cap2vid_without_cnn.py 文件源码

def align(hid_align, h_dec, scope):
    h_dec_align = linear3(h_dec, dim_align, "h_dec_align_"+scope) #batch_size x dimAlign
    h_dec_align = tf.reshape(h_dec_align,[batch_size,1,dim_align])
    h_dec_align_tiled = tf.tile(h_dec_align, [1, sentence_length, 1])
    all_align = tf.tanh(h_dec_align + hid_align) 

    with tf.variable_scope("v_align_"+scope, reuse = DO_SHARE):
        v_align=tf.get_variable("v_align_"+scope, [dim_align], initializer=tf.constant_initializer(0.0))
    e_t = all_align * v_align
    e_t = tf.reduce_sum(e_t, 2)

    # normalise
    alpha = tf.nn.softmax(e_t) # batch_size x sentence_length
    alpha_t = tf.reshape(alpha, [batch_size, sentence_length, 1])
    alpha_tile = tf.tile(alpha_t, [1, 1, 2*y_enc_size])
    s_t = tf.multiply(alpha_tile, h_t_lang)

    s_t = tf.reduce_sum(s_t, 1)

    return s_t,alpha