Resolve "release v1.4.0"

Closes #317 (closed)

mlair/model_modules/convolutional_networks.py

@@ -12,17 +12,22 @@ import keras
 
 
 class CNN(AbstractModelClass):
+
     _activation = {"relu": keras.layers.ReLU, "tanh": partial(keras.layers.Activation, "tanh"),
                    "sigmoid": partial(keras.layers.Activation, "sigmoid"),
                    "linear": partial(keras.layers.Activation, "linear"),
-                   "selu": partial(keras.layers.Activation, "selu")}
-    _initializer = {"selu": keras.initializers.lecun_normal()}
-    _optimizer = {"adam": keras.optimizers.adam}
+                   "selu": partial(keras.layers.Activation, "selu"),
+                   "prelu": partial(keras.layers.PReLU, alpha_initializer=keras.initializers.constant(value=0.25))}
+    _initializer = {"tanh": "glorot_uniform", "sigmoid": "glorot_uniform", "linear": "glorot_uniform",
+                    "relu": keras.initializers.he_normal(), "selu": keras.initializers.lecun_normal(),
+                    "prelu": keras.initializers.he_normal()}
+    _optimizer = {"adam": keras.optimizers.adam, "sgd": keras.optimizers.SGD}
     _regularizer = {"l1": keras.regularizers.l1, "l2": keras.regularizers.l2, "l1_l2": keras.regularizers.l1_l2}
-    _requirements = ["lr", "beta_1", "beta_2", "epsilon", "decay", "amsgrad"]
+    _requirements = ["lr", "beta_1", "beta_2", "epsilon", "decay", "amsgrad", "momentum", "nesterov", "l1", "l2"]
+    _dropout = {"selu": keras.layers.AlphaDropout}
 
     def __init__(self, input_shape: list, output_shape: list, activation="relu", activation_output="linear",
-                 optimizer="adam", regularizer=None, kernel_size=1, **kwargs):
+                 optimizer="adam", regularizer=None, kernel_size=1, dropout=None, **kwargs):
 
         assert len(input_shape) == 1
         assert len(output_shape) == 1
@@ -37,6 +42,7 @@ class CNN(AbstractModelClass):
         self.kernel_regularizer = self._set_regularizer(regularizer, **kwargs)
         self.kernel_size = kernel_size
         self.optimizer = self._set_optimizer(optimizer, **kwargs)
+        self.dropout, self.dropout_rate = self._set_dropout(activation, dropout)
 
         # apply to model
         self.set_model()
@@ -56,6 +62,8 @@ class CNN(AbstractModelClass):
             opt_kwargs = {}
             if opt_name == "adam":
                 opt_kwargs = select_from_dict(kwargs, ["lr", "beta_1", "beta_2", "epsilon", "decay", "amsgrad"])
+            elif opt_name == "sgd":
+                opt_kwargs = select_from_dict(kwargs, ["lr", "momentum", "decay", "nesterov"])
             return opt(**opt_kwargs)
         except KeyError:
             raise AttributeError(f"Given optimizer {optimizer} is not supported in this model class.")
@@ -77,6 +85,12 @@ class CNN(AbstractModelClass):
         except KeyError:
             raise AttributeError(f"Given regularizer {regularizer} is not supported in this model class.")
 
+    def _set_dropout(self, activation, dropout_rate):
+        if dropout_rate is None:
+            return None, None
+        assert 0 <= dropout_rate < 1
+        return self._dropout.get(activation, keras.layers.Dropout), dropout_rate
+
     def set_model(self):
         """
         Build the model.