import scipy.io as sio # import scipy.io for .mat file I/O
import numpy as np # import numpy
import function_wmmse_powercontrol as wf # import our function file
import function_dnn_powercontrol as df # import our function file
for K in [10, 20, 30]:
# Problem Setup, K: number of users.
print('Gaussian IC Case: K=%d' % K)
# Load model from this path, ! Please modify this path !
model_location = "./DNNmodel/model_%d.ckpt" % (K)
# Generate Testing Data
num_test = 1000 # number of testing samples
X, Y, wmmsetime = wf.generate_Gaussian(K, num_test, seed=7)
# Testing Deep Neural Networks
dnntime = df.test(X,
model_location,
"Prediction_%d" % K,
K * K,
K,
binary=1)
print('wmmse time: %0.3f s, dnn time: %0.3f s, time speed up: %0.1f X' %
(wmmsetime, dnntime, wmmsetime / dnntime))
# Evaluate Performance of DNN and WMMSE
H = np.reshape(X, (K, K, X.shape[1]), order="F")
NNVbb = sio.loadmat('Prediction_%d.mat' % K)['pred']
wf.perf_eval(H, Y, NNVbb, K)
import function_dnn_powercontrol as df # import our function file
K = 10 # number of users
num_H = 25000 # number of training samples
num_test = 5000 # number of testing samples
training_epochs = 300 # number of training epochs
trainseed = 0 # set random seed for training set
testseed = 7 # set random seed for test set
Beta = [10**(-3), 10**(-4), 10**(-6)]
epochs = np.arange(training_epochs)
# Problem Setup
print('Gaussian IC Case: K=%d, Total Samples: %d, Total Iterations: %d\n' %
(K, num_H, training_epochs))
# Generate Training Data
Xtrain, Ytrain, wtime = wf.generate_Gaussian(K, num_H, seed=trainseed)
# Training Deep Neural Networks
print('train DNN ...')
# Save & Load model from this path
model_location = "./DNNmodel/model_demo.ckpt"
train = []
time = []
val = []
for i in range(np.size(Beta)):
df.train(Xtrain,
Ytrain,
model_location,
training_epochs=training_epochs,
traintestsplit=0.2,
batch_size=200,