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"source": [
"# 多层感知机的从零开始实现\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "0be61c4f",
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"tab": [
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]
},
"outputs": [],
"source": [
"import torch\n",
"from torch import nn\n",
"from d2l import torch as d2l\n",
"\n",
"batch_size = 256\n",
"train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)"
]
},
{
"cell_type": "markdown",
"id": "1484071d",
"metadata": {
"slideshow": {
"slide_type": "slide"
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},
"source": [
"实现一个具有单隐藏层的多层感知机，\n",
"它包含256个隐藏单元"
]
},
{
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"id": "7730f280",
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"tab": [
"pytorch"
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"outputs": [],
"source": [
"num_inputs, num_outputs, num_hiddens = 784, 10, 256\n",
"\n",
"W1 = nn.Parameter(torch.randn(\n",
" num_inputs, num_hiddens, requires_grad=True) * 0.01)\n",
"b1 = nn.Parameter(torch.zeros(num_hiddens, requires_grad=True))\n",
"W2 = nn.Parameter(torch.randn(\n",
" num_hiddens, num_outputs, requires_grad=True) * 0.01)\n",
"b2 = nn.Parameter(torch.zeros(num_outputs, requires_grad=True))\n",
"\n",
"params = [W1, b1, W2, b2]"
]
},
{
"cell_type": "markdown",
"id": "609b91ed",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"实现ReLU激活函数"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "5f46a813",
"metadata": {
"execution": {
"iopub.execute_input": "2023-08-18T06:59:24.528151Z",
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"origin_pos": 13,
"tab": [
"pytorch"
]
},
"outputs": [],
"source": [
"def relu(X):\n",
" a = torch.zeros_like(X)\n",
" return torch.max(X, a)"
]
},
{
"cell_type": "markdown",
"id": "10221d2d",
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"source": [
"实现我们的模型"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "f55fe0ea",
"metadata": {
"execution": {
"iopub.execute_input": "2023-08-18T06:59:24.554675Z",
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"shell.execute_reply": "2023-08-18T06:59:24.559049Z"
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"origin_pos": 23,
"tab": [
"pytorch"
]
},
"outputs": [],
"source": [
"def net(X):\n",
" X = X.reshape((-1, num_inputs))\n",
" H = relu(X@W1 + b1)\n",
" return (H@W2 + b2)\n",
"\n",
"loss = nn.CrossEntropyLoss(reduction='none')"
]
},
{
"cell_type": "markdown",
"id": "09b086e0",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"多层感知机的训练过程与softmax回归的训练过程完全相同"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "c83cc0c7",
"metadata": {
"execution": {
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"tab": [
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"outputs": [
{
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"\n",
"\n",
"\n"
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}
],
"source": [
"num_epochs, lr = 10, 0.1\n",
"updater = torch.optim.SGD(params, lr=lr)\n",
"d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, updater)"
]
},
{
"cell_type": "markdown",
"id": "e08ce45c",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"在一些测试数据上应用这个模型"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "8230ba7c",
"metadata": {
"execution": {
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"tab": [
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},
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{
"data": {
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"\n",
"\n"
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"text/plain": [
""
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},
"metadata": {
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}
],
"source": [
"d2l.predict_ch3(net, test_iter)"
]
}
],
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