0MixUp helps models learn better by mixing two images and their labels. This makes the model more confident and less likely to guess wrong.
def mixup_data(x, y, alpha=1.0): '''Returns mixed inputs, pairs of targets, and lambda''' if alpha > 0: lam = np.random.beta(alpha, alpha) else: lam = 1 batch_size = x.shape[0] index = np.random.permutation(batch_size) mixed_x = lam * x + (1 - lam) * x[index] y_a, y_b = y, y[index] return mixed_x, y_a, y_b, lam
The function takes a batch of images x and labels y.
It mixes images and labels using a random weight lam from a Beta distribution.
mixed_x, y_a, y_b, lam = mixup_data(images, labels, alpha=0.4)mixed_x, y_a, y_b, lam = mixup_data(images, labels, alpha=0)This example loads a small image dataset, applies MixUp to one batch, trains a simple model on it, and then evaluates the model.
import numpy as np from sklearn.datasets import load_digits from sklearn.model_selection import train_test_split from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Flatten from tensorflow.keras.utils import to_categorical # Load digits dataset digits = load_digits() X = digits.images y = digits.target # Normalize images X = X / 16.0 # One-hot encode labels y = to_categorical(y, num_classes=10) # Split data X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # MixUp function def mixup_data(x, y, alpha=0.2): if alpha > 0: lam = np.random.beta(alpha, alpha) else: lam = 1 batch_size = x.shape[0] index = np.random.permutation(batch_size) mixed_x = lam * x + (1 - lam) * x[index] y_a, y_b = y, y[index] return mixed_x, y_a, y_b, lam # Build simple model model = Sequential([ Flatten(input_shape=(8,8)), Dense(64, activation='relu'), Dense(10, activation='softmax') ]) model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) # Prepare one batch with MixUp batch_size = 32 x_batch = X_train[:batch_size] y_batch = y_train[:batch_size] mixed_x, y_a, y_b, lam = mixup_data(x_batch, y_batch, alpha=0.2) mixed_y = lam * y_a + (1 - lam) * y_b # Train on mixed batch model.train_on_batch(mixed_x, mixed_y) # Using mixed soft labels # Evaluate on test data loss, acc = model.evaluate(X_test, y_test, verbose=0) print(f"Test loss: {loss:.4f}") print(f"Test accuracy: {acc:.4f}")
MixUp works best when applied during training, mixing batches on the fly.
The labels are mixed the same way as images, so the loss function must handle soft labels.
MixUp can slow training a bit but usually improves final accuracy.
MixUp blends images and labels to help models learn smoother decision boundaries.
It is useful to reduce overfitting and improve robustness.
MixUp requires adjusting the loss to handle mixed labels.
MixUp strategy in computer vision?x_mix using MixUp with images x1, x2 and mixing coefficient lambda?mixed_labels?
import torch labels1 = torch.tensor([[1, 0, 0], [0, 1, 0]]) labels2 = torch.tensor([[0, 0, 1], [1, 0, 0]]) lambda_ = 0.7 mixed_labels = lambda_ * labels1 + (1 - lambda_) * labels2 print(mixed_labels.shape)
def mixup_data(x1, x2, y1, y2, alpha=1.0):
lam = np.random.beta(alpha, alpha)
x_mix = lam * x1 + (1 - lam) * x2
y_mix = lam * y1 + lam * y2
return x_mix, y_mixalpha very high (e.g., 100), what is the likely effect on training?Pass the test and this pattern moves unknown strong while earning +1 Grip toward your certificate.