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output_layer.go
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/
output_layer.go
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// Copyright 2021 Anastasios Daris
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
package bp7
import (
"math/rand"
)
// The representation of the output layer of
// an MLP neural network. The output layer
// contains an array of neurons.
type OutputLayer struct {
Neurons []Neuron
}
// The structure function implementation of the
// output layer initialization. During the
// initialization, we assign a random weight in
// the weight array of each neuron.
// -Input neuronCount: How many neurons are in the
// output layer.
// -Input hiddenNeuronsCount: How many hidden layer
// inputs.
func (ol *OutputLayer) Init(neuronCount int, hiddenNeuronsCount int) {
neurons := make([]Neuron, 0)
for i := 0; i < neuronCount; i++ {
weights := make([]float32, 0)
for j := 0; j < hiddenNeuronsCount + 1; j++ {
weight := rand.Float32()
weights = append(weights, weight)
}
neuron := Neuron{}
neuron.Weights = weights
neurons = append(neurons, neuron)
}
ol.Neurons = neurons
}
// The standalone function implementation of the
// output layer initialization. During the
// initialization, we assign a random weight in
// the weight array of each neuron.
// -Input neuronCount: How many neurons are in the
// output layer.
// -Input hiddenNeuronsCount: How many hidden layer
// inputs.
func CreateOutputLayer(neuronCount int, hiddenNeuronsCount int) OutputLayer {
ol := OutputLayer{}
neurons := make([]Neuron, 0)
for i := 0; i < neuronCount; i++ {
weights := make([]float32, 0)
for j := 0; j < hiddenNeuronsCount + 1; j++ {
weight := rand.Float32()
weights = append(weights, weight)
}
neuron := Neuron{}
neuron.Weights = weights
neurons = append(neurons, neuron)
}
ol.Neurons = neurons
return ol
}