Method uniform

tinygrad/tensor.py:705–720

Creates a tensor with the given shape, filled with random values from a uniform distribution over the interval `[low, high)`. Requires `low < high`. You can pass in `dtype` and `device` keyword arguments to control the data type and device of the tensor. Additionally, all other key

(*shape, low=0.0, high=1.0, dtype:DTypeLike|None=None, **kwargs)

  @staticmethod
  def uniform(*shape, low=0.0, high=1.0, dtype:DTypeLike|None=None, **kwargs) -> Tensor:
    """
    Creates a tensor with the given shape, filled with random values from a uniform distribution over the interval `[low, high)`.
    Requires `low < high`.

    You can pass in `dtype` and `device` keyword arguments to control the data type and device of the tensor.
    Additionally, all other keyword arguments are passed to the constructor of the tensor.

    ```python exec="true" source="above" session="tensor" result="python"
    Tensor.manual_seed(42)
    print(Tensor.uniform(2, 3, low=2, high=10).numpy())
    ```
    """
    if not all_int(shape:=argfix(*shape)) or not all(s >= 0 for s in shape): raise ValueError(f"invalid input {shape=}")
    if low >= high: raise ValueError(f"Tensor.uniform requires low < high, got {low=}, {high=}")
    return ((high-low) * Tensor.rand(*shape, **kwargs)).cast(dtype or dtypes.default_float) + low

  @staticmethod
  def scaled_uniform(*shape, **kwargs) -> Tensor: