# HG changeset patch
# User bengioy@bengiomac.local
# Date 1206581888 14400
# Node ID 633453635d511f4e77c45f7ddd7e676e98888963
# Parent  ff4e551490f1a5501beadb889d07cca7cb0737d4
Starting to work on gradient_based_learner.py

diff -r ff4e551490f1 -r 633453635d51 gradient_learner.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/gradient_learner.py	Wed Mar 26 21:38:08 2008 -0400
@@ -0,0 +1,40 @@
+
+from learner import *
+from tensor import *
+import gradient
+from compile import Function
+from gradient_based_optimizer import *
+
+class GradientLearner(Learner):
+    """
+    Generic Learner for gradient-based optimization of a training criterion
+    that can consist in two parts, an additive part over examples, and
+    an example-independent part (usually called the regularizer).
+    The user provides a Theano formula that maps the fields of a training example
+    and parameters to output fields (for the use function), one of which must be a cost
+    that is the training criterion to be minimized. The user also provides
+    a GradientBasedOptimizer that implements the optimization strategy.
+    The inputs, parameters, outputs and lists of Theano tensors,
+    while the example_wise_cost and regularization_term are Theano tensors.
+    The user can specify a regularization coefficient that multiplies the regularization term.
+    The training algorithm looks for parameters that minimize
+       regularization_coefficienet * regularization_term(parameters) +
+       sum_{inputs in training_set} example_wise_cost(inputs,parameters)
+    i.e. the regularization_term should not depend on the inputs, only on the parameters.
+    The learned function can map a subset of inputs to a subset of outputs (as long as the inputs subset
+    includes all the inputs required in the Theano expression for the selected outputs).
+    """
+    def __init__(self, inputs, parameters, outputs, example_wise_cost, regularization_term,
+                 gradient_based_optimizer=StochasticGradientDescent(), regularization_coefficient = astensor(1.0)):
+        self.inputs = inputs
+        self.outputs = outputs
+        self.parameters = parameters
+        self.example_wise_cost = example_wise_cost
+        self.regularization_term = regularization_term
+        self.gradient_based_optimizer = gradient_based_optimizer
+        self.regularization_coefficient = regularization_coefficient
+        self.parameters_example_wise_gradient = gradient.grad(example_wise_cost, parameters)
+        self.parameters_regularization_gradient = gradient.grad(self.regularization_coefficient * regularization, parameters)
+
+#    def update(self,training_set):
+        
diff -r ff4e551490f1 -r 633453635d51 learner.py
--- a/learner.py	Wed Mar 26 18:21:57 2008 -0400
+++ b/learner.py	Wed Mar 26 21:38:08 2008 -0400
@@ -1,6 +1,5 @@
 
 from dataset import *
-from statscollector import *
     
 class Learner(object):
     """Base class for learning algorithms, provides an interface
@@ -32,7 +31,7 @@
         The result is a function that can be applied on data, with the same
         semantics of the Learner.use method.
         """
-        return self.use
+        return self.use # default behavior is 'non-adaptive', i.e. update does not do anything
     
     
     def __call__(self,training_set):