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?
This question is related to
python
tensorflow
deep-learning
tensor
The method tf.shape is a TensorFlow static method. However, there is also the method get_shape for the Tensor class. See
https://www.tensorflow.org/api_docs/python/tf/Tensor#get_shape
Let's make it simple as hell. If you want a single number for the number of dimensions like 2, 3, 4, etc.,
then just use tf.rank()
. But, if you want the exact shape of the tensor then use tensor.get_shape()
with tf.Session() as sess:
arr = tf.random_normal(shape=(10, 32, 32, 128))
a = tf.random_gamma(shape=(3, 3, 1), alpha=0.1)
print(sess.run([tf.rank(arr), tf.rank(a)]))
print(arr.get_shape(), ", ", a.get_shape())
# for tf.rank()
[4, 3]
# for tf.get_shape()
Output: (10, 32, 32, 128) , (3, 3, 1)
A function to access the values:
def shape(tensor):
s = tensor.get_shape()
return tuple([s[i].value for i in range(0, len(s))])
Example:
batch_size, num_feats = shape(logits)
Just print out the embed after construction graph (ops) without running:
import tensorflow as tf
...
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)
print (embed)
This will show the shape of the embed tensor:
Tensor("embedding_lookup:0", shape=(128, 2, 64), dtype=float32)
Usually, it's good to check shapes of all tensors before training your models.
I see most people confused about tf.shape(tensor)
and tensor.get_shape()
Let's make it clear:
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
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.
Source: Stackoverflow.com