Use Map base class

Author: lululxvi

deepxde/maps/fnn.py

@@ -9,11 +9,12 @@
 from . import activations
 from . import initializers
 from . import regularizers
+from .map import Map
 from .. import config
 from ..utils import timing
 
 
-class FNN(object):
+class FNN(Map):
     """feed-forward neural networks
     """
 
@@ -33,10 +34,7 @@ def __init__(
         self.dropout_rate = dropout_rate
         self.batch_normalization = batch_normalization
 
-        self.training, self.dropout = None, None
-        self.data_id = None  # 0: train data, 1: test data
-        self.x, self.y, self.y_ = None, None, None
-        self.build()
+        super(FNN, self).__init__()
 
     @property
     def inputs(self):
@@ -53,9 +51,6 @@ def targets(self):
     @timing
     def build(self):
         print("\nBuilding feed-forward neural network...")
-        self.training = tf.placeholder(tf.bool)
-        self.dropout = tf.placeholder(tf.bool)
-        self.data_id = tf.placeholder(tf.uint8)
         self.x = tf.placeholder(config.real(tf), [None, self.layer_size[0]])
 
         y = self.x

deepxde/maps/map.py

@@ -0,0 +1,37 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+from ..utils import timing
+
+
+class Map(object):
+    """Map base class."""
+
+    def __init__(self):
+        if not hasattr(self, "regularizer"):
+            self.regularizer = None
+
+        self.training = tf.placeholder(tf.bool)
+        self.dropout = tf.placeholder(tf.bool)
+        self.data_id = tf.placeholder(tf.uint8)  # 0: train data, 1: test data
+
+        self.build()
+
+    @property
+    def inputs(self):
+        """Return the mapping inputs."""
+
+    @property
+    def outputs(self):
+        """Return the mapping outputs."""
+
+    @property
+    def targets(self):
+        """Return the targets of the mapping outputs."""
+
+    @timing
+    def build(self):
+        """Construct the mapping."""

deepxde/maps/mfnn.py

@@ -7,11 +7,12 @@
 from . import activations
 from . import initializers
 from . import regularizers
+from .map import Map
 from .. import config
 from ..utils import timing
 
 
-class MfNN(object):
+class MfNN(Map):
     """Multifidelity neural networks
     """
 
@@ -31,16 +32,7 @@ def __init__(
         self.regularizer = regularizers.get(regularization)
         self.residue = residue
 
-        self.training = None
-        self.dropout = None
-        self.data_id = None
-        self.X = None
-        self.y_lo = None
-        self.y_hi = None
-        self.target_lo = None
-        self.target_hi = None
-
-        self.build()
+        super(MfNN, self).__init__()
 
     @property
     def inputs(self):
@@ -57,9 +49,6 @@ def targets(self):
     @timing
     def build(self):
         print("Building multifidelity neural network...")
-        self.training = tf.placeholder(tf.bool)
-        self.dropout = tf.placeholder(tf.bool)
-        self.data_id = tf.placeholder(tf.uint8)
         self.X = tf.placeholder(config.real(tf), [None, self.layer_size_lo[0]])
 
         # Low fidelity

deepxde/maps/opnn.py

@@ -7,11 +7,12 @@
 from . import activations
 from . import initializers
 from . import regularizers
+from .map import Map
 from .. import config
 from ..utils import timing
 
 
-class OpNN(object):
+class OpNN(Map):
     """Operator neural networks
     """
 
@@ -37,15 +38,7 @@ def __init__(
         )
         self.regularizer = regularizers.get(regularization)
 
-        self.training = None
-        self.dropout = None
-        self.data_id = None
-        self.X_func = None
-        self.X_loc = None
-        self.y = None
-        self.target = None
-
-        self.build()
+        super(OpNN, self).__init__()
 
     @property
     def inputs(self):
@@ -62,9 +55,6 @@ def targets(self):
     @timing
     def build(self):
         print("Building operator neural network...")
-        self.training = tf.placeholder(tf.bool)
-        self.dropout = tf.placeholder(tf.bool)
-        self.data_id = tf.placeholder(tf.uint8)
         self.X_func = tf.placeholder(config.real(tf), [None, self.layer_size_func[0]])
         self.X_loc = tf.placeholder(config.real(tf), [None, self.layer_size_loc[0]])
 

deepxde/maps/resnet.py

@@ -7,11 +7,12 @@
 from . import activations
 from . import initializers
 from . import regularizers
+from .map import Map
 from .. import config
 from ..utils import timing
 
 
-class ResNet(object):
+class ResNet(Map):
     """Residual neural network
     """
 
@@ -33,10 +34,7 @@ def __init__(
         self.kernel_initializer = initializers.get(kernel_initializer)
         self.regularizer = regularizers.get(regularization)
 
-        self.training, self.dropout = None, None
-        self.data_id = None  # 0: train data, 1: test data
-        self.x, self.y, self.y_ = None, None, None
-        self.build()
+        super(ResNet, self).__init__()
 
     @property
     def inputs(self):
@@ -53,9 +51,6 @@ def targets(self):
     @timing
     def build(self):
         print("Building residual neural network...")
-        self.training = tf.placeholder(tf.bool)
-        self.dropout = tf.placeholder(tf.bool)
-        self.data_id = tf.placeholder(tf.uint8)
         self.x = tf.placeholder(config.real(tf), [None, self.input_size])
 
         y = self.dense(self.x, self.num_neurons, activation=self.activation)