def decode(dec_lstm, vectors, output):
output = [EOS] + list(output) + [EOS]
output = [char2int[c] for c in output]
w = dy.parameter(decoder_w)
b = dy.parameter(decoder_b)
w1 = dy.parameter(attention_w1)
input_mat = dy.concatenate_cols(vectors)
w1dt = None
last_output_embeddings = output_lookup[char2int[EOS]]
s = dec_lstm.initial_state().add_input(dy.concatenate([dy.vecInput(STATE_SIZE*2), last_output_embeddings]))
loss = []
for char in output:
# w1dt can be computed and cached once for the entire decoding phase
w1dt = w1dt or w1 * input_mat
vector = dy.concatenate([attend(input_mat, s, w1dt), last_output_embeddings])
s = s.add_input(vector)
out_vector = w * s.output() + b
probs = dy.softmax(out_vector)
last_output_embeddings = output_lookup[char]
loss.append(-dy.log(dy.pick(probs, char)))
loss = dy.esum(loss)
return loss
def calc_scores(words): # Create a computation graph, and add parameters dy.renew_cg() # Take the sum of all the embedding vectors for each word score = dy.esum([dy.lookup(W, x) for x in words]) # Add the bias vector and return return score + b
def attend(blstm_outputs, h_t, W_c, v_a, W__a, U__a):
# iterate through input states to compute alphas
# print 'computing scores...'
# scores = [W_a * pc.concatenate([h_t, h_input]) for h_input in blstm_outputs]
scores = [v_a * pc.tanh(W__a * h_t + U__a * h_input) for h_input in blstm_outputs]
# print 'computed scores'
# normalize to alphas using softmax
# print 'computing alphas...'
alphas = pc.softmax(pc.concatenate(scores))
# print 'computed alphas...'
# compute c using alphas
# print 'computing c...'
# import time
# s = time.time()
# dim = len(blstm_outputs[0].vec_value())
# stacked_alphas = pc.concatenate_cols([alphas for j in xrange(dim)])
# stacked_vecs = pc.concatenate_cols([h_input for h_input in blstm_outputs])
# c = pc.esum(pc.cwise_multiply(stacked_vecs, stacked_alphas))
# print "stack time:", time.time() - s
# s = time.time()
c = pc.esum([h_input * pc.pick(alphas, j) for j, h_input in enumerate(blstm_outputs)])
# print "pick time:", time.time() - s
# print 'computed c'
# print 'c len is {}'.format(len(c.vec_value()))
# compute output state h~ using c and the decoder's h (global attention variation from Loung and Manning 2015)
# print 'computing h~...'
h_output = pc.tanh(W_c * pc.concatenate([h_t, c]))
# print 'len of h_output is {}'.format(len(h_output.vec_value()))
# print 'computed h~'
return h_output, alphas, W__a.value()
def finalize(self, finished_epoch=False, **kwargs):
"""
Fit this model on collected samples
:return self
"""
super().finalize(finished_epoch=finished_epoch, **kwargs)
assert self.model, "Cannot finalize a model without initializing it first"
if self.losses:
loss = dy.esum(self.losses)
loss.forward()
self.config.print(lambda: "Total loss from %d time steps: %g" % (self.steps, loss.value()), level=4)
loss.backward()
try:
self.trainer.update()
except RuntimeError as e:
Config().log("Error in update(): %s\n" % e)
self.init_cg()
self.losses = []
self.steps = 0
self.updates += 1
if finished_epoch:
self.trainer.learning_rate /= (1 - self.learning_rate_decay)
if self.config.args.verbose > 2:
self.trainer.status()
return self
def calc_sent_loss(sent):
# Create a computation graph
dy.renew_cg()
#add padding to the sentence equal to the size of the window
#as we need to predict the eos as well, the future window at that point is N past it
padded_sent = [S] * N + sent + [S] * N
padded_emb = [W_c_p[x] for x in padded_sent]
# Step through the sentence
all_losses = []
for i in range(N,len(sent)+N):
c = dy.esum(padded_emb[i-N:i] + padded_emb[i+1:i+N+1])
s = W_w * c
all_losses.append(dy.pickneglogsoftmax(s, padded_sent[i]))
return dy.esum(all_losses)
def calc_loss(sent):
dy.renew_cg()
# Transduce all batch elements with an LSTM
src = sent[0]
trg = sent[1]
#initialize the LSTM
init_state_src = LSTM_SRC_BUILDER.initial_state()
#get the output of the first LSTM
src_output = init_state_src.add_inputs([LOOKUP_SRC[x] for x in src])[-1].output()
#now step through the output sentence
all_losses = []
current_state = LSTM_TRG_BUILDER.initial_state().set_s([src_output, dy.tanh(src_output)])
prev_word = trg[0]
W_sm = dy.parameter(W_sm_p)
b_sm = dy.parameter(b_sm_p)
for next_word in trg[1:]:
#feed the current state into the
current_state = current_state.add_input(LOOKUP_TRG[prev_word])
output_embedding = current_state.output()
s = dy.affine_transform([b_sm, W_sm, output_embedding])
all_losses.append(dy.pickneglogsoftmax(s, next_word))
prev_word = next_word
return dy.esum(all_losses)
def sent_loss(words, tags):
vecs = build_tagging_graph(words)
errs = []
for v,t in zip(vecs,tags):
tid = vt.w2i[t]
err = dy.pickneglogsoftmax(v, tid)
errs.append(err)
return dy.esum(errs)
def calc_loss(sents):
dy.renew_cg()
# Transduce all batch elements with an LSTM
src_sents = [x[0] for x in sents]
tgt_sents = [x[1] for x in sents]
src_cws = []
src_len = [len(sent) for sent in src_sents]
max_src_len = np.max(src_len)
num_words = 0
for i in range(max_src_len):
src_cws.append([sent[i] for sent in src_sents])
#initialize the LSTM
init_state_src = LSTM_SRC_BUILDER.initial_state()
#get the output of the first LSTM
src_output = init_state_src.add_inputs([dy.lookup_batch(LOOKUP_SRC, cws) for cws in src_cws])[-1].output()
#now decode
all_losses = []
# Decoder
#need to mask padding at end of sentence
tgt_cws = []
tgt_len = [len(sent) for sent in sents]
max_tgt_len = np.max(tgt_len)
masks = []
for i in range(max_tgt_len):
tgt_cws.append([sent[i] if len(sent) > i else eos_trg for sent in tgt_sents])
mask = [(1 if len(sent) > i else 0) for sent in tgt_sents]
masks.append(mask)
num_words += sum(mask)
current_state = LSTM_TRG_BUILDER.initial_state().set_s([src_output, dy.tanh(src_output)])
prev_words = tgt_cws[0]
W_sm = dy.parameter(W_sm_p)
b_sm = dy.parameter(b_sm_p)
for next_words, mask in zip(tgt_cws[1:], masks):
#feed the current state into the
current_state = current_state.add_input(dy.lookup_batch(LOOKUP_TRG, prev_words))
output_embedding = current_state.output()
s = dy.affine_transform([b_sm, W_sm, output_embedding])
loss = (dy.pickneglogsoftmax_batch(s, next_words))
mask_expr = dy.inputVector(mask)
mask_expr = dy.reshape(mask_expr, (1,),len(sents))
mask_loss = loss * mask_expr
all_losses.append(mask_loss)
prev_words = next_words
return dy.sum_batches(dy.esum(all_losses)), num_words
def calc_reinforce_loss(words, tags, delta):
dy.renew_cg()
# Transduce all batch elements with an LSTM
word_reps = LSTM.transduce([LOOKUP[x] for x in words])
# Softmax scores
W = dy.parameter(W_sm)
b = dy.parameter(b_sm)
#calculate the probability distribution
scores = [dy.affine_transform([b, W, x]) for x in word_reps]
losses = [dy.pickneglogsoftmax(score, tag) for score, tag in zip(scores, tags)]
probs = [-dy.exp(loss).as_array() for loss in losses]
#then take samples from the probability distribution
samples = [np.random.choice(range(len(x)), p=x) for x in probs]
#calculate accuracy=reward
correct = [sample == tag for sample, tag in zip(samples, tags)]
r_i = float(sum(correct))/len(correct)
r = dy.constant((1), r_i)
# Reward baseline for each word
W_bl = dy.parameter(W_bl_p)
b_bl = dy.parameter(b_bl_p)
r_b = [dy.affine_transform([b_bl, W_bl, dy.nobackprop(x)]) for x in word_reps]
#we need to take the value in order to break the computation graph
#as the reward portion is trained seperatley and not backpropogated through during the overall score
rewards_over_baseline = [(r - dy.nobackprop(x)) for x in r_b]
#the scores for training the baseline
baseline_scores = [dy.square(r - x) for x in r_b]
#then calculate the reinforce scores using reinforce
reinforce_scores = [r_s*score for r_s, score in zip(rewards_over_baseline, scores)]
#we want the first len(sent)-delta scores from xent then delta scores from reinforce
#for mixer
if len(scores) > delta:
mixer_scores = scores[:len(scores)-delta] + reinforce_scores[delta-1:]
else:
mixer_scores = reinforce_scores
return dy.esum(mixer_scores), dy.esum(baseline_scores)
def calc_loss(sent):
dy.renew_cg()
# Transduce all batch elements with an LSTM
src = sent[0]
trg = sent[1]
# initialize the LSTM
init_state_src = LSTM_SRC_BUILDER.initial_state()
# get the output of the first LSTM
src_output = init_state_src.add_inputs([LOOKUP_SRC[x] for x in src])[-1].output()
# Now compute mean and standard deviation of source hidden state.
W_mean = dy.parameter(W_mean_p)
V_mean = dy.parameter(V_mean_p)
b_mean = dy.parameter(b_mean_p)
W_var = dy.parameter(W_var_p)
V_var = dy.parameter(V_var_p)
b_var = dy.parameter(b_var_p)
# The mean vector from the encoder.
mu = mlp(src_output, W_mean, V_mean, b_mean)
# This is the diagonal vector of the log co-variance matrix from the encoder
# (regard this as log variance is easier for furture implementation)
log_var = mlp(src_output, W_var, V_var, b_var)
# Compute KL[N(u(x), sigma(x)) || N(0, I)]
# 0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2)
kl_loss = -0.5 * dy.sum_elems(1 + log_var - dy.pow(mu, dy.inputVector([2])) - dy.exp(log_var))
z = reparameterize(mu, log_var)
# now step through the output sentence
all_losses = []
current_state = LSTM_TRG_BUILDER.initial_state().set_s([z, dy.tanh(z)])
prev_word = trg[0]
W_sm = dy.parameter(W_sm_p)
b_sm = dy.parameter(b_sm_p)
for next_word in trg[1:]:
# feed the current state into the
current_state = current_state.add_input(LOOKUP_TRG[prev_word])
output_embedding = current_state.output()
s = dy.affine_transform([b_sm, W_sm, output_embedding])
all_losses.append(dy.pickneglogsoftmax(s, next_word))
prev_word = next_word
softmax_loss = dy.esum(all_losses)
return kl_loss, softmax_loss
def loss(self, preds, y):
if self.do_crf is True:
return self.crf.neg_log_loss(preds, y.squeeze(0))
else:
element_loss = dy.pickneglogsoftmax
errs = []
for pred, y_i in zip(preds, y.T):
err = element_loss(pred, y_i)
errs.append(err)
return dy.esum(errs)
def transduce(seq,Y):
seq = [E[i] for i in seq]
fw = fwR.initial_state().transduce(seq)
# this UNUSED part affects strategy 2
XXX = fwR2.initial_state().transduce([E[3],E[5]])
W = W_.expr()
outs = [W*z for z in fw]
losses = [dy.pickneglogsoftmax(o,y) for o,y in zip(outs,Y)]
s = dy.esum(losses)
return s
def calc_sent_loss(sent):
# Create a computation graph
dy.renew_cg()
# The initial history is equal to end of sentence symbols
hist = [S] * N
# Step through the sentence, including the end of sentence token
all_losses = []
for next_word in sent + [S]:
s = calc_score_of_history(hist)
all_losses.append(dy.pickneglogsoftmax(s, next_word))
hist = hist[1:] + [next_word]
return dy.esum(all_losses)
def sent_lm_loss(self, sent):
rnn_cur = self.rnn.initial_state()
losses = []
prev_word = self.start
for word in sent:
x_t = self.embeddings[prev_word]
rnn_cur = rnn_cur.add_input(x_t)
logits = dy.affine_transform([self.lb,
self.h2l,
rnn_cur.output()])
losses.append(dy.pickneglogsoftmax(logits, word))
prev_word = word
return dy.esum(losses)
def BuildLMGraph(self, sent):
dy.renew_cg()
init_state = self.builder.initial_state()
errs = [] # will hold expressions
es=[]
state = init_state
inputs = [self.lookup[int(cw)] for cw in sent[:-1]]
expected_outputs = [int(nw) for nw in sent[1:]]
outputs = state.transduce(inputs)
r_ts = ((self.bias + (self.R * y_t)) for y_t in outputs)
errs = [dy.pickneglogsoftmax(r_t, eo) for r_t, eo in zip(r_ts, expected_outputs)]
nerr = dy.esum(errs)
return nerr
def attend(input_vectors, state):
global attention_w1
global attention_w2
global attention_v
w1 = dy.parameter(attention_w1)
w2 = dy.parameter(attention_w2)
v = dy.parameter(attention_v)
attention_weights = []
w2dt = w2*dy.concatenate(list(state.s()))
for input_vector in input_vectors:
attention_weight = v*dy.tanh(w1*input_vector + w2dt)
attention_weights.append(attention_weight)
attention_weights = dy.softmax(dy.concatenate(attention_weights))
output_vectors = dy.esum([vector*attention_weight for vector, attention_weight in zip(input_vectors, attention_weights)])
return output_vectors
def attend2(blstm_outputs, s_prev, y_feedback, v_a, W_a, U_a, U_o, V_o, C_o):
# attention mechanism - Bahdanau style
# iterate through input states to compute alphas
# print 'computing scores...'
# W_a: hidden x hidden, U_a: hidden x 2 hidden, v_a: hidden, each score: scalar
scores = [v_a * pc.tanh(W_a * s_prev + U_a * h_j) for h_j in blstm_outputs]
alphas = pc.softmax(pc.concatenate(scores))
# c_i: 2 hidden
c_i = pc.esum([h_input * pc.pick(alphas, j) for j, h_input in enumerate(blstm_outputs)])
# U_o = 2l x hidden, V_o = 2l x input, C_o = 2l x 2 hidden
attention_output_vector = U_o * s_prev + V_o * y_feedback + C_o * c_i
return attention_output_vector, alphas
def build_lm_graph(self, sent):
dy.renew_cg()
init_state = self.builder.initial_state()
errs = [] # will hold expressions
es=[]
state = init_state
for (cw,nw) in zip(sent,sent[1:]):
# assume word is already a word-id
x_t = dy.lookup(self.lookup, int(cw))
state = state.add_input(x_t)
y_t = state.output()
r_t = self.bias + (self.R * y_t)
err = dy.pickneglogsoftmax(r_t, int(nw))
errs.append(err)
nerr = dy.esum(errs)
return nerr
def calc_sent_loss(sent):
# Create a computation graph
dy.renew_cg()
# Get embeddings for the sentence
emb = [W_w_p[x] for x in sent]
# Step through the sentence and calculate binary prediction losses
all_losses = []
for i, my_emb in enumerate(emb):
scores = dy.logistic(W_c * my_emb)
pos_words = ([sent[x] if x >= 0 else S for x in range(i-N,i)] +
[sent[x] if x < len(sent) else S for x in range(i+1,i+N+1)])
word_repr = [[float(y) for y in np.binary_repr(x).zfill(nbits)] for x in pos_words]
word_repr = [dy.inputVector(x) for x in word_repr]
all_losses.extend([dy.binary_log_loss(scores, x) for x in word_repr])
return dy.esum(all_losses)
def BuildLMGraph(self, sents):
dy.renew_cg()
# initialize the RNN
init_state = self.builder.initial_state()
# parameters -> expressions
R = dy.parameter(self.R)
bias = dy.parameter(self.bias)
S = vocab.w2i["<s>"]
# get the cids and masks for each step
tot_chars = 0
cids = []
masks = []
for i in range(len(sents[0])):
cids.append([(vocab.w2i[sent[i]] if len(sent) > i else S) for sent in sents])
mask = [(1 if len(sent)>i else 0) for sent in sents]
masks.append(mask)
tot_chars += sum(mask)
# start the rnn with "<s>"
init_ids = cids[0]
s = init_state.add_input(lookup_batch(self.lookup, init_ids))
losses = []
# feed char vectors into the RNN and predict the next char
for cid, mask in zip(cids[1:], masks[1:]):
score = dy.affine_transform([bias, R, s.output()])
loss = dy.pickneglogsoftmax_batch(score, cid)
# mask the loss if at least one sentence is shorter
if mask[-1] != 1:
mask_expr = dy.inputVector(mask)
mask_expr = dy.reshape(mask_expr, (1,), len(sents))
loss = loss * mask_expr
losses.append(loss)
# update the state of the RNN
cemb = dy.lookup_batch(self.lookup, cid)
s = s.add_input(cemb)
return dy.sum_batches(dy.esum(losses)), tot_chars
def decode(dec_lstm, vectors, output):
output = [EOS] + list(output) + [EOS]
output = [char2int[c] for c in output]
w = dy.parameter(decoder_w)
b = dy.parameter(decoder_b)
last_output_embeddings = output_lookup[char2int[EOS]]
s = dec_lstm.initial_state().add_input(dy.concatenate([dy.vecInput(STATE_SIZE*2), last_output_embeddings]))
loss = []
for char in output:
vector = dy.concatenate([attend(vectors, s), last_output_embeddings])
s = s.add_input(vector)
out_vector = w * s.output() + b
probs = dy.softmax(out_vector)
last_output_embeddings = output_lookup[char]
loss.append(-dy.log(dy.pick(probs, char)))
loss = dy.esum(loss)
return loss
def calc_sent_loss(sent):
# Create a computation graph
dy.renew_cg()
# Get embeddings for the sentence
emb = [W_w_p[x] for x in sent]
# Sample K negative words for each predicted word at each position
all_neg_words = np.random.choice(nwords, size=2*N*K*len(emb), replace=True, p=word_probabilities)
# W_w = dy.parameter(W_w_p)
# Step through the sentence and calculate the negative and positive losses
all_losses = []
for i, my_emb in enumerate(emb):
neg_words = all_neg_words[i*K*2*N:(i+1)*K*2*N]
pos_words = ([sent[x] if x >= 0 else S for x in range(i-N,i)] +
[sent[x] if x < len(sent) else S for x in range(i+1,i+N+1)])
neg_loss = -dy.log(dy.logistic(-dy.dot_product(my_emb, dy.lookup_batch(W_c_p, neg_words))))
pos_loss = -dy.log(dy.logistic(dy.dot_product(my_emb, dy.lookup_batch(W_c_p, pos_words))))
all_losses.append(dy.sum_batches(neg_loss) + dy.sum_batches(pos_loss))
return dy.esum(all_losses)
def calc_lm_loss(sents):
dy.renew_cg()
# initialize the RNN
f_init = RNN.initial_state()
# get the wids and masks for each step
tot_words = 0
wids = []
masks = []
for i in range(len(sents[0])):
wids.append([(sent[i] if len(sent) > i else S) for sent in sents])
mask = [(1 if len(sent) > i else 0) for sent in sents]
masks.append(mask)
tot_words += sum(mask)
# start the rnn by inputting "<s>"
init_ids = [S] * len(sents)
s = f_init.add_input(dy.lookup_batch(WORDS_LOOKUP, init_ids))
# feed word vectors into the RNN and predict the next word
losses = []
for wid, mask in zip(wids, masks):
# calculate the softmax and loss
score = dy.affine_transform([b_exp, W_exp, s.output()])
loss = dy.pickneglogsoftmax_batch(score, wid)
# mask the loss if at least one sentence is shorter
if mask[-1] != 1:
mask_expr = dy.inputVector(mask)
mask_expr = dy.reshape(mask_expr, (1,), len(sents))
loss = loss * mask_expr
losses.append(loss)
# update the state of the RNN
wemb = dy.lookup_batch(WORDS_LOOKUP, wid)
s = s.add_input(wemb)
return dy.sum_batches(dy.esum(losses)), tot_words
def CalculateLossForDaf(daf, fValidation=False, fRunning=False):
dy.renew_cg()
tagged_daf = {"words": []}
# add a bos before and after
seq = ['*BOS*'] + list(' '.join([word for word, _ in daf])) + ['*BOS*']
# get all the char encodings for the daf
char_embeds = [let_enc(let) for let in seq]
# run it through the bilstm
char_bilstm_outputs = bilstm(char_embeds)
# now iterate and get all the separate word representations by concatenating the bilstm output
# before and after the word
word_bilstm_outputs = []
iLet_start = 0
for iLet, char in enumerate(seq):
# if it is a bos, check if it's at the end of the sequence
if char == '*BOS*':
if iLet + 1 == len(seq):
char = ' '
else:
continue
# if we are at a space, take this bilstm output and the one at the letter start
if char == ' ':
cur_word_bilstm_output = dy.concatenate([char_bilstm_outputs[iLet_start], char_bilstm_outputs[iLet]])
# add it in
word_bilstm_outputs.append(cur_word_bilstm_output)
# set the iLet_start ocunter to here
iLet_start = iLet
# safe-check, make sure word bilstm outputs length is the same as the daf
if len(word_bilstm_outputs) != len(daf):
log_message('Size mismatch!! word_bilstm_outputs: ' + str(len(word_bilstm_outputs)) + ', daf: ' + str(len(daf)))
prev_lang_lstm_state = prev_lang_lstm.initial_state().add_input(lang_enc('*BOS*'))
all_losses = []
lang_prec = 0.0
lang_items = 0
# now iterate through the bilstm outputs, and each word in the daf
for (word, gold_word_lang), bilstm_output in zip(daf, word_bilstm_outputs):
# create the mlp input, a concatenate of the bilstm output and of the prev pos output
mlp_input = dy.concatenate([bilstm_output, prev_lang_lstm_state.output()])
# run through the class mlp
lang_mlp_output = lang_mlp(mlp_input)
predicted_word_lang = lang_vocab.getItem(np.argmax(lang_mlp_output.npvalue()))
confidence = np.max(lang_mlp_output.npvalue()) / np.sum(lang_mlp_output.npvalue())
lang_prec += 1 if predicted_word_lang == gold_word_lang else 0
lang_items += 1
tagged_daf["words"].append(
{"word": word, "predicted_lang": predicted_word_lang, "confidence": confidence})
# if we aren't doing validation, calculate the loss
if not fValidation and not fRunning:
all_losses.append(-dy.log(dy.pick(lang_mlp_output, lang_vocab[gold_word_lang])))
word_pos_ans = gold_word_lang
# otherwise, set the answer to be the argmax
elif not fRunning and fValidation:
lang_conf_matrix(lang_vocab[predicted_word_lang], lang_vocab[gold_word_lang])
word_pos_ans = predicted_word_lang
else:
continue
# run through the prev-pos-mlp
prev_lang_lstm_state = prev_lang_lstm_state.add_input(lang_enc(word_pos_ans))
# prev_pos_lstm_state = prev_pos_lstm_state.add_input(pos_enc(''))
lang_prec = lang_prec / lang_items if lang_items > 0 else None
# class_prec = class_prec / class_items if class_items > 0 else None
if fValidation:
return lang_prec, tagged_daf
if fRunning:
return tagged_daf
total_loss = dy.esum(all_losses) if len(all_losses) > 0 else None
return total_loss, lang_prec
all_time = 0
for ITER in range(100):
random.shuffle(train)
closs = 0.0
cwords = 0
start = time.time()
batch = []
for i, tree in enumerate(train, 1):
sents += 1
W = dy.parameter(W_)
h, c = builder.expr_for_tree(tree, True)
nodes = tree.nonterms()
losses = [
dy.pickneglogsoftmax(W * nt._e, l2i[nt.label]) for nt in nodes
]
loss = dy.esum(losses)
batch.append(loss)
if len(batch) == 50:
loss = dy.esum(batch)
closs += loss.value()
cwords += len(nodes)
loss.backward()
trainer.update()
batch = []
dy.renew_cg()
if sents % 1000 == 0:
trainer.status()
print(closs / cwords, file=sys.stderr)
closs = 0.0
cwords = 0
all_time += time.time() - start
def em_example(self, example):
encoder_input = example[0]
ground_labels = example[1]
goal_vector = encoder_input[0]
encoder_input = encoder_input[1]
num_utterances = len(encoder_input)
logits = self.MLP(goal_vector)
sentence_initial_state = self.sentence_encoder.initial_state()
pzs = []
for sentence in encoder_input:
embedded_sentence = [self.embeddings[word] for word in sentence]
final_state = sentence_initial_state.transduce(
embedded_sentence)[-1]
final_state = dy.concatenate([final_state, logits])
# Stochastic node:
pzs.append(self.prob_z(final_state))
context_initial_state = self.context_encoder.initial_state()
context_state = context_initial_state
z_list = []
# Expectation
e_time = time.time()
for idx in range(num_utterances):
pz = dy.nobackprop(pzs[idx])
max_prob = dy.scalarInput(-999999999)
z_star = -999999999
z_star_onehot = self.onehotzs[0]
for z in range(self.num_clusters):
one_hot_z = self.onehotzs[z]
state = context_state.add_input(one_hot_z).h()[-1]
log_papx = dy.nobackprop(
self.log_prob_papx(example, idx, state))
log_pz = dy.nobackprop(dy.log(dy.pick(pz, z)))
# print("PZ: {}".format(log_pz.npvalue()))
# print("PAPX: {}".format(log_papx.npvalue()))
log_prob = dy.esum([log_papx, log_pz])
# print("PROB: {}".format(log_prob.npvalue()))
if log_prob.value() > max_prob.value():
max_prob = log_prob
z_star = z
z_star_onehot = one_hot_z
# print(z_star)
# print(max_prob.npvalue())
# print(z_star)
context_state = context_state.add_input(z_star_onehot)
z_list.append(z_star)
self.e_time += (time.time() - e_time)
# Maximization
m_time = time.time()
context_state = context_initial_state
probs = []
for idx in range(num_utterances):
pz = pzs[idx]
z = z_list[idx]
log_pz = dy.log(dy.pick(pz, z))
one_hot_z = self.onehotzs[z]
state = context_state.add_input(one_hot_z).h()[-1]
# NO BACKPROP:
log_papx = dy.nobackprop(self.log_prob_papx(example, idx, state))
log_prob = dy.esum([log_papx, log_pz])
probs.append(log_prob)
# probs.append(log_papx)
probs = dy.esum(probs)
self.m_time += (time.time() - m_time)
# TODO: check this:
return (-probs, z_list)
def identify_frames(builders,
tokens,
postags,
lexunit,
targetpositions,
goldframe=None):
renew_cg()
trainmode = (goldframe is not None)
sentlen = len(tokens) - 1
emb_x = [v_x[tok] for tok in tokens]
pos_x = [p_x[pos] for pos in postags]
emb2_xi = []
for i in range(sentlen + 1):
if tokens[i] in pretrained_embeddings_map:
# If update set to False, prevents pretrained embeddings from being updated.
emb_without_backprop = lookup(e_x, tokens[i], update=True)
features_at_i = concatenate(
[emb_x[i], pos_x[i], emb_without_backprop])
else:
features_at_i = concatenate([emb_x[i], pos_x[i], u_x])
emb2_xi.append(w_e * features_at_i + b_e)
emb2_x = [rectify(emb2_xi[i]) for i in range(sentlen + 1)]
# initializing the two LSTMs
if USE_DROPOUT and trainmode:
builders[0].set_dropout(DROPOUT_RATE)
builders[1].set_dropout(DROPOUT_RATE)
f_init, b_init = [i.initial_state() for i in builders]
fw_x = f_init.transduce(emb2_x)
bw_x = b_init.transduce(reversed(emb2_x))
# only using the first target position - summing them hurts :(
targetembs = [
concatenate([fw_x[targetidx], bw_x[sentlen - targetidx - 1]])
for targetidx in targetpositions
]
targinit = tlstm.initial_state()
target_vec = targinit.transduce(targetembs)[-1]
valid_frames = list(lufrmmap[lexunit.id])
chosenframe = valid_frames[0]
logloss = None
if len(valid_frames) > 1:
if USE_HIER and lexunit.id in relatedlus:
lu_vec = esum([lu_x[luid] for luid in relatedlus[lexunit.id]])
else:
lu_vec = lu_x[lexunit.id]
fbemb_i = concatenate([target_vec, lu_vec, lp_x[lexunit.posid]])
# TODO(swabha): Add more Baidu-style features here.
f_i = w_f * rectify(w_z * fbemb_i + b_z) + b_f
if trainmode and USE_DROPOUT:
f_i = dropout(f_i, DROPOUT_RATE)
logloss = log_softmax(f_i, valid_frames)
if not trainmode:
chosenframe = np.argmax(logloss.npvalue())
if trainmode:
chosenframe = goldframe.id
losses = []
if logloss is not None:
losses.append(pick(logloss, chosenframe))
prediction = {
tidx: (lexunit, Frame(chosenframe))
for tidx in targetpositions
}
objective = -esum(losses) if losses else None
return objective, prediction
def Train(self, trainData, options):
mloss = 0.0
eloss = 0.0
eerrors = 0
lerrors = 0
etotal = 0
ninf = -float('inf')
beg = time.time()
start = time.time()
random.shuffle(
trainData
) # in certain cases the data will already have been shuffled after being read from file or while creating dev data
print "Length of training data: ", len(trainData)
errs = []
self.feature_extractor.Init(options)
for iSentence, sentence in enumerate(trainData, 1):
if iSentence % 100 == 0:
loss_message = 'Processing sentence number: %d'%iSentence + \
' Loss: %.3f'%(eloss / etotal)+ \
' Errors: %.3f'%((float(eerrors)) / etotal)+\
' Labeled Errors: %.3f'%(float(lerrors) / etotal)+\
' Time: %.2gs'%(time.time()-start)
print loss_message
start = time.time()
eerrors = 0
eloss = 0.0
etotal = 0
lerrors = 0
sentence = deepcopy(
sentence
) # ensures we are working with a clean copy of sentence and allows memory to be recycled each time round the loop
conll_sentence = [
entry for entry in sentence
if isinstance(entry, utils.ConllEntry)
]
conll_sentence = conll_sentence[1:] + [conll_sentence[0]]
self.feature_extractor.getWordEmbeddings(conll_sentence, True,
options)
stack = ParseForest([])
buf = ParseForest(conll_sentence)
hoffset = 1 if self.headFlag else 0
for root in conll_sentence:
root.lstms = [root.vec] if self.headFlag else []
root.lstms += [
self.feature_extractor.paddingVec
for _ in range(self.nnvecs - hoffset)
]
root.relation = root.relation if root.relation in self.irels else 'runk'
while not (len(buf) == 1 and len(stack) == 0):
scores = self.__evaluate(stack, buf, True)
#to ensure that we have at least one wrong operation
scores.append([(None, 4, ninf, None)])
stack_ids = [sitem.id for sitem in stack.roots]
s1 = [stack.roots[-2]] if len(stack) > 1 else []
s0 = [stack.roots[-1]] if len(stack) > 0 else []
b = [buf.roots[0]] if len(buf) > 0 else []
beta = buf.roots[1:] if len(buf) > 1 else []
costs, shift_case = self.calculate_cost(
scores, s0, s1, b, beta, stack_ids)
bestValid = list(
(s for s in chain(*scores)
if costs[s[1]] == 0 and (s[1] == SHIFT or s[1] == SWAP
or s[0] == s0[0].relation)))
bestValid = max(bestValid, key=itemgetter(2))
bestWrong = max(
(s for s in chain(*scores)
if costs[s[1]] != 0 or (s[1] != SHIFT and s[1] != SWAP
and s[0] != s0[0].relation)),
key=itemgetter(2))
#force swap
if costs[SWAP] == 0:
best = bestValid
else:
#select a transition to follow
# + aggresive exploration
#1: might want to experiment with that parameter
if bestWrong[1] == SWAP:
best = bestValid
else:
best = bestValid if (
(not self.oracle) or
(bestValid[2] - bestWrong[2] > 1.0) or
(bestValid[2] > bestWrong[2]
and random.random() > 0.1)) else bestWrong
if best[1] == LEFT_ARC or best[1] == RIGHT_ARC:
child = s0[0]
#updates for the dynamic oracle
if self.oracle:
self.oracle_updates(best, b, s0, stack_ids, shift_case)
self.apply_transition(best, stack, buf, hoffset)
if bestValid[2] < bestWrong[2] + 1.0:
loss = bestWrong[3] - bestValid[3]
mloss += 1.0 + bestWrong[2] - bestValid[2]
eloss += 1.0 + bestWrong[2] - bestValid[2]
errs.append(loss)
#labeled errors
if best[1] == LEFT_ARC or best[1] == RIGHT_ARC:
if (child.pred_parent_id != child.parent_id
or child.pred_relation != child.relation):
lerrors += 1
#attachment error
if child.pred_parent_id != child.parent_id:
eerrors += 1
#??? when did this happen and why?
if best[1] == 0 or best[1] == 2:
etotal += 1
#footnote 8 in Eli's original paper
if len(errs) > 50: # or True:
eerrs = dy.esum(errs)
scalar_loss = eerrs.scalar_value() #forward
eerrs.backward()
self.trainer.update()
errs = []
lerrs = []
dy.renew_cg()
self.feature_extractor.Init(options)
if len(errs) > 0:
eerrs = (dy.esum(errs))
eerrs.scalar_value()
eerrs.backward()
self.trainer.update()
errs = []
lerrs = []
dy.renew_cg()
self.trainer.update()
print "Loss: ", mloss / iSentence
print "Total Training Time: %.2gs" % (time.time() - beg)
def train(self, train_data, dev_data, num_epochs=150, batch_size=10):
for I in range(num_epochs):
print("EPOCH NUMBER {}".format(I))
avg_loss = 0.
random.shuffle(train_data)
good, bad = 0., 0.
avg_edit_distance = 0.
q = 0.
losses = []
preds = []
for i, (x, y) in enumerate(train_data):
if i % batch_size == 0 and i > 0:
loss_sum = dy.esum(losses)
loss_sum.forward()
loss_sum.backward()
self.trainer.update()
losses = []
# evaluate trainset accuracy
for (word_probs, y_true) in preds:
generated_string = ""
for char_probs in word_probs:
generated_string += self.I2C[np.argmax(
char_probs.npvalue())]
if generated_string == y_true:
good += 1
else:
bad += 1
preds = []
dy.renew_cg()
encoded_state, encoded_x = self.encode(x, y, train=True)
loss, probs = self.decode(encoded_state,
y,
encoded_x,
train=True)
preds.append((probs, y))
losses.append(loss)
if i % 2000 == 0 and i > 0:
print(i)
#print (avg_loss)
avg_loss = 0.
#self.test(dev_data)
#print ('DROPOUT = 0.5')
#self.embedding_collector.collect()
print("training accuracy: {}".format(good / (good + bad)))
acc, edit_dis = self.evaluate(dev_data)
self.accs.append(acc)
patience = 20
if I > 8 and abs(
min(self.accs[-patience:]) -
max(self.accs[-patience:])) < 0.01:
return 0
if acc > self.best_acc:
self.best_acc = acc
self.model.save("preds-orto-no-diac-embs-cyclic.m")
#self.embedding_collector.collect()
return 0
def train(self, train_file, epochs, validation_file):
plot_on = True
# matplotlib config
loss_values = []
validation_data = pickle.load(open(validation_file, 'rb'))
validation_accs, train_accs = [], []
train_data_original = pickle.load(open(train_file, "rb" ))
for i in range(epochs):
print('started epoch', (i+1))
losses = []
train_data = pickle.load(open(train_file, "rb" ))
# shuffle the training data.
random.shuffle(train_data)
step = 0
for fl in train_data:
features, label = fl[:-1], fl[-1]
gold_label = self.vocab.tag2id(label)
result = self.build_graph(features)
# getting loss with respect to negative log softmax function and the gold label
loss = dynet.pickneglogsoftmax(result, gold_label)
# appending to the minibatch losses
losses.append(loss)
step += 1
if len(losses) >= self.properties.minibatch_size:
# now we have enough loss values to get loss for minibatch
minibatch_loss = dynet.esum(losses) / len(losses)
# calling dynet to run forward computation for all minibatch items
minibatch_loss.forward()
# getting float value of the loss for current minibatch
minibatch_loss_value = minibatch_loss.value()
# printing info and plotting
loss_values.append((len(loss_values), minibatch_loss_value))
if len(loss_values)%10==0:
progress = round(100 * float(step) / len(train_data), 2)
print('current minibatch loss', minibatch_loss_value, 'progress:', progress, '%')
# calling dynet to run backpropagation
minibatch_loss.backward()
# calling dynet to change parameter values with respect to current backpropagation
self.updater.update()
# empty the loss vector
losses = []
# refresh the memory of dynet
dynet.renew_cg()
# get validation set accuracy
if len(loss_values)%100==0:
validation_accs.append((len(loss_values), self.calc_acc(validation_data)))
train_accs.append((len(loss_values), self.calc_acc(train_data_original)))
# there are still some minibatch items in the memory but they are smaller than the minibatch size
# so we ask dynet to forget them
dynet.renew_cg()
# return these values just for plotting
return loss_values, validation_accs, train_accs
def node_iteration(rel, g, node, opts, assoc_model, trainer, log_file, is_source):
"""
Perform one iteration of trying to score a node's neighbors above negative samples.
"""
# true instances likelihood
trues = targets(g, node) if is_source else sources(g, node)
side = '->' if is_source else '<-'
if len(trues) == 0: return 0.0
if opts.debug:
dy.renew_cg(immediate_compute = True, check_validity = True)
else:
dy.renew_cg()
# compute association score as dynet expression (can't do this above due to staleness)
true_scores = []
for tr in trues:
if is_source:
j_assoc_score = assoc_model.word_assoc_score(node, tr, rel)
else:
j_assoc_score = assoc_model.word_assoc_score(tr, node, rel)
if log_file is not None:
log_file.write('{} {}\tTRUE_{}\t{:.3e}\n'\
.format(node, side, tr, j_assoc_score.scalar_value()))
true_scores.append(j_assoc_score)
# false targets likelihood - negative sampling (uniform)
# collect negative samples
if opts.nll:
sample_scores = [[ts] for ts in true_scores]
else:
margins = []
neg_samples = [np.random.choice(range(N)) for _ in range(opts.neg_samp * len(trues))]
# remove source and true targets if applicable
for t in [node] + trues:
if t in neg_samples:
neg_samples.remove(t)
neg_samples.append(np.random.choice(range(N)))
for (i,ns) in enumerate(neg_samples):
# compute association score as dynet expression
if is_source:
ns_assoc_score = assoc_model.word_assoc_score(node, ns, rel)
else:
ns_assoc_score = assoc_model.word_assoc_score(ns, node, rel)
if log_file is not None:
log_file.write('{} {}\tNEG_{}\t{:.3e}\n'\
.format(node, side, ns, ns_assoc_score.scalar_value()))
corresponding_true = i // opts.neg_samp
if opts.nll:
sample_scores[corresponding_true].append(ns_assoc_score)
else:
# TODO maybe use dy.hinge()
ctt_score = true_scores[corresponding_true]
margin = ctt_score - ns_assoc_score
margins.append(dy.rectify(dy.scalarInput(1.0) - margin))
# compute overall loss
if opts.nll:
if len(sample_scores) == 0:
dy_loss = dy.scalarInput(0.0)
else:
dy_loss = dy.esum([dy.pickneglogsoftmax(dy.concatenate(scrs), 0) for scrs in sample_scores])
else:
if len(margins) == 0:
dy_loss = dy.scalarInput(0.0)
else:
dy_loss = dy.esum(margins)
sc_loss = dy_loss.scalar_value()
if log_file is not None:
log_file.write('{}\tLOSS\t{:.3e}\n'\
.format(node, sc_loss))
# backprop and recompute score
if opts.v > 1:
timeprint('overall loss for relation {}, node {} as {} = {:.6f}'\
.format(rel, node, 'source' if is_source else 'target', sc_loss))
dy_loss.backward()
trainer.update()
return sc_loss
def CalculateLossForDaf(daf, fValidation=False, fRunning=False):
dy.renew_cg()
tagged_daf = {"words": [], "file": daf["file"]}
daf = daf["words"]
# add a bos before and after
seq = ['*BOS*'] + list(' '.join([word
for word, _, _, _ in daf])) + ['*BOS*']
# get all the char encodings for the daf
char_embeds = [let_enc(let) for let in seq]
# run it through the bilstm
char_bilstm_outputs = bilstm(char_embeds)
# now iterate and get all the separate word representations by concatenating the bilstm output
# before and after the word
word_bilstm_outputs = []
iLet_start = 0
for iLet, char in enumerate(seq):
# if it is a bos, check if it's at the end of the sequence
if char == '*BOS*':
if iLet + 1 == len(seq):
char = ' '
else:
continue
# if we are at a space, take this bilstm output and the one at the letter start
if char == ' ':
cur_word_bilstm_output = dy.concatenate(
[char_bilstm_outputs[iLet_start], char_bilstm_outputs[iLet]])
# add it in
word_bilstm_outputs.append(cur_word_bilstm_output)
# set the iLet_start ocunter to here
iLet_start = iLet
# safe-check, make sure word bilstm outputs length is the same as the daf
if len(word_bilstm_outputs) != len(daf):
log_message('Size mismatch!! word_bilstm_outputs: ' +
str(len(word_bilstm_outputs)) + ', daf: ' + str(len(daf)))
prev_pos_lstm_state = prev_pos_lstm.initial_state().add_input(
pos_enc('*BOS*'))
all_losses = []
pos_prec = 0.0
rough_pos_prec = 0.0
pos_items = 0
class_prec = 0.0
class_items = 0.0
# now iterate through the bilstm outputs, and each word in the daf
for (word, gold_word_class, gold_word_pos,
gold_word_lang), bilstm_output in zip(daf, word_bilstm_outputs):
should_backprop = gold_word_class == 1
# create the mlp input, a concatenate of the bilstm output and of the prev pos output
mlp_input = dy.concatenate(
[bilstm_output, prev_pos_lstm_state.output()])
# run through the class mlp
class_mlp_output = class_mlp(mlp_input)
predicted_word_class = np.argmax(class_mlp_output.npvalue())
confidence = np.max(class_mlp_output.npvalue()) / np.sum(
class_mlp_output.npvalue())
# prec
if should_backprop:
class_prec += 1 if predicted_word_class == gold_word_class else 0
class_items += 1
# if we aren't doing validation, calculate the loss
if not fValidation and not fRunning:
if should_backprop:
all_losses.append(
-dy.log(dy.pick(class_mlp_output, gold_word_class)))
word_class_ans = gold_word_class
# otherwise, set the answer to be the argmax
else:
word_class_ans = predicted_word_class
# if the word_class answer is 1, do the pos!
# alternatively, if validating and it's aramic, do the pos!
if word_class_ans or (fValidation
and gold_word_lang) or (fRunning
and gold_word_lang):
# run the pos mlp output
pos_mlp_output = pos_mlp(mlp_input)
try:
temp_pos_array = pos_mlp_output.npvalue()
possible_pos_array = np.zeros(temp_pos_array.shape)
pos_list = pos_hashtable[word]
# pos_list.add('') #concat 'unknown' as possible pos
possible_pos_indices = [
pos_vocab[temp_pos] for temp_pos in pos_list
]
possible_pos_array[possible_pos_indices] = temp_pos_array[
possible_pos_indices]
except KeyError:
possible_pos_array = pos_mlp_output.npvalue()
# if fValidation:
# possible_pos_array[pos_vocab['']] = 0.0 # don't allow validation to guess UNK b/c it never trained against that TODO this makes sense, right?
predicted_word_pos = pos_vocab.getItem(
np.argmax(possible_pos_array))
confidence = np.max(possible_pos_array) / np.sum(
possible_pos_array)
# prec
if should_backprop:
pos_prec += 1 if predicted_word_pos == gold_word_pos else 0
rough_pos_prec += 1 if predicted_word_pos[0] == gold_word_pos[
0] else 0 # you got at least the rough pos right
pos_items += 1
# if we aren't doing validation, calculate the loss
if not fValidation and not fRunning:
if should_backprop:
all_losses.append(-dy.log(
dy.pick(pos_mlp_output, pos_vocab[gold_word_pos])))
word_pos_ans = gold_word_pos
# otherwise, set the answer to be the argmax
elif not fRunning and fValidation:
if should_backprop:
pos_conf_matrix(pos_vocab[predicted_word_pos],
pos_vocab[gold_word_pos])
word_pos_ans = predicted_word_pos
else:
word_pos_ans = predicted_word_pos
# run through the prev-pos-mlp
predicted = predicted_word_pos
prev_pos_lstm_state = prev_pos_lstm_state.add_input(
pos_enc(word_pos_ans))
# if the answer is 0, put a '' through the prev-pos lstm
else:
predicted = 'UNK'
prev_pos_lstm_state = prev_pos_lstm_state.add_input(pos_enc(''))
tagged_daf["words"].append({
"word": word,
"gold_pos": gold_word_pos,
"gold_class": gold_word_class,
"predicted": predicted,
"confidence": confidence,
"lang": gold_word_lang
})
if fRunning:
return tagged_daf
pos_prec = pos_prec / pos_items if pos_items > 0 else None
rough_pos_prec = rough_pos_prec / pos_items if pos_items > 0 else None
class_prec = class_prec / class_items if class_items > 0 else None
if fValidation:
return class_prec, pos_prec, tagged_daf, rough_pos_prec
total_loss = dy.esum(all_losses) if len(all_losses) > 0 else None
return total_loss, class_prec, pos_prec, rough_pos_prec
def soft_average(buffer, word_weights):
"""soft attention"""
return dy.esum([
vector * attterion_weight
for vector, attterion_weight in zip(buffer, word_weights)
])
else:
with open(os.curdir + save_dir + 'parsed.txt', 'w') as f:
pass
for i in ids:
# Prepare a triple of the source word's character ids,
# the target word's character ids and morphosyntactic features.
d = data[step][i]
triple = ([vocab._char_dict.x2i[c] for c in d[0]], [
vocab._char_dict.x2i[c] for c in d[1]
], [vocab._feat_dicts[idx].x2i[c] for idx, c in enumerate(d[2])])
pred_word_indices, loss = mdl.run(triple, isTrain)
losses.extend(loss)
if isTrain:
if len(losses) >= config.batch_size:
sum_loss = dy.esum(losses)
tot_loss += sum_loss.value()
sum_loss.backward()
mdl.update_parameters()
mdl._global_step += 1
losses = []
dy.renew_cg()
else:
pred_word = ''.join(
[vocab._char_dict.i2x[c] for c in pred_word_indices[:-1]])
if pred_word == d[1]:
tot_cor += 1
with open(os.curdir + save_dir + 'parsed.txt', 'a') as f:
f.write(d[0] + '\t' + pred_word + '\n')
def calculate_scores_vector_for_list_of_words(word_idxs):
dy.renew_cg()
b = dy.parameter(mb)
score_vector = dy.esum([dy.lookup(mW, x) for x in word_idxs])
return b + score_vector
def node_iteration(rel, g, node, opts, assoc_model, trainer, log_file,
is_source):
"""
Perform one iteration of trying to score a node's neighbors above negative samples.
"""
# true instances likelihood
trues = targets(g, node) if is_source else sources(g, node)
side = '->' if is_source else '<-'
if len(trues) == 0: return 0.0
if opts.debug:
dy.renew_cg(immediate_compute=True, check_validity=True)
else:
dy.renew_cg()
# compute association score as dynet expression (can't do this above due to staleness)
true_scores = []
for tr in trues:
if is_source:
j_assoc_score = assoc_model.word_assoc_score(node, tr, rel)
else:
j_assoc_score = assoc_model.word_assoc_score(tr, node, rel)
if log_file is not None:
log_file.write('{} {}\tTRUE_{}\t{:.3e}\n'\
.format(node, side, tr, j_assoc_score.scalar_value()))
true_scores.append(j_assoc_score)
# false targets likelihood - negative sampling (uniform)
# collect negative samples
if opts.nll:
sample_scores = [[ts] for ts in true_scores]
else:
margins = []
neg_samples = [
np.random.choice(range(N)) for _ in range(opts.neg_samp * len(trues))
]
# remove source and true targets if applicable
for t in [node] + trues:
if t in neg_samples:
neg_samples.remove(t)
neg_samples.append(np.random.choice(range(N)))
for (i, ns) in enumerate(neg_samples):
# compute association score as dynet expression
if is_source:
ns_assoc_score = assoc_model.word_assoc_score(node, ns, rel)
else:
ns_assoc_score = assoc_model.word_assoc_score(ns, node, rel)
if log_file is not None:
log_file.write('{} {}\tNEG_{}\t{:.3e}\n'\
.format(node, side, ns, ns_assoc_score.scalar_value()))
corresponding_true = i // opts.neg_samp
if opts.nll:
sample_scores[corresponding_true].append(ns_assoc_score)
else:
# TODO maybe use dy.hinge()
ctt_score = true_scores[corresponding_true]
margin = ctt_score - ns_assoc_score
margins.append(dy.rectify(dy.scalarInput(1.0) - margin))
# compute overall loss
if opts.nll:
if len(sample_scores) == 0:
dy_loss = dy.scalarInput(0.0)
else:
dy_loss = dy.esum([
dy.pickneglogsoftmax(dy.concatenate(scrs), 0)
for scrs in sample_scores
])
else:
if len(margins) == 0:
dy_loss = dy.scalarInput(0.0)
else:
dy_loss = dy.esum(margins)
sc_loss = dy_loss.scalar_value()
if log_file is not None:
log_file.write('{}\tLOSS\t{:.3e}\n'\
.format(node, sc_loss))
# backprop and recompute score
if opts.v > 1:
timeprint('overall loss for relation {}, node {} as {} = {:.6f}'\
.format(rel, node, 'source' if is_source else 'target', sc_loss))
dy_loss.backward()
trainer.update()
return sc_loss
def pool(input_, _):
return dy.esum(input_) / len(input_)
def train(self, train_file, epochs):
# matplotlib config
loss_values = []
plt.ion()
ax = plt.gca()
ax.set_xlim([0, 10])
ax.set_ylim([0, 3])
plt.title("Loss over time")
plt.xlabel("Minibatch")
plt.ylabel("Loss")
for i in range(epochs):
print('started epoch', (i+1))
losses = []
train_data = open(train_file, 'r').read().strip().split('\n')
# shuffle the training data.
random.shuffle(train_data)
step = 0
for line in train_data:
fields = line.strip().split()
features, label = fields[:-1], fields[-1]
gold_label = self.vocab.action2id(label)
result = self.build_graph(features)
# getting loss with respect to negative log softmax function and the gold label.
loss = dynet.pickneglogsoftmax(result, gold_label)
# appending to the minibatch losses
losses.append(loss)
step += 1
if len(losses) >= self.properties.minibatch_size:
# now we have enough loss values to get loss for minibatch
minibatch_loss = dynet.esum(losses) / len(losses)
# calling dynet to run forward computation for all minibatch items
minibatch_loss.forward()
# getting float value of the loss for current minibatch
minibatch_loss_value = minibatch_loss.value()
# printing info and plotting
loss_values.append(minibatch_loss_value)
if len(loss_values)%10==0:
ax.set_xlim([0, len(loss_values)+10])
ax.plot(loss_values)
plt.draw()
plt.pause(0.0001)
progress = round(100 * float(step) / len(train_data), 2)
print('current minibatch loss', minibatch_loss_value, 'progress:', progress, '%')
# calling dynet to run backpropagation
minibatch_loss.backward()
# calling dynet to change parameter values with respect to current backpropagation
self.updater.update()
# empty the loss vector
losses = []
# refresh the memory of dynet
dynet.renew_cg()
# there are still some minibatch items in the memory but they are smaller than the minibatch size
# so we ask dynet to forget them
dynet.renew_cg()
def _trans_loss(self, superv_acts, superv_terms, buffer, stack_tail,
act_tail):
stack = []
loss_lst = []
reduction_flag = False
reducable_flag = False
while not (len(stack) == 1 and reduction_flag != False):
reduction_flag = False
act_choices = self._legal_acts(stack, reducable_flag)
w_weights = None
act = self._act_dict[superv_acts.pop(0)]
# Accumlate loss in action predication
if len(stack) > 0 and act_choices[0] != self._ACT_RED:
stack_emb = stack[-1][0].output()
act_emb = act_tail.output()
w_weights = self._atten(stack_emb, buffer)
buf_emb, _ = nnunits.attention_output(buffer, w_weights,
'soft_average')
# buf_emb=self._atten.output(buffer,w_weights)
for i in xrange(len(stack)):
re_idx = len(stack) - 1 - i
if stack[re_idx][1] == 'nl':
nl_emb = stack[re_idx][2]
# Find the raw embedding of the root of subtree
# for the leaves.
break
trans_state = dy.concatenate(
[buf_emb, stack_emb, nl_emb, act_emb])
out = self._mlp_layer(trans_state)
if self._dropout > 0:
out = dy.dropout(out, self._dropout)
if len(act_choices):
log_probs_act = dy.log_softmax(self._act_pred_layer(out),
act_choices)
assert act in act_choices, 'illegal action'
loss_lst.append(-dy.pick(log_probs_act, act))
act_emb = self._act_in_layer(self._lookup_act[act])
act_tail = act_tail.add_input(act_emb)
# Accumlate loss in term predication
if act == self._ACT_NT:
idx_nt = self._nt_dict[superv_terms.pop(0)]
if w_weights is not None:
buf_emb, _ = nnunits.attention_output(
buffer, w_weights, 'soft_average')
# buf_emb = self._atten.output(buffer, w_weights)
log_probs_nt = dy.log_softmax(self._nt_pred_layer(buf_emb))
loss_lst.append(-dy.pick(log_probs_nt, idx_nt))
stack_state, label, _ = stack[-1] if stack else (stack_tail,
'ROOT',
stack_tail)
nt_emb = self._nt_in_layer(self._lookup_nt[idx_nt])
# Here it is called 'raw embedding'
stack_state = stack_state.add_input(nt_emb)
stack.append((stack_state, 'nl', nt_emb))
# 'nl' label represents the non-leaf nodes
elif act in self._ACT_NT_dg:
idx_nt = self._nt_dict[superv_terms.pop(0)]
# There is no terms (operands) for this action
stack_state, label, _ = stack[-1] if stack else (stack_tail,
'ROOT',
stack_tail)
nt_emb = self._nt_in_layer(self._lookup_nt[idx_nt])
# Here it is called 'raw embedding'
stack_state = stack_state.add_input(nt_emb)
stack.append((stack_state, 'nl', nt_emb))
# 'nl' label represents the non-leaf nodes
elif act == self._ACT_TER:
idx_ter = self._ter_dict[superv_terms.pop(0)]
if buf_emb != None:
log_probs_ter = dy.log_softmax(
self._ter_pred_layer(buf_emb))
loss_lst.append(-dy.pick(log_probs_ter, idx_ter))
stack_state, label, _ = stack[-1] if stack else (stack_tail,
'ROOT',
stack_tail)
ter_emb = self._nt_in_layer(self._lookup_ter[idx_ter])
# Here it is called 'raw embedding'
stack_state = stack_state.add_input(ter_emb)
stack.append((stack_state, 'l', ter_emb))
# 'nl' label represents the non-leaf nodes
else:
leaf_raw_reps = []
while stack[-1][1] == 'l':
top = stack.pop()
rep, _, raw_rep = top
leaf_raw_reps.append(raw_rep)
nl_raw_rep = stack.pop()[2]
subtree_rep = self._red_in_layer(
dy.concatenate([dy.average(leaf_raw_reps), nl_raw_rep]))
# Append the new reduced node
stack_state, _, _ = stack[-1] if stack else (stack_tail,
'ROOT',
stack_tail)
stack_state = stack_state.add_input(subtree_rep)
stack.append((stack_state, 'l', subtree_rep))
reduction_flag = True
reducable_flag = True if stack[-1][1] != 'nl' else False
# for loss in loss_lst:
# print loss.vec_value()
return dy.esum(loss_lst)
def train(self, conll_path):
# pylint: disable=invalid-name
# pylint: disable=missing-docstring
eloss = 0.0
mloss = 0.0
eerrors = 0
etotal = 0
start = time.time()
shuffled_data = list(read_conll(conll_path))
random.shuffle(shuffled_data)
errs = []
lerrs = []
i_sentence = 0
for sentence in shuffled_data:
if i_sentence % 100 == 0 and i_sentence != 0:
print('Processing sentence number:', i_sentence, 'Loss:',
eloss / etotal, 'Errors:', (float(eerrors)) / etotal,
'Time',
time.time() - start)
start = time.time()
eerrors = 0
eloss = 0.0
etotal = 0
conll_sentence = [
entry for entry in sentence if isinstance(entry, ConllEntry)
]
for entry in conll_sentence:
c = float(self.words_count.get(entry.norm, 0))
drop_flag = (random.random() < (c / (0.25 + c)))
wordvec = self.wlookup[int(self.vocab.get(entry.norm, 0)) if drop_flag else 0] \
if self.wdims > 0 else None
posvec = self.plookup[int(
self.pos[entry.pos])] if self.pdims > 0 else None
entry.vec = concatenate(
[_f for _f in [wordvec, posvec, None] if _f])
entry.lstms = [entry.vec, entry.vec]
entry.headfov = None
entry.modfov = None
entry.rheadfov = None
entry.rmodfov = None
if self.blstm_flag:
lstm_forward = self.builders[0].initial_state()
lstm_backward = self.builders[1].initial_state()
for entry, rentry in zip(conll_sentence,
reversed(conll_sentence)):
lstm_forward = lstm_forward.add_input(entry.vec)
lstm_backward = lstm_backward.add_input(rentry.vec)
entry.lstms[1] = lstm_forward.output()
rentry.lstms[0] = lstm_backward.output()
if self.bibi_flag:
for entry in conll_sentence:
entry.vec = concatenate(entry.lstms)
blstm_forward = self.bbuilders[0].initial_state()
blstm_backward = self.bbuilders[1].initial_state()
for entry, rentry in zip(conll_sentence,
reversed(conll_sentence)):
blstm_forward = blstm_forward.add_input(entry.vec)
blstm_backward = blstm_backward.add_input(rentry.vec)
entry.lstms[1] = blstm_forward.output()
rentry.lstms[0] = blstm_backward.output()
scores, exprs = self._evaluate(conll_sentence)
gold = [entry.parent_id for entry in conll_sentence]
heads = decoder.parse_proj(scores,
gold if self.costaug_flag else None)
if self.labels_flag:
for modifier, head in enumerate(gold[1:]):
rscores, rexprs = self._evaluate_label(
conll_sentence, head, modifier + 1)
gold_label_ind = self.rels[conll_sentence[modifier +
1].relation]
wrong_label_ind = max(((label, scr)
for label, scr in enumerate(rscores)
if label != gold_label_ind),
key=itemgetter(1))[0]
if rscores[gold_label_ind] < rscores[wrong_label_ind] + 1:
lerrs.append(rexprs[wrong_label_ind] -
rexprs[gold_label_ind])
e = sum([1 for h, g in zip(heads[1:], gold[1:]) if h != g])
eerrors += e
if e > 0:
loss = [(exprs[h][i] - exprs[g][i])
for i, (h, g) in enumerate(zip(heads, gold))
if h != g] # * (1.0/float(e))
eloss += e
mloss += e
errs.extend(loss)
etotal += len(conll_sentence)
if i_sentence % 1 == 0 or errs > 0 or lerrs:
if errs or lerrs:
eerrs = (esum(errs + lerrs)) # * (1.0/(float(len(errs))))
eerrs.scalar_value()
eerrs.backward()
self.trainer.update()
errs = []
lerrs = []
renew_cg()
i_sentence += 1
if errs:
eerrs = (esum(errs + lerrs)) # * (1.0/(float(len(errs))))
eerrs.scalar_value()
eerrs.backward()
self.trainer.update()
renew_cg()
self.trainer.update()
print("Loss: ", mloss / i_sentence)
def get_score_for_hist(hist):
scores = [dy.parameter(b_m)]
for h_, W_ in zip(hist, W_m):
scores.append(dy.lookup(W_, h_))
return dy.esum(scores)
def calc_scores(words):
dy.renew_cg()
h = dy.esum([dy.lookup(W_emb, x) for x in words])
for W_h_i, b_h_i in zip(W_h, b_h):
h = dy.tanh(W_h_i * h + b_h_i)
return W_sm * h + b_sm
good = 0.0
bad = 0.0
errors = []
# batching
for i, (s1, s2, label) in enumerate(train.data):
prob = model(s1, s2)
softmax = dy.softmax(prob).npvalue()
pred = np.argmax(softmax)
error = dy.pickneglogsoftmax(prob, label)
errors.append(error)
if pred == label:
good += 1
else:
bad += 1
if i % batch_size == 0 and i > 0:
sum_errors = dy.esum(errors)
loss += sum_errors.value()
sum_errors.backward()
model.trainer.update()
checked += batch_size
dy.renew_cg()
errors = []
if i % (batch_size * 5) == 0 and i > 0:
avgLoss = loss / checked
losses.append(avgLoss)
print "-" * 20
print "Time: " + str(passed_time(start_time))
print "Epoch: " + str(epoch + 1) + ", Iteration: " + str(
i) + " Average loss: " + str(avgLoss)
loss = 0
checked = 0
def CalculateLossForDaf(daf, fValidation=False, fRunning=False):
dy.renew_cg()
tagged_daf = {"words":[],"file":daf["file"]}
daf = daf["words"]
# add a bos before and after
seq = ['*BOS*'] + list(' '.join([word for word, _, _, _ in daf])) + ['*BOS*']
# get all the char encodings for the daf
char_embeds = [let_enc(let) for let in seq]
# run it through the bilstm
char_bilstm_outputs = bilstm(char_embeds)
# now iterate and get all the separate word representations by concatenating the bilstm output
# before and after the word
word_bilstm_outputs = []
iLet_start = 0
for iLet, char in enumerate(seq):
# if it is a bos, check if it's at the end of the sequence
if char == '*BOS*':
if iLet + 1 == len(seq):
char = ' '
else:
continue
# if we are at a space, take this bilstm output and the one at the letter start
if char == ' ':
cur_word_bilstm_output = dy.concatenate([char_bilstm_outputs[iLet_start], char_bilstm_outputs[iLet]])
# add it in
word_bilstm_outputs.append(cur_word_bilstm_output)
# set the iLet_start ocunter to here
iLet_start = iLet
# safe-check, make sure word bilstm outputs length is the same as the daf
if len(word_bilstm_outputs) != len(daf):
log_message('Size mismatch!! word_bilstm_outputs: ' + str(len(word_bilstm_outputs)) + ', daf: ' + str(len(daf)))
s_0 = prev_pos_lstm.initial_state()
beam = [(['*BOS*'],1.0,s_0,[],0.0,0.0,0.0,0.0,0.0,[])] # seq, prob, lstm_state, losses, class_prec, class_items, pos_prec, rough_pos_prec, pos_items, confidences
i = 0
for (word, gold_word_class, gold_word_pos, gold_word_lang), bilstm_output in zip(daf, word_bilstm_outputs):
should_backprop = gold_word_class == 1
new_hypos = []
for hypo in beam:
seq, hyp_prob, hyp_state, losses, class_prec, class_items, pos_prec, rough_pos_prec, pos_items, confidences = hypo
new_seq = seq[:]
new_losses = losses[:]
new_confidences = confidences[:]
last_pos = seq[-1]
next_hyp_state = hyp_state.add_input(pos_enc(last_pos))
# create the mlp input, a concatenate of the bilstm output and of the prev pos output
mlp_input = dy.concatenate([bilstm_output, next_hyp_state.output()])
# run through the class mlp
class_mlp_output = class_mlp(mlp_input)
predicted_word_class = np.argmax(class_mlp_output.npvalue())
new_confidences.append(np.max(class_mlp_output.npvalue()) / np.sum(class_mlp_output.npvalue()))
# prec
if should_backprop:
class_prec += 1 if predicted_word_class == gold_word_class else 0
class_items += 1
# if we aren't doing validation, calculate the loss
if not fValidation and not fRunning:
if should_backprop: new_losses.append(-dy.log(dy.pick(class_mlp_output, gold_word_class)))
word_class_ans = gold_word_class
# otherwise, set the answer to be the argmax
else:
word_class_ans = predicted_word_class
# if the word_class answer is 1, do the pos!
# alternatively, if validating and it's aramic, do the pos!
if word_class_ans or (fValidation and gold_word_lang) or (fRunning and gold_word_lang):
# run the pos mlp output
pos_mlp_output = pos_mlp(mlp_input)
try:
temp_pos_array = pos_mlp_output.npvalue()
possible_pos_array = np.zeros(temp_pos_array.shape)
pos_list = pos_hashtable[word]
# pos_list.add('') #concat 'unknown' as possible pos
possible_pos_indices = [pos_vocab[temp_pos] for temp_pos in pos_list]
possible_pos_array[possible_pos_indices] = temp_pos_array[possible_pos_indices]
except KeyError:
possible_pos_array = pos_mlp_output.npvalue()
# if fValidation:
# possible_pos_array[pos_vocab['']] = 0.0 # don't allow validation to guess UNK b/c it never trained against that TODO this makes sense, right?
poss_pos_sum = np.sum(possible_pos_array)
for iprob, prob in enumerate(possible_pos_array):
new_seq = seq[:]
temp_picked_pos = pos_vocab.getItem(iprob)
temp_confidence = possible_pos_array[iprob] / poss_pos_sum
new_confidences[-1] = temp_confidence # overwrite class confidence
new_pos_prec = pos_prec
new_pos_items = pos_items
new_rough_pos_prec = rough_pos_prec
if should_backprop:
new_pos_prec += 1 if temp_picked_pos == gold_word_pos else 0
new_rough_pos_prec += 1 if len(temp_picked_pos) > 0 and temp_picked_pos[0] == gold_word_pos[
0] else 0 # you got at least the rough pos right
new_pos_items += 1
if not fValidation and not fRunning:
if should_backprop: new_losses.append(
-dy.log(dy.pick(pos_mlp_output, pos_vocab[gold_word_pos])))
new_seq += [temp_picked_pos]
new_prob = hyp_prob + math.log(prob) if prob != 0 else hyp_prob + math.log(1E-10) # which is log(0.00000001) or something like that
new_hypos += [(new_seq, new_prob, next_hyp_state, new_losses, class_prec, class_items, new_pos_prec,
new_rough_pos_prec, new_pos_items, new_confidences)]
else:
# assume prob is 1. It's really good at predicting hebrew / aramaic
new_seq = seq[:]
new_seq += ['']
new_prob = hyp_prob
new_hypos += [(new_seq, new_prob, next_hyp_state, new_losses, class_prec, class_items, pos_prec,
rough_pos_prec, pos_items, new_confidences)]
# pick the best hypos
new_probs = [p for (s, p, r, l, cp, ci, pp, rpp, pi, c) in new_hypos]
argmax_indices = util.argmax(new_probs, n=beam_width)
if type(argmax_indices) == int:
argmax_indices = [argmax_indices]
beam = [new_hypos[l] for l in argmax_indices]
i += 1
correct_answer_in_beam = False
for max_ind in argmax_indices:
if new_hypos[max_ind][0][-1] == gold_word_pos:
correct_answer_in_beam = True
break
if not correct_answer_in_beam and not fValidation and not fRunning and with_early_stop:
# early stop
break
final_probs = [p for (s, p, r, l, cp, ci, pp, rpp, pi, c) in beam]
argmax_index = util.argmax(final_probs)
final_seq, prob, lstm_state, all_losses, class_prec, class_items, pos_prec, rough_pos_prec, pos_items, confidences = beam[argmax_index]
for (word, gold_word_class, gold_word_pos, gold_word_lang), pred, conf in zip(daf, final_seq[1:], confidences): # VERY IMPORTANT. final_seq is off-by-one b/c we inited it with BOS
tagged_daf['words'].append({"word":word,"gold_pos":gold_word_pos,"gold_class":gold_word_class,"predicted":pred,"confidence":conf,"lang":gold_word_lang})
should_backprop = gold_word_class == 1
if should_backprop: pos_conf_matrix(pos_vocab[pred], pos_vocab[gold_word_pos])
if fRunning:
return tagged_daf
pos_prec = pos_prec / pos_items if pos_items > 0 else None
rough_pos_prec = rough_pos_prec / pos_items if pos_items > 0 else None
class_prec = class_prec / class_items if class_items > 0 else None
if fValidation:
return class_prec, pos_prec,tagged_daf, rough_pos_prec
total_loss = dy.esum(all_losses) if len(all_losses) > 0 else None
return total_loss, class_prec, pos_prec, rough_pos_prec
prev_word = next_word
softmax_loss = dy.esum(all_losses)
return kl_loss, softmax_loss
for ITER in range(100):
# Perform training
random.shuffle(train)
train_words, train_loss, train_kl_loss, train_reconstruct_loss = 0, 0.0, 0.0, 0.0
start = time.time()
for sent_id, sent in enumerate(train):
kl_loss, softmax_loss = calc_loss(sent)
total_loss = dy.esum([kl_loss, softmax_loss])
train_loss += total_loss.value()
# Record the KL loss and reconstruction loss separately help you monitor the training.
train_kl_loss += kl_loss.value()
train_reconstruct_loss += softmax_loss.value()
train_words += len(sent)
total_loss.backward()
trainer.update()
if (sent_id + 1) % 1000 == 0:
print("--finished %r sentences" % (sent_id + 1))
print("iter %r: train loss/word=%.4f, kl loss/word=%.4f, reconstruction loss/word=%.4f, ppl=%.4f, time=%.2fs" % (
ITER, train_loss / train_words, train_kl_loss / train_words, train_reconstruct_loss / train_words,
math.exp(train_loss / train_words), time.time() - start))
def train_item(args, model, document):
loss = None
word_lookups = []
for preprocessed_sentence in document.preprocessed_sentences:
seq = [
model.wlookup[int(model.w2i.get(entry, 0))]
for entry in preprocessed_sentence
]
if len(seq) > 0:
word_lookups.append(seq)
sentences_lookups = []
for seq in word_lookups:
sentence_encode = encode_sequence(model, seq, model.sentence_rnn)
global_max = max_pooling(sentence_encode)
global_min = average_pooling(sentence_encode)
if len(sentence_encode) > 0:
att_mlp_outputs = []
for e in sentence_encode:
mlp_out = (model.word_attention_w * e) + model.word_attention_b
att_mlp_outputs.append(mlp_out)
lst = []
for o in att_mlp_outputs:
lst.append(
dy.exp(dy.sum_elems(dy.cmult(o, model.word_att_context))))
sum_all = dy.esum(lst)
probs = [dy.cdiv(e, sum_all) for e in lst]
att_context = dy.esum(
[dy.cmult(p, h) for p, h in zip(probs, sentence_encode)])
context = dy.concatenate([att_context, global_max, global_min])
sentences_lookups.append(context)
document_encode = encode_sequence(model, sentences_lookups,
model.document_rnn)
global_max = max_pooling(document_encode)
global_min = average_pooling(document_encode)
if len(document_encode) > 0:
att_mlp_outputs = []
for e in document_encode:
mlp_out = (model.sentence_attention_w *
e) + model.sentence_attention_b
att_mlp_outputs.append(mlp_out)
lst = []
for o in att_mlp_outputs:
lst.append(
dy.exp(dy.sum_elems(dy.cmult(o, model.sentence_att_context))))
sum_all = dy.esum(lst)
probs = [dy.cdiv(e, sum_all) for e in lst]
att_context = dy.esum(
[dy.cmult(p, h) for p, h in zip(probs, document_encode)])
context = dy.concatenate([att_context, global_max, global_min])
y_pred = dy.logistic((model.mlp_w * context) + model.mlp_b)
if document.permissions[args.permission_type]:
loss = dy.binary_log_loss(y_pred, dy.scalarInput(1))
else:
loss = dy.binary_log_loss(y_pred, dy.scalarInput(0))
loss.backward()
model.trainer.update()
loss_val = loss.scalar_value()
dy.renew_cg()
return loss_val
return 0
def calc_loss(self, policy_reward, results={}):
"""
Calc policy networks loss.
"""
assert len(policy_reward) == len(self.states), "There should be a reward for every action taken"
batch_size = self.states[0].dim()[1]
loss = {}
# Calculate the baseline loss of the reinforce loss for each timestep:
# b = W_b * s + b_b
# R = r - b
# Also calculate the baseline loss
# b = r_p (predicted)
# loss_b = squared_distance(r_p - r_r)
rewards = []
baseline_loss = []
units = np.zeros(batch_size)
for i, state in enumerate(self.states):
r_p = self.baseline.transform(dy.nobackprop(state))
rewards.append(policy_reward[i] - r_p)
if self.valid_pos[i] is not None:
r_p = dy.pick_batch_elems(r_p, self.valid_pos[i])
r_r = dy.pick_batch_elems(policy_reward[i], self.valid_pos[i])
units[self.valid_pos[i]] += 1
else:
r_r = policy_reward[i]
units += 1
baseline_loss.append(dy.sum_batches(dy.squared_distance(r_p, r_r)))
loss["rl_baseline"] = losses.LossExpr(dy.esum(baseline_loss), units)
# Z Normalization
# R = R - mean(R) / std(R)
rewards = dy.concatenate(rewards, d=0)
r_dim = rewards.dim()
if self.z_normalization:
rewards_shape = dy.reshape(rewards, (r_dim[0][0], r_dim[1]))
rewards_mean = dy.mean_elems(rewards_shape)
rewards_std = dy.std_elems(rewards_shape) + 1e-20
rewards = (rewards - rewards_mean.value()) / rewards_std.value()
rewards = dy.nobackprop(rewards)
# Calculate Confidence Penalty
if self.confidence_penalty:
loss["rl_confpen"] = self.confidence_penalty.calc_loss(self.policy_lls)
# Calculate Reinforce Loss
# L = - sum([R-b] * pi_ll)
reinf_loss = []
units = np.zeros(batch_size)
for i, (policy, action) in enumerate(zip(self.policy_lls, self.actions)):
reward = dy.pick(rewards, i)
ll = dy.pick_batch(policy, action)
if self.valid_pos[i] is not None:
ll = dy.pick_batch_elems(ll, self.valid_pos[i])
reward = dy.pick_batch_elems(reward, self.valid_pos[i])
units[self.valid_pos[i]] += 1
else:
units += 1
reinf_loss.append(dy.sum_batches(dy.cmult(ll, reward)))
loss["rl_reinf"] = losses.LossExpr(-dy.esum(reinf_loss), units)
# Pack up + return
return losses.FactoredLossExpr(loss)
def pool(input_, _):
return dy.esum(input_) / len(input_)
def train(self, examples):
# Train action classifier:
classifier_data = []
for example in examples:
encoder_input = example[0]
ground_labels = example[1]
cdata = (encoder_input[0], ground_labels[0], ground_labels[1])
classifier_data.append(cdata)
self.classifier.train(classifier_data)
# Train cluster model:
# num_examples = len(examples)
num_examples = 100
trainer = dy.AdamTrainer(self.params)
for epoch in range(self.num_epochs):
batch_loss = []
loss_sum = 0
for idx in range(num_examples):
# if (idx % 1000 == 0):
# print("(Clusters) Epoch: {} | Example: {} | Loss sum: {}".format(epoch, idx, loss_sum))
loss = self.train_example(examples[idx])
batch_loss.append(loss)
# Minibatching:
if (idx % self.minibatch == 0) or (idx + 1 == num_examples):
batch_loss = dy.esum(batch_loss)
loss_sum += batch_loss.value()
batch_loss.backward()
batch_loss = []
trainer.update()
dy.renew_cg()
print("(Clusters) Epoch: {} | Loss: {}".format(
epoch + 1, loss_sum))
# Expectation maximization:
em_time = time.time()
self.e_time = 0
self.m_time = 0
self.back_time = 0
self.papx_time = 0
self.pa_time = 0
self.px_time = 0
zs = {}
# Initialize one-hot z's:
self.onehotzs = []
for idx in range(self.num_clusters):
one_hot_z = np.zeros(self.num_clusters)
one_hot_z[idx] = 1
one_hot_z = dy.inputVector(one_hot_z)
self.onehotzs.append(one_hot_z)
for epoch in range(10):
batch_loss = []
loss_sum = 0
for idx in range(num_examples):
# if (idx % 100 == 0):
# print("(EM) Epoch: {} | Example: {}".format(epoch, idx))
if len(examples[idx][1][1]) == 3: # Agreement occurs
loss, z_list = self.em_example(examples[idx])
zs[idx] = z_list
batch_loss.append(loss)
else:
zs[idx] = []
# Minibatching:
if (idx % self.minibatch
== 0) or (idx + 1
== num_examples) and (batch_loss != []):
batch_loss = dy.esum(batch_loss)
loss_sum += batch_loss.value()
back_time = time.time()
batch_loss.backward()
batch_loss = []
trainer.update()
dy.renew_cg()
self.onehotzs = []
for idx in range(self.num_clusters):
one_hot_z = np.zeros(self.num_clusters)
one_hot_z[idx] = 1
one_hot_z = dy.inputVector(one_hot_z)
self.onehotzs.append(one_hot_z)
self.back_time += (time.time() - back_time)
print("(EM) Epoch: {} | Loss: {}".format(epoch + 1, loss_sum))
# print("EM time: {}".format(time.time() - em_time))
# print("E time: {}".format(self.e_time))
# print("M time: {}".format(self.m_time))
# print("Backprop time: {}".format(self.back_time))
# print("PAPX time: {}".format(self.papx_time))
# print("PA time: {}".format(self.pa_time))
# print("PX time: {}".format(self.px_time))
# Print zs to file:
with open("data/clusters/clusters.txt", 'w') as f:
for idx in range(num_examples):
f.write(str(zs[idx]))
f.write('\n')
def calc_loss(scores, tags):
losses = [dy.pickneglogsoftmax(score, tag) for score, tag in zip(scores, tags)]
return dy.esum(losses)
def calc_loss(scores, tags):
losses = [
dy.pickneglogsoftmax(score, tag) for score, tag in zip(scores, tags)
]
return dy.esum(losses)
def calc_loss(sents):
dy.renew_cg()
# Transduce all batch elements with an LSTM
src_sents = [x[0] for x in sents]
tgt_sents = [x[1] for x in sents]
src_cws = []
src_len = [len(sent) for sent in src_sents]
max_src_len = np.max(src_len)
num_words = 0
for i in range(max_src_len):
src_cws.append([sent[i] for sent in src_sents])
#get the outputs of the first LSTM
src_outputs = [dy.concatenate([x.output(), y.output()]) for x,y in LSTM_SRC.add_inputs([dy.lookup_batch(LOOKUP_SRC, cws) for cws in src_cws])]
src_output = src_outputs[-1]
#gets the parameters for the attention
src_output_matrix = dy.concatenate_cols(src_outputs)
w1_att_src = dy.parameter(w1_att_src_p)
fixed_attentional_component = w1_att_src * src_output_matrix
#now decode
all_losses = []
# Decoder
#need to mask padding at end of sentence
tgt_cws = []
tgt_len = [len(sent) for sent in sents]
max_tgt_len = np.max(tgt_len)
masks = []
for i in range(max_tgt_len):
tgt_cws.append([sent[i] if len(sent) > i else eos_trg for sent in tgt_sents])
mask = [(1 if len(sent) > i else 0) for sent in tgt_sents]
masks.append(mask)
num_words += sum(mask)
current_state = LSTM_TRG_BUILDER.initial_state().set_s([src_output, dy.tanh(src_output)])
prev_words = tgt_cws[0]
W_sm = dy.parameter(W_sm_p)
b_sm = dy.parameter(b_sm_p)
W_m = dy.parameter(W_m_p)
b_m = dy.parameter(b_m_p)
for next_words, mask in zip(tgt_cws[1:], masks):
#feed the current state into the
current_state = current_state.add_input(dy.lookup_batch(LOOKUP_TRG, prev_words))
output_embedding = current_state.output()
att_output, _ = calc_attention(src_output_matrix, output_embedding, fixed_attentional_component)
middle_expr = dy.tanh(dy.affine_transform([b_m, W_m, dy.concatenate([output_embedding, att_output])]))
s = dy.affine_transform([b_sm, W_sm, middle_expr])
loss = (dy.pickneglogsoftmax_batch(s, next_words))
mask_expr = dy.inputVector(mask)
mask_expr = dy.reshape(mask_expr, (1,),len(sents))
mask_loss = loss * mask_expr
all_losses.append(mask_loss)
prev_words = next_words
return dy.sum_batches(dy.esum(all_losses)), num_words
def CalculateLossForDaf(daf, fValidation=False, fRunning=False):
dy.renew_cg()
tagged_daf = {"words":[],"file":daf["file"]}
daf = daf["words"]
# add a bos before and after
seq = ['*BOS*'] + list(' '.join([word for word, _, _, _ in daf])) + ['*BOS*']
# get all the char encodings for the daf
char_embeds = [let_enc(let) for let in seq]
# run it through the bilstm
char_bilstm_outputs = bilstm(char_embeds)
# now iterate and get all the separate word representations by concatenating the bilstm output
# before and after the word
word_bilstm_outputs = []
iLet_start = 0
for iLet, char in enumerate(seq):
# if it is a bos, check if it's at the end of the sequence
if char == '*BOS*':
if iLet + 1 == len(seq):
char = ' '
else:
continue
# if we are at a space, take this bilstm output and the one at the letter start
if char == ' ':
cur_word_bilstm_output = dy.concatenate([char_bilstm_outputs[iLet_start], char_bilstm_outputs[iLet]])
# add it in
word_bilstm_outputs.append(cur_word_bilstm_output)
# set the iLet_start ocunter to here
iLet_start = iLet
# safe-check, make sure word bilstm outputs length is the same as the daf
if len(word_bilstm_outputs) != len(daf):
log_message('Size mismatch!! word_bilstm_outputs: ' + str(len(word_bilstm_outputs)) + ', daf: ' + str(len(daf)))
prev_pos_lstm_state = prev_pos_lstm.initial_state().add_input(pos_enc('*BOS*'))
all_losses = []
pos_prec = 0.0
rough_pos_prec = 0.0
pos_items = 0
class_prec = 0.0
class_items = 0.0
# now iterate through the bilstm outputs, and each word in the daf
for (word, gold_word_class, gold_word_pos, gold_word_lang), bilstm_output in zip(daf, word_bilstm_outputs):
should_backprop = gold_word_class == 1
# create the mlp input, a concatenate of the bilstm output and of the prev pos output
mlp_input = dy.concatenate([bilstm_output, prev_pos_lstm_state.output()])
# run through the class mlp
class_mlp_output = class_mlp(mlp_input)
predicted_word_class = np.argmax(class_mlp_output.npvalue())
confidence = np.max(class_mlp_output.npvalue()) / np.sum(class_mlp_output.npvalue())
# prec
if should_backprop:
class_prec += 1 if predicted_word_class == gold_word_class else 0
class_items += 1
# if we aren't doing validation, calculate the loss
if not fValidation and not fRunning:
if should_backprop: all_losses.append(-dy.log(dy.pick(class_mlp_output, gold_word_class)))
word_class_ans = gold_word_class
# otherwise, set the answer to be the argmax
else:
word_class_ans = predicted_word_class
# if the word_class answer is 1, do the pos!
# alternatively, if validating and it's aramic, do the pos!
if word_class_ans or (fValidation and gold_word_lang) or (fRunning and gold_word_lang):
# run the pos mlp output
pos_mlp_output = pos_mlp(mlp_input)
try:
temp_pos_array = pos_mlp_output.npvalue()
possible_pos_array = np.zeros(temp_pos_array.shape)
pos_list = pos_hashtable[word]
# pos_list.add('') #concat 'unknown' as possible pos
possible_pos_indices = [pos_vocab[temp_pos] for temp_pos in pos_list]
possible_pos_array[possible_pos_indices] = temp_pos_array[possible_pos_indices]
except KeyError:
possible_pos_array = pos_mlp_output.npvalue()
# if fValidation:
# possible_pos_array[pos_vocab['']] = 0.0 # don't allow validation to guess UNK b/c it never trained against that TODO this makes sense, right?
predicted_word_pos = pos_vocab.getItem(np.argmax(possible_pos_array))
confidence = np.max(possible_pos_array) / np.sum(possible_pos_array)
# prec
if should_backprop:
pos_prec += 1 if predicted_word_pos == gold_word_pos else 0
rough_pos_prec += 1 if predicted_word_pos[0] == gold_word_pos[0] else 0 # you got at least the rough pos right
pos_items += 1
# if we aren't doing validation, calculate the loss
if not fValidation and not fRunning:
if should_backprop: all_losses.append(-dy.log(dy.pick(pos_mlp_output, pos_vocab[gold_word_pos])))
word_pos_ans = gold_word_pos
# otherwise, set the answer to be the argmax
elif not fRunning and fValidation:
if should_backprop: pos_conf_matrix(pos_vocab[predicted_word_pos], pos_vocab[gold_word_pos])
word_pos_ans = predicted_word_pos
else:
word_pos_ans = predicted_word_pos
# run through the prev-pos-mlp
predicted = predicted_word_pos
prev_pos_lstm_state = prev_pos_lstm_state.add_input(pos_enc(word_pos_ans))
# if the answer is 0, put a '' through the prev-pos lstm
else:
predicted = 'UNK'
prev_pos_lstm_state = prev_pos_lstm_state.add_input(pos_enc(''))
tagged_daf["words"].append({"word":word,"gold_pos":gold_word_pos,"gold_class":gold_word_class,"predicted":predicted,"confidence":confidence, "lang": gold_word_lang})
if fRunning:
return tagged_daf
pos_prec = pos_prec / pos_items if pos_items > 0 else None
rough_pos_prec = rough_pos_prec / pos_items if pos_items > 0 else None
class_prec = class_prec / class_items if class_items > 0 else None
if fValidation:
return class_prec, pos_prec,tagged_daf, rough_pos_prec
total_loss = dy.esum(all_losses) if len(all_losses) > 0 else None
return total_loss, class_prec, pos_prec, rough_pos_prec
def train_beam_graph(e, beam_size, traj_type, loss_fn):
dy.renew_cg()
tags = e[tk_tags_key]
m = model_init(e)
beam_costs_prev = np.array([0], dtype="int")
beam_costs = []
losses = []
for i in xrange(len(e["tk_words"])):
scores = model_scores(m)
# transition
scores_np = scores.npvalue()
beam_indices, tag_indices = beam_argtopk(scores_np, beam_size)
beam_costs_cur = beam_costs_prev[beam_indices] + (tag_indices !=
tags[i]).astype('int')
# compute the loss if there is score accumulation or always
next_beam_size = beam_size if i < len(e["tk_words"]) - 1 else 1
if (not cfg["update_only_on_cost_increase"]) or (
cfg["update_only_on_cost_increase"] and
beam_costs_prev.min() < beam_costs_cur[:next_beam_size].min()):
loss = loss_fn(tags, i, beam_costs_prev, scores, beam_size)
losses.append(loss)
if traj_type == "stop":
if beam_costs_cur.min() > 0:
break
elif traj_type == "continue":
pass
elif traj_type == "reset":
if beam_costs_cur.min() > 0:
b_gold_idx = beam_costs_prev.argmin()
beam_indices = np.array([b_gold_idx], dtype='int')
tag_indices = np.array([tags[i]], dtype='int')
beam_costs_cur = np.array([0], dtype='int')
elif traj_type == "reset_multiple":
# NOTE: this is similar to the reset option. replace the last element
# in the beam with the correct one.
if beam_costs_cur.min() > 0:
b_gold_idx = beam_costs_prev.argmin()
beam_indices[-1] = b_gold_idx
tag_indices[-1] = tags[i]
beam_costs_cur[-1] = beam_costs_prev[b_gold_idx]
# this should be zero
# assert beam_costs_prev[-1] == 0
# NOTE: there is probably a less repetitive way of doing this.
elif traj_type == "oracle":
t_idx = tags[i]
beam_size_prev = beam_costs_prev.shape[0]
costs = beam_costs_prev.reshape((beam_size_prev, 1)) * np.ones(
(1, num_tags))
costs += 1.0
costs[:, t_idx] -= 1.0
beam_indices, tag_indices = beam_argtopk(-costs, beam_size)
beam_costs_cur = beam_costs_prev[beam_indices] + (
tag_indices != tags[i]).astype('int')
else:
raise ValueError
beam_costs.append(beam_costs_cur)
beam_costs_prev = beam_costs_cur
model_step(m, beam_indices, tag_indices)
if len(losses) > 0:
return dy.esum(losses)
else:
return dy.zeros(1)
def on_calc_additional_loss(self, translator_loss):
if not self.learn_segmentation or self.segment_decisions is None:
return None
reward = -translator_loss["mle"]
if not self.log_reward:
reward = dy.exp(reward)
reward = dy.nobackprop(reward)
# Make sure that reward is not scalar, but rather based on the each batch item
assert reward.dim()[1] == len(self.src_sent)
# Mask
enc_mask = self.enc_mask.get_active_one_mask().transpose(
) if self.enc_mask is not None else None
# Compose the lose
ret = LossBuilder()
## Length prior
alpha = self.length_prior_alpha.value(
) if self.length_prior_alpha is not None else 0
if alpha > 0:
reward += self.segment_length_prior * alpha
# reward z-score normalization
if self.z_normalization:
reward = dy.cdiv(reward - dy.mean_batches(reward),
dy.std_batches(reward) + EPS)
## Baseline Loss
if self.use_baseline:
baseline_loss = []
for i, baseline in enumerate(self.bs):
loss = dy.squared_distance(reward, baseline)
if enc_mask is not None:
loss = dy.cmult(dy.inputTensor(enc_mask[i], batched=True),
loss)
baseline_loss.append(loss)
ret.add_loss("Baseline", dy.esum(baseline_loss))
if self.print_sample:
print(
dy.exp(self.segment_logsoftmaxes[i]).npvalue().transpose()[0])
## Reinforce Loss
lmbd = self.lmbd.value()
if lmbd > 0.0:
reinforce_loss = []
# Calculating the loss of the baseline and reinforce
for i in range(len(self.segment_decisions)):
ll = dy.pick_batch(self.segment_logsoftmaxes[i],
self.segment_decisions[i])
if self.use_baseline:
r_i = reward - dy.nobackprop(self.bs[i])
else:
r_i = reward
if enc_mask is not None:
ll = dy.cmult(dy.inputTensor(enc_mask[i], batched=True),
ll)
reinforce_loss.append(r_i * -ll)
loss = dy.esum(reinforce_loss) * lmbd
ret.add_loss("Reinforce", loss)
if self.confidence_penalty:
ls_loss = self.confidence_penalty(self.segment_logsoftmaxes,
enc_mask)
ret.add_loss("Confidence Penalty", ls_loss)
# Total Loss
return ret
def __call__(self,
context,
F2I,
only_train_words,
dropout_rate=1.0,
activate_sub_word=False,
stop_updating_lookup=False):
num_params = len(self.params) # get the length of params vector
lookup = self.lookup
# if the user choose to continue learning the pre-trained words
if not stop_updating_lookup:
# if sub word feature is not activated
if not activate_sub_word:
emb_vectors = [lookup[F2I.get(i)]
for i in context] # get embedding of the words
else: # sum embedding of word, suffix and prefix for words that allow it
emb_vectors = []
for word in context:
# get the word alone if len<=3 or word=start/end/unk
if len(word) <= 3 or (word in ["<s>", "</s>", "UUUNKKK"]):
emb_vectors.append(lookup[F2I.get(word)])
else:
pref = False
suff = False
# check if prefix exist in F2I. relevant for test/dev sets
if F2I.has_key(word[:3]):
prefix_embd = lookup[F2I.get(word[:3])]
pref = True
# check if suffix exist in F2I. relevant for test/dev sets
if F2I.has_key(word[-3:]):
suffix_embd = lookup[F2I.get(word[-3:])]
suff = True
word_embd = lookup[F2I.get(word)]
# sum vectors of word with existing prefix/suffix
if pref and suff:
sum_embd = dy.esum(
[prefix_embd, suffix_embd, word_embd])
elif pref and suff == False:
sum_embd = dy.esum([prefix_embd, word_embd])
elif suff and pref == False:
sum_embd = dy.esum([suffix_embd, word_embd])
else:
sum_embd = dy.esum([word_embd])
emb_vectors.append(sum_embd)
# if the user choose to stop learning the pre-trained words
if stop_updating_lookup:
# if sub word feature is not activated
if not activate_sub_word:
emb_vectors = []
for word in context:
# if it's a word from the corpus continue training it
if word in only_train_words:
emb_vectors.append(lookup[F2I.get(word)])
# if it's a word from the pre-train stop training it
else:
emb_vectors.append(dy.nobackprop(
lookup[F2I.get(word)]))
else: # sum embedding of word, suffix and prefix for words that allow it
emb_vectors = []
for word in context:
# get the word alone if len<=3 or word=start/end/unk
if len(word) <= 3 or (word in ["<s>", "</s>", "UUUNKKK"]):
# if it's a word from the corpus continue training it
if word in only_train_words:
emb_vectors.append(lookup[F2I.get(word)])
# if it's a word from the pre-train stop training it
else:
emb_vectors.append(
dy.nobackprop(lookup[F2I.get(word)]))
else:
pref = False
suff = False
# check if prefix exist in F2I. relevant for test/dev sets
if F2I.has_key(word[:3]):
# if it's a word from the corpus continue training it
if word[:3] in only_train_words:
prefix_embd = lookup[F2I.get(word[:3])]
# if it's a word from the pre-train stop training it
else:
prefix_embd = dy.nobackprop(lookup[F2I.get(
word[:3])])
pref = True
# check if suffix exist in F2I. relevant for test/dev sets
if F2I.has_key(word[-3:]):
# if it's a word from the corpus continue training it
if word[-3:] in only_train_words:
suffix_embd = lookup[F2I.get(word[-3:])]
# if it's a word from the pre-train stop training it
else:
suffix_embd = dy.nobackprop(lookup[F2I.get(
word[-3:])])
suff = True
# if it's a word from the corpus continue training it
if word in only_train_words:
word_embd = lookup[F2I.get(word)]
# if it's a word from the pre-train stop training it
else:
word_embd = dy.nobackprop(lookup[F2I.get(word)])
# sum vectors of word with existing prefix/suffix
if pref and suff:
sum_embd = dy.esum(
[prefix_embd, suffix_embd, word_embd])
elif pref and suff == False:
sum_embd = dy.esum([prefix_embd, word_embd])
elif suff and pref == False:
sum_embd = dy.esum([suffix_embd, word_embd])
else:
sum_embd = dy.esum([word_embd])
emb_vectors.append(sum_embd)
net_input = dy.concatenate(emb_vectors)
for i in xrange(
0, num_params - 2, 2
): # calculate the activation of each subsequent layers and apply the bernoulli mask
W = dy.parameter(self.params[i]) # from parameters to expressions
b = dy.parameter(self.params[i + 1])
if i == 0: # first layer
activation = dy.tanh((W * net_input) + b)
else: # other layers
activation = dy.tanh((W * activation) + b)
if dropout_rate != 1.0:
activation = dy.dropout(activation, dropout_rate)
W = dy.parameter(self.params[num_params -
2]) # from parameters to expressions
b = dy.parameter(self.params[num_params - 1])
net_output = dy.softmax(
(W * activation) + b) # apply sfotmax on last layer
return net_output
def test_item(model, document):
word_lookups = []
for preprocessed_sentence in document.preprocessed_sentences:
seq = [
model.wlookup[int(model.w2i.get(entry, 0))]
for entry in preprocessed_sentence
]
if len(seq) > 0:
word_lookups.append(seq)
sentences_lookups = []
for seq in word_lookups:
sentence_encode = encode_sequence(model, seq, model.sentence_rnn)
global_max = max_pooling(sentence_encode)
global_min = average_pooling(sentence_encode)
if len(sentence_encode) > 0:
att_mlp_outputs = []
for e in sentence_encode:
mlp_out = (model.word_attention_w * e) + model.word_attention_b
att_mlp_outputs.append(mlp_out)
lst = []
for o in att_mlp_outputs:
lst.append(
dy.exp(dy.sum_elems(dy.cmult(o, model.word_att_context))))
sum_all = dy.esum(lst)
probs = [dy.cdiv(e, sum_all) for e in lst]
att_context = dy.esum(
[dy.cmult(p, h) for p, h in zip(probs, sentence_encode)])
context = dy.concatenate([att_context, global_max, global_min])
sentences_lookups.append(context)
document_encode = encode_sequence(model, sentences_lookups,
model.document_rnn)
global_max = max_pooling(document_encode)
global_min = average_pooling(document_encode)
if len(document_encode) > 0:
att_mlp_outputs = []
for e in document_encode:
mlp_out = (model.sentence_attention_w *
e) + model.sentence_attention_b
att_mlp_outputs.append(mlp_out)
lst = []
for o in att_mlp_outputs:
lst.append(
dy.exp(dy.sum_elems(dy.cmult(o, model.sentence_att_context))))
sum_all = dy.esum(lst)
probs = [dy.cdiv(e, sum_all) for e in lst]
att_context = dy.esum(
[dy.cmult(p, h) for p, h in zip(probs, document_encode)])
context = dy.concatenate([att_context, global_max, global_min])
y_pred = dy.logistic((model.mlp_w * context) + model.mlp_b)
document.prediction_result = y_pred.scalar_value()
dy.renew_cg()
return document.prediction_result
return 0
# make a forward pass
pred = forward_pass(x)
# calculate loss for each example
loss = dy.binary_log_loss(pred, y)
losses.append(loss)
pred
y
# Now let's accumulate the loss and backpropogate it.
# In[24]:
# get total loss for dataset
total_loss = dy.esum(losses)
# apply the calculations of the computational graph
total_loss.forward()
# calculate loss to backpropogate
total_loss.backward()
# update parameters with backpropogated error
trainer.update()
# Let's make sure that our parameter `W_1` has been updated (e.g. it "learned" something).
# In[25]:
# confirm that parameters updated
dy.renew_cg()
def __call__(self,
inputs,
masks,
truth,
iters,
is_train=True,
is_tree=True):
sent_len = len(inputs)
batch_size = inputs[0].dim()[1]
flat_len = sent_len * batch_size
print('===Vào call===')
print('input length: ', inputs.__len__()) # input length: 46
print('input dim: ', inputs[1].dim()) # input dim: ((400,), 2)
print('sent_len', sent_len) # sent_len 46
print('batch_size', batch_size) # batch_size 2
print('flat_len', flat_len) # flat_len 92
# H -> hidden size, L -> sentence length, B -> batch size
# ((H, L), B)
X = dy.concatenate_cols(inputs)
print('X dim: ', X.dim()) # X dim: ((400, 46), 2)
if is_train:
X = dy.dropout_dim(X, 1, self.cfg.MLP_DROP)
# A_H -> ARC MLP hidden size, R_H -> REL MLP hidden size
# ((A_H, L), B)
head_arc = self.head_arc_MLP(X, is_train)
dept_arc = self.dept_arc_MLP(X, is_train)
print('head_arc dim: ', head_arc.dim())
print('dept_arc dim: ', dept_arc.dim())
# head_arc dim: ((300, 46), 2)
# dept_arc dim: ((300, 46), 2)
# ((R_H, L), B)
head_rel = self.head_rel_MLP(X, is_train)
dept_rel = self.dept_rel_MLP(X, is_train)
print('head_rel dim: ', head_rel.dim())
print('dept_rel dim: ', dept_rel.dim())
# head_rel dim: ((100, 46), 2)
# dept_rel dim: ((100, 46), 2)
if is_train:
total_token = sum(masks['flat'].tolist())
head_arc = dy.dropout_dim(head_arc, 1, self.cfg.MLP_DROP)
head_rel = dy.dropout_dim(head_rel, 1, self.cfg.MLP_DROP)
dept_arc = dy.dropout_dim(dept_arc, 1, self.cfg.MLP_DROP)
dept_rel = dy.dropout_dim(dept_rel, 1, self.cfg.MLP_DROP)
# ((L, L), B)
masks_2D = 1e9 * (1 - dy.inputTensor(masks['2D'], True))
masks_flat = dy.inputTensor(masks['flat'], True)
gnn_losses = []
arc_norm = math.sqrt(self.arc_size)
rel_norm = math.sqrt(self.rel_size)
for k in range(self.cfg.GRAPH_LAYERS):
print('----layer-----', k)
# Graph Weights
# ((L, L), B)
arc_mat = self.arc_attn_mat[k](head_arc,
dept_arc) / arc_norm - masks_2D
arc_prob = dy.softmax(arc_mat)
# arc_mat dim: ((46, 46), 2)
# arc_prob dim: ((46, 46), 2)
# Layer-wise Loss
if is_train:
arc_prob = dy.dropout(arc_prob, self.cfg.ARC_DROP)
# ((L,), L*B)
arc_mat = dy.reshape(arc_mat, (sent_len, ), flat_len)
# ((1,), L*B)
print('arc_mat val', arc_mat.value())
print('arc_mat dim', arc_mat.dim())
print("truth['head'] value", truth['head'])
print("truth['head'] lengt", truth['head'].__len__())
arc_loss = dy.pickneglogsoftmax_batch(arc_mat, truth['head'])
print('arc_loss', arc_loss.value())
print('arc_loss', arc_loss.dim())
# (1,)
arc_loss = dy.sum_batches(arc_loss * masks_flat) / total_token
print('arc_loss', arc_loss.value)
print('arc_loss', arc_loss.dim())
gnn_losses.append(arc_loss.value())
input("pause")
# Aggregation Function
# Fusion head and dept representation
# ((A_H, L), B)
HX = head_arc * arc_prob
DX = dept_arc * dy.transpose(arc_prob)
FX = HX + DX
print('HX dim: ', HX.dim())
print('DX dim: ', DX.dim())
print('FX dim: ', FX.dim())
# HX dim: ((300, 46), 2)
# DX dim: ((300, 46), 2)
# FX dim: ((300, 46), 2)
# Async Update Function
# Head-first
# ((A_H, L), B)
head_arc = self.head_gnn(FX, head_arc)
FX_new = head_arc * arc_prob + DX
dept_arc = self.dept_gnn(FX_new, dept_arc)
print('head_arc dim: ', head_arc.dim())
print('FX_new dim: ', FX_new.dim())
print('dept_arc dim: ', dept_arc.dim())
# head_arc dim: ((300, 46), 2)
# FX_new dim: ((300, 46), 2)
# dept_arc dim: ((300, 46), 2)
# Relation Aggregation Function
# Sync update
# ((R_H, L), B)
HR = head_rel * arc_prob
DR = dept_rel * dy.transpose(arc_prob)
FX = HR + DR
head_rel = self.head_rel_gnn(FX, head_rel) + head_rel
dept_rel = self.dept_rel_gnn(FX, dept_rel) + dept_rel
print('HR dim: ', HR.dim())
print('DR dim: ', DR.dim())
print('FX dim: ', FX.dim())
# HR dim: ((100, 46), 2)
# DR dim: ((100, 46), 2)
# FX dim: ((100, 46), 2)
print('head_rel dim: ', head_rel.dim())
print('dept_rel dim: ', dept_rel.dim())
# head_rel dim: ((100, 46), 2)
# dept_rel dim: ((100, 46), 2)
# ((L, L), B)
arc_mat = self.arc_attn_mat[-1](head_arc,
dept_arc) / arc_norm - masks_2D
# ((L,), L*B)
arc_mat = dy.reshape(arc_mat, (sent_len, ), flat_len)
# Predict Relation
# (R_H, L*B)
head_rel = dy.reshape(head_rel, (self.rel_size, flat_len))
# ((R_H,), L*B)
dept_rel = dy.reshape(dept_rel, (self.rel_size, ), flat_len)
print('arc_mat dim: ', arc_mat.dim())
print('head_rel dim: ', head_rel.dim())
print('dept_rel dim: ', dept_rel.dim())
# arc_mat dim: ((46,), 92)
# head_rel dim: ((100, 92), 1)
# dept_rel dim: ((100,), 92)
if is_train:
# ((1,), L*B)
arc_losses = dy.pickneglogsoftmax_batch(arc_mat, truth['head'])
# (1,)
arc_loss = dy.sum_batches(arc_losses * masks_flat) / total_token
# ((R_H,), L*B)
truth_rel = dy.pick_batch(head_rel, truth['flat_head'], 1)
# R -> Relation Set Size
# ((R,), L*B)
rel_mask = 1e9 * dy.inputTensor(self.rel_mask)
rel_mat = self.rel_attn(dept_rel, truth_rel) / rel_norm - rel_mask
# Calculate Relation Classification Loss
# ((1,), L*B)
rel_losses = dy.pickneglogsoftmax_batch(rel_mat, truth['rel'])
# (1,)
rel_loss = dy.sum_batches(rel_losses * masks_flat) / total_token
# Final Total Loss with Layer-wise
warm = [int(iters >= x) for x in self.warm_list]
losses = rel_loss*self.cfg.LAMBDA2 * \
warm[-1]+arc_loss*self.cfg.LAMBDA2*warm[-1]
if gnn_losses:
for i in range(self.cfg.GRAPH_LAYERS):
gnn_losses[i] *= warm[i]
losses += dy.esum(gnn_losses) * self.cfg.LAMBDA1
losses_list = gnn_losses + [arc_loss, rel_loss]
return losses, losses_list
else:
if is_tree:
# MST Inference, Achieve Tree Edge.
arc_probs = dy.softmax(arc_mat).npvalue()
arc_probs = np.reshape(arc_probs,
(sent_len, sent_len, batch_size), 'F')
arc_probs = np.transpose(arc_probs)
# Mask PAD
arc_masks = [
np.array(masks['flat'][i:i + sent_len])
for i in range(0, flat_len, sent_len)
]
arc_pred = []
# Inference One By One.
for msk, arc_prob in zip(arc_masks, arc_probs):
msk[0] = 1
seq_len = int(np.sum(msk))
tmp_pred = MST_inference(arc_prob, seq_len, msk)
tmp_pred[0] = 0
arc_pred.extend(tmp_pred)
else:
# Greedy Inference (argmax)
arc_pred = np.argmax(arc_mat.npvalue(), 0)
# Pick Predicted Edge's <Head, Dept> pair.
flat_pred = [
j + (i // sent_len) * sent_len for i, j in enumerate(arc_pred)
]
pred_rel = dy.pick_batch(head_rel, flat_pred, 1)
# Predict Relation (mask ROOT)
rel_mask = 1e9 * dy.inputTensor(self.rel_mask)
rel_mat = self.rel_attn(dept_rel, pred_rel) / rel_norm - rel_mask
rel_mat = dy.reshape(rel_mat, (self.rel_num, )).npvalue()
rel_pred = np.argmax(rel_mat, 0)
pred = {}
pred['head'], pred['rel'] = arc_pred, rel_pred
return pred
def calc_score_of_history(words):
# Create a list of things to sum up with only the bias vector at first
score_vecs = [dy.parameter(b_sm)]
for word_id, lookup_param in zip(words, W_sm):
score_vecs.append(lookup_param[word_id])
return dy.esum(score_vecs)
