added possibility to freeze layers during training
diff --git a/examples/mnist_transfer_cnn.py b/examples/mnist_transfer_cnn.py
new file mode 100644
index 00000000000..579df76d08f
--- /dev/null
+++ b/examples/mnist_transfer_cnn.py
@@ -0,0 +1,114 @@
+from __future__ import absolute_import
+from __future__ import print_function
+import numpy as np
+import datetime
+
+np.random.seed(1337)  # for reproducibility
+
+from keras.datasets import mnist
+from keras.models import Sequential
+from keras.layers.core import Dense, Dropout, Activation, Flatten
+from keras.layers.convolutional import Convolution2D, MaxPooling2D
+from keras.utils import np_utils
+
+'''
+    Transfer learning toy example:
+        1- Train a simple convnet on the MNIST dataset the first 5 digits [0..4].
+        2- Freeze convolutional layers and fine-tune dense layers for the classification of digits [5..9].
+
+    Run on GPU: THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python mnist_cnn.py
+
+    Get to 99.8% test accuracy after 5 epochs for the first five digits classifier
+    and 99.2% for the last five digits after transfer + fine-tuning.
+'''
+
+now = datetime.datetime.now
+
+batch_size = 128
+nb_classes = 5
+nb_epoch = 5
+
+# input image dimensions
+img_rows, img_cols = 28, 28
+# number of convolutional filters to use
+nb_filters = 32
+# size of pooling area for max pooling
+nb_pool = 2
+# convolution kernel size
+nb_conv = 3
+
+
+def train_model(model, train, test, nb_classes):
+    X_train = train[0].reshape(train[0].shape[0], 1, img_rows, img_cols)
+    X_test = test[0].reshape(test[0].shape[0], 1, img_rows, img_cols)
+    X_train = X_train.astype("float32")
+    X_test = X_test.astype("float32")
+    X_train /= 255
+    X_test /= 255
+    print('X_train shape:', X_train.shape)
+    print(X_train.shape[0], 'train samples')
+    print(X_test.shape[0], 'test samples')
+
+    # convert class vectors to binary class matrices
+    Y_train = np_utils.to_categorical(train[1], nb_classes)
+    Y_test = np_utils.to_categorical(test[1], nb_classes)
+
+    model.compile(loss='categorical_crossentropy', optimizer='adadelta')
+
+    t = now()
+    model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=True, verbose=1,
+              validation_data=(X_test, Y_test))
+    print('Training time: %s' % (now() - t))
+    score = model.evaluate(X_test, Y_test, show_accuracy=True, verbose=0)
+    print('Test score:', score[0])
+    print('Test accuracy:', score[1])
+
+
+# the data, shuffled and split between train and test sets
+(X_train, y_train), (X_test, y_test) = mnist.load_data()
+
+# create two datasets one with digits below 5 and one with 5 and above
+X_train_lt5 = X_train[y_train < 5]
+y_train_lt5 = y_train[y_train < 5]
+X_test_lt5 = X_test[y_test < 5]
+y_test_lt5 = y_test[y_test < 5]
+
+X_train_gte5 = X_train[y_train >= 5]
+y_train_gte5 = y_train[y_train >= 5] - 5  # make classes start at 0 for
+X_test_gte5 = X_test[y_test >= 5]         # np_utils.to_categorical
+y_test_gte5 = y_test[y_test >= 5] - 5
+
+# define two groups of layers: feature (convolutions) and classification (dense)
+feature_layers = [
+    Convolution2D(nb_filters, nb_conv, nb_conv,
+                  border_mode='full',
+                  input_shape=(1, img_rows, img_cols)),
+    Activation('relu'),
+    Convolution2D(nb_filters, nb_conv, nb_conv),
+    Activation('relu'),
+    MaxPooling2D(pool_size=(nb_pool, nb_pool)),
+    Dropout(0.25),
+    Flatten(),
+]
+classification_layers = [
+    Dense(128),
+    Activation('relu'),
+    Dropout(0.5),
+    Dense(nb_classes),
+    Activation('softmax')
+]
+
+# create complete model
+model = Sequential()
+for l in feature_layers + classification_layers:
+    model.add(l)
+
+# train model for 5-digit classification [0..4]
+train_model(model, (X_train_lt5, y_train_lt5), (X_test_lt5, y_test_lt5), nb_classes)
+
+# freeze feature layers and rebuild model
+for l in feature_layers:
+    l.trainable = False
+
+# transfer: train dense layers for new classification task [5..9]
+train_model(model, (X_train_gte5, y_train_gte5), (X_test_gte5, y_test_gte5), nb_classes)
diff --git a/keras/layers/containers.py b/keras/layers/containers.py
index 8c4ea8604c3..422359a9037 100644
--- a/keras/layers/containers.py
+++ b/keras/layers/containers.py
@@ -2,6 +2,7 @@
 from __future__ import absolute_import
 from __future__ import print_function
 
+from collections import OrderedDict
 import theano.tensor as T
 from ..layers.core import Layer, Merge
 from ..utils.theano_utils import ndim_tensor
@@ -20,11 +21,6 @@ class Sequential(Layer):
 
     def __init__(self, layers=[]):
         self.layers = []
-        self.params = []
-        self.regularizers = []
-        self.constraints = []
-        self.updates = []
-
         for layer in layers:
             self.add(layer)
 
@@ -38,11 +34,37 @@ def add(self, layer):
             if not hasattr(self.layers[0], 'input'):
                 self.set_input()
 
-        params, regularizers, constraints, updates = layer.get_params()
-        self.params += params
-        self.regularizers += regularizers
-        self.constraints += constraints
-        self.updates += updates
+    @property
+    def params(self):
+        params = []
+        for l in self.layers:
+            if l.trainable:
+                params += l.get_params()[0]
+        return params
+
+    @property
+    def regularizers(self):
+        regularizers = []
+        for l in self.layers:
+            if l.trainable:
+                regularizers += l.get_params()[1]
+        return regularizers
+
+    @property
+    def constraints(self):
+        constraints = []
+        for l in self.layers:
+            if l.trainable:
+                constraints += l.get_params()[2]
+        return constraints
+
+    @property
+    def updates(self):
+        updates = []
+        for l in self.layers:
+            if l.trainable:
+                updates += l.get_params()[3]
+        return updates
 
     @property
     def output_shape(self):
@@ -97,7 +119,6 @@ class Graph(Layer):
         when it has exactly one input and one output.
 
         inherited from Layer:
-            - get_params
             - get_output_mask
             - supports_masked_input
             - get_weights
@@ -105,7 +126,7 @@ class Graph(Layer):
     '''
     def __init__(self):
         self.namespace = set()  # strings
-        self.nodes = {}  # layer-like
+        self.nodes = OrderedDict()  # layer-like
         self.inputs = {}  # layer-like
         self.input_order = []  # strings
         self.outputs = {}  # layer-like
@@ -114,11 +135,6 @@ def __init__(self):
         self.output_config = []  # dicts
         self.node_config = []  # dicts
 
-        self.params = []
-        self.regularizers = []
-        self.constraints = []
-        self.updates = []
-
     @property
     def nb_input(self):
         return len(self.inputs)
@@ -127,6 +143,38 @@ def nb_input(self):
     def nb_output(self):
         return len(self.outputs)
 
+    @property
+    def params(self):
+        params = []
+        for l in self.nodes.values():
+            if l.trainable:
+                params += l.get_params()[0]
+        return params
+
+    @property
+    def regularizers(self):
+        regularizers = []
+        for l in self.nodes.values():
+            if l.trainable:
+                regularizers += l.get_params()[1]
+        return regularizers
+
+    @property
+    def constraints(self):
+        constraints = []
+        for l in self.nodes.values():
+            if l.trainable:
+                constraints += l.get_params()[2]
+        return constraints
+
+    @property
+    def updates(self):
+        updates = []
+        for l in self.nodes.values():
+            if l.trainable:
+                updates += l.get_params()[3]
+        return updates
+
     def set_previous(self, layer, connection_map={}):
         if self.nb_input != layer.nb_output:
             raise Exception('Cannot connect layers: input count does not match output count.')
@@ -220,11 +268,6 @@ def add_node(self, layer, name, input=None, inputs=[],
                                  'merge_mode': merge_mode,
                                  'concat_axis': concat_axis,
                                  'create_output': create_output})
-        params, regularizers, constraints, updates = layer.get_params()
-        self.params += params
-        self.regularizers += regularizers
-        self.constraints += constraints
-        self.updates += updates
 
         if create_output:
             self.add_output(name, input=name)
diff --git a/keras/layers/core.py b/keras/layers/core.py
index 5c216b0d91c..f21a3cbefae 100644
--- a/keras/layers/core.py
+++ b/keras/layers/core.py
@@ -20,9 +20,11 @@
 class Layer(object):
     def __init__(self, **kwargs):
         for kwarg in kwargs:
-            assert kwarg in {'input_shape'}, "Keyword argument not understood: " + kwarg
+            assert kwarg in {'input_shape', 'trainable'}, "Keyword argument not understood: " + kwarg
         if 'input_shape' in kwargs:
             self.set_input_shape(kwargs['input_shape'])
+        if 'trainable' in kwargs:
+            self._trainable = kwargs['trainable']
         if not hasattr(self, 'params'):
             self.params = []
 
@@ -45,6 +47,17 @@ def build(self):
         '''
         pass
 
+    @property
+    def trainable(self):
+        if hasattr(self, '_trainable'):
+            return self._trainable
+        else:
+            return True
+
+    @trainable.setter
+    def trainable(self, value):
+        self._trainable = value
+
     @property
     def nb_input(self):
         return 1
@@ -133,6 +146,8 @@ def get_config(self):
         config = {"name": self.__class__.__name__}
         if hasattr(self, '_input_shape'):
             config['input_shape'] = self._input_shape[1:]
+        if hasattr(self, '_trainable'):
+            config['trainable'] = self._trainable
         return config
 
     def get_params(self):

Following the discussion in #622, here is a simple strategy to "freeze" specific layers during training. IT works for `Sequential` and `Graph` models.

It adds a `trainable` attribute to the core `Layer` class and check its value when creating the model. If a layer's `trainable` flag is modified after addition to a model, the model needs to be rebuilt and recompiled. For this, a `rebuild` function has been added to `Sequential` and `Graph` classes.
