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未验证 提交 adcdb7b8 编写于 作者: L Li Fuchen 提交者： GitHub
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Unify 1.6 api in 06.understand_sentiment (#815) 

* Modify the formula of lstm

* Update README.cn.md

* Update README.cn.md

* Unify 1.6 api in 06.understand_sentiment

* fix a bug in README.md
上级 40fe9714
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Showing with 99 addition and 77 deletion
06.understand_sentiment/README.cn.md
浏览文件 @ adcdb7b8
......@@ -151,7 +151,7 @@ BATCH_SIZE = 128 #batch的大小
```python
#文本卷积神经网络
def convolution_net(data, input_dim, class_dim, emb_dim, hid_dim):
emb = fluid.layers.embedding(
emb = fluid.embedding(
input=data, size=[input_dim, emb_dim], is_sparse=True)
conv_3 = fluid.nets.sequence_conv_pool(
input=emb,
......@@ -183,7 +183,7 @@ def convolution_net(data, input_dim, class_dim, emb_dim, hid_dim):
def stacked_lstm_net(data, input_dim, class_dim, emb_dim, hid_dim, stacked_num):
#计算词向量
emb = fluid.layers.embedding(
emb = fluid.embedding(
input=data, size=[input_dim, emb_dim], is_sparse=True)
#第一层栈
......@@ -218,8 +218,8 @@ def stacked_lstm_net(data, input_dim, class_dim, emb_dim, hid_dim, stacked_num):
```python
def inference_program(word_dict):
data = fluid.layers.data(
name="words", shape=[1], dtype="int64", lod_level=1)
data = fluid.data(
name="words", shape=[-1], dtype="int64", lod_level=1)
dict_dim = len(word_dict)
net = convolution_net(data, dict_dim, CLASS_DIM, EMB_DIM, HID_DIM)
......@@ -235,7 +235,7 @@ def inference_program(word_dict):
```python
def train_program(prediction):
label = fluid.layers.data(name="label", shape=[1], dtype="int64")
label = fluid.data(name="label", shape=[-1,1], dtype="int64")
cost = fluid.layers.cross_entropy(input=prediction, label=label)
avg_cost = fluid.layers.mean(cost)
accuracy = fluid.layers.accuracy(input=prediction, label=label)
......@@ -269,12 +269,12 @@ print("Loading IMDB word dict....")
word_dict = paddle.dataset.imdb.word_dict()
print ("Reading training data....")
train_reader = paddle.batch(
train_reader = fluid.io.batch(
paddle.reader.shuffle(
paddle.dataset.imdb.train(word_dict), buf_size=25000),
batch_size=BATCH_SIZE)
print("Reading testing data....")
test_reader = paddle.batch(
test_reader = fluid.io.batch(
paddle.dataset.imdb.test(word_dict), batch_size=BATCH_SIZE)
```
word_dict是一个字典序列，是词和label的对应关系，运行下一行可以看到具体内容：
......@@ -401,11 +401,15 @@ reviews = [c.split() for c in reviews_str]
UNK = word_dict['<unk>']
lod = []
for c in reviews:
lod.append([word_dict.get(words, UNK) for words in c])
base_shape = []
base_shape = [[len(c) for c in lod]]
for c in reviews:
re = np.array([np.int64(word_dict.get(words, UNK)) for words in c])
lod = np.concatenate([lod,re],axis = 0)
base_shape.insert(-1, re.shape[0])
base_shape = [base_shape]
lod = np.array(lod).astype('int64')
tensor_words = fluid.create_lod_tensor(lod, base_shape, place)
```
......
06.understand_sentiment/README.md
浏览文件 @ adcdb7b8
......@@ -140,7 +140,7 @@ Note that `fluid.nets.sequence_conv_pool` contains both convolution and pooling
```python
#Textconvolution neural network
def convolution_net(data, input_dim, class_dim, emb_dim, hid_dim):
emb = fluid.layers.embedding(
emb = fluid.embedding(
input=data, size=[input_dim, emb_dim], is_sparse=True)
conv_3 = fluid.nets.sequence_conv_pool(
input=emb,
......@@ -172,7 +172,7 @@ The code of the stack bidirectional LSTM `stacked_lstm_net` is as follows:
def stacked_lstm_net(data, input_dim, class_dim, emb_dim, hid_dim, stacked_num):
# Calculate word vectorvector
emb = fluid.layers.embedding(
emb = fluid.embedding(
input=data, size=[input_dim, emb_dim], is_sparse=True)
#First stack
......@@ -191,7 +191,7 @@ def stacked_lstm_net(data, input_dim, class_dim, emb_dim, hid_dim, stacked_num):
inputs = [fc, lstm]
#pooling layer
pc_last = fluid.layers.sequence_pool(input=inputs[0], pool_type='max')
fc_last = fluid.layers.sequence_pool(input=inputs[0], pool_type='max')
lstm_last = fluid.layers.sequence_pool(input=inputs[1], pool_type='max')
#Fully connected layer, softmax prediction
......@@ -207,8 +207,8 @@ Next we define the prediction program (`inference_program`). We use `convolution
```python
def inference_program(word_dict):
data = fluid.layers.data(
name="words", shape=[1], dtype="int64", lod_level=1)
data = fluid.data(
name="words", shape=[-1], dtype="int64", lod_level=1)
dict_dim = len(word_dict)
net = convolution_net(data, dict_dim, CLASS_DIM, EMB_DIM, HID_DIM)
......@@ -224,7 +224,7 @@ During the testing, the classifier calculates the probability of each output. Th
```python
def train_program(prediction):
label = fluid.layers.data(name="label", shape=[1], dtype="int64")
label = fluid.data(name="label", shape=[-1, 1], dtype="int64")
cost = fluid.layers.cross_entropy(input=prediction, label=label)
avg_cost = fluid.layers.mean(cost)
accuracy = fluid.layers.accuracy(input=prediction, label=label)
......@@ -258,12 +258,12 @@ print("Loading IMDB word dict....")
word_dict = paddle.dataset.imdb.word_dict()
print ("Reading training data....")
train_reader = paddle.batch(
train_reader = fluid.io.batch(
paddle.reader.shuffle(
paddle.dataset.imdb.train(word_dict), buf_size=25000),
batch_size=BATCH_SIZE)
print("Reading testing data....")
test_reader = paddle.batch(
test_reader = fluid.io.batch(
paddle.dataset.imdb.test(word_dict), batch_size=BATCH_SIZE)
```
Word_dict is a dictionary sequence, which is the correspondence between words and labels. You can see it specifically by running the next code:
......@@ -390,11 +390,15 @@ reviews = [c.split() for c in reviews_str]
UNK = word_dict['<unk>']
lod = []
for c in reviews:
lod.append([word_dict.get(words, UNK) for words in c])
base_shape = []
base_shape = [[len(c) for c in lod]]
for c in reviews:
re = np.array([np.int64(word_dict.get(words, UNK)) for words in c])
lod = np.concatenate([lod,re],axis = 0)
base_shape.insert(-1, re.shape[0])
base_shape = [base_shape]
lod = np.array(lod).astype('int64')
tensor_words = fluid.create_lod_tensor(lod, base_shape, place)
```
......
06.understand_sentiment/index.cn.html
浏览文件 @ adcdb7b8
......@@ -193,7 +193,7 @@ BATCH_SIZE = 128 #batch的大小
```python
#文本卷积神经网络
def convolution_net(data, input_dim, class_dim, emb_dim, hid_dim):
emb = fluid.layers.embedding(
emb = fluid.embedding(
input=data, size=[input_dim, emb_dim], is_sparse=True)
conv_3 = fluid.nets.sequence_conv_pool(
input=emb,
......@@ -225,7 +225,7 @@ def convolution_net(data, input_dim, class_dim, emb_dim, hid_dim):
def stacked_lstm_net(data, input_dim, class_dim, emb_dim, hid_dim, stacked_num):
#计算词向量
emb = fluid.layers.embedding(
emb = fluid.embedding(
input=data, size=[input_dim, emb_dim], is_sparse=True)
#第一层栈
......@@ -260,8 +260,8 @@ def stacked_lstm_net(data, input_dim, class_dim, emb_dim, hid_dim, stacked_num):
```python
def inference_program(word_dict):
data = fluid.layers.data(
name="words", shape=[1], dtype="int64", lod_level=1)
data = fluid.data(
name="words", shape=[-1], dtype="int64", lod_level=1)
dict_dim = len(word_dict)
net = convolution_net(data, dict_dim, CLASS_DIM, EMB_DIM, HID_DIM)
......@@ -277,7 +277,7 @@ def inference_program(word_dict):
```python
def train_program(prediction):
label = fluid.layers.data(name="label", shape=[1], dtype="int64")
label = fluid.data(name="label", shape=[-1,1], dtype="int64")
cost = fluid.layers.cross_entropy(input=prediction, label=label)
avg_cost = fluid.layers.mean(cost)
accuracy = fluid.layers.accuracy(input=prediction, label=label)
......@@ -311,12 +311,12 @@ print("Loading IMDB word dict....")
word_dict = paddle.dataset.imdb.word_dict()
print ("Reading training data....")
train_reader = paddle.batch(
train_reader = fluid.io.batch(
paddle.reader.shuffle(
paddle.dataset.imdb.train(word_dict), buf_size=25000),
batch_size=BATCH_SIZE)
print("Reading testing data....")
test_reader = paddle.batch(
test_reader = fluid.io.batch(
paddle.dataset.imdb.test(word_dict), batch_size=BATCH_SIZE)
```
word_dict是一个字典序列，是词和label的对应关系，运行下一行可以看到具体内容：
......@@ -443,11 +443,15 @@ reviews = [c.split() for c in reviews_str]
UNK = word_dict['<unk>']
lod = []
for c in reviews:
lod.append([word_dict.get(words, UNK) for words in c])
base_shape = []
base_shape = [[len(c) for c in lod]]
for c in reviews:
re = np.array([np.int64(word_dict.get(words, UNK)) for words in c])
lod = np.concatenate([lod,re],axis = 0)
base_shape.insert(-1, re.shape[0])
base_shape = [base_shape]
lod = np.array(lod).astype('int64')
tensor_words = fluid.create_lod_tensor(lod, base_shape, place)
```
......
06.understand_sentiment/index.html
浏览文件 @ adcdb7b8
......@@ -182,7 +182,7 @@ Note that `fluid.nets.sequence_conv_pool` contains both convolution and pooling
```python
#Textconvolution neural network
def convolution_net(data, input_dim, class_dim, emb_dim, hid_dim):
emb = fluid.layers.embedding(
emb = fluid.embedding(
input=data, size=[input_dim, emb_dim], is_sparse=True)
conv_3 = fluid.nets.sequence_conv_pool(
input=emb,
......@@ -214,7 +214,7 @@ The code of the stack bidirectional LSTM `stacked_lstm_net` is as follows:
def stacked_lstm_net(data, input_dim, class_dim, emb_dim, hid_dim, stacked_num):
# Calculate word vectorvector
emb = fluid.layers.embedding(
emb = fluid.embedding(
input=data, size=[input_dim, emb_dim], is_sparse=True)
#First stack
......@@ -233,7 +233,7 @@ def stacked_lstm_net(data, input_dim, class_dim, emb_dim, hid_dim, stacked_num):
inputs = [fc, lstm]
#pooling layer
pc_last = fluid.layers.sequence_pool(input=inputs[0], pool_type='max')
fc_last = fluid.layers.sequence_pool(input=inputs[0], pool_type='max')
lstm_last = fluid.layers.sequence_pool(input=inputs[1], pool_type='max')
#Fully connected layer, softmax prediction
......@@ -249,8 +249,8 @@ Next we define the prediction program (`inference_program`). We use `convolution
```python
def inference_program(word_dict):
data = fluid.layers.data(
name="words", shape=[1], dtype="int64", lod_level=1)
data = fluid.data(
name="words", shape=[-1], dtype="int64", lod_level=1)
dict_dim = len(word_dict)
net = convolution_net(data, dict_dim, CLASS_DIM, EMB_DIM, HID_DIM)
......@@ -266,7 +266,7 @@ During the testing, the classifier calculates the probability of each output. Th
```python
def train_program(prediction):
label = fluid.layers.data(name="label", shape=[1], dtype="int64")
label = fluid.data(name="label", shape=[-1, 1], dtype="int64")
cost = fluid.layers.cross_entropy(input=prediction, label=label)
avg_cost = fluid.layers.mean(cost)
accuracy = fluid.layers.accuracy(input=prediction, label=label)
......@@ -300,12 +300,12 @@ print("Loading IMDB word dict....")
word_dict = paddle.dataset.imdb.word_dict()
print ("Reading training data....")
train_reader = paddle.batch(
train_reader = fluid.io.batch(
paddle.reader.shuffle(
paddle.dataset.imdb.train(word_dict), buf_size=25000),
batch_size=BATCH_SIZE)
print("Reading testing data....")
test_reader = paddle.batch(
test_reader = fluid.io.batch(
paddle.dataset.imdb.test(word_dict), batch_size=BATCH_SIZE)
```
Word_dict is a dictionary sequence, which is the correspondence between words and labels. You can see it specifically by running the next code:
......@@ -432,11 +432,15 @@ reviews = [c.split() for c in reviews_str]
UNK = word_dict['<unk>']
lod = []
for c in reviews:
lod.append([word_dict.get(words, UNK) for words in c])
base_shape = []
base_shape = [[len(c) for c in lod]]
for c in reviews:
re = np.array([np.int64(word_dict.get(words, UNK)) for words in c])
lod = np.concatenate([lod,re],axis = 0)
base_shape.insert(-1, re.shape[0])
base_shape = [base_shape]
lod = np.array(lod).astype('int64')
tensor_words = fluid.create_lod_tensor(lod, base_shape, place)
```
......
06.understand_sentiment/train_conv.py
浏览文件 @ adcdb7b8
......@@ -42,8 +42,7 @@ def parse_args():
def convolution_net(data, input_dim, class_dim, emb_dim, hid_dim):
emb = fluid.layers.embedding(
input=data, size=[input_dim, emb_dim], is_sparse=True)
emb = fluid.embedding(input=data, size=[input_dim, emb_dim], is_sparse=True)
conv_3 = fluid.nets.sequence_conv_pool(
input=emb,
num_filters=hid_dim,
......@@ -62,16 +61,15 @@ def convolution_net(data, input_dim, class_dim, emb_dim, hid_dim):
def inference_program(word_dict):
data = fluid.layers.data(
name="words", shape=[1], dtype="int64", lod_level=1)
dict_dim = len(word_dict)
data = fluid.data(name="words", shape=[-1], dtype="int64", lod_level=1)
net = convolution_net(data, dict_dim, CLASS_DIM, EMB_DIM, HID_DIM)
return net
def train_program(prediction):
label = fluid.layers.data(name="label", shape=[1], dtype="int64")
label = fluid.data(name="label", shape=[-1, 1], dtype="int64")
cost = fluid.layers.cross_entropy(input=prediction, label=label)
avg_cost = fluid.layers.mean(cost)
accuracy = fluid.layers.accuracy(input=prediction, label=label)
......@@ -90,16 +88,16 @@ def train(use_cuda, params_dirname):
print("Reading training data....")
if args.enable_ce:
train_reader = paddle.batch(
train_reader = fluid.io.batch(
paddle.dataset.imdb.train(word_dict), batch_size=BATCH_SIZE)
else:
train_reader = paddle.batch(
paddle.reader.shuffle(
train_reader = fluid.io.batch(
fluid.io.shuffle(
paddle.dataset.imdb.train(word_dict), buf_size=25000),
batch_size=BATCH_SIZE)
print("Reading testing data....")
test_reader = paddle.batch(
test_reader = fluid.io.batch(
paddle.dataset.imdb.test(word_dict), batch_size=BATCH_SIZE)
feed_order = ['words', 'label']
......@@ -213,11 +211,15 @@ def infer(use_cuda, params_dirname=None):
UNK = word_dict['<unk>']
lod = []
for c in reviews:
lod.append([np.int64(word_dict.get(words, UNK)) for words in c])
base_shape = []
base_shape = [[len(c) for c in lod]]
for c in reviews:
re = np.array([np.int64(word_dict.get(words, UNK)) for words in c])
lod = np.concatenate([lod, re], axis=0)
base_shape.insert(-1, re.shape[0])
base_shape = [base_shape]
lod = np.array(lod).astype('int64')
tensor_words = fluid.create_lod_tensor(lod, base_shape, place)
assert feed_target_names[0] == "words"
results = exe.run(
......
06.understand_sentiment/train_dyn_rnn.py
浏览文件 @ adcdb7b8
......@@ -42,8 +42,7 @@ def parse_args():
def dynamic_rnn_lstm(data, input_dim, class_dim, emb_dim, lstm_size):
emb = fluid.layers.embedding(
input=data, size=[input_dim, emb_dim], is_sparse=True)
emb = fluid.embedding(input=data, size=[input_dim, emb_dim], is_sparse=True)
sentence = fluid.layers.fc(input=emb, size=lstm_size * 4, act='tanh')
lstm, _ = fluid.layers.dynamic_lstm(sentence, size=lstm_size * 4)
......@@ -54,8 +53,7 @@ def dynamic_rnn_lstm(data, input_dim, class_dim, emb_dim, lstm_size):
def inference_program(word_dict):
data = fluid.layers.data(
name="words", shape=[1], dtype="int64", lod_level=1)
data = fluid.data(name="words", shape=[-1], dtype="int64", lod_level=1)
dict_dim = len(word_dict)
pred = dynamic_rnn_lstm(data, dict_dim, CLASS_DIM, EMB_DIM, LSTM_SIZE)
......@@ -63,7 +61,7 @@ def inference_program(word_dict):
def train_program(prediction):
label = fluid.layers.data(name="label", shape=[1], dtype="int64")
label = fluid.data(name="label", shape=[-1, 1], dtype="int64")
cost = fluid.layers.cross_entropy(input=prediction, label=label)
avg_cost = fluid.layers.mean(cost)
accuracy = fluid.layers.accuracy(input=prediction, label=label)
......@@ -81,16 +79,16 @@ def train(use_cuda, params_dirname):
print("Reading training data....")
if args.enable_ce:
train_reader = paddle.batch(
train_reader = fluid.io.batch(
paddle.dataset.imdb.train(word_dict), batch_size=BATCH_SIZE)
else:
train_reader = paddle.batch(
train_reader = fluid.io.batch(
paddle.reader.shuffle(
paddle.dataset.imdb.train(word_dict), buf_size=25000),
batch_size=BATCH_SIZE)
print("Reading testing data....")
test_reader = paddle.batch(
test_reader = fluid.io.batch(
paddle.dataset.imdb.test(word_dict), batch_size=BATCH_SIZE)
feed_order = ['words', 'label']
......@@ -202,11 +200,15 @@ def infer(use_cuda, params_dirname=None):
UNK = word_dict['<unk>']
lod = []
for c in reviews:
lod.append([np.int64(word_dict.get(words, UNK)) for words in c])
base_shape = []
base_shape = [[len(c) for c in lod]]
for c in reviews:
re = np.array([np.int64(word_dict.get(words, UNK)) for words in c])
lod = np.concatenate([lod, re], axis=0)
base_shape.insert(-1, re.shape[0])
base_shape = [base_shape]
lod = np.array(lod).astype('int64')
tensor_words = fluid.create_lod_tensor(lod, base_shape, place)
assert feed_target_names[0] == "words"
results = exe.run(
......
06.understand_sentiment/train_stacked_lstm.py
浏览文件 @ adcdb7b8
......@@ -46,8 +46,7 @@ def parse_args():
def stacked_lstm_net(data, input_dim, class_dim, emb_dim, hid_dim, stacked_num):
assert stacked_num % 2 == 1
emb = fluid.layers.embedding(
input=data, size=[input_dim, emb_dim], is_sparse=True)
emb = fluid.embedding(input=data, size=[input_dim, emb_dim], is_sparse=True)
fc1 = fluid.layers.fc(input=emb, size=hid_dim)
lstm1, cell1 = fluid.layers.dynamic_lstm(input=fc1, size=hid_dim)
......@@ -69,8 +68,7 @@ def stacked_lstm_net(data, input_dim, class_dim, emb_dim, hid_dim, stacked_num):
def inference_program(word_dict):
data = fluid.layers.data(
name="words", shape=[1], dtype="int64", lod_level=1)
data = fluid.data(name="words", shape=[-1], dtype="int64", lod_level=1)
dict_dim = len(word_dict)
net = stacked_lstm_net(data, dict_dim, CLASS_DIM, EMB_DIM, HID_DIM,
......@@ -80,7 +78,7 @@ def inference_program(word_dict):
def train_program(prediction):
# prediction = inference_program(word_dict)
label = fluid.layers.data(name="label", shape=[1], dtype="int64")
label = fluid.data(name="label", shape=[-1, 1], dtype="int64")
cost = fluid.layers.cross_entropy(input=prediction, label=label)
avg_cost = fluid.layers.mean(cost)
accuracy = fluid.layers.accuracy(input=prediction, label=label)
......@@ -100,16 +98,16 @@ def train(use_cuda, params_dirname):
print("Reading training data....")
if args.enable_ce:
train_reader = paddle.batch(
train_reader = fluid.io.batch(
paddle.dataset.imdb.train(word_dict), batch_size=BATCH_SIZE)
else:
train_reader = paddle.batch(
train_reader = fluid.io.batch(
paddle.reader.shuffle(
paddle.dataset.imdb.train(word_dict), buf_size=25000),
batch_size=BATCH_SIZE)
print("Reading testing data....")
test_reader = paddle.batch(
test_reader = fluid.io.batch(
paddle.dataset.imdb.test(word_dict), batch_size=BATCH_SIZE)
feed_order = ['words', 'label']
......@@ -223,11 +221,15 @@ def infer(use_cuda, params_dirname=None):
UNK = word_dict['<unk>']
lod = []
for c in reviews:
lod.append([np.int64(word_dict.get(words, UNK)) for words in c])
base_shape = []
base_shape = [[len(c) for c in lod]]
for c in reviews:
re = np.array([np.int64(word_dict.get(words, UNK)) for words in c])
lod = np.concatenate([lod, re], axis=0)
base_shape.insert(-1, re.shape[0])
base_shape = [base_shape]
lod = np.array(lod).astype('int64')
tensor_words = fluid.create_lod_tensor(lod, base_shape, place)
assert feed_target_names[0] == "words"
results = exe.run(
......
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