Convert to new programing API (apache#4307)

iblislin · Dec 29, 2016 · a9ec282 · a9ec282
1 parent e18ff28
commit a9ec282
Showing 1 changed file with 27 additions and 18 deletions.
diff --git a/example/image-classification/common/fit.py b/example/image-classification/common/fit.py
@@ -76,6 +76,8 @@ def add_fit_args(parser):
                        help='show progress for every n batches')
     train.add_argument('--model-prefix', type=str,
                        help='model prefix')
+    parser.add_argument('--monitor', dest='monitor', type=int, default=0,
+                        help='log network parameters every N iters if larger than 0')
     train.add_argument('--load-epoch', type=int,
                        help='load the model on an epoch using the model-load-prefix')
     train.add_argument('--top-k', type=int, default=0,
@@ -134,23 +136,23 @@ def fit(args, network, data_loader, **kwargs):
     lr, lr_scheduler = _get_lr_scheduler(args, kv)
 
     # create model
-    model = mx.model.FeedForward(
-        ctx           = devs,
-        symbol        = network,
-        begin_epoch   = args.load_epoch if args.load_epoch else 0,
-        num_epoch     = args.num_epochs,
-        arg_params    = arg_params,
-        aux_params    = aux_params,
-        learning_rate = lr,
-        lr_scheduler  = lr_scheduler,
-        momentum      = args.mom,
-        wd            = args.wd,
-        optimizer     = args.optimizer,
-        initializer   = mx.init.Xavier(
-            rnd_type='gaussian', factor_type="in", magnitude=2)
-        # initializer   = mx.init.Xavier(factor_type="in", magnitude=2.34),
+    model = mx.mod.Module(
+        context       = devs,
+        symbol        = network
     )
 
+    lr_scheduler  = lr_scheduler
+    optimizer_params = {
+            'learning_rate': lr,
+            'momentum' : args.mom,
+            'wd' : args.wd}
+
+    monitor = mx.mon.Monitor(args.monitor, pattern=".*") if args.monitor > 0 else None
+
+    initializer   = mx.init.Xavier(
+       rnd_type='gaussian', factor_type="in", magnitude=2)
+    # initializer   = mx.init.Xavier(factor_type="in", magnitude=2.34),
+
     # evaluation metrices
     eval_metrics = ['accuracy']
     if args.top_k > 0:
@@ -163,10 +165,17 @@ def fit(args, network, data_loader, **kwargs):
         batch_end_callbacks += cbs if isinstance(cbs, list) else [cbs]
 
     # run
-    model.fit(
-        X                  = train,
+    model.fit(train,
+        begin_epoch        = args.load_epoch if args.load_epoch else 0,
+        num_epoch          = args.num_epochs,
         eval_data          = val,
         eval_metric        = eval_metrics,
         kvstore            = kv,
+        optimizer          = args.optimizer,
+        optimizer_params   = optimizer_params,
+        initializer        = initializer,
+        arg_params         = arg_params,
+        aux_params         = aux_params,
         batch_end_callback = batch_end_callbacks,
-        epoch_end_callback = checkpoint)
+        epoch_end_callback = checkpoint,
+        monitor            = monitor)