conf

examples/updates_examples.py

@@ -91,17 +91,12 @@
 with open('model.json', 'w') as f:
     json.dump(model.to_json(),f)
 
-for layer in layers:
-    l = getattr(yadll.layers, 'InputLayer')(vars(model.network.layers[0]))
-
-
-class A(object):
-    def __init__(self, a, b=1, **kwargs):
-        self.a=a
-        self.b=b
-
-    def p(self):
-        print self.a
-
+net.load_params('net_params.yp')
+model_2.network.load_params('net_params.yp')
+predicted_values_2 = model_2.predict(test_set_x[:30])
+print predicted_values_2
+print test_set_y[:30]
 
+model_2.data = data
 
+model_2.train()

yadll/layers.py

@@ -10,7 +10,7 @@
 from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams
 from theano.tensor.signal import pool
 from theano.tensor.nnet import conv
-
+import yadll
 import logging
 
 logger = logging.getLogger(__name__)
@@ -213,6 +213,8 @@ def __init__(self, incoming, nb_units, W=glorot_uniform, b=constant,
             self.b = initializer(b, shape=(self.shape[1],), name='b')
         self.params.append(self.b)
         self.activation = activation
+        if isinstance(activation, basestring):
+            self.activation = getattr(yadll.activation, activation)
         self.l1 = l1
         self.l2 = l2
         if l1 and l1 != 0:
@@ -283,7 +285,11 @@ def __init__(self, incoming, nb_class, W=constant, activation=softmax, **kwargs)
         super(LogisticRegression, self).__init__(incoming, nb_class, W=W,
                                                  activation=activation, **kwargs)
 
-    def
+    def to_conf(self):
+        conf = super(LogisticRegression, self).to_conf()
+        conf['nb_class'] = conf.pop('nb_units')
+        return conf
+
 
 class Dropout(Layer):
     """

yadll/model.py

@@ -311,7 +311,7 @@ def from_conf(self, conf):
         self.network = yadll.network.Network()
         self.network.from_conf(_conf['network'])
         self.updates = getattr(yadll.updates, _conf['updates'])
-        self.report = _conf['file']
+        self.report = _conf['report']
         self.file = _conf['file']
         pass