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model.py
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model.py
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#!/usr/bin/python3
import tensorflow as tf
import numpy as np
import glob, time, os, functools
from network import Network
from data import Data
from config import directories
from adversary import Adversary
from utils import Utils
class Model():
def __init__(self, config, features, labels, args=None, evaluate=False):
if args is not None: # merge args and config
config_d = dict((n, getattr(config, n)) for n in dir(config) if not n.startswith('__'))
config_d.update(vars(args))
config = Utils.Struct(**config_d)
# Build the computational graph
arch = functools.partial(Network.dense_network, actv=tf.nn.elu)
sequential = False
self.global_step = tf.Variable(0, trainable=False)
self.MINE_step = tf.Variable(0, trainable=False)
self.handle = tf.placeholder(tf.string, shape=[])
self.training_phase = tf.placeholder(tf.bool)
self.rnn_keep_prob = tf.placeholder(tf.float32)
self.features_placeholder = tf.placeholder(tf.float32, [features.shape[0], features.shape[1]])
self.labels_placeholder = tf.placeholder(tf.int32, labels.shape)
self.test_features_placeholder = tf.placeholder(tf.float32)
self.test_labels_placeholder = tf.placeholder(tf.int32)
self.pivots_placeholder = tf.placeholder(tf.float32)
self.test_pivots_placeholder = tf.placeholder(tf.float32)
if config.use_adversary and not evaluate:
self.pivot_labels_placeholder = tf.placeholder(tf.int32)
self.test_pivot_labels_placeholder = tf.placeholder(tf.int32)
train_dataset = Data.load_dataset(self.features_placeholder, self.labels_placeholder,
self.pivots_placeholder, batch_size=config.batch_size, sequential=sequential, adversary=True,
pivot_labels_placeholder=self.pivot_labels_placeholder)
test_dataset = Data.load_dataset(self.test_features_placeholder, self.test_labels_placeholder,
self.test_pivots_placeholder, batch_size=config.batch_size, sequential=sequential, adversary=True,
pivot_labels_placeholder=self.test_pivot_labels_placeholder, test=True)
else:
train_dataset = Data.load_dataset(self.features_placeholder, self.labels_placeholder,
self.pivots_placeholder, batch_size=config.batch_size, sequential=sequential)
test_dataset = Data.load_dataset(self.test_features_placeholder, self.test_labels_placeholder,
self.pivots_placeholder, config.batch_size, test=True, sequential=sequential)
val_dataset = Data.load_dataset(self.features_placeholder, self.labels_placeholder, self.pivots_placeholder,
config.batch_size, evaluate=True, sequential=sequential)
self.iterator = tf.data.Iterator.from_string_handle(self.handle, train_dataset.output_types, train_dataset.output_shapes)
self.train_iterator = train_dataset.make_initializable_iterator()
self.test_iterator = test_dataset.make_initializable_iterator()
self.val_iterator = val_dataset.make_initializable_iterator()
if config.use_adversary and not evaluate:
self.example, self.labels, self.pivots, self.pivot_labels = self.iterator.get_next()
if len(config.pivots) == 1:
# self.pivots = tf.expand_dims(self.pivots, axis=1)
self.pivot_labels = tf.expand_dims(self.pivot_labels, axis=1)
else:
self.example, self.labels, self.pivots = self.iterator.get_next()
self.example.set_shape([None, features.shape[1]])
self.pivots.set_shape([None, 2*len(config.pivots)])
if evaluate:
with tf.variable_scope('classifier') as scope:
self.logits, *hreps = arch(self.example, config, self.training_phase)
self.softmax = tf.nn.sigmoid(self.logits)
self.pred = tf.cast(tf.greater(self.softmax, 0.5), tf.int32)
self.ema = tf.train.ExponentialMovingAverage(decay=config.ema_decay, num_updates=self.global_step)
print('Y shape:', self.labels.shape)
print('Logits shape:', self.logits.shape)
self.cross_entropy = tf.nn.sigmoid_cross_entropy_with_logits(logits=self.logits,
labels=(1-tf.one_hot(self.labels, depth=1)))
return
with tf.variable_scope('classifier') as scope:
self.logits, self.hrep = arch(self.example, config, self.training_phase)
self.softmax = tf.nn.sigmoid(self.logits)[:,0]
self.pred = tf.cast(tf.greater(self.softmax, 0.5), tf.int32)
self.pred_boolean = tf.cast(tf.greater(self.softmax, 0.5), tf.bool)
true_background_pivots = tf.boolean_mask(self.pivots, tf.cast((1-self.labels), tf.bool))
pred_background_pivots = tf.boolean_mask(self.pivots, tf.cast((1-self.pred), tf.bool))
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
self.cross_entropy = tf.nn.sigmoid_cross_entropy_with_logits(logits=self.logits,
labels=(1-tf.one_hot(self.labels, depth=1)))
self.cost = tf.reduce_mean(self.cross_entropy)
theta_f = Utils.scope_variables('classifier')
self.ema = tf.train.ExponentialMovingAverage(decay=config.ema_decay, num_updates=self.global_step)
with tf.control_dependencies(update_ops):
config.optimizer = config.optimizer.lower()
if config.optimizer=='adam':
self.opt = tf.train.AdamOptimizer(config.learning_rate)
elif config.optimizer=='momentum':
self.opt = tf.train.MomentumOptimizer(config.learning_rate, config.momentum,
use_nesterov=True)
elif config.optimizer == 'rmsprop':
self.opt = tf.train.RMSPropOptimizer(config_learning_rate)
elif config.optimizer == 'sgd':
self.opt = tf.train.GradientDescentOptimizer(config.learning_rate)
self.MI_logits_theta_kraskov = tf.py_func(Utils.mutual_information_1D_kraskov, inp=[tf.squeeze(self.logits),
tf.squeeze(self.pivots[:,0])], Tout=tf.float64)
self.MI_xent_theta_kraskov = tf.py_func(Utils.mutual_information_1D_kraskov, inp=[tf.squeeze(self.cross_entropy),
tf.squeeze(self.pivots[:,0])], Tout=tf.float64)
self.MI_logits_labels_kraskov = tf.py_func(Utils.mutual_information_1D_kraskov, inp=[tf.squeeze(self.logits),
tf.squeeze(self.labels)], Tout=tf.float64)
X, Z = self.logits, tf.squeeze(self.pivots[:,0])
with tf.variable_scope('LABEL_MINE') as scope:
Y = tf.cast(self.labels, tf.float32)
Y_prime = tf.random_shuffle(Y)
*reg_terms, self.MI_logits_labels_MINE = Network.MINE(x=X, y=Y, y_prime=Y_prime, batch_size=config.batch_size,
dimension=2, training=self.training_phase, actv=tf.nn.elu)
if config.use_adversary:
adv = Adversary(config,
classifier_logits=self.logits,
labels=self.labels,
pivots=self.pivots,
pivot_labels=self.pivot_labels,
training_phase=self.training_phase,
classifier_opt=self.opt)
self.adv_loss = adv.adversary_combined_loss
self.total_loss = adv.total_loss
self.predictor_train_op = adv.predictor_train_op
self.adversary_train_op = adv.adversary_train_op
self.joint_step = adv.joint_step
self.joint_train_op = adv.joint_train_op
self.MI_logits_theta = tf.constant(0.0)
else:
X_bkg = tf.boolean_mask(X, tf.cast((1-self.labels), tf.bool))
# Calculate mutual information
with tf.variable_scope('MINE') as scope:
Z_prime = tf.random_shuffle(tf.squeeze(self.pivots[:,1]))
Z_bkg = tf.boolean_mask(Z, tf.cast((1-self.labels), tf.bool))
Z_prime_bkg = tf.random_shuffle(Z_bkg)
if config.bkg_only:
(x_joint, x_marginal), (joint_f, marginal_f), self.MI_logits_theta = Network.MINE(x=X_bkg, y=Z_bkg, y_prime=Z_prime_bkg,
batch_size=config.batch_size, dimension=2, training=True, actv=tf.nn.elu, labels=self.labels, bkg_only=True,
jensen_shannon=config.JSD, apply_sn=config.spectral_norm)
else:
(x_joint, x_marginal), (joint_f, marginal_f), self.MI_logits_theta = Network.MINE(x=X, y=Z, y_prime=Z_prime,
batch_size=config.batch_size, dimension=2, training=True, actv=tf.nn.elu, labels=self.labels, bkg_only=False,
jensen_shannon=config.JSD, apply_sn=config.spectral_norm)
theta_MINE = Utils.scope_variables('MINE')
theta_MINE_NY = Utils.scope_variables('LABEL_MINE')
Utils.get_parameter_overview(theta_f, 'CLASSIFIER PARAMETERS')
Utils.get_parameter_overview(theta_MINE, 'MI-EST PARAMETERS')
Utils.get_parameter_overview(theta_MINE_NY, 'MI-EST PARAMETERS (LABELS)')
with tf.control_dependencies(update_ops):
# Ensures that we execute the update_ops before performing the train_step
self.MINE_lower_bound = self.MI_logits_theta
self.MINE_labels_lower_bound = self.MI_logits_labels_MINE
if config.jsd_regularizer and config.JSD:
print('Using Jensen-Shannon regularizer')
joint_logit_grads = tf.gradients(self.joint_f, self.x_joint)[0]
marginal_logit_grads = tf.gradients(self.marginal_f, self.x_marginal)[0]
gamma_0, alpha_0 = 2.0, 0.1
gamma = tf.train.exponential_decay(gamma_0, decay_rate=alpha_0, global_step=self.global_step,
decay_steps=10**6)
self.jsd_regularizer = tf.reduce_mean((1.0 - tf.nn.sigmoid(self.joint_f))**2 * tf.square(joint_logit_grads)) + tf.reduce_mean( tf.nn.sigmoid(self.marginal_f)**2 * tf.square(marginal_logit_grads))
if config.mutual_information_penalty:
print('Penalizing mutual information')
if config.jsd_regularizer and config.JSD:
self.cost += config.MI_lambda * (tf.nn.relu(self.MINE_lower_bound) - config.gamma/2.0 *
self.jsd_regularizer)
else:
# Alternative cost
# (Naive) Minimize log(1 - D(E(x),z)) for (x,z) ~ marginals
E_update_1 = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=marginal_f, labels=tf.zeros_like(marginal_f)))
# Maximize log(D(E(x),z)) for (x,z) ~ marginals
E_update_2 = - tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=marginal_f, labels=tf.ones_like(marginal_f)))
self.NS_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=marginal_f, labels=tf.ones_like(marginal_f)))
# Minimize log(D(E(x),z)) for (x,z) ~ joint
E_update_3 = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=joint_f, labels=tf.zeros_like(joint_f)))
# sum of update 2 and 3
alt_update = E_update_2 + E_update_3 # maximize this
# kl update: Minimize E_{p(x,y)} [ \log D(x,z) / 1 - D(x,z) ]
log_D_xz = - tf.nn.sigmoid_cross_entropy_with_logits(logits=joint_f, labels=tf.ones_like(marginal_f))
log_D_xz_c = tf.nn.sigmoid_cross_entropy_with_logits(logits=joint_f, labels=tf.zeros_like(marginal_f))
kl_update = tf.reduce_mean(log_D_xz + log_D_xz_c)
if config.heuristic:
self.cost += config.MI_lambda * tf.nn.relu(-E_update_2)
elif config.combined:
self.cost += config.MI_lambda * tf.nn.relu(self.MINE_lower_bound - E_update_2)
elif config.kl_update:
self.cost += config.MI_lambda * kl_update
else:
# 'Minmax' cost
self.cost += config.MI_lambda * tf.nn.relu(self.MINE_lower_bound)
self.grad_loss = tf.get_variable(name='grad_loss', shape=[], trainable=False)
self.grads = self.opt.compute_gradients(self.MI_logits_theta, grad_loss=self.grad_loss)
self.opt_op = self.opt.minimize(self.cost, global_step=self.global_step, var_list=theta_f)
MINE_opt = tf.train.AdamOptimizer(config.MINE_learning_rate)
self.MINE_opt_op = MINE_opt.minimize(-self.MINE_lower_bound, var_list=theta_MINE,
global_step=self.MINE_step)
self.MINE_labels_opt_op = MINE_opt.minimize(-self.MINE_labels_lower_bound, var_list=theta_MINE_NY)
self.MINE_ema = tf.train.ExponentialMovingAverage(decay=0.95, num_updates=self.MINE_step)
maintain_averages_clf_op = self.ema.apply(theta_f)
maintain_averages_MINE_op = self.MINE_ema.apply(theta_MINE)
maintain_averages_MINE_labels_op = self.ema.apply(theta_MINE_NY)
with tf.control_dependencies(update_ops+[self.opt_op]):
self.train_op = tf.group(maintain_averages_clf_op)
with tf.control_dependencies(update_ops+[self.MINE_opt_op]):
self.MINE_train_op = tf.group(maintain_averages_MINE_op)
with tf.control_dependencies(update_ops+[self.MINE_labels_opt_op]):
self.MINE_labels_train_op = tf.group(maintain_averages_MINE_labels_op)
self.str_accuracy, self.update_accuracy = tf.metrics.accuracy(self.labels, self.pred)
correct_prediction = tf.equal(self.labels, tf.cast(self.pred, tf.int32))
_, self.auc_op = tf.metrics.auc(predictions=self.pred, labels=self.labels, num_thresholds=2048)
self.accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
tf.summary.scalar('accuracy', self.accuracy)
tf.summary.scalar('cost', self.cost)
tf.summary.scalar('auc', self.auc_op)
tf.summary.scalar('logits_theta_MI', self.MI_logits_theta_kraskov)
tf.summary.scalar('xent_theta_MI', self.MI_xent_theta_kraskov)
tf.summary.scalar('logits_labels_MI', self.MI_logits_labels_kraskov)
self.merge_op = tf.summary.merge_all()
self.train_writer = tf.summary.FileWriter(
os.path.join(directories.tensorboard, '{}_train_{}'.format(config.name, time.strftime('%d-%m_%I:%M'))), graph=tf.get_default_graph())
self.test_writer = tf.summary.FileWriter(
os.path.join(directories.tensorboard, '{}_test_{}'.format(config.name, time.strftime('%d-%m_%I:%M'))))