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Modify sentiment analysis book chapter #539

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Modify sentiment analysis book chapter #539

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016995e
Add python files and edit readme
sidgoyal78 Jun 7, 2018
6c82719
Update README and confirm everything runs with jupyter for sentimenta…
nickyfantasy Jun 13, 2018
cec2c1d
clean up
nickyfantasy Jun 13, 2018
89d558c
Add inference example instead of random data and update per team's co…
nickyfantasy Jun 14, 2018
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125 changes: 41 additions & 84 deletions 06.understand_sentiment/README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -102,14 +102,11 @@ After issuing a command `python train.py`, training will start immediately. The

### Initialize PaddlePaddle

We must import and initialize PaddlePaddle (enable/disable GPU, set the number of trainers, etc).
We must import and initialize Paddle.

```python
import sys
import paddle.v2 as paddle

# PaddlePaddle init
paddle.init(use_gpu=False, trainer_count=1)
import paddle
import paddle.fluid as fluid
```

As alluded to in section [Model Overview](#model-overview), here we provide the implementations of both Text CNN and Stacked-bidirectional LSTM models.
Expand All @@ -121,20 +118,26 @@ We create a neural network `convolution_net` as the following snippet code.
Note: `paddle.networks.sequence_conv_pool` includes both convolution and pooling layer operations.

```python
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)
conv_3 = paddle.networks.sequence_conv_pool(
input=emb, context_len=3, hidden_size=hid_dim)
conv_4 = paddle.networks.sequence_conv_pool(
input=emb, context_len=4, hidden_size=hid_dim)
output = paddle.layer.fc(input=[conv_3, conv_4],
size=class_dim,
act=paddle.activation.Softmax())
lbl = paddle.layer.data("label", paddle.data_type.integer_value(2))
cost = paddle.layer.classification_cost(input=output, label=lbl)
return cost, output
def convolution_net(data, input_dim, class_dim=2, emb_dim=128, hid_dim=128):
emb = fluid.layers.embedding(
input=data, size=[input_dim, emb_dim], is_sparse=True)
conv_3 = fluid.nets.sequence_conv_pool(
input=emb,
num_filters=hid_dim,
filter_size=3,
act="tanh",
pool_type="sqrt")
conv_4 = fluid.nets.sequence_conv_pool(
input=emb,
num_filters=hid_dim,
filter_size=4,
act="tanh",
pool_type="sqrt")
prediction = fluid.layers.fc(input=[conv_3, conv_4],
size=class_dim,
act="softmax")
return prediction

```

1. Define input data and its dimension
Expand All @@ -154,70 +157,31 @@ def convolution_net(input_dim, class_dim=2, emb_dim=128, hid_dim=128):
We create a neural network `stacked_lstm_net` as below.

```python
def stacked_lstm_net(input_dim,
class_dim=2,
emb_dim=128,
hid_dim=512,
stacked_num=3):
"""
A Wrapper for sentiment classification task.
This network uses a bi-directional recurrent network,
consisting of three LSTM layers. This configuration is
motivated from the following paper, but uses few layers.
http://www.aclweb.org/anthology/P15-1109
input_dim: here is word dictionary dimension.
class_dim: number of categories.
emb_dim: dimension of word embedding.
hid_dim: dimension of hidden layer.
stacked_num: number of stacked lstm-hidden layer.
"""
def stacked_lstm_net(data, input_dim, class_dim, emb_dim, hid_dim, stacked_num):
assert stacked_num % 2 == 1

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 = paddle.attr.Param(initial_std=0., l2_rate=0.)
relu = paddle.activation.Relu()
linear = paddle.activation.Linear()

data = paddle.layer.data("word",
paddle.data_type.integer_value_sequence(input_dim))
emb = paddle.layer.embedding(input=data, size=emb_dim)
emb = fluid.layers.embedding(
input=data, size=[input_dim, emb_dim], is_sparse=True)

fc1 = paddle.layer.fc(input=emb,
size=hid_dim,
act=linear,
bias_attr=bias_attr)
lstm1 = paddle.layer.lstmemory(
input=fc1, act=relu, bias_attr=bias_attr)
fc1 = fluid.layers.fc(input=emb, size=hid_dim)
lstm1, cell1 = fluid.layers.dynamic_lstm(input=fc1, size=hid_dim)

inputs = [fc1, lstm1]

for i in range(2, stacked_num + 1):
fc = paddle.layer.fc(input=inputs,
size=hid_dim,
act=linear,
param_attr=para_attr,
bias_attr=bias_attr)
lstm = paddle.layer.lstmemory(
input=fc,
reverse=(i % 2) == 0,
act=relu,
bias_attr=bias_attr)
fc = fluid.layers.fc(input=inputs, size=hid_dim)
lstm, cell = fluid.layers.dynamic_lstm(
input=fc, size=hid_dim, is_reverse=(i % 2) == 0)
inputs = [fc, lstm]

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(),
bias_attr=bias_attr,
param_attr=para_attr)

lbl = paddle.layer.data("label", paddle.data_type.integer_value(2))
cost = paddle.layer.classification_cost(input=output, label=lbl)
return cost, output
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')
return prediction

```

1. Define input data and its dimension
Expand All @@ -236,14 +200,7 @@ def stacked_lstm_net(input_dim,
To reiterate, we can either invoke `convolution_net` or `stacked_lstm_net`.

```python
word_dict = paddle.dataset.imdb.word_dict()
dict_dim = len(word_dict)
class_dim = 2

# option 1
[cost, output] = convolution_net(dict_dim, class_dim=class_dim)
# option 2
# [cost, output] = stacked_lstm_net(dict_dim, class_dim=class_dim, stacked_num=3)
TODO
```

## Model Training
Expand Down
125 changes: 41 additions & 84 deletions 06.understand_sentiment/index.html
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -144,14 +144,11 @@

### Initialize PaddlePaddle

We must import and initialize PaddlePaddle (enable/disable GPU, set the number of trainers, etc).
We must import and initialize Paddle.

```python
import sys
import paddle.v2 as paddle

# PaddlePaddle init
paddle.init(use_gpu=False, trainer_count=1)
import paddle
import paddle.fluid as fluid
```

As alluded to in section [Model Overview](#model-overview), here we provide the implementations of both Text CNN and Stacked-bidirectional LSTM models.
Expand All @@ -163,20 +160,26 @@
Note: `paddle.networks.sequence_conv_pool` includes both convolution and pooling layer operations.

```python
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)
conv_3 = paddle.networks.sequence_conv_pool(
input=emb, context_len=3, hidden_size=hid_dim)
conv_4 = paddle.networks.sequence_conv_pool(
input=emb, context_len=4, hidden_size=hid_dim)
output = paddle.layer.fc(input=[conv_3, conv_4],
size=class_dim,
act=paddle.activation.Softmax())
lbl = paddle.layer.data("label", paddle.data_type.integer_value(2))
cost = paddle.layer.classification_cost(input=output, label=lbl)
return cost, output
def convolution_net(data, input_dim, class_dim=2, emb_dim=128, hid_dim=128):
emb = fluid.layers.embedding(
input=data, size=[input_dim, emb_dim], is_sparse=True)
conv_3 = fluid.nets.sequence_conv_pool(
input=emb,
num_filters=hid_dim,
filter_size=3,
act="tanh",
pool_type="sqrt")
conv_4 = fluid.nets.sequence_conv_pool(
input=emb,
num_filters=hid_dim,
filter_size=4,
act="tanh",
pool_type="sqrt")
prediction = fluid.layers.fc(input=[conv_3, conv_4],
size=class_dim,
act="softmax")
return prediction

```

1. Define input data and its dimension
Expand All @@ -196,70 +199,31 @@
We create a neural network `stacked_lstm_net` as below.

```python
def stacked_lstm_net(input_dim,
class_dim=2,
emb_dim=128,
hid_dim=512,
stacked_num=3):
"""
A Wrapper for sentiment classification task.
This network uses a bi-directional recurrent network,
consisting of three LSTM layers. This configuration is
motivated from the following paper, but uses few layers.
http://www.aclweb.org/anthology/P15-1109
input_dim: here is word dictionary dimension.
class_dim: number of categories.
emb_dim: dimension of word embedding.
hid_dim: dimension of hidden layer.
stacked_num: number of stacked lstm-hidden layer.
"""
def stacked_lstm_net(data, input_dim, class_dim, emb_dim, hid_dim, stacked_num):
assert stacked_num % 2 == 1

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 = paddle.attr.Param(initial_std=0., l2_rate=0.)
relu = paddle.activation.Relu()
linear = paddle.activation.Linear()

data = paddle.layer.data("word",
paddle.data_type.integer_value_sequence(input_dim))
emb = paddle.layer.embedding(input=data, size=emb_dim)
emb = fluid.layers.embedding(
input=data, size=[input_dim, emb_dim], is_sparse=True)

fc1 = paddle.layer.fc(input=emb,
size=hid_dim,
act=linear,
bias_attr=bias_attr)
lstm1 = paddle.layer.lstmemory(
input=fc1, act=relu, bias_attr=bias_attr)
fc1 = fluid.layers.fc(input=emb, size=hid_dim)
lstm1, cell1 = fluid.layers.dynamic_lstm(input=fc1, size=hid_dim)

inputs = [fc1, lstm1]

for i in range(2, stacked_num + 1):
fc = paddle.layer.fc(input=inputs,
size=hid_dim,
act=linear,
param_attr=para_attr,
bias_attr=bias_attr)
lstm = paddle.layer.lstmemory(
input=fc,
reverse=(i % 2) == 0,
act=relu,
bias_attr=bias_attr)
fc = fluid.layers.fc(input=inputs, size=hid_dim)
lstm, cell = fluid.layers.dynamic_lstm(
input=fc, size=hid_dim, is_reverse=(i % 2) == 0)
inputs = [fc, lstm]

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(),
bias_attr=bias_attr,
param_attr=para_attr)

lbl = paddle.layer.data("label", paddle.data_type.integer_value(2))
cost = paddle.layer.classification_cost(input=output, label=lbl)
return cost, output
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')
return prediction

```

1. Define input data and its dimension
Expand All @@ -278,14 +242,7 @@
To reiterate, we can either invoke `convolution_net` or `stacked_lstm_net`.

```python
word_dict = paddle.dataset.imdb.word_dict()
dict_dim = len(word_dict)
class_dim = 2

# option 1
[cost, output] = convolution_net(dict_dim, class_dim=class_dim)
# option 2
# [cost, output] = stacked_lstm_net(dict_dim, class_dim=class_dim, stacked_num=3)
TODO
```

## Model Training
Expand Down
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