Tensor in PyTorch QUIZ (MCQ QUESTIONS AND ANSWERS)

torch.matmul(tensor1, tensor2)

tensor1 * tensor2

torch.multiply(tensor1, tensor2)

torch.dot(tensor1, tensor2)

It creates a tensor with linearly spaced values.

It generates random values between a specified range.

It creates a tensor with logarithmically spaced values.

It creates a tensor with constant values.

torch.identity(size)

torch.eye(size)

torch.ones(size)

torch.zeros(size)

tensor.maximum()

torch.find_max(tensor)

tensor.max()

torch.max_element(tensor)

tensor.dot(other_tensor)

tensor.multiply(other_tensor)

torch.matmul(tensor, other_tensor)

tensor @ other_tensor

It changes the data type of the tensor.

It reshapes the tensor without changing its data.

It performs element-wise multiplication.

It transposes the tensor.

tensor.size()

tensor.ndim()

tensor.dim()

tensor.shape()

torch.tensor()

tensor.from_numpy()

torch.from_array()

torch.convert()

tensor.to_numpy()

tensor.numpy()

numpy.convert(tensor)

tensor.convert_to_numpy()

It creates a tensor with random values.

It creates a tensor with all elements initialized to zero.

It creates a tensor without initializing its values.

It creates an identity matrix.

Converts a tensor to a NumPy array

Reshapes a tensor without changing its data

Flattens a tensor into a one-dimensional tensor

Computes the element-wise square root

torch.sum(tensor, dim)

tensor.total(dim)

torch.reduce_sum(tensor, dim)

tensor.sum(dim)

Computes the element-wise square root

Reshapes a tensor without changing its data

Clips the values of a tensor to be within a specified range

Applies a mathematical function element-wise

torch.mean(tensor)

tensor.mean(dim)

torch.average(tensor, dim)

torch.calculate_mean(tensor, dim)

Computes the dot product of two tensors

Finds the maximum value in a tensor

Returns the indices of the maximum values in a tensor

Computes the element-wise square root

torch.find_max(tensor)

torch.max(tensor)

tensor.maximum()

torch.maximum(tensor)

torch.multiply(tensor1, tensor2)

tensor1 * tensor2

torch.mul(tensor1, tensor2)

torch.dot(tensor1, tensor2)

Increases the size of a tensor along a specified dimension

Removes dimensions with size 1

Swaps two dimensions of a tensor

Computes the element-wise square root

torch.linspace(start, end, steps)

torch.range(start, end, step)

torch.arange(start, end, step)

torch.even_space(start, end, steps)

Converts a tensor to a NumPy array

Reshapes a tensor without changing its data

Applies a mathematical function element-wise

Computes the dot product of two tensors

torch.zero_tensor(size)

torch.zeros(size)

torch.tensor(size)

torch.create_tensor(size)

torch.tensor(data, dtype)

torch.create_tensor(data, dtype)

torch.as_tensor(data, dtype)

torch.tensor(data).astype(dtype)

torch.BoolTensor

torch.IntTensor

torch.FloatTensor

torch.DoubleTensor

torch.stack(tensors, dim)

torch.concat(tensors, dim)

torch.merge(tensors, dim)

torch.cat(tensors, dim)

tensor.to_numpy()

tensor.as_array()

numpy.array(tensor)

tensor.numpy()

A tensor with all elements initialized to zero

A tensor with random values

An identity matrix of size n

A one-dimensional tensor with n elements

torch.FloatTensor

torch.IntTensor

torch.DoubleTensor

torch.LongTensor

tensor.size

tensor.num_elements

tensor.shape

tensor.numel()

torch.random(size)

torch.uniform(size)

torch.randn(size)

torch.random_uniform(size)

torch.IntTensor

torch.DoubleTensor

torch.CharTensor

torch.BoolTensor