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158 lines
9.0 KiB
158 lines
9.0 KiB
2 years ago
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import numpy as np
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import tensorflow as tf
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class OptimizerCycle(object):
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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, settings):
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preds_sub = preds
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labels_sub = labels
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self.real = d_real
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self.settings = settings
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# Discrimminator Loss
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self.dc_loss_real = tf.reduce_mean(
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tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(self.real), logits=self.real, name='dclreal'))
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# self.dc_loss_real = - tf.reduce_mean(self.real)
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self.dc_loss_fake = tf.reduce_mean(
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tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.zeros_like(d_fake), logits=d_fake, name='dcfake'))
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# self.dc_loss_fake = tf.reduce_mean(d_fake)
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# GP_loss = tf.reduce_mean(tf.square(tf.sqrt(tf.reduce_mean(tf.square(gradient), axis = [0, 1])) - 1))
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# GP_loss_z = tf.reduce_mean(tf.square(tf.sqrt(tf.reduce_mean(tf.square(gradient_z), axis = [0, 1])) - 1))
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# self.dc_loss = self.dc_loss_fake + self.dc_loss_real + 10.0 * GP_loss
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self.GD_loss_real = tf.reduce_mean(
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tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(GD_real), logits=GD_real, name='GD_real'))
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# self.GD_loss_real = - tf.reduce_mean(GD_real)
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self.GD_loss_fake = tf.reduce_mean(
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tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.zeros_like(GD_fake), logits=GD_fake, name='GD_fake'))
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# self.GD_loss_fake = tf.reduce_mean(GD_fake)
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self.dc_loss = self.dc_loss_fake + self.dc_loss_real
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self.GD_loss = self.GD_loss_fake + self.GD_loss_real
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# Generator loss
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generator_loss = tf.reduce_mean(
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tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(d_fake), logits=d_fake, name='gl'))
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# generator_loss = -self.dc_loss_fake
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generator_loss_z2g = tf.reduce_mean(
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tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(GD_fake), logits=GD_fake, name='G_z2g'))
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# generator_loss_z2g = -self.GD_loss_fake
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# 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
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self.cost = norm * tf.reduce_mean(tf.nn.weighted_cross_entropy_with_logits(logits=preds_sub, targets=labels_sub, pos_weight=pos_weight))
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cost_cycle = norm * tf.reduce_mean(tf.square(preds_cycle - labels_cycle))
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cost_z2g = norm * tf.reduce_mean(tf.square(preds_z2g - labels_z2g))
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self.cost = self.cost + cost_cycle
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self.generator_loss = generator_loss + self.cost
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self.generator_loss_z2g = generator_loss_z2g
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all_variables = tf.trainable_variables()
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dc_var = [var for var in all_variables if 'dc_' in var.name]
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en_var = [var for var in all_variables if 'e_' in var.name]
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GG_var = [var for var in all_variables if 'GG' in var.name]
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GD_var = [var for var in all_variables if 'GD' in var.name]
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with tf.variable_scope(tf.get_variable_scope()):
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self.discriminator_optimizer = tf.train.AdamOptimizer(learning_rate=self.settings.discriminator_learning_rate,
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beta1=0.9, name='adam1').minimize(self.dc_loss,
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var_list=dc_var) # minimize(dc_loss_real, var_list=dc_var)
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self.generator_optimizer = tf.train.AdamOptimizer(learning_rate=self.settings.discriminator_learning_rate,
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beta1=0.9, name='adam2').minimize(self.generator_loss, var_list=en_var)
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self.discriminator_optimizer_z2g = tf.train.AdamOptimizer(learning_rate=self.settings.discriminator_learning_rate,
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beta1=0.9, name='adam1').minimize(self.GD_loss, var_list=GD_var)
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self.generator_optimizer_z2g = tf.train.AdamOptimizer(learning_rate=self.settings.discriminator_learning_rate,
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beta1=0.9, name='adam2').minimize(self.generator_loss_z2g, var_list=GG_var)
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# 值得注意的是，这个地方，除了对抗优化之外，
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# 还单纯用cost损失又优化了一遍，
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# 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
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self.optimizer = tf.train.AdamOptimizer(learning_rate=self.settings.learning_rate) # Adam Optimizer
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self.opt_op = self.optimizer.minimize(self.cost)
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# self.grads_vars = self.optimizer.compute_gradients(self.cost)
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# self.optimizer_z2g = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate) # Adam Optimizer
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# self.opt_op_z2g = self.optimizer.minimize(cost_z2g)
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# self.grads_vars_z2g = self.optimizer.compute_gradients(cost_z2g)
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class Optimizer(object):
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def __init__(self, model, model_z2g, D_Graph, discriminator, placeholders, pos_weight, norm, d_real, num_nodes, GD_real, settings):
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self.opt = self.construct_optimizer(model, model_z2g, D_Graph, discriminator, placeholders, pos_weight, norm, d_real, num_nodes, GD_real, settings)
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def construct_optimizer(self, model, model_z2g, D_Graph, discriminator, placeholders, pos_weight, norm, d_real, num_nodes, GD_real, settings):
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z2g = model_z2g.construct()
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hidden = z2g[1]
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z2g = z2g[0]
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preds_z2g = model.construct(hidden=hidden, reuse=True)[0]
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g2z = model.construct()
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embeddings = g2z[0]
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reconstructions = g2z[1]
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d_fake = discriminator.construct(embeddings, reuse=True)
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GD_fake = D_Graph.construct(z2g, reuse=True)
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epsilon = tf.random_uniform(shape=[1], minval=0.0, maxval=1.0)
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interpolated_input = epsilon * placeholders['real_distribution'] + (1 - epsilon) * embeddings
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gradient = tf.gradients(discriminator.construct(interpolated_input, reuse=True), [interpolated_input])[0]
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epsilon = tf.random_uniform(shape=[1], minval=0.0, maxval=1.0)
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interpolated_input = epsilon * placeholders['features_dense'] + (1 - epsilon) * z2g
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gradient_z = tf.gradients(D_Graph.construct(interpolated_input, reuse=True), [interpolated_input])[0]
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opt = OptimizerCycle(preds=reconstructions,
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labels=tf.reshape(tf.sparse_tensor_to_dense(placeholders['adj_orig'], validate_indices=False), [-1]),
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pos_weight=pos_weight,
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norm=norm,
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d_real=d_real,
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d_fake=d_fake,
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GD_real=GD_real,
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GD_fake=GD_fake,
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preds_z2g=preds_z2g,
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labels_z2g=placeholders['real_distribution'],
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preds_cycle=model_z2g.construct(embeddings, reuse=True)[0],
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labels_cycle=placeholders['features_dense'],
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gradient=gradient,
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gradient_z=gradient_z,
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settings=settings)
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return opt
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def construct_feed_dict(adj_normalized, adj, features, placeholders):
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# construct feed dictionary
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# .update()用法就是将()内的字段增加到dict当中
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feed_dict = dict() # 创建一个空字典
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feed_dict.update({placeholders['features']: features})
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feed_dict.update({placeholders['adj']: adj_normalized})
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feed_dict.update({placeholders['adj_orig']: adj})
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return feed_dict
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def update(model, opt, sess, adj_norm, adj_label, features, placeholders, adj, distribution, adj_dense, settings):
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# Construct feed dictionary
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feed_dict = construct_feed_dict(adj_norm, adj_label, features, placeholders)
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feed_dict.update({placeholders['dropout']: settings.dropout})
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feed_dict.update({placeholders['features_dense']: adj_dense})
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feed_dict.update({placeholders['dropout']: 0})
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z_real_dist = np.random.randn(adj.shape[0], settings.hidden2)
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z_real_dist = distribution.sample(adj.shape[0])
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feed_dict.update({placeholders['real_distribution']: z_real_dist})
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for j in range(5):
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_, reconstruct_loss = sess.run([opt.opt_op, opt.cost], feed_dict=feed_dict)
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g_loss, _ = sess.run([opt.generator_loss, opt.generator_optimizer], feed_dict=feed_dict)
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d_loss, _ = sess.run([opt.dc_loss, opt.discriminator_optimizer], feed_dict=feed_dict)
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GD_loss, _ = sess.run([opt.GD_loss, opt.discriminator_optimizer_z2g], feed_dict=feed_dict)
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GG_loss, _ = sess.run([opt.generator_loss_z2g, opt.generator_optimizer_z2g], feed_dict=feed_dict)
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# GD_loss = sess.run(opt.GD_loss, feed_dict=feed_dict)
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# GG_loss = sess.run(opt.generator_loss_z2g, feed_dict=feed_dict)
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# g_loss = sess.run(opt.generator_loss, feed_dict=feed_dict)
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# d_loss = sess.run(opt.dc_loss, feed_dict=feed_dict)
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emb = sess.run(model.z_mean, feed_dict=feed_dict)
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avg_cost = [reconstruct_loss, d_loss, g_loss, GD_loss, GG_loss]
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return emb, avg_cost
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