public Layer(int inputs_size, int output_size, Squash_func.SQUASH_FUNC func, uint index = 0, double learn_rate = 0.01)
{
out_size = output_size;
input_size = inputs_size;
neurons = new Neuron[out_size];
for (int i = 0; i < neurons.Length; i++)
{
neurons[i] = new Neuron(func, inputs_size, learn_rate);
}
inputs = DenseVector.Build.Dense(input_size, 0);
outputs = DenseVector.Build.Dense(out_size, 0);
layer_index = index;
}
public Neuron(Squash_func.SQUASH_FUNC func, int input_size, double learn_rate)
{
//Init the squash function
squash_func = new Func <double, double>(x => Squash_func.squash(func, x));
//Init the derivative of the squash function
squash_func_derivate = new Func <double, double>(x => Squash_func.derivative_squash(func, x));
Random random = new Random();
//Initialize the weights of the neuron to random value between 0 and 1
weigths = DenseVector.Build.Dense(input_size).Map(x => random.NextDouble());
//Initilize the bias to a random value between 0 and 1
bias = random.NextDouble();
forward_propagation_done = false;
learning_rate = learn_rate;
}
public void add_layer(int in_size, int out_size, Squash_func.SQUASH_FUNC func = Squash_func.SQUASH_FUNC.TANH)
{
neural_net_layers_list.Add(new Layer(in_size, out_size, func, (uint)neural_net_layers_list.Count, learn_rate: this.learn_rate));
}
