机器学习/深度学习常见算法实现(秋招版)

包括BN层、卷积层、池化层、交叉熵、随机梯度下降法、非极大抑制、k均值聚类等秋招常见的代码实现。文章来源地址https://www.toymoban.com/news/detail-582474.html

1. BN层

import numpy as np

def batch_norm(outputs, gamma, beta, epsilon=1e-6, momentum=0.9, running_mean=0, running_var=1):
    '''
    :param outputs: [B, L]
    :param gamma: mean
    :param beta:
    :param epsilon:
    :return:
    '''
    mean = np.mean(outputs, axis=(0, 2, 3), keepdims=True) # 1, C, H, W
    var = np.var(outputs, axis=(0,2,3), keepdims=True) # 1, C, H, W

    # mean = np.mean(outputs, axis=0)
    # var = np.var(outputs, axis=0)


    # 滑动平均用于记录mean和var,用于测试
    running_mean = momentum * running_mean + (1-momentum) * mean
    running_var = momentum * running_var + (1-momentum) * var

    res = gamma * ( outputs - mean ) / np.sqrt(var + epsilon) + beta
    return res, running_mean, running_var

if __name__ == '__main__':
    outputs = np.random.random((16, 64, 8, 8))
    tmp, _, _ = batch_norm(outputs, 1, 1, 1e-6)
    # print(tmp.shape)
    mean = np.mean(tmp[:, 1, :, :])
    std = np.sqrt(np.var(tmp[:, 1, :, :]))
    print(mean, std)

2. 卷积层

import numpy as np

def conv_forward_naive(x, w, b, conv_param):
    '''
    :param x: [N, C_in, H, W]
    :param w: [C_out, C_in, k1, k2]
    :param b: [C_out]
    :param conv_param:
        - 'stride':
        - 'pad': the number of pixels that will be used to zero-pad the input
    :return:
        - 'out': (N, C_out, H', W')
        - 'cache': (x, w, b, conv_param)
    '''
    out = None
    N, C_in, H, W = x.shape
    C_out, _, k1, k2 = w.shape
    stride, padding = conv_param['stride'], conv_param['pad']
    H_out = (H-k1+2*padding) // stride + 1
    W_out = (W-k2+2*padding) // stride + 1
    out = np.zeros((N, C_out, H_out, W_out))

    x_pad = np.zeros((N, C_in, H+2*padding, W+2*padding))
    x_pad[:, :, padding:padding+H, padding:padding+W] = x

    for i in range(H_out):
        for j in range(W_out):
            x_pad_mask = x_pad[:, :, i*stride:i*stride+k1, j*stride:j*stride+k2]
            for c in range(C_out):
                out[:, c, i, j] = np.sum(x_pad_mask*w[c, :, :, :], axis=(1,2,3))
    out += b[None, :, None, None]

    cache = (x, w, b, conv_param)

    return out, cache

3. maxpooling

import numpy as np

def maxpooling_forward(feature, kernel, stride):
    '''
    :param feature: [N, C, H, W]
    :param kernel: [k1, k2]
    :param stride: [s1, s2]
    :return:
    '''
    N, C, H, W = feature.shape
    k1, k2 = kernel
    s1, s2 = stride

    H_out = (H - k1) // s1 + 1
    W_out = (W - k2) // s2 + 1

    out = np.zeros((N, C, H_out, W_out))
    for i in range(H_out):
        for j in range(W_out):
            feature_mask = feature[:, :, i*s1:i*s1+k1, j*s2:j*s2+k2]
            out[:, :, i, j] = np.max(feature_mask, axis=(2,3)) # 注意这里的2,3!!!

    return out

4. cross Entropy

import numpy as np

def cross_entropy(label, outputs, reduce=True):
    '''
    :param label: B x 1
    :param outputs: B x c
    :return: loss
    '''
    loss_list = []
    for i in range(len(label)):
        y = label[i]
        output = outputs[i]
        sum_exp = np.sum([np.exp(k) for k in output])
        prop = np.exp(output[y]) / sum_exp
        loss_list.append(-np.log(prop))
    if reduce:
        return np.mean(loss_list)
    else:
        return np.sum(loss_list)

def softmax(t):
    return np.exp(t) / np.sum(np.exp(t), axis=1, keepdims=True)

def softmax2(t):
    return np.exp(t) / np.sum(np.exp(t), axis=1, keepdims=True)

def cross_entropy_2(y, y_, onehot=True, reduce=True):
    y = softmax(y)
    if not onehot:
        cates = y.shape
        y_ = np.eye(cates[-1])[y_]
    if reduce:
        return np.mean(-np.sum(y_ * np.log(y), axis=1))
    else:
        return np.sum(-np.sum(y_ * np.log(y), axis=1))




if __name__ == '__main__':
    outputs = [[0.5, 0.5], [0, 1], [1, 0]]
    label = [0, 0, 1]
    print(cross_entropy(label, outputs, True))
    print(cross_entropy_2(outputs, label, False))

5. sgd

import numpy as np
import random
class MYSGD:
    def __init__(self, training_data, epochs, batch_size, lr, model):
        self.training_data = training_data
        self.epochs = epochs
        self.batch_size = batch_size
        self.lr = lr
        self.weight = [...]
        self.bias = [...]

    def run(self):
         n = len(self.training_data)
         for j in range(self.epochs):
             random.shuffle(self.training_data)
             mini_batches = [self.training_data[k*self.batch_size: (k+1)*self.batch_size]
                             for k in range(n//self.batch_size)]
             for mini_batch in mini_batches:
                 self.updata(mini_batch)
    def update(self, mini_batch):
        nabla_b = [np.zeros(b.shape) for b in self.bias]
        nabla_w = [np.zeros(w.shape) for w in self.weight]
        for x, y in mini_batch:
            delta_nabla_b, delta_nabla_w = self.backprop(x, y)
            nabla_b = [nb+dnb for nb, dnb in zip(nabla_b, delta_nabla_b)]
            nabla_w = [nw+dnw for nw, dnw in zip(nabla_w, delta_nabla_w)]
        self.weight = [w-(self.eta/len(mini_batch))*nw for w, nw in zip(self.weight, nabla_w)]
        self.bias = [b-(self.eta/len(mini_batch))*nb for b, nb in zip(self.bias, nabla_b)]

    def backprop(self, x, y):

6. nms

import numpy as np

def iou_calculate(bbox1, bbox2, mode='x1y1x2y2'):
    # 我的
    x11, y11, x12, y12 = bbox1
    x21, y21, x22, y22 = bbox2
    area1 = (y12-y11+1)*(x12-x11+1)
    area2 = (y22-y21+1)*(x22-x21+1)
    overlap = max(min(y12, y22) - max(y11, y21) + 1, 0) * max(min(x12, x22) - max(x11, x21) + 1, 0)

    return overlap / (area2 + area1 - overlap + 1e-6)

def bb_intersection_over_union(boxA, boxB):
    # 别人的
    boxA = [int(x) for x in boxA]
    boxB = [int(x) for x in boxB]

    xA = max(boxA[0], boxB[0])
    yA = max(boxA[1], boxB[1])
    xB = min(boxA[2], boxB[2])
    yB = min(boxA[3], boxB[3])

    interArea = max(0, xB - xA + 1) * max(0, yB - yA + 1)

    boxAArea = (boxA[2] - boxA[0] + 1) * (boxA[3] - boxA[1] + 1)
    boxBArea = (boxB[2] - boxB[0] + 1) * (boxB[3] - boxB[1] + 1)

    iou = interArea / float(boxAArea + boxBArea - interArea)

    return iou

def nms(outputs, scores, T):
    '''
    :param outputs: bboxes, x1y1x2y2
    :param scores: confidence of each bbox
    :param T: threshold
    :return:
    '''
    # 我的
    outputs = np.array(outputs)[np.argsort(-np.array(scores))]
    saved = [True for _ in range(outputs.shape[0])]
    for i in range(outputs.shape[0]):
        if saved[i]:
            for j in range(i+1, outputs.shape[0]):
                if saved[j]:
                    iou = iou_calculate(outputs[i], outputs[j])
                    if iou >= T:
                        saved[j] = False
    scores = np.sort(-np.array(scores))
    return outputs[saved], -scores[saved]


# 别人的
def nms_others(bboxes, scores, iou_thresh):
    """

    :param bboxes: 检测框列表
    :param scores: 置信度列表
    :param iou_thresh: IOU阈值
    :return:
    """

    x1 = bboxes[:, 0]
    y1 = bboxes[:, 1]
    x2 = bboxes[:, 2]
    y2 = bboxes[:, 3]
    areas = (y2 - y1) * (x2 - x1)

    # 结果列表
    result = []
    index = scores.argsort()[::-1]  # 对检测框按照置信度进行从高到低的排序，并获取索引
    # 下面的操作为了安全，都是对索引处理
    while index.size > 0:
        # 当检测框不为空一直循环
        i = index[0]
        result.append(i)  # 将置信度最高的加入结果列表

        # 计算其他边界框与该边界框的IOU
        x11 = np.maximum(x1[i], x1[index[1:]])
        y11 = np.maximum(y1[i], y1[index[1:]])
        x22 = np.minimum(x2[i], x2[index[1:]])
        y22 = np.minimum(y2[i], y2[index[1:]])
        w = np.maximum(0, x22 - x11 + 1) # 两个边重叠时,也有1列/行像素点是重叠的
        h = np.maximum(0, y22 - y11 + 1)
        overlaps = w * h
        ious = overlaps / (areas[i] + areas[index[1:]] - overlaps)
        # 只保留满足IOU阈值的索引
        idx = np.where(ious <= iou_thresh)[0]
        index = index[idx + 1]  # 处理剩余的边框
    bboxes, scores = bboxes[result], scores[result]
    return bboxes, scores

def mynms(bboxes, scores, iou_T):
    x1 = bboxes[:, 0]
    y1 = bboxes[:, 1]
    x2 = bboxes[:, 2]
    y2 = bboxes[:, 3]
    areas = (y2-y1+1) * (x2-x1+1)

    ids = np.argsort(scores)[::-1]
    res = []
    while len(ids) > 0:
        i = ids[0]
        res.append(i)

        x11 = np.maximum(x1[i], x1[ids[1:]])
        x22 = np.minimum(x2[i], x2[ids[1:]])
        y11 = np.maximum(y1[i], y1[ids[1:]])
        y22 = np.minimum(y2[i], y1[ids[1:]])

        # np.maximum(X,Y,None) : X与Y逐位取最大者. 最少两个参数
        overlap = np.maximum(x22-x11+1, 0) * np.maximum(y22-y11+1, 0)
        iou = overlap / (areas[i] +areas[ids[1:]] - overlap)
        ids = ids[1:][iou<T]
    return bboxes[res], scores[res]



if __name__ == '__main__':
    outputs = [[10, 10, 20, 20], [15, 15, 25, 25], [9, 15, 25, 13]]
    scores = [0.6, 0.8, 0.7]
    T = 0.1
    print(nms(outputs, scores, T))
    print(nms_others(np.array(outputs), np.array(scores), T))
    print(mynms(np.array(outputs), np.array(scores), T))

7. k-means

import numpy as np
import copy

def check(clusters_last, clusters_center):
    # clusters_last.sort()
    # clusters_center.sort()
    if len(clusters_last) == 0:
        return False
    for c1, c2 in zip(clusters_last, clusters_center):
        if np.linalg.norm(c1 - c2) > 0:
            return False
    return True


def kMeans(data, k):
    '''
    :param data: [n, c]
    :param k: the number of clusters
    :return:
    '''
    clusters_last = []
    clusters_center = [data[i] for i in range(k)] # random choosed

    while not check(clusters_last, clusters_center):
        clusters_last = copy.deepcopy(clusters_center)
        clusters = [[] for _ in range(k)]
        for i in range(data.shape[0]):
            min_dis = float('inf')
            for j, center in enumerate(clusters_center):
                distance = np.linalg.norm(center-data[i])
                if distance < min_dis:
                    min_dis = distance
                    idx = j
            clusters[idx].append(data[i])
        clusters_center = []
        for i in range(k):
            clusters_center.append(np.mean(clusters[i], axis=0))
    return clusters_center

def kMeans2(data, k):
    '''
    :param data: [n, c]
    :param k: the number of clusters
    :return:
    '''
    clusters_last = []
    clusters_center = copy.deepcopy(data[:k]) # random choosed

    while not check(clusters_last, clusters_center):
        clusters_last = copy.deepcopy(clusters_center)
        clusters = [[] for _ in range(k)]
        for i in range(data.shape[0]):
            distance = np.linalg.norm(clusters_center - data[i], axis=1)
            idx = np.argmin(distance)
            clusters[idx].append(data[i])
        clusters_center = []
        for i in range(k):
            clusters_center.append(np.mean(clusters[i], axis=0))
        clusters_center = np.array(clusters_center)
    return clusters_center

if __name__ == '__main__':
    data = np.random.random((20, 2))
    print(kMeans(data, 5))
    print(kMeans2(data, 5))

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