问题
I am trying to use tensorflow to create a recurrent neural network. My code is something like this:
import tensorflow as tf
rnn_cell = tf.nn.rnn_cell.GRUCell(3)
inputs = [tf.constant([[0, 1]], dtype=tf.float32), tf.constant([[2, 3]], dtype=tf.float32)]
outputs, end = tf.nn.rnn(rnn_cell, inputs, dtype=tf.float32)
Now, everything runs just fine. However, I am rather confused by what is actually going on. The output dimensions are always the batch size x the size of the rnn cell's hidden state - how can they be completely independent of the input size?
If my understanding is correct, the inputs are concatenated to the rnn's hidden state at each step, and then multiplied by a weight matrix (among other operations). This means that the dimensions of the weight matrix need to depend on the input size, which is impossible, because the rnn_cell is created before the inputs are even declared!
回答1:
After seeing the answer to a question about tensorflow's GRU implementation, I've realized what's going on. Counter to my intuition, the GRUCell constructor doesn't create any weight or bias variables at all. Instead, it creates its own variable scope, and then instantiates the variables on demand when actually called. Tensorflow's variable scoping mechanism ensures that the variables are only created once, and shared across subsequent calls to the GRU.
I'm not sure why they decided to go with this rather confusing implementation, which is as far as I can tell is undocumented. To me it seems more appropriate to use python's object-level variable scoping to encapsulate the tensorflow variables within the GRUCell itself, rather than relying on an additional implicit scoping mechanism.
来源：https://stackoverflow.com/questions/39302344/tensorflow-rnn-input-size