//input is a picture's matrix
public Network(int inputs_count, int input_w, int input_h, int mpl1_neurons_number, int mpl2_neurons_number, int result_vector_lenght)
{
mpl_first = new MPL_Layer(inputs_count, input_w, input_h, mpl1_neurons_number, mpl2_neurons_number);
mpl_second = new MPL_Layer(mpl_first, mpl2_neurons_number, result_vector_lenght);
output = new OutPutLayer(mpl_second, result_vector_lenght);
this.result_vector_lenght = result_vector_lenght;
}
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);
}
