Tensorflow字符级CNN-输入形状

我正在尝试将2个堆栈的字符级CNN添加到一个较大的神经网络系统中，但是在输入维数方面出现了ValueError。

我要实现的是通过替换字符（根据大写字母，数字或字母）并将输入的字符输入CNN中来获得输入字的正交表示形式。我知道可以使用LSTM / RNN来实现，但是要求表明使用CNN，因此使用其他NN并不是可选的。

那里的大多数示例自然使用图像数据集（MNIST等），而不使用文本数据集。因此，我很困惑，不确定如何对字符嵌入进行“重塑”，以使它们可以成为CNN的有效输入。

因此，这是我要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。
谢谢。

更新

所以通过一些博客文章下面后1，2，感谢维杰男，我明白，我们必须提供输入尺寸事先（与提供sequence_lengths的RNN / LSTM）。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)