output = model(batch_img.to(device)).cpu() predict = torch.softmax(output, dim=1) probs, classes = torch.max(predict, dim=1)

这段代码用于对当前批次中的所有图像进行推断，并返回每个图像的预测结果。具体地，代码首先将当前批次中的所有图像作为输入，传入已经定义并加载了预训练权重的模型中进行推断。在该代码中，`batch_img.to(device)`将图像张量移动到指定的计算设备上（如GPU），以加速计算。

然后，代码使用`torch.softmax`函数对模型输出进行归一化，以获得每个类别的概率。`dim=1`表示在第1维上进行归一化，即对每个图像的预测结果进行归一化。

接着，代码使用`torch.max`函数返回每个图像预测结果中概率最高的类别和对应的概率值。在该代码中，`dim=1`表示在第1维上查找最大值，即对每个图像的预测结果进行查找。

最终，代码将每个图像的预测结果命名为`probs`和`classes`并返回。`probs`是一个张量，存储了每个图像预测结果中概率最高的类别的概率值；`classes`是一个张量，存储了每个图像预测结果中概率最高的类别的下标。

需要注意的是，这段代码假定了已经定义并加载了预训练权重的模型。如果模型没有被定义或未加载权重，程序会抛出`NameError`或`RuntimeError`异常。此外，代码还假定了计算设备已经正确设置并且可用。如果计算设备不存在或不可用，程序会抛出`RuntimeError`异常。

相关问题

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：

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。

from keras import applications from keras.preprocessing.image import ImageDataGenerator from keras import optimizers from keras.models import Sequential, Model from keras.layers import Dropout, Flatten, Dense img_width, img_height = 256, 256 batch_size = 16 epochs = 50 train_data_dir = 'C:/Users/Z-/Desktop/kaggle/train' validation_data_dir = 'C:/Users/Z-/Desktop/kaggle/test1' OUT_CATAGORIES = 1 nb_train_samples = 2000 nb_validation_samples = 100 base_model = applications.VGG16(weights='imagenet', include_top=False, input_shape=(img_width, img_height, 3)) base_model.summary() for layer in base_model.layers[:15]: layer.trainable = False top_model = Sequential() top_model.add(Flatten(input_shape=base_model.output_shape[1:])) top_model.add(Dense(256, activation='relu')) top_model.add(Dropout(0.5)) top_model.add(Dense(OUT_CATAGORIES, activation='sigmoid')) model = Model(inputs=base_model.input, outputs=top_model(base_model.output)) model.compile(loss='binary_crossentropy', optimizer=optimizers.SGD(learning_rate=0.0001, momentum=0.9), metrics=['accuracy']) train_datagen = ImageDataGenerator(rescale=1. / 255, horizontal_flip=True) test_datagen = ImageDataGenerator(rescale=1. / 255) train_generator = train_datagen.flow_from_directory( train_data_dir, target_size=(img_height, img_width), batch_size=batch_size, class_mode='binary') validation_generator = test_datagen.flow_from_directory( validation_data_dir, target_size=(img_height, img_width), batch_size=batch_size, class_mode='binary', shuffle=False ) model.fit_generator( train_generator, steps_per_epoch=nb_train_samples / batch_size, epochs=epochs, validation_data=validation_generator, validation_steps=nb_validation_samples / batch_size, verbose=2, workers=12 ) score = model.evaluate_generator(validation_generator, nb_validation_samples / batch_size) scores = model.predict_generator(validation_generator, nb_validation_samples / batch_size)看看这段代码有什么错误

在段代码中，有一些错误和需要注意的地方：

1. `img_width` 和 `img_height` 变量没有被定义，需要先定义它们的值。
2. `OUT_CATAGORIES` 变量也没有被定义，需要给它赋值。
3. 在使用 `train_generator` 和 `validation_generator` 时，`steps_per_epoch` 和 `validation_steps` 应该为整数值，而不是浮点数。可以使用 `//` 运算符来进行整除操作。
4. 在评估模型的性能时，`evaluate_generator` 和 `predict_generator` 方法的参数应该是整数值，而不是浮点数。可以使用 `int()` 函数来将浮点数转换为整数。

下面是修改后的代码：

from keras import applications
from keras.preprocessing.image import ImageDataGenerator
from keras import optimizers
from keras.models import Sequential, Model
from keras.layers import Dropout, Flatten, Dense

img_width, img_height = 256, 256
batch_size = 16
epochs = 50
train_data_dir = 'C:/Users/Z-/Desktop/kaggle/train'
validation_data_dir = 'C:/Users/Z-/Desktop/kaggle/test1'
OUT_CATAGORIES = 1
nb_train_samples = 2000
nb_validation_samples = 100

base_model = applications.VGG16(weights='imagenet',
                                include_top=False,
                                input_shape=(img_width, img_height, 3))
base_model.summary()

for layer in base_model.layers[:15]:
    layer.trainable = False

top_model = Sequential()
top_model.add(Flatten(input_shape=base_model.output_shape[1:]))
top_model.add(Dense(256, activation='relu'))
top_model.add(Dropout(0.5))
top_model.add(Dense(OUT_CATAGORIES, activation='sigmoid'))

model = Model(inputs=base_model.input,
              outputs=top_model(base_model.output))
model.compile(loss='binary_crossentropy',
              optimizer=optimizers.SGD(learning_rate=0.0001, momentum=0.9),
              metrics=['accuracy'])

train_datagen = ImageDataGenerator(rescale=1. / 255,
                                   horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1. / 255)

train_generator = train_datagen.flow_from_directory(
    train_data_dir,
    target_size=(img_height, img_width),
    batch_size=batch_size,
    class_mode='binary')

validation_generator = test_datagen.flow_from_directory(
    validation_data_dir,
    target_size=(img_height, img_width),
    batch_size=batch_size,
    class_mode='binary',
    shuffle=False)

model.fit_generator(
    train_generator,
    steps_per_epoch=nb_train_samples // batch_size,
    epochs=epochs,
    validation_data=validation_generator,
    validation_steps=nb_validation_samples // batch_size,
    verbose=2,
    workers=12)

score = model.evaluate_generator(validation_generator, int(nb_validation_samples / batch_size))
scores = model.predict_generator(validation_generator, int(nb_validation_samples / batch_size))