train_img_gen.__next__()

`train_img_gen.__next__()` 是一个生成器表达式或者迭代器的方法，通常在图像数据集生成器（如 Keras 的 `ImageDataGenerator`）中使用。当你创建了一个用于训练图片的生成器对象 `train_img_gen`，这个 `__next__()` 方法会在每次调用时返回训练集中的下一个批次的数据样本。

例如，在使用 `ImageDataGenerator.flow_from_directory` 函数加载数据时，可以创建一个生成器，它会对图片进行随机翻转、裁剪等数据增强操作，然后在训练过程中逐批返回这些增强后的图片及其相应的标签。通过 `train_img_gen.__next__()`，模型可以在训练时不断接收到新的、多样化的数据，从而提高泛化能力。

代码片段可能会看起来像这样：

train_img_gen = train_datagen.flow_from_directory(train_dir, batch_size=batch_size, shuffle=True)
for _ in range(steps_per_epoch):
    images, labels = train_img_gen.__next__()
    model.train_on_batch(images, labels)

在这里，`images` 和 `labels` 分别存储了批次内的图片和对应的标签。

相关问题

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))

如何调用image_generator.flow_from_directory读取的图片

在Keras的ImageDataGenerator类中，`flow_from_directory`方法是一个用于从目录结构中生成图像数据集的便捷工具。以下是调用这个方法的基本步骤：

1. 首先，你需要导入所需的库和模块：

from tensorflow.keras.preprocessing.image import ImageDataGenerator

2. 创建ImageDataGenerator实例，并设置必要的参数。例如，指定数据源的路径、图像大小、批处理大小等：

img_datagen = ImageDataGenerator(rescale=1./255, # 对像素值归一化
                                 rotation_range=20,  # 图像随机旋转角度范围
                                 width_shift_range=0.2, # 水平平移范围
                                 height_shift_range=0.2, # 垂直平移范围
                                 shear_range=0.2,     # 切割角度
                                 zoom_range=0.2,      # 缩放比例
                                 horizontal_flip=True, # 水平翻转
                                 vertical_flip=False)  # 垂直翻转

3. 使用`flow_from_directory`方法，传入训练或验证数据的根目录，以及一些额外选项，如目标类别数（如果有的话）、shuffle（是否打乱顺序）和batch_size：

train_data_gen = img_datagen.flow_from_directory(
    'path/to/train/directory',  
    target_size=(img_height, img_width),  # 根据需要设定的图像尺寸
    batch_size=batch_size,
    class_mode='binary' if binary_classification else 'categorical',  # 类别模式
    shuffle=True,
)

4. 如果你想同时获取标签和图像，可以这样调用：

(x_train, y_train) = train_data_gen.next()

这里，x_train就是包含图像的数据，y_train则是对应的标签。