Trivial code correction to run mlp

train.py

@@ -148,7 +148,7 @@ def get_mnist_cnn():
 
     return (nb_classes, batch_size, input_shape, x_train, x_test, y_train, y_test, epochs)
 
-def compile_model_mlp(geneparam, nb_classes, input_shape):
+def compile_model_mlp(genome, nb_classes, input_shape):
     """Compile a sequential model.
 
     Args:
@@ -159,12 +159,12 @@ def compile_model_mlp(geneparam, nb_classes, input_shape):
 
     """
     # Get our network parameters.
-    nb_layers  = geneparam['nb_layers' ]
-    nb_neurons = geneparam['nb_neurons']
-    activation = geneparam['activation']
-    optimizer  = geneparam['optimizer' ]
+    nb_layers  = genome.geneparam['nb_layers' ]
+    nb_neurons = genome.nb_neurons()
+    activation = genome.geneparam['activation']
+    optimizer  = genome.geneparam['optimizer' ]
 
-    logging.info("Architecture:%d,%s,%s,%d" % (nb_neurons, activation, optimizer, nb_layers))
+    logging.info("Architecture:%s,%s,%s,%d" % (str(nb_neurons), activation, optimizer, nb_layers))
 
     model = Sequential()
 
@@ -173,9 +173,9 @@ def compile_model_mlp(geneparam, nb_classes, input_shape):
 
         # Need input shape for first layer.
         if i == 0:
-            model.add(Dense(nb_neurons, activation=activation, input_shape=input_shape))
+            model.add(Dense(nb_neurons[i], activation=activation, input_shape=input_shape))
         else:
-            model.add(Dense(nb_neurons, activation=activation))
+            model.add(Dense(nb_neurons[i], activation=activation))
 
         model.add(Dropout(0.2))  # hard-coded dropout for each layer
 
@@ -265,11 +265,11 @@ def train_and_score(genome, dataset):
     logging.info("Compling Keras model")
 
     if dataset   == 'cifar10_mlp':
-        model = compile_model_mlp(geneparam, nb_classes, input_shape)
+        model = compile_model_mlp(genome, nb_classes, input_shape)
     elif dataset == 'cifar10_cnn':
         model = compile_model_cnn(genome, nb_classes, input_shape)
     elif dataset == 'mnist_mlp':
-        model = compile_model_mlp(geneparam, nb_classes, input_shape)
+        model = compile_model_mlp(genome, nb_classes, input_shape)
     elif dataset == 'mnist_cnn':
         model = compile_model_cnn(genome, nb_classes, input_shape)