How to Create Tensors with Known Values

By Matthew Scarpino

Just as most programs start by declaring variables, most TensorFlow applications start by creating tensors. A tensor is an array with zero or more dimensions. A zero-dimensional tensor is called a scalar, a one-dimensional tensor is called a vector, and a two-dimensional tensor is called a matrix. Keep in mind these three points about tensors:

  • Every tensor is an instance of the Tensor class.
  • A tensor may contain numbers, strings, or Boolean values. Every element of a tensor must have the same type.
  • Tensors can be created, transformed, and operated upon using functions of the tf package.

The tf package provides seven functions that form tensors with known values. The following table lists them and provides a description of each.

Creating Tensors with Known Values

Function Description
constant(value, dtype=None, shape = None, name = 'Const', verify_shape=False)
Returns a tensor containing the given value
zeros(shape, dtype=tf.float32, name = None)
Returns a tensor filled with zeros
ones(shape, dtype=tf.float32, name=None)
Returns a tensor filled with ones
fill(dims, value, name=None)
Returns a tensor filled with the given value
linspace(start, stop, num, name=None)
Returns a tensor containing a linear range of values
range(start, limit, delta=1, dtype=None, name='range')
Returns a tensor containing a range of values
range(limit, delta=1, dtype=None, name='range')
Returns a tensor containing a range of values


A tensor may have multiple dimensions, and the number of dimensions in a tensor is its rank. The lengths of a tensor’s dimensions form an array called the tensor’s shape. Many of the functions in the table accept a shape parameter that identifies the desired shape of the new tensor. The following examples demonstrate how you can set this parameter:

  • [] — The tensor contains a single value.
  • [3] — The tensor is a one-dimensional array containing three values.
  • [3, 4] — The tensor is a 3-x-4 matrix.
  • [3, 4, 5] — The tensor is a multidimensional array whose dimensions equal 3, 4, and 5.

Most of the functions in the table have a dtype argument that identifies the data type of the tensor’s elements. The default value of dtype is float32, which indicates that, by default, tensors contain single-precision floating-point values. The following table lists float32 and other possible data types.

Tensor Data Types

Data Type Description
bool Boolean values
uint8/uint16 Unsigned integers
quint8/quint16 Quantized unsigned integers
int8/int16/int32/int64 Signed integers
qint8/qint32 Quantized signed integers
float16/float32/float64 Floating-point values
complex64/complex128 Complex floating-point values
string Strings


Each function in the preceding table accepts an optional name argument that serves as an identifier for the tensor. Applications can access a tensor by name through the tensor’s graph.

The constant function

The most popular function in the table is constant. Its only required argument is the first, which defines the value or values to be stored in the tensor. You can provide these values in a list, and the following code creates a one-dimensional tensor containing three floating-point values:

t1 = tf.constant([1.5, 2.5, 3.5])

Multidimensional arrays use similar notation. The following code creates a 2-x-2 matrix and sets each of its elements to the letter b:

t2 = tf.constant([['b', 'b'], ['b', 'b']])

By default, TensorFlow won’t raise an error if the function’s first argument doesn’t have the shape given by the shape argument. But if you set the last argument, verify_shape, to True, TensorFlow will verify that the two shapes are equal. The following code provides an example of mismatched shapes:

t3 = tf.constant([4, 2], tf.int16, [3], 'Const', True)

In this case, the given shape, [3], doesn’t match the shape of the first argument, which is [2]. As a result, TensorFlow displays the following error:

TypeError: Expected Tensor's shape: (3,), got (2,).

zeros, ones, and fill

The functions zeros, ones, and fill create tensors whose elements all have the same value. For zeros and ones, the only required argument is shape, which identifies the shape of the desired tensor. As an example, the following code creates a simple 1-x-3 vector whose elements equal 0.0:

zero_tensor = tf.zeros([3])

Similarly, the following function call creates a 4-x-4 matrix whose elements equal 1.0:

one_tensor = tf.ones([4, 4])

The fill function requires a value parameter, which sets the value of the tensor’s elements. The following code creates a three-dimensional tensor whose values are set to 81.0:

fill_tensor = tf.fill([1, 2, 3], 81.0)

Unlike zeros and ones, fill doesn’t have a dtype argument. It can only create tensors containing 32-bit floating point values.

Creating sequences

The linspace and range functions create tensors whose elements change regularly between a start and end value. The difference between them is that linspace creates a tensor with a specific number of values. For example, the following code creates a 1-x-5 tensor whose elements range from 5.0 to 9.0:

lin_tensor = tf.linspace(5., 9., 5)

# Result: [5. 6. 7. 8. 9.]

Unlike linspace, range doesn’t accept the number of elements in the tensor. Instead, it computes successive elements by adding a value called a delta. In the following code, delta is set to 0.5:

range_tensor = tf.range(3., 7., delta=0.5)

# Result: [3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5]

Like Python’s range function, TensorFlow’s range function can be called without the start parameter. In this case, the starting value is assumed to be 0.0. The following code demonstrates this:

range_tensor = tf.range(1.5, delta=0.3)

# Result: [0.0 0.3 0.6 0.9 1.2]

If the delta parameter is positive, the starting value must be less than the ending value. If delta is negative, the starting value must be greater than the ending value.