A tensor is a **vector** or **matrix** of n-dimensions that represents all types of data. All the values in a tensor are having the same data type known as shape. The shape is the dimension of any matrix.

To create a tensor, you can use tf.constant().

Here is the syntax for the same:

tf.constant(value, dtype, name = "")

The arguments are as follows:

-value: Value of n dimension to define the tensor. Optional

- dtype: Define the type of data:

- tf.string: String variable

- tf.float32: Float variable

- tf.int16: Integer variable

- "name": Name of the tensor.

To create a tensor of dimension 0, run the following code:

r1 = tf.constant(1, tf.int16)

print(r1)

You can define a tensor with decimal values or with a string by changing the type of data.

# Decimal

r1_decimal = tf.constant(0.9, tf.float32)

print(r1_decimal)

# String

r1_string = tf.constant("any name", tf.string)

print(r1_string)

The following fill operation creates a tensor of shape dims and fills it with value.

tf.fill(dims,value,name=None)

Now for the specified shape, When you print the tensor, TensorFlow guesses the shape. However, you can get the shape of the tensor with the shape property.

m_shape = tf.constant(0.9, tf.float32)

m_shape.shape

Hope this helps!

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