解释一下 img_predicted = imgs_masked.clone() img_predicted[:, :, x:x + h, y:y + w] = fake_imgs[:, :, x:x + h, y:y + w]
时间: 2024-05-21 22:14:13 浏览: 15
这段代码的作用是将混合了遮罩的图像(imgs_masked)中的部分像素替换为生成的假图像(fake_imgs)中对应位置的像素。具体来说,img_predicted的所有通道、所有行、所有列都与imgs_masked相同,但是在指定的区域内(即x到h行和y到w列),像素值被替换为fake_imgs中对应位置的像素值。换句话说,这个代码片段实现了图像中的部分重建。
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knn.fit(X_train, y_train) y_predicted = knn.predict(X_test)作用
这段代码使用了K近邻算法对鸢尾花数据集进行了分类,并预测了测试集的类别。
首先,knn.fit(X_train, y_train)根据训练集X_train和对应的目标变量y_train,构建出K近邻模型。其中knn是使用scikit-learn库中的KNeighborsClassifier类创建的一个实例,可以设置不同的K值和距离度量方式,以调整模型的参数。
接着,y_predicted = knn.predict(X_test)使用训练好的K近邻模型对测试集X_test进行分类,并将预测结果保存到y_predicted中。最后,我们可以将y_predicted与真实的测试集标签y_test进行比较,来评估模型的性能。
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))