Flatten, Reshape, and Squeeze Explained - Tensors for Deep Learning with PyTorch

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Tensors for neural network programming and deep learning with PyTorch. A deeper look into the tensor reshaping options like flattening, squeezing, and unsqueezing.

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[philtrem]

def flatten(t):
return t.reshape(-1)

[prakhardixit]

thanks deeplizard for making pytorch so easy to understand, an excellent series!!!

[sethatkins]

This was very helpful. I came here looking for what the squeeze operation does. Was happy to find answers to some other questions in the back of my mind. (what does the -1 mean in resize operations) I will say though, that sometimes the visuals can be a little distracting. (Mainly the pancake one. I was having a hard time listening to what you were saying.) Other than that, this was very helpful. I might watch more of your videos in the future!

[luis.barragan]

When you're writing code, it sounds in the background like airplane cabin white sound and it's relaxing. Nice tutorial! I'm a PyTorch Scholar and since the resources in Udacity aren't good enough for me, I'm watching your very helpful tutorials. Thanks.

[philtrem]

Really love the analogy ! And I'm really enjoying the course so far.

[rohtashbeniwal]

best work u guys are doing, lot of hard work u both did, to get this knowledge, love from India

[Aditya-nelk]

i was waiting for the pancakes to be squished 'flat'...

[amaytrivedi]

Hi there,

i followed your tutorials and they seem great!!

I implemented in the following manner (Squeeze function in reshape)


def flatten_by_reshaping():
rdata = data.reshape(1, -1)
rd = rdata.reshape(rdata.shape[1], )
return rd
print(flatten_by_reshaping(), len(flatten_by_reshaping()))

[AIRoboticsCreator]

Great video! I’m halfway thru the playlist!

[hosseinaboutalebi]

Great videos. Best content. Nice animations.
The fact that you make your videos like a meme is exceptional and make watching them enjoyable.
Keep up the good work! I am your fan :)

[Murmur]

Gratulations on your 100.000 followers here! :)

[SaiKrishna-trdz]

awesome content and the way you deliver hats off!

[srikarvalluri]

def flatten(t):
t = t.reshape(1, t.size()[0] * t.size()[1])
t = t.squeeze()

return t


I know this is terribly ineffective but it's something different then all the other ones out there. ;)

[Normalizing-polyamory]

def flatten(t):
numel = t.numel()
return t.reshape(numel)

[thepresistence]

6:00 why you are showing food, I am hungry 😅. But it look like delicious :)

[philtrem]

When you have a sentence like this: "The primary ingredient we use to produce our product, a function that maps inputs to correct outputs, is data.", it's better to write it as: "The primary ingredient we use to produce our product – a function that maps inputs to correct outputs – is data." (ie. using dashes instead of commas). Otherwise it's confusing and difficult to make out that it's a 'parenthetical statement'.

[narroric]

I don't know what the low rumbling sound is, but it is ridiculously soothing. Like doing coding on a star trek starship.

[M]

I haven’t installed PyTorch yet, but assuming that it works the same as a list, would the answer be: reshape(1, -1)[0]?

[thak]

@deeplizard thank you very much for this video and the full playlist. Can you make a series on NLP with Pytorch ?

[heller]

5:45 why you distracting me with food !