Source code for example_cvffnn_1

# -*- coding: utf-8 -*-
"""**RosenPy: An Open Source Python Framework for Complex-Valued Neural Networks**.
*Copyright © A. A. Cruz, K. S. Mayer, D. S. Arantes*.

*License*

This file is part of RosenPy.
RosenPy is an open-source framework distributed under the terms of the GNU General 
Public License, as published by the Free Software Foundation, either version 3 of 
the License, or (at your option) any later version. For additional information on 
license terms, please open the Readme.md file.

RosenPy is distributed in the hope that it will be useful to every user, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. 
See the GNU General Public License for more details. 

You should have received a copy of the GNU General Public License
along with RosenPy. If not, see <http://www.gnu.org/licenses/>.
"""
import sys
import os

project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))

if project_root not in sys.path:
    sys.path.insert(0, project_root)


import numpy as np
import rosenpy.model.cvffnn as mynn
import rosenpy.model.rp_optimizer as opt
import rosenpy.dataset.beamforming as dt


[docs]def set_data():
    """
    Set up the data for training.

    Returns:
        tuple: Tuple containing the normalized input and output datasets.
    """
    f = 850e6
    sinr_db = 20
    snr_dbs = 25
    snr_dbi = 20
    phi = [1, 60, 90, 120, 160, 200, 240, 260, 280, 300, 330]
    theta = [90] * 11
    desired = np.array([1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0])
    modulation = ["QAM", "WGN", "QAM", "PSK", "QAM", "WGN", "QAM", "WGN", "QAM", "PSK", "PSK"]
    mmod = [4, 0, 64, 8, 256, 0, 16, 0, 64, 16, 8]

    len_data = int(1e4)

    # Create the dataset
    set_in, set_out = dt.create_dataset_beam(modulation, mmod, f, phi, theta, desired, len_data, sinr_db, snr_dbs, snr_dbi)

    return set_in, set_out


# Initialize input_data and output_data using the set_data function
input_data, output_data = set_data()

# Create an instance of the CVFFNN Neural Network
nn = mynn.CVFFNN(gpu_enable=False)

# Add layers to the neural network
nn.add_layer(ishape=input_data.shape[1], neurons=15)
nn.add_layer(neurons=output_data.shape[1])

# Train the neural network
nn.fit(input_data, output_data, epochs=500, verbose=100, batch_size=100, optimizer=opt.CVAdamax())

# Make predictions using the trained model
y_pred = nn.predict(input_data)

# Calculate and print accuracy
print(f'Accuracy: {nn.accuracy(output_data, y_pred):.2f}%')