How to get the dimensions of a tensor (in TensorFlow) at graph construction time?

Question

I am trying an Op that is not behaving as expected.

graph = tf.Graph()
with graph.as_default():
  train_dataset = tf.placeholder(tf.int32, shape=[128, 2])
  embeddings = tf.Variable(
    tf.random_uniform([50000, 64], -1.0, 1.0))
  embed = tf.nn.embedding_lookup(embeddings, train_dataset)
  embed = tf.reduce_sum(embed, reduction_indices=0)

So I need to know the dimensions of the Tensor embed. I know that it can be done at the run time but it's too much work for such a simple operation. What's the easier way to do it?

Answer 1

I see most people confused about tf.shape(tensor) and tensor.get_shape() Let's make it clear:

1. tf.shape

tf.shape is used for dynamic shape. If your tensor's shape is changable, use it. An example: a input is an image with changable width and height, we want resize it to half of its size, then we can write something like:
new_height = tf.shape(image)[0] / 2

2. tensor.get_shape

tensor.get_shape is used for fixed shapes, which means the tensor's shape can be deduced in the graph.

Conclusion: tf.shape can be used almost anywhere, but t.get_shape only for shapes can be deduced from graph.