
preact = tensor.dot(h_, U)
preact += x_
i = tensor.nnet.sigmoid(_slice(preact, 0, n_h))
f = tensor.nnet.sigmoid(_slice(preact, 1, n_h))
o = tensor.nnet.sigmoid(_slice(preact, 2, n_h))
c = tensor.tanh(_slice(preact, 3, n_h))
c = f * c_ + i * c
c = m_[:, None] * c + (1. - m_)[:, None] * c_
h = o * tensor.tanh(c)
h = m_[:, None] * h + (1. - m_)[:, None] * h_
return h, c
seqs = [mask, state_below_]
rval, updates = theano.scan(_step,
sequences=seqs,
outputs_info=[tensor.alloc(numpy_floatX(0.),
n_samples,n_h),
tensor.alloc(numpy_floatX(0.),
n_samples,n_h)],
non_sequences = [tparams[_p(prefix, 'U')]],
name=_p(prefix, '_layers'),
n_steps=n_steps,
strict=True)
h_rval = rval[0]
if seq_output:
return h_rval
else:
# size of n_samples * n_h
return h_rval[-1]
def decoder_layer(tparams, state_below, prefix='decoder_lstm'):
""" state_below: size of n_steps * n_samples * n_x
"""
nsteps = state_below.shape[0]
n_h = tparams[_p(prefix, 'U')].shape[0]
if state_below.ndim == 3:
n_samples = state_below.shape[1]
else:
n_samples = 1
def _slice(_x, n, dim):
if _x.ndim == 3:
return _x[:, :, n * dim:(n + 1) * dim]
return _x[:, n * dim:(n + 1) * dim]
def _step(x_, h_, c_, U):
preact = tensor.dot(h_, U)
preact += x_
i = tensor.nnet.sigmoid(_slice(preact, 0, n_h))
f = tensor.nnet.sigmoid(_slice(preact, 1, n_h))
o = tensor.nnet.sigmoid(_slice(preact, 2, n_h))
c = tensor.tanh(_slice(preact, 3, n_h))
c = f * c_ + i * c
h = o * tensor.tanh(c)
return h, c
state_below_ = tensor.dot(state_below, tparams[_p(
prefix, 'W')]) + tparams[_p(prefix, 'b')]
seqs = [state_below_]
rval, updates = theano.scan(_step,
sequences=seqs,
outputs_info=[
tensor.alloc(numpy_floatX(0.), n_samples,
n_h),
tensor.alloc(numpy_floatX(0.), n_samples,
n_h)
],
non_sequences=[tparams[_p(prefix, 'U')]],
name=_p(prefix, '_layers'),
n_steps=nsteps,
strict=True)
h_rval = rval[0]
return h_rval
def rmsprop(lr, tparams, grads, iin, out, updates):
"""
A variant of SGD that scales the step size by running average of the
recent step norms.
Notes
-----
For more information, see [Hint2014]_.
.. [Hint2014] Geoff Hinton, *Neural Networks for Machine Learning*,
lecture 6a,
http://cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf
"""
zipped_grads = [
theano.shared(p.get_value() * utils.numpy_floatX(0.),
name='%s_grad' % k) for k, p in tparams.items()
]
running_grads = [
theano.shared(p.get_value() * utils.numpy_floatX(0.),
name='%s_rgrad' % k) for k, p in tparams.items()
]
running_grads2 = [
theano.shared(p.get_value() * utils.numpy_floatX(0.),
name='%s_rgrad2' % k) for k, p in tparams.items()
]
zgup = [(zg, g) for zg, g in zip(zipped_grads, grads)]
rgup = [(rg, 0.95 * rg + 0.05 * g) for rg, g in zip(running_grads, grads)]
rg2up = [(rg2, 0.95 * rg2 + 0.05 * (g**2))
for rg2, g in zip(running_grads2, grads)]
f_grad_shared = theano.function(iin,
out,
updates=zgup + rgup + rg2up + updates,
name='rmsprop_f_grad_shared')
updir = [
theano.shared(p.get_value() * utils.numpy_floatX(0.),
name='%s_updir' % k) for k, p in tparams.items()
]
updir_new = [(ud, 0.9 * ud - 1e-4 * zg / tensor.sqrt(rg2 - rg**2 + 1e-4))
for ud, zg, rg, rg2 in zip(updir, zipped_grads, running_grads,
running_grads2)]
param_up = [(p, p + udn[1]) for p, udn in zip(tparams.values(), updir_new)]
f_update = theano.function([lr], [],
updates=updir_new + param_up,
on_unused_input='ignore',
name='rmsprop_f_update')
return f_grad_shared, f_update
def perform(self, x):
x1, x2 = x[0], x[1]
nsteps = x1.shape[0]
n_samples = x1.shape[1]
# if x1.ndim == 3:
# n_samples = x1.shape[1]
# else:
# n_samples = 1
#
def _slice(x_t, idx, ndim):
if x_t.ndim == 3:
return x_t[:, :, idx * ndim: (idx + 1) * ndim]
return x_t[:, idx * ndim:(idx + 1) * ndim]
def _step(x_t, h_tm1, c_tm1, W, U, b):
# z = sigmoid( W * x(t) + U * h(t-1) + b)
# zi = W * x(t) + U * h(t-1) + b
zi = T.dot(x_t, W) + T.dot(h_tm1, U) + b
# zi = T.dot(h_tm1, self.Uh)
# zi += x_t
# W = [Wi, Wf, Wo, Wc], U = [Ui, Uf, Uo, Uc], b = [bi, bf, bo, bc]
i = T.nnet.sigmoid(_slice(zi, 0, self.n_output))
f = T.nnet.sigmoid(_slice(zi, 1, self.n_output))
o = T.nnet.sigmoid(_slice(zi, 2, self.n_output))
c = T.tanh(_slice(zi, 3, self.n_output))
c = f * c_tm1 + i * c;
h = o * T.tanh(c)
# output at each time
# s = softmax(w * h_t + b)
return h, c
# h0 and c0 are initialized randomly
h0 = T.alloc(numpy_floatX(0.), n_samples, self.n_output);
c0 = T.alloc(numpy_floatX(0.), n_samples, self.n_output)
h0 = theano.tensor.unbroadcast(h0, 1);
c0 = theano.tensor.unbroadcast(c0, 1)
[h, c], _ = theano.scan(_step, sequences=[x1],
outputs_info=[h0, c0],
non_sequences=[self.Wh, self.Uh, self.bh],
name='blstm_layers', n_steps=nsteps)
[h_reverse, c_reverse], _ = theano.scan(_step, sequences=[x2],
outputs_info=[h0, c0],
non_sequences=[self.Wh_reverse, self.Uh_reverse, self.bh_reverse],
name='blstm_layers_reverse', n_steps=nsteps,
go_backwards=True)
self.input = x
self.output = [h, h_reverse]
def adadelta(lr, tparams, grads, iin, out, updates):
"""
An adaptive learning rate optimizer
Notes
-----
For more information, see [ADADELTA]_.
.. [ADADELTA] Matthew D. Zeiler, *ADADELTA: An Adaptive Learning
Rate Method*, arXiv:1212.5701.
"""
zipped_grads = [
theano.shared(p.get_value() * utils.numpy_floatX(0.),
name='%s_grad' % k) for k, p in tparams.items()
]
running_up2 = [
theano.shared(p.get_value() * utils.numpy_floatX(0.),
name='%s_rup2' % k) for k, p in tparams.items()
]
running_grads2 = [
theano.shared(p.get_value() * utils.numpy_floatX(0.),
name='%s_rgrad2' % k) for k, p in tparams.items()
]
zgup = [(zg, g) for zg, g in zip(zipped_grads, grads)]
rg2up = [(rg2, 0.95 * rg2 + 0.05 * (g**2))
for rg2, g in zip(running_grads2, grads)]
f_grad_shared = theano.function(iin,
out,
updates=zgup + rg2up + updates,
name='adadelta_f_grad_shared')
updir = [
-tensor.sqrt(ru2 + 1e-6) / tensor.sqrt(rg2 + 1e-6) * zg
for zg, ru2, rg2 in zip(zipped_grads, running_up2, running_grads2)
]
ru2up = [(ru2, 0.95 * ru2 + 0.05 * (ud**2))
for ru2, ud in zip(running_up2, updir)]
param_up = [(p, p + ud) for p, ud in zip(tparams.values(), updir)]
f_update = theano.function([lr], [],
updates=ru2up + param_up,
on_unused_input='ignore',
name='adadelta_f_update')
return f_grad_shared, f_update
def lstm_layer(tparams, state_below, options, prefix='lstm', mask=None):
nsteps = state_below.shape[0]
if state_below.ndim == 3:
n_samples = state_below.shape[1]
else:
n_samples = 1
assert mask is not None
def _slice(_x, n, dim):
if _x.ndim == 3:
return _x[:, :, n * dim:(n + 1) * dim]
return _x[:, n * dim:(n + 1) * dim]
def _step(m_, x_, h_, c_):
preact = tensor.dot(h_, tparams[_p(prefix, 'U')])
preact += x_
i = tensor.nnet.sigmoid(_slice(preact, 0, options['dim_proj']))
f = tensor.nnet.sigmoid(_slice(preact, 1, options['dim_proj']))
o = tensor.nnet.sigmoid(_slice(preact, 2, options['dim_proj']))
c = tensor.tanh(_slice(preact, 3, options['dim_proj']))
c = f * c_ + i * c
c = m_[:, None] * c + (1. - m_)[:, None] * c_
h = o * tensor.tanh(c)
h = m_[:, None] * h + (1. - m_)[:, None] * h_
return h, c
state_below = (tensor.dot(state_below, tparams[_p(prefix, 'W')]) +
tparams[_p(prefix, 'b')])
dim_proj = options['dim_proj']
rval, updates = theano.scan(_step,
sequences=[mask, state_below],
outputs_info=[
tensor.alloc(numpy_floatX(0.), n_samples,
dim_proj),
tensor.alloc(numpy_floatX(0.), n_samples,
dim_proj)
],
name=_p(prefix, '_layers'),
n_steps=nsteps)
# outputs_info include h_ and c_
# return only hidden states, so return rval[0]
return rval[0]
def Adam(tparams, cost, inps, lr, b1=0.1, b2=0.001, e=1e-8):
""" default: lr=0.0002 """
grads = tensor.grad(cost, tparams.values())
norm = tensor.sqrt(sum([tensor.sum(g ** 2) for g in grads]))
if tensor.ge(norm, 5):
grads = [g * 5 / norm for g in grads]
gshared = [theano.shared(p.get_value() * 0.0, name="%s_grad" % k) for k, p in tparams.iteritems()]
gsup = [(gs, g) for gs, g in zip(gshared, grads)]
f_grad_shared = theano.function(inps, cost, updates=gsup)
updates = []
i = theano.shared(numpy_floatX(0.0))
i_t = i + 1.0
fix1 = 1.0 - b1 ** (i_t)
fix2 = 1.0 - b2 ** (i_t)
lr_t = lr * (tensor.sqrt(fix2) / fix1)
for p, g in zip(tparams.values(), gshared):
m = theano.shared(p.get_value() * 0.0)
v = theano.shared(p.get_value() * 0.0)
m_t = (b1 * g) + ((1.0 - b1) * m)
v_t = (b2 * tensor.sqr(g)) + ((1.0 - b2) * v)
g_t = m_t / (tensor.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))
f_update = theano.function([lr], [], updates=updates)
return f_grad_shared, f_update
def build_model(tparams, options):
trng = RandomStreams(options['SEED'])
# Used for dropout.
use_noise = theano.shared(numpy_floatX(0.))
# size of n_samples * n_z
z = tensor.matrix('z', dtype=config.floatX)
# size of n_samples * n_y
y = tensor.matrix('y', dtype=config.floatX)
z = dropout(z, trng, use_noise)
h = tensor.tanh(tensor.dot(z, tparams['Wy1']) + tparams['by1'])
h = dropout(h, trng, use_noise)
# size of n_samples * n_y
pred = tensor.nnet.sigmoid(tensor.dot(h, tparams['Wy2']) + tparams['by2'])
f_pred = theano.function([z], pred, name='f_pred')
cost = (-y * tensor.log(pred + 1e-6) -
(1. - y) * tensor.log(1. - pred + 1e-6)).sum() / z.shape[0]
return use_noise, z, y, cost, f_pred
def build_model(tparams,options):
trng = RandomStreams(options['SEED'])
# Used for dropout.
use_noise = theano.shared(numpy_floatX(0.))
# input sentences, size of n_steps * n_samples
x = tensor.matrix('x', dtype='int64')
# the corresponding masks padding zeros
mask = tensor.matrix('mask', dtype=config.floatX)
# size of n_samples * n_z
z = tensor.matrix('z', dtype=config.floatX)
y = tensor.matrix('y', dtype=config.floatX)
z = dropout(z, trng, use_noise)
y = dropout(y, trng, use_noise)
n_steps = x.shape[0] # the sentence length in this mini-batch
n_samples = x.shape[1] # the number of sentences in this mini-batch
n_x = tparams['Wemb'].shape[1] # the dimension of the word embedding
# size of n_steps,n_samples,n_x
emb = tparams['Wemb'][x.flatten()].reshape([n_steps,n_samples,n_x])
emb = dropout(emb, trng, use_noise)
# 1 * n_samples * n_x
z0 =tensor.dot(z,tparams['C0']).dimshuffle('x',0,1)
# n_steps * n_samples * n_x
emb_input = tensor.concatenate((z0,emb[:n_steps-1]))
# n_steps * n_samples
mask0 =mask[0].dimshuffle('x',0)
mask_input = tensor.concatenate((mask0,mask[:n_steps-1]))
# decoding the sentence vector z back into the original sentence
h_decoder = encoder_layer(tparams, emb_input, mask_input,y, seq_output=True)
h_decoder = dropout(h_decoder, trng, use_noise)
shape = h_decoder.shape
h_decoder = h_decoder.reshape((shape[0]*shape[1], shape[2]))
Vhid = tensor.dot(tparams['Vhid'],tparams['Wemb'].T)
pred_x = tensor.dot(h_decoder, Vhid) + tparams['bhid']
pred = tensor.nnet.softmax(pred_x)
x_vec = x.reshape((shape[0]*shape[1],))
index = tensor.arange(shape[0]*shape[1])
pred_word = pred[index, x_vec]
mask_word = mask.reshape((shape[0]*shape[1],))
index_list = theano.tensor.eq(mask_word, 1.).nonzero()[0]
pred_word = pred_word[index_list]
# the cross-entropy loss
cost = -tensor.log(pred_word + 1e-6).sum() / n_samples
return use_noise, x, mask, y, z, cost
def decoder_layer(tparams, state_below, prefix='decoder_vanilla'):
""" state_below: size of n_steps * n_samples * n_x
"""
n_steps = state_below.shape[0]
if state_below.ndim == 3:
n_samples = state_below.shape[1]
else:
n_samples = 1
n_h = tparams[_p(prefix, 'U')].shape[0]
def _step_slice(x_, h_, U):
preact = tensor.dot(h_, U)
preact += x_
h = tensor.tanh(preact)
return h
state_below_ = tensor.dot(state_below, tparams[_p(prefix, 'W')]) + \
tparams[_p(prefix, 'b')]
rval, updates = theano.scan(
_step_slice,
sequences=[state_below_],
outputs_info=[tensor.alloc(numpy_floatX(0.), n_samples, n_h)],
non_sequences=[tparams[_p(prefix, 'U')]],
name=_p(prefix, '_layers'),
n_steps=n_steps)
return rval
def rmsprop(self, lr, tparams, grads, inp_list, cost, params):
clip = params["grad_clip"]
decay_rate = tensor.constant(params["decay_rate"], dtype=theano.config.floatX)
smooth_eps = tensor.constant(params["smooth_eps"], dtype=theano.config.floatX)
zipped_grads = [theano.shared(np.zeros_like(p.get_value()), name="%s_grad" % k) for k, p in tparams.iteritems()]
running_grads2 = [
theano.shared(np.zeros_like(p.get_value()), name="%s_rgrad2" % k) for k, p in tparams.iteritems()
]
zgup = [(zg, g) for zg, g in zip(zipped_grads, grads)]
if clip > 0.0:
rg2up = [
(
rg2,
tensor.clip(decay_rate * rg2 + (1 - decay_rate) * (tensor.clip(g, -clip, clip) ** 2), 0.0, np.inf),
)
for rg2, g in zip(running_grads2, grads)
]
else:
rg2up = [
(rg2, tensor.clip(decay_rate * rg2 + (1 - decay_rate) * (g ** 2), 0.0, np.inf))
for rg2, g in zip(running_grads2, grads)
]
f_grad_shared = theano.function(inp_list, cost, updates=zgup + rg2up, name="rmsprop_f_grad_shared")
updir = [theano.shared(p.get_value() * numpy_floatX(0.0), name="%s_updir" % k) for k, p in tparams.iteritems()]
updir_new = [
(ud, -lr * zg / (tensor.sqrt(rg2) + smooth_eps)) for ud, zg, rg2 in zip(updir, zipped_grads, running_grads2)
]
param_up = [(p, p + udn[1]) for p, udn in zip(tparams.values(), updir_new)]
f_update = theano.function(
[lr], [], updates=updir_new + param_up, on_unused_input="ignore", name="rmsprop_f_update"
)
return f_grad_shared, f_update, zipped_grads, running_grads2, updir
def pred_error(f_pred, prepare_data, data, iterator, fname='', verbose=False):
"""
Just compute the error
f_pred: Theano fct computing the prediction
prepare_data: usual prepare_data for that dataset.
"""
valid_err = 0
if verbose:
f = open('../data/trec/TREC_10.label')
lines = f.readlines()
f.close()
f_out = open(fname + 'trec_out_dscnn.txt', 'w')
cat_ind = ['ABBR', 'ENTY', 'DESC', 'HUM', 'LOC', 'NUM']
cnt = 0
for b, valid_index in iterator:
x, mask, y = prepare_data([data[0][t] for t in valid_index],
numpy.array(data[1])[valid_index])
preds = f_pred(x, mask)
targets = numpy.array(data[1])[valid_index]
if verbose:
for i in range(len(preds)):
p = preds[i]
if p != targets[i]:
f_out.write('*')
f_out.write(cat_ind[p] + ' ')
f_out.write(lines[cnt])
cnt += 1
valid_err += (preds == targets).sum()
valid_err = 1. - numpy_floatX(valid_err) / len(data[0])
return valid_err * 100
def lstm_layer(tparams, state_below, options, prefix='lstm', mask=None):
nsteps = state_below.shape[0]
if state_below.ndim == 3:
n_samples = state_below.shape[1]
else:
n_samples = 1
assert mask is not None
def _slice(_x, n, dim):
if _x.ndim == 3:
return _x[:, :, n * dim:(n+1) * dim]
return _x[:, n * dim:(n+1) * dim]
def _step(m_, x_, h_, c_):
preact = tensor.dot(h_, tparams[_p(prefix, 'U')])
preact += x_
i = tensor.nnet.sigmoid(_slice(preact, 0, options['dim_proj']))
f = tensor.nnet.sigmoid(_slice(preact, 1, options['dim_proj']))
o = tensor.nnet.sigmoid(_slice(preact, 2, options['dim_proj']))
c = tensor.tanh(_slice(preact, 3, options['dim_proj']))
c = f * c_ + i * c
c = m_[:, None] * c + (1. - m_)[:, None] * c_
h = o * tensor.tanh(c)
h = m_[:, None] * h + (1. - m_)[:, None] * h_
return h, c
state_below = (tensor.dot(state_below, tparams[_p(prefix, 'W')]) +
tparams[_p(prefix, 'b')])
dim_proj = options['dim_proj']
rval, updates = theano.scan(_step,
sequences=[mask, state_below],
outputs_info=[tensor.alloc(numpy_floatX(0.),
n_samples,
dim_proj),
tensor.alloc(numpy_floatX(0.),
n_samples,
dim_proj)],
name=_p(prefix, '_layers'),
n_steps=nsteps)
return rval[0][-1]
def decoder_layer(tparams, state_below, z, mask, prefix='decoder_lstm'):
""" state_below: size of n_steps * n_samples * n_x
z: size of n_samples * n_z
"""
nsteps = state_below.shape[0]
n_h = tparams[_p(prefix, 'U')].shape[0]
if state_below.ndim == 3:
n_samples = state_below.shape[1]
else:
n_samples = 1
state_belowx0 = tensor.dot(z, tparams[_p(prefix, 'C0')]) + \
tparams[_p(prefix, 'b0')]
h0 = tensor.tanh(state_belowx0)
def _slice(_x, n, dim):
if _x.ndim == 3:
return _x[:, :, n * dim:(n + 1) * dim]
return _x[:, n * dim:(n + 1) * dim]
state_below_ = tensor.dot(state_below, tparams[_p(
prefix, 'W')]) + tparams[_p(prefix, 'b')]
# tensor.dot(z, tparams[_p(prefix, 'C')])
def _step(m_, x_, h_, c_, U):
preact = tensor.dot(h_, U)
preact += x_
i = tensor.nnet.sigmoid(_slice(preact, 0, n_h))
f = tensor.nnet.sigmoid(_slice(preact, 1, n_h))
o = tensor.nnet.sigmoid(_slice(preact, 2, n_h))
c = tensor.tanh(_slice(preact, 3, n_h))
c = f * c_ + i * c
c = m_[:, None] * c + (1. - m_)[:, None] * c_
h = o * tensor.tanh(c)
h = m_[:, None] * h + (1. - m_)[:, None] * h_
return h, c
seqs = [mask[:nsteps - 1], state_below_[:nsteps - 1]]
rval, updates = theano.scan(
_step,
sequences=seqs,
outputs_info=[h0, tensor.alloc(numpy_floatX(0.), n_samples, n_h)],
non_sequences=[tparams[_p(prefix, 'U')]],
name=_p(prefix, '_layers'),
n_steps=nsteps - 1,
strict=True)
h0x = h0.dimshuffle('x', 0, 1)
h_rval = rval[0]
return tensor.concatenate((h0x, h_rval))
def lstm_layer(tparams, x, mask, prefix):
n_steps = x.shape[0]
n_samples = x.shape[1]
n_h = tparams[_p(prefix, 'U_i')].shape[0]
x_i = tensor.dot(x, tparams[_p(prefix, 'W_i')]) + tparams[_p(
prefix, 'b_i')]
x_f = tensor.dot(x, tparams[_p(prefix, 'W_f')]) + tparams[_p(
prefix, 'b_f')]
x_o = tensor.dot(x, tparams[_p(prefix, 'W_o')]) + tparams[_p(
prefix, 'b_o')]
x_c = tensor.dot(x, tparams[_p(prefix, 'W_c')]) + tparams[_p(
prefix, 'b_c')]
def _step(m_, xt_i, xt_f, xt_o, xt_c, h_, c_, U_i, U_f, U_o, U_c):
i = tensor.nnet.sigmoid(tensor.dot(h_, U_i) + xt_i)
f = tensor.nnet.sigmoid(tensor.dot(h_, U_f) + xt_f)
o = tensor.nnet.sigmoid(tensor.dot(h_, U_o) + xt_o)
c = tensor.tanh(tensor.dot(h_, U_c) + xt_c)
c = f * c_ + i * c
c = m_[:, None] * c + (1. - m_)[:, None] * c_
h = o * tensor.tanh(c)
h = m_[:, None] * h + (1. - m_)[:, None] * h_
return h, c
seqs = [mask, x_i, x_f, x_o, x_c]
non_seqs = [
tparams[_p(prefix, 'U_i')], tparams[_p(prefix, 'U_f')],
tparams[_p(prefix, 'U_o')], tparams[_p(prefix, 'U_c')]
]
rval, updates = theano.scan(_step,
sequences=seqs,
outputs_info=[
tensor.alloc(numpy_floatX(0.), n_samples,
n_h),
tensor.alloc(numpy_floatX(0.), n_samples,
n_h)
],
non_sequences=non_seqs,
name=_p(prefix, '_layers'),
n_steps=n_steps,
strict=True)
# hseq, cseq
return rval
def perform(self, x):
nsteps = x.shape[0]
# if x.ndim == 3:
# n_samples = x.shape[1]
# else:
# n_samples = 1
#
n_samples = x.shape[1]
def _slice(x_t, idx, ndim):
if x_t.ndim == 3:
return x_t[:, :, idx * ndim: (idx + 1) * ndim]
return x_t[:, idx * ndim:(idx + 1) * ndim]
def _step(x_t, h_tm1, c_tm1):
# z = sigmoid( W * x(t) + U * h(t-1) + b)
# zi = W * x(t) + U * h(t-1) + b
zi = T.dot(x_t, self.Wh) + T.dot(h_tm1, self.Uh) + self.bh
# zi = T.dot(h_tm1, self.Uh)
# zi += x_t
# W = [Wi, Wf, Wo, Wc], U = [Ui, Uf, Uo, Uc], b = [bi, bf, bo, bc]
i = T.nnet.sigmoid(_slice(zi, 0, self.n_output))
f = T.nnet.sigmoid(_slice(zi, 1, self.n_output))
o = T.nnet.sigmoid(_slice(zi, 2, self.n_output))
c = T.tanh(_slice(zi, 3, self.n_output))
c = f * c_tm1 + i * c;
h = o * T.tanh(c)
# output at each time
# s = softmax(w * h_t + b)
return [h, c]
# h0 and c0 are initialized randomly
h0 = T.alloc(numpy_floatX(0.), n_samples, self.n_output);
c0 = T.alloc(numpy_floatX(0.), n_samples, self.n_output)
h0 = theano.tensor.unbroadcast(h0, 1);
c0 = theano.tensor.unbroadcast(c0, 1)
[h, c], _ = theano.scan(fn=_step, sequences=x,
outputs_info=[h0, c0],
n_steps=nsteps)
self.input = x
self.output = h
def Santa(tparams, cost, inps, lr, eidx, nframes, max_epoch, rho=0.95, anne_rate=0.5, e=1e-8, clip_norm=5):
""" The implementation of Santa algorithm.
tparams: theano shared variables, params that we need to optimize
cost: cost function, the cross-entropy loss in our case
inps: input theano variables
lr: learning rate, in our case, we choose it to be 1.*1e-3, or 2.*1e-4
eidx: the current epochs we are running, used to decide when to change
from exploration to refinement
nframes: how many time-steps we have in the training dataset.
max_epoch: the maximum of epochs we run
rho, anne_rate, e, clip_norm: hyper-parameters we used in all the algorithms.
"""
trng = RandomStreams(123)
grads = tensor.grad(cost, tparams.values())
norm = tensor.sqrt(sum([tensor.sum(g**2) for g in grads]))
if tensor.ge(norm, clip_norm):
grads = [g*clip_norm/norm for g in grads]
gshared = [theano.shared(p.get_value() * 0., name='%s_grad'%k)
for k, p in tparams.iteritems()]
gsup = [(gs, g) for gs, g in zip(gshared, grads)]
f_grad_shared = theano.function(inps, cost, updates=gsup)
updates = []
i = theano.shared(numpy_floatX(0.))
i_t = i + 1.
for p, g in zip(tparams.values(), gshared):
m = theano.shared(p.get_value() * 0.)
v = theano.shared(p.get_value() * 0.)
alpha = theano.shared(np.ones(p.get_value().shape)*.5)
alpha_t = alpha + (m**2 - lr/(i_t ** anne_rate)) * tensor.lt(eidx, 0.15*max_epoch)
v_t = rho * v + (1.-rho) * (g ** 2)
pcder = tensor.sqrt(tensor.sqrt(v_t)+e)
eps = trng.normal(p.get_value().shape, avg = 0.0, std = 1.0,
dtype=theano.config.floatX)
m_t = -lr*g/pcder + (1. - alpha_t) * m + (tensor.sqrt(2*lr*v_t/(i_t ** anne_rate)/nframes) *eps) * tensor.lt(eidx, 0.15*max_epoch)
p_t = p + (m_t/ pcder)
updates.append((alpha, alpha_t))
updates.append((m, m_t))
updates.append((v, v_t))
updates.append((p, p_t))
updates.append((i, i_t))
f_update = theano.function([lr,eidx,nframes,max_epoch], [], updates=updates)
return f_grad_shared, f_update
def encoder(tparams, state_below, mask, seq_output=False, prefix='gru_encoder'):
""" state_below: size of n_steps * n_samples * n_x
"""
n_steps = state_below.shape[0]
n_samples = state_below.shape[1]
n_h = tparams[_p(prefix,'Ux')].shape[1]
def _slice(_x, n, dim):
if _x.ndim == 3:
return _x[:, :, n*dim:(n+1)*dim]
return _x[:, n*dim:(n+1)*dim]
state_below_ = tensor.dot(state_below, tparams[_p(prefix, 'W')]) + \
tparams[_p(prefix, 'b')]
state_belowx = tensor.dot(state_below, tparams[_p(prefix, 'Wx')]) + \
tparams[_p(prefix, 'bx')]
def _step(m_, x_, xx_, h_, U, Ux):
preact = tensor.dot(h_, U)
preact += x_
r = tensor.nnet.sigmoid(_slice(preact, 0, n_h))
u = tensor.nnet.sigmoid(_slice(preact, 1, n_h))
preactx = tensor.dot(h_, Ux)
preactx = preactx * r
preactx = preactx + xx_
h = tensor.tanh(preactx)
h = u * h_ + (1. - u) * h
h = m_[:,None] * h + (1. - m_)[:,None] * h_
return h
seqs = [mask, state_below_, state_belowx]
rval, updates = theano.scan(_step,
sequences=seqs,
outputs_info = [tensor.alloc(numpy_floatX(0.),
n_samples, n_h)],
non_sequences = [tparams[_p(prefix, 'U')],
tparams[_p(prefix, 'Ux')]],
name=_p(prefix, '_layers'),
n_steps=n_steps,
strict=True)
if seq_output:
return rval
else:
# size of n_samples * n_h
return rval[-1]
def Adam(tparams, cost, inps, lr, b1=0.1, b2=0.001, e=1e-8, clip_norm=5):
""" default: lr=0.0002
This is the implementation of the Adam algorithm
Reference: http://arxiv.org/pdf/1412.6980v8.pdf
"""
grads = tensor.grad(cost, tparams.values())
norm = tensor.sqrt(sum([tensor.sum(g**2) for g in grads]))
if tensor.ge(norm, clip_norm):
grads = [g * clip_norm / (norm + e) for g in grads]
zero = numpy.float32(0)
gshared = [
theano.shared(p.get_value() * zero, name='%s_grad' % k)
for k, p in tparams.iteritems()
]
gsup = [(gs, g) for gs, g in zip(gshared, grads)]
f_grad_shared = theano.function(inps, cost, updates=gsup)
updates = []
i = theano.shared(numpy_floatX(0.))
i_t = i + 1.
fix1 = 1. - b1**(i_t)
fix2 = 1. - b2**(i_t)
lr_t = lr * (tensor.sqrt(fix2) / fix1)
_s = tensor.scalar('s', dtype='float32')
for p, g in zip(tparams.values(), gshared):
m = theano.shared(p.get_value() * zero)
v = theano.shared(p.get_value() * zero)
m_t = (b1 * g) + ((1. - b1) * m)
v_t = (b2 * tensor.sqr(g)) + ((1. - b2) * v)
g_t = m_t / (tensor.sqrt(v_t) + e)
p_t = p - (lr_t * g_t)
if tensor.eq(_s, 0.) and (p.name is 'gp_beta' or p.name is 'gp_alpha'
or p.name is 'r'):
p_t = p - (_s * lr_t * g_t)
#elif tensor.eq(_s,1.) and (p.name is not 'gp_beta' and p.name is not 'gp_alpha' and p.name is not 'r'):
# p_t = p - ((1-_s) * lr_t * g_t)
if p.name == 'e_beta' or p.name == 'd_beta':
p_t = p_t * (p_t > 0)
elif p.name is 'gp_beta' or p.name is 'gp_alpha':
m_t = m_t.astype('float32')
v_t = v_t.astype('float32')
p_t = p_t.astype('float32')
updates.append((m, m_t))
updates.append((v, v_t))
updates.append((p, p_t))
updates.append((i, i_t))
f_update = theano.function([lr, _s], [], updates=updates)
return f_grad_shared, f_update
def fully_layer(params, input, results, nCategories=101, nout=512, weights_path=None):
trng = RandomStreams(SEED)
# Used for dropout.
use_noise = theano.shared(numpy_floatX(0.))
ninput = tensor.prod(input.shape[1:])
denselayer1 = tensor.dot(input, params['fc1_w']) + params['fc1_b']
denselayer1 = relu(denselayer1)
denselayer2 = tensor.dot(denselayer1, params['fc2_w']) + params['fc2_b']
denselayer2 = relu(denselayer2)
results['fc1'] = denselayer1
results['fc2'] = denselayer2
return params, results
def build_model(tparams, options):
trng = RandomStreams(SEED)
# Used for dropout.
use_noise = theano.shared(numpy_floatX(0.))
# input sentences, size of n_steps * n_samples
x = tensor.matrix('x', dtype='int64')
# the corresponding masks padding zeros
mask = tensor.matrix('mask', dtype=config.floatX)
# size of n_z * n_samples
z = tensor.matrix('z', dtype=config.floatX)
z = dropout(z, trng, use_noise)
n_steps = x.shape[0] # the sentence length in this mini-batch
n_samples = x.shape[1] # the number of sentences in this mini-batch
n_x = tparams['Wemb'].shape[1] # the dimension of the word embedding
emb = tparams['Wemb'][x.flatten()].reshape([n_steps, n_samples, n_x])
emb = dropout(emb, trng, use_noise)
# decoding the sentence vector z back into the original sentence
h_decoder = decoder_layer(tparams, emb, z, mask=mask)
h_decoder = dropout(h_decoder, trng, use_noise)
shape = h_decoder.shape
h_decoder = h_decoder.reshape((shape[0] * shape[1], shape[2]))
Vhid = tensor.dot(tparams['Vhid'], tparams['Wemb'].T)
pred_x = tensor.dot(h_decoder, Vhid) + tparams['bhid']
pred = tensor.nnet.softmax(pred_x)
x_vec = x.reshape((shape[0] * shape[1], ))
index = tensor.arange(shape[0] * shape[1])
pred_word = pred[index, x_vec]
mask_word = mask.reshape((shape[0] * shape[1], ))
index_list = theano.tensor.eq(mask_word, 1.).nonzero()[0]
pred_word = pred_word[index_list]
# the cross-entropy loss
cost = -tensor.log(pred_word + 1e-6).sum() / n_samples
f_pred_prob = theano.function([x, mask, z], pred_word, name='f_pred_prob')
return use_noise, x, mask, z, f_pred_prob, cost
def decoder_layer(tparams, state_below, prefix='decoder_gru'):
""" state_below: size of n_steps * n_samples * n_x
"""
nsteps = state_below.shape[0]
n_h = tparams[_p(prefix, 'Ux')].shape[1]
if state_below.ndim == 3:
n_samples = state_below.shape[1]
else:
n_samples = 1
def _slice(_x, n, dim):
if _x.ndim == 3:
return _x[:, :, n * dim:(n + 1) * dim]
return _x[:, n * dim:(n + 1) * dim]
state_below_ = tensor.dot(state_below, tparams[_p(
prefix, 'W')]) + tparams[_p(prefix, 'b')]
state_belowx = tensor.dot(state_below, tparams[_p(
prefix, 'Wx')]) + tparams[_p(prefix, 'bx')]
def _step_slice(x_, xx_, h_, U, Ux):
preact = tensor.dot(h_, U)
preact += x_
r = tensor.nnet.sigmoid(_slice(preact, 0, n_h))
u = tensor.nnet.sigmoid(_slice(preact, 1, n_h))
preactx = tensor.dot(h_, Ux)
preactx = preactx * r
preactx = preactx + xx_
h = tensor.tanh(preactx)
h = u * h_ + (1. - u) * h
return h
seqs = [state_below_, state_belowx]
_step = _step_slice
rval, updates = theano.scan(
_step,
sequences=seqs,
outputs_info=[tensor.alloc(numpy_floatX(0.), n_samples, n_h)],
non_sequences=[tparams[_p(prefix, 'U')], tparams[_p(prefix, 'Ux')]],
name=_p(prefix, '_layers'),
n_steps=nsteps,
strict=True)
return rval
def pSGLD_test(tparams,
cost,
inps,
lr,
rho=0.99,
epsilon=1e-6,
eta=0.01,
anne_rate=0.55,
clip_norm=5):
""" default: lr=0.001 """
trng = RandomStreams(123)
grads = tensor.grad(cost, tparams.values())
norm = tensor.sqrt(sum([tensor.sum(g**2) for g in grads]))
if tensor.ge(norm, clip_norm):
grads = [g * clip_norm / norm for g in grads]
gshared = [
theano.shared(p.get_value() * 0., name='%s_grad' % k)
for k, p in tparams.iteritems()
]
gsup = [(gs, g) for gs, g in zip(gshared, grads)]
f_grad_shared = theano.function(inps, cost, updates=gsup)
updates = []
i = theano.shared(numpy_floatX(0.))
i_t = i + 1.
for p, g in zip(tparams.values(), gshared):
acc = theano.shared(p.get_value() * 0.)
acc_new = rho * acc + (1 - rho) * g**2
updates.append((acc, acc_new))
G = tensor.sqrt(acc_new + epsilon)
eps = trng.normal(p.get_value().shape,
avg=0.0,
std=1.0,
dtype=theano.config.floatX)
updated_p = p - lr * g / G + tensor.sqrt(
lr / G) * eta / (1 + i_t)**anne_rate * eps
updates.append((p, updated_p))
updates.append((i, i_t))
f_update = theano.function([lr], [], updates=updates)
return f_grad_shared, f_update
def pred_error(f_pred, prepare_data, data, iterator, verbose=False):
""" compute the prediction error.
"""
valid_err = 0
for _, valid_index in iterator:
x, mask, y = prepare_data([data[0][t] for t in valid_index],
np.array(data[1])[valid_index],
maxlen=None)
preds = f_pred(x, mask)
targets = np.array(data[1])[valid_index]
valid_err += (preds == targets).sum()
valid_err = 1. - numpy_floatX(valid_err) / len(data[0])
return valid_err
def pred_error(f_pred, prepare_data, data, iterator, fname='', verbose=False):
"""
Just compute the error
f_pred: Theano fct computing the prediction
prepare_data: usual prepare_data for that dataset.
"""
valid_err = 0
preds_all = []
targets_all = []
if verbose:
true_labels = []
f_label = open("paper2_labels_without_175_repeat_with_3200.txt", "r")
for line_label in f_label:
true_labels.append(int(line_label.strip()))
f_label.close()
f_out = open(fname + 'hdf_out_dscnn.txt', 'w')
cat_ind = ['1', '2']
cnt = 0
for b, valid_index in iterator:
x, mask, y = prepare_data([data[0][t] for t in valid_index],
numpy.array(data[1])[valid_index])
preds = f_pred(x, mask)
for pred_item in preds:
preds_all.append(pred_item)
targets = numpy.array(data[1])[valid_index]
for target_item in targets:
targets_all.append(target_item)
if verbose:
for i in range(len(preds)):
p = preds[i]
if p != targets[i]:
f_out.write('*')
else:
f_out.write(' ')
f_out.write(str(preds[i]) + ' ')
f_out.write(str(targets[i]) + ' ')
f_out.write(cat_ind[p] + ' ')
f_out.write(str(true_labels[cnt]) + '\n')
cnt += 1
equals = 0
for i in range(len(preds)):
if preds[i] == targets[i]:
equals += 1
valid_err += equals
valid_err = 1. - numpy_floatX(valid_err) / len(data[0])
print 'len(preds_all):', len(preds_all)
print 'len(targets_all):', len(targets_all)
return valid_err * 100, preds_all, targets_all
def rmsprop(self, lr, tparams, grads, inp_list, cost, params):
clip = params['grad_clip']
decay_rate = tensor.constant(params['decay_rate'],
dtype=theano.config.floatX)
smooth_eps = tensor.constant(params['smooth_eps'],
dtype=theano.config.floatX)
zipped_grads = [
theano.shared(np.zeros_like(p.get_value()), name='%s_grad' % k)
for k, p in tparams.iteritems()
]
running_grads2 = [
theano.shared(np.zeros_like(p.get_value()), name='%s_rgrad2' % k)
for k, p in tparams.iteritems()
]
zgup = [(zg, g) for zg, g in zip(zipped_grads, grads)]
if clip > 0.0:
rg2up = [(rg2,
tensor.clip(
decay_rate * rg2 + (1 - decay_rate) *
(tensor.clip(g, -clip, clip)**2), 0.0, np.inf))
for rg2, g in zip(running_grads2, grads)]
else:
rg2up = [(rg2,
tensor.clip(decay_rate * rg2 + (1 - decay_rate) * (g**2),
0.0, np.inf))
for rg2, g in zip(running_grads2, grads)]
f_grad_shared = theano.function(inp_list,
cost,
updates=zgup + rg2up,
name='rmsprop_f_grad_shared')
updir = [
theano.shared(p.get_value() * numpy_floatX(0.),
name='%s_updir' % k) for k, p in tparams.iteritems()
]
updir_new = [
(ud, -lr * zg / (tensor.sqrt(rg2) + smooth_eps))
for ud, zg, rg2 in zip(updir, zipped_grads, running_grads2)
]
param_up = [(p, p + udn[1])
for p, udn in zip(tparams.values(), updir_new)]
f_update = theano.function([lr], [],
updates=updir_new + param_up,
on_unused_input='ignore',
name='rmsprop_f_update')
return f_grad_shared, f_update, zipped_grads, running_grads2, updir
def build_model(self, tparams):
# sents -> word_indices * #batch_size
sents = tensor.matrix('sents', dtype="int64")
# mask -> n_word_indices * #batch_size
mask = tensor.matrix('mask', dtype=config.floatX)
# imgs -> #4098 * #batch_size
imgs = tensor.matrix('imgs', dtype=config.floatX)
# gt_sents -> word_indices * #batch_size
gt_sents = tensor.matrix('gt_sents', dtype="int64")
# Used for dropout.
use_noise = theano.shared(numpy_floatX(1.))
with open("testTagData.pkl", "rb") as f:
sents_tag, mask_tag, imgs_tag, gt_sents_tag = pickle.load(f)
sents.tag.test_value = sents_tag
mask.tag.test_value = mask_tag
imgs.tag.test_value = imgs_tag
gt_sents.tag.test_value = gt_sents_tag
n_timesteps = sents.shape[0]
n_samples = sents.shape[1]
# Image encoding
# Xe -> #batch_size * #image_encoding_size
x_e = (tensor.dot(imgs.T, tparams['We']) + tparams['be'])
# sentences (i.e. captions) encoding
# Xs -> #no_of_words * #batch_size * #word_encoding_size
x_s = tparams['Ws'][sents.flatten()].reshape([n_timesteps,
n_samples,
self.word_img_embed_hidden_dim])
# Xes has the image vector as the first timestep
# Xes -> #no_timesteps (no_of_words + 1 (for image)) * #batch_size * #word_image_encoding_size
x_es = tensor.zeros([n_timesteps + 1, n_samples, self.word_img_embed_hidden_dim], dtype=config.floatX)
x_es = tensor.set_subtensor(x_es[1:], x_s)
x_es = tensor.set_subtensor(x_es[0], x_e)
mask_es = tensor.ones([mask.shape[0] + 1, mask.shape[1]], dtype=config.floatX)
mask_es = tensor.set_subtensor(mask_es[1:], mask)
# pred_softmax -> #batch_size * #no_of_words * #vocab_size
pred_softmax = self._lstm_build_model(tparams, x_es, mask_es, use_noise)
cost = negative_log_likelihood(pred_softmax, gt_sents)
# pred_prob = lstm_output.max(axis=2)
# pred = lstm_output.argmax(axis=2)
return sents, mask, imgs, gt_sents, use_noise, cost
def build_model(tparams, options):
trng = RandomStreams(SEED)
# Used for dropout.
use_noise = theano.shared(numpy_floatX(0.))
# input sentence: n_steps * n_samples
x = tensor.matrix('x', dtype='int32')
# label: (n_samples,)
y = tensor.vector('y', dtype='int32')
layer0_input = tparams['Wemb'][tensor.cast(x.flatten(),
dtype='int32')].reshape(
(x.shape[0], 1, x.shape[1],
tparams['Wemb'].shape[1]))
layer0_input = dropout(layer0_input, trng, use_noise)
layer1_inputs = []
for i in xrange(len(options['filter_hs'])):
filter_shape = options['filter_shapes'][i]
pool_size = options['pool_sizes'][i]
conv_layer = encoder(tparams,
layer0_input,
filter_shape=filter_shape,
pool_size=pool_size,
prefix=_p('cnn_encoder', i))
layer1_input = conv_layer
layer1_inputs.append(layer1_input)
layer1_input = tensor.concatenate(layer1_inputs, 1)
layer1_input = dropout(layer1_input, trng, use_noise)
# this is the label prediction you made
pred = tensor.nnet.softmax(
tensor.dot(layer1_input, tparams['Wy']) + tparams['by'])
f_pred_prob = theano.function([x], pred, name='f_pred_prob')
f_pred = theano.function([x], pred.argmax(axis=1), name='f_pred')
# get the expression of how we calculate the cost function
# i.e. corss-entropy loss
index = tensor.arange(x.shape[0])
cost = -tensor.log(pred[index, y] + 1e-6).mean()
return use_noise, x, y, f_pred_prob, f_pred, cost
def build_model(tparams, options):
trng = RandomStreams(SEED)
# Used for dropout.
use_noise = theano.shared(numpy_floatX(0.))
# n_samples * n_chars
x = tensor.matrix('x', dtype='int32')
y = tensor.matrix('y', dtype='int32')
# (ncons*n_samples) * n_chars
cy = tensor.matrix('cy', dtype='int32')
# n_samples * n_h
tmp_x = tensor.tanh(tensor.dot(x, tparams['W1']) + tparams['b1'])
tmp_y = tensor.tanh(tensor.dot(y, tparams['W1']) + tparams['b1'])
# (ncons*n_samples) * n_h
tmp_cy = tensor.tanh(tensor.dot(cy, tparams['W1']) + tparams['b1'])
# n_samples * n_h
feats_x = tensor.tanh(tensor.dot(tmp_x, tparams['W2']) + tparams['b2'])
feats_y = tensor.tanh(tensor.dot(tmp_y, tparams['W2']) + tparams['b2'])
# (ncons*n_samples) * n_h
feats_cy = tensor.tanh(tensor.dot(tmp_cy, tparams['W2']) + tparams['b2'])
feats_x = dropout(feats_x, trng, use_noise)
feats_y = dropout(feats_y, trng, use_noise)
feats_cy = dropout(feats_cy, trng, use_noise)
feats_x = l2norm(feats_x)
feats_y = l2norm(feats_y)
feats_cy = l2norm(feats_cy)
# Tile by number of contrast terms
# (ncon*n_samples) * n_h
feats_x = tensor.tile(feats_x, (options['ncon'], 1))
feats_y = tensor.tile(feats_y, (options['ncon'], 1))
cost = tensor.log(1 + tensor.sum(
tensor.exp(-options['gamma'] * ((feats_x * feats_y).sum(axis=1) -
(feats_x * feats_cy).sum(axis=1)))))
return use_noise, [x, y, cy], cost
def build_model(tparams, options):
"""first model - blind single answer qa without masking the response at ?
:tparams: TODO
:options: TODO
:returns: TODO
"""
trng = RandomStreams(SEED)
# Used for dropout
use_noise = theano.shared(utils.numpy_floatX(0.))
x = tensor.matrix('x', dtype='int64')
mask = tensor.matrix('mask', dtype=config.floatX)
y = tensor.vector('y', dtype='int64')
n_timesteps = x.shape[0]
n_samples = x.shape[1]
emb = tparams['Wemb'][x.flatten()].reshape([n_timesteps,
n_samples,
options['dim_proj']])
proj = get_layer(options['encoder'])[1](tparams, emb, options,
prefix=options['encoder'],
mask=mask)
if options['use_dropout']:
proj = utils.dropout_layer(proj, use_noise, trng)
pred = tensor.nnet.softmax(tensor.dot(proj, tparams['U']) + tparams['b'])
f_pred_prob = theano.function([x, mask], pred, name='f_pred_prob')
f_pred = theano.function([x, mask], pred.argmax(axis=1), name='f_pred')
off = 1e-8
if pred.dtype == 'float16':
off = 1e-6
cost = -tensor.log(pred[tensor.arange(n_samples), y] + off).mean()
return use_noise, x, mask, y, f_pred_prob, f_pred, cost
def build_model(tparams,options):
trng = RandomStreams(SEED)
# Used for dropout.
use_noise = theano.shared(numpy_floatX(0.))
# input sentence: n_steps * n_samples
x = tensor.matrix('x', dtype='int32')
mask = tensor.matrix('mask', dtype=config.floatX)
# label: (n_samples,)
y = tensor.vector('y',dtype='int32')
n_steps = x.shape[0] # the length of the longest sentence in this minibatch
n_samples = x.shape[1] # how many samples we have in this minibatch
n_x = tparams['Wemb'].shape[1] # the dimension of the word-embedding
emb = tparams['Wemb'][x.flatten()].reshape([n_steps,n_samples,n_x])
emb = dropout(emb, trng, use_noise)
# encoding of the sentence, size of n_samples * n_h
h_encoder = encoder(tparams, emb, mask=mask, prefix='lstm_encoder')
h_encoder_rev = encoder(tparams, emb[::-1], mask=mask[::-1], prefix='lstm_encoder_rev')
# size of n_samples * (2*n_h)
z = tensor.concatenate((h_encoder,h_encoder_rev),axis=1)
z = dropout(z, trng, use_noise)
# this is the label prediction you made
# size of n_samples * n_y
pred = tensor.nnet.softmax(tensor.dot(z, tparams['Wy'])+tparams['by'])
f_pred_prob = theano.function([x, mask], pred, name='f_pred_prob')
f_pred = theano.function([x, mask], pred.argmax(axis=1), name='f_pred')
# get the expression of how we calculate the cost function
# i.e. corss-entropy loss
index = tensor.arange(n_samples)
cost = -tensor.log(pred[index, y] + 1e-6).mean()
return use_noise, x, mask, y, f_pred_prob, f_pred, cost
def Adam(tparams, cost, inps, lr, b1=0.1, b2=0.001, e=1e-8):
""" default: lr=0.0002
This is the implementation of the Adam algorithm
Reference: http://arxiv.org/pdf/1412.6980v8.pdf
"""
grads = tensor.grad(cost, tparams.values())
norm = tensor.sqrt(sum([tensor.sum(g**2) for g in grads]))
if tensor.ge(norm, 5):
grads = [g * 5 / norm for g in grads]
gshared = [
theano.shared(p.get_value() * 0., name='%s_grad' % k)
for k, p in tparams.iteritems()
]
gsup = [(gs, g) for gs, g in zip(gshared, grads)]
f_grad_shared = theano.function(inps, cost, updates=gsup)
updates = []
i = theano.shared(numpy_floatX(0.))
i_t = i + 1.
fix1 = 1. - b1**(i_t)
fix2 = 1. - b2**(i_t)
lr_t = lr * (tensor.sqrt(fix2) / fix1)
for p, g in zip(tparams.values(), gshared):
m = theano.shared(p.get_value() * 0.)
v = theano.shared(p.get_value() * 0.)
m_t = (b1 * g) + ((1. - b1) * m)
v_t = (b2 * tensor.sqr(g)) + ((1. - b2) * v)
g_t = m_t / (tensor.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))
f_update = theano.function([lr], [], updates=updates)
return f_grad_shared, f_update
def build_model(tparams, options):
trng = RandomStreams(SEED)
# Used for dropout.
use_noise = theano.shared(numpy_floatX(0.))
# x: n_steps * n_samples
x = tensor.matrix('x', dtype='int64')
y = tensor.matrix('y', dtype='int64')
n_steps = x.shape[0]
n_samples = x.shape[1]
n_x = tparams['Wemb'].shape[1]
emb = tparams['Wemb'][x.flatten()].reshape([n_steps, n_samples, n_x])
emb = dropout(emb, trng, use_noise)
h_decoder = decoder_layer(tparams, emb, prefix='decoder_h1')
h_decoder = dropout(h_decoder, trng, use_noise)
h_decoder = decoder_layer(tparams, h_decoder, prefix='decoder_h2')
h_decoder = dropout(h_decoder, trng, use_noise)
# n_steps * n_samples * n_h
shape = h_decoder.shape
h_decoder = h_decoder.reshape((shape[0] * shape[1], shape[2]))
pred = tensor.dot(h_decoder, tparams['Vhid']) + tparams['bhid']
pred = tensor.nnet.softmax(pred)
y_vec = y.reshape((shape[0] * shape[1], ))
index = tensor.arange(shape[0] * shape[1])
y_pred = pred[index, y_vec]
f_pred_prob = theano.function([x, y], y_pred, name='f_pred_prob')
cost = -tensor.log(y_pred + 1e-6).sum() / n_steps / n_samples
return use_noise, x, y, f_pred_prob, cost
def Adam(tparams, cost, inps, lr, b1=0.1, b2=0.001, e=1e-8, clip_norm=5):
""" default: lr=0.0002
This is the implementation of the Adam algorithm
Reference: http://arxiv.org/pdf/1412.6980v8.pdf
"""
grads = tensor.grad(cost, tparams.values())
norm = tensor.sqrt(sum([tensor.sum(g**2) for g in grads]))
if tensor.ge(norm, clip_norm):
grads = [g*clip_norm/norm for g in grads]
gshared = [theano.shared(p.get_value() * 0., name='%s_grad'%k)
for k, p in tparams.iteritems()]
gsup = [(gs, g) for gs, g in zip(gshared, grads)]
f_grad_shared = theano.function(inps, cost, updates=gsup)
updates = []
i = theano.shared(numpy_floatX(0.))
i_t = i + 1.
fix1 = 1. - b1**(i_t)
fix2 = 1. - b2**(i_t)
lr_t = lr * (tensor.sqrt(fix2) / fix1)
for p, g in zip(tparams.values(), gshared):
m = theano.shared(p.get_value() * 0.)
v = theano.shared(p.get_value() * 0.)
m_t = (b1 * g) + ((1. - b1) * m)
v_t = (b2 * tensor.sqr(g)) + ((1. - b2) * v)
g_t = m_t / (tensor.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))
f_update = theano.function([lr], [], updates=updates)
return f_grad_shared, f_update
def build_model(tparams,options):
trng = RandomStreams(SEED)
# Used for dropout.
use_noise = theano.shared(numpy_floatX(0.))
# input sentence: n_steps * n_samples
x = tensor.matrix('x', dtype='int32')
# label: (n_samples,)
y = tensor.vector('y',dtype='int32')
layer0_input = tparams['Wemb'][tensor.cast(x.flatten(),dtype='int32')].reshape((x.shape[0],1,x.shape[1],tparams['Wemb'].shape[1]))
layer0_input = dropout(layer0_input, trng, use_noise)
layer1_inputs = []
for i in xrange(len(options['filter_hs'])):
filter_shape = options['filter_shapes'][i]
pool_size = options['pool_sizes'][i]
conv_layer = encoder(tparams, layer0_input,filter_shape=filter_shape, pool_size=pool_size,prefix=_p('cnn_encoder',i))
layer1_input = conv_layer
layer1_inputs.append(layer1_input)
layer1_input = tensor.concatenate(layer1_inputs,1)
layer1_input = dropout(layer1_input, trng, use_noise)
# this is the label prediction you made
pred = tensor.nnet.softmax(tensor.dot(layer1_input, tparams['Wy']) + tparams['by'])
f_pred_prob = theano.function([x], pred, name='f_pred_prob')
f_pred = theano.function([x], pred.argmax(axis=1), name='f_pred')
# get the expression of how we calculate the cost function
# i.e. corss-entropy loss
index = tensor.arange(x.shape[0])
cost = -tensor.log(pred[index, y] + 1e-6).mean()
return use_noise, x, y, f_pred_prob, f_pred, cost
def Adam(tparams, cost, inps, lr, b1=0.1, b2=0.001, e=1e-8, clip_norm=5):
grads = tensor.grad(cost, tparams.values())
norm = tensor.sqrt(sum([tensor.sum(g**2) for g in grads]))
if tensor.ge(norm, clip_norm):
grads = [g * clip_norm / norm for g in grads]
gshared = [
theano.shared(p.get_value() * 0., name='%s_grad' % k)
for k, p in tparams.iteritems()
]
gsup = [(gs, g) for gs, g in zip(gshared, grads)]
f_grad_shared = theano.function(inps, cost, updates=gsup)
updates = []
i = theano.shared(numpy_floatX(0.))
i_t = i + 1.
fix1 = 1. - b1**(i_t)
fix2 = 1. - b2**(i_t)
lr_t = lr * (tensor.sqrt(fix2) / fix1)
for p, g in zip(tparams.values(), gshared):
m = theano.shared(p.get_value() * 0.)
v = theano.shared(p.get_value() * 0.)
m_t = (b1 * g) + ((1. - b1) * m)
v_t = (b2 * tensor.sqr(g)) + ((1. - b2) * v)
g_t = m_t / (tensor.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))
f_update = theano.function([lr], [], updates=updates)
return f_grad_shared, f_update
def SGMGHMC(tparams,
cost,
inps,
ntrain,
lr,
iterations,
rho=0.9,
epsilon=1e-6,
clip_norm=1):
""" Additional parameters """
mom_tparams = OrderedDict()
xi_tparams = OrderedDict()
for k, p0 in tparams.iteritems():
mom_tparams[k] = theano.shared(p0.get_value() * 0. + 1e-10,
name='%s_mom' % k)
xi_tparams[k] = theano.shared(p0.get_value() * 0. + 1e-10,
name='%s_xi' % k)
#a = theano.shared(numpy_floatX(1.))
m = theano.shared(numpy_floatX(1.))
c = theano.shared(numpy_floatX(5.))
sigma_p = theano.shared(numpy_floatX(1.))
sigma_xi = theano.shared(numpy_floatX(1.))
gamma_xi = theano.shared(numpy_floatX(0.001))
logger = logging.getLogger('eval_ptb_sgmgnht')
logger.setLevel(logging.INFO)
fh = logging.FileHandler('eval_ptb_sgmgnht.log')
logger.info('a = 1, m {} c {} s_p{} s_xi{} g_xi{}'.format(
m.get_value(), c.get_value(), sigma_p.get_value(),
sigma_xi.get_value(), gamma_xi.get_value()))
p = tensor.vector('p', dtype=theano.config.floatX)
""" default: lr=0.001 """
trng = RandomStreams(123)
grads = tensor.grad(cost, tparams.values())
norm = tensor.sqrt(sum([tensor.sum(g**2) for g in grads]))
if tensor.ge(norm, clip_norm):
grads = [g * clip_norm / norm for g in grads]
gshared = [
theano.shared(p0.get_value() * 0., name='%s_grad' % k)
for k, p0 in tparams.iteritems()
]
gsup = [(gs, g) for gs, g in zip(gshared, grads)]
f_grad_shared = theano.function(inps, cost, updates=gsup)
updates = []
for p, mom, xi, g in zip(tparams.values(), mom_tparams.values(),
xi_tparams.values(), gshared):
g_f = mom / m
K_f = -g_f + 2 / c * (c * g_f + tensor.log(1 + tensor.exp(-c * g_f)))
psi_f_1 = (1 - tensor.exp(-c * g_f)) / (1 + tensor.exp(-c * g_f))
f1_f_1 = 1 / m * psi_f_1
psi_grad_f_1 = 2 * c * tensor.exp(
-c * g_f) / (1 + tensor.exp(-c * g_f))**2
f3_f_1 = 1 / m**2 * (psi_f_1**2 - psi_grad_f_1)
psi_f = (tensor.exp(c * g_f) - 1) / (tensor.exp(c * g_f) + 1)
f1_f = 1 / m * psi_f
psi_grad_f = 2 * c * tensor.exp(c * g_f) / (tensor.exp(c * g_f) + 1)**2
f3_f = 1 / m**2 * (psi_f**2 - psi_grad_f)
temp_f1 = tensor.switch(tensor.ge(g_f, 0), f1_f_1, f1_f)
temp_f3 = tensor.switch(tensor.ge(g_f, 0), f3_f_1, f3_f)
noise_p = trng.normal(p.get_value().shape,
avg=0.0,
std=1.,
dtype=theano.config.floatX)
noise_xi = trng.normal(p.get_value().shape,
avg=0.0,
std=1.,
dtype=theano.config.floatX)
# generata gamma(a,2): N(0,1)^2 = gamma(1/2,2)
noise_temp = tensor.zeros(p.get_value().shape)
for aa in xrange(2):
this_noise = trng.normal(p.get_value().shape,
avg=0.0,
std=1.,
dtype=theano.config.floatX)
noise_temp = tensor.inc_subtensor(noise_temp[:], this_noise**2)
randmg = (noise_temp * m / 2) * tensor.sgn(
trng.normal(p.get_value().shape,
avg=0.0,
std=1.,
dtype=theano.config.floatX))
updated_p = p + temp_f1 * lr
updated_mom = (mom - temp_f1 * xi * lr - g * lr * ntrain +
tensor.sqrt(2 * sigma_p * lr) * noise_p) * (
1 - tensor.eq(tensor.mod(iterations, 50), 0)
) + randmg * tensor.eq(tensor.mod(iterations, 50), 0)
#updated_mom = mom - temp_f1* xi *lr - g * lr * ntrain + tensor.sqrt(2*sigma_p*lr) * noise_p
temp_xi = trng.normal(p.get_value().shape,
avg=sigma_p,
std=tensor.sqrt(sigma_xi / 2),
dtype=theano.config.floatX)
updated_xi = (xi + temp_f3 * sigma_xi * lr -
(xi - sigma_p) * gamma_xi * lr +
tensor.sqrt(2 * sigma_xi * gamma_xi * lr) * noise_xi) * (
1 - tensor.eq(tensor.mod(iterations, 100), 50)
) + temp_xi * tensor.eq(tensor.mod(iterations, 100), 50)
updates.append((p, updated_p))
updates.append((mom, updated_mom))
updates.append((xi, updated_xi))
f_update = theano.function([lr, ntrain, iterations], [p, mom, xi],
updates=updates)
#f_params = theano.function([], [a, m, c, mom.shape])
return f_grad_shared, f_update
