small fix to training script

script/mhcflurry-train-class1-allele-specific-models.py

@@ -46,13 +46,8 @@ from keras.optimizers import RMSprop
 from mhcflurry.common import normalize_allele_name
 from mhcflurry.data import load_allele_datasets
 from mhcflurry.class1_binding_predictor import Class1BindingPredictor
-from mhcflurry.class1_allele_specific_hyperparameters import (
-    add_hyperparameter_arguments_to_parser
-)
-from mhcflurry.paths import (
-    CLASS1_MODEL_DIRECTORY,
-    CLASS1_DATA_DIRECTORY
-)
+from mhcflurry.feedforward_hyperparameters import add_hyperparameter_arguments_to_parser
+from mhcflurry.paths import (CLASS1_MODEL_DIRECTORY, CLASS1_DATA_DIRECTORY)
 from mhcflurry.imputation import create_imputed_datasets, imputer_from_name
 
 CSV_FILENAME = "combined_human_class1_dataset.csv"

test/test_neural_nets.py

@@ -17,7 +17,7 @@ def test_make_embedding_network_properties():
         optimizer=RMSprop(lr=0.7, rho=0.9, epsilon=1e-6))
     eq_(nn.layers[0].input_dim, 3)
     eq_(nn.loss, mse)
-    eq_(nn.optimizer.lr, 0.7)
+    eq_(nn.optimizer.lr.eval(), 0.7)
     print(nn.layers)
     # embedding + flatten + (dense->activation) * hidden layers and last layer
     eq_(len(nn.layers), 2 + 2 * (1 + len(layer_sizes)))
@@ -34,7 +34,7 @@ def test_make_hotshot_network_properties():
         optimizer=RMSprop(lr=0.7, rho=0.9, epsilon=1e-6))
     eq_(nn.layers[0].input_dim, 6)
     eq_(nn.loss, mse)
-    eq_(nn.optimizer.lr, 0.7)
+    eq_(nn.optimizer.lr.eval(), 0.7)
     print(nn.layers)
     eq_(len(nn.layers), 2 + 2 * (1 + len(layer_sizes)))