{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# %pip install sklearn" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import torch\n", "import torch.nn as nn\n", "import pandas as pd\n", "import numpy as np\n", "from sklearn.preprocessing import MinMaxScaler" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(40, 2)\n" ] } ], "source": [ "# 加载数据集\n", "data = pd.read_csv('translated_speed_strain_useOriginSpeed.csv')\n", "data=data.head(200)\n", "# 提取特征\n", "# features = data[['焊接电压', '焊接电流', '焊接速度', 'x', 'y', 'z', '3', '4', '5','strain']]\n", "\n", "\n", "features=pd.DataFrame()\n", "features['UIV']=data['焊接电压']*data['焊接电流']/data['焊接速度']\n", "features[\"Q\"]=(data['焊接电压']*data['焊接电流']/data['焊接速度'])*0.75\n", "mu,sigma=0,5\n", "noise= np.random.normal(mu, sigma, len(features[\"Q\"]))\n", "features[\"Q\"]=features[\"Q\"]+noise\n", "\n", "\n", "features=features.to_numpy()\n", "\n", "train_size = int(0.8*len(features))\n", "train_sequences = features[:train_size]\n", "test_sequences = features[train_size:]\n", "\n", "\n", "print(test_sequences.shape)" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [], "source": [ "class LSTM(nn.Module):\n", " def __init__(self, input_size, hidden_size, num_layers, output_size):\n", " super(LSTM, self).__init__()\n", " # self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)\n", " # self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)\n", " # self.lstm=nn.RNN(input_size, hidden_size, num_layers, batch_first=True)\n", " self.fc0=nn.Linear(input_size,hidden_size,bias=False)\n", " self.relu = nn.ReLU()\n", " self.fc = nn.Linear(hidden_size, output_size)\n", " \n", " nn.init.constant_(self.fc0.weight,0.78)\n", " nn.init.xavier_uniform_(self.fc.weight)\n", " nn.init.zeros_(self.fc.bias)\n", "\n", " def forward(self, x):\n", " # out, _ = self.lstm(x)\n", " out0=self.fc0(x)\n", " # out1=self.relu(out0)\n", " # print(f\"out{out[0]}\")\n", " # print(f\"out{out[-1]}\")\n", " # out2 = self.fc(out1)\n", " return out0" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [], "source": [ "input_size = 1\n", "hidden_size = 1\n", "num_layers = 2\n", "output_size = 1\n", "model = LSTM(input_size, hidden_size, num_layers, output_size)\n", "\n", "# 定义优化器和损失函数\n", "learning_rate = 0.01\n", "# learning_rate=1\n", "criterion = nn.MSELoss()\n", "# criterion=nn.L1Loss()\n", "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/50, Loss: 26.1416\n", "[[0.7336207032203674]]\n", "Epoch 2/50, Loss: 20.0536\n", "[[0.7503769993782043]]\n", "Epoch 3/50, Loss: 18.3776\n", "[[0.7569617033004761]]\n", "Epoch 4/50, Loss: 20.1773\n", "[[0.7479583024978638]]\n", "Epoch 5/50, Loss: 19.8454\n", "[[0.7500355839729309]]\n", "Epoch 6/50, Loss: 19.2315\n", "[[0.7522401809692383]]\n", "Epoch 7/50, Loss: 19.7850\n", "[[0.7499220967292786]]\n", "Epoch 8/50, Loss: 19.5623\n", "[[0.7510177493095398]]\n", "Epoch 9/50, Loss: 19.5571\n", "[[0.7509536743164062]]\n", "Epoch 10/50, Loss: 19.6496\n", "[[0.7506616711616516]]\n", "Epoch 11/50, Loss: 19.5702\n", "[[0.7510122060775757]]\n", "Epoch 12/50, Loss: 19.6270\n", "[[0.7507977485656738]]\n", "Epoch 13/50, Loss: 19.6025\n", "[[0.7509378790855408]]\n", "Epoch 14/50, Loss: 19.6167\n", "[[0.7508964538574219]]\n", "Epoch 15/50, Loss: 19.6162\n", "[[0.7509273290634155]]\n", "Epoch 16/50, Loss: 19.6183\n", "[[0.7509388327598572]]\n", "Epoch 17/50, Loss: 19.6221\n", "[[0.7509452700614929]]\n", "Epoch 18/50, Loss: 19.6225\n", "[[0.7509629726409912]]\n", "Epoch 19/50, Loss: 19.6260\n", "[[0.7509673833847046]]\n", "Epoch 20/50, Loss: 19.6267\n", "[[0.7509824633598328]]\n", "Epoch 21/50, Loss: 19.6294\n", "[[0.7509878873825073]]\n", "Epoch 22/50, Loss: 19.6303\n", "[[0.7510002255439758]]\n", "Epoch 23/50, Loss: 19.6325\n", "[[0.7510063052177429]]\n", "Epoch 24/50, Loss: 19.6334\n", "[[0.7510166168212891]]\n", "Epoch 25/50, Loss: 19.6352\n", "[[0.7510228157043457]]\n", "Epoch 26/50, Loss: 19.6362\n", "[[0.7510318756103516]]\n", "Epoch 27/50, Loss: 19.6378\n", "[[0.7510377168655396]]\n", "Epoch 28/50, Loss: 19.6386\n", "[[0.7510459423065186]]\n", "Epoch 29/50, Loss: 19.6401\n", "[[0.7510514259338379]]\n", "Epoch 30/50, Loss: 19.6409\n", "[[0.7510588765144348]]\n", "Epoch 31/50, Loss: 19.6421\n", "[[0.7510640621185303]]\n", "Epoch 32/50, Loss: 19.6429\n", "[[0.7510709166526794]]\n", "Epoch 33/50, Loss: 19.6440\n", "[[0.7510756850242615]]\n", "Epoch 34/50, Loss: 19.6447\n", "[[0.7510820627212524]]\n", "Epoch 35/50, Loss: 19.6458\n", "[[0.7510864734649658]]\n", "Epoch 36/50, Loss: 19.6464\n", "[[0.7510923743247986]]\n", "Epoch 37/50, Loss: 19.6473\n", "[[0.7510964870452881]]\n", "Epoch 38/50, Loss: 19.6479\n", "[[0.7511020302772522]]\n", "Epoch 39/50, Loss: 19.6488\n", "[[0.751105785369873]]\n", "Epoch 40/50, Loss: 19.6493\n", "[[0.7511110901832581]]\n", "Epoch 41/50, Loss: 19.6502\n", "[[0.7511143684387207]]\n", "Epoch 42/50, Loss: 19.6505\n", "[[0.7511195540428162]]\n", "Epoch 43/50, Loss: 19.6515\n", "[[0.7511224150657654]]\n", "Epoch 44/50, Loss: 19.6517\n", "[[0.7511274814605713]]\n", "Epoch 45/50, Loss: 19.6527\n", "[[0.7511299252510071]]\n", "Epoch 46/50, Loss: 19.6528\n", "[[0.751134991645813]]\n", "Epoch 47/50, Loss: 19.6537\n", "[[0.7511370182037354]]\n", "Epoch 48/50, Loss: 19.6538\n", "[[0.7511419653892517]]\n", "Epoch 49/50, Loss: 19.6548\n", "[[0.7511436939239502]]\n", "Epoch 50/50, Loss: 19.6548\n", "[[0.7511485815048218]]\n" ] } ], "source": [ "# 训练模型\n", "num_epochs = 50\n", "batch_size = 16\n", "for epoch in range(num_epochs):\n", " for i in range(0, len(train_sequences), batch_size):\n", " batch_sequences = train_sequences[i:i+batch_size]\n", " # print(batch_sequences.shape)\n", " inputs = torch.tensor(batch_sequences[:, :-1], dtype=torch.float32)\n", " \n", " targets = torch.tensor(batch_sequences[:,-1], dtype=torch.float32)\n", " if len(inputs) != batch_size:\n", " continue\n", " \n", " # 前向传播\n", " outputs = model(inputs)\n", " # print(f\"targets {targets}\")\n", " \n", " # print(f\"input:{inputs}\")\n", " \n", " # print(f\"outputs {outputs}\")\n", " \n", " outputs=outputs.reshape(targets.shape)\n", " # print(outputs.shape)\n", " # print(f\"output:{outputs.shape}\")\n", " # print(f\"target:{targets.shape}\")\n", " loss = criterion(outputs, targets)\n", " # print(f\"loss:{loss}\")\n", " # loss=loss*1000\n", " # 反向传播和优化\n", " loss.backward()\n", " optimizer.step()\n", " optimizer.zero_grad()\n", " \n", " \n", " \n", " # if epoch>996:\n", " if True:\n", " print(f'Epoch {epoch+1}/{num_epochs}, Loss: {loss.item():.4f}')\n", " \n", " print(model.fc0.weight.tolist())\n", " # print(model.fc.weight.tolist())\n", " # print(model.fc.bias)\n", " # print(model.fc.weight[0][0])\n", " \n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "ename": "", "evalue": "", "output_type": "error", "traceback": [ "\u001b[1;31mnotebook controller is DISPOSED. \n", "\u001b[1;31mView Jupyter log for further details." ] } ], "source": [ "# num_epochs = 30\n", "# batch_size = 32\n", "# learning_rate=0.05\n", "# for k in range(8):\n", "# learning_rate*=0.5\n", "# print(f\"learning_rate:{learning_rate}\")\n", "# for epoch in range(num_epochs):\n", "# for i in range(0, len(train_sequences), batch_size):\n", "# batch_sequences = train_sequences[i:i+batch_size]\n", "# inputs = torch.tensor(batch_sequences[:, :-1,:-1], dtype=torch.float32)\n", "# targets = torch.tensor(batch_sequences[:, -1, -1], dtype=torch.float32)\n", "# if len(inputs) != batch_size:\n", "# continue\n", " \n", "# # 前向传播\n", "# # print(inputs.shape)\n", "# outputs = model(inputs)\n", "# loss = criterion(outputs, targets)\n", " \n", "# # 反向传播和优化\n", "# optimizer.zero_grad()\n", "# loss.backward()\n", "# optimizer.step()\n", " \n", "# print(f'Epoch {epoch+1}/{num_epochs}, Loss: {loss.item():.4f}')" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "test_inputs_shapetorch.Size([40, 1])\n", "Test Loss: 21.8248\n" ] } ], "source": [ "# 在测试集上进行评估\n", "model.eval()\n", "test_inputs = torch.tensor(test_sequences[ :,:-1], dtype=torch.float32)\n", "test_targets = torch.tensor(test_sequences[ :, -1], dtype=torch.float32)\n", "print(f\"test_inputs_shape{test_inputs.shape}\")\n", "test_outputs = model(test_inputs)\n", "test_loss = criterion(test_outputs, test_targets)\n", "# print(test_outputs)\n", "# print(test_targets)\n", "print(f'Test Loss: {test_loss.item():.4f}')" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Parameter containing:\n", "tensor([[0.7608]], requires_grad=True)\n", "Parameter containing:\n", "tensor([-1.3928], requires_grad=True)\n", "tensor([6.2156], grad_fn=)\n" ] } ], "source": [ "print(model.fc0.weight)\n", "\n", "one_point=torch.tensor([10],dtype=torch.float32)\n", "print(model(one_point))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "读取点修正模型" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.7511485815048218\n" ] } ], "source": [ "model.fc0.weight.tolist()[0][0]" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": false }, "data": [ { "mode": "lines+markers", "type": "scatter", "x": [ 1, 2, 3, 4, 5 ], "y": [ 1, 3, 2, 4, 3 ] } ], "layout": { "template": { "data": { "bar": [ { "error_x": { "color": "#2a3f5f" }, "error_y": { "color": "#2a3f5f" }, "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "bar" } ], "barpolar": [ { "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "barpolar" } ], "carpet": [ { "aaxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "baxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "type": "carpet" } ], "choropleth": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "choropleth" } ], "contour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "contour" } ], "contourcarpet": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "contourcarpet" } ], "heatmap": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmap" } ], "heatmapgl": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmapgl" } ], "histogram": [ { "marker": { "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "histogram" } ], "histogram2d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2d" } ], "histogram2dcontour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2dcontour" } ], "mesh3d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "mesh3d" } ], "parcoords": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "parcoords" } ], "pie": [ { "automargin": true, "type": "pie" } ], "scatter": [ { "fillpattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 }, "type": "scatter" } ], "scatter3d": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter3d" } ], "scattercarpet": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattercarpet" } ], "scattergeo": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergeo" } ], "scattergl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergl" } ], "scattermapbox": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattermapbox" } ], "scatterpolar": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolar" } ], "scatterpolargl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolargl" } ], "scatterternary": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterternary" } ], "surface": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "surface" } ], "table": [ { "cells": { "fill": { "color": "#EBF0F8" }, "line": { "color": "white" } }, "header": { "fill": { "color": "#C8D4E3" }, "line": { "color": "white" } }, "type": "table" } ] }, "layout": { "annotationdefaults": { "arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1 }, "autotypenumbers": "strict", "coloraxis": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "colorscale": { "diverging": [ [ 0, "#8e0152" ], [ 0.1, "#c51b7d" ], [ 0.2, "#de77ae" ], [ 0.3, "#f1b6da" ], [ 0.4, "#fde0ef" ], [ 0.5, "#f7f7f7" ], [ 0.6, "#e6f5d0" ], [ 0.7, "#b8e186" ], [ 0.8, "#7fbc41" ], [ 0.9, "#4d9221" ], [ 1, "#276419" ] ], "sequential": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "sequentialminus": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ] }, "colorway": [ "#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52" ], "font": { "color": "#2a3f5f" }, "geo": { "bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": { "angularaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "radialaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import plotly.graph_objs as go\n", "from plotly.offline import iplot, init_notebook_mode\n", "\n", "# 初始化Plotly的Notebook模式\n", "init_notebook_mode(connected=True)\n", "\n", "# 创建初始的折线图数据\n", "x = [1, 2, 3, 4, 5]\n", "y = [1, 3, 2, 4, 3]\n", "\n", "trace = go.Scatter(x=x, y=y, mode='lines+markers')\n", "\n", "data = [trace]\n", "\n", "# 绘制初始的折线图\n", "fig = go.Figure(data=data)\n", "\n", "iplot(fig)\n", "\n", "# 动态添加新的点\n", "new_x = [6]\n", "new_y = [2]\n", "fig.add_trace(go.Scatter(x=new_x, y=new_y, mode='markers'))\n", "\n", "# 更新折线图数据\n", "fig.update_traces(x=[list(fig.data[0].x) + new_x],\n", " y=[list(fig.data[0].y) + new_y])\n", "\n", "\n", "# iplot(fig)" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "mode": "lines+markers", "type": "scatter", "x": [ [ 1, 2, 3, 4, 5, 6 ] ], "y": [ [ 1, 3, 2, 4, 3, 2 ] ] }, { "mode": "markers", "type": "scatter", "x": [ [ 1, 2, 3, 4, 5, 6 ] ], "y": [ [ 1, 3, 2, 4, 3, 2 ] ] } ], "layout": { "template": { "data": { "bar": [ { "error_x": { "color": "#2a3f5f" }, "error_y": { "color": "#2a3f5f" }, "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "bar" } ], "barpolar": [ { "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "barpolar" } ], "carpet": [ { "aaxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "baxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "type": "carpet" } ], "choropleth": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "choropleth" } ], "contour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "contour" } ], "contourcarpet": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "contourcarpet" } ], "heatmap": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmap" } ], "heatmapgl": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmapgl" } ], "histogram": [ { "marker": { "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "histogram" } ], "histogram2d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2d" } ], "histogram2dcontour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2dcontour" } ], "mesh3d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "mesh3d" } ], "parcoords": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "parcoords" } ], "pie": [ { "automargin": true, "type": "pie" } ], "scatter": [ { "fillpattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 }, "type": "scatter" } ], "scatter3d": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter3d" } ], "scattercarpet": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattercarpet" } ], "scattergeo": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergeo" } ], "scattergl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergl" } ], "scattermapbox": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattermapbox" } ], "scatterpolar": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolar" } ], "scatterpolargl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolargl" } ], "scatterternary": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterternary" } ], "surface": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "surface" } ], "table": [ { "cells": { "fill": { "color": "#EBF0F8" }, "line": { "color": "white" } }, "header": { "fill": { "color": "#C8D4E3" }, "line": { "color": "white" } }, "type": "table" } ] }, "layout": { "annotationdefaults": { "arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1 }, "autotypenumbers": "strict", "coloraxis": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "colorscale": { "diverging": [ [ 0, "#8e0152" ], [ 0.1, "#c51b7d" ], [ 0.2, "#de77ae" ], [ 0.3, "#f1b6da" ], [ 0.4, "#fde0ef" ], [ 0.5, "#f7f7f7" ], [ 0.6, "#e6f5d0" ], [ 0.7, "#b8e186" ], [ 0.8, "#7fbc41" ], [ 0.9, "#4d9221" ], [ 1, "#276419" ] ], "sequential": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "sequentialminus": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ] }, "colorway": [ "#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52" ], "font": { "color": "#2a3f5f" }, "geo": { "bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": { "angularaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "radialaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "\n", "\n", "# for epoch in range(num_epochs):\n", "# for i in range(0, len(train_sequences), batch_size):\n", "# batch_sequences = train_sequences[i:i+batch_size]\n", "# # print(batch_sequences.shape)\n", "# inputs = torch.tensor(batch_sequences[:, :-1], dtype=torch.float32)\n", " \n", "# targets = torch.tensor(batch_sequences[:,-1], dtype=torch.float32)\n", "# if len(inputs) != batch_size:\n", "# continue\n", " \n", "# # 前向传播\n", "# outputs = model(inputs)\n", "# # print(f\"targets {targets}\")\n", " \n", "# # print(f\"input:{inputs}\")\n", " \n", "# # print(f\"outputs {outputs}\")\n", " \n", "# outputs=outputs.reshape(targets.shape)\n", "# # print(outputs.shape)\n", "# # print(f\"output:{outputs.shape}\")\n", "# # print(f\"target:{targets.shape}\")\n", "# loss = criterion(outputs, targets)\n", "# # print(f\"loss:{loss}\")\n", "# # loss=loss*1000\n", "# # 反向传播和优化\n", "# loss.backward()\n", "# optimizer.step()\n", "# optimizer.zero_grad()\n", " \n", " \n", " \n", "# # if epoch>996:\n", "# if True:\n", "# print(f'Epoch {epoch+1}/{num_epochs}, Loss: {loss.item():.4f}')\n", " \n", "# print(model.fc0.weight.tolist())\n", "# print(model.fc0.bias)" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [], "source": [ "torch.save(model.state_dict(), 'lstm_model.pt')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "读取" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "ename": "", "evalue": "", "output_type": "error", "traceback": [ "\u001b[1;31mnotebook controller is DISPOSED. \n", "\u001b[1;31mView Jupyter log for further details." ] } ], "source": [ "model = LSTM(input_size, hidden_size, num_layers, output_size)\n", "\n", "model.load_state_dict(torch.load('lstm_model.pt'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "下面是尝试反缩放归一化的数据并在测试集测试，发现误差很大，所以决定不再缩放" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "ename": "", "evalue": "", "output_type": "error", "traceback": [ "\u001b[1;31mnotebook controller is DISPOSED. \n", "\u001b[1;31mView Jupyter log for further details." ] } ], "source": [ "\n", "model.eval()\n", "test_inputs = torch.tensor(test_sequences[:, :-1,:-1], dtype=torch.float32)\n", "test_targets = torch.tensor(test_sequences[:, -1, -1], dtype=torch.float32)\n", "test_outputs = model(test_inputs)\n", "\n", "print(test_outputs.shape)\n", "print(test_outputs.size())\n", "t=np.zeros((test_outputs.shape[0],1),dtype=float)\n", "\n", "t=torch.Tensor(t)\n", "print(t.shape)\n", "test_outputs=torch.cat([t, test_outputs], dim=1)\n", "print(test_outputs.shape)\n", "\n", "test_outputs = scaler.inverse_transform(test_outputs.detach().numpy() )\n", "test_targets = scaler.inverse_transform(torch.tensor(test_sequences[:, -1, :]).detach().numpy())\n", "test_outputs=torch.tensor(test_outputs[:,-1], dtype=torch.float32)\n", "test_targets=torch.tensor(test_targets[:,-1], dtype=torch.float32)\n", "print(test_outputs)\n", "print(test_targets)\n", "# print(test_outputs.shape)\n", "# print(test_targets.shape)\n", "\n", "test_loss = criterion(test_outputs, test_targets)\n", "print(f'Test Loss: {test_loss:.4f}')\n", "# a=test_outputs>0.00007\n", "# print(torch.sum(a))\n", "# b=test_targets>0.00007\n", "# print(torch.sum(b))\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3.9.16 ('torch_py3.9')", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.16" }, "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "2c93e8e09754cf1cd40b55c7c8fefeaff6ac58d6a85dd394fdad2eae466f5690" } } }, "nbformat": 4, "nbformat_minor": 2 }