cfeef8cb854a24371f0ef5d7d9c68f23675c97ee,a2_decoder.py,,init,#,91

Before Change

    Embedding = tf.get_variable("Embedding_d", shape=[vocab_size, embed_size], initializer=initializer)
    decoder_input_x = tf.placeholder(tf.int32, [batch_size, decoder_sent_length], name="input_x")  // [4,10]
    print("1.decoder_input_x:", decoder_input_x)
    decoder_input_x_ = tf.reshape(decoder_input_x, (batch_size * decoder_sent_length,))  // [batch_size*sequence_length]
    embedded_words = tf.nn.embedding_lookup(Embedding, decoder_input_x_)  // [batch_size*sequence_length,embed_size]
    Q = embedded_words  // [batch_size*sequence_length,embed_size]
    K_s = embedded_words  // [batch_size*sequence_length,embed_size]
    K_v_encoder = tf.placeholder(tf.float32, [batch_size * sequence_length, d_model], name="input_x") //sequence_length
    print("2.output from encoder:",K_v_encoder)

After Change

    K_s=decoder_input_embedding
    K_v_encoder= tf.get_variable("v_variable",shape=[batch_size,decoder_sent_length, d_model],initializer=initializer) //tf.float32,
    print("2.output from encoder:",K_v_encoder)
    mask = get_mask(decoder_sent_length) //sequence_length
    decoder = Decoder(d_model, d_k, d_v, sequence_length, h, batch_size, Q, K_s, K_v_encoder,decoder_sent_length,mask=mask,num_layer=num_layer)
    return decoder,Q, K_s

Instances