TypeError: The value of a feed cannot be a tf.Tensor object. Acceptable feed values include Python s

一. 背景

今天继续完成“字嵌入+LSTM+CRF”实现分词的大业， 不过一上来又出现了新的问题。

二. 代码

Chars, vectors, labels = read(path)
vectors = vectors.reshape((batch_size, max_seq_len, 128))
labels = labels.reshape((batch_size, max_seq_len))

with tf.Graph().as_default() as g:
    inputs = tf.placeholder('float', [batch_size, max_seq_len, 128])
    tags = tf.placeholder('float', [batch_size, max_seq_len])
    model = Model(inputs, tags)
    initer = tf.global_variables_initializer()

with tf.Session(graph=g) as sess:
    sess.run(initer)
    train_batch_size = batch_size * 0.9
    test_batch_size = batch_size * 0.1
    # batch_size = train_batch_size
    for i in range(1000):
        print(type(vectors))
        print(type(labels))
        loss, accuracy, size, _ = sess.run([model.loss, model.accuracy, model.batch_size, model.optimizer], feed_dict={inputs : inputs, tags : labels})
        if i % 100 == 0:
            print('loss:', loss, 'accuracy', accuracy, 'size', size)

三. 错误 

以上代码报如下错误：

TypeError: The value of a feed cannot be a tf.Tensor object. Acceptable feed values include Python scalars, strings, lists, numpy ndarrays, or TensorHandles.For reference, the tensor object was Tensor("Placeholder:0", shape=(1000, 10, 128), dtype=float32) which was passed to the feed with key Tensor("Placeholder:0", shape=(1000, 10, 128), dtype=float32).

这是什么鬼，怎么会这样， 明明传进入的是np.ndarray啊。

四. 解决方案

后来仔细看了好久，原来是是这里没有写对：

 feed_dict={inputs : inputs, tags : labels}

改成如下就解决问题了：

 feed_dict={inputs : vectors, tags : labels}