Associative Neural Network Code Implementation

I recently read a Correlational Neural Networks paper, which mainly uses the idea of ​​migration learning

The following is the simple implementation code

def Autoencoder(left_input,right_input,left_units=None, right_units=None, hidden_units=None):
    
    #Initialization    
    
    #weight initialization
    w_left =tf.Variable(tf.truncated_normal([left_units, hidden_units], stddev=0.1))
    b_left =tf.Variable(tf.random_normal([hidden_units]))
    w_right =tf.Variable(tf.truncated_normal([right_units,hidden_units], stddev =0.1))
    b_right =tf.Variable(tf.random_normal([hidden_units]))
    
    b = tf.Variable(tf.random_normal([hidden_units]))
    #
    left_w =tf.Variable(tf.truncated_normal([hidden_units, left_units], stddev=0.1))
    left_b =tf.Variable(tf.random_normal([left_units]))
    
    right_w =tf.Variable(tf.truncated_normal([hidden_units,right_units], stddev =0.1))
    right_b =tf.Variable(tf.random_normal([right_units]))
    
   
    #encoder
    #common= tf.matmul(left_input, w_left)+ tf.matmul(right_input, w_right)+b
    encoder = tf.nn.tanh(tf.add(tf.add(tf.matmul(left_input, w_left), tf.matmul(right_input, w_right)),b))
    
    #decoder
    # y_left =tf.matmul(encoder,left_w) + left_b
    #y_right =tf.matmul(encoder,right_w) + right_b
    
    left_decode =tf.nn.tanh(tf.add(tf.matmul(encoder,left_w),left_b))
    right_decode =tf.nn.tanh(tf.add(tf.matmul(encoder,right_w),right_b))
    
    return left_decode, right_decode

def model():
     
    left_input = tf.placeholder(tf.float32, [None, 41])
    
    right_input =tf.placeholder(tf.float32, [None, 21])
    
    left_decode, right_decode =Autoencoder(left_input,right_input,41,21,10)
    
    left_cost = 0.5*tf.reduce_sum(tf.pow(tf.subtract(left_input,left_decode),2.0))
    right_cost = 0.5*tf.reduce_sum(tf.pow(tf.subtract(right_input,right_decode),2.0))
    cost =left_cost+right_cost
    
    optimizer =tf.train.AdamOptimizer(0.05, 0.9, 0.999, 1e-5).minimize(cost)
    
    sex = tf.InteractiveSession ()
    
    sess.run( tf.global_variables_initializer())
    
    data=read_data_sets()
    
    for i in range(50000):
        batch = data.train.next_batch(10)
        if i % 100==0:
            c = sess.run([optimizer, cost],feed_dict={left_input:batch[0],right_input:batch[1]})
            print("cost=",c)
   
if __name__=="__main__":
    
    model()