df['label'] = df['label'].map(label_dict)
时间: 2023-10-19 10:06:30 浏览: 20
你的这个问题是关于Python中对DataFrame进行数据映射的操作。DataFrame是一种二维表格数据结构,label_dict是一个字典类型的数据,可以通过map()函数进行数据映射,将df['label']中的每个元素映射为对应的label_dict中的值。具体来说,这段代码将df['label']中的每个元素替换为label_dict中对应的值。
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这段代码中加一个test loss功能 class LSTM(nn.Module): def __init__(self, input_size, hidden_size, num_layers, output_size, batch_size, device): super().__init__() self.device = device self.input_size = input_size self.hidden_size = hidden_size self.num_layers = num_layers self.output_size = output_size self.num_directions = 1 # 单向LSTM self.batch_size = batch_size self.lstm = nn.LSTM(self.input_size, self.hidden_size, self.num_layers, batch_first=True) self.linear = nn.Linear(65536, self.output_size) def forward(self, input_seq): h_0 = torch.randn(self.num_directions * self.num_layers, self.batch_size, self.hidden_size).to(self.device) c_0 = torch.randn(self.num_directions * self.num_layers, self.batch_size, self.hidden_size).to(self.device) output, _ = self.lstm(input_seq, (h_0, c_0)) pred = self.linear(output.contiguous().view(self.batch_size, -1)) return pred if __name__ == '__main__': # 加载已保存的模型参数 saved_model_path = '/content/drive/MyDrive/危急值/model/dangerous.pth' device = 'cuda:0' lstm_model = LSTM(input_size=1, hidden_size=64, num_layers=1, output_size=3, batch_size=256, device='cuda:0').to(device) state_dict = torch.load(saved_model_path) lstm_model.load_state_dict(state_dict) dataset = ECGDataset(X_train_df.to_numpy()) dataloader = DataLoader(dataset, batch_size=256, shuffle=True, num_workers=0, drop_last=True) loss_fn = nn.CrossEntropyLoss() optimizer = optim.SGD(lstm_model.parameters(), lr=1e-4) for epoch in range(200000): print(f'epoch:{epoch}') lstm_model.train() epoch_bar = tqdm(dataloader) for x, y in epoch_bar: optimizer.zero_grad() x_out = lstm_model(x.to(device).type(torch.cuda.FloatTensor)) loss = loss_fn(x_out, y.long().to(device)) loss.backward() epoch_bar.set_description(f'loss:{loss.item():.4f}') optimizer.step() if epoch % 100 == 0 or epoch == epoch - 1: torch.save(lstm_model.state_dict(), "/content/drive/MyDrive/危急值/model/dangerous.pth") print("权重成功保存一次")
以下是加入 Test Loss 功能后的代码:
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
class LSTM(nn.Module):
def __init__(self, input_size, hidden_size, num_layers, output_size, batch_size, device):
super().__init__()
self.device = device
self.input_size = input_size
self.hidden_size = hidden_size
self.num_layers = num_layers
self.output_size = output_size
self.num_directions = 1 # 单向LSTM
self.batch_size = batch_size
self.lstm = nn.LSTM(self.input_size, self.hidden_size, self.num_layers, batch_first=True)
self.linear = nn.Linear(65536, self.output_size)
def forward(self, input_seq):
h_0 = torch.randn(self.num_directions * self.num_layers, self.batch_size, self.hidden_size).to(self.device)
c_0 = torch.randn(self.num_directions * self.num_layers, self.batch_size, self.hidden_size).to(self.device)
output, _ = self.lstm(input_seq, (h_0, c_0))
pred = self.linear(output.contiguous().view(self.batch_size, -1))
return pred
if __name__ == '__main__':
# 加载已保存的模型参数
saved_model_path = '/content/drive/MyDrive/危急值/model/dangerous.pth'
device = 'cuda:0'
lstm_model = LSTM(input_size=1, hidden_size=64, num_layers=1, output_size=3, batch_size=256, device=device).to(device)
state_dict = torch.load(saved_model_path)
lstm_model.load_state_dict(state_dict)
# 加载测试数据集
test_dataset = ECGDataset(X_test_df.to_numpy())
test_loader = DataLoader(test_dataset, batch_size=256, shuffle=False, num_workers=0, drop_last=False)
# 定义损失函数和优化器
loss_fn = nn.CrossEntropyLoss()
optimizer = optim.SGD(lstm_model.parameters(), lr=1e-4)
# 开始训练
for epoch in range(200000):
print(f'epoch:{epoch}')
lstm_model.train()
epoch_bar = tqdm(dataloader)
for x, y in epoch_bar:
optimizer.zero_grad()
x_out = lstm_model(x.to(device).type(torch.cuda.FloatTensor))
loss = loss_fn(x_out, y.long().to(device))
loss.backward()
epoch_bar.set_description(f'loss:{loss.item():.4f}')
optimizer.step()
# 计算测试集上的平均损失
test_loss = 0.0
lstm_model.eval()
with torch.no_grad():
for x, y in test_loader:
x_out = lstm_model(x.to(device).type(torch.cuda.FloatTensor))
loss = loss_fn(x_out, y.long().to(device))
test_loss += loss.item() * x.size(0)
test_loss /= len(test_dataset)
print(f'Test Loss: {test_loss:.4f}')
if epoch % 100 == 0 or epoch == epoch - 1:
torch.save(lstm_model.state_dict(), "/content/drive/MyDrive/危急值/model/dangerous.pth")
print("权重成功保存一次")
```
在这个代码中,我们首先加载了测试数据集 `test_dataset` 和测试数据加载器 `test_loader`。在每个 epoch 完成后,我们计算测试集上的平均损失值。我们使用 `lstm_model.eval()` 将模型设为评估模式,并利用 `torch.no_grad()` 避免计算梯度,以加速计算。最后,我们输出测试集上的平均损失值。
import tkinter as tk from tkinter import filedialog from PIL import ImageTk, Image # 创建窗口 window = tk.Tk() window.title("宝石预测") window.geometry("400x400") # 加载模型参数 para_state_dict = paddle.load("MyCNN") model = MyCNN() model.set_state_dict(para_state_dict) model.eval() # 加载标签字典 label_dict = train_parameters['label_dict'] # 创建预测函数 def predict(): # 获取待预测图片路径 img_path = filedialog.askopenfilename() img = Image.open(img_path) # 预处理图片 img = img.resize((224, 224), Image.BILINEAR) img = np.array(img).astype('float32') img = img.transpose((2, 0, 1)) img = img / 255 img = np.array([img]) # 进行预测 img = paddle.to_tensor(img) out = model(img) label = np.argmax(out.numpy()) result = label_dict[str(label)] # 显示预测结果 result_label.config(text="预测结果:{}".format(result)) # 显示待预测图片 img = ImageTk.PhotoImage(Image.open(img_path).resize((200, 200))) img_label.config(image=img) img_label.image = img # 创建选择图片按钮 select_button = tk.Button(window, text="选择图片", command=predict) select_button.pack(pady=20) # 创建待预测图片区域 img_label = tk.Label(window) img_label.pack() # 创建预测结果区域 result_label = tk.Label(window, font=("Helvetica", 16)) result_label.pack(pady=20) # 进入消息循环 window.mainloop()在这段代码的功能中加入使用cv2对选择的图片进行降噪,之后再对上传图片进行识别
好的,你可以使用 OpenCV 库对选择的图片进行降噪,然后将处理后的图片传给预测函数进行识别。下面是修改后的代码:
```
import tkinter as tk
from tkinter import filedialog
from PIL import ImageTk, Image
import cv2
import numpy as np
# 创建窗口
window = tk.Tk()
window.title("宝石预测")
window.geometry("400x400")
# 加载模型参数
para_state_dict = paddle.load("MyCNN")
model = MyCNN()
model.set_state_dict(para_state_dict)
model.eval()
# 加载标签字典
label_dict = train_parameters['label_dict']
# 创建预测函数
def predict():
# 获取待预测图片路径
img_path = filedialog.askopenfilename()
# 降噪处理
img = cv2.imread(img_path)
img = cv2.fastNlMeansDenoisingColored(img, None, 10, 10, 7, 21)
# 预处理图片
img = cv2.resize(img, (224, 224))
img = img.transpose((2, 0, 1))
img = img / 255
img = np.array([img])
# 进行预测
img = paddle.to_tensor(img)
out = model(img)
label = np.argmax(out.numpy())
result = label_dict[str(label)]
# 显示预测结果
result_label.config(text="预测结果:{}".format(result))
# 显示待预测图片
img = ImageTk.PhotoImage(Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)).resize((200, 200)))
img_label.config(image=img)
img_label.image = img
# 创建选择图片按钮
select_button = tk.Button(window, text="选择图片", command=predict)
select_button.pack(pady=20)
# 创建待预测图片区域
img_label = tk.Label(window)
img_label.pack()
# 创建预测结果区域
result_label = tk.Label(window, font=("Helvetica", 16))
result_label.pack(pady=20)
# 进入消息循环
window.mainloop()
```