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

def build_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]) for gv in grads_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 Encoding    self.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_i    Ain_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_i    Bin_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)    for h in xrange(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)        if self.lindim == self.edim:            self.hid.append(Dout)        elif self.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
Blog: Memory-network

Github: MemN2N-tensorflow

Paper: End-To-End Memory Networks

原文链接