tensorflow中的sess.run()方法详解

run()方法详解链接

feed_dict={y:3}

把3赋值给y，当次计算有效。

在lstm和cnn进行train和predict

def run_step(self, sess, is_train, batch):
        """
        :param sess: session to run the batch
        :param is_train: a flag indicate if it is a train batch
        :param batch: a dict containing batch data
        :return: batch result, loss of the batch or logits
        """
        feed_dict = self.create_feed_dict(is_train, batch)
        if is_train:
            global_step, loss, _ = sess.run(
                [self.global_step, self.loss, self.train_op],
                feed_dict)
            return global_step, loss
        else:
            lengths, logits = sess.run([self.lengths, self.logits], feed_dict)
            return lengths, logits

训练的时候图全部计算而预测的时候图不用计算损失值

def predict(self, sent):
        """
        Evaluates model on a dev set
        """
        x_list = [data_helpers.preprocess(sent)]
        x = np.array(list(self.vocab_processor.fit_transform(x_list)))
        x_batch = (x)
        feed_dict = {
            self.cnn.input_x: x_batch,
            self.cnn.dropout_keep_prob: 1.0
        }
        prediction, softmax_scores = self.sess.run(
            [self.cnn.predictions, self.cnn.softmax_scores],
            feed_dict)
        score = max(softmax_scores[0])
        intent = label_dict.get(str(prediction[0]))
        # print(intent)
        return intent, score

def train_step(x_batch, y_batch):
                """
                A single training step
                """
                feed_dict = {
                    cnn.input_x: x_batch,
                    cnn.input_y: y_batch,
                    cnn.dropout_keep_prob: FLAGS.dropout_keep_prob
                }
                _, step, summaries, loss, accuracy = sess.run(
                    [train_op, global_step, train_summary_op, cnn.loss, cnn.accuracy],
                    feed_dict)
                time_str = datetime.datetime.now().isoformat()
                print("{}: step {}, loss {:g}, acc {:g}".format(time_str, step, loss, accuracy))
                train_summary_writer.add_summary(summaries, step)

import tensorflow as tf
y = tf.placeholder(dtype=tf.float32)
b = tf.Variable(1.0,dtype=tf.float32)
new_val = tf.add(y, b)
update = tf.assign(b, new_val)
init = tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init)
    for _ in range(3):
        print(sess.run(update,feed_dict={y:3}))
    # print(sess.run(y))

lstm和cnn训练的时候，就可以通过把b设置为变量，y设置为输入。就可以循环训练参数b。达到训练模型的目的。