deep learning学习笔记---MemN2N

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