tf.contrib.seq2seq.sequence_loss example:seqence loss 实例代码

#!/usr/bin/env python
# -*- coding: utf-8 -*-

import tensorflow as tf

import numpy as np

params=np.random.normal(loc=0.0,scale=1.0,size=[10,10])

encoder_inputs=tf.placeholder(dtype=tf.int32,shape=[10,10])
decoder_inputs=tf.placeholder(dtype=tf.int32,shape=[10,10])

logits=tf.placeholder(dtype=tf.float32,shape=[10,10,10])
targets=tf.placeholder(dtype=tf.int32,shape=[10,10])
weights=tf.placeholder(dtype=tf.float32,shape=[10,10])


train_encoder_inputs=np.ones(shape=[10,10],dtype=np.int32)
train_decoder_inputs=np.ones(shape=[10,10],dtype=np.int32)
train_weights=np.ones(shape=[10,10],dtype=np.float32)

num_encoder_symbols=10
num_decoder_symbols=10
embedding_size=10
cell=tf.nn.rnn_cell.BasicLSTMCell(10)

def seq2seq(encoder_inputs,decoder_inputs,cell,num_encoder_symbols,num_decoder_symbols,embedding_size):
    encoder_inputs = tf.unstack(encoder_inputs, axis=0)
    decoder_inputs = tf.unstack(decoder_inputs, axis=0)
    results,states=tf.contrib.legacy_seq2seq.embedding_rnn_seq2seq(
    encoder_inputs,
    decoder_inputs,
    cell,
    num_encoder_symbols,
    num_decoder_symbols,
    embedding_size,
    output_projection=None,
    feed_previous=False,
    dtype=None,
    scope=None
)
    return results

def get_loss(logits,targets,weights):
    loss=tf.contrib.seq2seq.sequence_loss(
        logits,
        targets=targets,
        weights=weights
    )
    return loss

results=seq2seq(encoder_inputs,decoder_inputs,cell,num_encoder_symbols,num_decoder_symbols,embedding_size)
logits=tf.stack(results,axis=0)
print(logits)
loss=get_loss(logits,targets,weights)

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    results_value=sess.run(results,feed_dict={encoder_inputs:train_encoder_inputs,decoder_inputs:train_decoder_inputs})
    print(type(results_value[0]))
    print(len(results_value))
    cost = sess.run(loss, feed_dict={encoder_inputs: train_encoder_inputs, targets: train_decoder_inputs,
                                     weights:train_weights,decoder_inputs:train_decoder_inputs})
    print(cost)

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