A function to access the values:
def shape(tensor):
s = tensor.get_shape()
return tuple([s.value for i in range(0, len(s))])
Example:
batch_size, num_feats = shape(logits)
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?
A function to access the values:
def shape(tensor):
s = tensor.get_shape()
return tuple([s.value for i in range(0, len(s))])
Example:
batch_size, num_feats = shape(logits)
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)
c = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
print(c.get_shape())
==> TensorShape([Dimension(2), Dimension(3)])
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
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.