CS231n assignment1 Q5 Level Representations: Image Feature

这个作业是讨论对图像像素进行进一步计算得到的特征来训练线性分类器是否可以提高性能。
对于每张图，我们会计算梯度方向直方图(HOG)特征和用HSV（Hue色调，Saturation饱和度,Value明度）颜色空间的色调特征。把每张图的梯度方向直方图和颜色直方图特征合并形成我们最后的特征向量。
HOG大致可以捕捉到图像的纹理特征而忽略了颜色信息，颜色直方图会表示图像的颜色特征而忽略了纹理特征。

svm的验证部分:

# Use the validation set to tune the learning rate and regularization strength

from cs231n.classifiers.linear_classifier import LinearSVM

learning_rates = [1e-9, 1e-8, 1e-7]
regularization_strengths = [5e4, 5e5, 5e6]

results = {}
best_val = -1
best_svm = None

################################################################################
# TODO:                                                                        #
# Use the validation set to set the learning rate and regularization strength. #
# This should be identical to the validation that you did for the SVM; save    #
# the best trained classifer in best_svm. You might also want to play          #
# with different numbers of bins in the color histogram. If you are careful    #
# you should be able to get accuracy of near 0.44 on the validation set.       #
################################################################################
for lr in learning_rates:
    for rs in regularization_strengths:
        svm = LinearSVM()
        loss_hist = svm.train(X_train_feats,y_train,lr,rs,num_iters=6000)
        y_train_pred = svm.predict(X_train_feats)
        train_accuracy = np.mean(y_train == y_train_pred)
        y_val_pred = svm.predict(X_val_feats)
        val_accuracy = np.mean(y_val == y_val_pred)
        if val_accuracy > best_val:
            best_val = val_accuracy
            best_svm = svm
        results[(lr,rs)] = train_accuracy,val_accuracy
################################################################################
#                              END OF YOUR CODE                                #
################################################################################

# Print out results.
for lr, reg in sorted(results):
    train_accuracy, val_accuracy = results[(lr, reg)]
    print('lr %e reg %e train accuracy: %f val accuracy: %f' % (
                lr, reg, train_accuracy, val_accuracy))
    
print('best validation accuracy achieved during cross-validation: %f' % best_val)

最终测试集的准确率为0.422

两层神经网络的验证部分:

from cs231n.classifiers.neural_net import TwoLayerNet

input_dim = X_train_feats.shape[1]
hidden_dim = 500
num_classes = 10

net = TwoLayerNet(input_dim, hidden_dim, num_classes)
best_net = None
best_val = -1

################################################################################
# TODO: Train a two-layer neural network on image features. You may want to    #
# cross-validate various parameters as in previous sections. Store your best   #
# model in the best_net variable.                                              #
################################################################################
learning_rates = [1e-2,1e-1,5e-1,1,5]
regularization_strengths = [1e-3,5e-3,1e-2,1e-1,0.5,1]

for lr in learning_rates:
    for rs in regularization_strengths:
        net = TwoLayerNet(input_dim,hidden_dim,num_classes)
        stats = net.train(X_train_feats,y_train,X_val_feats,y_val,num_iters = 1500,
                          batch_size = 200,learning_rate = lr,learning_rate_decay = 0.95,reg= rs,verbose = False)
        val_acc = (net.predict(X_val_feats) == y_val).mean()
        if val_acc > best_val:
            best_val = val_acc
            best_net = net
        results[(lr,rs)] = val_acc

for lr,rs in sorted(results):
    val_acc = results[(lr,rs)]
    print('lr:',lr,"reg:",rs,"accuracy:",val_acc)
print('best validation accuracy achieved during cross-validation:%f' %best_val)
################################################################################
#                              END OF YOUR CODE                                #
################################################################################

最终测试集的准确率为0.579