fixed deprecation

civodlu · Jan 31, 2020 · c48d90d · c48d90d
1 parent d129c6d
commit c48d90d
Show file tree

Hide file tree

Showing 3 changed files with 4 additions and 4 deletions.
diff --git a/tests/test_simple_layers.py b/tests/test_simple_layers.py
@@ -53,7 +53,7 @@ def test_denses(self):
             i,
             sizes=[16, 32],
             dropout_probability=0.5,
-            with_batchnorm=True,
+            batch_norm_kwargs={},
             activation=torch.nn.ReLU6,
             last_layer_is_output=True)
         net = trw.simple_layers.compile_nn([o])

diff --git a/tutorials/classification_cirfar10_darts.py b/tutorials/classification_cirfar10_darts.py
@@ -10,7 +10,7 @@ def __init__(self, options):
 
         dropout_probability = options['training_parameters']['dropout_probability']
         self.convs = trw.layers.convs_2d(3, [64, 128, 256], batch_norm_kwargs={}, convolution_repeats=[3, 3, 3])
-        self.denses = trw.layers.denses([4096, 256, 10], dropout_probability=None, with_batchnorm=True, last_layer_is_output=True)
+        self.denses = trw.layers.denses([4096, 256, 10], dropout_probability=None, batch_norm_kwargs={}, last_layer_is_output=True)
 
     def forward(self, batch):
         # a batch should be a dictionary of features

diff --git a/tutorials/explanation_synthetic.py b/tutorials/explanation_synthetic.py
@@ -5,9 +5,9 @@
 def create_net_simple(options):
     activation = nn.ReLU
     n = trw.simple_layers.Input([None, 3, 64, 64], 'image')
-    n = trw.simple_layers.convs_2d(n, channels=[4, 8, 16], with_batchnorm=True, activation=activation)
+    n = trw.simple_layers.convs_2d(n, channels=[4, 8, 16], batch_norm_kwargs={}, activation=activation)
     n = trw.simple_layers.global_max_pooling_2d(n)
-    n = trw.simple_layers.denses(n, sizes=[32, 2], with_batchnorm=True, activation=activation, last_layer_is_output=True)
+    n = trw.simple_layers.denses(n, sizes=[32, 2], batch_norm_kwargs={}, activation=activation, last_layer_is_output=True)
     n = trw.simple_layers.OutputClassification(n, output_name='softmax', classes_name='triangle')
     return trw.simple_layers.compile_nn([n])