- input_activation for Dense_plasticity

few_shot_tests.py

@@ -61,7 +61,7 @@ def parser():
     parser.add_argument('--binary_siamese', dest='binary_siamese', action='store_true') #seems to be a bad idea
     parser.add_argument('--square_siamese', dest='square_siamese', action='store_true')
     parser.add_argument('--eta', dest='eta',  type=float, default=0)
-
+    parser.add_argument('--input_activation', dest='input_activation',  type=str, default=None)
     args = parser.parse_args()
 
 parser()
@@ -445,6 +445,7 @@ def __init__(self,
                kernel_constraint=None,
                bias_constraint=None,
                eta = 0.0,
+               input_activation=None,
                **kwargs):
     if 'input_shape' not in kwargs and 'input_dim' in kwargs:
       kwargs['input_shape'] = (kwargs.pop('input_dim'),)
@@ -464,6 +465,7 @@ def __init__(self,
     self.supports_masking = True
     self.input_spec = InputSpec(min_ndim=2)
     self.eta = eta
+    self.input_activation = activations.get(input_activation)
 
   def build(self, input_shape):
     dtype = dtypes.as_dtype(self.dtype or K.floatx())
@@ -518,6 +520,8 @@ def build(self, input_shape):
     self.built = True
 
   def call(self, inputs):
+    if self.input_activation is not None:
+        inputs=self.input_activation(inputs)
     rank = len(inputs.shape)
     placticity = tf.multiply(self.kernel_p,self.hebb)
     if rank > 2:
@@ -616,7 +620,7 @@ def get_config(self):
 
 flat = TimeDistributed(Flatten())(pool5)
 if args.dense_img_num > 0:
-    x = TimeDistributed(Dense_plasticity(args.dense_img_num, eta = args.eta, activation = 'sigmoid'))(flat)
+    x = TimeDistributed(Dense_plasticity(args.dense_img_num, eta = args.eta, input_activation = args.input_activation, activation = 'sigmoid'))(flat)
 else:
     if args.binary_siamese:
         x = Activation('sigmoid')(flat)
@@ -650,15 +654,15 @@ def get_config(self):
     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_plasticity(1, eta = args.eta, name = 'dense_siamese')(L1_distance)
+    prediction = Dense_plasticity(1, eta = args.eta, input_activation = args.input_activation, name = 'dense_siamese')(L1_distance)
 else:
     # Add a customized layer to compute the absolute difference between the encodings
     if args.square_siamese:
         L1_layer = Lambda(lambda tensors:K.pow(tensors[0] - tensors[1], 2))
     else:
         L1_layer = Lambda(lambda tensors:K.abs(tensors[0] - tensors[1]))
     L1_distance = L1_layer([encoded_l, encoded_rb_scale])
-    prediction = Dense_plasticity(1, eta = args.eta, name = 'dense_siamese')(L1_distance)
+    prediction = Dense_plasticity(1, eta = args.eta, input_activation = args.input_activation, name = 'dense_siamese')(L1_distance)
 
 # Connect the inputs with the outputs
 if args.load_subnet: