Basic-SRGNN-Demo.zip

import tensorflow as tf import math class Model(object): def __init__(self, hidden_size=100, out_size=100, batch_size=100, nonhybrid=True): self.hidden_size = hidden_size self.out_size = out_size self.batch_size = batch_size self.mask = tf.placeholder(dtype=tf.float32) self.alias = tf.placeholder(dtype=tf.int32) # 给给每个输入重新 self.item = tf.placeholder(dtype=tf.int32) # 重新编号的序列构成的矩阵 self.tar = tf.placeholder(dtype=tf.int32) self.nonhybrid = nonhybrid self.stdv = 1.0 / math.sqrt(self.hidden_size) self.nasr_w1 = tf.get_variable('nasr_w1', [self.out_size, self.out_size], dtype=tf.float32, initializer=tf.random_uniform_initializer(-self.stdv, self.stdv)) self.nasr_w2 = tf.get_variable('nasr_w2', [self.out_size, self.out_size], dtype=tf.float32, initializer=tf.random_uniform_initializer(-self.stdv, self.stdv)) self.nasr_v = tf.get_variable('nasrv', [1, self.out_size], dtype=tf.float32, initializer=tf.random_uniform_initializer(-self.stdv, self.stdv)) self.nasr_b = tf.get_variable('nasr_b', [self.out_size], dtype=tf.float32, initializer=tf.zeros_initializer()) def forward(self, re_embedding, train=True): rm = tf.reduce_sum(self.mask, 1) # 序列中有效点击的个数 last_id = tf.gather_nd(self.alias, tf.stack([tf.range(self.batch_size), tf.to_int32(rm)-1], axis=1)) last_h = tf.gather_nd(re_embedding, tf.stack([tf.range(self.batch_size), last_id], axis=1)) seq_h = tf.stack([tf.nn.embedding_lookup(re_embedding[i], self.alias[i]) for i in range(self.batch_size)], axis=0) #batch_size*T*d last = tf.matmul(last_h, self.nasr_w1) seq = tf.matmul(tf.reshape(seq_h, [-1, self.out_size]), self.nasr_w2) last = tf.reshape(last, [self.batch_size, 1, -1]) m = tf.nn.sigmoid(last + tf.reshape(seq, [self.batch_size, -1, self.out_size]) + self.nasr_b) coef = tf.matmul(tf.reshape(m, [-1, self.out_size]), self.nasr_v, transpose_b=True) * tf.reshape( self.mask, [-1, 1]) b = self.embedding[1:] if not self.nonhybrid: ma = tf.concat([tf.reduce_sum(tf.reshape(coef, [self.batch_size, -1, 1]) * seq_h, 1), tf.reshape(last, [-1, self.out_size])], -1) self.B = tf.get_variable('B', [2 * self.out_size, self.out_size], initializer=tf.random_uniform_initializer(-self.stdv, self.stdv)) y1 = tf.matmul(ma, self.B) logits = tf.matmul(y1, b, transpose_b=True) else: ma = tf.reduce_sum(tf.reshape(coef, [self.batch_size, -1, 1]) * seq_h, 1) logits = tf.matmul(ma, b, transpose_b=True) loss = tf.reduce_mean( tf.nn.sparse_softmax_cross_entropy_with_logits(labels=self.tar - 1, logits=logits)) self.vars = tf.trainable_variables() if train: lossL2 = tf.add_n([tf.nn.l2_loss(v) for v in self.vars if v.name not in ['bias', 'gamma', 'b', 'g', 'beta']]) * self.L2 loss = loss + lossL2 return loss, logits def run(self, fetches, tar, item, adj_in, adj_out, alias, mask): return self.sess.run(fetches, feed_dict={self.tar: tar, self.item: item, self.adj_in: adj_in, self.adj_out: adj_out, self.alias: alias, self.mask: mask}) class GGNN(Model): def __init__(self,hidden_size=100, out_size=100, batch_size=300, n_node=None, lr=None, l2=None, step=1, decay=None, lr_dc=0.1, nonhybrid=False): super(GGNN,self).__init__(hidden_size, out_size, batch_size, nonhybrid) self.embedding = tf.get_variable(shape=[n_node, hidden_size], name='embedding', dtype=tf.float32, initializer=tf.random_uniform_initializer(-self.stdv, self.stdv)) self.adj_in = tf.placeholder(dtype=tf.float32, shape=[self.batch_size, None, None]) self.adj_out = tf.placeholder(dtype=tf.float32, shape=[self.batch_size, None, None]) self.n_node = n_node self.L2 = l2 self.step = step self.nonhybrid = nonhybrid self.W_in = tf.get_variable('W_in', shape=[self.out_size, self.out_size], dtype=tf.float32, initializer=tf.random_uniform_initializer(-self.stdv, self.stdv)) self.b_in = tf.get_variable('b_in', [self.out_size], dtype=tf.float32, initializer=tf.random_uniform_initializer(-self.stdv, self.stdv)) self.W_out = tf.get_variable('W_out', [self.out_size, self.out_size], dtype=tf.float32, initializer=tf.random_uniform_initializer(-self.stdv, self.stdv)) self.b_out = tf.get_variable('b_out', [self.out_size], dtype=tf.float32, initializer=tf.random_uniform_initializer(-self.stdv, self.stdv)) with tf.variable_scope('ggnn_model', reuse=None): self.loss_train, _ = self.forward(self.ggnn()) with tf.variable_scope('ggnn_model', reuse=True): self.loss_test, self.score_test = self.forward(self.ggnn(), train=False) self.global_step = tf.Variable(0) self.learning_rate = tf.train.exponential_decay(lr, global_step=self.global_step, decay_steps=decay, decay_rate=lr_dc, staircase=True) self.opt = tf.train.AdamOptimizer(self.learning_rate).minimize(self.loss_train, global_step=self.global_step) gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.8) config = tf.ConfigProto(gpu_options=gpu_options) config.gpu_options.allow_growth = True self.sess = tf.Session(config=config) self.sess.run(tf.global_variables_initializer()) def ggnn(self): fin_state = tf.nn.embedding_lookup(self.embedding, self.item) cell = tf.nn.rnn_cell.GRUCell(self.out_size) with tf.variable_scope('gru'): for i in range(self.step): fin_state = tf.reshape(fin_state, [self.batch_size, -1, self.out_size]) fin_state_in = tf.reshape(tf.matmul(tf.reshape(fin_state, [-1, self.out_size]), self.W_in) + self.b_in, [self.batch_size, -1, self.out_size]) fin_state_out = tf.reshape(tf.matmul(tf.reshape(fin_state, [-1, self.out_size]), self.W_out) + self.b_out, [self.batch_size, -1, self.out_size]) av = tf.concat([tf.matmul(self.adj_in, fin_state_in), tf.matmul(self.adj_out, fin_state_out)], axis=-1) state_output, fin_state = \ tf.nn.dynamic_rnn(cell, tf.expand_dims(tf.reshape(av, [-1, 2*self.out_size]), axis=1), initial_state=tf.reshape(fin_state, [-1, self.out_size])) return tf.reshape(fin_state, [self.batch_size, -1, self.out_size])