import torch import torch.nn as nn import torch.optim as optim from sklearn.datasets import make_classification
时间: 2024-09-21 13:00:59 浏览: 59
上述代码导入了必要的PyTorch库,包括`torch`、`torch.nn`和`torch.optim`。它们分别是用于张量操作、神经网络模块和优化算法的核心组件。
- `torch`:提供了一系列功能强大的张量(类似于NumPy的ndarray,但在GPU上运行速度更快)以及相关的数学运算和自动微分功能。
- `torch.nn`:这是神经网络层和模块的集合,如线性层(Linear)、激活函数(Sigmoid、ReLU等)、卷积层等,用于构建和定义神经网络架构。
- `torch.optim`:包含了各种优化器,如SGD(随机梯度下降)、Adam等,用于更新模型参数以最小化损失函数。
`from sklearn.datasets import make_classification`这部分来自scikit-learn库,这是一个流行的数据科学工具包,用于创建模拟数据集,这里用来生成分类问题的数据,如二进制或多类别分类任务。
通过这些库,你可以编写出基于PyTorch的机器学习模型,例如上面提到的Logistic回归实例。在实际应用中,你会使用这些库加载和预处理数据,定义模型结构,配置优化过程,然后训练模型并进行预测。
相关问题
import pytorch_lightning as pl
# As an AI language model, I cannot provide a personal experience with PyTorch Lightning, but I can provide information and examples on how to use it.
PyTorch Lightning is a lightweight PyTorch wrapper for high-performance AI research. It allows researchers and practitioners to focus on the core research problem by abstracting the engineering details. PyTorch Lightning provides a high-level interface for building complex deep learning models and training pipelines. It also simplifies the process of scaling models to multiple GPUs or TPUs.
Here is an example of how to use PyTorch Lightning to train a simple neural network for image classification:
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision.datasets import MNIST
from torchvision.transforms import ToTensor
import pytorch_lightning as pl
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 32, 3, 1)
self.conv2 = nn.Conv2d(32, 64, 3, 1)
self.dropout1 = nn.Dropout2d(0.25)
self.dropout2 = nn.Dropout2d(0.5)
self.fc1 = nn.Linear(9216, 128)
self.fc2 = nn.Linear(128, 10)
def forward(self, x):
x = self.conv1(x)
x = nn.ReLU()(x)
x = self.conv2(x)
x = nn.ReLU()(x)
x = nn.MaxPool2d(2)(x)
x = self.dropout1(x)
x = torch.flatten(x, 1)
x = self.fc1(x)
x = nn.ReLU()(x)
x = self.dropout2(x)
x = self.fc2(x)
output = nn.LogSoftmax(dim=1)(x)
return output
class LitMNIST(pl.LightningModule):
def __init__(self):
super().__init__()
self.net = Net()
def forward(self, x):
return self.net(x)
def training_step(self, batch, batch_idx):
x, y = batch
y_hat = self(x)
loss = nn.NLLLoss()(y_hat, y)
self.log('train_loss', loss)
return loss
def configure_optimizers(self):
optimizer = optim.Adam(self.parameters(), lr=1e-3)
return optimizer
train_data = MNIST('.', train=True, download=True, transform=ToTensor())
train_loader = DataLoader(train_data, batch_size=64)
trainer = pl.Trainer(gpus=1, max_epochs=10)
model = LitMNIST()
trainer.fit(model, train_loader)
```
In this example, we define a simple neural network for image classification using PyTorch. We then wrap the model in a PyTorch Lightning module, which provides hooks for training and validation steps. We define a training step that calculates the loss and logs it to the PyTorch Lightning log, and we configure the optimizer to use the Adam optimizer. Finally, we create a PyTorch DataLoader for the MNIST dataset, create a PyTorch Lightning trainer with one GPU, and fit the model to the training data for 10 epochs.
Overall, PyTorch Lightning simplifies the process of training deep learning models while still allowing for flexibility and customization.
帮我把下面这个代码从TensorFlow改成pytorch 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 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 = torch.utils.data.DataLoader(torchvision.datasets.ImageFolder(train_dir, transform=transforms.Compose([transforms.Resize((IMG_HEIGHT, IMG_WIDTH)), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])), batch_size=batch_size, shuffle=True) validation_image_generator = torch.utils.data.DataLoader(torchvision.datasets.ImageFolder(validation_dir, transform=transforms.Compose([transforms.Resize((IMG_HEIGHT, IMG_WIDTH)), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])), batch_size=batch_size) model = torch.nn.Sequential( torch.nn.Conv2d(3, 16, kernel_size=3, padding=1), torch.nn.ReLU(), torch.nn.MaxPool2d(2), torch.nn.Conv2d(16, 32, kernel_size=3, padding=1), torch.nn.ReLU(), torch.nn.MaxPool2d(2), torch.nn.Conv2d(32, 64, kernel_size=3, padding=1), torch.nn.ReLU(), torch.nn.MaxPool2d(2), torch.nn.Flatten(), torch.nn.Linear(64*16*16, 256), torch.nn.ReLU(), torch.nn.Linear(256, 2), torch.nn.Softmax() ) criterion = torch.nn.CrossEntropyLoss() optimizer = torch.optim.Adam(model.parameters(), lr=0.001) for epoch in range(epochs): running_loss = 0.0 for i, data in enumerate(train_image_generator, 0): inputs, labels = data optimizer.zero_grad() outputs = model(inputs) loss = criterion(outputs, labels) loss.backward() optimizer.step() running_loss += loss.item() epoch_loss = running_loss / (len(train_data_gen) / batch_size) print('Epoch: %d, Loss: %.3f' % (epoch + 1, epoch_loss)) correct = 0 total = 0 with torch.no_grad(): for data in validation_image_generator: images, labels = data outputs = model(images) _, predicted = torch.max(outputs.data, 1) total += labels.size(0) correct += (predicted == labels).sum().item() print('Validation Accuracy: %.2f%%' % (100 * correct / total))
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