def show_examples_tf(model, es, rlut1, rlut2, vocab, mxlen, sample, prob_clip,
max_examples, reverse):
si = np.random.randint(0, len(es))
batch_dict = es[si]
i = 0
src_lengths_key = model.src_lengths_key
src_key = src_lengths_key.split('_')[0]
while True:
example = {}
for k in batch_dict.keys():
if i >= len(batch_dict[k]):
return
example[k] = batch_dict[k][i]
print(
'========================================================================'
)
src_i = example[src_key]
src_len_i = example[src_lengths_key]
tgt_i = example['tgt']
sent = lookup_sentence(rlut1, src_i, reverse=reverse)
print('[OP] %s' % sent)
sent = lookup_sentence(rlut2, tgt_i)
print('[Actual] %s' % sent)
tgt_i = np.zeros((1, mxlen))
example['tgt'] = tgt_i
src_i = src_i[np.newaxis, :]
example[src_key] = src_i
example[src_lengths_key] = np.array([src_len_i])
next_value = Offsets.GO
for j in range(mxlen):
tgt_i[0, j] = next_value
tgt_len_i = np.array([j + 1])
example['tgt_lengths'] = tgt_len_i
output = model.step(example).squeeze()[j]
if sample is False:
next_value = np.argmax(output)
else:
# This is going to zero out low prob. events so they are not
# sampled from
next_value = beam_multinomial(prob_clip, output)
if next_value == Offsets.EOS:
break
sent = lookup_sentence(rlut2, tgt_i.squeeze())
print('Guess: %s' % sent)
print(
'------------------------------------------------------------------------'
)
i += 1
if i == max_examples:
return
def show_examples_tf(model, es, rlut1, rlut2, embed2, mxlen, sample, prob_clip,
max_examples, reverse):
si = np.random.randint(0, len(es))
batch_dict = es[si]
src_array = batch_dict['src']
tgt_array = batch_dict['dst']
src_len = batch_dict['src_len']
if max_examples > 0:
max_examples = min(max_examples, src_array.shape[0])
src_array = src_array[0:max_examples]
tgt_array = tgt_array[0:max_examples]
src_len = src_len[0:max_examples]
GO = embed2.vocab['<GO>']
EOS = embed2.vocab['<EOS>']
for src_len_i, src_i, tgt_i in zip(src_len, src_array, tgt_array):
print(
'========================================================================'
)
sent = lookup_sentence(rlut1, src_i, reverse=reverse)
print('[OP] %s' % sent)
sent = lookup_sentence(rlut2, tgt_i)
print('[Actual] %s' % sent)
dst_i = np.zeros((1, mxlen))
src_i = src_i[np.newaxis, :]
src_len_i = np.array([src_len_i])
next_value = GO
for j in range(mxlen):
dst_i[0, j] = next_value
tgt_len_i = np.array([j + 1])
output = model.step({
'src': src_i,
'src_len': src_len_i,
'dst': dst_i,
'dst_len': tgt_len_i
})[j]
if sample is False:
next_value = np.argmax(output)
else:
# This is going to zero out low prob. events so they are not
# sampled from
next_value = beam_multinomial(prob_clip, output)
if next_value == EOS:
break
sent = lookup_sentence(rlut2, dst_i.squeeze())
print('Guess: %s' % sent)
print(
'------------------------------------------------------------------------'
)
def show_examples_tf(model, es, rlut1, rlut2, vocab, mxlen, sample, prob_clip, max_examples, reverse):
si = np.random.randint(0, len(es))
batch_dict = es[si]
i = 0
src_lengths_key = model.src_lengths_key
src_key = src_lengths_key.split('_')[0]
while True:
example = {}
for k in batch_dict.keys():
if i >= len(batch_dict[k]):
return
example[k] = batch_dict[k][i]
print('========================================================================')
src_i = example[src_key]
src_len_i = example[src_lengths_key]
tgt_i = example['tgt']
sent = lookup_sentence(rlut1, src_i, reverse=reverse)
print('[OP] %s' % sent)
sent = lookup_sentence(rlut2, tgt_i)
print('[Actual] %s' % sent)
tgt_i = np.zeros((1, mxlen))
example['tgt'] = tgt_i
src_i = src_i[np.newaxis, :]
example[src_key] = src_i
example[src_lengths_key] = np.array([src_len_i])
next_value = Offsets.GO
for j in range(mxlen):
tgt_i[0, j] = next_value
tgt_len_i = np.array([j+1])
example['tgt_lengths'] = tgt_len_i
output = model.step(example).squeeze()[j]
if sample is False:
next_value = np.argmax(output)
else:
# This is going to zero out low prob. events so they are not
# sampled from
next_value = beam_multinomial(prob_clip, output)
if next_value == Offsets.EOS:
break
sent = lookup_sentence(rlut2, tgt_i.squeeze())
print('Guess: %s' % sent)
print('------------------------------------------------------------------------')
i += 1
if i == max_examples:
return
