Пример #1
0
        print("Target:\t" + str(sin_desired_output[i]) + "\t\tOutput:\t" +
              str(mlp.o) + "\n")
        f.write('Target:\t' + str(sin_desired_output[i]) + '\t\tOutput:\t' +
                str(mlp.o) + '\n')

    f.write('MLP Size\t\t\t(' + str(INPUTS) + ', ' + str(HIDDEN) + ', ' +
            str(OUTPUTS) + ')\n')
    f.write('Epochs:\t\t\t\t' + str(MAX_EPOCHS) + '\n')
    f.write('Learning Rate:\t\t' + str(LEARNING_RATE) + '\n\n')

    print("Training:\n")
    f.write('Training:\n')
    for i in range(0, MAX_EPOCHS):
        error = 0
        mlp.forward(sin_inputs[:len(sin_inputs) - 10], True)
        error = mlp.backwards(sin_inputs[:(len(sin_inputs) - 10)],
                              sin_desired_output[:len(sin_inputs) - 10], True)
        mlp.update_weights(LEARNING_RATE)

        if (i + 1) % (MAX_EPOCHS / 10) == 0:
            print("Epoch:\t" + str(i + 1) + "\tError:\t" + str(error))
            f.write('Epoch:\t' + str(i + 1) + '\tError:\t' + str(error) + '\n')

    print("\nTesting:\n")
    f.write('\nTesting:\n')
    for i in range(len(sin_inputs) - 10, len(sin_inputs)):
        mlp.forward(sin_inputs[i], True)
        print("Target:\t" + str(sin_desired_output[i]) + "\t\tOutput:\t" +
              str(mlp.o) + "\n")
        f.write('Target:\t' + str(sin_desired_output[i]) + '\t\tOutput:\t' +
                str(mlp.o) + '\n')
# print(mlp)
Пример #2
0
        print("Target:\t" + str(xor_desired_output[i]) + "\t\tOutput:\t" +
              str(mlp.o) + "\n")
        f.write('Target:\t' + str(xor_desired_output[i]) + '\t\tOutput:\t' +
                str(mlp.o) + '\n')

    f.write('MLP Size\t(' + str(INPUTS) + ', ' + str(HIDDEN) + ', ' +
            str(OUTPUTS) + ')\n\n')
    f.write('Epochs:\t\t' + str(MAX_EPOCHS) + '\n')
    f.write('Learning Rate:\t' + str(LEARNING_RATE) + '\n\n')

    print("Training:\n")
    f.write('Training:\n')
    for i in range(0, MAX_EPOCHS):
        error = 0
        mlp.forward(xor_inputs, False)
        error = mlp.backwards(xor_inputs, xor_desired_output, False)
        mlp.update_weights(LEARNING_RATE)

        if (i + 1) % (MAX_EPOCHS / 10) == 0:
            length = len(str(i))
            print("Epoch:\t" + str(i + 1) + "\tError:\t" + str(error))
            f.write('Epoch:\t' + str(i + 1) + '\tError:\t' + str(error) + '\n')

    print("\nTesting:\n")
    f.write('\nTesting:\n')
    for i in range(len(xor_inputs)):
        mlp.forward(xor_inputs[i], False)
        print("Target:\t" + str(xor_desired_output[i]) + "\t\tOutput:\t" +
              str(mlp.o) + "\n")
        f.write('Target:\t' + str(xor_desired_output[i]) + '\t\tOutput:\t' +
                str(mlp.o) + '\n')