ppxasjsm
commented
1 year ago @ryankzhu can you fix these please thanks!
Open marmatti opened 1 year ago
ppxasjsm
commented
1 year ago @ryankzhu can you fix these please thanks!
Deep neural network built on an(?) autograd system
The following cell installs(?) necessary packages...
...and it(?) is just a generic n-dimensional array to be used...
We can also specify a(?) data type
Casting tensor x as a(?) CUDA datatype if CUDA is(?) available
Attention: x.to(device) will not cast x as a(?) cuda datatype - we need x = x.to(device)
I do not think this flows super clearly. I would rewrite: Note: As mentioned above, whenever we work with tensors, x.to(device) does not move x to cuda and we need to write x = x.to(device) instead.
DoubleTensor is a(?) 64-bit floating point and FloatTensor is a(?) 32-bit floating point tensor. So a FloatTensor uses half of the memory as a DoubleTensor with the same size (?) ... So PyTorch leaves it up(?) to the(?) user to choose...
Set a default tensor type for your notebook
The MNIST hand-written digits dataset consists of 60,000 training...
...Since we have been using scikit-learn, we will use the(?) dataset in the example here: Dataset and data set equally valid for spelling, just a suggestion to uniform.
Splitting the data into train and test sets
... This is just an example full stop "." missing
If np.argmax(out,axis=1)-y is non-zero, (I would move this here -->) i.e. every time there is a mismatch between prediction and label, e.g.(?) label y is 9 and prediction is 5 comma(?) then diff is incremented by 1.
we can't call numpy() on Tensors that require grad.
The optimiser module gives access to a large number of standard optimisers that try to help minimise the loss.
Typically for a classification problem one would use a cross entropy loss :(Colon?) the torch documentation has some more details on this.
This is a parameter you set in your optimiser and you can play around with different values for it.