Tensorflow is a high-level library. A variable is a state or value that can be modified by performing operations on it. In TensorFlow variables are created using the Variable() constructor.
The Variable() constructor expects an initial value for the variable, which can be any kind or shape of Tensor. The type and form of the variable are defined by its initial value. The shape and the variables are fixed once they are created. let’s look at a few examples of how to create variables in TensorFlow.
Syntax :
tf.Variable(initial_value=None, trainable=None, validate_shape=True,
caching_device=None, name=None, variable_def=None, dtype=None, import_scope=None,
constraint=None,synchronization=tf.VariableSynchronization.AUTO,
aggregation=tf.compat.v1.VariableAggregation.NONE, shape=None)​
Parameters :
initial_value : by default None. The initial value for the Variable is a Tensor, or a Python object convertible to a Tensor.
trainable : by default None. If True, GradientTapes will keep an eye on this variable’s usage.
validate_shape : by default True. Allows the variable to be initialised with an unknown shape value if False. The shape of initial value must be known if True, which is the default.
name : by default None. The variable’s optional name. Defaults to ‘Variable’ and is automatically uniquified.
variable_def : by default None.
dtype : by default None. If set, initial_value will be converted to the given type. If None, either the datatype will be kept (if initial_value is a Tensor), or convert_to_tensor will decide.
shape : by default None. if None the shape of initial_value will be used. if any shape is specified, the variable will be assigned with that particular shape.
