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train_unsuper.py
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"""
Build a unsupervised model for indiviudal power and individual beta optimization
Author : Khin Thandar Kyaw
Reference : DL Framework for Optimization of MISO Downlink Beamforming, TCOM,
TianLin0509/BF-design-with-DL
Date : 8 Nov 2023
Last Modified : 15 Nov 2023
"""
import numpy as np
import tensorflow as tf
import keras
from nn_utils import *
from super_utils import *
from keras import layers
import matplotlib.pyplot as plt
# ------------------------------------
# load and generate simulation data
# ------------------------------------
total_users = total_users()
for user_size in total_users:
print(f'Total # of Users: {user_size}')
print_line()
antenna_size, _, _, _, _, _, _ = parameters(user_size)
snr_fixed = fixed_snr()
covariance_complex = np.load(f'train/{user_size}users/cov_train.npy')
e_max_complex = np.load(f'train/{user_size}users/eMax_train.npy')
# ----------Prepare Data---------
batch_size, sample_size, covariance_stacked, snr_total, noise_var_total, power_total = data_preparation(covariance_complex)
e_max_stacked = stacking(e_max_complex)
# (sampleSize, antennaSize, antennaSize)
identity_matrix = tf.cast(tf.eye(antenna_size, batch_shape=[sample_size]), dtype=tf.complex64)
# (sampleSize, 1, antennaSize, antennaSize)
identity_matrix = tf.expand_dims(identity_matrix, axis=1)
# (sampleSize, userSize, antennaSize, antennaSize)
identity_matrix = tf.tile(identity_matrix, [1, user_size, 1, 1])
# ------------------------------------
# Construct the Unsupervised Model
# ------------------------------------
# (userSize, real/imag, anteannaSize, antennaSize)
covariance_stacked_input = layers.Input(name='CovarianceStackedInput',
shape=(covariance_stacked.shape[1:5]),
dtype=tf.float32)
power_total_input = layers.Input(name='PowerTotalInput',
shape=(1,),
dtype=tf.float32)
identity_matrix_input = layers.Input(name='identityMatrixInput',
shape=(identity_matrix.shape[1:4]),
dtype=tf.complex64)
covariance_complex_input = layers.Input(name='CovarianceComplexInput',
shape=(covariance_complex.shape[1:4]),
dtype=tf.complex64)
e_max_complex_input = layers.Input(name='eMaxComplexInput',
shape=(e_max_complex.shape[1:4]),
dtype=tf.complex64)
e_max_stacked_input = layers.Input(name="eMaxStackedInput",
shape=(e_max_stacked.shape[1:5]),
dtype=tf.float32)
temp1 = layers.BatchNormalization()(covariance_stacked_input)
temp1 = layers.Flatten()(temp1)
temp2 = layers.BatchNormalization()(power_total_input)
temp2 = layers.Flatten()(temp2)
temp3 = layers.BatchNormalization()(e_max_stacked_input)
temp3 = layers.Flatten()(temp3)
temp = layers.concatenate([temp1, temp2, temp3])
temp = layers.BatchNormalization()(temp)
# temp = layers.Dense(512, activation='softplus')(temp)
# temp = layers.BatchNormalization()(temp)
temp = layers.Dense(256, activation='softplus')(temp)
temp = layers.BatchNormalization()(temp)
temp = layers.Dense(128, activation='softplus')(temp)
temp = layers.BatchNormalization()(temp)
temp = layers.Dense(64, activation='softplus')(temp)
temp = layers.BatchNormalization()(temp)
temp_first_half = layers.Lambda(lambda x: x[:, :32])(temp)
temp_second_half = layers.Lambda(lambda x: x[:, 32:])(temp)
power_temp = layers.Dense(user_size, activation='softplus')(temp_first_half)
beta_temp = layers.Dense(user_size, activation='softplus')(temp_second_half)
individual_power_output = layers.Lambda(trans_power,
dtype=tf.float32,
output_shape=(user_size, 1, 1))([power_temp, power_total_input])
individual_beta_output = layers.Lambda(trans_Beta,
dtype=tf.float32,
output_shape=(user_size, 1, 1))([beta_temp, power_total_input])
beam = layers.Lambda(compute_beam,
dtype=tf.complex64,
output_shape=(user_size, antenna_size, 1))([individual_power_output,
individual_beta_output,
e_max_complex_input,
identity_matrix_input,
covariance_complex_input])
loss = layers.Lambda(loss_func_unsuper,
dtype=tf.float32,
output_shape=(1,))([covariance_complex_input, beam])
model = keras.Model(inputs=[covariance_stacked_input,
power_total_input,
identity_matrix_input,
covariance_complex_input,
e_max_complex_input,
e_max_stacked_input], outputs=loss)
optimizer = keras.optimizers.Adam(learning_rate=1e-5)
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR) # suppress warning # cos I believe that the warning is due to internal issues
model.compile(optimizer=optimizer, loss=lambda y_true, y_pred: y_pred)
model.summary()
# ------------------------------------
# Train the Model
# ------------------------------------
reduced_lr = keras.callbacks.ReduceLROnPlateau(monitor='val_loss',
factor=0.1,
patience=3,
mode= 'min',
min_delta=0.01,
min_lr=1e-7)
checkpoint = keras.callbacks.ModelCheckpoint(f'train/{user_size}users/trainedSuper.h5',
monitor='val_loss',
verbose=0,
save_best_only=True,
mode='min',
save_weights_only=True)
early_stopping = keras.callbacks.EarlyStopping(monitor='val_loss',
min_delta=0,
patience=5,
verbose=0,
mode='min',
restore_best_weights=True)
history = model.fit(x=[covariance_stacked, power_total,
identity_matrix, covariance_complex,
e_max_complex, e_max_stacked],
y=covariance_complex, # Dummy target
batch_size=batch_size,
epochs=500,
verbose=2,
validation_split= 0.3,
callbacks=[reduced_lr, checkpoint, early_stopping]
)
# ------------------------------------
# Plot the loss curve
# ------------------------------------
loss_curve(history, user_size, 'Indiviudal beta and Individual power Constraints', snr_fixed)
plt.savefig(f'train/{user_size}users/LossCurve_Super.png')
plt.close()