def do_cpu(): C.renew_cg() W = C.parameter(cpW) W = W*W*W*W*W*W*W z = C.squared_distance(W,W) z.value() z.backward()
def _build_word_expression_list(self, sentence, is_train=False):
renew_cg()
sentence_word_vectors = []
for word in sentence:
sentence_word_vectors.append(self._get_word_vector(word, use_dropout=is_train))
lstm_forward = self.word_builders[0].initial_state()
lstm_backward = self.word_builders[1].initial_state()
embeddings_forward = []
embeddings_backward = []
for word_vector, reverse_word_vector in zip(sentence_word_vectors, reversed(sentence_word_vectors)):
lstm_forward = lstm_forward.add_input(word_vector)
lstm_backward = lstm_backward.add_input(reverse_word_vector)
embeddings_forward.append(lstm_forward.output())
embeddings_backward.append(lstm_backward.output())
O = parameter(self.param_out)
sentence_word_expressions = []
for word_f_embedding, word_b_embedding in zip(embeddings_forward, reversed(embeddings_backward)):
word_concat_embedding = concatenate([word_f_embedding, word_b_embedding])
word_expression = O * word_concat_embedding
sentence_word_expressions.append(word_expression)
return sentence_word_expressions
def predict_output_sequence(model, encoder_frnn, encoder_rrnn, decoder_rnn, lemma, feats, alphabet_index,
inverse_alphabet_index, feat_index, feature_types):
pc.renew_cg()
# read the parameters
char_lookup = model["char_lookup"]
feat_lookup = model["feat_lookup"]
R = pc.parameter(model["R"])
bias = pc.parameter(model["bias"])
W_c = pc.parameter(model["W_c"])
W__a = pc.parameter(model["W__a"])
U__a = pc.parameter(model["U__a"])
v__a = pc.parameter(model["v__a"])
# encode the lemma
blstm_outputs = encode_chars(alphabet_index, char_lookup, encoder_frnn, encoder_rrnn, lemma)
# convert features to matching embeddings, if UNK handle properly
feats_input = encode_feats(feat_index, feat_lookup, feats, feature_types)
# initialize the decoder rnn
s_0 = decoder_rnn.initial_state()
s = s_0
# set prev_output_vec for first lstm step as BEGIN_WORD
prev_output_vec = char_lookup[alphabet_index[BEGIN_WORD]]
i = 0
predicted_sequence = []
# run the decoder through the sequence and predict characters
while i < MAX_PREDICTION_LEN:
# get current h of the decoder
s = s.add_input(pc.concatenate([prev_output_vec, feats_input]))
decoder_rnn_output = s.output()
# perform attention step
attention_output_vector, alphas, W = task1_attention_implementation.attend(blstm_outputs, decoder_rnn_output,
W_c, v__a, W__a, U__a)
# compute output probabilities
# print 'computing readout layer...'
readout = R * attention_output_vector + bias
# find best candidate output
probs = pc.softmax(readout)
next_char_index = common.argmax(probs.vec_value())
predicted_sequence.append(inverse_alphabet_index[next_char_index])
# check if reached end of word
if predicted_sequence[-1] == END_WORD:
break
# prepare for the next iteration - "feedback"
prev_output_vec = char_lookup[next_char_index]
i += 1
# remove the end word symbol
return predicted_sequence[0:-1]
def compute_loss(model, encoder_frnn, encoder_rrnn, decoder_rnn, lemma, feats, word, alphabet_index, feat_index,
feature_types):
pc.renew_cg()
# read the parameters
char_lookup = model["char_lookup"]
feat_lookup = model["feat_lookup"]
R = pc.parameter(model["R"])
bias = pc.parameter(model["bias"])
W_c = pc.parameter(model["W_c"])
W__a = pc.parameter(model["W__a"])
U__a = pc.parameter(model["U__a"])
v__a = pc.parameter(model["v__a"])
blstm_outputs = encode_chars(alphabet_index, char_lookup, encoder_frnn, encoder_rrnn, lemma)
# initialize the decoder rnn
s_0 = decoder_rnn.initial_state()
s = s_0
# convert features to matching embeddings, if UNK handle properly
feats_input = encode_feats(feat_index, feat_lookup, feats, feature_types)
# set prev_output_vec for first lstm step as BEGIN_WORD
prev_output_vec = char_lookup[alphabet_index[BEGIN_WORD]]
loss = []
padded_word = word + END_WORD
# run the decoder through the output sequence and aggregate loss
for i, output_char in enumerate(padded_word):
# get current h of the decoder
s = s.add_input(pc.concatenate([prev_output_vec, feats_input]))
decoder_rnn_output = s.output()
attention_output_vector, alphas, W = task1_attention_implementation.attend(blstm_outputs, decoder_rnn_output,
W_c, v__a, W__a, U__a)
# compute output probabilities
# print 'computing readout layer...'
readout = R * attention_output_vector + bias
current_loss = pc.pickneglogsoftmax(readout, alphabet_index[output_char])
# print 'computed readout layer'
loss.append(current_loss)
# prepare for the next iteration - "feedback"
prev_output_vec = char_lookup[alphabet_index[output_char]]
total_sequence_loss = pc.esum(loss)
# loss = average(loss)
return total_sequence_loss
def tag_sentence(self, sentence):
renew_cg()
for word in sentence:
word.vector = self._get_word_vector(word, use_dropout=False)
sentence_expressions = self._build_sentence_expressions(sentence)
for word, word_expression in zip(sentence, sentence_expressions):
out = softmax(word_expression)
tag_index = np.argmax(out.npvalue())
word.tag = self.tag_indexer.get_object(tag_index)
def predict(self, word_indices, char_indices, task_id, train=False):
"""
predict tags for a sentence represented as char+word embeddings
"""
pycnn.renew_cg() # new graph
char_emb = []
rev_char_emb = []
# get representation for words
for chars_of_token in char_indices:
# use last state as word representation
last_state = self.char_rnn.predict_sequence([self.cembeds[c] for c in chars_of_token])[-1]
rev_last_state = self.char_rnn.predict_sequence([self.cembeds[c] for c in reversed(chars_of_token)])[-1]
char_emb.append(last_state)
rev_char_emb.append(rev_last_state)
wfeatures = [self.wembeds[w] for w in word_indices]
features = [pycnn.concatenate([w,c,rev_c]) for w,c,rev_c in zip(wfeatures,char_emb,reversed(rev_char_emb))]
if train: # only do at training time
features = [pycnn.noise(fe,self.noise_sigma) for fe in features]
output_expected_at_layer = self.predictors["task_expected_at"][task_id]
output_expected_at_layer -=1
# go through layers
# input is now combination of w + char emb
prev = features
num_layers = self.h_layers
# for i in range(0,num_layers-1):
for i in range(0,num_layers):
predictor = self.predictors["inner"][i]
forward_sequence, backward_sequence = predictor.predict_sequence(prev)
if i > 0 and self.activation:
# activation between LSTM layers
forward_sequence = [self.activation(s) for s in forward_sequence]
backward_sequence = [self.activation(s) for s in backward_sequence]
if i == output_expected_at_layer:
output_predictor = self.predictors["output_layers_dict"][task_id]
concat_layer = [pycnn.concatenate([f, b]) for f, b in zip(forward_sequence,reversed(backward_sequence))]
if train and self.noise_sigma > 0.0:
concat_layer = [pycnn.noise(fe,self.noise_sigma) for fe in concat_layer]
output = output_predictor.predict_sequence(concat_layer)
return output
prev = forward_sequence
prev_rev = backward_sequence # not used
raise "oops should not be here"
return None
def calc_sentence_error(self, sentence):
renew_cg()
for word in sentence:
# word.vector = noise(self._get_word_vector(word), 0.1)
word.vector = self._get_word_vector(word, use_dropout=True)
sentence_expressions = self._build_sentence_expressions(sentence)
sentence_errors = []
for word, word_expression in zip(sentence, sentence_expressions):
gold_label_index = self.tag_indexer.get_index(word.gold_label)
word_error = pickneglogsoftmax(word_expression, gold_label_index)
sentence_errors.append(word_error)
return esum(sentence_errors)
def build_tagging_graph(word_indices, model, builder):
"""
build the computational graph
:param word_indices: list of indices
:param model: current model to access parameters
:param builder: builder to create state combinations
:return: forward and backward sequence
"""
pycnn.renew_cg()
f_init = builder.initial_state()
# retrieve embeddings from the model and add noise
word_embeddings = [pycnn.lookup(model["word_lookup"], w) for w in word_indices]
word_embeddings = [pycnn.noise(we, args.noise) for we in word_embeddings]
# compute the expressions for the forward pass
forward_sequence = [x.output() for x in f_init.add_inputs(word_embeddings)]
return forward_sequence
def build_tagging_graph(words, model, builders):
"""
build the computational graph
:param words: list of indices
:param model: current model to access parameters
:param builders: builder to create state combinations
:return: forward and backward sequence
"""
pycnn.renew_cg()
f_init, b_init = [b.initial_state() for b in builders]
# retrieve embeddings from the model and add noise
word_embeddings = [pycnn.lookup(model["lookup"], w) for w in words]
word_embeddings = [pycnn.noise(we, 0.1) for we in word_embeddings]
# compute the expressions for the forward and backward pass
forward_sequence = [x.output() for x in f_init.add_inputs(word_embeddings)]
backward_sequence = [x.output() for x in b_init.add_inputs(reversed(word_embeddings))]
return list(zip(forward_sequence, reversed(backward_sequence)))
def predict_output_sequence(model, encoder_frnn, encoder_rrnn, decoder_rnn, lemma, feats, alphabet_index,
inverse_alphabet_index, feat_index, feature_types):
pc.renew_cg()
# read the parameters
char_lookup = model["char_lookup"]
feat_lookup = model["feat_lookup"]
R = pc.parameter(model["R"])
bias = pc.parameter(model["bias"])
# convert characters to matching embeddings, if UNK handle properly
padded_lemma = BEGIN_WORD + lemma + END_WORD
lemma_char_vecs = []
for char in padded_lemma:
try:
lemma_char_vecs.append(char_lookup[alphabet_index[char]])
except KeyError:
# handle UNK
lemma_char_vecs.append(char_lookup[alphabet_index[UNK]])
# convert features to matching embeddings, if UNK handle properly
feat_vecs = []
for feat in sorted(feature_types):
# TODO: is it OK to use same UNK for all feature types? and for unseen feats as well?
# if this feature has a value, take it from the lookup. otherwise use UNK
if feat in feats:
feat_str = feat + ':' + feats[feat]
try:
feat_vecs.append(feat_lookup[feat_index[feat_str]])
except KeyError:
# handle UNK or dropout
feat_vecs.append(feat_lookup[feat_index[UNK_FEAT]])
else:
feat_vecs.append(feat_lookup[feat_index[UNK_FEAT]])
feats_input = pc.concatenate(feat_vecs)
# BiLSTM forward pass
s_0 = encoder_frnn.initial_state()
s = s_0
frnn_outputs = []
for c in lemma_char_vecs:
s = s.add_input(c)
frnn_outputs.append(s.output())
# BiLSTM backward pass
s_0 = encoder_rrnn.initial_state()
s = s_0
rrnn_outputs = []
for c in reversed(lemma_char_vecs):
s = s.add_input(c)
rrnn_outputs.append(s.output())
# BiLTSM outputs
blstm_outputs = []
lemma_char_vecs_len = len(lemma_char_vecs)
for i in xrange(lemma_char_vecs_len):
blstm_outputs.append(pc.concatenate([frnn_outputs[i], rrnn_outputs[lemma_char_vecs_len - i - 1]]))
# initialize the decoder rnn
s_0 = decoder_rnn.initial_state()
s = s_0
# set prev_output_vec for first lstm step as BEGIN_WORD
prev_output_vec = char_lookup[alphabet_index[BEGIN_WORD]]
prev_char_vec = char_lookup[alphabet_index[BEGIN_WORD]]
# i is input index, j is output index
i = j = 0
num_outputs = 0
predicted_output_sequence = []
# run the decoder through the sequence and predict characters, twice max prediction as step outputs are added
while num_outputs < MAX_PREDICTION_LEN * 3:
# prepare input vector and perform LSTM step
decoder_input = pc.concatenate([prev_output_vec,
prev_char_vec,
# char_lookup[alphabet_index[str(i)]],
# char_lookup[alphabet_index[str(j)]],
blstm_outputs[i],
feats_input])
s = s.add_input(decoder_input)
# compute softmax probs vector and predict with argmax
decoder_rnn_output = s.output()
probs = pc.softmax(R * decoder_rnn_output + bias)
probs = probs.vec_value()
predicted_output_index = common.argmax(probs)
predicted_output = inverse_alphabet_index[predicted_output_index]
predicted_output_sequence.append(predicted_output)
# check if step or char output to promote i or j.
if predicted_output == STEP:
prev_char_vec = char_lookup[alphabet_index[EPSILON]]
if i < len(padded_lemma) - 1:
i += 1
else:
if predicted_output.isdigit():
# handle copy
# try:
# prev_char_vec = char_lookup[alphabet_index[padded_lemma[i]]]
# except KeyError:
# prev_char_vec = char_lookup[alphabet_index[UNK]]
try:
# this way END_WORD cannot be copied (as it is in the training stage)
if i < len(lemma) + 1:
prev_char_vec = char_lookup[alphabet_index[padded_lemma[i]]]
else:
# if trying to copy from a non-existent index, pad with last lemma character
prev_char_vec = char_lookup[alphabet_index[lemma[-1]]]
except KeyError:
prev_char_vec = char_lookup[alphabet_index[UNK]]
else:
# handle char
prev_char_vec = char_lookup[predicted_output_index]
j += 1
num_outputs += 1
# check if reached end of word
if predicted_output_sequence[-1] == END_WORD:
break
# prepare for the next iteration - "feedback"
prev_output_vec = char_lookup[predicted_output_index]
# remove the end word symbol
return predicted_output_sequence[0:-1]
def one_word_loss(model, encoder_frnn, encoder_rrnn, decoder_rnn, lemma, feats, word, alphabet_index, aligned_pair,
feat_index, feature_types):
pc.renew_cg()
# read the parameters
char_lookup = model["char_lookup"]
feat_lookup = model["feat_lookup"]
R = pc.parameter(model["R"])
bias = pc.parameter(model["bias"])
padded_lemma = BEGIN_WORD + lemma + END_WORD
# convert characters to matching embeddings
lemma_char_vecs = []
for char in padded_lemma:
try:
lemma_char_vecs.append(char_lookup[alphabet_index[char]])
except KeyError:
# handle UNK
lemma_char_vecs.append(char_lookup[alphabet_index[UNK]])
# convert features to matching embeddings, if UNK handle properly
feat_vecs = []
for feat in sorted(feature_types):
# TODO: is it OK to use same UNK for all feature types? and for unseen feats as well?
# if this feature has a value, take it from the lookup. otherwise use UNK
if feat in feats:
feat_str = feat + ':' + feats[feat]
try:
feat_vecs.append(feat_lookup[feat_index[feat_str]])
except KeyError:
# handle UNK or dropout
feat_vecs.append(feat_lookup[feat_index[UNK_FEAT]])
else:
feat_vecs.append(feat_lookup[feat_index[UNK_FEAT]])
feats_input = pc.concatenate(feat_vecs)
# BiLSTM forward pass
s_0 = encoder_frnn.initial_state()
s = s_0
frnn_outputs = []
for c in lemma_char_vecs:
s = s.add_input(c)
frnn_outputs.append(s.output())
# BiLSTM backward pass
s_0 = encoder_rrnn.initial_state()
s = s_0
rrnn_outputs = []
for c in reversed(lemma_char_vecs):
s = s.add_input(c)
rrnn_outputs.append(s.output())
# BiLTSM outputs
blstm_outputs = []
lemma_char_vecs_len = len(lemma_char_vecs)
for i in xrange(lemma_char_vecs_len):
blstm_outputs.append(pc.concatenate([frnn_outputs[i], rrnn_outputs[lemma_char_vecs_len - i - 1]]))
# initialize the decoder rnn
s_0 = decoder_rnn.initial_state()
s = s_0
# set prev_output_vec for first lstm step as BEGIN_WORD
prev_output_vec = char_lookup[alphabet_index[BEGIN_WORD]]
prev_char_vec = char_lookup[alphabet_index[BEGIN_WORD]]
loss = []
# i is input index, j is output index
i = 0
j = 0
# go through alignments, progress j when new output is introduced, progress i when new char is seen on lemma (no ~)
# TODO: try sutskever flip trick?
# TODO: attention on the lemma chars/feats could help here?
aligned_lemma, aligned_word = aligned_pair
aligned_lemma += END_WORD
aligned_word += END_WORD
# run through the alignments
for index, (input_char, output_char) in enumerate(zip(aligned_lemma, aligned_word)):
possible_outputs = []
# feedback, i, j, blstm[i], feats
decoder_input = pc.concatenate([prev_output_vec,
prev_char_vec,
# char_lookup[alphabet_index[str(i)]],
# char_lookup[alphabet_index[str(j)]],
blstm_outputs[i],
feats_input])
# if reached the end word symbol
if output_char == END_WORD:
s = s.add_input(decoder_input)
decoder_rnn_output = s.output()
probs = pc.softmax(R * decoder_rnn_output + bias)
# compute local loss
loss.append(-pc.log(pc.pick(probs, alphabet_index[END_WORD])))
continue
# if there is no prefix, step
if padded_lemma[i] == BEGIN_WORD and aligned_lemma[index] != ALIGN_SYMBOL:
# perform rnn step
# feedback, i, j, blstm[i], feats
s = s.add_input(decoder_input)
decoder_rnn_output = s.output()
probs = pc.softmax(R * decoder_rnn_output + bias)
# compute local loss
loss.append(-pc.log(pc.pick(probs, alphabet_index[STEP])))
# prepare for the next iteration - "feedback"
prev_output_vec = char_lookup[alphabet_index[STEP]]
prev_char_vec = char_lookup[alphabet_index[EPSILON]]
i += 1
# if there is new output
if aligned_word[index] != ALIGN_SYMBOL:
decoder_input = pc.concatenate([prev_output_vec,
prev_char_vec,
# char_lookup[alphabet_index[str(i)]],
# char_lookup[alphabet_index[str(j)]],
blstm_outputs[i],
feats_input])
# copy i action - maybe model as a single action?
if padded_lemma[i] == aligned_word[j]:
possible_outputs.append(str(i))
possible_outputs.append(padded_lemma[i])
else:
possible_outputs.append(aligned_word[index])
# perform rnn step
s = s.add_input(decoder_input)
decoder_rnn_output = s.output()
probs = pc.softmax(R * decoder_rnn_output + bias)
local_loss = pc.scalarInput(0)
max_output_loss = -pc.log(pc.pick(probs, alphabet_index[possible_outputs[0]]))
max_likelihood_output = possible_outputs[0]
# sum over all correct output possibilities and pick feedback output to be the one with the highest
# probability
for output in possible_outputs:
neg_log_likelihood = -pc.log(pc.pick(probs, alphabet_index[output]))
if neg_log_likelihood < max_output_loss:
max_likelihood_output = output
max_output_loss = neg_log_likelihood
local_loss += neg_log_likelihood
loss.append(local_loss)
# prepare for the next iteration - "feedback"
prev_output_vec = char_lookup[alphabet_index[max_likelihood_output]]
prev_char_vec = char_lookup[alphabet_index[aligned_word[index]]]
j += 1
# now check if it's time to progress on input
if i < len(padded_lemma) - 1 and aligned_lemma[index + 1] != ALIGN_SYMBOL:
# perform rnn step
# feedback, i, j, blstm[i], feats
decoder_input = pc.concatenate([prev_output_vec,
prev_char_vec,
# char_lookup[alphabet_index[str(i)]],
# char_lookup[alphabet_index[str(j)]],
blstm_outputs[i],
feats_input])
s = s.add_input(decoder_input)
decoder_rnn_output = s.output()
probs = pc.softmax(R * decoder_rnn_output + bias)
# compute local loss
loss.append(-pc.log(pc.pick(probs, alphabet_index[STEP])))
# prepare for the next iteration - "feedback"
prev_output_vec = char_lookup[alphabet_index[STEP]]
prev_char_vec = char_lookup[alphabet_index[EPSILON]]
i += 1
# TODO: maybe here a "special" loss function is appropriate?
# loss = esum(loss)
loss = pc.average(loss)
return loss
if args.target in ['joint']:
pOutAge = model.add_parameters("OUT_AGE", (num_labels, MLP_HIDDEN_LAYER_SIZE))
biasOutAge = model.add_parameters("BIAS_OUT_AGE", (num_labels))
elif args.target in ['age', 'both']:
pOutAge = model.add_parameters("OUT_AGE", (len(age_labels), MLP_HIDDEN_LAYER_SIZE))
biasOutAge = model.add_parameters("BIAS_OUT_AGE", (len(age_labels)))
if args.target in ['gender', 'both']:
pOutGender = model.add_parameters("OUT2", (len(gender_labels), MLP_HIDDEN_LAYER_SIZE))
biasOutGender = model.add_parameters("BIAS_OUT2", (len(gender_labels)))
print("declared variables", file=sys.stderr)
pycnn.renew_cg()
new_word_embeddings = [pycnn.lookup(model["word_lookup"], w) for w in train[0][0][0]]
print(new_word_embeddings)
pycnn.conv1d_narrow(new_word_embeddings, pycnn.cg())
sys.exit()
def build_tagging_graph(word_indices, model, builder):
"""
build the computational graph
:param word_indices: list of indices
:param model: current model to access parameters
:param builder: builder to create state combinations
:return: forward and backward sequence
def get_loss(model, input_sentence, output_sentence, enc_fwd_lstm, enc_bwd_lstm, dec_lstm):
pc.renew_cg()
embedded = embedd_sentence(model, input_sentence)
encoded = encode_sentence(model, enc_fwd_lstm, enc_bwd_lstm, embedded)
return decode(model, dec_lstm, encoded, output_sentence)
def compute_loss(model, encoder_frnn, encoder_rrnn, decoder_rnn, lemma, feats, word, alphabet_index, feat_index,
feature_types, alignment):
pc.renew_cg()
# read the parameters
char_lookup = model["char_lookup"]
feat_lookup = model["feat_lookup"]
R = pc.parameter(model["R"])
bias = pc.parameter(model["bias"])
W_c = pc.parameter(model["W_c"])
W__a = pc.parameter(model["W__a"])
U__a = pc.parameter(model["U__a"])
v__a = pc.parameter(model["v__a"])
template = task1_ms2s.generate_template_from_alignment(alignment)
blstm_outputs = task1_attention_implementation.encode_feats_and_chars(alphabet_index, char_lookup, encoder_frnn, encoder_rrnn, feat_index,
feat_lookup, feats, feature_types, lemma)
# initialize the decoder rnn
s_0 = decoder_rnn.initial_state()
s = s_0
# set prev_output_vec for first lstm step as BEGIN_WORD
prev_output_vec = char_lookup[alphabet_index[BEGIN_WORD]]
prev_char_vec = char_lookup[alphabet_index[BEGIN_WORD]]
loss = []
padded_word = word + END_WORD
padded_template = template + [END_WORD]
# run the decoder through the output sequence and aggregate loss
for i, output_char in enumerate(padded_word):
# find all possible actions - copy from index, output specific character etc.
possible_outputs = list(set([padded_template[i]]))# + [output_char]))
# get current h of the decoder
s = s.add_input(pc.concatenate([prev_output_vec, prev_char_vec]))
decoder_rnn_output = s.output()
attention_output_vector, alphas, W = task1_attention_implementation.attend(blstm_outputs, decoder_rnn_output,
W_c, v__a, W__a, U__a)
# compute output probabilities
# print 'computing readout layer...'
readout = R * attention_output_vector + bias
# choose which feedback based on minimum neg. log likelihood: initialize with the character loss
min_neg_log_loss = pc.pickneglogsoftmax(readout, alphabet_index[output_char])
prev_output_char = output_char
prev_output_action = output_char
for output in possible_outputs:
current_loss = pc.pickneglogsoftmax(readout, alphabet_index[output])
# append the loss of all options
loss.append(current_loss)
if current_loss < min_neg_log_loss:
min_neg_log_loss = current_loss
prev_output_action = output
# prepare for the next iteration - "feedback"
prev_output_vec = char_lookup[alphabet_index[prev_output_action]]
prev_char_vec = char_lookup[alphabet_index[prev_output_char]]
total_sequence_loss = pc.esum(loss)
# loss = average(loss)
return total_sequence_loss
def compute_loss(model, encoder_frnn, encoder_rrnn, decoder_rnn, lemma, feats,
word, alphabet_index, feat_index, feature_types, alignment):
pc.renew_cg()
# read the parameters
char_lookup = model["char_lookup"]
feat_lookup = model["feat_lookup"]
R = pc.parameter(model["R"])
bias = pc.parameter(model["bias"])
W_c = pc.parameter(model["W_c"])
W__a = pc.parameter(model["W__a"])
U__a = pc.parameter(model["U__a"])
v__a = pc.parameter(model["v__a"])
template = task1_ms2s.generate_template_from_alignment(alignment)
blstm_outputs = task1_attention_implementation.encode_feats_and_chars(
alphabet_index, char_lookup, encoder_frnn, encoder_rrnn, feat_index,
feat_lookup, feats, feature_types, lemma)
# initialize the decoder rnn
s_0 = decoder_rnn.initial_state()
s = s_0
# set prev_output_vec for first lstm step as BEGIN_WORD
prev_output_vec = char_lookup[alphabet_index[BEGIN_WORD]]
prev_char_vec = char_lookup[alphabet_index[BEGIN_WORD]]
loss = []
padded_word = word + END_WORD
padded_template = template + [END_WORD]
# run the decoder through the output sequence and aggregate loss
for i, output_char in enumerate(padded_word):
# find all possible actions - copy from index, output specific character etc.
possible_outputs = list(set([padded_template[i]])) # + [output_char]))
# get current h of the decoder
s = s.add_input(pc.concatenate([prev_output_vec, prev_char_vec]))
decoder_rnn_output = s.output()
attention_output_vector, alphas, W = task1_attention_implementation.attend(
blstm_outputs, decoder_rnn_output, W_c, v__a, W__a, U__a)
# compute output probabilities
# print 'computing readout layer...'
readout = R * attention_output_vector + bias
# choose which feedback based on minimum neg. log likelihood: initialize with the character loss
min_neg_log_loss = pc.pickneglogsoftmax(readout,
alphabet_index[output_char])
prev_output_char = output_char
prev_output_action = output_char
for output in possible_outputs:
current_loss = pc.pickneglogsoftmax(readout,
alphabet_index[output])
# append the loss of all options
loss.append(current_loss)
if current_loss < min_neg_log_loss:
min_neg_log_loss = current_loss
prev_output_action = output
# prepare for the next iteration - "feedback"
prev_output_vec = char_lookup[alphabet_index[prev_output_action]]
prev_char_vec = char_lookup[alphabet_index[prev_output_char]]
total_sequence_loss = pc.esum(loss)
# loss = average(loss)
return total_sequence_loss
