- try binary cross entropy for simese

few_shot_tests.py

@@ -57,6 +57,7 @@ def parser():
     parser.add_argument('--EarlyStop', dest='EarlyStop',  type=str, default='EarlyStop')
     parser.add_argument('--max_idx', dest='max_idx',  type=int, default=-1)
     parser.add_argument('--dense_img_num', dest='dense_img_num',  type=int, default=-1)
+    parser.add_argument('--binary_siamese', dest='binary_siamese', action='store_true')
 
     args = parser.parse_args()
 
@@ -377,9 +378,13 @@ def get_config(self):
 
 flat = TimeDistributed(Flatten())(pool5)
 if args.dense_img_num > 0:
-    x = TimeDistributed(Dense(args.dense_img_num, activation = 'tanh'))(flat)
+    x = TimeDistributed(Dense(args.dense_img_num, activation = 'sigmoid'))(flat)
 else:
-    x = flat
+    if args.binary_siamese:
+        x = Activation('sigmoid')(flat)
+    else:
+        x = flat
+
 #predictions = Activation('softmax')(x)
 
 model_img = FindModel(inputs=inputs, outputs=x)
@@ -400,13 +405,20 @@ def get_config(self):
     encoded_rb_scale = ScaleGradientLayer()(encoded_rb)
 else:
     encoded_rb_scale = encoded_rb
-# Add a customized layer to compute the absolute difference between the encodings
-L1_layer = Lambda(lambda tensors:K.abs(tensors[0] - tensors[1]))
-L1_distance = L1_layer([encoded_l, encoded_rb_scale])
-
+
 # Add a dense layer with a sigmoid unit to generate the similarity score
-prediction = Dense(1, name = 'dense_siamese')(L1_distance)
-
+if args.binary_siamese:
+    # Add a customized layer to compute the absolute difference between the encodings
+    L1_layer = Lambda(lambda tensors:K.binary_crossentropy(tensors[0], tensors[1]))
+    print(encoded_l,encoded_rb_scale)
+    L1_distance = L1_layer([encoded_l, encoded_rb_scale])
+    prediction = Dense(1, name = 'dense_siamese')(L1_distance)
+else:
+    # Add a customized layer to compute the absolute difference between the encodings
+    L1_layer = Lambda(lambda tensors:K.abs(tensors[0] - tensors[1]))
+    L1_distance = L1_layer([encoded_l, encoded_rb_scale])
+    prediction = Dense(1, name = 'dense_siamese')(L1_distance)
+
 # Connect the inputs with the outputs
 if args.load_subnet:
     submodel_name = "model_changed"