Hi! Iâm not sure what :eqref:eq_matrix_def is supposed to refer to in section 2.3.3

Hi @Briana_Brady, sorry for not rendered labels, we will fix it. It should point to the equation 2.3.2.

Thank you for your quick response!

Here in the Linear-Algebra-Exercises sectionâs 6th question, it is asked to

âRun `A / A.sum(axis=1)`

and see what happens.â and find out why but Iâm getting an error.

At first it said

**AttributeError: âtensorflow.python.framework.ops.EagerTensorâ object has no attribute 'sumâ**

So I used reduce_sum and again it raised an error.

**InvalidArgumentError: Incompatible shapes: [5,4] vs. [5] [Op:RealDiv]**

Can you help me with this?

Thanking you in advance.

Hi @Sumanth, good question! The numerator and denominator need to have the same type. (Think of how the machine knows which axis to apply division. ) It is pretty common to see the shape mismatch in deep learning. Just check the document and find more details, for e.g. TF division here.

Hi, may I join the discussion , since I also got the same problem trying to run that line, even with tf.math.divide. What I think the question is asking is: divide the matrix to an array, so for each row of the matrix, it is divided by the sum of that row, so I would suggest mine: A / tf.reshape(tf.reduce_sum(A,axis=1),(-1,1))

Hey @randomonlinedude, thanks for the great suggestion! Would you like to post a PR on our git and become a contributor?

@goldpiggy Hi, sorry for the late reply, I didnt see the notice yes, if I can do something for this book, I would love to. Thank you for the work of your team and you

In the scalars section of this chapter, I attempted to write âimport tensorflow as tfâ and am now getting this error: â[WinError 193] %1 is not a valid Win32 applicationâ. It seems like some library I have downloaded may be made for 32-bit only. Does anyone know any fixes?

Hello, thanks for writing this book. I have a small doubt. In the book, its written axis=1 represents along the column. So if I have:

A = tf.reshape(tf.range(20), shape=(5,4))

tf.reduce_sum(A, axis=1)

# The output comes to be of shape [5] why? If we are doing along with the columns

# shouldnât the answer be [4] as there are 4 columns and we are adding along

# the columns.