criterion = nn.MSELoss() content_layers_default = ['conv_4', 'conv_5', 'conv_6']
时间: 2024-01-13 14:02:36 浏览: 24
你好!对于你的第一个问题,这是一段 Python 代码。在这段代码中,`criterion` 是一个实例化的 `nn.MSELoss()` 对象,它表示均方误差损失函数。`content_layers_default` 是一个包含字符串元素的列表,其中包含了要在神经网络模型中用于计算内容损失的卷积层的名称。根据代码的上下文,这些层可能是用于提取图像特征的层。
相关问题
class ARMA4NC(nn.Module): def __init__(self, in_dim, hid_dim, out_dim, num_stacks, num_layers, activation=None, dropout=0.0): super(ARMA4NC, self).__init__() self.conv1 = ARMAConv(in_dim=in_dim, out_dim=hid_dim, num_stacks=num_stacks, num_layers=num_layers, activation=activation, dropout=dropout) self.conv2 = ARMAConv(in_dim=hid_dim, out_dim=out_dim, num_stacks=num_stacks, num_layers=num_layers, activation=activation, dropout=dropout) self.dropout = nn.Dropout(p=dropout) def forward(self, g, feats): feats = F.relu(self.conv1(g, feats)) feats = self.dropout(feats) feats = self.conv2(g, feats) return feats。写出对这个进行训练的代码
以下是一个示例训练代码,包括定义损失函数、优化器,以及数据加载器和训练循环:
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
# Define the loss function
criterion = nn.CrossEntropyLoss()
# Define the optimizer
optimizer = optim.Adam(model.parameters(), lr=0.01)
# Create a DataLoader for the training data
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
# Train the model
for epoch in range(num_epochs):
for batch_idx, (g, feats, labels) in enumerate(train_loader):
# Zero the gradients
optimizer.zero_grad()
# Forward pass
output = model(g, feats)
loss = criterion(output, labels)
# Backward pass and optimization step
loss.backward()
optimizer.step()
# Print progress
if batch_idx % log_interval == 0:
print('Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(g), len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.item()))
```
在此示例中,`train_dataset` 是一个包含图形、特征和标签的数据集,`log_interval` 是打印日志的间隔(例如每100个批次)。在训练过程中,每个批次的图形、特征和标签被加载到 `g`、`feats` 和 `labels` 变量中,分别表示图形、特征和标签的张量。在每个批次中,模型的输出和损失都被计算,并且通过反向传播和优化器进行了更新。最后,训练循环打印进度并在每个日志间隔时输出当前损失。
帮我把这段代码从tensorflow框架改成pytorch框架: import tensorflow as tf import os import numpy as np import matplotlib.pyplot as plt os.environ["CUDA_VISIBLE_DEVICES"] = "0" base_dir = 'E:/direction/datasetsall/' train_dir = os.path.join(base_dir, 'train_img/') validation_dir = os.path.join(base_dir, 'val_img/') train_cats_dir = os.path.join(train_dir, 'down') train_dogs_dir = os.path.join(train_dir, 'up') validation_cats_dir = os.path.join(validation_dir, 'down') validation_dogs_dir = os.path.join(validation_dir, 'up') batch_size = 64 epochs = 50 IMG_HEIGHT = 128 IMG_WIDTH = 128 num_cats_tr = len(os.listdir(train_cats_dir)) num_dogs_tr = len(os.listdir(train_dogs_dir)) num_cats_val = len(os.listdir(validation_cats_dir)) num_dogs_val = len(os.listdir(validation_dogs_dir)) total_train = num_cats_tr + num_dogs_tr total_val = num_cats_val + num_dogs_val train_image_generator = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1. / 255) validation_image_generator = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1. / 255) train_data_gen = train_image_generator.flow_from_directory(batch_size=batch_size, directory=train_dir, shuffle=True, target_size=(IMG_HEIGHT, IMG_WIDTH), class_mode='categorical') val_data_gen = validation_image_generator.flow_from_directory(batch_size=batch_size, directory=validation_dir, target_size=(IMG_HEIGHT, IMG_WIDTH), class_mode='categorical') sample_training_images, _ = next(train_data_gen) model = tf.keras.models.Sequential([ tf.keras.layers.Conv2D(16, 3, padding='same', activation='relu', input_shape=(IMG_HEIGHT, IMG_WIDTH, 3)), tf.keras.layers.MaxPooling2D(), tf.keras.layers.Conv2D(32, 3, padding='same', activation='relu'), tf.keras.layers.MaxPooling2D(), tf.keras.layers.Conv2D(64, 3, padding='same', activation='relu'), tf.keras.layers.MaxPooling2D(), tf.keras.layers.Flatten(), tf.keras.layers.Dense(256, activation='relu'), tf.keras.layers.Dense(2, activation='softmax') ]) model.compile(optimizer='adam', loss=tf.keras.losses.BinaryCrossentropy(from_logits=True), metrics=['accuracy']) model.summary() history = model.fit_generator( train_data_gen, steps_per_epoch=total_train // batch_size, epochs=epochs, validation_data=val_data_gen, validation_steps=total_val // batch_size ) # 可视化训练结果 acc = history.history['accuracy'] val_acc = history.history['val_accuracy'] loss = history.history['loss'] val_loss = history.history['val_loss'] epochs_range = range(epochs) model.save("./model/timo_classification_128_maxPool2D_dense256.h5")
import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader from torchvision import datasets, transforms import os BATCH_SIZE = 64 EPOCHS = 50 IMG_HEIGHT = 128 IMG_WIDTH = 128 train_transforms = transforms.Compose([ transforms.Resize((IMG_HEIGHT,IMG_WIDTH)), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize([0.5,0.5,0.5], [0.5,0.5,0.5])]) test_transforms = transforms.Compose([ transforms.Resize((IMG_HEIGHT,IMG_WIDTH)), transforms.ToTensor(), transforms.Normalize([0.5,0.5,0.5], [0.5,0.5,0.5])]) base_dir = 'E:/direction/datasetsall/' train_dir = os.path.join(base_dir, 'train_img/') validation_dir = os.path.join(base_dir, 'val_img/') train_cats_dir = os.path.join(train_dir, 'down') train_dogs_dir = os.path.join(train_dir, 'up') validation_cats_dir = os.path.join(validation_dir, 'down') validation_dogs_dir = os.path.join(validation_dir, 'up') train_dataset = datasets.ImageFolder(train_dir, transform=train_transforms) train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True) test_dataset = datasets.ImageFolder(validation_dir, transform=test_transforms) test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=False) device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") model = nn.Sequential( nn.Conv2d(3, 16, 3, padding=1), nn.ReLU(), nn.MaxPool2d(2), nn.Conv2d(16, 32, 3, padding=1), nn.ReLU(), nn.MaxPool2d(2), nn.Conv2d(32, 64, 3, padding=1), nn.ReLU(), nn.MaxPool2d(2), nn.Flatten(), nn.Linear(64 * (IMG_HEIGHT // 8) * (IMG_WIDTH // 8), 256), nn.ReLU(), nn.Linear(256, 2), nn.Softmax(dim=1) ) model.to(device) criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=0.001) for epoch in range(EPOCHS): train_loss = 0.0 train_acc = 0.0 model.train() for images, labels in train_loader: images = images.to(device) labels = labels.to(device) optimizer.zero_grad() outputs = model(images) loss = criterion(outputs, labels) loss.backward() optimizer.step() train_loss += loss.item() * images.size(0) _, preds = torch.max(outputs, 1) train_acc += torch.sum(preds == labels.data) train_loss = train_loss / len(train_loader.dataset) train_acc = train_acc / len(train_loader.dataset) print('Epoch: {} \tTraining Loss: {:.6f} \tTraining Accuracy: {:.6f}'.format(epoch+1, train_loss,train_acc)) with torch.no_grad(): test_loss = 0.0 test_acc = 0.0 model.eval() for images, labels in test_loader: images = images.to(device) labels = labels.to(device) outputs = model(images) loss = criterion(outputs, labels) test_loss += loss.item() * images.size(0) _, preds = torch.max(outputs, 1) test_acc += torch.sum(preds == labels.data) test_loss = test_loss / len(test_loader.dataset) test_acc = test_acc / len(test_loader.dataset) print('Test Loss: {:.6f} \tTest Accuracy: {:.6f}'.format(test_loss,test_acc))