Package org.jblas

Examples of org.jblas.DoubleMatrix.neg()

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    /**
     * backward
     */
    // 1 last layer
    DoubleMatrix ai = activation[curr_pbparam.nl - 1];
    l_bias[curr_pbparam.nl - 1] = ai.sub(x_samples).muli(ai).muli(ai.neg().addi(1));
   
    //2 back
    for(int i = curr_pbparam.nl - 2; i >= 1; i--) {
      l_bias[i] = l_bias[i + 1].mmul(curr_pbparam.w[i]);
      if(config.isForceSparsity()) {
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      /**
       * backward
       */
      // 1 last layer
      DoubleMatrix ai = activation[my_bpparam.nl - 1];
      l_bias[my_bpparam.nl - 1] = ai.sub(activation[0]).muli(ai).muli(ai.neg().addi(1));
     
      //2 back(no layer0 error need)
      for(int i = my_bpparam.nl - 2; i >= 1; i--) {
        l_bias[i] = l_bias[i + 1].mmul(my_bpparam.w[i]);
        if(my_config.isForceSparsity()) {
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    /**
     * backward
     */
    // 1 last layer
    DoubleMatrix ai = activation[curr_pbparam.nl - 1];
    l_bias[curr_pbparam.nl - 1] = ai.sub(y_samples).muli(ai).muli(ai.neg().addi(1));
   
    //2 back
    for(int i = curr_pbparam.nl - 2; i >= 1; i--) {
      ai = activation[i];
      l_bias[i] = l_bias[i + 1].mmul(curr_pbparam.w[i]).muli(ai).muli(ai.neg().addi(1));
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    l_bias[curr_pbparam.nl - 1] = ai.sub(y_samples).muli(ai).muli(ai.neg().addi(1));
   
    //2 back
    for(int i = curr_pbparam.nl - 2; i >= 1; i--) {
      ai = activation[i];
      l_bias[i] = l_bias[i + 1].mmul(curr_pbparam.w[i]).muli(ai).muli(ai.neg().addi(1));
    }
   
    /**
     * delta
     */
 
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      /**
       * backward
       */
      // 1 last layer
      DoubleMatrix ai = activation[my_bpparam.nl - 1];
      l_bias[my_bpparam.nl - 1] = ai.sub(my_y_samples).muli(ai).muli(ai.neg().addi(1));
     
      //2 back(no layer0 error need)
      for(int i = my_bpparam.nl - 2; i >= 1; i--) {
        ai = activation[i];
        l_bias[i] = l_bias[i + 1].mmul(my_bpparam.w[i]).muli(ai).muli(ai.neg().addi(1));
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      l_bias[my_bpparam.nl - 1] = ai.sub(my_y_samples).muli(ai).muli(ai.neg().addi(1));
     
      //2 back(no layer0 error need)
      for(int i = my_bpparam.nl - 2; i >= 1; i--) {
        ai = activation[i];
        l_bias[i] = l_bias[i + 1].mmul(my_bpparam.w[i]).muli(ai).muli(ai.neg().addi(1));
      }
     
      /**
       * delta
       */
 
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