simulations/models/autoencoder.py

import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf

from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from tensorflow.keras import layers, losses
from tensorflow.keras.models import Model
from tensorflow.python.keras.layers import LeakyReLU, ReLU

from functools import partial
import misc
import defs
from models import basic
import os

latent_dim = 64

print("# GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))


class AutoencoderMod(defs.Modulator):
    def __init__(self, autoencoder):
        super().__init__(2 ** autoencoder.N)
        self.autoencoder = autoencoder

    def forward(self, binary: np.ndarray) -> defs.Signal:
        reshaped = binary.reshape((-1, self.N))
        reshaped_ho = misc.bit_matrix2one_hot(reshaped)
        encoded = self.autoencoder.encoder(reshaped_ho)
        x = encoded.numpy()
        x2 = x * 2 - 1

        f = np.zeros(x2.shape[0])
        x3 = misc.rect2polar(np.c_[x2[:, 0], x2[:, 1], f])
        return defs.Signal(x3)


class AutoencoderDemod(defs.Demodulator):
    def __init__(self, autoencoder):
        super().__init__(2 ** autoencoder.N)
        self.autoencoder = autoencoder

    def forward(self, values: defs.Signal) -> np.ndarray:
        decoded = self.autoencoder.decoder(values.rect).numpy()
        result = misc.int2bit_array(decoded.argmax(axis=1), self.N)
        return result.reshape(-1, )


class Autoencoder(Model):
    def __init__(self, N, channel, signal_dim=2):
        super(Autoencoder, self).__init__()
        self.N = N
        self.encoder = tf.keras.Sequential()
        self.encoder.add(tf.keras.Input(shape=(2 ** N,), dtype=bool))
        self.encoder.add(layers.Dense(units=2 ** (N + 1)))
        self.encoder.add(LeakyReLU(alpha=0.001))
        # self.encoder.add(layers.Dropout(0.2))
        self.encoder.add(layers.Dense(units=2 ** (N + 1)))
        self.encoder.add(LeakyReLU(alpha=0.001))
        self.encoder.add(layers.Dense(units=signal_dim, activation="tanh"))
        # self.encoder.add(layers.ReLU(max_value=1.0))

        self.decoder = tf.keras.Sequential()
        self.decoder.add(tf.keras.Input(shape=(signal_dim,)))
        self.decoder.add(layers.Dense(units=2 ** (N + 1)))
        # leaky relu with alpha=1 gives by far best results
        self.decoder.add(LeakyReLU(alpha=1))
        self.decoder.add(layers.Dense(units=2 ** N, activation="softmax"))

        # self.randomiser = tf.random_normal_initializer(mean=0.0, stddev=0.1, seed=None)

        self.mod = None
        self.demod = None
        self.compiled = False

        if isinstance(channel, int) or isinstance(channel, float):
            self.channel = basic.AWGNChannel(channel)
        else:
            if not hasattr(channel, 'forward_tensor'):
                raise ValueError("Channel has no forward_tensor function")
            if not callable(channel.forward_tensor):
                raise ValueError("Channel.forward_tensor is not callable")
            self.channel = channel

        # self.decoder.add(layers.Softmax(units=4, dtype=bool))

        # [
        #     layers.Input(shape=(28, 28, 1)),
        #     layers.Conv2D(16, (3, 3), activation='relu', padding='same', strides=2),
        #     layers.Conv2D(8, (3, 3), activation='relu', padding='same', strides=2)
        # ])
        # self.decoder = tf.keras.Sequential([
        #     layers.Conv2DTranspose(8, kernel_size=3, strides=2, activation='relu', padding='same'),
        #     layers.Conv2DTranspose(16, kernel_size=3, strides=2, activation='relu', padding='same'),
        #     layers.Conv2D(1, kernel_size=(3, 3), activation='sigmoid', padding='same')
        # ])

    def call(self, x, **kwargs):
        signal = self.encoder(x)
        signal = signal * 2 - 1
        signal = self.channel.forward_tensor(signal)
        # encoded = encoded * 2 - 1
        # encoded = tf.clip_by_value(encoded, clip_value_min=0, clip_value_max=1, name=None)
        # noise = self.randomiser(shape=(-1, 2), dtype=tf.float32)
        # noise = np.random.normal(0, 1, (1, 2)) * self.noise
        # noisy = tf.convert_to_tensor(noise, dtype=tf.float32)
        decoded = self.decoder(signal)
        return decoded

    def fit_encoder(self, modulation, sample_size, train_size=0.8, epochs=1, batch_size=1, shuffle=False):
        alphabet = basic.load_alphabet(modulation, polar=False)

        if not alphabet.shape[0] == self.N ** 2:
            raise Exception("Cardinality of modulation scheme is different from cardinality of autoencoder!")

        x_train = np.random.randint(self.N ** 2, size=int(sample_size * train_size))
        y_train = alphabet[x_train]
        x_train_ho = np.zeros((int(sample_size * train_size), self.N ** 2))
        for idx, x in np.ndenumerate(x_train):
            x_train_ho[idx, x] = 1

        x_test = np.random.randint(self.N ** 2, size=int(sample_size * (1 - train_size)))
        y_test = alphabet[x_test]
        x_test_ho = np.zeros((int(sample_size * (1 - train_size)), self.N ** 2))
        for idx, x in np.ndenumerate(x_test):
            x_test_ho[idx, x] = 1

        self.encoder.compile(optimizer='adam', loss=tf.keras.losses.MeanSquaredError())
        self.encoder.fit(x_train_ho, y_train,
                         epochs=epochs,
                         batch_size=batch_size,
                         shuffle=shuffle,
                         validation_data=(x_test_ho, y_test))

    def fit_decoder(self, modulation, samples):
        samples = int(samples * 1.3)
        demod = basic.AlphabetDemod(modulation, 0)
        x = np.random.rand(samples, 2) * 2 - 1
        x = x.reshape((-1, 2))
        f = np.zeros(x.shape[0])
        xf = np.c_[x[:, 0], x[:, 1], f]
        y = demod.forward(defs.Signal(misc.rect2polar(xf)))
        y_ho = misc.bit_matrix2one_hot(y.reshape((-1, 4)))

        X_train, X_test, y_train, y_test = train_test_split(x, y_ho)
        self.decoder.compile(optimizer='adam', loss=tf.keras.losses.MeanSquaredError())
        self.decoder.fit(X_train, y_train, shuffle=False, validation_data=(X_test, y_test))
        y_pred = self.decoder(X_test).numpy()
        y_pred2 = np.zeros(y_test.shape, dtype=bool)
        y_pred2[np.arange(y_pred2.shape[0]), np.argmax(y_pred, axis=1)] = True

        print("Decoder accuracy: %.4f" % accuracy_score(y_pred2, y_test))

    def train(self, samples=1e6):
        if samples % self.N:
            samples += self.N - (samples % self.N)
        x_train = misc.generate_random_bit_array(samples).reshape((-1, self.N))
        x_train_ho = misc.bit_matrix2one_hot(x_train)

        test_samples = samples * 0.3
        if test_samples % self.N:
            test_samples += self.N - (test_samples % self.N)
        x_test_array = misc.generate_random_bit_array(test_samples)
        x_test = x_test_array.reshape((-1, self.N))
        x_test_ho = misc.bit_matrix2one_hot(x_test)

        if not self.compiled:
            self.compile(optimizer='adam', loss=losses.MeanSquaredError())
            self.compiled = True

        self.fit(x_train_ho, x_train_ho, shuffle=False, validation_data=(x_test_ho, x_test_ho))
        # encoded_data = self.encoder(x_test_ho)
        # decoded_data = self.decoder(encoded_data).numpy()

    def get_modulator(self):
        if self.mod is None:
            self.mod = AutoencoderMod(self)
        return self.mod

    def get_demodulator(self):
        if self.demod is None:
            self.demod = AutoencoderDemod(self)
        return self.demod


def view_encoder(encoder, N, samples=1000):
    test_values = misc.generate_random_bit_array(samples).reshape((-1, N))
    test_values_ho = misc.bit_matrix2one_hot(test_values)
    mvector = np.array([2 ** i for i in range(N)], dtype=int)
    symbols = (test_values * mvector).sum(axis=1)
    encoded = encoder(test_values_ho).numpy()
    # encoded = misc.polar2rect(encoded)
    for i in range(2 ** N):
        xy = encoded[symbols == i]
        plt.plot(xy[:, 0], xy[:, 1], 'x', markersize=12, label=format(i, f'0{N}b'))
        plt.annotate(xy=[xy[:, 0].mean() + 0.01, xy[:, 1].mean() + 0.01], text=format(i, f'0{N}b'))
    plt.xlabel('Real')
    plt.ylabel('Imaginary')
    plt.title("Autoencoder generated alphabet")
    # plt.legend()
    plt.show()

    pass


if __name__ == '__main__':
    # (x_train, _), (x_test, _) = fashion_mnist.load_data()
    #
    # x_train = x_train.astype('float32') / 255.
    # x_test = x_test.astype('float32') / 255.
    #
    # print(f"Train data: {x_train.shape}")
    # print(f"Test data: {x_test.shape}")

    n = 4

    # samples = 1e6
    # x_train = misc.generate_random_bit_array(samples).reshape((-1, n))
    # x_train_ho = misc.bit_matrix2one_hot(x_train)
    # x_test_array = misc.generate_random_bit_array(samples * 0.3)
    # x_test = x_test_array.reshape((-1, n))
    # x_test_ho = misc.bit_matrix2one_hot(x_test)

    autoencoder = Autoencoder(n, -8)
    autoencoder.compile(optimizer='adam', loss=losses.MeanSquaredError())

    autoencoder.fit_encoder(modulation='16qam',
                            sample_size=2e6,
                            train_size=0.8,
                            epochs=1,
                            batch_size=256,
                            shuffle=True)

    view_encoder(autoencoder.encoder, n)
    autoencoder.fit_decoder(modulation='16qam', samples=2e6)
    autoencoder.train()
    view_encoder(autoencoder.encoder, n)

    # view_encoder(autoencoder.encoder, n)
    # view_encoder(autoencoder.encoder, n)


    # autoencoder.compile(optimizer='adam', loss=losses.MeanSquaredError())
    #
    # autoencoder.fit(x_train_ho, x_train_ho,
    #                 epochs=1,
    #                 shuffle=False,
    #                 validation_data=(x_test_ho, x_test_ho))
    #
    # encoded_data = autoencoder.encoder(x_test_ho)
    # decoded_data = autoencoder.decoder(encoded_data).numpy()
    #
    # result = misc.int2bit_array(decoded_data.argmax(axis=1), n)
    # print("Accuracy: %.4f" % accuracy_score(x_test_array, result.reshape(-1, )))
    # view_encoder(autoencoder.encoder, n)

    pass