Issues about codes？

[Yuzhou96]

Hi， Dr. Guo.I’m confused when I run your codes, there are several details I want to consult you.

Firstly, the class 'emb_classifier' in 'main_multiclass.py' as follows, why you only calculate the most relevant class's embedding in multi_class and what is the significance of y_emb, which you didn't use in following codes?

​    y_pos = tf.argmax(y, -1) #？
    y_emb, W_class = embedding_class(y_pos, opt, 'class_emb') # b * e, c * e
    y_emb=tf.cast(y_emb,tf.float32)
    W_class=tf.cast(W_class,tf.float32) # c * e
    W_class_tran = tf.transpose(W_class, [1,0]) # e * c

​

Secondly, in the class of 'att_emb_ngram_encoder_cnn' in 'model.py', the input of convolution you designed here absolutely impossible to get the output size of 'b * s * c' and is not the same with your paper. What is your thinking of here for this convolution and the detailed design for padding, kernel, and output?

    x_emb_0 = tf.squeeze(x_emb,) # b * s * e
    x_emb_1 = tf.multiply(x_emb_0, x_mask) # b * s * e

   H = tf.contrib.layers.conv2d(x_emb_0, num_outputs=opt.embed_size,kernel_size=[10], padding='SAME',activation_fn=tf.nn.relu) #b * s *  c

​Thirdly, why you only calculate the most relevant class as accuracy in your 'main_multiclass.py' as follow? Shouldn't it calculate the whole classes?

​correct_prediction = tf.equal(tf.argmax(prob, 1), tf.argmax(y, 1))

​