Merge branch 'develop' of github.com:baidu/Paddle into feature/better_infer_interface

Author: reyoung

demo/image_classification/api_v2_train.py

@@ -13,26 +13,27 @@
 # limitations under the License
 
 import sys
+
 import paddle.v2 as paddle
+
 from api_v2_vgg import vgg_bn_drop
-from api_v2_resnet import resnet_cifar10
 
 
 def main():
     datadim = 3 * 32 * 32
     classdim = 10
 
     # PaddlePaddle init
-    paddle.init(use_gpu=True, trainer_count=1)
+    paddle.init(use_gpu=False, trainer_count=1)
 
     image = paddle.layer.data(
         name="image", type=paddle.data_type.dense_vector(datadim))
 
     # Add neural network config
     # option 1. resnet
-    net = resnet_cifar10(image, depth=32)
+    # net = resnet_cifar10(image, depth=32)
     # option 2. vgg
-    # net = vgg_bn_drop(image)
+    net = vgg_bn_drop(image)
 
     out = paddle.layer.fc(input=net,
                           size=classdim,
@@ -68,8 +69,8 @@ def event_handler(event):
             result = trainer.test(
                 reader=paddle.batch(
                     paddle.dataset.cifar.test10(), batch_size=128),
-                reader_dict={'image': 0,
-                             'label': 1})
+                feeding={'image': 0,
+                         'label': 1})
             print "\nTest with Pass %d, %s" % (event.pass_id, result.metrics)
 
     # Create trainer
@@ -83,8 +84,8 @@ def event_handler(event):
             batch_size=128),
         num_passes=5,
         event_handler=event_handler,
-        reader_dict={'image': 0,
-                     'label': 1})
+        feeding={'image': 0,
+                 'label': 1})
 
 
 if __name__ == '__main__':

demo/introduction/api_train_v2.py

@@ -30,26 +30,26 @@ def main():
     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)
+                print "Pass %d, Batch %d, Cost %f" % (
+                    event.pass_id, event.batch_id, event.cost)
 
         if isinstance(event, paddle.event.EndPass):
-            result = trainer.test(
-                reader=paddle.reader.batched(
-                    uci_housing.test(), batch_size=2),
-                reader_dict={'x': 0,
+            if (event.pass_id + 1) % 10 == 0:
+                result = trainer.test(
+                    reader=paddle.batch(
+                        uci_housing.test(), batch_size=2),
+                    feeding={'x': 0,
                              'y': 1})
-            if event.pass_id % 10 == 0:
-                print "Test %d, %s" % (event.pass_id, result.metrics)
+                print "Test %d, %.2f" % (event.pass_id, result.cost)
 
     # training
     trainer.train(
-        reader=paddle.reader.batched(
+        reader=paddle.batch(
             paddle.reader.shuffle(
                 uci_housing.train(), buf_size=500),
             batch_size=2),
-        reader_dict={'x': 0,
-                     'y': 1},
+        feeding={'x': 0,
+                 'y': 1},
         event_handler=event_handler,
         num_passes=30)
 

demo/mnist/.gitignore

@@ -5,3 +5,6 @@ plot.png
 train.log
 *pyc
 .ipynb_checkpoints
+params.pkl
+params.tar
+params.tar.gz

demo/mnist/api_train_v2.py

@@ -1,4 +1,5 @@
 import paddle.v2 as paddle
+import gzip
 
 
 def softmax_regression(img):
@@ -71,7 +72,11 @@ def main():
 
     cost = paddle.layer.classification_cost(input=predict, label=label)
 
-    parameters = paddle.parameters.create(cost)
+    try:
+        with gzip.open('params.tar.gz', 'r') as f:
+            parameters = paddle.parameters.Parameters.from_tar(f)
+    except IOError:
+        parameters = paddle.parameters.create(cost)
 
     optimizer = paddle.optimizer.Momentum(
         learning_rate=0.1 / 128.0,
@@ -86,10 +91,18 @@ def main():
 
     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 isinstance(event, paddle.event.EndPass):
+            if event.batch_id % 1000 == 0:
+                result = trainer.test(reader=paddle.batch(
+                    paddle.dataset.mnist.test(), 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)
+
+                with gzip.open('params.tar.gz', 'w') as f:
+                    parameters.to_tar(f)
+
+        elif isinstance(event, paddle.event.EndPass):
             result = trainer.test(reader=paddle.batch(
                 paddle.dataset.mnist.test(), batch_size=128))
             print "Test with Pass %d, Cost %f, %s\n" % (

demo/semantic_role_labeling/api_train_v2.py

@@ -163,11 +163,11 @@ def event_handler(event):
                                  update_equation=optimizer)
     parameters.set('emb', load_parameter(conll05.get_embedding(), 44068, 32))
 
-    trn_reader = paddle.reader.batched(
+    trn_reader = paddle.batch(
         paddle.reader.shuffle(
             conll05.test(), buf_size=8192), batch_size=10)
 
-    reader_dict = {
+    feeding = {
         'word_data': 0,
         'ctx_n2_data': 1,
         'ctx_n1_data': 2,
@@ -183,7 +183,7 @@ def event_handler(event):
         reader=trn_reader,
         event_handler=event_handler,
         num_passes=10000,
-        reader_dict=reader_dict)
+        feeding=feeding)
 
 
 if __name__ == '__main__':

demo/sentiment/train_v2.py

@@ -18,11 +18,7 @@
 import paddle.v2 as paddle
 
 
-def convolution_net(input_dim,
-                    class_dim=2,
-                    emb_dim=128,
-                    hid_dim=128,
-                    is_predict=False):
+def convolution_net(input_dim, class_dim=2, emb_dim=128, hid_dim=128):
     data = paddle.layer.data("word",
                              paddle.data_type.integer_value_sequence(input_dim))
     emb = paddle.layer.embedding(input=data, size=emb_dim)
@@ -42,8 +38,7 @@ def stacked_lstm_net(input_dim,
                      class_dim=2,
                      emb_dim=128,
                      hid_dim=512,
-                     stacked_num=3,
-                     is_predict=False):
+                     stacked_num=3):
     """
     A Wrapper for sentiment classification task.
     This network uses bi-directional recurrent network,
@@ -110,7 +105,7 @@ def stacked_lstm_net(input_dim,
 
 if __name__ == '__main__':
     # init
-    paddle.init(use_gpu=True, trainer_count=4)
+    paddle.init(use_gpu=False, trainer_count=4)
 
     # network config
     print 'load dictionary...'
@@ -143,11 +138,11 @@ def event_handler(event):
                 sys.stdout.flush()
         if isinstance(event, paddle.event.EndPass):
             result = trainer.test(
-                reader=paddle.reader.batched(
+                reader=paddle.batch(
                     lambda: paddle.dataset.imdb.test(word_dict),
                     batch_size=128),
-                reader_dict={'word': 0,
-                             'label': 1})
+                feeding={'word': 0,
+                         'label': 1})
             print "\nTest with Pass %d, %s" % (event.pass_id, result.metrics)
 
     # create trainer
@@ -156,11 +151,11 @@ def event_handler(event):
                                  update_equation=adam_optimizer)
 
     trainer.train(
-        reader=paddle.reader.batched(
+        reader=paddle.batch(
             paddle.reader.shuffle(
                 lambda: paddle.dataset.imdb.train(word_dict), buf_size=1000),
             batch_size=100),
         event_handler=event_handler,
-        reader_dict={'word': 0,
-                     'label': 1},
+        feeding={'word': 0,
+                 'label': 1},
         num_passes=10)