i have a very simple doubt here. why we reshaped the X_train[0] values to (1,1)

```
a1 = linear_layer(X_train[0].reshape(1,1))
print(a1)
```

i have a very simple doubt here. why we reshaped the X_train[0] values to (1,1)

```
a1 = linear_layer(X_train[0].reshape(1,1))
print(a1)
```

Hello @Utsav_Sharma1,

I think you quoted that line of code from this lab: “C2_W1_Lab01_Neurons_and_Layers”. Next time, please state it in your post.

`linear_layer`

is a `tf.keras.layers.Dense`

object and it is the tensorflow’s requirement that the minimum number of dimensions to be 2. We take the zeroth axis as the “sample axis” (or “batch axis” as called in many TF documentations) . By reshaping it to (1, 1), the zeroth axis becomes the “batch axis”, and the first axis be the “feature axis”.

So, it is a requirement.

Cheers,

Raymond

I did not understand what you mean by sample axis and feature axis

Hey @Mohammed_Ahmed3,

This is about how we arrange the samples. For example, let’s say we have 2 samples, where the first sample is `[1., 2., 3.]`

, and the second one is `[4., 5., 6.]`

.

Now, obviously each sample has 3 features.

There are 2 ways we can arrange them into one 2D array.

First way:

```
import numpy as np
data1 = np.array([
[1., 2., 3.],
[4., 5., 6.],
])
```

Second way:

```
data2 = np.array([
[1., 4.],
[2., 5.],
[3., 6.],
])
```

Here, it is important that you realize their difference. The first way stacks the sample vertically (it grows in the vectrical direction) whereas the second way horizontally.

Another way to speak their difference is that, in the first way we take the zeroth axis (aka 0th axis) as the sample axis because if you index an “element” along the zeroth axis, you get a sample. Note here that an element of a 2D array is a 1D array. For example, `data1[0, :]`

gives us `[1., 2., 3.]`

and `data1[1, :]`

gives us `[4., 5., 6.]`

, and they are obviously the first and the second samples.

**Because when we index along the zeroth axis, we get a sample, the zeroth axis can be called as the sample axis.**

On the other hand, when we index along the first axis, for example, `data1[:, 0]`

gives us `[1., 4.]`

. Here, The two values corresponds to the first feature of the first and second samples respectively, therefore, in short, `data1[:, 0]`

gives us the first feature. Similarity, `data1[:, 1]`

gives us the second feature, and `data1[:, 2]`

the third.

**Because when we index along the first axis, we get a feature, the first axis can be called as the feature axis.**

In summary, the first way of arrangement, which is `data1`

, stacks the samples vertically to make the zeroth axis as the sample axis because indexing along that axis gives us a sample, and make the first axis as the feature axis because indexing along which gives us a feature.

The second way arranges the samples in the opposite way but I will let you figure out the rest yourself if you are interested. It is important to note that both this machine learning specialization and Tensorflow adopt the first way, and that’s why I spend all the time explaining the first way, but if you understand everything, you should be able to tell which axis is the sample axis in the second way.

Good luck, and cheers,

Raymond

@Mohammed_Ahmed3, I am going to stay around in the next 30 minutes for you, let me know here if you have any follow up.

Keep trying!

Raymond

data2[0, :] gives us [4., 5., 6.] or give us array([1., 4.]) if it’s vertically so the first sample is 1,4 and that is good because data2[0,:] give us that but data1[0,:] give us [1,2,3] zeroth axis as the sample aixs is that mean wwe should get the same sample

what first axis mean?

I need proof to this. Please share a screenshot like you did last time that shows how you define `data2`

and you ran `data2[0, :]`

.

Also, I have a hard time following your last reply, please use punctuations and new paragraphs so that I can read your statements in a statement-by-statement manner. Whenever possible, supply a screenshot of running the code you mention in your statement, because in this way, it supports the statement.

Lastly, please explain what “wwe” means.

Thank you.

data2[0,:] give us [1,4] as you see

first look at your reply underlined by black line first samples stacked verticaly .

you mean by that samples are [1,4] [2,5] [3,6] where the second way horizontally already so data1[0,:] = [1,4] so that zero axis beacuse it give us sample

second what you mean by sample highlighted in second image ?

third is that the axis in first picture?

This is the first way:

They are stacked vertically because it grows vertically. The meaning of growing vertically is that, `data1`

has 2 rows because it has 2 samples. If there were 10 samples, since they were stacked vertically, it was going to have 10 rows, in other words, it was 10 rows tall.

3 samples → 3 rows tall.

4 samples → 4 rows tall.

5 samples → 5 rows tall.

Is this clear?

This is wrong. Run the code and share your screenshot.

Don’t mix up data1 with data2.

I won’t go into these questions because it seems to me you were mixing up data1 and data2. As I said, support your argument with screenshot because it helps avoid mixing up.

Run the code, compare the result with my previous reply again, and re-ask the questions if they remain.

Keep trying!

Raymond

you say that data2[0,:] give us [4,5,6] and that is wrong

if we accesses data[1,:] it give us [4,5,6]

so how the zeroth axis is the sample axis

Thank you for telling me my mistakes! That helps, and I have corrected my previous post. My previous post should only give examples about `data1`

.

I stand by this explanation:

and your experiments have shown the above statement. You said:

- data1[0,:] gives us [1,2,3]
- data1[1,:] gives us [4,5,6]

and they are the first and the second samples.

Cheers,

Raymond

one also is a sample axis?

if data2[:,0] = [1,2,3] so axis zero is sample axis or first axis is the sample axis because we

index the first axis

That depends on which variable we are talking about, right? For `data1`

, the zeroth axis is the sample axis, and for `data2`

, the first axis is the sample axis. Agree?

So can you tell me what causes the sample axis to change from the zeroth to the first axis? What is the difference between `data1`

and `data2`

?

because we stacked the samples in data1 vertically and in data2 horizontally

is that right?

Exactly!

Additionally, in these MLS courses, we mostly adopt the `data1`

way, meaning we almost always stack samples vertically, and thus we treat the zeroth axis as the sample axis.