#将分类标签变为onehot编码 #Keras采用独热编码输出 num_class = 10 y_train = keras.utils.to_categorical(y_train, num_class) y_test = keras.utils.to_categorical(y_test, num_class) print(y_train[0]) 查看结果: y_train[0]: ,该样本对应数字是: y_test[0]: ,该样本对应数字是:
时间: 2024-02-14 07:11:45 浏览: 32
以下是将分类标签变为onehot编码的代码:
```python
import tensorflow as tf
from tensorflow import keras
# 加载MNIST数据集
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
# 将标签转换为one-hot编码
num_classes = 10
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
# 输出转换后的标签
print("y_train[0]:", y_train[0])
print("y_test[0]:", y_test[0])
```
运行结果:
```
y_train[0]: [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]
y_test[0]: [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]
```
可以看到,训练集和测试集的标签已经变成了one-hot编码形式。`y_train[0]`对应的样本数字是5,`y_test[0]`对应的样本数字是7。
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))
将MNIST变成三通道,向MNIST_M进行迁移的方法、代码
将MNIST变成三通道的方法可以有多种,这里提供两种简单的方法:
方法一:将MNIST的灰度图像复制三份作为RGB三通道。这种方法可能会导致图像失真,但是可以快速实现。
```python
import numpy as np
from tensorflow.keras.datasets import mnist
# 加载MNIST数据集
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
# 复制灰度图像3份作为RGB三通道
train_images_rgb = np.stack((train_images,) * 3, axis=-1)
test_images_rgb = np.stack((test_images,) * 3, axis=-1)
```
方法二:使用OpenCV库将灰度图像转换成RGB三通道。这种方法可以更好地保留图像的信息和质量。
```python
import cv2
import numpy as np
from tensorflow.keras.datasets import mnist
# 加载MNIST数据集
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
# 将灰度图像转换成RGB三通道
train_images_rgb = np.zeros((train_images.shape[0], 28, 28, 3))
test_images_rgb = np.zeros((test_images.shape[0], 28, 28, 3))
for i in range(train_images.shape[0]):
train_images_rgb[i] = cv2.cvtColor(train_images[i], cv2.COLOR_GRAY2RGB)
for i in range(test_images.shape[0]):
test_images_rgb[i] = cv2.cvtColor(test_images[i], cv2.COLOR_GRAY2RGB)
```
接下来,将转换后的MNIST数据集应用到MNIST_M数据集的迁移上,可以使用迁移学习的方法,将预训练的模型在MNIST数据集上进行微调,然后应用到MNIST_M数据集上。
```python
import tensorflow as tf
from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D
from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.preprocessing.image import ImageDataGenerator
# 定义模型
model = Sequential([
Conv2D(32, (3,3), activation='relu', input_shape=(28,28,3)),
MaxPooling2D(pool_size=(2,2)),
Flatten(),
Dense(10, activation='softmax')
])
# 编译模型
model.compile(optimizer=Adam(lr=0.001),
loss='categorical_crossentropy',
metrics=['accuracy'])
# 定义数据增强器
train_datagen = ImageDataGenerator(
rescale=1./255,
rotation_range=10,
width_shift_range=0.1,
height_shift_range=0.1,
shear_range=0.1,
zoom_range=0.1,
horizontal_flip=False,
fill_mode='nearest'
)
test_datagen = ImageDataGenerator(
rescale=1./255
)
# 加载MNIST_M数据集
train_data = train_datagen.flow_from_directory(
'mnist_m/train',
target_size=(28,28),
batch_size=32,
class_mode='categorical'
)
test_data = test_datagen.flow_from_directory(
'mnist_m/test',
target_size=(28,28),
batch_size=32,
class_mode='categorical'
)
# 在MNIST数据集上进行微调
model.fit(train_images_rgb, tf.keras.utils.to_categorical(train_labels, num_classes=10),
epochs=5, batch_size=32, validation_data=(test_images_rgb, tf.keras.utils.to_categorical(test_labels, num_classes=10)))
# 应用到MNIST_M数据集上
model.fit(train_data,
epochs=5,
validation_data=test_data)
```