pytor

Question 1

pytorch: To create an integer tensor, type

Answer 1

torch.tensor([1,2,3])

Question 2

pytorch: To create a float tensor, type

Answer 2

torch.FloatTensor([1,2,3])

Question 3

pytorch: To reshape an existing tensor by choosing the column and row dimensions, type

Answer 3

my_tensor.view(10, 10)

Note: It will error if the existing tensor does not properly fit into the new shape’s slots.

Question 4

pytorch: To reshape an existing tensor by choosing the number of columns and then have the row number automatically accommodate it, type

Answer 4

my_tensor.view(10, -1)

Question 5

pytorch: To create a tensor from a numpy array, type

Answer 5

my array = np.array([1, 2, 3])

my_tensor = torch.from_numpy(array)

Question 6

pytorch: Adding or multiplying two tensors

Answer 6

applies the operation to only the value in the same index

Question 7

pytorch: The dot product is when you

Answer 7

multiply two tensors and then sum all the resulting values.

Question 8

pytorch: To create a tensor for 10 equally spaced numbers between 2 numbers, type

Answer 8

torch.linspace(0, 10)

Question 9

pytorch: To plot a graph of 2 tensors, type

Answer 9

plt.plot(x.numpy(), v.numpy())

Question 10

pytorch: To create a 1 dimensional range in pytorch, type

Answer 10

torch.arange(1, 10)

Question 11

pytorch: To get the dimensions of a tensor, type

Answer 11

my_tensor.dim()

Question 12

pytorch: To slice a 2 dimensional tensor, type

Answer 12

my_tensor[0, 0]

Question 13

pytorch: A 3 dimensional array can be imagined as

Answer 13

multiple separate 2 dimensional tables

Question 14

pytorch: To multiply two tensors matrices, type

Answer 14

my_tensor_matrix_1 @ my_tensor_matrix_2

Question 15

pytorch: To set the seed, type

Answer 15

torch.manual_seed(1)