get_layer

docs/user/tutorial.rst

@@ -71,6 +71,8 @@ We now create each layers of the network by implementing :class:`yadll.layers` c
 The first layers must be a :class:`yadll.layers.Input` that give the shape of the input data.
 This network will be a mlp with two dense layer with rectified linear unit activation and dropout.
 Each layer receive as `incoming` the previous layer.
+Each layer has a name. You can provide it or it will be, by default, the name of the layer class, space, the number of
+the instantiation.
 The last layer is a :class:`yadll.layers.LogisticRegression` which is a dense layer with softmax activation.
 Layers names are optional.
 

yadll/layers.py

@@ -28,7 +28,8 @@ class Layer(object):
     incoming : a `Layer` or a `tuple` of `int`
         The incoming layer or shape if input layer
     name : `string`, optional
-        The layer name.
+        The layer name. default name is the class name
+        plus instantiation number i.e: 'DenseLayer 3'
 
     """
     nb_instances = 0

yadll/network.py

@@ -1,6 +1,7 @@
 # -*- coding: UTF-8 -*-
+from collections import OrderedDict
 import cPickle
-import yadll
+
 from .layers import *
 
 import logging
@@ -33,6 +34,7 @@ class Network(object):
     """
     def __init__(self, name=None, layers=None):
         self.layers = []
+        self.layer_names = []
         self.params = []
         self.reguls = 0
         self.has_unsupervised_layer = False
@@ -51,6 +53,7 @@ def add(self, layer):
 
         """
         self.layers.append(layer)
+        self.layer_names.append(layer.name)
         self.params.extend(layer.params)
         self.reguls += layer.reguls
         if isinstance(layer, UnsupervisedLayer):
@@ -68,6 +71,23 @@ def get_output(self, **kwargs):
         """
         return self.layers[-1].get_output(**kwargs)
 
+    def get_layer(self, layer_name):
+        """
+        Get a layer of the network from its name
+
+        Parameters
+        ----------
+        layer_name : `string`
+            name of the layer requested
+
+        Returns
+        -------
+        yaddl.layers object
+            a yadll layer object in the
+
+        """
+        return self.layers[self.layer_names.index(layer_name)]
+
     def save_params(self, file):
         """
         Save the parameters of the network to file with cPickle