print('train dogs total images : %d' % (len(os.listdir(base_dir_train + '//dog'))))是什么意思
时间: 2024-03-30 10:40:54 浏览: 17
这段代码是用来输出训练集中狗的图片数量的,具体来说,它的含义是:
1. `os.listdir()`函数可以列出指定目录下的所有文件和文件夹的名称,返回一个列表。
2. `base_dir_train`是训练集所在的目录,`'//dog'`是指训练集中狗的图片所在的文件夹,`os.listdir(base_dir_train + '//dog')`返回的是训练集中狗的图片文件名列表。
3. `len()`函数用来计算列表的长度,即训练集中狗的图片数量。
4. 最后,使用字符串格式化输出狗的图片数量,`%d`是一个占位符,表示输出一个整数。
综上所述,这段代码的输出结果是训练集中狗的图片数量。
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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))
dog_image_paths = 'dog vs cat/dataset/training_set/dogs' dog_images = os.listdir(dog_image_paths) new_style = {'grid': False} plt.rc('axes', **new_style) _, ax = plt.subplots(3, 3, sharex='col', sharey='row', figsize=(12, 12)) for i in range(9): image = dog_images[i] img = plt.imread( dog_image_paths + '/' + image) ax[i // 3, i % 3].imshow(img)
这段代码是用来读取狗的图像数据,并将其显示在一个 3x3 的图像网格中。具体解释如下:
- `dog_image_paths` 是存放狗图像数据的路径。
- `dog_images = os.listdir(dog_image_paths)` 读取 `dog_image_paths` 中的所有图像文件名,并存储在 `dog_images` 列表中。
- `new_style = {'grid': False}` 定义一个新的样式字典,其中 `grid` 键设置为 `False`,表示不显示网格线。
- `plt.rc('axes', **new_style)` 设置 matplotlib 中的 `axes` 样式为 `new_style`。
- `_, ax = plt.subplots(3, 3, sharex='col', sharey='row', figsize=(12, 12))` 创建一个 3x3 的子图,并返回一个 `ax` 数组,用于访问每个子图。`sharex` 和 `sharey` 参数表示共享 x 轴和 y 轴,`figsize` 参数设置整个图像的大小。
- `for i in range(9):` 对于每个子图:
- `image = dog_images[i]` 获取第 `i` 张狗的图像文件名。
- `img = plt.imread( dog_image_paths + '/' + image)` 读取图像数据,并存储在 `img` 变量中。
- `ax[i // 3, i % 3].imshow(img)` 在第 `i` 个子图中显示图像数据。
这段代码是使用 Matplotlib 库来可视化图像数据的一个例子。