public MPL_Layer(int inps_number, int inp_w, int inp_h, int neurons_number, int next_layer_neurons_number)
{
this.input_w = inp_w;
this.input_h = inp_h;
//total number of previous layer's neurons
this.full_inputs_number = inps_number * inp_w * inp_h;
//full-connected layer
this.full_input = new float[full_inputs_number];
this.inputs = new List <float[, ]>();
this.packed_errors_for_prev_subs = new List <float[, ]>();
for (int i = 0; i < inps_number; i++)
{
packed_errors_for_prev_subs.Add(new float[inp_w, inp_h]);
}
this.outputs_number = neurons_number;
// this.bj_prev = prev_subsampling.bj;
this.next_layer_outputs_number = next_layer_neurons_number;
this.output = new float[outputs_number];
this.non_activated_stages = new float[outputs_number];
this.errors = new float[outputs_number];
this.weights = new float[full_inputs_number, outputs_number];
//
MatrixOperations.init_matrix_random(weights, full_inputs_number, outputs_number);
}
public OutPutLayer(MPL_Layer mpl, int outp_number)
{
this.input = mpl.output;
this.inputs_number = mpl.outputs_number;
this.outputs_number = outp_number;
this.weights = new float[inputs_number, outputs_number];
MatrixOperations.init_matrix_random(weights, inputs_number, outputs_number);
non_activated_stages = new float[outputs_number];
error = new float[outputs_number];
output = new float[outputs_number];
}
public MPL_Layer(MPL_Layer prev_mpl, int neurons_number, int next_layer_neurons_number)
{
//total number of previous layer's neurons
this.full_inputs_number = prev_mpl.outputs_number;
//full-connected layer
this.full_input = prev_mpl.output;
this.outputs_number = neurons_number;
// this.bj_prev = prev_subsampling.bj;
this.next_layer_outputs_number = next_layer_neurons_number;
this.output = new float[outputs_number];
this.non_activated_stages = new float[outputs_number];
this.errors = new float[outputs_number];
this.weights = new float[full_inputs_number, outputs_number];
//
MatrixOperations.init_matrix_random(weights, full_inputs_number, outputs_number);
}
public string Recognize(List <float[, ]> inputs)
{
string result = "";
connect_inputs(inputs);
// connect_input(input);
send_signal_front();
for (int i = 0; i < result_vector_lenght; i++)
{
result += Math.Round(output.output[i], 1).ToString() + " | ";
}
/* MatrixOperations.print_vector("mpl hidden input: ", mpl.full_input, mpl.full_inputs_number);
* MatrixOperations.print_vector("mpl hidden output: ", mpl.output, mpl.outputs_number);
* MatrixOperations.print_vector("outp_layer input: ", output.input, mpl.outputs_number);
*/
MatrixOperations.print_vector("outp_layer output: ", output.output, result_vector_lenght);
return(result);
}
