From 1ff95e3de121fb72bd230b31c92c75b46f798c0e Mon Sep 17 00:00:00 2001
From: hedaoyuan <hedaoyuan@github.com>
Date: Wed, 8 Mar 2017 20:42:10 +0800
Subject: [PATCH] Use v2 attr and pooling fix sentiment train_v2.py
---
demo/sentiment/train_v2.py | 48 ++++++++++++++++++--------------------
1 file changed, 23 insertions(+), 25 deletions(-)
diff --git a/demo/sentiment/train_v2.py b/demo/sentiment/train_v2.py
index fd7243cbe6..1c856556bd 100644
--- a/demo/sentiment/train_v2.py
+++ b/demo/sentiment/train_v2.py
@@ -13,8 +13,6 @@
# limitations under the License.
import sys
-import paddle.trainer_config_helpers.attrs as attrs
-from paddle.trainer_config_helpers.poolings import MaxPooling
import paddle.v2 as paddle
@@ -51,16 +49,14 @@ def stacked_lstm_net(input_dim,
emb_dim: dimension of word embedding.
hid_dim: dimension of hidden layer.
stacked_num: number of stacked lstm-hidden layer.
- is_predict: is predicting or not.
- Some layers is not needed in network when predicting.
"""
assert stacked_num % 2 == 1
- layer_attr = attrs.ExtraLayerAttribute(drop_rate=0.5)
- fc_para_attr = attrs.ParameterAttribute(learning_rate=1e-3)
- lstm_para_attr = attrs.ParameterAttribute(initial_std=0., learning_rate=1.)
+ layer_attr = paddle.attr.Extra(drop_rate=0.5)
+ fc_para_attr = paddle.attr.Param(learning_rate=1e-3)
+ lstm_para_attr = paddle.attr.Param(initial_std=0., learning_rate=1.)
para_attr = [fc_para_attr, lstm_para_attr]
- bias_attr = attrs.ParameterAttribute(initial_std=0., l2_rate=0.)
+ bias_attr = paddle.attr.Param(initial_std=0., l2_rate=0.)
relu = paddle.activation.Relu()
linear = paddle.activation.Linear()
@@ -90,8 +86,10 @@ def stacked_lstm_net(input_dim,
layer_attr=layer_attr)
inputs = [fc, lstm]
- fc_last = paddle.layer.pooling(input=inputs[0], pooling_type=MaxPooling())
- lstm_last = paddle.layer.pooling(input=inputs[1], pooling_type=MaxPooling())
+ fc_last = paddle.layer.pooling(
+ input=inputs[0], pooling_type=paddle.pooling.Max())
+ lstm_last = paddle.layer.pooling(
+ input=inputs[1], pooling_type=paddle.pooling.Max())
output = paddle.layer.fc(input=[fc_last, lstm_last],
size=class_dim,
act=paddle.activation.Softmax(),
@@ -105,14 +103,23 @@ def stacked_lstm_net(input_dim,
if __name__ == '__main__':
# init
- paddle.init(use_gpu=False, trainer_count=4)
+ paddle.init(use_gpu=False)
- # network config
+ #data
print 'load dictionary...'
word_dict = paddle.dataset.imdb.word_dict()
dict_dim = len(word_dict)
class_dim = 2
+ train_reader = paddle.batch(
+ paddle.reader.shuffle(
+ lambda: paddle.dataset.imdb.train(word_dict), buf_size=1000),
+ batch_size=100)
+ test_reader = paddle.batch(
+ lambda: paddle.dataset.imdb.test(word_dict), batch_size=100)
+
+ feeding = {'word': 0, 'label': 1}
+ # network config
# Please choose the way to build the network
# by uncommenting the corresponding line.
cost = convolution_net(dict_dim, class_dim=class_dim)
@@ -137,12 +144,7 @@ if __name__ == '__main__':
sys.stdout.write('.')
sys.stdout.flush()
if isinstance(event, paddle.event.EndPass):
- result = trainer.test(
- reader=paddle.batch(
- lambda: paddle.dataset.imdb.test(word_dict),
- batch_size=128),
- feeding={'word': 0,
- 'label': 1})
+ result = trainer.test(reader=test_reader, feeding=feeding)
print "\nTest with Pass %d, %s" % (event.pass_id, result.metrics)
# create trainer
@@ -151,11 +153,7 @@ if __name__ == '__main__':
update_equation=adam_optimizer)
trainer.train(
- reader=paddle.batch(
- paddle.reader.shuffle(
- lambda: paddle.dataset.imdb.train(word_dict), buf_size=1000),
- batch_size=100),
+ reader=train_reader,
event_handler=event_handler,
- feeding={'word': 0,
- 'label': 1},
- num_passes=10)
+ feeding=feeding,
+ num_passes=2)