Refine serialize

demo/mnist/.gitignore

@@ -6,3 +6,4 @@ train.log
 *pyc
 .ipynb_checkpoints
 params.pkl
+params.tar

demo/mnist/api_train_v2.py

@@ -103,6 +103,9 @@ def main():
                     cPickle.dump(
                         parameters, f, protocol=cPickle.HIGHEST_PROTOCOL)
 
+                with open('params.tar', 'w') as f:
+                    parameters.serialize_to_tar(f)
+
         elif isinstance(event, paddle.event.EndPass):
             result = trainer.test(reader=paddle.reader.batched(
                 paddle.dataset.mnist.test(), batch_size=128))

python/paddle/v2/parameters.py

 import numpy as np
 import py_paddle.swig_paddle as api
 from paddle.proto.ParameterConfig_pb2 import ParameterConfig
-
+import struct
+import tarfile
+import cStringIO
 from topology import Topology
 
 __all__ = ['Parameters', 'create']
@@ -235,6 +237,42 @@ class Parameters(object):
 
         return {'conf': param_conf, 'params': params}
 
+    def serialize(self, name, f):
+        """
+
+        :param name:
+        :param f:
+        :type f: file
+        :return:
+        """
+        param = self.get(name)
+        size = reduce(lambda a, b: a * b, param.shape)
+        f.write(struct.pack("IIQ", 0, 4, size))
+        param = param.astype(np.float32)
+        f.write(param.tobytes())
+
+    def deserialize(self, name, f):
+        """
+
+        :param name:
+        :param f:
+        :type f: file
+        :return:
+        """
+        f.read(16)  # header
+        arr = np.fromfile(f, dtype=np.float32)
+        self.set(name, arr.reshape(self.get_shape(name)))
+
+    def serialize_to_tar(self, f):
+        tar = tarfile.TarFile(fileobj=f, mode='w')
+        for nm in self.names():
+            buf = cStringIO.StringIO()
+            self.serialize(nm, buf)
+            tarinfo = tarfile.TarInfo(name=nm)
+            buf.seek(0)
+            tarinfo.size = len(buf.getvalue())
+            tar.addfile(tarinfo, buf)
+
     def __setstate__(self, obj):
         Parameters.__init__(self)
 

python/paddle/v2/trainer.py

@@ -101,7 +101,7 @@ class SGD():
                 for each_param in self.__gradient_machine__.getNonStaticParameters(
                 ):
                     updater.update(each_param)
-                cost_sum = out_args.sumCosts()
+                cost_sum = out_args.sum()
                 cost = cost_sum / len(data_batch)
                 updater.finishBatch(cost)
                 batch_evaluator.finish()
@@ -137,7 +137,7 @@ class SGD():
             num_samples += len(data_batch)
             self.__gradient_machine__.forward(
                 feeder(data_batch), out_args, api.PASS_TEST)
-            total_cost += out_args.sumCosts()
+            total_cost += out_args.sum()
             self.__gradient_machine__.eval(evaluator)
 
         evaluator.finish()