def linearLSTM_over_TreeLstm(self, num_classes, sent_lstm_num_units):
self.sent_cell = td.ScopedLayer(tf.contrib.rnn.BasicLSTMCell(
num_units=sent_lstm_num_units), name_or_scope = self._sent_lstm_default_scope_name)
sent_lstm = (td.Map(self.tree_lstm.tree_lstm()
>> td.Concat()) >> td.RNN(self.sent_cell))
self.output_layer = td.FC(
num_classes, activation=None, name=self._output_layer_default_scope_name)
return (td.Scalar('int32'), sent_lstm >> td.GetItem(1)
>> td.GetItem(0) >> self.output_layer) \
>> self.set_metrics()
def bidirectional_dynamic_FC(fw_cell, bw_cell, hidden):
bidir_conv_lstm = td.Composition()
with bidir_conv_lstm.scope():
fw_seq = td.Identity().reads(bidir_conv_lstm.input[0])
labels = (
td.GetItem(1) >> td.Map(td.Metric("labels")) >> td.Void()).reads(
bidir_conv_lstm.input)
bw_seq = td.Slice(step=-1).reads(fw_seq)
forward_dir = (td.RNN(fw_cell) >> td.GetItem(0)).reads(fw_seq)
back_dir = (td.RNN(bw_cell) >> td.GetItem(0)).reads(bw_seq)
back_to_leftright = td.Slice(step=-1).reads(back_dir)
output_transform = td.FC(1, activation=None)
bidir_common = (td.ZipWith(
td.Concat() >> output_transform >> td.Metric('logits'))).reads(
forward_dir, back_to_leftright)
bidir_conv_lstm.output.reads(bidir_common)
return bidir_conv_lstm
def build_program_decoder_for_analysis(token_emb_size, rnn_cell):
"""
Does the same as build_program_decoder_for_analysis, but also returns
the final hidden state of the decoder
"""
decoder_rnn = td.ScopedLayer(rnn_cell, 'decoder')
decoder_rnn_output = td.RNN(decoder_rnn,
initial_state_from_input=True) >> td.GetItem(0)
fc_layer = td.FC(token_emb_size,
activation=tf.nn.relu,
initializer=tf.contrib.layers.xavier_initializer(),
name='encoder_fc')
# decoder_rnn_output.reads()
un_normalised_token_probs = td.Map(fc_layer)
return decoder_rnn_output >> td.AllOf(un_normalised_token_probs,
td.Identity())
def build_token_level_RVAE(z_size, token_emb_size, look_behind_length):
c = td.Composition()
c.set_input_type(
td.SequenceType(td.TensorType(([token_emb_size]), 'float32')))
with c.scope():
padded_input_sequence = c.input
# build encoder block
encoder_rnn_cell = build_program_encoder(default_gru_cell(2 * z_size))
output_sequence = td.RNN(encoder_rnn_cell) >> td.GetItem(0)
mus_and_log_sigs = output_sequence >> td.GetItem(-1)
reparam_z = resampling_block(z_size)
if look_behind_length > 0:
decoder_input_sequence = (
td.Slice(stop=-1) >> td.NGrams(look_behind_length) >> td.Map(
td.Concat()))
else:
decoder_input_sequence = td.Map(
td.Void() >> td.FromTensor(tf.zeros((0, ))))
# build decoder block
un_normalised_token_probs = build_program_decoder(
token_emb_size, default_gru_cell(z_size), just_tokens=True)
# remove padding for input sequence
input_sequence = td.Slice(start=look_behind_length)
input_sequence.reads(padded_input_sequence)
mus_and_log_sigs.reads(input_sequence)
reparam_z.reads(mus_and_log_sigs)
decoder_input_sequence.reads(padded_input_sequence)
td.Metric('encoder_sequence_length').reads(
td.Length().reads(input_sequence))
td.Metric('decoder_sequence_length').reads(
td.Length().reads(decoder_input_sequence))
un_normalised_token_probs.reads(decoder_input_sequence, reparam_z)
c.output.reads(un_normalised_token_probs, mus_and_log_sigs)
return c
def build_program_decoder(token_emb_size, rnn_cell, just_tokens=False):
"""
Used for blind or 'look-behind' decoders
"""
decoder_rnn = td.ScopedLayer(rnn_cell, 'decoder')
decoder_rnn_output = td.RNN(decoder_rnn,
initial_state_from_input=True) >> td.GetItem(0)
fc_layer = td.FC(
token_emb_size,
activation=tf.nn.relu,
initializer=tf.contrib.layers.xavier_initializer(),
name='encoder_fc' # this is fantastic
)
# un_normalised_token_probs = decoder_rnn_output >> td.Map(fc_layer)
if just_tokens:
return decoder_rnn_output >> td.Map(fc_layer)
else:
return decoder_rnn_output >> td.AllOf(td.Map(fc_layer), td.Identity())
def _compile(self):
with self.sess.as_default():
import tensorflow_fold as td
output_size = len(self.labels)
self.keep_prob = tf.placeholder_with_default(tf.constant(1.0),shape=None)
char_emb = td.Embedding(num_buckets=self.char_buckets,
num_units_out=self.embedding_size)
#initializer=tf.truncated_normal_initializer(stddev=0.15))
char_cell = td.ScopedLayer(tf.contrib.rnn.LSTMCell(num_units=self.rnn_dim), 'char_cell')
char_lstm = (td.InputTransform(lambda s: [ord(c) for c in s])
>> td.Map(td.Scalar('int32') >> char_emb)
>> td.RNN(char_cell) >> td.GetItem(1) >> td.GetItem(1))
rnn_fwdcell = td.ScopedLayer(tf.contrib.rnn.LSTMCell(num_units=self.rnn_dim), 'lstm_fwd')
fwdlayer = td.RNN(rnn_fwdcell) >> td.GetItem(0)
rnn_bwdcell = td.ScopedLayer(tf.contrib.rnn.LSTMCell(num_units=self.rnn_dim), 'lstm_bwd')
bwdlayer = (td.Slice(step=-1) >> td.RNN(rnn_bwdcell)
>> td.GetItem(0) >> td.Slice(step=-1))
pos_emb = td.Embedding(num_buckets=300,
num_units_out=32,
initializer=tf.truncated_normal_initializer(stddev=0.1))
pos_x = (td.InputTransform(lambda x: x + 150)
>> td.Scalar(dtype='int32')
>> pos_emb)
pos_y = (td.InputTransform(lambda x: x + 150)
>> td.Scalar(dtype='int32')
>> pos_emb)
input_layer = td.Map(td.Record((char_lstm,pos_x,pos_y)) >> td.Concat())
maxlayer = (td.AllOf(fwdlayer, bwdlayer)
>> td.ZipWith(td.Concat())
>> td.Max())
output_layer = (input_layer >>
maxlayer >> td.FC(output_size,
input_keep_prob=self.keep_prob,
activation=None))
self.compiler = td.Compiler.create((output_layer,
td.Vector(output_size,dtype=tf.int32)))
self.y_out, self.y_true = self.compiler.output_tensors
self.y_loss = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(
logits=self.y_out,labels=self.y_true))
self.y_prob = tf.nn.softmax(self.y_out)
self.y_true_idx = tf.argmax(self.y_true,axis=1)
self.y_pred_idx = tf.argmax(self.y_prob,axis=1)
self.y_pred = tf.one_hot(self.y_pred_idx,depth=output_size,dtype=tf.int32)
epoch_step = tf.Variable(0, trainable=False)
self.epoch_step_op = tf.assign(epoch_step, epoch_step+1)
lrate_decay = tf.train.exponential_decay(self.lrate, epoch_step, 1, self.decay)
if self.optimizer == 'adam':
self.opt = tf.train.AdamOptimizer(learning_rate=lrate_decay)
elif self.optimizer == 'adagrad':
self.opt = tf.train.AdagradOptimizer(learning_rate=lrate_decay,
initial_accumulator_value=1e-08)
elif self.optimizer == 'rmsprop' or self.optimizer == 'default':
self.opt = tf.train.RMSPropOptimizer(learning_rate=lrate_decay,
epsilon=1e-08)
else:
raise Exception(('The optimizer {} is not in list of available '
+ 'optimizers: default, adam, adagrad, rmsprop.')
.format(self.optimizer))
# apply learning multiplier on on embedding learning rate
embeds = [pos_emb.weights, char_emb.weights]
grads_and_vars = self.opt.compute_gradients(self.y_loss)
found = 0
for i, (grad, var) in enumerate(grads_and_vars):
if var in embeds:
found += 1
grad = tf.scalar_mul(self.embedding_factor, grad)
grads_and_vars[i] = (grad, var)
assert found == len(embeds) # internal consistency check
self.train_step = self.opt.apply_gradients(grads_and_vars)
self.sess.run(tf.global_variables_initializer())
self.saver = tf.train.Saver(max_to_keep=100)
def _compile(self):
with self.sess.as_default():
import tensorflow_fold as td
output_size = len(self.labels)
self.keep_prob = tf.placeholder_with_default(tf.constant(1.0),shape=None)
fshape = (self.window_size * (self.char_embedding_size + self.char_feature_embedding_size), self.num_filters)
filt_w3 = tf.Variable(tf.random_normal(fshape, stddev=0.05))
def CNN_Window3(filters):
return td.Function(lambda a, b, c: cnn_operation([a,b,c],filters))
def cnn_operation(window_sequences,filters):
windows = tf.concat(window_sequences,axis=-1)
products = tf.multiply(tf.expand_dims(windows,axis=-1),filters)
return tf.reduce_sum(products,axis=-2)
char_emb = td.Embedding(num_buckets=self.char_buckets,
num_units_out=self.char_embedding_size)
cnn_layer = (td.NGrams(self.window_size)
>> td.Map(CNN_Window3(filt_w3))
>> td.Max())
# --------- char features
def charfeature_lookup(c):
if c in string.lowercase:
return 0
elif c in string.uppercase:
return 1
elif c in string.punctuation:
return 2
else:
return 3
char_input = td.Map(td.InputTransform(lambda c: ord(c.lower()))
>> td.Scalar('int32') >> char_emb)
char_features = td.Map(td.InputTransform(charfeature_lookup)
>> td.Scalar(dtype='int32')
>> td.Embedding(num_buckets=4,
num_units_out=self.char_feature_embedding_size))
charlevel = (td.InputTransform(lambda s: ['~'] + [ c for c in s ] + ['~'])
>> td.AllOf(char_input,char_features) >> td.ZipWith(td.Concat())
>> cnn_layer)
# --------- word features
word_emb = td.Embedding(num_buckets=len(self.word_vocab),
num_units_out=self.embedding_size,
initializer=self.word_embeddings)
wordlookup = lambda w: (self.word_vocab.index(w.lower())
if w.lower() in self.word_vocab else 0)
wordinput = (td.InputTransform(wordlookup)
>> td.Scalar(dtype='int32')
>> word_emb)
def wordfeature_lookup(w):
if re.match('^[a-z]+$',w):
return 0
elif re.match('^[A-Z][a-z]+$',w):
return 1
elif re.match('^[A-Z]+$',w):
return 2
elif re.match('^[A-Za-z]+$',w):
return 3
else:
return 4
wordfeature = (td.InputTransform(wordfeature_lookup)
>> td.Scalar(dtype='int32')
>> td.Embedding(num_buckets=5,
num_units_out=32))
#-----------
rnn_fwdcell = td.ScopedLayer(tf.contrib.rnn.LSTMCell(
num_units=self.rnn_dim), 'lstm_fwd')
fwdlayer = td.RNN(rnn_fwdcell) >> td.GetItem(0)
rnn_bwdcell = td.ScopedLayer(tf.contrib.rnn.LSTMCell(
num_units=self.rnn_dim), 'lstm_bwd')
bwdlayer = (td.Slice(step=-1) >> td.RNN(rnn_bwdcell)
>> td.GetItem(0) >> td.Slice(step=-1))
rnn_layer = td.AllOf(fwdlayer, bwdlayer) >> td.ZipWith(td.Concat())
output_layer = td.FC(output_size,
input_keep_prob=self.keep_prob,
activation=None)
wordlevel = td.AllOf(wordinput,wordfeature) >> td.Concat()
network = (td.Map(td.AllOf(wordlevel,charlevel) >> td.Concat())
>> rnn_layer
>> td.Map(output_layer)
>> td.Map(td.Metric('y_out'))) >> td.Void()
groundlabels = td.Map(td.Vector(output_size,dtype=tf.int32)
>> td.Metric('y_true')) >> td.Void()
self.compiler = td.Compiler.create((network, groundlabels))
self.y_out = self.compiler.metric_tensors['y_out']
self.y_true = self.compiler.metric_tensors['y_true']
self.y_loss = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(
logits=self.y_out,labels=self.y_true))
self.y_prob = tf.nn.softmax(self.y_out)
self.y_true_idx = tf.argmax(self.y_true,axis=-1)
self.y_pred_idx = tf.argmax(self.y_prob,axis=-1)
self.y_pred = tf.one_hot(self.y_pred_idx,depth=output_size,dtype=tf.int32)
epoch_step = tf.Variable(0, trainable=False)
self.epoch_step_op = tf.assign(epoch_step, epoch_step+1)
lrate_decay = tf.train.exponential_decay(self.lrate, epoch_step, 1, self.decay)
if self.optimizer == 'adam':
self.opt = tf.train.AdamOptimizer(learning_rate=lrate_decay)
elif self.optimizer == 'adagrad':
self.opt = tf.train.AdagradOptimizer(learning_rate=lrate_decay,
initial_accumulator_value=1e-08)
elif self.optimizer == 'rmsprop':
self.opt = tf.train.RMSPropOptimizer(learning_rate=lrate_decay,
epsilon=1e-08)
else:
raise Exception(('The optimizer {} is not in list of available '
+ 'optimizers: default, adam, adagrad, rmsprop.')
.format(self.optimizer))
# apply learning multiplier on on embedding learning rate
embeds = [word_emb.weights]
grads_and_vars = self.opt.compute_gradients(self.y_loss)
found = 0
for i, (grad, var) in enumerate(grads_and_vars):
if var in embeds:
found += 1
grad = tf.scalar_mul(self.embedding_factor, grad)
grads_and_vars[i] = (grad, var)
assert found == len(embeds) # internal consistency check
self.train_step = self.opt.apply_gradients(grads_and_vars)
self.sess.run(tf.global_variables_initializer())
self.saver = tf.train.Saver(max_to_keep=100)
def build_encoder(z_size, token_emb_size):
input_sequence = td.Map(td.Vector(token_emb_size))
encoder_rnn_cell = build_program_encoder(default_gru_cell(2 * z_size))
output_sequence = td.RNN(encoder_rnn_cell) >> td.GetItem(0)
mus_and_log_sigs = output_sequence >> td.GetItem(-1)
return input_sequence >> mus_and_log_sigs
def build_VAE(z_size, token_emb_size):
c = td.Composition()
c.set_input_type(td.SequenceType(td.TensorType(([token_emb_size]), 'float32')))
with c.scope():
# input_sequence = td.Map(td.Vector(token_emb_size)).reads(c.input)
input_sequence = c.input
# encoder composition TODO: refactor this out
# rnn_cell = td.ScopedLayer(
# tf.contrib.rnn.LSTMCell(
# num_units=2*z_size,
# initializer=tf.contrib.layers.xavier_initializer(),
# activation=tf.tanh
# ),
# 'encoder'
# )
encoder_rnn_cell = td.ScopedLayer(
tf.contrib.rnn.GRUCell(
num_units=2*z_size,
# initializer=tf.contrib.layers.xavier_initializer(),
activation=tf.tanh
),
'encoder'
)
output_sequence = td.RNN(encoder_rnn_cell) >> td.GetItem(0)
mus_and_log_sigs = output_sequence >> td.GetItem(-1)
# reparam_z = mus_and_log_sigs >> td.Function(resampling)
reparam_z = td.Function(resampling, name='resampling')
reparam_z.set_input_type(td.TensorType((2 * z_size,)))
reparam_z.set_output_type(td.TensorType((z_size,)))
# A list of same length of input_sequence, but with empty values
# this is used for the decoder to map over
list_of_nothing = td.Map(
td.Void() >> td.FromTensor(tf.zeros((0,)))
)
# decoder composition
# TODO: refactor this out
# decoder_rnn = td.ScopedLayer(
# tf.contrib.rnn.LSTMCell(
# num_units=z_size,
# initializer=tf.contrib.layers.xavier_initializer(),
# activation=tf.tanh
# ),
# 'decoder'
# )
decoder_rnn = td.ScopedLayer(
tf.contrib.rnn.GRUCell(
num_units=z_size,
# initializer=tf.contrib.layers.xavier_initializer(),
activation=tf.tanh
),
'decoder'
)
decoder_rnn_output = td.RNN(
decoder_rnn,
initial_state_from_input=True
) >> td.GetItem(0)
fc_layer = td.FC(
token_emb_size,
activation=tf.nn.relu,
initializer=tf.contrib.layers.xavier_initializer()
)
un_normalised_token_probs = decoder_rnn_output >> td.Map(fc_layer)
# reparam_z.reads(input_sequence)
mus_and_log_sigs.reads(input_sequence)
reparam_z.reads(mus_and_log_sigs)
list_of_nothing.reads(input_sequence)
un_normalised_token_probs.reads(list_of_nothing, reparam_z)
c.output.reads(un_normalised_token_probs, mus_and_log_sigs)
return c