Fix same weights in MCD, MDD

Author: antoinedemathelin

adapt/feature_based/_mcd.py

@@ -214,6 +214,23 @@ def _initialize_networks(self):
         if self.task is None:
             self.discriminator_ = get_default_task(name="discriminator")
         else:
+            # Impose Copy, else undesired behaviour
             self.discriminator_ = check_network(self.task,
-                                                copy=self.copy,
-                                                name="discriminator")
+                                                copy=True, 
+                                                name="discriminator")
+            
+    
+    def _initialize_weights(self, shape_X):
+        # Init weights encoder
+        self(np.zeros((1,) + shape_X))
+        X_enc = self.encoder_(np.zeros((1,) + shape_X))
+        self.task_(X_enc)
+        self.discriminator_(X_enc)
+        
+        # Add noise to discriminator in order to
+        # differentiate from task
+        weights = self.discriminator_.get_weights()
+        for i in range(len(weights)):
+            weights[i] += (0.01 * weights[i] *
+                           np.random.standard_normal(weights[i].shape))
+        self.discriminator_.set_weights(weights)

adapt/feature_based/_mdd.py

@@ -187,6 +187,22 @@ def _initialize_networks(self):
         if self.task is None:
             self.discriminator_ = get_default_task(name="discriminator")
         else:
+            # Impose Copy, else undesired behaviour
             self.discriminator_ = check_network(self.task,
-                                                copy=self.copy,
+                                                copy=True,
                                                 name="discriminator")
+            
+    def _initialize_weights(self, shape_X):
+        # Init weights encoder
+        self(np.zeros((1,) + shape_X))
+        X_enc = self.encoder_(np.zeros((1,) + shape_X))
+        self.task_(X_enc)
+        self.discriminator_(X_enc)
+        
+        # Add noise to discriminator in order to
+        # differentiate from task
+        weights = self.discriminator_.get_weights()
+        for i in range(len(weights)):
+            weights[i] += (0.01 * weights[i] *
+                           np.random.standard_normal(weights[i].shape))
+        self.discriminator_.set_weights(weights)

tests/test_mdd.py

@@ -63,3 +63,20 @@ def test_fit():
                   model.encoder_.get_weights()[0][0][0]) < 0.25
     assert np.sum(np.abs(model.predict(Xs).ravel() - ys)) < 0.1
     assert np.sum(np.abs(model.predict(Xt).ravel() - yt)) < 5.
+    
+    
+def test_not_same_weights():
+    tf.random.set_seed(0)
+    np.random.seed(0)
+    task = _get_task()
+    encoder = _get_encoder()
+    X_enc = encoder.predict(Xs)
+    task.predict(X_enc)
+    model = MDD(encoder, task, copy=False,
+                loss="mse", optimizer=Adam(0.01), metrics=["mse"])
+    model.fit(Xs, ys, Xt, yt,
+              epochs=0, batch_size=34, verbose=0)
+    assert np.any(model.task_.get_weights()[0] !=
+                  model.discriminator_.get_weights()[0])
+    assert np.all(model.task_.get_weights()[0] ==
+                  task.get_weights()[0])