- mor flexible shapes in BiasLayer

few_shot_tests.py

@@ -177,7 +177,7 @@ def __init__(self, proto_num, mult_bias = 1, **kwargs):
     def build(self, input_shape):
         # Create a trainable weight variable for this layer.
         self.bias = self.add_weight(name='bias',
-                                      shape=(self.proto_num,input_shape[2], input_shape[3], input_shape[4]),
+                                      shape=(self.proto_num) + input_shape[2:],
                                       initializer='zeros',
                                       trainable=True)
         super(BiasLayer, self).build(input_shape)  # Be sure to call this at the end
@@ -223,10 +223,11 @@ def get_config(self):
 input2b = BiasLayer(shots * cathegories, mult_bias = 0)(input2)
 encoded_l = model_img(input1)
 encoded_r = model_img(input2b)
-
+
+encoded_rb = BiasLayer(shots * cathegories, mult_bias = 0)(encoded_r)
 # 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_r])
+L1_distance = L1_layer([encoded_l, encoded_rb])
 
 # Add a dense layer with a sigmoid unit to generate the similarity score
 prediction = Dense(1)(L1_distance)