deep learning学习笔记---MemN2N
摘要：MemN2N简单介绍
MemN2N
1. Summary
MemN2N is a generalization of RNN
1) The sentence in MemN2N is equivalent to the word in RNN;
2. Kernel Code
Build Model
defbuild_model(self):self.build_memory()self.W = tf.Variable(tf.random_normal([self.edim,self.nwords], stddev=self.init_std)) z = tf.matmul(self.hid[-1],self.W)self.loss = tf.nn.softmax_cross_entropy_with_logits(logits=z, labels=self.target)self.lr = tf.Variable(self.current_lr)self.opt = tf.train.GradientDescentOptimizer(self.lr) params = [self.A,self.B,self.C,self.T_A,self.T_B,self.W] grads_and_vars =self.opt.compute_gradients(self.loss,params) clipped_grads_and_vars = [(tf.clip_by_norm(gv[0],self.max_grad_norm), gv[1])forgvingrads_and_vars] inc =self.global_step.assign_add(1) with tf.control_dependencies([inc]):self.optim =self.opt.apply_gradients(clipped_grads_and_vars) tf.global_variables_initializer().run()self.saver = tf.train.Saver()
Build Memory
def build_memory(self):self.global_step = tf.Variable(0, name="global_step")self.A = tf.Variable(tf.random_normal([self.nwords,self.edim], stddev=self.init_std))self.B = tf.Variable(tf.random_normal([self.nwords,self.edim], stddev=self.init_std))self.C = tf.Variable(tf.random_normal([self.edim,self.edim], stddev=self.init_std))# Temporal Encodingself.T_A = tf.Variable(tf.random_normal([self.mem_size,self.edim], stddev=self.init_std))self.T_B = tf.Variable(tf.random_normal([self.mem_size,self.edim], stddev=self.init_std))# m_i = sum A_ij * x_ij + T_A_iAin_c = tf.nn.embedding_lookup(self.A,self.context) Ain_t = tf.nn.embedding_lookup(self.T_A,self.time) Ain= tf.add(Ain_c, Ain_t)# c_i = sum B_ij * u + T_B_iBin_c = tf.nn.embedding_lookup(self.B,self.context) Bin_t = tf.nn.embedding_lookup(self.T_B,self.time) Bin= tf.add(Bin_c, Bin_t)forhinxrange(self.nhop):self.hid3dim = tf.reshape(self.hid[-1], [-1,1,self.edim]) Aout= tf.matmul(self.hid3dim, Ain, adjoint_b=True) Aout2dim = tf.reshape(Aout, [-1,self.mem_size]) P = tf.nn.softmax(Aout2dim) probs3dim = tf.reshape(P, [-1,1,self.mem_size]) Bout= tf.matmul(probs3dim, Bin) Bout2dim = tf.reshape(Bout, [-1,self.edim]) Cout= tf.matmul(self.hid[-1],self.C) Dout= tf.add(Cout, Bout2dim)self.share_list[0].append(Cout) ifself.lindim ==self.edim:self.hid.append(Dout) elifself.lindim ==0:self.hid.append(tf.nn.relu(Dout)) else: F = tf.slice(Dout, [0,0], [self.batch_size,self.lindim]) G = tf.slice(Dout, [0,self.lindim], [self.batch_size,self.edim-self.lindim]) K = tf.nn.relu(G)self.hid.append(tf.concat(axis=1, values=[F, K]))
3. Reference
