Refine test_understand_sentiment_conv.py

python/paddle/v2/fluid/tests/book/test_understand_sentiment_conv.py

@@ -4,21 +4,25 @@
 import paddle.v2.fluid as fluid
 
 
-def convolution_net(data, label, input_dim, class_dim=2, emb_dim=32,
- hid_dim=32):
+def convolution_net(data,
+ label,
+ input_dim,
+ class_dim=2,
+ emb_dim=128,
+ hid_dim=128):
  emb = fluid.layers.embedding(input=data, size=[input_dim, emb_dim])
  conv_3 = fluid.nets.sequence_conv_pool(
  input=emb,
  num_filters=hid_dim,
  filter_size=3,
  act="tanh",
- pool_type="sqrt")
+ pool_type="max")
  conv_4 = fluid.nets.sequence_conv_pool(
  input=emb,
  num_filters=hid_dim,
  filter_size=4,
  act="tanh",
- pool_type="sqrt")
+ pool_type="max")
  prediction = fluid.layers.fc(input=[conv_3, conv_4],
  size=class_dim,
  act="softmax")

python/paddle/v2/fluid/tests/book/test_understand_sentiment_dynamic_lstm.py

@@ -1,6 +1,9 @@
+import math
 import numpy as np
 import paddle.v2 as paddle
 import paddle.v2.fluid as fluid
+from paddle.v2.fluid.param_attr import ParamAttr
+from paddle.v2.fluid.initializer import NormalInitializer
 
 
 def stacked_lstm_net(data,
@@ -9,32 +12,85 @@ def stacked_lstm_net(data,
  class_dim=2,
  emb_dim=128,
  hid_dim=512,
- stacked_num=3):
+ stacked_num=3,
+ batch_size=100):
  assert stacked_num % 2 == 1
 
- emb = fluid.layers.embedding(input=data, size=[input_dim, emb_dim])
- # add bias attr
-
- # TODO(qijun) linear act
- fc1 = fluid.layers.fc(input=emb, size=hid_dim)
- lstm1, cell1 = fluid.layers.dynamic_lstm(input=fc1, size=hid_dim)
+ emb = fluid.layers.embedding(
+ input=data,
+ size=[input_dim, emb_dim],
+ param_attr=ParamAttr(
+ initializer=NormalInitializer(
+ loc=0., scale=1.0 / math.sqrt(input_dim)), ))
+
+ fc1 = fluid.layers.fc(input=emb,
+ size=hid_dim,
+ bias_attr=ParamAttr(initializer=NormalInitializer(
+ loc=0., scale=0.)),
+ param_attr=ParamAttr(
+ name='fc1',
+ initializer=NormalInitializer(
+ loc=0., scale=1.0 / math.sqrt(emb_dim)), ))
+ lstm1, cell1 = fluid.layers.dynamic_lstm(
+ input=fc1,
+ size=hid_dim,
+ candidate_activation='relu',
+ bias_attr=ParamAttr(initializer=NormalInitializer(
+ loc=0., scale=0.)),
+ param_attr=ParamAttr(
+ initializer=NormalInitializer(
+ loc=0., scale=1.0 / math.sqrt(emb_dim)), ))
 
  inputs = [fc1, lstm1]
 
  for i in range(2, stacked_num + 1):
- fc = fluid.layers.fc(input=inputs, size=hid_dim)
+ fc = fluid.layers.fc(input=inputs,
+ size=hid_dim,
+ bias_attr=ParamAttr(initializer=NormalInitializer(
+ loc=0., scale=0.)),
+ param_attr=[
+ ParamAttr(
+ learning_rate=1e-3,
+ initializer=NormalInitializer(
+ loc=0., scale=1.0 /
+ math.sqrt(hid_dim))), ParamAttr(
+ learning_rate=1.,
+ initializer=NormalInitializer(
+ loc=0., scale=0.), )
+ ])
  lstm, cell = fluid.layers.dynamic_lstm(
- input=fc, size=hid_dim, is_reverse=(i % 2) == 0)
+ input=fc,
+ size=hid_dim,
+ is_reverse=(i % 2) == 0,
+ candidate_activation='relu',
+ bias_attr=ParamAttr(initializer=NormalInitializer(
+ loc=0., scale=0.)),
+ param_attr=ParamAttr(
+ initializer=NormalInitializer(
+ loc=0., scale=1.0 / math.sqrt(emb_dim)), ))
  inputs = [fc, lstm]
 
  fc_last = fluid.layers.sequence_pool(input=inputs[0], pool_type='max')
  lstm_last = fluid.layers.sequence_pool(input=inputs[1], pool_type='max')
 
- prediction = fluid.layers.fc(input=[fc_last, lstm_last],
- size=class_dim,
- act='softmax')
+ prediction = fluid.layers.fc(
+ input=[fc_last, lstm_last],
+ size=class_dim,
+ bias_attr=ParamAttr(initializer=NormalInitializer(
+ loc=0., scale=0.)),
+ param_attr=[
+ ParamAttr(
+ learning_rate=1e-3,
+ initializer=NormalInitializer(
+ loc=0., scale=1.0 / math.sqrt(hid_dim)), ), ParamAttr(
+ learning_rate=1.,
+ initializer=NormalInitializer(
+ loc=0., scale=0.), )
+ ],
+ act='softmax')
  cost = fluid.layers.cross_entropy(input=prediction, label=label)
  avg_cost = fluid.layers.mean(x=cost)
+ avg_cost = fluid.layers.scale(x=avg_cost, scale=float(batch_size))
  adam_optimizer = fluid.optimizer.Adam(learning_rate=0.002)
  adam_optimizer.minimize(avg_cost)
  accuracy = fluid.evaluator.Accuracy(input=prediction, label=label)
@@ -69,7 +125,11 @@ def main():
  name="words", shape=[1], dtype="int64", lod_level=1)
  label = fluid.layers.data(name="label", shape=[1], dtype="int64")
  cost, accuracy, acc_out = stacked_lstm_net(
- data, label, input_dim=dict_dim, class_dim=class_dim)
+ data,
+ label,
+ input_dim=dict_dim,
+ class_dim=class_dim,
+ batch_size=BATCH_SIZE)
 
  train_data = paddle.batch(
  paddle.reader.shuffle(