import tensorflow as tf flags = tf.app.flags FLAGS = flags.FLAGS class OptimizerAE(object): def __init__(self, preds, labels, pos_weight, norm, d_real, d_fake): preds_sub = preds labels_sub = labels self.real = d_real # Discrimminator Loss # self.dc_loss_real = tf.reduce_mean( # tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(self.real), logits=self.real,name='dclreal')) self.dc_loss_real = - tf.reduce_mean(self.real) # self.dc_loss_fake = tf.reduce_mean( # tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.zeros_like(d_fake), logits=d_fake,name='dcfake')) self.dc_loss_fake = tf.reduce_mean(d_fake) GP_loss = tf.reduce_mean(tf.square(tf.sqrt(tf.reduce_mean(tf.square(gradient), axis=[0, 1])) - 1)) self.dc_loss = self.dc_loss_fake + self.dc_loss_real + GP_loss # self.dc_loss = self.dc_loss_fake + self.dc_loss_real # Generator loss # generator_loss = tf.reduce_mean( # tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(d_fake), logits=d_fake, name='gl')) generator_loss = -self.dc_loss_fake # pos_weight，允许人们通过向上或向下加权相对于负误差的正误差的成本来权衡召回率和精确度 self.cost = norm * tf.reduce_mean(tf.nn.weighted_cross_entropy_with_logits(logits=preds_sub, targets=labels_sub, pos_weight=pos_weight)) self.generator_loss = generator_loss + self.cost all_variables = tf.trainable_variables() dc_var = [var for var in all_variables if 'dc_' in var.name] en_var = [var for var in all_variables if 'e_' in var.name] with tf.variable_scope(tf.get_variable_scope()): self.discriminator_optimizer = tf.train.AdamOptimizer(learning_rate=FLAGS.discriminator_learning_rate, beta1=0.9, name='adam1').minimize(self.dc_loss, var_list=dc_var) # minimize(dc_loss_real, var_list=dc_var) self.generator_optimizer = tf.train.AdamOptimizer(learning_rate=FLAGS.discriminator_learning_rate, beta1=0.9, name='adam2').minimize(self.generator_loss, var_list=en_var) # 值得注意的是，这个地方，除了对抗优化之外， # 还单纯用cost损失又优化了一遍， # 待会儿看训练的时候注意看是在哪部分进行的这部分优化操作 self.optimizer = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate) # Adam Optimizer self.opt_op = self.optimizer.minimize(self.cost) self.grads_vars = self.optimizer.compute_gradients(self.cost) class OptimizerCycle(object): def __init__(self, preds, labels, pos_weight, norm, d_real, d_fake, GD_real, GD_fake, preds_z2g, labels_z2g, preds_cycle, labels_cycle, gradient, gradient_z): preds_sub = preds labels_sub = labels self.real = d_real # Discrimminator Loss self.dc_loss_real = tf.reduce_mean( tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(self.real), logits=self.real, name='dclreal')) # self.dc_loss_real = - tf.reduce_mean(self.real) self.dc_loss_fake = tf.reduce_mean( tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.zeros_like(d_fake), logits=d_fake, name='dcfake')) # self.dc_loss_fake = tf.reduce_mean(d_fake) # GP_loss = tf.reduce_mean(tf.square(tf.sqrt(tf.reduce_mean(tf.square(gradient), axis = [0, 1])) - 1)) # GP_loss_z = tf.reduce_mean(tf.square(tf.sqrt(tf.reduce_mean(tf.square(gradient_z), axis = [0, 1])) - 1)) # self.dc_loss = self.dc_loss_fake + self.dc_loss_real + 10.0 * GP_loss self.GD_loss_real = tf.reduce_mean( tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(GD_real), logits=GD_real, name='GD_real')) # self.GD_loss_real = - tf.reduce_mean(GD_real) self.GD_loss_fake = tf.reduce_mean( tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.zeros_like(GD_fake), logits=GD_fake, name='GD_fake')) # self.GD_loss_fake = tf.reduce_mean(GD_fake) self.dc_loss = self.dc_loss_fake + self.dc_loss_real self.GD_loss = self.GD_loss_fake + self.GD_loss_real # Generator loss generator_loss = tf.reduce_mean( tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(d_fake), logits=d_fake, name='gl')) # generator_loss = -self.dc_loss_fake generator_loss_z2g = tf.reduce_mean( tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(GD_fake), logits=GD_fake, name='G_z2g')) # generator_loss_z2g = -self.GD_loss_fake # pos_weight，允许人们通过向上或向下加权相对于负误差的正误差的成本来权衡召回率和精确度 self.cost = norm * tf.reduce_mean(tf.nn.weighted_cross_entropy_with_logits(logits=preds_sub, targets=labels_sub, pos_weight=pos_weight)) cost_cycle = norm * tf.reduce_mean(tf.square(preds_cycle - labels_cycle)) cost_z2g = norm * tf.reduce_mean(tf.square(preds_z2g - labels_z2g)) # with tf.device("/gpu:1"): # self.cost = 0.00001*self.cost + cost_cycle #for citseer cluster self.cost = self.cost + cost_cycle self.generator_loss = generator_loss + self.cost self.generator_loss_z2g = generator_loss_z2g all_variables = tf.trainable_variables() dc_var = [var for var in all_variables if 'dc_' in var.name] en_var = [var for var in all_variables if 'e_' in var.name] GG_var = [var for var in all_variables if 'GG' in var.name] GD_var = [var for var in all_variables if 'GD' in var.name] with tf.variable_scope(tf.get_variable_scope()): self.discriminator_optimizer = tf.train.AdamOptimizer(learning_rate=FLAGS.discriminator_learning_rate, beta1=0.9, name='adam1').minimize(self.dc_loss, var_list=dc_var) # minimize(dc_loss_real, var_list=dc_var) self.generator_optimizer = tf.train.AdamOptimizer(learning_rate=FLAGS.discriminator_learning_rate, beta1=0.9, name='adam2').minimize(self.generator_loss, var_list=en_var) self.discriminator_optimizer_z2g = tf.train.AdamOptimizer(learning_rate=FLAGS.discriminator_learning_rate, beta1=0.9, name='adam1').minimize(self.GD_loss, var_list=GD_var) self.generator_optimizer_z2g = tf.train.AdamOptimizer(learning_rate=FLAGS.discriminator_learning_rate, beta1=0.9, name='adam2').minimize(self.generator_loss_z2g, var_list=GG_var) # 值得注意的是，这个地方，除了对抗优化之外， # 还单纯用cost损失又优化了一遍， # 待会儿看训练的时候注意看是在哪部分进行的这部分优化操作 self.optimizer = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate) # Adam Optimizer self.opt_op = self.optimizer.minimize(self.cost) # self.grads_vars = self.optimizer.compute_gradients(self.cost) # self.optimizer_z2g = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate) # Adam Optimizer # self.opt_op_z2g = self.optimizer.minimize(cost_z2g) # self.grads_vars_z2g = self.optimizer.compute_gradients(cost_z2g)