def ar2word2sent2doc(document,
*,
words,
char_embeddings,
word_embedding_size,
context_vector_size,
save_memory=True,
**rd2sent2doc_hyperparams):
"""char2word2sent2doc model without character embeddings as parameters
"""
assert ex.static_rank(document) == 3
assert ex.static_rank(words) == 2
assert ex.static_rank(char_embeddings) == 2
with tf.variable_scope("char2word"):
word_embeddings = ex.bidirectional_id_vector_to_embedding(
tf.gather(words, ex.flatten(document)) if save_memory else words,
char_embeddings,
output_size=word_embedding_size,
context_vector_size=context_vector_size,
dynamic_length=True)
return rd2sent2doc(document,
word_embeddings,
context_vector_size=context_vector_size,
save_memory=save_memory,
**rd2sent2doc_hyperparams)
def font2char2word2sent2doc(document, *, words, fonts, char_embedding_size,
dropout_keep_prob, mode,
**ar2word2sent2doc_hyperparams):
assert ex.static_rank(document) == 3
assert ex.static_rank(words) == 2
assert ex.static_rank(fonts) == 3
return ar2word2sent2doc(
document,
words=words,
char_embeddings=font2char(fonts,
char_embedding_size=char_embedding_size),
**ar2word2sent2doc_hyperparams)
def sequence_labeling_loss(logits, labels, sequence_length=None):
assert ex.static_rank(logits) == 3
assert ex.static_rank(labels) == 2
losses = tf.reshape(
tf.nn.sparse_softmax_cross_entropy_with_logits(
tf.reshape(logits, [-1, ex.static_shape(logits)[-1]]),
tf.reshape(labels, [-1])), [-1, *ex.static_shape(labels)[1:]])
if sequence_length == None:
return tf.reduce_mean(losses)
mask = tf.sequence_mask(sequence_length, dtype=losses.dtype)
return tf.reduce_sum(losses * mask) / tf.reduce_sum(mask)
def test_font2char():
assert (
ex.static_rank(font2char.font2char(
tf.zeros([64, 224, 224]),
nums_of_channels=[32] * 4,
nums_of_attention_channels=[32] * 3))
== 2)
def font2char2word2sent2doc(document, *, words, fonts, dropout_keep_prob,
nums_of_cnn_channels,
nums_of_attention_cnn_channels, mode,
**ar2word2sent2doc_hyperparams):
assert ex.static_rank(document) == 3
assert ex.static_rank(words) == 2
assert ex.static_rank(fonts) == 3
return ar2word2sent2doc(
document,
words=words,
char_embeddings=font2char(
fonts,
nums_of_channels=nums_of_cnn_channels,
nums_of_attention_channels=nums_of_attention_cnn_channels),
**ar2word2sent2doc_hyperparams)
def batch_linear(h, output_size):
assert ex.static_rank(h) == 3
shape = ex.static_shape(h)
return (tf.batch_matmul(
h,
tf.tile(tf.expand_dims(ex.variable([shape[2], output_size]), 0),
[shape[0], 1, 1])) + ex.variable([output_size]))
def _cnn(h, nums_of_channels):
assert ex.static_rank(h) == 4
for index, num_of_channels in enumerate(nums_of_channels):
h = tf.contrib.slim.conv2d(
h, num_of_channels, 3, scope='conv{}'.format(index))
h = tf.contrib.slim.max_pool2d(h, 2, 2, scope='pool{}'.format(index))
return h
def _attend_to_image(images, nums_of_channels):
assert ex.static_rank(images) == 4
attentions = _calculate_attention(images, nums_of_channels)
collections.add_attention(attentions)
return tf.transpose(
tf.transpose(images) *
tf.transpose(attentions))
def char2word2sent2doc(document, *, words, char_space_size,
char_embedding_size, **ar2word2sent2doc_hyperparams):
"""
The argument `document` is in the shape of
(#examples, #sentences per document, #words per sentence).
"""
assert ex.static_rank(document) == 3
assert ex.static_rank(words) == 2
with tf.variable_scope("char_embeddings"):
char_embeddings = ex.embeddings(id_space_size=char_space_size,
embedding_size=char_embedding_size,
name="char_embeddings")
return ar2word2sent2doc(document,
words=words,
char_embeddings=char_embeddings,
**ar2word2sent2doc_hyperparams)
def font2char(font, *, nums_of_channels, nums_of_attention_channels):
assert ex.static_rank(font) == 3
h = tf.contrib.slim.flatten(
_cnn(_attend_to_image(tf.expand_dims(font, -1),
nums_of_attention_channels),
nums_of_channels))
ex.summary.image(tf.expand_dims(tf.expand_dims(h[:256], 0), 3))
return h
def font2char2word2sent2doc(document,
*,
words,
fonts,
dropout_keep_prob,
nums_of_cnn_channels,
nums_of_attention_cnn_channels,
mode,
**ar2word2sent2doc_hyperparams):
assert ex.static_rank(document) == 3
assert ex.static_rank(words) == 2
assert ex.static_rank(fonts) == 3
return ar2word2sent2doc(
document,
words=words,
char_embeddings=font2char(
fonts,
nums_of_channels=nums_of_cnn_channels,
nums_of_attention_channels=nums_of_attention_cnn_channels),
**ar2word2sent2doc_hyperparams)
def rd2sent2doc(document,
word_embeddings,
*,
sentence_embedding_size,
document_embedding_size,
context_vector_size,
save_memory=False):
"""
word2sent2doc model lacking word embeddings as parameters
"""
assert ex.static_rank(document) == 3
assert ex.static_rank(word_embeddings) == 2
embeddings_to_embedding = functools.partial(
ex.bidirectional_embeddings_to_embedding,
context_vector_size=context_vector_size)
with tf.variable_scope("word2sent"):
# word_embeddings.shape == (#batch * #sent * #word, emb_size)
# if save_memory else
# (vocab_size, emb_size)
sentences = _flatten_document_into_sentences(document)
sentence_embeddings = _restore_document_shape(
embeddings_to_embedding(
(_restore_sentence_shape(word_embeddings, sentences)
if save_memory else
tf.gather(word_embeddings, sentences)),
sequence_length=ex.id_vector_to_length(sentences),
output_size=sentence_embedding_size),
document)
with tf.variable_scope("sent2doc"):
return embeddings_to_embedding(
sentence_embeddings,
sequence_length=ex.id_tensor_to_length(document),
output_size=document_embedding_size)
def char2word2sent2doc(document,
*,
words,
char_space_size,
char_embedding_size,
**ar2word2sent2doc_hyperparams):
"""
The argument `document` is in the shape of
(#examples, #sentences per document, #words per sentence).
"""
assert ex.static_rank(document) == 3
assert ex.static_rank(words) == 2
with tf.variable_scope("char_embeddings"):
char_embeddings = ex.embeddings(id_space_size=char_space_size,
embedding_size=char_embedding_size,
name="char_embeddings")
return ar2word2sent2doc(document,
words=words,
char_embeddings=char_embeddings,
**ar2word2sent2doc_hyperparams)
def word2sent2doc(document, *, word_space_size, word_embedding_size,
**rd2sent2doc_hyperparams):
assert ex.static_rank(document) == 3
with tf.variable_scope("word_embeddings"):
word_embeddings = tf.gather(
ex.embeddings(id_space_size=word_space_size,
embedding_size=word_embedding_size,
name="word_embeddings"), ex.flatten(document))
return rd2sent2doc(document,
word_embeddings,
save_memory=True,
**rd2sent2doc_hyperparams)
def _calculate_attention(images, nums_of_channels):
assert ex.static_rank(images) == 4
logits = tf.squeeze(
tf.image.resize_nearest_neighbor(
tf.expand_dims(tf.reduce_sum(_cnn(images, nums_of_channels),
axis=3),
axis=-1),
tf.shape(images)[1:3]),
axis=3)
return tf.reshape(
tf.nn.softmax(tf.reshape(logits, [tf.shape(logits)[0], -1])),
tf.shape(logits))
def rd2sent2doc(document,
word_embeddings,
*,
sentence_embedding_size,
document_embedding_size,
context_vector_size,
save_memory=False):
"""
word2sent2doc model lacking word embeddings as parameters
"""
assert ex.static_rank(document) == 3
assert ex.static_rank(word_embeddings) == 2
embeddings_to_embedding = functools.partial(
ex.bidirectional_embeddings_to_embedding,
context_vector_size=context_vector_size)
with tf.variable_scope("word2sent"):
# word_embeddings.shape == (#batch * #sent * #word, emb_size)
# if save_memory else
# (vocab_size, emb_size)
sentences = _flatten_document_into_sentences(document)
sentence_embeddings = _restore_document_shape(
embeddings_to_embedding(
(_restore_sentence_shape(word_embeddings, sentences)
if save_memory else tf.gather(word_embeddings, sentences)),
sequence_length=ex.id_vector_to_length(sentences),
output_size=sentence_embedding_size), document)
with tf.variable_scope("sent2doc"):
return embeddings_to_embedding(
sentence_embeddings,
sequence_length=ex.id_tensor_to_length(document),
output_size=document_embedding_size)
def word2sent2doc(document,
*,
word_space_size,
word_embedding_size,
**rd2sent2doc_hyperparams):
assert ex.static_rank(document) == 3
with tf.variable_scope("word_embeddings"):
word_embeddings = tf.gather(
ex.embeddings(id_space_size=word_space_size,
embedding_size=word_embedding_size,
name="word_embeddings"),
ex.flatten(document))
return rd2sent2doc(document,
word_embeddings,
save_memory=True,
**rd2sent2doc_hyperparams)
def font2char(fonts, char_embedding_size):
assert ex.static_rank(fonts) == 3
h = ex.lenet(tf.expand_dims(fonts, -1), output_size=char_embedding_size)
ex.summary.image(tf.expand_dims(tf.expand_dims(h[:256], 0), 3))
return h
def test_attend_to_image():
assert (
ex.static_rank(font2char._attend_to_image(
tf.zeros([64, 224, 224, 1]),
nums_of_channels=[32] * 3))
== 4)