84 lines
2.5 KiB
Plaintext
84 lines
2.5 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"MultiLayerPerceptron(\n",
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" (hidden_layer1): Linear(in_features=10, out_features=20, bias=True)\n",
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" (hidden_layer2): Linear(in_features=20, out_features=10, bias=True)\n",
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" (output_layer): Linear(in_features=10, out_features=2, bias=True)\n",
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" (activation1): ReLU()\n",
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" (activation2): Sigmoid()\n",
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")\n"
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]
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}
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],
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"source": [
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"import torch\n",
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"import torch.nn as nn\n",
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"\n",
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"# 设置使用gpu7\n",
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"device = torch.device(\"cuda:7\" if torch.cuda.is_available() else \"cpu\")\n",
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"\n",
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"# 定义一个简单的神经元层\n",
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"class MultiLayerPerceptron(nn.Module):\n",
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" def __init__(self, input_size, hidden_size1, hidden_size2, output_size):\n",
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" super(MultiLayerPerceptron, self).__init__()\n",
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" self.hidden_layer1 = nn.Linear(input_size, hidden_size1)\n",
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" self.hidden_layer2 = nn.Linear(hidden_size1, hidden_size2)\n",
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" self.output_layer = nn.Linear(hidden_size2, output_size)\n",
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" \n",
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" # 定义不同的激活函数\n",
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" self.activation1 = nn.ReLU() # 第一个隐藏层使用 ReLU\n",
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" self.activation2 = nn.Sigmoid() # 第二个隐藏层使用 Sigmoid\n",
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"\n",
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" def forward(self, x):\n",
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" # 第一个隐藏层及其激活函数\n",
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" x = self.hidden_layer1(x)\n",
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" x = self.activation1(x)\n",
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" \n",
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" # 第二个隐藏层及其激活函数\n",
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" x = self.hidden_layer2(x)\n",
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" x = self.activation2(x)\n",
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" \n",
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" # 输出层\n",
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" x = self.output_layer(x)\n",
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" return x\n",
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"\n",
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"# 创建一个MLP实例\n",
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"mlp = MultiLayerPerceptron(input_size=10, hidden_size1=20, hidden_size2=10, output_size=2)\n",
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"\n",
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"# 打印模型结构\n",
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"print(mlp)\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "ail",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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