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main_model.py
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main_model.py
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import tensorflow as tf
from tensorflow.contrib.rnn.python.ops import core_rnn_cell
import numpy as np
import copy
import importlib
def xavier_init(size):
in_dim = size[0]
xavier_stddev = 1. / tf.sqrt(in_dim / 2.)
return tf.random_normal(shape=size, stddev=xavier_stddev)
class main_model:
def __init__(
self,
method,
model,
kdim,
edim,
kbembed_size,
triples_num,
size,
num_layers,
vocab_size,
buckets,
hops_num=1,#TODO
kgpath_len=1,#TODO
learning_rate=0.5,
learning_rate_decay_factor=0.99,
max_gradient_norm=5.0,
feed_prev=False,
batch_size=32,
dtype=tf.float32):
model_funcs = importlib.import_module('models.' + model)
globals().update(model_funcs.__dict__)
# for knowledge graph
self.kdim = kdim
self.edim = edim
self.kbembed_size = kbembed_size
self.triples_num = triples_num
self.hops_num = hops_num#TODO
self.kgpath_len = kgpath_len#TODO
# basic
self.size = size
self.num_layers = num_layers
self.vocab_size = vocab_size
print('VOCABSIZE:{}'.format(vocab_size))
self.buckets = buckets
self.feed_prev = feed_prev
self.batch_size = batch_size
self.learning_rate = tf.Variable(float(learning_rate), trainable=False, dtype=dtype)
self.op_lr_decay = self.learning_rate.assign(self.learning_rate * learning_rate_decay_factor)
self.global_step = tf.Variable(0, trainable=False)
# main model
self.cell = tf.nn.rnn_cell.MultiRNNCell([tf.nn.rnn_cell.GRUCell(size) for _ in range(num_layers)])
self.enc_cell = tf.nn.rnn_cell.MultiRNNCell([tf.nn.rnn_cell.GRUCell(size) for _ in range(num_layers)])
self.enc_cell = core_rnn_cell.EmbeddingWrapper(
cell=self.enc_cell,
embedding_classes=vocab_size,
embedding_size=size)
# input embedding
self.embedding = variable_scope.get_variable('embedding', [vocab_size, size])
# encoder's placeholder
self.encoder_inputs = []
for bid in range(buckets[-1][0]):
self.encoder_inputs.append(
tf.placeholder(tf.int32, shape = [None],
name = 'encoder{0}'.format(bid)))
self.seq_len = tf.placeholder(
tf.int32, shape = [None],
name = 'enc_seq_len')
# decoder's placeholder
self.decoder_inputs = []
self.targets = []
self.target_weights = []
self.masks = []
for bid in range(buckets[-1][1] + 1):
self.decoder_inputs.append(
tf.placeholder(tf.int32, shape = [None],
name = 'decoder{0}'.format(bid)))
self.targets.append(
tf.placeholder(tf.int32, shape = [None],
name = 'target{0}'.format(bid)))
self.target_weights.append(
tf.placeholder(tf.float32, shape = [None],
name = 'weight{0}'.format(bid)))
self.masks.append(
tf.placeholder(tf.float32, shape = [None],
name = 'mask_unit{0}'.format(bid)))
# TODO passed args funcs
self.output_projection = build_out_proj(size, vocab_size, kdim)
self.kg_projection = build_kg_proj(size, kdim)
self.memA, self.memC = build_memnet(size, num_layers, kbembed_size, xavier_init)
self.Tpred_W, self.Tpred_b = build_transit_mat(size, kdim, edim, xavier_init)
self.S, self.neA = hold_graph(kdim, edim, dtype)
self.facts = hold_facts(triples_num, kbembed_size, dtype)
self.kg_indices = hold_kg_indices()
more_args = (self.Tpred_W, self.Tpred_b, self.kdim, self.edim, self.neA, self.S, self.hops_num, self.kgpath_len, self.kg_projection)
mem_args = (self.batch_size, self.size, self.num_layers, self.hops_num, self.facts, self.kg_indices, self.memA, self.memC)
if method == 'TRAIN':
self.enc_state = []
self.losses = []
self.logits = []
self.decKB_losses = []
self.decN_losses = []
self.ptr_losses = []
self.outputs = []
self.a1s = []
self.kdists = []
self.Ndists = []
self.Rdebugs = []
for j, bucket in enumerate(buckets):
with variable_scope.variable_scope(
variable_scope.get_variable_scope(), reuse=True if j > 0 else None):
_, enc_state = \
encode(self.enc_cell, self.encoder_inputs[:bucket[0]], self.seq_len)
enc_state = enc_state_transform(enc_state, mem_args)
logits, hiddens, dec_state = \
decode(self.cell, enc_state, \
self.vocab_size, self.embedding, \
self.decoder_inputs[:bucket[1]], \
self.output_projection, \
bucket[1]+1, more_args, \
None, feed_prev=False, \
copy_transform=copy_transform)
outputs, a1s, kdists, Ndists, final_logits, Rdebug = copymech(logits, self.output_projection, self.vocab_size, self.kdim, more_args, mem_args, copy_transform)
loss = compute_loss(final_logits, self.targets[:bucket[1]], self.target_weights[:bucket[1]], self.output_projection, self.vocab_size)
self.enc_state.append(enc_state)
self.losses.append(loss)
self.logits.append(logits)
self.outputs.append(outputs)
self.a1s.append(a1s)
self.kdists.append(kdists)
self.Ndists.append(Ndists)
self.Rdebugs.append(Rdebug)
# TODO check
self.softmax_outputs, self.argmax_outputs = to_check(self.logits, self.outputs, self.output_projection)
# update methods
self.op_update = []
optimizer = tf.train.GradientDescentOptimizer(self.learning_rate)
params = tf.trainable_variables()
print(params)
for j in range(len(self.buckets)):
gradients = tf.gradients(self.losses[j], params)
clipped_gradients, _ = tf.clip_by_global_norm(gradients, max_gradient_norm)
self.op_update.append(optimizer.apply_gradients(zip(clipped_gradients, params),
global_step=self.global_step))
elif method == 'TEST':
self.enc_state = []
self.argmax_outputs = []
self.logits = []
self.a1s = []
self.kdists = []
self.Ndists = []
self.Rdebugs = []
for j, bucket in enumerate(buckets):
with variable_scope.variable_scope(
variable_scope.get_variable_scope(), reuse=True if j > 0 else None):
_, enc_state = \
encode(self.enc_cell, self.encoder_inputs[:bucket[0]], self.seq_len)
enc_state = enc_state_transform(enc_state, mem_args)
logits, argmax_outputs, hiddens, a1s, kdists, Ndists, Rdebugs = \
decode(self.cell, enc_state, \
self.vocab_size, self.embedding, \
self.decoder_inputs[:bucket[1]], \
self.output_projection, \
bucket[1], more_args, \
mem_args, feed_prev=True, \
loop_function=loop_function, \
copy_transform=copy_transform)
self.enc_state.append(enc_state)
self.argmax_outputs.append(argmax_outputs)
self.logits.append(logits)
self.a1s.append(a1s)
self.kdists.append(kdists)
self.Ndists.append(Ndists)
self.Rdebugs.append(Rdebugs)
params = tf.trainable_variables()
print(params)
# saver
self.saver = tf.train.Saver(var_list=tf.trainable_variables(), max_to_keep=None, sharded=True)
def train_step(self, sess,
encoder_inputs, decoder_inputs,
targets, target_weights, masks,
bucket_id, encoder_lens,
neAs, Ss, facts, kg_indices,
forward=False):
batch_size = encoder_inputs[0].shape[0]
encoder_size, decoder_size = self.buckets[bucket_id]
input_feed = {}
for l in range(encoder_size):
input_feed[self.encoder_inputs[l].name] = encoder_inputs[l]
input_feed[self.seq_len] = encoder_lens
for l in range(decoder_size):
input_feed[self.decoder_inputs[l].name] = decoder_inputs[l]
input_feed[self.targets[l].name] = targets[l]
input_feed[self.target_weights[l].name] = target_weights[l]
input_feed[self.masks[l].name] = masks[l]
if self.neA != None:
input_feed[self.neA] = neAs
if self.S != None:
input_feed[self.S] = Ss
if self.facts != None:
input_feed[self.facts] = facts
if self.kg_indices != None:
input_feed[self.kg_indices] = kg_indices
if forward:
#output_feed = [self.losses[bucket_id],
# self.argmax_outputs[bucket_id],
# self.softmax_outputs[bucket_id]]
output_feed = [self.losses[bucket_id],
self.argmax_outputs[bucket_id],
self.softmax_outputs[bucket_id],
self.a1s[bucket_id],
self.kdists[bucket_id],
self.Ndists[bucket_id],
self.Rdebugs[bucket_id]]
else:
output_feed = [self.losses[bucket_id],
self.op_update[bucket_id]]
return sess.run(output_feed, input_feed)
def dynamic_decode(self, sess, encoder_inputs, encoder_lens, decoder_inputs, neAs, Ss, facts, kg_indices, bucket_id):
encoder_size, decoder_size = self.buckets[bucket_id]
input_feed = {}
for l in range(encoder_size):
input_feed[self.encoder_inputs[l].name] = encoder_inputs[l]
input_feed[self.seq_len] = encoder_lens
input_feed[self.decoder_inputs[0].name] = decoder_inputs[0]
if self.neA != None:
input_feed[self.neA] = neAs
if self.S != None:
input_feed[self.S] = Ss
if self.facts != None:
input_feed[self.facts] = facts
if self.kg_indices != None:
input_feed[self.kg_indices] = kg_indices
output_feed = [self.argmax_outputs[bucket_id],
self.enc_state[bucket_id],
self.a1s[bucket_id],
self.kdists[bucket_id],
self.Ndists[bucket_id],
self.logits[bucket_id],
self.Rdebugs[bucket_id]]
return sess.run(output_feed, input_feed)