PyTorch
#######

* https://pytorch.org/tutorials/index.html

Learning PyTorch
================

PyTorch is a Python-based scientific computing package serving two broad purposes::

    A replacement for NumPy to use the power of GPUs and other accelerators.
    An automatic differentiation library that is useful to implement neural networks.


Tensors
-------

* 张量是一种特殊的数据结构，与数组和矩阵非常相似。在 PyTorch 中，我们使用张量来编码模型的输入和输出，以及模型的参数。
* 张量类似于 NumPy 的 ndarray，但它们可以在 GPU 或其他专用硬件上运行以加速计算。


Tensor Initialization
^^^^^^^^^^^^^^^^^^^^^

::

    # Directly from data
    data = [[1, 2], [3, 4]]
    x_data = torch.tensor(data)

    # From a NumPy array
    np_array = np.array(data)
    x_np = torch.from_numpy(np_array)

    # From another tensor
    x_ones = torch.ones_like(x_data) # retains the properties of x_data
    print(f"Ones Tensor: \n {x_ones} \n")

    x_rand = torch.rand_like(x_data, dtype=torch.float) # overrides the datatype of x_data
    print(f"Random Tensor: \n {x_rand} \n")

    # With random or constant values:
    shape = (2, 3,)
    rand_tensor = torch.rand(shape)
    ones_tensor = torch.ones(shape)
    zeros_tensor = torch.zeros(shape)

    print(f"Random Tensor: \n {rand_tensor} \n")
    print(f"Ones Tensor: \n {ones_tensor} \n")
    print(f"Zeros Tensor: \n {zeros_tensor}")



Tensor Attributes
^^^^^^^^^^^^^^^^^

::

    tensor = torch.rand(3, 4)

    print(f"Shape of tensor: {tensor.shape}")
    print(f"Datatype of tensor: {tensor.dtype}")
    print(f"Device tensor is stored on: {tensor.device}")


Tensor Operations
^^^^^^^^^^^^^^^^^

* 超过 100 种张量操作，包括转置、索引、切片、数学运算、线性代数、随机采样等


::

    # We move our tensor to the GPU if available
    if torch.cuda.is_available():
      tensor = tensor.to('cuda')
      print(f"Device tensor is stored on: {tensor.device}")


.. note:: 其他函数参见 ``demo-python`` 项目