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tp_amtl.py
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import tensorflow as tf
import numpy as np
import pdb
class TP_AMTL(object):
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, config.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.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 output(self, task_id, embed, beta_output):
with tf.variable_scope("task_"+str(task_id)+'/output'):
beta_att = tf.layers.dense(beta_output,self.num_hidden,activation=tf.nn.tanh,use_bias=True,name='beta_att')
c_i = tf.reduce_mean(beta_att * embed, 1)
logits = tf.layers.dense(c_i,1,activation=None,use_bias=True,name='output_layer')
self.beta_att_each.append(beta_att)
return logits
def transfer(self,information_source,information_target,V,to_task,from_task):
with tf.variable_scope('transfer_to%d_from%d'%(to_task,from_task),reuse=tf.AUTO_REUSE):
helper = np.zeros([self.num_steps,self.num_steps])
for i in range(self.num_steps):
for j in range(self.num_steps):
if i>=j:
helper[i][j] = 1.0
helper = tf.convert_to_tensor(helper,dtype=tf.float32)
information_target = tf.expand_dims(information_target,2)
information_target = tf.tile(information_target,[1,1,self.num_steps,1])
information_source = tf.expand_dims(information_source,1)
information_source = tf.tile(information_source,[1,self.num_steps,1,1])
information = tf.concat([information_target,information_source],3)
att = tf.layers.dense(information,self.num_hidden,activation=tf.nn.relu,use_bias=True)
att = tf.layers.dense(att,self.num_hidden,activation=tf.nn.relu,use_bias=True)
att = tf.layers.dense(att,1,activation=tf.nn.softplus,use_bias=True)
att = tf.squeeze(att,axis=[3])
att = att * helper
self.att_each[to_task,from_task].append(att)
with tf.variable_scope('transfer_from%d'%(from_task),reuse=tf.AUTO_REUSE):
transformed_V = tf.layers.dense(V,self.num_hidden,activation=tf.nn.leaky_relu,use_bias=True,name="layer1")
transformed_V = transformed_V / tf.norm(transformed_V,axis=2,keepdims=True)
transfer_amount = tf.matmul(att,transformed_V)
with tf.variable_scope('transfer_to%d'%(to_task),reuse=tf.AUTO_REUSE):
transfer_amount = tf.layers.dense(transfer_amount,self.num_hidden,activation=tf.nn.leaky_relu,use_bias=True,name="layer1")
return transfer_amount
def build_model(self, use_lstm=True):
print('Start building model')
self.loss_each = []
self.preds_each = []
self.beta_att_each = []
self.beta_main_each = []
loss_task = 0
self.att_each = {}
self.test = []
self.KL=[[0 for _ in range(self.num_tasks)] for i in range(self.num_tasks)]
self.att_loc = {}
self.att_scale = {}
self.att_each = {(i,j):[] for i in range(self.num_tasks) for j in range(self.num_tasks)}
self.att_each_before = {}
self.z_in = {}
self.test = {}
self.beta_output = []
self.helper = tf.contrib.distributions.fill_triangular(tf.ones([int(self.num_steps*(self.num_steps+1))/2]))
with tf.variable_scope("base"):
with tf.variable_scope("embed"):
embed = tf.layers.dense(self.x,self.num_hidden,activation=None,use_bias=False)
with tf.variable_scope("rnn"):
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,
variational_recurrent=True,
dtype=tf.float32
)
cell = tf.contrib.rnn.MultiRNNCell([single_cell() for _ in range(self.num_shared_rnn_layers)])
r, _ = tf.nn.dynamic_rnn(cell,
embed,
dtype=tf.float32)
for task_id in range(self.num_tasks):
f = tf.reshape(r,[-1,self.num_hidden])
with tf.variable_scope('task_%d/ffw'%(task_id)):
for i in range(self.num_layers-1):
W = tf.get_variable('weight_'+str(i),[self.num_hidden,self.num_hidden])
B = tf.get_variable('bias_'+str(i),[self.num_hidden])
W = tf.nn.dropout(W,keep_prob=self.keep_prob)
f = tf.nn.leaky_relu(tf.matmul(f,W)+B)
W_loc1 = tf.get_variable('weight_loc1',[self.num_hidden,self.num_hidden])
B_loc1 = tf.get_variable('bias_loc1',[self.num_hidden])
W_loc1 = tf.nn.dropout(W_loc1,keep_prob=self.keep_prob)
beta_loc = tf.nn.leaky_relu(tf.matmul(f,W_loc1)+B_loc1)
W_loc2 = tf.get_variable('weight_loc2',[self.num_hidden,self.num_hidden])
B_loc2 = tf.get_variable('bias_loc2',[self.num_hidden])
W_loc2 = tf.nn.dropout(W_loc2,keep_prob=self.keep_prob)
beta_loc = tf.nn.leaky_relu(tf.matmul(beta_loc,W_loc2)+B_loc2)
beta_loc = tf.reshape(beta_loc,[-1,self.num_steps,self.num_hidden])
W_scale1 = tf.get_variable('weight_scale1',[self.num_hidden,self.num_hidden])
B_scale1 = tf.get_variable('bias_scale1',[self.num_hidden])
W_scale1 = tf.nn.dropout(W_scale1,keep_prob=self.keep_prob)
beta_scale = tf.nn.leaky_relu(tf.matmul(f,W_scale1)+B_scale1)
W_scale2 = tf.get_variable('weight_scale2',[self.num_hidden,self.num_hidden])
B_scale2 = tf.get_variable('bias_scale2',[self.num_hidden])
W_scale2 = tf.nn.dropout(W_scale2,keep_prob=self.keep_prob)
beta_scale = tf.nn.leaky_relu(tf.matmul(beta_scale,W_scale2)+B_scale2)
beta_scale = tf.reshape(beta_scale,[-1,self.num_steps,self.num_hidden])
beta_output = tf.distributions.Normal(beta_loc,beta_scale).sample()
self.beta_output.append(beta_output)
self.variance = []
for task_id in range(self.num_tasks):
beta_outputs = []
for s in range(self.num_training_samples):
with tf.variable_scope("rnn",reuse=True):
r, _ = tf.nn.dynamic_rnn(cell,
embed,
dtype=tf.float32)
f = tf.reshape(r,[-1,self.num_hidden])
with tf.variable_scope('task_%d/ffw'%(task_id),reuse=True):
for i in range(self.num_layers-1):
W = tf.get_variable('weight_'+str(i),[self.num_hidden,self.num_hidden])
B = tf.get_variable('bias_'+str(i),[self.num_hidden])
W = tf.nn.dropout(W,keep_prob=self.keep_prob)
f = tf.nn.leaky_relu(tf.matmul(f,W)+B)
W_loc1 = tf.get_variable('weight_loc1',[self.num_hidden,self.num_hidden])
B_loc1 = tf.get_variable('bias_loc1',[self.num_hidden])
W_loc1 = tf.nn.dropout(W_loc1,keep_prob=self.keep_prob)
beta_loc = tf.nn.leaky_relu(tf.matmul(f,W_loc1)+B_loc1)
W_loc2 = tf.get_variable('weight_loc2',[self.num_hidden,self.num_hidden])
B_loc2 = tf.get_variable('bias_loc2',[self.num_hidden])
W_loc2 = tf.nn.dropout(W_loc2,keep_prob=self.keep_prob)
beta_loc = tf.nn.leaky_relu(tf.matmul(beta_loc,W_loc2)+B_loc2)
beta_loc = tf.reshape(beta_loc,[-1,self.num_steps,self.num_hidden])
W_scale1 = tf.get_variable('weight_scale1',[self.num_hidden,self.num_hidden])
B_scale1 = tf.get_variable('bias_scale1',[self.num_hidden])
W_scale1 = tf.nn.dropout(W_scale1,keep_prob=self.keep_prob)
beta_scale = tf.nn.leaky_relu(tf.matmul(f,W_scale1)+B_scale1)
W_scale2 = tf.get_variable('weight_scale2',[self.num_hidden,self.num_hidden])
B_scale2 = tf.get_variable('bias_scale2',[self.num_hidden])
W_scale2 = tf.nn.dropout(W_scale2,keep_prob=self.keep_prob)
beta_scale = tf.nn.leaky_relu(tf.matmul(beta_scale,W_scale2)+B_scale2)
beta_scale = tf.reshape(beta_scale,[-1,self.num_steps,self.num_hidden])
beta_output = tf.stop_gradient(tf.distributions.Normal(beta_loc,beta_scale).sample())
beta_outputs.append(tf.expand_dims(beta_output,0))
_,variance = tf.nn.moments(tf.concat(beta_outputs,0), axes=[0])
self.variance.append(variance)
# transfer
self.beta_output_transfer = {}
for task_id in range(self.num_tasks):
beta_output_comb = self.beta_output[task_id]
for source_id in range(self.num_tasks):
if source_id != task_id:
source_knowledge = tf.stop_gradient(self.beta_output[source_id])
else:
source_knowledge = self.beta_output[source_id]
information_source = tf.concat([self.beta_output[source_id],self.variance[source_id]],2)
information_target = tf.concat([self.beta_output[task_id],self.variance[task_id]],2)
beta_output_transfer = self.transfer(tf.stop_gradient(information_source),tf.stop_gradient(information_target),source_knowledge,task_id,source_id)
beta_output_comb = beta_output_comb + beta_output_transfer
self.beta_output_transfer[source_id,task_id] = beta_output_transfer
# attention_sum
logits = self.output(task_id, embed, beta_output_comb)
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.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='base') if ('kernel' in v.name or 'weight' in v.name)]
loss_l2 = self.l2_coeff*tf.add_n(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')