argmax: 返回向量中最大元素索引 dot:向量点乘 matmul:矩阵乘法 read_matrix:读取矩阵文件 write_matrix:写入矩阵文件 calssify:调用以上函数连接各层 同时我们需要编写测试文件来测试程序的正确性,让我们用向量点乘举个例子 dot.s 功能:将两个向量点乘 输入: a0 (int*) 指向v0第一个元素的指针 a1 (int*) 指...
int* argmax_data = max_idx_.mutable_cpu_data(); caffe_set(top_count, -1, argmax_data); // For each ROI R = [batch_index x1 y1 x2 y2]: max pool over R for (int n = 0; n < num_rois; ++n) { int roi_batch_ind = bottom_rois[0]; CHECK_GE(roi_batch_ind, 0); CH...
autoprediction = output.argmax(1); std::cout <<"prediction:"<< prediction << std::endl; intmaxk =3; autotop3 = std::get<1>(output.topk(maxk,1,true,true)); std::cout <<"top3: "<< top3 <<'\n'; std::vector<int> res; for(autoi =0; i < maxk; i++) { res.push_...
cout << "result:" << result << endl; auto pred = result.argmax(1); cout << "max index:" << pred << endl; /*std::tuple<torch::Tensor,torch::Tensor> res_sort = result.sort(-1, true); torch::Tensor top_scores = get<0>(res_sort)[0]; torch::Tensor top_idxs = get<1>...
correct_prediction=tf.equal(tf.argmax(y_conv,1),tf.argmax(y_,1)) accurace=tf.reduce_mean(tf.cast(correct_prediction,tf.float32)) tf.global_variables_initializer().run() start = 0 end = BATCH duaration=0 #开始训练 for i in range(500): ...
RNN是recurrent neural network的简称，中文叫做循环神经网络。 MNIST是Mixed National Institue of Standar...
8 # # === # # CALCULATE CLASSIFICATION/LABEL # # === # # Call argmax mv a0, a1 #pointer to the start of the vector mv a1, t1 #size of the elements jal argmax mv s11, a0 # # === # # Print if a2 is 0 # # === # ...
MNN_PUBLIC VARP _EltwiseMaxInt8(VARP x, VARP y, std::vector<int8_t> x_weight, std::vector<int32_t> x_bias, std::vector<float> x_scale, std::vector<float> x_tensorScale, std::vector<int8_t> y_weight, std::vector<int32_t> y_bias, std::vector<float> y_scale, std::...
核心公式两个 $$ \begin{align} w^&=\sum_{i=1}^{N}\alpha_i^y_ix_i\ b^&=\color{red}y_j\color{black}-\sum_{i=1}^{N}\alpha_i^y_i(x_i\cdot \color{red}x_j\color{black}) \end{align} $$ 这里面比较重要的是$b^$的公式的理解,通过$\arg\max \alpha^$实现,因为支持...
return-1returnnp.argmax(info_gain)defget_best_feature_info_gain_ratio(dataset,labels,threshold):entropy=get_entropy(labels)info_gain_ratio=0.0info_gain=[]info_value=[]foriinrange(len(dataset[0])):# 对每个特征计算 经验条件熵 entropy_condentropy_cond=0.0# 数据集关于分裂特征A的熵entropy_feat...