diff --git a/few_shot_tests.py b/few_shot_tests.py
index b992a48..3bf9c43 100755
--- a/few_shot_tests.py
+++ b/few_shot_tests.py
@@ -14,6 +14,7 @@
 from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard, Callback
 import tensorflow.keras.backend as K
 import tensorflow.keras
+from tensorflow import keras
 from tensorflow.keras import optimizers as op
 import tensorflow as tf
 import argparse
@@ -464,11 +465,11 @@ def __init__(self,
   def build(self, input_shape):
     dtype = dtypes.as_dtype(self.dtype or K.floatx())
     if not (dtype.is_floating or dtype.is_complex):
-      raise TypeError('Unable to build `Dense` layer with non-floating point '
+      raise TypeError('Unable to build `Dense_plasticity` layer with non-floating point '
                       'dtype %s' % (dtype,))
     input_shape = tensor_shape.TensorShape(input_shape)
     if tensor_shape.dimension_value(input_shape[-1]) is None:
-      raise ValueError('The last dimension of the inputs to `Dense` '
+      raise ValueError('The last dimension of the inputs to `Dense_plasticity` '
                        'should be defined. Found `None`.')
     last_dim = tensor_shape.dimension_value(input_shape[-1])
     self.input_spec = InputSpec(min_ndim=2,
@@ -481,6 +482,25 @@ def build(self, input_shape):
         constraint=self.kernel_constraint,
         dtype=self.dtype,
         trainable=True)
+
+    #plasticity
+    self.kernel_p = self.add_weight(
+        'kernel_p',
+        shape=[last_dim, self.units],
+        initializer=keras.initializers.Constant(),
+        regularizer=self.kernel_regularizer,
+        constraint=self.kernel_constraint,
+        dtype=self.dtype,
+        trainable=True)
+    self.hebb = self.add_weight(
+        'hebb',
+        shape=[last_dim, self.units],
+        initializer=self.kernel_initializer,
+        regularizer=self.kernel_regularizer,
+        constraint=self.kernel_constraint,
+        dtype=self.dtype,
+        trainable=False)
+
     if self.use_bias:
       self.bias = self.add_weight(
           'bias',
@@ -496,9 +516,12 @@ def build(self, input_shape):
 
   def call(self, inputs):
     rank = len(inputs.shape)
+    placticity = tf.multiply(self.kernel_p,self.hebb)
     if rank > 2:
       # Broadcasting is required for the inputs.
       outputs = standard_ops.tensordot(inputs, self.kernel, [[rank - 1], [0]])
+      outputs2 = standard_ops.tensordot(inputs, placticity, [[rank - 1], [0]])
+      outputs = tf.add(outputs,outputs2)
       # Reshape the output back to the original ndim of the input.
       if not context.executing_eagerly():
         shape = inputs.shape.as_list()
@@ -512,8 +535,12 @@ def call(self, inputs):
         inputs = math_ops.cast(inputs, self.dtype)
       if K.is_sparse(inputs):
         outputs = sparse_ops.sparse_tensor_dense_matmul(inputs, self.kernel)
+        outputs2 = sparse_ops.sparse_tensor_dense_matmul(inputs, placticity)
+        outputs.set_shape(output_shape)
       else:
         outputs = gen_math_ops.mat_mul(inputs, self.kernel)
+        outputs2 = gen_math_ops.mat_mul(inputs, placticity)
+        outputs.set_shape(output_shape)
     if self.use_bias:
       outputs = nn.bias_add(outputs, self.bias)
     if self.activation is not None:
@@ -543,7 +570,7 @@ def get_config(self):
         'kernel_constraint': constraints.serialize(self.kernel_constraint),
         'bias_constraint': constraints.serialize(self.bias_constraint)
     }
-    base_config = super(Dense, self).get_config()
+    base_config = super(Dense_plasticity, self).get_config()
     return dict(list(base_config.items()) + list(config.items()))
 
 
@@ -639,7 +666,7 @@ def call(x):
 
 if args.pretrained_name is not None:
     from tensorflow.keras.models import load_model
-    lambda_model = load_model(args.pretrained_name, custom_objects = { "keras": tensorflow.keras , "args":args, "BiasLayer": BiasLayer, "FindModel": FindModel})
+    lambda_model = load_model(args.pretrained_name, custom_objects = { "keras": tensorflow.keras , "args":args, "BiasLayer": BiasLayer, "FindModel": FindModel, "Dense_plasticity": Dense_plasticity})
     print("loaded model",lambda_model)
 
 if args.load_weights_name: