def get_data_from_line(self, batch_left):
utt_id = self.utt_ids[self.align_ind]
align = self.alignments[self.align_ind]
ll = self.likelihoods[utt_id]
# + 1 since add </s>, need this to match the batches from CharStream
N = min(len(align) + 1, batch_left)
data = empty((self.feat_dim, N))
for k in xrange(0, N):
if len(align) > 0:
a = align[max(self.char_ind - 1, 0)]
llk = ll[:, a:a + SOURCE_CONTEXT]
else:
llk = blank_loglikes(1)
if llk.shape[1] < SOURCE_CONTEXT:
#llk = gnp.concatenate((llk, uniform_loglikes(SOURCE_CONTEXT - llk.shape[1])), axis=1)
#llk = np.hstack((llk, uniform_loglikes(SOURCE_CONTEXT - llk.shape[1])))
llk = np.hstack(
(llk, blank_loglikes(SOURCE_CONTEXT - llk.shape[1])))
data[:, k] = llk.ravel()
self.char_ind += 1
return data
def bprop(self):
logger.debug("%s backprop" % str(self))
# FIXME Assuming just 1 successor for now
assert len(self.succ) == 1
succ_grad = self.succ[0].full_grad
# FIXME Hack to avoid large sparse matrix multiply
if type(self.succ[0]) is SumNode: # Which leads to softmax...
if self.full_grad is None:
self.full_grad = empty((self.W.shape[1], succ_grad.shape[1]))
self.W.grad = empty(self.W.shape)
for k in range(self.full_grad.shape[0]):
# TODO self.full_grad[k, :] = ?
# TODO self.W.grad[k, :] = ?
pass
else:
self.full_grad = mult(self.W.out.T, succ_grad)
# FIXME Multiplication below is wrong
self.W.grad = mult(succ_grad, self.succ[0].out.T)
# TODO Check this
self.b.grad = succ_grad
def bprop(self):
logger.debug('%s backprop' % str(self))
# FIXME Assuming just 1 successor for now
assert len(self.succ) == 1
succ_grad = self.succ[0].full_grad
# FIXME Hack to avoid large sparse matrix multiply
if type(self.succ[0]) is SumNode: # Which leads to softmax...
if self.full_grad is None:
self.full_grad = empty((self.W.shape[1], succ_grad.shape[1]))
self.W.grad = empty(self.W.shape)
for k in range(self.full_grad.shape[0]):
# TODO self.full_grad[k, :] = ?
# TODO self.W.grad[k, :] = ?
pass
else:
self.full_grad = mult(self.W.out.T, succ_grad)
# FIXME Multiplication below is wrong
self.W.grad = mult(succ_grad, self.succ[0].out.T)
# TODO Check this
self.b.grad = succ_grad
def alloc_params(self):
hps = self.hps
self.params['Wih'] = vp_init((hps.hidden_size, hps.input_size))
self.params['Wsh'] = vp_init((hps.hidden_size, hps.source_size))
self.params['bih'] = zeros((hps.hidden_size, 1))
for k in xrange(hps.hidden_layers - 1):
self.params['W%d' % (k+1)] = vp_init((hps.hidden_size, hps.hidden_size))
self.params['b%d' % (k+1)] = zeros((hps.hidden_size, 1))
self.params['Who'] = vp_init((hps.output_size, hps.hidden_size))
self.params['bho'] = zeros((hps.output_size, 1))
self.count_params()
# Allocate grads as well
self.grads = {}
for k in self.params:
self.grads[k] = empty(self.params[k].shape)
logger.info('Allocated gradients')
def get_data_from_line(self, batch_left):
utt_id = self.utt_ids[self.align_ind]
align = self.alignments[self.align_ind]
ll = self.likelihoods[utt_id]
# + 1 since add </s>, need this to match the batches from CharStream
N = min(len(align) + 1, batch_left)
data = empty((self.feat_dim, N))
for k in xrange(0, N):
if len(align) > 0:
a = align[max(self.char_ind-1, 0)]
llk = ll[:, a:a+SOURCE_CONTEXT]
else:
llk = blank_loglikes(1)
if llk.shape[1] < SOURCE_CONTEXT:
#llk = gnp.concatenate((llk, uniform_loglikes(SOURCE_CONTEXT - llk.shape[1])), axis=1)
#llk = np.hstack((llk, uniform_loglikes(SOURCE_CONTEXT - llk.shape[1])))
llk = np.hstack((llk, blank_loglikes(SOURCE_CONTEXT - llk.shape[1])))
data[:, k] = llk.ravel()
self.char_ind += 1
return data
def __init__(self, name, data_inp, shape, init_fn=None):
super(IndexedParamNode, self).__init__(name, shape, init_fn=init_fn)
self.data_inp = data_inp
self.params_batch = empty(
(data_inp.feat_dim * self.params.shape[0], data_inp.batch_size))
def cost_and_grad(self, data, labels, back=True, prev_h0=None):
hps = self.hps
T = data.shape[1]
bsize = data.shape[2]
# FIXME gnumpy reallocates if try and use same parameters?
#us = self.us[:, 0:T, 0:bsize]
#dus = self.dus[:, 0:T, 0:bsize]
#hs = self.hs[:, 0:T, 0:bsize]
#dhs = self.dhs[:, 0:T, 0:bsize]
#probs = self.probs[:, 0:T, 0:bsize]
#dprobs = self.dprobs[:, 0:T, 0:bsize]
#costs = self.costs[0:T, 0:bsize]
us = list()
dus = list()
hs = list()
dhs = list()
h0 = list()
for k in xrange(hps.hidden_layers):
us.append(list())
dus.append(list())
hs.append(list())
dhs.append(list())
h0.append(empty((hps.hidden_size, bsize)))
for t in xrange(T):
us[k].append(zeros((hps.hidden_size, bsize)))
dus[k].append(zeros((hps.hidden_size, bsize)))
hs[k].append(zeros((hps.hidden_size, bsize)))
dhs[k].append(zeros((hps.hidden_size, bsize)))
probs = list()
for t in xrange(T):
probs.append(zeros((hps.output_size, bsize)))
costs = np.zeros((T, bsize))
if prev_h0 is not None:
h0 = prev_h0
else:
for k in xrange(hps.hidden_layers):
h0[k] = tile(self.params['h0'][:, k].reshape(-1, 1), bsize)
bih = self.params['bih']
Wih = self.params['Wih']
Whh = self.params['Whh']
bhh = self.params['bhh']
Who = self.params['Who']
bho = self.params['bho']
# Forward prop
for t in xrange(T):
for k in xrange(hps.hidden_layers):
if t == 0:
hprev = h0[k]
else:
hprev = hs[k][t-1]
if k == 0:
us[k][t] = mult(Wih, data[:, t, :]) + bih
else:
us[k][t] = mult(self.params['Wh%d' % k], hs[k-1][t])
if k == hps.recurrent_layer - 1:
us[k][t] += mult(Whh, hprev) + bhh
# Clip maximum activation
mask = us[k][t] < hps.max_act
us[k][t] = us[k][t] * mask + hps.max_act * (1 - mask)
elif k != 0:
us[k][t] += self.params['bh%d' % k]
hs[k][t] = self.nl(us[k][t])
probs[t] = softmax(mult(Who, hs[-1][t]) + bho)
self.last_h = list()
for k in xrange(hps.hidden_layers):
self.last_h.append(hs[k][-1])
if labels is None:
return None, probs
probs_neg_log = list()
dprobs = list()
for t in xrange(T):
probs_neg_log.append(as_np(-1 * log(probs[t])))
dprobs.append(as_np(probs[t].copy()))
for k in xrange(bsize):
for t in xrange(len(labels[k])):
costs[t, k] = probs_neg_log[t][labels[k][t], k]
dprobs[t][labels[k][t], k] -= 1
for t in xrange(T):
dprobs[t] = array(dprobs[t])
# NOTE Summing costs over time
# NOTE FIXME Dividing by T to get better sense if objective
# is decreasing, remove for grad checking
cost = costs.sum() / bsize / float(T)
if not back:
return cost, probs
# Backprop
for k in self.grads:
self.grads[k][:] = 0
for t in reversed(xrange(T)):
self.grads['bho'] += dprobs[t][:, :].sum(axis=-1).reshape((-1, 1)) / bsize
self.grads['Who'] += mult(dprobs[t], hs[-1][t].T) / bsize
for k in reversed(xrange(hps.hidden_layers)):
if k == hps.hidden_layers - 1:
dhs[k][t] += mult(Who.T, dprobs[t])
else:
dhs[k][t] += mult(self.params['Wh%d' % (k+1)].T, dhs[k+1][t])
dus[k][t] += get_nl_grad(self.hps.nl, us[k][t]) * dhs[k][t]
if k > 0:
self.grads['Wh%d' % k] += mult(dus[k][t], hs[k-1][t].T) / bsize
self.grads['bh%d' % k] += dus[k][t].sum(axis=-1).reshape((-1, 1)) / bsize
if k == hps.recurrent_layer - 1:
if t == 0:
hprev = h0[k]
self.grads['h0'][:, k] = mult(Whh.T, dus[k][t]).sum(axis=-1) / bsize
else:
hprev = hs[k][t-1]
dhs[k][t-1] = mult(Whh.T, dus[k][t])
self.grads['Whh'] += mult(dus[k][t], hprev.T) / bsize
self.grads['bhh'] += dus[k][t].sum(axis=-1).reshape((-1, 1)) / bsize
self.grads['Wih'] += mult(dus[0][t], data[:, t, :].T) / bsize
self.grads['bih'] += dus[0][t].sum(axis=-1).reshape((-1, 1)) / bsize
return cost, self.grads
def cost_and_grad(self, data, labels, back=True, prev_h0=None):
hps = self.hps
T = data.shape[1]
bsize = data.shape[2]
# FIXME gnumpy reallocates if try and use same parameters?
#us = self.us[:, 0:T, 0:bsize]
#dus = self.dus[:, 0:T, 0:bsize]
#hs = self.hs[:, 0:T, 0:bsize]
#dhs = self.dhs[:, 0:T, 0:bsize]
#probs = self.probs[:, 0:T, 0:bsize]
#dprobs = self.dprobs[:, 0:T, 0:bsize]
#costs = self.costs[0:T, 0:bsize]
us = list()
dus = list()
hs = list()
dhs = list()
h0 = list()
for k in xrange(hps.hidden_layers):
us.append(list())
dus.append(list())
hs.append(list())
dhs.append(list())
h0.append(empty((hps.hidden_size, bsize)))
for t in xrange(T):
us[k].append(zeros((hps.hidden_size, bsize)))
dus[k].append(zeros((hps.hidden_size, bsize)))
hs[k].append(zeros((hps.hidden_size, bsize)))
dhs[k].append(zeros((hps.hidden_size, bsize)))
probs = list()
for t in xrange(T):
probs.append(zeros((hps.output_size, bsize)))
costs = np.zeros((T, bsize))
if prev_h0 is not None:
h0 = prev_h0
else:
for k in xrange(hps.hidden_layers):
h0[k] = tile(self.params['h0'][:, k].reshape(-1, 1), bsize)
bih = self.params['bih']
Wih = self.params['Wih']
Whh = self.params['Whh']
bhh = self.params['bhh']
Who = self.params['Who']
bho = self.params['bho']
# Forward prop
for t in xrange(T):
for k in xrange(hps.hidden_layers):
if t == 0:
hprev = h0[k]
else:
hprev = hs[k][t - 1]
if k == 0:
us[k][t] = mult(Wih, data[:, t, :]) + bih
else:
us[k][t] = mult(self.params['Wh%d' % k], hs[k - 1][t])
if k == hps.recurrent_layer - 1:
us[k][t] += mult(Whh, hprev) + bhh
# Clip maximum activation
mask = us[k][t] < hps.max_act
us[k][t] = us[k][t] * mask + hps.max_act * (1 - mask)
elif k != 0:
us[k][t] += self.params['bh%d' % k]
hs[k][t] = self.nl(us[k][t])
probs[t] = softmax(mult(Who, hs[-1][t]) + bho)
self.last_h = list()
for k in xrange(hps.hidden_layers):
self.last_h.append(hs[k][-1])
if labels is None:
return None, probs
probs_neg_log = list()
dprobs = list()
for t in xrange(T):
probs_neg_log.append(as_np(-1 * log(probs[t])))
dprobs.append(as_np(probs[t].copy()))
for k in xrange(bsize):
for t in xrange(len(labels[k])):
costs[t, k] = probs_neg_log[t][labels[k][t], k]
dprobs[t][labels[k][t], k] -= 1
for t in xrange(T):
dprobs[t] = array(dprobs[t])
# NOTE Summing costs over time
# NOTE FIXME Dividing by T to get better sense if objective
# is decreasing, remove for grad checking
cost = costs.sum() / bsize / float(T)
if not back:
return cost, probs
# Backprop
for k in self.grads:
self.grads[k][:] = 0
for t in reversed(xrange(T)):
self.grads['bho'] += dprobs[t][:, :].sum(axis=-1).reshape(
(-1, 1)) / bsize
self.grads['Who'] += mult(dprobs[t], hs[-1][t].T) / bsize
for k in reversed(xrange(hps.hidden_layers)):
if k == hps.hidden_layers - 1:
dhs[k][t] += mult(Who.T, dprobs[t])
else:
dhs[k][t] += mult(self.params['Wh%d' % (k + 1)].T,
dhs[k + 1][t])
dus[k][t] += get_nl_grad(self.hps.nl, us[k][t]) * dhs[k][t]
if k > 0:
self.grads['Wh%d' %
k] += mult(dus[k][t], hs[k - 1][t].T) / bsize
self.grads['bh%d' % k] += dus[k][t].sum(axis=-1).reshape(
(-1, 1)) / bsize
if k == hps.recurrent_layer - 1:
if t == 0:
hprev = h0[k]
self.grads['h0'][:, k] = mult(
Whh.T, dus[k][t]).sum(axis=-1) / bsize
else:
hprev = hs[k][t - 1]
dhs[k][t - 1] = mult(Whh.T, dus[k][t])
self.grads['Whh'] += mult(dus[k][t], hprev.T) / bsize
self.grads['bhh'] += dus[k][t].sum(axis=-1).reshape(
(-1, 1)) / bsize
self.grads['Wih'] += mult(dus[0][t], data[:, t, :].T) / bsize
self.grads['bih'] += dus[0][t].sum(axis=-1).reshape(
(-1, 1)) / bsize
return cost, self.grads
def __init__(self, name, data_inp, shape, init_fn=None):
super(IndexedParamNode, self).__init__(name, shape, init_fn=init_fn)
self.data_inp = data_inp
self.params_batch = empty((data_inp.feat_dim * self.params.shape[0], data_inp.batch_size))
def alloc_grads(self):
# Call after allocating parameters
self.grads = {}
for k in self.params:
self.grads[k] = empty(self.params[k].shape)
logger.info('Allocated gradients')
def alloc_grads(self):
# Call after allocating parameters
self.grads = {}
for k in self.params:
self.grads[k] = empty(self.params[k].shape)
logger.info('Allocated gradients')
