Tensor in TensorFlow QUIZ (MCQ QUESTIONS AND ANSWERS)

Using tf.matmul

Using tf.multiply

Using tf.dot

Using tf.add

To remove dimensions of size 1 from a tensor.

To add an extra dimension to a tensor.

To calculate the mean of a tensor.

To concatenate tensors along a specified axis.

tf.argmax

tf.max_index

tf.maximum_index

tf.find_max

Using tensor.numpy()

Using numpy.array(tensor)

Using tensor.to_numpy()

Using numpy.tensor(tensor)

It is a mutable tensor.

It is a constant tensor.

It represents a multidimensional array or list.

It cannot be used for deep learning computations.

tf.constant creates a mutable tensor, while tf.Variable creates an immutable tensor.

tf.constant creates a tensor with fixed values, while tf.Variable creates a mutable tensor.

tf.constant creates a tensor with random values, while tf.Variable creates a tensor with fixed values.

There is no difference between tf.constant and tf.Variable.

tf.concat

tf.stack

tf.split

tf.join

Computes the softmax activations of logits.

Computes the sigmoid activations of logits.

Computes the ReLU activations of logits.

Computes the tanh activations of logits.

Using tf.pow

Using tf.exp

Using tf.square

Using tf.sqrt

tf.sqrt

tf.square_root

tf.power

tf.square

To concatenate tensors along a specified axis.

To change the data type of a tensor.

To reshape a tensor into a new shape.

To perform element-wise multiplication of tensors.

Generates a tensor filled with random values from a normal distribution with the specified shape.

Generates a tensor filled with zeros with the specified shape.

Generates a tensor filled with ones with the specified shape.

Generates a tensor with the specified shape without initializing its values.

tf.mean

tf.reduce_mean

tf.average

tf.calculate_mean

To apply a function to each element of a tensor.

To reduce the dimensions of a tensor.

To perform element-wise operations on tensors with different shapes.

To initialize the values of a tensor.

tf.max

tf.argmax

tf.reduce_max

tf.maximum

Using tf.zeros

Using tf.ones

Using tf.fill

Using tf.constant with all values set to zero

To add an extra dimension to a tensor.

To remove dimensions of size 1 from a tensor.

To transpose the dimensions of a tensor.

To perform element-wise multiplication of tensors.

It creates a constant tensor.

It creates a mutable tensor.

It creates a placeholder for input data whose values are provided during execution.

It creates a tensor with fixed values.

A data structure that represents a multidimensional array or list.

A specialized type of variable used for deep learning computations.

A type of neural network layer in TensorFlow.

An object used for storing metadata in TensorFlow operations.

TensorFlow tensors are faster for numerical computations.

TensorFlow tensors support automatic differentiation for gradient-based optimization.

TensorFlow tensors have built-in support for deep learning operations.

TensorFlow tensors have a more intuitive indexing syntax.

tf.numpy_array

tf.convert_to_tensor

tf.tensor_from_numpy

tf.numpy_to_tensor

An immutable tensor.

A constant tensor.

A mutable tensor that can be changed during execution.

A placeholder tensor used for feeding input data.

tf.variable

tf.constant

tf.placeholder

tf.Tensor

tf.int32

tf.float16

tf.string

tf.boolean

Creates a tensor filled with ones with the same shape as the input tensor.

Creates a tensor filled with ones with a specified shape.

Creates a tensor filled with zeros with the same shape as the input tensor.

Creates a tensor filled with zeros with a specified shape.

tf.reshape

tf.rearrange

tf.resize

tf.reform

tf.Variable(initial_value=0)

tf.Variable(initial_value=[0])

tf.Variable([0])

tf.Variable()

Using tf.multiply

Using tf.add

Using tf.subtract

Using tf.divide

tf.multiply(x, y)

tf.concat(x, y)

tf.divide(x, y, z)

tf.subtract(x, y)

The number of elements in a tensor.

The data type of a tensor.

The shape of a tensor.

The number of dimensions in a tensor.