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class_AC_TPC.py
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class_AC_TPC.py
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import numpy as np
import tensorflow as tf
import random
import os,sys
from tensorflow.python.ops.rnn import _transpose_batch_time
#user defined
import utils_network as utils
def log(x):
return tf.log(x + 1e-8)
def div(x, y):
return tf.div(x, (y + 1e-8))
def get_seq_length(sequence):
used = tf.sign(tf.reduce_max(tf.abs(sequence), 2))
tmp_length = tf.reduce_sum(used, 1)
tmp_length = tf.cast(tmp_length, tf.int32)
return tmp_length
class AC_TPC:
def __init__(self, sess, name, input_dims, network_settings):
self.sess = sess
self.name = name
# INPUT/OUTPUT DIMENSIONS
self.x_dim = input_dims['x_dim'] #features + delta
self.y_dim = input_dims['y_dim']
self.y_type = input_dims['y_type']
self.K = input_dims['max_cluster']
self.max_length = input_dims['max_length']
# Encoder
self.h_dim_f = network_settings['h_dim_encoder'] #encoder nodes
self.num_layers_f = network_settings['num_layers_encoder'] #encoder layers
self.rnn_type = network_settings['rnn_type']
self.rnn_activate_fn = network_settings['rnn_activate_fn']
# Selector
self.h_dim_h = network_settings['h_dim_selector'] #selector nodes
self.num_layers_h = network_settings['num_layers_selector'] #selector layers
# Predictor
self.h_dim_g = network_settings['h_dim_predictor'] #predictor nodes
self.num_layers_g = network_settings['num_layers_predictor'] #predictor layers
self.fc_activate_fn = network_settings['fc_activate_fn'] #selector & predictor
# Latent Space
self.z_dim = self.h_dim_f * self.num_layers_f
self._build_net()
def _build_net(self):
with tf.variable_scope(self.name):
self.mb_size = tf.placeholder(tf.int32, [], name='batch_size')
self.lr_rate1 = tf.placeholder(tf.float32, name='learning_rate1')
self.lr_rate2 = tf.placeholder(tf.float32, name='learning_rate2')
self.keep_prob = tf.placeholder(tf.float32, name='keep_probability')
# Input and Output
self.x = tf.placeholder(tf.float32, [None, self.max_length, self.x_dim], name='inputs')
self.y = tf.placeholder(tf.float32, [None, self.max_length, self.y_dim], name='labels_onehot')
# Embedding
self.E = tf.placeholder(tf.float32, [self.K, self.z_dim], name='embeddings_input')
self.EE = tf.Variable(self.E, name='embeddings_var')
self.embeddings = tf.nn.tanh(self.EE)
# self.embde = tf.nn.tanh(self.EE)
# self.EE = tf.Variable(self.E, name='embeddings_var')
self.s = tf.placeholder(tf.int32, [None], name='cluster_label')
self.s_onehot = tf.one_hot(self.s, self.K)
# LOSS PARAMETERS
self.alpha = tf.placeholder(tf.float32, name = 'alpha') #For sample-wise entropy
self.beta = tf.placeholder(tf.float32, name = 'beta') #For prediction loss (i.e., mle)
self.gamma = tf.placeholder(tf.float32, name = 'gamma') #For batch-wise entropy
self.delta = tf.placeholder(tf.float32, name = 'delta') #For embedding
'''
### CREATE RNN MASK
- This is to flexibly handle sequences with different length
- rnn_mask1: last observation; [mb_size, max_length]
- rnn_mask2: all available observations; [mb_size, max_length]
'''
# CREATE RNN MASK:
seq_length = get_seq_length(self.x)
tmp_range = tf.expand_dims(tf.range(0, self.max_length, 1), axis=0)
self.rnn_mask1 = tf.cast(tf.equal(tmp_range, tf.expand_dims(seq_length-1, axis=1)), tf.float32) #last observation
self.rnn_mask2 = tf.cast(tf.less_equal(tmp_range, tf.expand_dims(seq_length-1, axis=1)), tf.float32) #all available observation
### DEFINE SELECTOR
def selector(x_, o_dim_=self.K, num_layers_=2, h_dim_=self.h_dim_h, activation_fn=self.fc_activate_fn, reuse=tf.AUTO_REUSE):
out_fn = tf.nn.softmax
with tf.variable_scope('selector', reuse=reuse):
if num_layers_ == 1:
out = tf.contrib.layers.fully_connected(inputs=x_, num_outputs=o_dim_, activation_fn=out_fn, scope='selector_out')
else: #num_layers > 1
for tmp_layer in range(num_layers_-1):
if tmp_layer == 0:
net = x_
net = tf.contrib.layers.fully_connected(inputs=net, num_outputs=h_dim_, activation_fn=activation_fn, scope='selector_'+str(tmp_layer))
net = tf.nn.dropout(net, keep_prob=self.keep_prob)
out = tf.contrib.layers.fully_connected(inputs=net, num_outputs=o_dim_, activation_fn=out_fn, scope='selector_out')
return out
### DEFINE PREDICTOR
def predictor(x_, o_dim_=self.y_dim, o_type_=self.y_type, num_layers_=1, h_dim_=self.h_dim_g, activation_fn=self.fc_activate_fn, reuse=tf.AUTO_REUSE):
if o_type_ == 'continuous':
out_fn = None
elif o_type_ == 'categorical':
out_fn = tf.nn.softmax #for classification task
elif o_type_ == 'binary':
out_fn = tf.nn.sigmoid
else:
raise Exception('Wrong output type. The value {}!!'.format(o_type_))
with tf.variable_scope('predictor', reuse=reuse):
if num_layers_ == 1:
out = tf.contrib.layers.fully_connected(inputs=x_, num_outputs=o_dim_, activation_fn=out_fn, scope='predictor_out')
else: #num_layers > 1
for tmp_layer in range(num_layers_-1):
if tmp_layer == 0:
net = x_
net = tf.contrib.layers.fully_connected(inputs=net, num_outputs=h_dim_, activation_fn=activation_fn, scope='predictor_'+str(tmp_layer))
net = tf.nn.dropout(net, keep_prob=self.keep_prob)
out = tf.contrib.layers.fully_connected(inputs=net, num_outputs=o_dim_, activation_fn=out_fn, scope='predictor_out')
return out
### DEFINE LOOP FUNCTION FOR ENCODRER (f-g, f-h relations are created here)
def loop_fn(time, cell_output, cell_state, loop_state):
emit_output = cell_output
if cell_output is None: # time == 0
next_cell_state = cell.zero_state(self.mb_size, tf.float32)
next_loop_state = loop_state_ta
else:
next_cell_state = cell_state
tmp_z = utils.create_concat_state_h(next_cell_state, self.num_layers_f, self.rnn_type)
tmp_y = predictor(tmp_z, self.y_dim, self.y_type, self.num_layers_g, self.h_dim_g, self.fc_activate_fn)
tmp_pi = selector(tmp_z, self.K, self.num_layers_h, self.h_dim_h, self.fc_activate_fn)
next_loop_state = (loop_state[0].write(time-1, tmp_z), # save all the hidden states
loop_state[1].write(time-1, tmp_y), # save all the output
loop_state[2].write(time-1, tmp_pi)) # save all the selector_net output (i.e., pi)
elements_finished = (time >= self.max_length)
#this gives the break-point (no more recurrence after the max_length)
finished = tf.reduce_all(elements_finished)
next_input = tf.cond(finished,
lambda: tf.zeros([self.mb_size, self.x_dim], dtype=tf.float32),
lambda: inputs_ta.read(time))
return (elements_finished, next_input, next_cell_state, emit_output, next_loop_state)
'''
##### CREATE RNN NETWORK
- (INPUT) inputs_ta: TensorArray with [max_length, mb_size, x_dim] #x_dim included delta
- (OUTPUT)
. zs = rnn states (h) in LSTM/GRU ; [mb_size, max_length z_dim]
. y_hats = output of predictor taking zs as inputs; [mb_size, max_length, y_dim]
. pis = output of selector ; [mb_size, max_length, K]
'''
inputs = self.x
inputs_ta = tf.TensorArray(
dtype=tf.float32, size=self.max_length
).unstack(_transpose_batch_time(inputs), name = 'rnn_input')
cell = utils.create_rnn_cell(
self.h_dim_f, self.num_layers_f,
self.keep_prob, self.rnn_type, self.rnn_activate_fn
)
#define the loop_state TensorArray for information from rnn time steps
loop_state_ta = (
tf.TensorArray(size=self.max_length, dtype=tf.float32, clear_after_read=False), #zs (j=1,...,J)
tf.TensorArray(size=self.max_length, dtype=tf.float32, clear_after_read=False), #y_hats (j=1,...,J)
tf.TensorArray(size=self.max_length, dtype=tf.float32, clear_after_read=False) #pis (j=1,...,J)
)
_, _, loop_state_ta = tf.nn.raw_rnn(cell, loop_fn) #, parallel_iterations=1)
self.zs = _transpose_batch_time(loop_state_ta[0].stack())
self.y_hats = _transpose_batch_time(loop_state_ta[1].stack())
self.pis = _transpose_batch_time(loop_state_ta[2].stack())
### SAMPLING PROCESS
s_dist = tf.distributions.Categorical(probs=tf.reshape(self.pis, [-1, self.K])) #define the categorical dist.
s_sample = s_dist.sample()
mask_e = tf.cast(tf.equal(tf.expand_dims(tf.range(0, self.K, 1), axis=0), tf.expand_dims(s_sample, axis=1)), tf.float32)
z_bars = tf.matmul(mask_e, self.embeddings)
pi_sample = tf.reduce_sum(mask_e * tf.reshape(log(self.pis), [-1, self.K]), axis=1)
with tf.variable_scope('rnn', reuse=True):
y_bars = predictor(z_bars, self.y_dim, self.y_type, self.num_layers_g, self.h_dim_g, self.fc_activate_fn)
self.z_bars = tf.reshape(z_bars, [-1, self.max_length, self.z_dim])
self.y_bars = tf.reshape(y_bars, [-1, self.max_length, self.y_dim])
self.pi_sample = tf.reshape(pi_sample, [-1, self.max_length])
self.s_sample = tf.reshape(s_sample, [-1, self.max_length])
### DEFINE LOSS FUNCTIONS
#\ell_{1}: KL divergence loss for regression and binary/categorical-classification task
def loss_1(y_true_, y_pred_, y_type_ = self.y_type):
if y_type_ == 'continuous':
tmp_loss = tf.reduce_sum((y_true_ - y_pred_)**2, axis=-1)
elif y_type_ == 'categorical':
tmp_loss = - tf.reduce_sum(y_true_ * log(y_pred_), axis=-1)
elif y_type_ == 'binary':
tmp_loss = - tf.reduce_sum(y_true_ * log(y_pred_) + (1.-y_true_) * log(1.-y_pred_), axis=-1)
else:
raise Exception('Wrong output type. The value {}!!'.format(y_type_))
return tmp_loss
#batch-wise entropy
tmp_pis = tf.tile(tf.expand_dims(self.rnn_mask2, axis=2), [1,1,self.K]) * self.pis
mean_pis = tf.reduce_sum(tf.reduce_sum(tmp_pis, axis=1), axis=0) / tf.reduce_sum(tf.reduce_sum(self.rnn_mask2, axis=1), axis=0, keepdims=True)
## LOSS_MLE: MLE prediction loss (for initalization)
self.LOSS_MLE = tf.reduce_mean(tf.reduce_sum(self.rnn_mask2 * loss_1(self.y, self.y_hats, self.y_type), axis=1))
## LOSS1: predictive clustering loss
self.LOSS_1 = tf.reduce_mean(tf.reduce_sum(self.rnn_mask2 * loss_1(self.y, self.y_bars, self.y_type), axis=1))
self.LOSS_1_AC = tf.reduce_mean(tf.reduce_sum(self.rnn_mask2 * self.pi_sample * loss_1(self.y, self.y_bars, self.y_type), axis=1))
## LOSS2: sample-wise entropy loss
self.LOSS_2 = tf.reduce_mean(-tf.reduce_sum(self.rnn_mask2 * tf.reduce_sum(self.pis * log(self.pis), axis=2), axis=1))
predictor_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope= self.name+'/rnn/predictor')
selecter_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope= self.name+'/rnn/selector')
embedding_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope= self.name+'/embeddings_var')
encoder_vars = [vars_ for vars_ in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
if vars_ not in predictor_vars+selecter_vars+embedding_vars]
### EMBEDDING TRAINING
with tf.variable_scope('rnn', reuse=True):
Ey = predictor(self.embeddings, self.y_dim, self.y_type, self.num_layers_g, self.h_dim_g, self.fc_activate_fn)
# Ey = predictor(self.EE, self.y_dim, self.y_type, self.num_layers_g, self.h_dim_g, self.fc_activate_fn)
## LOSS3: embedding separation loss (prevents embedding from collapsing)
self.LOSS_3 = 0
for i in range(self.K):
for j in range(i+1, self.K):
self.LOSS_3 += - loss_1(Ey[i, :], Ey[j, :], y_type_ = self.y_type) / ((self.K-1)*(self.K-2)) # negative because we want to increase this;
### DEFINE OPTIMIZATION SOLVERS
self.solver_MLE = tf.train.AdamOptimizer(self.lr_rate1).minimize(
self.LOSS_MLE, var_list=encoder_vars+predictor_vars
)
self.solver_L1_critic = tf.train.AdamOptimizer(self.lr_rate1).minimize(
self.LOSS_1,
var_list=encoder_vars+predictor_vars
)
self.solver_L1_actor = tf.train.AdamOptimizer(self.lr_rate2).minimize(
self.LOSS_1_AC + self.alpha*self.LOSS_2,
var_list=encoder_vars + selecter_vars
)
self.solver_E = tf.train.AdamOptimizer(self.lr_rate1).minimize(
self.LOSS_1 + self.beta*self.LOSS_3,
var_list=embedding_vars
)
### INITIALIZE SELECTOR
self.zz = tf.placeholder(tf.float32, [None, self.z_dim])
with tf.variable_scope('rnn', reuse=True):
self.yy = predictor(self.zz, self.y_dim, self.y_type, self.num_layers_g, self.h_dim_g, self.fc_activate_fn) #to check the predictor output given z
self.s_out = selector(self.zz, self.K, self.num_layers_h, self.h_dim_h, self.fc_activate_fn)
## LOSS_S: selector initialization (cross-entropy wrt initialized class)
self.LOSS_S = tf.reduce_mean(- tf.reduce_sum(self.s_onehot*log(self.s_out), axis=1))
self.solver_S = tf.train.AdamOptimizer(self.lr_rate1).minimize(
self.LOSS_S, var_list=selecter_vars
)
### TRAINING FUNCTIONS
def train_mle(self, x_, y_, lr_train, k_prob):
return self.sess.run([self.solver_MLE, self.LOSS_MLE],
feed_dict={self.x: x_, self.y: y_,
self.mb_size:np.shape(x_)[0], self.lr_rate1: lr_train, self.keep_prob: k_prob})
def train_critic(self, x_, y_, lr_train, k_prob):
return self.sess.run([self.solver_L1_critic, self.LOSS_1],
feed_dict={self.x: x_, self.y: y_,
self.mb_size:np.shape(x_)[0], self.lr_rate1: lr_train, self.keep_prob: k_prob})
def train_actor(self, x_, y_, alpha_, lr_train, k_prob):
return self.sess.run([self.solver_L1_actor, self.LOSS_1, self.LOSS_2],
feed_dict={self.x: x_, self.y: y_,
self.alpha: alpha_,
self.mb_size:np.shape(x_)[0], self.lr_rate2: lr_train, self.keep_prob: k_prob})
def train_selector(self, z_, s_, lr_train, k_prob):
return self.sess.run([self.solver_S, self.LOSS_S],
feed_dict={self.zz: z_, self.s: s_,
self.lr_rate1: lr_train, self.keep_prob: k_prob})
def train_embedding(self, x_, y_, beta_, lr_train, k_prob):
return self.sess.run([self.solver_E, self.LOSS_1, self.LOSS_3],
feed_dict={self.x:x_, self.y:y_,
self.beta:beta_,
self.mb_size:np.shape(x_)[0],
self.lr_rate1:lr_train, self.keep_prob:k_prob})
def get_losses(self, x_, y_):
return self.sess.run([self.LOSS_1, self.LOSS_2, self.LOSS_3],
feed_dict={self.x:x_, self.y:y_,
self.mb_size:np.shape(x_)[0],
self.keep_prob:1.0})
### PREDICTION FUNCTIONS
def predict_y_hats(self, x_):
return self.sess.run([self.y_hats, self.rnn_mask2],
feed_dict={self.x:x_, self.mb_size:np.shape(x_)[0], self.keep_prob:1.0})
def predict_y_bars(self, x_):
return self.sess.run([self.y_bars, self.rnn_mask2],
feed_dict={self.x:x_, self.mb_size:np.shape(x_)[0], self.keep_prob:1.0})
def predict_yy(self, z_):
return self.sess.run(self.yy,
feed_dict={self.zz:z_, self.mb_size:np.shape(z_)[0], self.keep_prob:1.0})
def predict_zs_and_pis_m2(self, x_):
return self.sess.run([self.zs, self.pis, self.rnn_mask2],
feed_dict={self.x:x_, self.mb_size:np.shape(x_)[0], self.keep_prob:1.0})
def predict_s_sample(self, x_):
return self.sess.run([self.s_sample, self.rnn_mask2],
feed_dict={self.x:x_, self.mb_size:np.shape(x_)[0], self.keep_prob:1.0})
def predict_zbars_and_pis_m1(self, x_):
return self.sess.run([self.z_bars, self.pis, self.rnn_mask1],
feed_dict={self.x:x_, self.mb_size:np.shape(x_)[0], self.keep_prob:1.0})
def predict_zs_and_pis_m1(self, x_):
return self.sess.run([self.zs, self.pis, self.rnn_mask1],
feed_dict={self.x:x_, self.mb_size:np.shape(x_)[0], self.keep_prob:1.0})
def predict_zbars_and_pis_m2(self, x_):
return self.sess.run([self.z_bars, self.pis, self.rnn_mask2],
feed_dict={self.x:x_, self.mb_size:np.shape(x_)[0], self.keep_prob:1.0})
### INITIALIZE EMBEDDING AND SELECTOR
from sklearn.cluster import MiniBatchKMeans, KMeans
def initialize_embedding(model, x, K):
tmp_z, _, _ = model.predict_zs_and_pis_m2(x)
tmp_y, tmp_m = model.predict_y_hats(x)
z_dim = np.shape(tmp_z)[-1]
y_dim = np.shape(tmp_y)[-1]
tmp_z = (tmp_z * np.tile(np.expand_dims(tmp_m, axis=2), [1,1,z_dim])).reshape([-1, z_dim])
tmp_z = tmp_z[np.sum(np.abs(tmp_z), axis=1) != 0]
tmp_y = (tmp_y * np.tile(np.expand_dims(tmp_m, axis=2), [1,1,y_dim])).reshape([-1, y_dim])
tmp_y = tmp_y[np.sum(np.abs(tmp_y), axis=1) != 0]
km = KMeans(n_clusters = K, init='k-means++')
_ = km.fit(tmp_y)
tmp_ey = km.cluster_centers_
tmp_s = km.predict(tmp_y)
tmp_e = np.zeros([K, z_dim])
for k in range(K):
# tmp_e[k, :] = np.mean(tmp_z[tmp_s == k])
tmp_e[k,:] = tmp_z[np.argmin(np.sum(np.abs(tmp_y - tmp_ey[k, :]),axis=1)), :]
return tmp_e, tmp_s, tmp_z