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Agent.py.bak
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import tensorflow as tf
import numpy as np
import random, os
from NonStructured_Encoder import NonStructured_Encoder
from Structured_Encoder import Structured_Encoder
from utils import update_buffer, init_grad
from libs import dropout
class Agent():
def __init__(self, sess, FLAGS, embed, scope, is_multi, encoders=None):
self.sess = sess
self.is_multi = is_multi
self.num_relations = FLAGS.num_relations
self.num_units = FLAGS.num_units
self.dim_feature_bi = FLAGS.dim_feature_bi
self.use_structured = FLAGS.use_structured
self.use_speaker_attn = FLAGS.use_speaker_attn
self.dim_state = 4 * self.num_units + (self.dim_feature_bi if FLAGS.use_traditional else 0)
self.regularizer_scale = FLAGS.regularizer_scale
self.train_keep_prob = FLAGS.keep_prob
self.fixed_noise = tf.placeholder(tf.int32)
self.keep_prob = tf.placeholder_with_default(1.0, ())
self.learning_rate = tf.placeholder(tf.float32)
if FLAGS.use_adam:
self.optimizer = tf.train.AdamOptimizer(self.learning_rate)
else:
self.optimizer = tf.train.GradientDescentOptimizer(self.learning_rate)
with tf.variable_scope(scope):
self._build_input()
self._build_policy_network()
if encoders:
self.ns_encoder = encoders[0]
self.s_encoder_attn = encoders[1]
self.s_encoder_general = encoders[2]
else:
self._build_encoders(FLAGS, embed)
self.params_policy_network = []
self.params = []
for var in tf.trainable_variables():
if var.name.find(os.path.join(tf.contrib.framework.get_name_scope(), os.path.join(scope, "policy_network"))) == 0:
self.params_policy_network.append(var)
if var.name.find(os.path.join(tf.contrib.framework.get_name_scope(), scope)) == 0:
self.params.append(var)
self._build_gradients()
self.grad_policy_in = [
tf.placeholder(tf.float32, param.shape)
for param in self.params_policy_network
]
self.train_op = self.optimizer.apply_gradients(zip(self.grad_policy_in, self.params_policy_network))
def get_policy(self, state, mask=None):
input_feed = { self.state: state }
if not self.is_multi:
input_feed[self.mask] = mask
policy = self.sess.run(self.policy, input_feed)
if not self.is_multi:
policy = policy * mask
return policy
def get_gradients(self, state, golden, mask=None):
input_feed = {
self.state: state,
self.golden: golden
}
output_feed = [self.loss, self.grad_policy_out, self.grad_state_out]
if not self.is_multi:
input_feed[self.mask] = mask
return self.sess.run(output_feed, input_feed)
def clear_gradients(self):
self.grad_policy = init_grad(self.params_policy_network)
if self.use_structured:
self.grad_s_encoder_attn = init_grad(self.s_encoder_attn.params)
self.grad_s_encoder_general = init_grad(self.s_encoder_general.params)
self.grad_ns_encoder = init_grad(self.ns_encoder.params)
def train(self, learning_rate, buffered=False):
output_feed, input_feed = [], {}
# update policy network
input_feed = {}
for i in range(len(self.grad_policy)):
input_feed[self.grad_policy_in[i]] = self.grad_policy[i]
input_feed[self.learning_rate] = learning_rate
output_feed.append(self.train_op)
# update structured encoder
if self.use_structured:
output_feed, input_feed = update_buffer(
output_feed, input_feed,
self.s_encoder_general.train(self.grad_s_encoder_general, learning_rate, buffered=True)
)
if self.use_speaker_attn:
output_feed, input_feed = update_buffer(
output_feed, input_feed,
self.s_encoder_attn.train(self.grad_s_encoder_attn, learning_rate, buffered=True)
)
# update ns encoder
output_feed, input_feed = update_buffer(
output_feed, input_feed,
self.ns_encoder.train(self.grad_ns_encoder, learning_rate, buffered=True)
)
if buffered:
return (output_feed, input_feed)
else:
self.sess.run(output_feed, input_feed)
def _build_gradients(self):
if self.is_multi:
self.loss = -tf.reduce_mean(tf.reduce_sum(
tf.log(self.policy) * tf.one_hot(self.golden, self.num_relations), axis=-1))
else:
self.loss = -tf.reduce_mean(tf.reduce_sum(
tf.log(self.policy) * tf.one_hot(self.golden, tf.shape(self.state)[1]) * self.mask,
axis=-1
))
self.grad_policy_out = tf.gradients(self.loss, self.params_policy_network)
self.grad_state_out = tf.gradients(self.loss, self.state)[0]
def _build_input(self):
if self.is_multi:
self.state = tf.placeholder(tf.float32, (None, self.dim_state), name="state")
else:
self.state = tf.placeholder(tf.float32, (None, None, self.dim_state), name="state")
self.mask = tf.placeholder(tf.float32, (None, None), name="mask")
self.golden = tf.placeholder(tf.int32, (None,), name="golden")
def _build_policy_network(self):
with tf.variable_scope("policy_network"):
h = tf.layers.dense(
self.state, self.num_units * 2, activation=tf.tanh,
kernel_regularizer=tf.contrib.layers.l2_regularizer(self.regularizer_scale),
bias_regularizer=tf.contrib.layers.l2_regularizer(self.regularizer_scale)
)
if self.is_multi:
self.policy = tf.nn.softmax(tf.layers.dense(h, self.num_relations))
else:
self.policy = self.softmax_with_mask(
tf.reduce_sum(tf.layers.dense(h, 1), axis=-1),
self.mask
)
def _build_encoders(self, FLAGS, embed):
num_units = self.num_units
self.ns_encoder = NonStructured_Encoder(self.sess, FLAGS, embed, num_units=num_units)
if self.use_structured:
self.s_encoder_general = Structured_Encoder(self.sess, FLAGS, scope="Structured_Encoder_general")
if self.use_speaker_attn:
self.s_encoder_attn = Structured_Encoder(self.sess, FLAGS, scope="Structured_Encoder_attn")
else:
self.s_encoder_attn = self.s_encoder_general
else:
self.s_encoder_general, self.s_encoder_attn = None, None
def softmax_with_mask(self, h, mask):
exp_with_mask = tf.exp(h * mask) * mask
s = tf.reduce_sum(exp_with_mask, axis=-1)
return tf.transpose(tf.transpose(exp_with_mask) / s) + (1 - mask)