多标签分类评价指标 precision_class = tp.sum(axis=0) / np.maximum(tp.sum(axis=0) + fp.sum(axis=0), eps) recall_class = tp.sum(axis=0) / np.maximum(tp.sum(axis=0) + fn.sum(axis=0), eps) CP = precision_class.mean() * 100.0 CR = recall_class.mean() * 100.0 CF1 = 2 ...
1)59softmax_output = np.exp(shift_scores)/np.sum(np.exp(shift_scores), axis = 1).reshape(-1,1)60loss=np.sum(-np.log(softmax_output[range(num_train),y]))61loss=loss/num_train+reg * np.sum(W *W)6263dW=softmax_output.copy()64dW[range(num_train),y]-=165dW=np.dot...
快来一起探索好肌肤的秘密~
self._gamma = np.ones(shape=(num_features, )) 初始化中_beta和_gamma对应于BN中需要学习的参数,分别初始化为0和1,接下来就是前向传播的实现: def batch_norm(self, x): """BN向传播:param x: 数据:return: BN输出""" x_mean = x.mean(axis=0) x_var = x.var(axis=0) # 对应running_me...
x=np.array([[-0.7715,-0.6205,-0.2562]])y=np.exp(x)/np.sum(np.exp(x),axis=1,keepdims=True)y=np.log(y) array([[-1.27508877, -0.81683591, -1.27738109], [-1.15045104, -1.47835858, -0.78637192]]) 在pytorch中 代码语言:javascript ...
对参数和初始值进行调用 def forward(self, input, label): ret = paddle.nn.functional.cross_entropy( input, label, weight=self.weight, ignore_index=self.ignore_index, reduction=self.reduction, soft_label=self.soft_label, axis=self.axis, use_softmax=self.use_softmax, name=self.name, ) ...
公式为max(0,1-y_true*y_pred)^2.mean(axis=-1),取1减去预测值与实际值的乘积的结果与0比相对大的值的平方的累加均值。 hinge 公式为为max(0,1-y_true*y_pred).mean(axis=-1),取1减去预测值与实际值的乘积的结果与0比相对大的值的累加均值。
(y_pred, axis=1) # 句向量归一化 similarities = K.dot(y_pred, K.transpose(y_pred)) # 相似度矩阵 similarities = similarities - K.eye(K.shape(y_pred)[0]) * 1e12 # 排除对角线 similarities = similarities * 30 # scale loss = K.categorical_crossentropy( y_true, similarities, from_...
(idxs, samples_per_class, replace=False)43fori, idxinenumerate(idxs):44plt_idx = i * num_classes + y + 145plt.subplot(samples_per_class, num_classes, plt_idx)46plt.imshow(X_train[idx].astype('uint8'))47plt.axis('off')48ifi ==0:49plt.title(cls)50plt.show()5152#把数据分...
# imshow()其他参数可百度 f.set_title(lbl)# 设置图像标题 f.axes.get_xaxis().set_visible(False)# 设置x轴不可见 f.axes.get_yaxis().set_visible(False)# 设置y轴不可见 plt.show() 查看数据集前10个图片及对应标签 X, y =[], []foriinrange(10): ...