如何使用无维张量流重塑张量

Question

I have a tensor of shape (3, None, 80, 10) and I want to reshape it to (3*None, 80, 10) . I tried with the following lines of code but didn't get the desired shape (3*None, 80, 10) , I got a tensor of shape (None, None, None) :

shape = [tf.shape(node_embed_tmp)[k] for k in range(4)]
Y = tf.reshape(node_embed_tmp, [shape[0]*shape[1], shape[2], shape[3]])

Any suggestions, Please!

EDIT:

I also tried the following code:

shape = [tf.shape(node_embed_tmp)[k] for k in range(4)]
Y=tf.reshape(node_embed_tmp, [-1, shape[2], shape[3]])

But I got the shape (None, None, None) and the following error: ValueError: Input size (depth of inputs) must be accessible via shape inference, but saw value None.

Answer 1

It is challenging to reshape the Variable dimension tensor, you can use keras.Input library

from tensorflow import keras
tensor_shape = (3, None, 80, 10)
input = keras.Input(shape=((None,) + tensor_shape[1:]), dtype = 'int32')
input.shape

Output

TensorShape([None, None, None, 80, 10])