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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