y_val = y_train[mask] mask = range(num_training) X_train = X_train[mask] y_train = y_train[mask] mask = range(num_test) X_test = X_test[mask] y_test = y_test[mask] return X_train, y_train, X_val, y_val, X_test, y_test X_train, y_train, X_val, y_val, X_test, y_test = get_CIFAR10_data() from cs231n.features import * num_color_bins = 10 # Number of bins in the color histogram feature_fns = [hog_feature, lambda img: color_histogram_hsv(img, nbin=num_color_bins)] X_train_feats = extract_features(X_train, feature_fns, verbose=True) X_val_feats = extract_features(X_val, feature_fns) X_test_feats = extract_features(X_test, feature_fns) print X_train_feats.shape # Preprocessing: Subtract the mean feature mean_feat = np.mean(X_train_feats, axis=0, keepdims=True) X_train_feats -= mean_feat X_val_feats -= mean_feat X_test_feats -= mean_feat # Preprocessing: Divide by standard deviation. This ensures that each feature # has roughly the same scale. std_feat = np.std(X_train_feats, axis=0, keepdims=True) X_train_feats /= std_feat
mask = range(num_training)
x_train = x_train[mask]
y_train = y_train[mask]
mask = range(num_test)
x_test = x_test[mask]
y_test = y_test[mask]
return x_train, y_train, x_val, y_val, x_test, y_test
x_train, y_train, x_val, y_val, x_test, y_test = get_CIFAR10_data()
from cs231n.features import *
num_color_bins = 10 # Number of bins in the color histogram
feature_fns = [
hog_feature, lambda img: color_histogram_hsv(img, nbin=num_color_bins)
]
print(x_train.shape)
print(x_val.shape)
x_train_feats = extract_features(x_train, feature_fns, verbose=True)
x_val_feats = extract_features(x_val, feature_fns)
x_test_feats = extract_features(x_test, feature_fns)
# Preprocessing: Subtract the mean feature
mean_feat = np.mean(x_train_feats, axis=0, keepdims=True)
x_train_feats -= mean_feat
x_val_feats -= mean_feat
x_test_feats -= mean_feat
# Preprocessing: Divide by standard deviation. This ensures that each feature
# has roughly the same scale.
mask = list(range(num_training))
X_train = X_train[mask]
y_train = y_train[mask]
mask = list(range(num_test))
X_test = X_test[mask]
y_test = y_test[mask]
return X_train, y_train, X_val, y_val, X_test, y_test
X_train, y_train, X_val, y_val, X_test, y_test = get_CIFAR10_data()
from cs231n.features import *
num_color_bins = 10 # Number of bins in the color histogram
feature_fns = [hog_feature, lambda img: color_histogram_hsv(img, nbin=num_color_bins)]# lambda argument_list: expression
X_train_feats = extract_features(X_train, feature_fns, verbose=True)
X_val_feats = extract_features(X_val, feature_fns)
X_test_feats = extract_features(X_test, feature_fns)
# Preprocessing: Subtract the mean feature
mean_feat = np.mean(X_train_feats, axis=0, keepdims=True)
X_train_feats -= mean_feat
X_val_feats -= mean_feat
X_test_feats -= mean_feat
# Preprocessing: Divide by standard deviation. This ensures that each feature
# has roughly the same scale.
std_feat = np.std(X_train_feats, axis=0, keepdims=True)
X_train_feats /= std_feat
X_val_feats /= std_feat
