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mtl_ua.py
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mtl_ua.py
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import tensorflow as tf
import numpy as np
import pdb
def attention_op(str_id, rnn_outputs, hidden_units, embed_size, steps):
with tf.variable_scope(str_id+'_'):
if str_id == 'alpha':
p_att_shape = [hidden_units, 1]
elif str_id == 'beta':
p_att_shape = [hidden_units, embed_size]
else:
raise ValueError('You must re-check the attention id. required to \'alpha\' or \'beta\'')
#Create MU
with tf.variable_scope(str_id+'MU'):
mu_w = tf.Variable(tf.random_normal(p_att_shape, stddev=0.01), name='_mu')
mu_b = tf.Variable(tf.zeros(p_att_shape[1], name='_mu'))
mu =[]
for _i in range(steps):
mu_tmp = tf.matmul(rnn_outputs[:, _i, :], mu_w) + mu_b
mu.append(mu_tmp)
mu = tf.reshape(tf.concat(mu, 1), [-1, steps, p_att_shape[1]])
#Create sigma
with tf.variable_scope(str_id+'SIGMA'):
sigma_w = tf.Variable(tf.random_normal(p_att_shape, stddev=0.01), name='sigma_weight')
sigma_b = tf.Variable(tf.zeros(p_att_shape[1], name='sigma_bias'))
sigma=[]
for _k in range(steps):
sigma_tmp = tf.matmul(rnn_outputs[:, _k, :], sigma_w) + sigma_b
sigma.append(sigma_tmp)
sigma = tf.reshape(tf.concat(sigma, 1), [-1, steps, p_att_shape[1]])
sigma = tf.nn.softplus(sigma)
distribution = tf.distributions.Normal(loc=mu, scale=sigma)
att = distribution.sample([1])
att = tf.squeeze(att, 0)
if str_id == 'alpha':
squashed_att = tf.nn.softmax(att, 1)
print('Done with generating alpha attention.')
elif str_id == 'beta':
squashed_att = tf.nn.tanh(att)
print('Done with generating beta attention.')
else:
raise ValueError('You must re-check the attention id. required to \'alpha\' or \'beta\'')
return squashed_att
class MTL_UA(object):
dic = {}
def __init__(self, config):
for name in config.__dict__:
setattr(self,name,getattr(config,name))
self.x = tf.placeholder(shape=[None, config.num_steps, config.num_features], dtype=tf.float32, name='data')
self.y = tf.placeholder(shape=[None, self.num_tasks], dtype=tf.float32, name='labels')
self.num_samples_ph = tf.placeholder(dtype=tf.int32,name='num_samples')
self.train = tf.placeholder(dtype=tf.bool,name='train')
self.keep_prob = 0.7
# self.lr = config.LR
self.global_step = tf.Variable(0, trainable=False)
self.lr_decay = tf.train.exponential_decay(self.lr, self.global_step,
10000, 0.8, staircase=False)
self.build_model()
def ua(self):
with tf.variable_scope("ua", reuse=tf.AUTO_REUSE):
def single_cell():
lstm_cell = tf.contrib.rnn.LSTMCell(self.num_hidden)
return tf.contrib.rnn.DropoutWrapper(cell=lstm_cell,
output_keep_prob=self.keep_prob,
dtype=tf.float32
)
cell = tf.contrib.rnn.MultiRNNCell([single_cell() for _ in range(self.num_layers)])
with tf.variable_scope('embedded'):
self.V = tf.get_variable('v_weight', shape=[self.num_features, self.num_hidden], dtype=tf.float32)
v_emb = []
with tf.variable_scope('embedded', reuse=True):
for _j in range(self.num_steps):
self.V = tf.get_variable(name='v_weight')
embbed = tf.matmul(self.x[:, _j, :], self.V)
v_emb.append(embbed)
self.embedded_v = tf.reshape(tf.concat(v_emb, 1), [-1, self.num_steps, self.num_hidden])
#Reverse embedded_v
reversed_v_outputs = tf.reverse(self.embedded_v, [1])
with tf.variable_scope("myrnns_alpha") as scope:
alpha_rnn_outputs, _ = tf.nn.dynamic_rnn(cell,
reversed_v_outputs,
dtype=tf.float32
)
with tf.variable_scope("myrnns_beta") as scope:
beta_rnn_outputs, _ = tf.nn.dynamic_rnn(cell,
reversed_v_outputs,
dtype=tf.float32
)
#alpha
alpha_embed_output = attention_op('alpha', alpha_rnn_outputs, self.num_hidden, self.num_hidden, self.num_steps)
self.rev_alpha_embed_output = tf.reverse(alpha_embed_output, [1])
#beta
beta_embed_output = attention_op('beta', beta_rnn_outputs, self.num_hidden, self.num_hidden, self.num_steps)
self.rev_beta_embed_output = tf.reverse(beta_embed_output, [1])
# attention_multiplication
c_i = tf.reduce_sum(self.rev_alpha_embed_output * (self.rev_beta_embed_output * self.embedded_v), 1)
return c_i
def output(self, task_id, output):
with tf.variable_scope("decoder_"+str(task_id)):
logits = tf.layers.dense(output,1,activation=None,use_bias=True,name='output_layer')
return logits
def build_model(self, use_lstm=True):
print('Start building model')
self.loss_each = []
self.preds_each = []
self.alpha_att_each = []
self.beta_att_each = []
loss_task = 0
output = self.ua()
for task_id in range(self.num_tasks):
# device = task_id%self.num_gpus
# with tf.device('/device:GPU:%d'%(device)):
logits = self.output(task_id, output)
preds = tf.nn.sigmoid(logits)
loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=self.y[:,task_id:task_id+1]))
self.loss_each.append(loss)
self.preds_each.append(preds)
loss_task += loss
l2_losses = [tf.nn.l2_loss(v) for v in tf.trainable_variables() if ('weight' in v.name or 'kernel' in v.name)]
loss_l2 = self.l2_coeff*tf.reduce_sum(l2_losses)
self.loss_sum = loss_task + loss_l2
self.loss_all = {'loss_task':loss_task, 'loss_l2':loss_l2}
self.optim = tf.train.AdamOptimizer(self.lr).minimize(self.loss_sum)
print ('Model built')