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parameter_introduction.ipynb 3.90 KB

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    "# Parameter\n",
    "\n",
    "`Ascend` `GPU` `CPU` `入门`\n",
    "\n",
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    "`Parameter`是变量张量，代表在训练网络时，需要被更新的参数，一般包括权重`weight`和偏置`bias`。\n",
    "\n",
    "例如，对于卷积算子`nn.Conv2D`， `weight`是`[out_channel, in_channel, kernel_size, kernel_size]`大小的张量， `bias`是`[out_channel]`大小的向量。而对于全连接操作`nn.Dense`，`weight`是`[out_channel, in_channel]`大小的张量， `bias`是`[out_channel]`大小的向量。\n",
    "\n",
    "```python\n",
    "import numpy as np\n",
    "from mindspore import Tensor\n",
    "from mindspore import nn\n",
    "\n",
    "conv_net = nn.Conv2d(in_channels=120, out_channels=240, kernel_size=4, has_bias=True, weight_init='normal')\n",
    "dense_net = nn.Dense(in_channels=3, out_channels=4, weight_init='normal', bias_init='zeros', has_bias=True)\n",
    "\n",
    "print(\"conv_net, weight shape:\", conv_net.weight.shape, \", bias shape:\", conv_net.bias.shape)\n",
    "print(\"dense_net, weight_shape：\", dense_net.weight.shape, \", bias shape:\", dense_net.bias.shape)\n",
    "```\n",
    "\n",
    "```python\n",
    "conv_net, weight shape: (240, 120, 4, 4) , bias shape:  (240,)\n",
    "dense_net, weight_shape: (4, 3) , bias shape:  (4,)\n",
    "```\n",
    "\n",
    "`nn`下的算子接口中一般集成了权重和偏置，用户可以指定初始化方式来初始化这些`Parameter`。\n",
    "\n",
    "在网络训练过程中，优化器根据反向传播计算出的梯度不断更新`Parameter`的值，最优的`Parameter`最终会被保存下来供推理使用。\n",
    "\n",
    "`Parameter`不支持稀疏矩阵，如[mindspore.RowTensor](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/mindspore/mindspore.RowTensor.html)、[mindspore.SparseTensor](https://www.mindspore.cn/docs/api/zh-CN/r1.6/api_python/mindspore/mindspore.SparseTensor.html)。其初始化和更新的详细介绍可参考[参数初始化](https://www.mindspore.cn/docs/programming_guide/zh-CN/r1.6/initializer.html)和[参数更新](https://www.mindspore.cn/docs/programming_guide/zh-CN/r1.6/parameter.html)。"
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