數據增強

數據增強的方式有很多，比如對圖像進行幾何變換（如翻轉、旋轉、變形、縮放等）、顏色變換（包括噪聲、模糊、顏色變換、檫除、填充等），將有限的數據，進行充分的利用。這里將介紹的僅僅是對圖像數據進行任意方向的移動操作（上下左右）來擴充數據。

這里將使用scipy中的shift變換工具（from scipy.ndimage.interpolation import shift）

 　　常用的參數：input輸入圖像數據為ndarray類型的，

　　　　　　　　shift參數代表表示各個維度的偏移量[1,1]表示第一個第二個維度均偏移1，

　　　　　　　　cval參數代表偏移后原來位置用什么來填充

from scipy.ndimage.interpolation import shift
def shift_digit(digit_array,dx,dy,new = 0):
    return shift(digit_array.reshape(28,28),[dy,dx],cval = new).reshape(784)
plot_digit(shift_digit(some_digit,5,1,new =100))

 　　一個簡單的數據偏移完成，接下來對整個訓練集進行擴充

X_train_expanded = [X_train]
y_train_expanded = [y_train]
for dx,dy in ((1,0),(-1,0),(0,1),(0,-1)):
    shifted_image = np.apply_along_axis(shift_digit,axis = 1,arr = X_train,dx = dx,dy = dy)
    X_train_expanded.append(shifted_image)
    y_train_expanded.append(y_train)
    
X_train_expanded = np.concatenate(X_train_expanded)
y_train_expanded = np.concatenate(y_train_expanded)
X_train_expanded.shape,y_train_expanded.shape

 　　數據增加大了30萬之多，有了更多的數據，接下來進行訓練、預測，計算精度

knn_clf.fit(X_train_expanded,y_train_expanded)

 

 

y_knn_expanded_pred = knn_clf.predict(X_test)
accuracy_score(y_test,y_knn_expanded_pred)

 另一種表示方式：

def shift_image(image,dx,dy):
    image = image.reshape((28,28))
    shifted_image = shift(image,[dy,dx],cval = 0,mode = 'constant')
    return shifted_image.reshape([-1])

image = X_train[1000]
shifted_image_down = shift_image(image,0,5)
shifted_image_left = shift_image(image,-5,0)

plt.figure(figsize=(12,3))
plt.subplot(131)
plt.title("Original",fontsize= 14)
plt.imshow(image.reshape(28,28),interpolation='nearest',cmap = 'Greys')
plt.subplot(132)
plt.title("shifted down",fontsize= 14)
plt.imshow(shifted_image_down.reshape(28,28),interpolation='nearest',cmap = 'Greys')
plt.subplot(133)
plt.title("shifted left",fontsize= 14)
plt.imshow(shifted_image_left.reshape(28,28),interpolation='nearest',cmap = 'Greys')
plt.show()

X_train_augmented = [image for image in X_train]
y_train_augmented = [label for label in y_train]

for dx,dy in ((1,0),(-1,0),(0,1),(0,-1)):
    for image,label in zip(X_train,y_train):
        X_train_augmented.append(shift_image(image,dx,dy))
        y_train_augmented.append(label)

X_train_augmented = np.array(X_train_augmented)
y_train_augmented = np.array(y_train_augmented)

#打亂順序
shuffle_idx = np.random.permutation(len(X_train_augmented))
X_train_augmented = X_train_augmented[shuffle_idx]
y_train_augmented = y_train_augmented[shuffle_idx]

knn_clf = KNeighborsClassifier(**grid_search.best_params_)
knn_clf.fit(X_train_augmented,y_train_augmented)

y_pred = knn_clf.predict(X_test)
accuracy_score(y_test,y_pred)

 此時准確率已達到97%以上

關於knn_clf = KNeighborsClassifier(**grid_search.best_params_)中的**犯傻了很久，**代表着該參數中包含了多個參數，在C++中也會有這種參數表示，
也可參看python中*args與**kwargs的介紹（https://pythontips.com/2013/08/04/args-and-kwargs-in-python-explained/）

當然，scipy.ndimage.interpolation 也包含了其他的數據增強的方法，如旋轉、縮放等（參考：https://blog.csdn.net/songchunxiao1991/article/details/88531086）