siamese_lstm_network.py 文件源码

def buildRNN(self,x,scope):
        print(x)
        x = tf.transpose(x, [1, 0, 2])        
        #print(x)
        x = tf.reshape(x, [-1,self.nfeatures])
        #print(x)
        x = tf.split(x, self.n_steps, 0)
        print(x)
        #lstm_cell = rnn.MultiRNNCell([rnn.BasicLSTMCell(self.n_hidden, forget_bias=1.0) for _ in range(self.n_layers)], state_is_tuple=True)
        #outputs, states = tf.nn.dynamic_rnn(lstm_cell, x, dtype=tf.float64)
        with tf.name_scope("fw"+scope),tf.variable_scope("fw"+scope):
            fw_cell_array = []
            print(tf.get_variable_scope().name)
            for _ in range(self.n_layers):
                fw_cell = rnn.BasicLSTMCell(self.n_hidden, forget_bias=1.0, state_is_tuple=True)
                #fw_cell = rnn.DropoutWrapper(fw_cell,output_keep_prob=self.dropout)                
                fw_cell_array.append(fw_cell)
            fw_cell = rnn.MultiRNNCell(fw_cell_array, state_is_tuple=True)
        with tf.name_scope("bw"+scope),tf.variable_scope("bw"+scope):
            bw_cell_array = []
            print(tf.get_variable_scope().name)
            for _ in range(self.n_layers):
                bw_cell = rnn.BasicLSTMCell(self.n_hidden, forget_bias=1.0, state_is_tuple=True)
                #bw_cell = rnn.DropoutWrapper(bw_cell,output_keep_prob=self.dropout)
                bw_cell_array.append(bw_cell)
            bw_cell = rnn.MultiRNNCell(bw_cell_array, state_is_tuple=True)

        outputs, _,_ = tf.contrib.rnn.static_bidirectional_rnn(fw_cell, bw_cell, x, dtype=tf.float64)
        #outputs, = tf.nn.bidirectional_dynamic_rnn(fw_cell, bw_cell, x, dtype=tf.float64)


        print(outputs)
        print(outputs[-1])

        return outputs[-1]