Tensorflow:ValueError:形状必须为2级但为3级 [英] Tensorflow : ValueError: Shape must be rank 2 but is rank 3

问题描述

我是tensorflow的新手，我正在尝试将双向LSTM的一些代码从tensorflow的旧版本更新为最新版本(1.0)，但出现此错误:

I'm new to tensorflow and I'm trying to update some code for a bidirectional LSTM from an old version of tensorflow to the newest (1.0), but I get this error:

对于"MatMul_3"(操作:"MatMul")，形状必须为2级，但其排名为3级，输入形状为[100，?，400]，[400,2].

Shape must be rank 2 but is rank 3 for 'MatMul_3' (op: 'MatMul') with input shapes: [100,?,400], [400,2].

该错误发生在pred_mod上.

The error happens on pred_mod.

    _weights = {
    # Hidden layer weights => 2*n_hidden because of foward + backward cells
        'w_emb' : tf.Variable(0.2 * tf.random_uniform([max_features,FLAGS.embedding_dim], minval=-1.0, maxval=1.0, dtype=tf.float32),name='w_emb',trainable=False),
        'c_emb' : tf.Variable(0.2 * tf.random_uniform([3,FLAGS.embedding_dim],minval=-1.0, maxval=1.0, dtype=tf.float32),name='c_emb',trainable=True),
        't_emb' : tf.Variable(0.2 * tf.random_uniform([tag_voc_size,FLAGS.embedding_dim], minval=-1.0, maxval=1.0, dtype=tf.float32),name='t_emb',trainable=False),
        'hidden_w': tf.Variable(tf.random_normal([FLAGS.embedding_dim, 2*FLAGS.num_hidden])),
        'hidden_c': tf.Variable(tf.random_normal([FLAGS.embedding_dim, 2*FLAGS.num_hidden])),
        'hidden_t': tf.Variable(tf.random_normal([FLAGS.embedding_dim, 2*FLAGS.num_hidden])),
        'out_w': tf.Variable(tf.random_normal([2*FLAGS.num_hidden, FLAGS.num_classes]))}

    _biases = {
         'hidden_b': tf.Variable(tf.random_normal([2*FLAGS.num_hidden])),
         'out_b': tf.Variable(tf.random_normal([FLAGS.num_classes]))}


    #~ input PlaceHolders
    seq_len = tf.placeholder(tf.int64,name="input_lr")
    _W = tf.placeholder(tf.int32,name="input_w")
    _C = tf.placeholder(tf.int32,name="input_c")
    _T = tf.placeholder(tf.int32,name="input_t")
    mask = tf.placeholder("float",name="input_mask")

    # Tensorflow LSTM cell requires 2x n_hidden length (state & cell)
    istate_fw = tf.placeholder("float", shape=[None, 2*FLAGS.num_hidden])
    istate_bw = tf.placeholder("float", shape=[None, 2*FLAGS.num_hidden])
    _Y = tf.placeholder("float", [None, FLAGS.num_classes])

    #~ transfortm into Embeddings
    emb_x = tf.nn.embedding_lookup(_weights['w_emb'],_W)
    emb_c = tf.nn.embedding_lookup(_weights['c_emb'],_C)
    emb_t = tf.nn.embedding_lookup(_weights['t_emb'],_T)

    _X = tf.matmul(emb_x, _weights['hidden_w']) + tf.matmul(emb_c, _weights['hidden_c']) + tf.matmul(emb_t, _weights['hidden_t']) + _biases['hidden_b']

    inputs = tf.split(_X, FLAGS.max_sent_length, axis=0, num=None, name='split')

    lstmcell = tf.contrib.rnn.BasicLSTMCell(FLAGS.num_hidden, forget_bias=1.0, 
    state_is_tuple=False)

    bilstm = tf.contrib.rnn.static_bidirectional_rnn(lstmcell, lstmcell, inputs, 
    sequence_length=seq_len, initial_state_fw=istate_fw, initial_state_bw=istate_bw)


    pred_mod = [tf.matmul(item, _weights['out_w']) + _biases['out_b'] for item in bilstm]

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推荐答案

对于以后遇到此问题的任何人，都不应使用上方的摘录.

For anyone encountering this issue in the future, the snippet above should not be used.

来自 tf.contrib.rnn.static_bidirectional_rnn v1.1文档:

From tf.contrib.rnn.static_bidirectional_rnn v1.1 documentation:

返回:

元组(outputs, output_state_fw, output_state_bw)其中:输出是输出的长度T列表(每个输入一个)，它们是深度连接的前向和后向输出. output_state_fw是前向rnn的最终状态. output_state_bw是向后rnn的最终状态.

A tuple (outputs, output_state_fw, output_state_bw) where: outputs is a length T list of outputs (one for each input), which are depth-concatenated forward and backward outputs. output_state_fw is the final state of the forward rnn. output_state_bw is the final state of the backward rnn.

上面的列表理解是期望LSTM输出，而获得这些输出的正确方法是:

The list comprehension above is expecting LSTM outputs, and the correct way to get those is this:

outputs, _, _ = tf.contrib.rnn.static_bidirectional_rnn(lstmcell, lstmcell, ...)
pred_mod = [tf.matmul(item, _weights['out_w']) + _biases['out_b'] 
            for item in outputs]

这将起作用，因为outputs中的每个item都具有形状[batch_size, 2 * num_hidden]，并且可以将权重乘以tf.matmul().

This will work, because each item in outputs has the shape [batch_size, 2 * num_hidden] and can be multiplied with the weights by tf.matmul().

附加组件:从tensorflow v1.2 +起，推荐使用的功能在另一个软件包中: tf.nn.static_bidirectional_rnn .返回的张量相同，因此代码变化不大:

Add-on: from tensorflow v1.2+, the recommended function to use is in another package: tf.nn.static_bidirectional_rnn. The returned tensors are the same, so the code doesn't change much:

outputs, _, _ = tf.nn.static_bidirectional_rnn(lstmcell, lstmcell, ...)
pred_mod = [tf.matmul(item, _weights['out_w']) + _biases['out_b'] 
            for item in outputs]

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