class Parsing_Model(object):
def add_placeholders(self):
self.inputs_placeholder_dict = {}
for feature in self.features:
if feature == 'chars':
self.inputs_placeholder_dict[feature] = tf.placeholder(
tf.int32, shape=[None, None, None])
else:
self.inputs_placeholder_dict[feature] = tf.placeholder(
tf.int32, shape=[None, None])
self.keep_prob = tf.placeholder(tf.float32)
self.input_keep_prob = tf.placeholder(tf.float32)
self.hidden_prob = tf.placeholder(tf.float32)
self.mlp_prob = tf.placeholder(tf.float32)
def add_word_embedding(self):
with tf.device('/cpu:0'):
with tf.variable_scope('word_embedding') as scope:
embedding = tf.get_variable(
'word_embedding_mat',
self.loader.word_embeddings.shape,
initializer=tf.constant_initializer(
self.loader.word_embeddings))
inputs = tf.nn.embedding_lookup(
embedding, self.inputs_placeholder_dict['words']
) ## [batch_size, seq_len, embedding_dim]
inputs = tf.transpose(
inputs, perm=[1, 0,
2]) # [seq_length, batch_size, embedding_dim]
return inputs
def add_jackknife_embedding(self):
with tf.device('/cpu:0'):
with tf.variable_scope('jk_embedding') as scope:
embedding = tf.get_variable(
'jk_embedding_mat',
[self.loader.nb_jk + 1, self.opts.jk_dim
]) # +1 for padding
inputs = tf.nn.embedding_lookup(
embedding, self.inputs_placeholder_dict['jk']
) ## [batch_size, seq_len, embedding_dim]
inputs = tf.transpose(
inputs, perm=[1, 0,
2]) # [seq_length, batch_size, embedding_dim]
return inputs
def add_stag_embedding(self):
with tf.device('/cpu:0'):
with tf.variable_scope('stag_embedding') as scope:
embedding = tf.get_variable(
'stag_embedding_mat',
[self.loader.nb_stags + 1, self.opts.stag_dim
]) # +1 for padding
inputs = tf.nn.embedding_lookup(
embedding, self.inputs_placeholder_dict['stags']
) ## [batch_size, seq_len, embedding_dim]
inputs = tf.transpose(
inputs, perm=[1, 0,
2]) # [seq_length, batch_size, embedding_dim]
self.stag_embeddings = embedding
return inputs
def add_char_embedding(self):
with tf.device('/cpu:0'):
with tf.variable_scope('char_embedding') as scope:
embedding = tf.get_variable(
'char_embedding_mat',
[self.loader.nb_chars + 1, self.opts.chars_dim
]) # +1 for padding
inputs = tf.nn.embedding_lookup(
embedding, self.inputs_placeholder_dict['chars']
) ## [batch_size, seq_len-1, word_len, embedding_dim]
## -1 because we don't have ROOT
inputs = tf.transpose(inputs, perm=[1, 0, 2, 3])
## [seq_len-1, batch_size, word_len, embedding_dim]
inputs = self.add_dropout(inputs, self.input_keep_prob)
weights = get_char_weights(self.opts, 'char_encoding')
inputs = encode_char(
inputs, weights) ## [seq_len-1, batch_size, nb_filters]
shape = tf.shape(inputs)
## add 0 vectors for <-root->
inputs = tf.concat([tf.zeros([1, shape[1], shape[2]]), inputs], 0)
return inputs
def add_lstm(self, inputs, i, name, backward=False):
prev_init = tf.zeros([2, tf.shape(inputs)[1],
self.opts.units]) # [2, batch_size, num_units]
#prev_init = tf.zeros([2, 100, self.opts.units]) # [2, batch_size, num_units]
if i == 0:
inputs_dim = self.inputs_dim
else:
inputs_dim = self.opts.units * 2 ## concat after each layer
weights = get_lstm_weights('{}_LSTM_layer{}'.format(name, i),
inputs_dim, self.opts.units,
tf.shape(inputs)[1], self.hidden_prob)
if backward:
## backward: reset states after zero paddings
non_paddings = tf.transpose(
self.weight,
[1, 0]) ## [batch_size, seq_len] => [seq_len, batch_size]
non_paddings = tf.reverse(non_paddings, [0])
cell_hidden = tf.scan(
lambda prev, x: lstm(prev, x, weights, backward=backward),
[inputs, non_paddings], prev_init)
else:
cell_hidden = tf.scan(lambda prev, x: lstm(prev, x, weights),
inputs, prev_init)
#cell_hidden [seq_len, 2, batch_size, units]
h = tf.unstack(cell_hidden, 2,
axis=1)[1] #[seq_len, batch_size, units]
return h
def add_dropout(self, inputs, keep_prob):
## inputs [seq_len, batch_size, inputs_dims/units]
dummy_dp = tf.ones(tf.shape(inputs)[1:])
dummy_dp = tf.nn.dropout(dummy_dp, keep_prob)
return tf.map_fn(lambda x: dummy_dp * x, inputs)
def add_projection(self, inputs):
with tf.variable_scope('Projection') as scope:
proj_U = tf.get_variable('weight',
[self.outputs_dim, self.loader.nb_tags])
proj_b = tf.get_variable('bias', [self.loader.nb_tags])
outputs = tf.matmul(inputs, proj_U) + proj_b
return outputs
def add_loss_op(self, output, gold):
cross_entropy = sequence_loss(output, gold, self.weight)
loss = tf.reduce_sum(cross_entropy)
return loss
def add_accuracy(self, output, gold):
predictions = tf.cast(tf.argmax(output, 2),
tf.int32) ## [batch_size, seq_len]
correct_predictions = self.weight * tf.cast(
tf.equal(predictions, gold), tf.float32)
accuracy = tf.reduce_sum(tf.cast(correct_predictions,
tf.float32)) / tf.reduce_sum(
tf.cast(self.weight, tf.float32))
return predictions, accuracy
def add_train_op(self, loss):
optimizer = tf.train.AdamOptimizer()
train_op = optimizer.minimize(loss)
return train_op
def get_features(self):
self.features = ['words', 'arcs', 'rels']
if self.opts.jk_dim > 0:
self.features.append('jk')
if self.opts.stag_dim > 0:
self.features.append('stags')
if self.opts.chars_dim > 0:
self.features.append('chars')
def add_biaffine(self, inputs):
## inputs [seq_len, batch_size, units]
## first define four different MLPs
arc_roles = ['arc-dep', 'arc-head']
rel_roles = ['rel-dep', 'rel-head']
vectors = {}
for arc_role in arc_roles:
for i in xrange(self.opts.mlp_num_layers):
if i == 0:
inputs_dim = self.outputs_dim
vector_mlp = inputs
else:
inputs_dim = self.opts.arc_mlp_units
weights = get_mlp_weights('{}_MLP_Layer{}'.format(arc_role, i),
inputs_dim, self.opts.arc_mlp_units)
vector_mlp = self.add_dropout(
tf.map_fn(lambda x: mlp(x, weights), vector_mlp),
self.mlp_prob)
## [seq_len, batch_size, 2*mlp_units]
vectors[arc_role] = vector_mlp
weights = get_arc_weights('arc', self.opts.arc_mlp_units)
arc_output = arc_equation(
vectors['arc-head'], vectors['arc-dep'], weights
) # [batch_size, seq_len, seq_len] dim 1: deps, dim 2: heads
# arc_predictions = get_arcs(arc_output, self.test_opts) # [batch_size, seq_len]
arc_predictions = tf.argmax(arc_output, 2) # [batch_size, seq_len]
for rel_role in rel_roles:
for i in xrange(self.opts.mlp_num_layers):
if i == 0:
inputs_dim = self.outputs_dim
vector_mlp = inputs
else:
inputs_dim = self.opts.rel_mlp_units
weights = get_mlp_weights('{}_MLP_Layer{}'.format(rel_role, i),
inputs_dim, self.opts.rel_mlp_units)
vector_mlp = self.add_dropout(
tf.map_fn(lambda x: mlp(x, weights), vector_mlp),
self.mlp_prob)
## [seq_len, batch_size, 2*mlp_units]
vectors[rel_role] = vector_mlp
weights = get_rel_weights('rel', self.opts.rel_mlp_units,
self.loader.nb_rels)
rel_output, rel_scores = rel_equation(
vectors['rel-head'], vectors['rel-dep'], weights,
arc_predictions) #[batch_size, seq_len, nb_rels]
return arc_output, rel_output, rel_scores
def __init__(self, opts, test_opts=None):
self.opts = opts
self.test_opts = test_opts
self.loader = Dataset(opts, test_opts)
self.batch_size = 100
self.get_features()
self.add_placeholders()
self.inputs_dim = self.opts.embedding_dim + self.opts.jk_dim + self.opts.stag_dim + self.opts.nb_filters
self.outputs_dim = (1 + self.opts.bi) * self.opts.units
inputs_list = [self.add_word_embedding()]
if self.opts.jk_dim:
inputs_list.append(self.add_jackknife_embedding())
if self.opts.stag_dim > 0:
inputs_list.append(self.add_stag_embedding())
if self.opts.chars_dim > 0:
inputs_list.append(self.add_char_embedding())
inputs_tensor = tf.concat(inputs_list,
2) ## [seq_len, batch_size, inputs_dim]
inputs_tensor = self.add_dropout(inputs_tensor, self.input_keep_prob)
inputs_shape = tf.shape(self.inputs_placeholder_dict['words'])
## no need to worry about the heads of <-root-> and zero-pads
## Let's get those non-padding places so we can reinitialize hidden states after each padding in the backward path
### because the backward path starts with zero pads.
self.weight = tf.cast(
tf.not_equal(self.inputs_placeholder_dict['words'],
tf.zeros(inputs_shape, tf.int32)),
tf.float32) ## [batch_size, seq_len]
for i in xrange(self.opts.num_layers):
forward_outputs_tensor = self.add_dropout(
self.add_lstm(inputs_tensor, i, 'Forward'),
self.keep_prob) ## [seq_len, batch_size, units]
if self.opts.bi:
backward_outputs_tensor = self.add_dropout(
self.add_lstm(tf.reverse(inputs_tensor, [0]), i,
'Backward', True),
self.keep_prob) ## [seq_len, batch_size, units]
inputs_tensor = tf.concat([
forward_outputs_tensor,
tf.reverse(backward_outputs_tensor, [0])
], 2)
else:
inputs_tensor = forward_outputs_tensor
self.weight = self.weight * tf.cast(
tf.not_equal(
self.inputs_placeholder_dict['words'],
tf.ones(inputs_shape, tf.int32) *
self.loader.word_index['<-root->']),
tf.float32) ## [batch_size, seq_len]
lstm_outputs = inputs_tensor ## [seq_len, batch_size, outputs_dim]
self.arc_outputs, rel_outputs, self.rel_scores = self.add_biaffine(
lstm_outputs)
# projected_outputs = tf.map_fn(lambda x: self.add_projection(x), lstm_outputs) #[seq_len, batch_size, nb_tags]
# projected_outputs = tf.transpose(projected_outputs, perm=[1, 0, 2]) # [batch_size, seq_len, nb_tags]
self.loss = self.add_loss_op(
self.arc_outputs,
self.inputs_placeholder_dict['arcs']) + self.add_loss_op(
rel_outputs, self.inputs_placeholder_dict['rels'])
self.predicted_arcs, self.UAS = self.add_accuracy(
self.arc_outputs, self.inputs_placeholder_dict['arcs'])
self.predicted_rels, self.rel_acc = self.add_accuracy(
rel_outputs, self.inputs_placeholder_dict['rels'])
self.train_op = self.add_train_op(self.loss)
def run_batch(self, session, testmode=False):
if not testmode:
feed = {}
for feat in self.inputs_placeholder_dict.keys():
feed[self.inputs_placeholder_dict[
feat]] = self.loader.inputs_train_batch[feat]
feed[self.keep_prob] = self.opts.dropout_p
feed[self.hidden_prob] = self.opts.hidden_p
feed[self.input_keep_prob] = self.opts.input_dp
feed[self.mlp_prob] = self.opts.mlp_prob
train_op = self.train_op
_, loss, UAS, rel_acc = session.run(
[train_op, self.loss, self.UAS, self.rel_acc], feed_dict=feed)
return loss, UAS, rel_acc
else:
feed = {}
predictions_batch = {}
for feat in self.inputs_placeholder_dict.keys():
feed[self.inputs_placeholder_dict[
feat]] = self.loader.inputs_test_batch[feat]
feed[self.keep_prob] = 1.0
feed[self.hidden_prob] = 1.0
feed[self.input_keep_prob] = 1.0
feed[self.mlp_prob] = 1.0
# loss, accuracy, predictions, weight = session.run([self.loss, self.accuracy, self.predictions, self.weight], feed_dict=feed)
loss, predicted_arcs, predicted_rels, UAS, weight, arc_outputs, rel_scores = session.run(
[
self.loss, self.predicted_arcs, self.predicted_rels,
self.UAS, self.weight, self.arc_outputs, self.rel_scores
],
feed_dict=feed)
weight = weight.astype(bool)
predicted_arcs_greedy = predicted_arcs[weight]
predicted_rels_greedy = predicted_rels[weight]
predictions_batch['arcs_greedy'] = predicted_arcs_greedy
predictions_batch['rels_greedy'] = predicted_rels_greedy
non_padding = weight.astype(bool)
non_padding[:, 0] = True ## take the dummy root nodes
predicted_arcs, predicted_rels = predict_arcs_rels(
arc_outputs, rel_scores, non_padding)
predictions_batch['arcs'] = predicted_arcs
predictions_batch['rels'] = predicted_rels
# print(predicted_greedy_arcs.shape)
# print(predicted_arcs.shape)
#print(arc_outputs.shape)
return loss, predictions_batch, UAS
def run_epoch(self, session, testmode=False):
if not testmode:
epoch_start_time = time.time()
next_batch = self.loader.next_batch
epoch_incomplete = next_batch(self.batch_size)
while epoch_incomplete:
loss, UAS, rel_acc = self.run_batch(session)
print('{}/{}, loss {:.4f}, Raw UAS {:.4f}, Rel Acc {:.4f}'.
format(self.loader._index_in_epoch,
self.loader.nb_train_samples, loss, UAS, rel_acc),
end='\r')
epoch_incomplete = next_batch(self.batch_size)
print('\nEpoch Training Time {}'.format(time.time() -
epoch_start_time))
return loss, UAS
else:
next_test_batch = self.loader.next_test_batch
test_incomplete = next_test_batch(self.batch_size)
output_types = ['arcs', 'rels', 'arcs_greedy', 'rels_greedy']
predictions = {output_type: [] for output_type in output_types}
while test_incomplete:
loss, predictions_batch, UAS = self.run_batch(session, True)
for name, pred in predictions_batch.items():
predictions[name].append(pred)
#print('Testmode {}/{}, loss {}, accuracy {}'.format(self.loader._index_in_test, self.loader.nb_validation_samples, loss, accuracy), end = '\r')
print('Test mode {}/{}, Raw UAS {:.4f}'.format(
self.loader._index_in_test,
self.loader.nb_validation_samples, UAS),
end='\r') #, end = '\r')
test_incomplete = next_test_batch(self.batch_size)
for name, pred in predictions.items():
predictions[name] = np.hstack(pred)
if self.test_opts is not None:
self.loader.output_arcs(predictions['arcs'],
self.test_opts.predicted_arcs_file)
self.loader.output_rels(predictions['rels'],
self.test_opts.predicted_rels_file)
self.loader.output_arcs(
predictions['arcs_greedy'],
self.test_opts.predicted_arcs_file_greedy)
self.loader.output_rels(
predictions['rels_greedy'],
self.test_opts.predicted_rels_file_greedy)
scores = self.loader.get_scores(predictions, self.opts,
self.test_opts)
if self.test_opts.get_weight:
stag_embeddings = session.run(self.stag_embeddings)
self.loader.output_weight(stag_embeddings)
#scores['UAS'] = np.mean(predictions['arcs'][self.loader.punc] == self.loader.gold_arcs[self.loader.punc])
#scores['UAS_greedy'] = np.mean(predictions['arcs_greedy'][self.loader.punc] == self.loader.gold_arcs[self.loader.punc])
return scores
class Stagging_Model_Concat_Hw(object):
def add_placeholders(self):
#self.inputs_placeholder_list = [tf.placeholder(tf.int32, shape = [None, None]) for _ in xrange(2+self.opts.suffix+self.opts.num+self.opts.cap+self.opts.jackknife)] # 2 for text_sequences and tag_sequences, necessary no matter what
#self.inputs_placeholder_list = [tf.placeholder(tf.int32, shape = [None, None]) for _ in xrange(6)] # 2 for text_sequences and tag_sequences, necessary no matter what
self.inputs_placeholder_dict = {}
for feature in self.features:
if feature == 'chars':
self.inputs_placeholder_dict[feature] = tf.placeholder(tf.int32, shape = [None, None, None])
else:
self.inputs_placeholder_dict[feature] = tf.placeholder(tf.int32, shape = [None, None])
self.keep_prob = tf.placeholder(tf.float32)
self.input_keep_prob = tf.placeholder(tf.float32)
self.hidden_prob = tf.placeholder(tf.float32)
def add_word_embedding(self):
with tf.device('/cpu:0'):
with tf.variable_scope('word_embedding') as scope:
embedding = tf.get_variable('word_embedding_mat', self.loader.word_embeddings.shape, initializer=tf.constant_initializer(self.loader.word_embeddings))
inputs = tf.nn.embedding_lookup(embedding, self.inputs_placeholder_dict['words']) ## [batch_size, seq_len, embedding_dim]
inputs = tf.transpose(inputs, perm=[1, 0, 2]) # [seq_length, batch_size, embedding_dim]
return inputs
def add_suffix_embedding(self):
with tf.device('/cpu:0'):
with tf.variable_scope('suffix_embedding') as scope:
embedding = tf.get_variable('suffix_embedding_mat', [self.loader.nb_suffixes+1, self.opts.suffix_dim]) # +1 for padding
inputs = tf.nn.embedding_lookup(embedding, self.inputs_placeholder_dict['suffix']) ## [batch_size, seq_len, embedding_dim]
inputs = tf.transpose(inputs, perm=[1, 0, 2]) # [seq_length, batch_size, embedding_dim]
return inputs
def add_cap(self):
inputs = tf.cast(tf.expand_dims(self.inputs_placeholder_dict['cap'], -1), tf.float32)
inputs = tf.transpose(inputs, perm=[1, 0, 2]) # [seq_length, batch_size, 1]
return inputs # [seq_length, batch_size, 1]
def add_num(self):
inputs = tf.cast(tf.expand_dims(self.inputs_placeholder_dict['num'], -1), tf.float32)
inputs = tf.transpose(inputs, perm=[1, 0, 2]) # [seq_length, batch_size, 1]
return inputs # [seq_length, batch_size, 1]
def add_char_embedding(self):
with tf.device('/cpu:0'):
with tf.variable_scope('char_embedding') as scope:
embedding = tf.get_variable('char_embedding_mat', [self.loader.nb_chars+1, self.opts.chars_dim]) # +1 for padding
inputs = tf.nn.embedding_lookup(embedding, self.inputs_placeholder_dict['chars']) ## [batch_size, seq_len, word_len, embedding_dim]
inputs = tf.transpose(inputs, perm=[1, 0, 2, 3])
## [seq_len, batch_size, word_len, embedding_dim]
inputs = self.add_dropout(inputs, self.input_keep_prob)
weights = get_char_weights(self.opts, 'char_encoding')
inputs = encode_char(inputs, weights) ## [seq_len, batch_size, nb_filters]
return inputs
def add_jackknife_embedding(self):
with tf.device('/cpu:0'):
with tf.variable_scope('jk_embedding') as scope:
embedding = tf.get_variable('jk_embedding_mat', [self.loader.nb_jk+1, self.opts.jk_dim]) # +1 for padding
inputs = tf.nn.embedding_lookup(embedding, self.inputs_placeholder_dict['jk']) ## [batch_size, seq_len, embedding_dim]
inputs = tf.transpose(inputs, perm=[1, 0, 2]) # [seq_length, batch_size, embedding_dim]
return inputs
def add_lstm(self, inputs, i, name, backward=False):
prev_init = tf.zeros([2, tf.shape(inputs)[1], self.opts.units]) # [2, batch_size, num_units]
#prev_init = tf.zeros([2, 100, self.opts.units]) # [2, batch_size, num_units]
if i == 0:
inputs_dim = self.inputs_dim
else:
inputs_dim = self.opts.units*2 ## concat after each layer
weights = get_lstm_weights('{}_LSTM_layer{}'.format(name, i), inputs_dim, self.opts.units, tf.shape(inputs)[1], self.hidden_prob)
if backward:
## backward: reset states after zero paddings
non_paddings = tf.transpose(self.weight, [1, 0]) ## [batch_size, seq_len] => [seq_len, batch_size]
non_paddings = tf.reverse(non_paddings, [0])
cell_hidden = tf.scan(lambda prev, x: lstm(prev, x, weights, backward=backward), [inputs, non_paddings], prev_init)
else:
cell_hidden = tf.scan(lambda prev, x: lstm(prev, x, weights), inputs, prev_init)
#cell_hidden [seq_len, 2, batch_size, units]
h = tf.unstack(cell_hidden, 2, axis=1)[1] #[seq_len, batch_size, units]
return h
def add_dropout(self, inputs, keep_prob):
## inputs [seq_len, batch_size, inputs_dims/units]
dummy_dp = tf.ones(tf.shape(inputs)[1:])
dummy_dp = tf.nn.dropout(dummy_dp, keep_prob)
return tf.map_fn(lambda x: dummy_dp*x, inputs)
def add_projection(self, inputs, reuse=False, name=None):
if name is None:
name = 'Projection'
with tf.variable_scope(name) as scope:
if reuse:
scope.reuse_variables()
proj_U = tf.get_variable('weight', [self.outputs_dim, self.loader.nb_tags])
tf.add_to_collection('stag_embedding', proj_U)
self.proj_U = proj_U
proj_b = tf.get_variable('bias', [self.loader.nb_tags])
outputs = tf.matmul(inputs, proj_U)+proj_b
return outputs
def add_loss_op(self, output):
cross_entropy = sequence_loss(output, self.inputs_placeholder_dict['tags'], self.weight)
tf.add_to_collection('total loss', cross_entropy)
loss = tf.add_n(tf.get_collection('total loss'))
return loss
def add_accuracy(self, output):
self.predictions = tf.cast(tf.argmax(output, 2), tf.int32) ## [batch_size, seq_len]
correct_predictions = self.weight*tf.cast(tf.equal(self.predictions, self.inputs_placeholder_dict['tags']), tf.float32)
self.accuracy = tf.reduce_sum(tf.cast(correct_predictions, tf.float32))/tf.reduce_sum(tf.cast(self.weight, tf.float32))
def add_probs(self, output):
self.probs = tf.nn.softmax(output)
def add_train_op(self, loss):
optimizer = tf.train.AdamOptimizer()
train_op = optimizer.minimize(loss)
return train_op
def get_features(self):
self.features = ['words', 'tags']
if self.opts.suffix_dim > 0:
self.features.append('suffix')
if self.opts.cap:
self.features.append('cap')
if self.opts.num:
self.features.append('num')
if self.opts.jk_dim > 0:
self.features.append('jk')
if self.opts.chars_dim > 0:
self.features.append('chars')
def __init__(self, opts, test_opts=None):
self.opts = opts
self.test_opts = test_opts
self.loader = Dataset(opts, test_opts)
self.batch_size = opts.batch_size
self.get_features()
self.add_placeholders()
self.inputs_dim = self.opts.embedding_dim + self.opts.suffix_dim + self.opts.cap + self.opts.num + self.opts.jk_dim + self.opts.nb_filters
self.outputs_dim = (1+self.opts.bi)*self.opts.units
inputs_list = [self.add_word_embedding()]
if self.opts.suffix_dim > 0:
inputs_list.append(self.add_suffix_embedding())
if self.opts.cap:
inputs_list.append(self.add_cap())
if self.opts.num:
inputs_list.append(self.add_num())
if self.opts.jk_dim > 0:
inputs_list.append(self.add_jackknife_embedding())
if self.opts.chars_dim > 0:
inputs_list.append(self.add_char_embedding())
inputs_tensor = tf.concat(inputs_list, 2) ## [seq_len, batch_size, inputs_dim]
inputs_tensor = self.add_dropout(inputs_tensor, self.input_keep_prob)
self.weight = tf.cast(tf.not_equal(self.inputs_placeholder_dict['words'], tf.zeros(tf.shape(self.inputs_placeholder_dict['words']), tf.int32)), tf.float32) ## [batch_size, seq_len]
for i in xrange(self.opts.num_layers):
forward_outputs_tensor = self.add_dropout(self.add_lstm(inputs_tensor, i, 'Forward'), self.keep_prob) ## [seq_len, batch_size, units]
if self.opts.bi:
backward_outputs_tensor = self.add_dropout(self.add_lstm(tf.reverse(inputs_tensor, [0]), i, 'Backward', True), self.keep_prob) ## [seq_len, batch_size, units]
inputs_tensor = tf.concat([forward_outputs_tensor, tf.reverse(backward_outputs_tensor, [0])], 2)
else:
inputs_tensor = forward_outputs_tensor
lstm_outputs = inputs_tensor ## [seq_len, batch_size, outputs_dim]
projected_outputs = tf.map_fn(lambda x: self.add_projection(x), lstm_outputs) #[seq_len, batch_size, nb_tags]
projected_outputs = tf.transpose(projected_outputs, perm=[1, 0, 2]) # [batch_size, seq_len, nb_tags]
self.loss = self.add_loss_op(projected_outputs)
self.train_op = self.add_train_op(self.loss)
self.add_accuracy(projected_outputs)
self.add_probs(projected_outputs)
def run_batch(self, session, testmode = False):
if not testmode:
feed = {}
#for placeholder, data in zip(self.inputs_placeholder_list, self.loader.inputs_train_batch):
# feed[placeholder] = data
for feat in self.inputs_placeholder_dict.keys():
feed[self.inputs_placeholder_dict[feat]] = self.loader.inputs_train_batch[feat]
feed[self.keep_prob] = self.opts.dropout_p
feed[self.hidden_prob] = self.opts.hidden_p
feed[self.input_keep_prob] = self.opts.input_dp
train_op = self.train_op
_, loss, accuracy = session.run([train_op, self.loss, self.accuracy], feed_dict=feed)
return loss, accuracy
else:
feed = {}
for feat in self.inputs_placeholder_dict.keys():
feed[self.inputs_placeholder_dict[feat]] = self.loader.inputs_test_batch[feat]
feed[self.keep_prob] = 1.0
feed[self.hidden_prob] = 1.0
feed[self.input_keep_prob] = 1.0
loss, accuracy, predictions, weight, probs = session.run([self.loss, self.accuracy, self.predictions, self.weight, self.probs], feed_dict=feed)
weight = weight.astype(bool)
predictions = predictions[weight]
probs = probs[weight]
return loss, accuracy, predictions, probs
def run_epoch(self, session, testmode = False):
if not testmode:
epoch_start_time = time.time()
next_batch = self.loader.next_batch
epoch_incomplete = next_batch(self.batch_size)
while epoch_incomplete:
loss, accuracy = self.run_batch(session)
print('{}/{}, loss {:.4f}, accuracy {:.4f}'.format(self.loader._index_in_epoch, self.loader.nb_train_samples, loss, accuracy), end = '\r')
epoch_incomplete = next_batch(self.batch_size)
print('\nEpoch Training Time {}'.format(time.time() - epoch_start_time))
return loss, accuracy
else:
next_test_batch = self.loader.next_test_batch
test_incomplete = next_test_batch(self.batch_size)
predictions = []
probs = []
while test_incomplete:
loss, accuracy, predictions_batch, probs_batch = self.run_batch(session, True)
predictions.append(predictions_batch)
probs.append(probs_batch)
#print('Testmode {}/{}, loss {}, accuracy {}'.format(self.loader._index_in_test, self.loader.nb_validation_samples, loss, accuracy), end = '\r')
print('Test mode {}/{}'.format(self.loader._index_in_test, self.loader.nb_validation_samples), end = '\r')
test_incomplete = next_test_batch(self.batch_size)
predictions = np.hstack(predictions)
probs = np.vstack(probs)
if self.test_opts is not None:
self.loader.output_stags(predictions, self.test_opts.save_tags)
if self.test_opts.save_probs:
self.loader.output_probs(probs)
if self.test_opts.get_weight:
stag_embeddings = session.run(self.proj_U)
self.loader.output_weight(stag_embeddings)
accuracy = np.mean(predictions == self.loader.test_gold)
return accuracy