Question

我正在尝试将2个堆叠的字符级CNN添加到更大的神经网络系统中，但我得到输入维度的ValueError。

我想要实现的是通过替换字符（根据大小写，或数字或字母）并将它们馈入CNN来获得输入单词的正交表示。我知道这可以通过LSTM / RNN实现，但是要求表明使用CNN因此使用另一个NN不是可选的。

那里的大多数例子自然使用图像数据集（MNIST等），但不使用文本数据集。所以我很困惑，不知道如何“重塑”字符嵌入，以便它们可以成为CNN的有效输入。

所以这是我正在尝试运行的代码的一部分：

# ...

# shape = (batch size, max length of sentence, max length of word)
self.char_ids = tf.placeholder(tf.int32, shape=[None, None, None],
                name="char_ids")

# ...

# Char embedding lookup
_char_embeddings = tf.get_variable(
        name="_char_embeddings",
        dtype=tf.float32,
        shape=[self.config.nchars, self.config.dim_char])
char_embeddings = tf.nn.embedding_lookup(_char_embeddings,
        self.char_ids, name="char_embeddings")

# Reshape for CNN?
s = tf.shape(char_embeddings)
char_embeddings = tf.reshape(char_embeddings, shape=[s[0]*s[1], self.config.dim_char, s[2]])

# Conv #1
conv1 = tf.layers.conv1d(
    inputs=char_embeddings,
    filters=64,
    kernel_size=3,
    padding="valid",
    activation=tf.nn.relu)

# Conv #2
conv2 = tf.layers.conv1d(
    inputs=conv1,
    filters=64,
    kernel_size=3,
    padding="valid",
    activation=tf.nn.relu)
pool2 = tf.layers.max_pooling1d(inputs=conv2, pool_size=2, strides=2)

# Dense Layer
output = tf.layers.dense(inputs=pool2, units=32, activation=tf.nn.relu)

# ...

这就是我得到的错误：

File "/home/emre/blstm-crf-ner/model/ner_model.py", line 159, in add_word_embeddings_op activation=tf.nn.relu)
File "/home/emre/blstm-crf-ner/virtner/lib/python3.4/site-packages/tensorflow/python/layers/convolutional.py", line 411, in conv1d return layer.apply(inputs)
File "/home/emre/blstm-crf-ner/virtner/lib/python3.4/site-packages/tensorflow/python/layers/base.py", line 809, in apply return self.__call__(inputs, *args, **kwargs)
File "/home/emre/blstm-crf-ner/virtner/lib/python3.4/site-packages/tensorflow/python/layers/base.py", line 680, in __call__ self.build(input_shapes)
File "/home/emre/blstm-crf-ner/virtner/lib/python3.4/site-packages/tensorflow/python/layers/convolutional.py", line 132, in build raise ValueError('The channel dimension of the inputs '
ValueError: The channel dimension of the inputs should be defined. Found `None`.

任何帮助将不胜感激感谢。

更新

因此，在完成了一些博客帖子1，2之后，感谢vijay m，我了解到我们必须事先提供输入维度（不同于提供带有RNN / LSTM的sequence_length ）。所以这是最终的代码片段：

# Char embedding lookup
_char_embeddings = tf.get_variable(
        name="_char_embeddings",
        dtype=tf.float32,
        shape=[self.config.nchars, self.config.dim_char])
char_embeddings = tf.nn.embedding_lookup(_char_embeddings,
        self.char_ids, name="char_embeddings")

# max_len_of_word: 20
# Just pad shorter words and truncate the longer ones.
s = tf.shape(char_embeddings)
char_embeddings = tf.reshape(char_embeddings, shape=[-1, self.config.dim_char, self.config.max_len_of_word])

# Conv #1
conv1 = tf.layers.conv1d(
    inputs=char_embeddings,
    filters=64,
    kernel_size=3,
    padding="valid",
    activation=tf.nn.relu)

# Conv #2
conv2 = tf.layers.conv1d(
    inputs=conv1,
    filters=64,
    kernel_size=3,
    padding="valid",
    activation=tf.nn.relu)
pool2 = tf.layers.max_pooling1d(inputs=conv2, pool_size=2, strides=2)

# Dense Layer
output = tf.layers.dense(inputs=pool2, units=32, activation=tf.nn.relu)

Answer 1

conv1d期望在创建图表期间定义渠道维度。因此，您无法将维度传递为None。

您需要进行以下更改：

char_ids = tf.placeholder(tf.int32, shape=[None, max_len_sen, max_len_word],
            name="char_ids")
#max_len_sen and max_len_word has to be set.

#Reshapping for CNN, should be
s = char_embeddings.get_shape()
char_embeddings = tf.reshape(char_embeddings, shape=[-1, dim_char, s[2]])

Answer 2

Conv1d中输入的默认格式为形状（批，长度，通道），也许char_embeddings应该像这样：

s = char_embeddings.get_shape()
char_embeddings = tf.reshape(char_embeddings, shape=[-1, s[2], dim_char])

谢谢！

Tensorflow字符级CNN - 输入形状

2 个答案: