mleq_cvurves.sh set up for different frames, loss_phase working again

ml_eq.py

@@ -24,14 +24,13 @@
 parser.add_argument('--load_model', type=str, default="", help='before inference, load model using this filename')
 parser.add_argument('--curve', type=str, default="", help='before inference, load model using this filename')
 parser.add_argument('--framer', type=int, default=1, help='framer design')
+parser.add_argument('--batch_size', type=int, help="batch size, default: 32", default=32)
 parser.set_defaults(train=True)
 parser.set_defaults(plots=True)
 args = parser.parse_args()
 n_syms = args.n_syms
 
 bps = 2
-batch_size = 16
-w1 = 32
 
 # Get cpu, gpu or mps device for training.
 device = (
@@ -237,10 +236,10 @@ def forward(self, tx_data):
 
 # sym and sym hat in float format (real,imag pairs)
 def loss_phase_mse(sym_hat, sym):
-    sym = sym[:,0] + 1j*sym[:,1]
-    sym_hat = sym_hat[:,0] + 1j*sym_hat[:,1]
+    sym = sym[:,0::2] + 1j*sym[:,1::2]
+    sym_hat = sym_hat[:,0::2] + 1j*sym_hat[:,1::2]
     error = torch.angle(sym*torch.conj(sym_hat))
-    loss = torch.sum(error**2)
+    loss = torch.mean(error**2)
     return loss
 
 if args.framer == 1:
@@ -259,11 +258,12 @@ def loss_phase_mse(sym_hat, sym):
     if args.loss_phase:
         loss_fn = loss_phase_mse
     else:
-        loss_fn = nn.MSELoss(reduction='sum')
+        loss_fn = nn.MSELoss(reduction='mean')
+
     optimizer = torch.optim.SGD(model.parameters(), lr=args.lr)
 
     dataset = aQPSKDataset(n_syms, model.n_data)
-    dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size)
+    dataloader = torch.utils.data.DataLoader(dataset, batch_size=args.batch_size)
 
     # Train model
     for epoch in range(args.epochs):

mleq_curves.sh

@@ -2,25 +2,36 @@
 # ML EQ experiments - generates sets of curves
 
 epochs=100
+n_syms=100000
+batch_size=128
 
-function frame01_train() {
+function train() {
+    f=$1
     # train with mse and phase loss functions
-    python3 ml_eq.py --EbNodB 4 --phase_offset --lr 0.001 --epochs ${epochs} --save_model mleq01_mse.model --noplots
-    python3 ml_eq.py --EbNodB 4 --phase_offset --lr 0.001 --epochs ${epochs} --loss_phase --save_model mleq01_phase.model --noplots
+    python3 ml_eq.py --frame ${f} --EbNodB 4 --phase_offset \
+    --lr 0.05 --n_syms ${n_syms} --epochs ${epochs} --batch_size ${batch_size} --save_model mleq0${f}_mse.model --noplots
+    python3 ml_eq.py --frame ${f} --loss_phase --EbNodB 4 --phase_offset \
+    --lr 0.05 --n_syms ${n_syms} --epochs ${epochs} --batch_size ${batch_size} --save_model mleq0${f}_phase.model --noplots
 }
 
-function frame01_curve() {
+function curve() {
+    f=$1
     # run BER v Eb/No curves, including DSP lin as control
-    python3 ml_eq.py --eq dsp --notrain --phase_offset --curve mleq01_ber_lin.txt
-    python3 ml_eq.py --notrain --curve mleq01_ber_mse.txt --phase_offset --load_model mleq01_mse.model
-    python3 ml_eq.py --notrain --curve mleq01_ber_phase.txt --phase_offset --load_model mleq01_phase.model
+    python3 ml_eq.py --frame ${f} --notrain --eq dsp --phase_offset --curve mleq0${f}_ber_lin.txt
+    python3 ml_eq.py --frame ${f} --notrain --load_model mleq0${f}_mse.model --phase_offset --curve mleq0${f}_ber_mse.txt 
+    python3 ml_eq.py --frame ${f} --notrain --load_model mleq0${f}_phase.model --phase_offset --curve mleq0${f}_ber_phase.txt
 
     # generate EPS plots for paper
     echo "radae_plots; plot_ber_EbNodB( \
-    'mleq01_ber_lin.txt','mleq01_ber_mse.txt','mleq01_ber_phase.txt', \
-    'mleq01_ber_EbNodB.png','mleq01_ber_EbNodB.eps')" | octave -qf
+    'mleq0${f}_ber_lin.txt','mleq0${f}_ber_mse.txt','mleq0${f}_ber_phase.txt', \
+    'mleq0${f}_ber_EbNodB.png','mleq0${f}_ber_EbNodB.eps')" | octave -qf
 }
 
-frame01_train
-frame01_curve
+if [ $# -ne 1 ]; then
+  echo "usage mleq_cruves.sh framer[1|2]"
+  exit 1
+fi
+
+train $1
+curve $1