V2.testing complete

demo/mnist/api_train_v2.py

@@ -21,28 +21,29 @@ def main():
 
     adam_optimizer = paddle.optimizer.Adam(learning_rate=0.01)
 
+    trainer = paddle.trainer.SGD(topology=cost,
+                                 parameters=parameters,
+                                 update_equation=adam_optimizer)
+
     def event_handler(event):
         if isinstance(event, paddle.event.EndIteration):
-            if event.batch_id % 100 == 0:
-                print "Pass %d, Batch %d, Cost %f, %s" % (
-                    event.pass_id, event.batch_id, event.cost, event.metrics)
+            if event.batch_id % 1000 == 0:
+                result = trainer.test(reader=paddle.reader.batched(
+                    paddle.dataset.mnist.test_creator(), batch_size=256))
+
+                print "Pass %d, Batch %d, Cost %f, %s, Testing metrics %s" % (
+                    event.pass_id, event.batch_id, event.cost, event.metrics,
+                    result.metrics)
+
         else:
             pass
 
-    trainer = paddle.trainer.SGD(update_equation=adam_optimizer)
     trainer.train(
         reader=paddle.reader.batched(
-            paddle.reader.shuffle(paddle.dataset.mnist.train_creator(),
-                                  buf_size=8192), batch_size=32),
-        topology=cost,
-        parameters=parameters,
-        event_handler=event_handler,
-        data_types=[  # data_types will be removed, It should be in
-            # network topology
-            ('pixel', images.type),
-            ('label', label.type)],
-        reader_dict={'pixel': 0, 'label': 1}
-    )
+            paddle.reader.shuffle(
+                paddle.dataset.mnist.train_creator(), buf_size=8192),
+            batch_size=32),
+        event_handler=event_handler)
 
 
 if __name__ == '__main__':

python/paddle/v2/event.py

@@ -11,7 +11,10 @@ There are:
 TODO(yuyang18): Complete it!
 """
 import py_paddle.swig_paddle as api
-__all__ = ['EndIteration', 'BeginIteration', 'BeginPass', 'EndPass']
+
+__all__ = [
+    'EndIteration', 'BeginIteration', 'BeginPass', 'EndPass', 'TestResult'
+]
 
 
 class WithMetric(object):
@@ -30,6 +33,11 @@ class WithMetric(object):
         return retv
 
 
+class TestResult(WithMetric):
+    def __init__(self, evaluator):
+        super(TestResult, self).__init__(evaluator)
+
+
 class BeginPass(object):
     """
     Event On One Pass Training Start.

python/paddle/v2/trainer.py

@@ -23,6 +23,13 @@ def default_event_handler(event):
     pass
 
 
+def __bfs_travel_topology__(callback, *topologies):
+    for each_layer in topologies:
+        callback(each_layer)
+        __bfs_travel_topology__(callback,
+                                *each_layer.__parent_layers__.values())
+
+
 class ITrainer(object):
     """
     The interface of Trainer. The only exposed method is `train`.
@@ -43,26 +50,49 @@ class ITrainer(object):
 
 
 class SGD(ITrainer):
-    def __init__(self, update_equation):
+    def __init__(self, topology, parameters, update_equation):
         """
         Simple SGD Trainer.
 
         :param update_equation: The optimizer object.
         :type update_equation: v2_optimizer.Optimizer
         """
+        if not isinstance(parameters, v2_parameters.Parameters):
+            raise TypeError('parameters should be parameters')
+
         if not isinstance(update_equation, v2_optimizer.Optimizer):
-            raise ValueError("update equation parameter must be "
-                             "paddle.v2.optimizer.Optimizer")
+            raise TypeError("update equation parameter must be "
+                            "paddle.v2.optimizer.Optimizer")
         self.__optimizer__ = update_equation
+        self.__topology__ = topology
+        self.__parameters__ = parameters
+        self.__topology_in_proto__ = v2_layer.parse_network(topology)
+        data_types = dict()
+
+        def __travel__(l):
+            if hasattr(l, 'type'):
+                data_types[l.name] = l.type
+
+        if not isinstance(topology, collections.Sequence):
+            topology = [topology]
+        __bfs_travel_topology__(__travel__, *topology)
+        self.__data_types__ = [
+            (iname, data_types[iname])
+            for iname in self.__topology_in_proto__.input_layer_names
+        ]
+
+        if not isinstance(self.__topology_in_proto__, ModelConfig):
+            raise TypeError('topology should be a model config')
 
-    def train(self,
-              reader,
-              topology,
-              parameters,
-              num_passes=1,
-              event_handler=None,
-              data_types=None,
-              reader_dict=None):
+        gm = api.GradientMachine.createFromConfigProto(
+            self.__topology_in_proto__, api.CREATE_MODE_NORMAL,
+            self.__optimizer__.enable_types())
+        assert isinstance(gm, api.GradientMachine)
+        parameters.append_gradient_machine(gm)
+        self.__gradient_machine__ = gm
+        self.__gradient_machine__.randParameters()
+
+    def train(self, reader, num_passes=1, event_handler=None, reader_dict=None):
         """
         Training method. Will train num_passes of input data.
 
@@ -79,26 +109,21 @@ class SGD(ITrainer):
         if event_handler is None:
             event_handler = default_event_handler
 
-        topology = v2_layer.parse_network(topology)
+        if reader_dict is None:
+            reader_dict = self.default_reader_dict()
 
         __check_train_args__(**locals())
-
-        gm = api.GradientMachine.createFromConfigProto(
-            topology, api.CREATE_MODE_NORMAL, self.__optimizer__.enable_types())
-        assert isinstance(gm, api.GradientMachine)
-        parameters.append_gradient_machine(gm)
-        gm.randParameters()
         updater = self.__optimizer__.create_local_updater()
-        updater.init(gm)
+        updater.init(self.__gradient_machine__)
 
-        gm.start()
-        batch_evaluator = gm.makeEvaluator()
+        self.__gradient_machine__.start()
+        batch_evaluator = self.__gradient_machine__.makeEvaluator()
         assert isinstance(batch_evaluator, api.Evaluator)
-        pass_evaluator = gm.makeEvaluator()
+        pass_evaluator = self.__gradient_machine__.makeEvaluator()
         assert isinstance(pass_evaluator, api.Evaluator)
         out_args = api.Arguments.createArguments(0)
 
-        feeder = DataFeeder(data_types, reader_dict)
+        feeder = DataFeeder(self.__data_types__, reader_dict)
 
         for pass_id in xrange(num_passes):
             event_handler(v2_event.BeginPass(pass_id))
@@ -106,16 +131,18 @@ class SGD(ITrainer):
             updater.startPass()
             for batch_id, data_batch in enumerate(reader()):
                 pass_type = updater.startBatch(len(data_batch))
-                gm.forwardBackward(feeder(data_batch), out_args, pass_type)
+                self.__gradient_machine__.forwardBackward(
+                    feeder(data_batch), out_args, pass_type)
                 batch_evaluator.start()
                 event_handler(
                     v2_event.BeginIteration(
                         pass_id=pass_id, batch_id=batch_id))
                 pass_type = updater.startBatch(len(data_batch))
-                gm.forwardBackward(feeder(data_batch), out_args, pass_type)
-                gm.eval(pass_evaluator)
-                gm.eval(batch_evaluator)
-                for each_param in gm.getParameters():
+                self.__gradient_machine__.forwardBackward(
+                    feeder(data_batch), out_args, pass_type)
+                self.__gradient_machine__.eval(pass_evaluator)
+                self.__gradient_machine__.eval(batch_evaluator)
+                for each_param in self.__gradient_machine__.getParameters():
                     updater.update(each_param)
                 # Get cost. We use numpy to calculate total cost for this batch.
                 cost_vec = out_args.getSlotValue(0)
@@ -133,10 +160,32 @@ class SGD(ITrainer):
             updater.finishPass()
             pass_evaluator.finish()
             event_handler(v2_event.EndPass(pass_id, evaluator=pass_evaluator))
-        gm.finish()
+        self.__gradient_machine__.finish()
+
+    def default_reader_dict(self):
+        reader_dict = dict()
+        for i, tp in enumerate(self.__data_types__):
+            reader_dict[tp[0]] = i
+        return reader_dict
 
+    def test(self, reader, reader_dict=None):
+        if reader_dict is None:
+            reader_dict = self.default_reader_dict()
+
+        feeder = DataFeeder(self.__data_types__, reader_dict)
+        evaluator = self.__gradient_machine__.makeEvaluator()
+        out_args = api.Arguments.createArguments(0)
+        evaluator.start()
+        for data_batch in reader():
+            self.__gradient_machine__.forward(
+                feeder(data_batch), out_args, api.PASS_TEST)
+            self.__gradient_machine__.eval(evaluator)
 
-def __check_train_args__(reader, topology, parameters, event_handler, **kwargs):
+        evaluator.finish()
+        return v2_event.TestResult(evaluator=evaluator)
+
+
+def __check_train_args__(reader, event_handler, **kwargs):
     """
     Check train function's argument types
     """
@@ -144,11 +193,5 @@ def __check_train_args__(reader, topology, parameters, event_handler, **kwargs):
         raise TypeError('train_data_reader should be a function, '
                         'which can return a iterator')
 
-    if not isinstance(topology, ModelConfig):
-        raise TypeError('topology should be a model config')
-
-    if not isinstance(parameters, v2_parameters.Parameters):
-        raise TypeError('parameters should be a parameter pool')
-
     if not callable(event_handler):
         raise TypeError('event handler should be a function')