mygrad.nnet.initializers.uniform#

mygrad.nnet.initializers.uniform(*shape, lower_bound=0, upper_bound=1, dtype=<class 'numpy.float32'>, constant=None)[source]#

Initialize a mygrad.Tensor by drawing from a uniform distribution.

Parameters:

shapeSequence[int]

The output shape.

lower_boundReal, optional (default=0)

Lower bound on the output interval, inclusive.

upper_boundReal, optional (default=1)

Upper bound on the output interval, exclusive.

dtypedata-type, optional (default=float32)

The data type of the output tensor; must be a floating-point type.

constantbool, optional (default=False)

If True, the returned tensor is a constant (it: does not back-propagate a gradient).

Returns:

mygrad.Tensor, shape=``shape``: A Tensor, with values drawn uniformly from [lower_bound, upper_bound).

Examples

>>> from mygrad.nnet.initializers import uniform
>>> uniform(2, 3)
Tensor([[0.8731087 , 0.30872548, 0.75528544],
        [0.55404514, 0.7652222 , 0.4955769 ]], dtype=float32)

>>> uniform(2, 2, lower_bound=-1, upper_bound=3)
Tensor([[ 1.9151938 , -0.28968155],
        [-0.01240687, -0.24448799]], dtype=float32)

>>> uniform(5, dtype="float16", constant=True)
Tensor([0.5186, 0.1481, 0.3745, 0.941 , 0.331 ], dtype=float16)