[Tensorflow] RNN - 02. Movie Review Sentiment Prediction with LSTM
From: Predicting Movie Review Sentiment with TensorFlow and TensorBoard
Ref: http://www.cnblogs.com/libinggen/p/6939577.html
使用LSTM的原因之一是: 解决RNN Deep Network的Gradient错误累积太多，以至于Gradient归零或者成为无穷大，所以无法继续进行优化的问题。
Thanks to Jürgen Schmidhuber
Using the data from an old Kaggle competition “Bag of Words Meets Bags of Popcorn”
import pandas as pd
import numpy as np
import tensorflow as tf
import nltk, re, time
from nltk.corpus import stopwords
from collections import defaultdict
from tqdm import tqdm
from sklearn.model_selection import train_test_split
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from collections import namedtuple
Preprocessing
The data is formatted as .tsv
- remove stopwords
- Convert words to lower case
def clean_text(text, remove_stopwords=True): '''Clean the text, with the option to remove stopwords''' # Convert words to lower case and split them text = text.lower().split() # Optionally, remove stop words if remove_stopwords: stops = set(stopwords.words("english")) text = [w for w in text if not w in stops] text = " ".join(text) # Clean the text text = re.sub(r"<br />", " ", text) text = re.sub(r"[^a-z]", " ", text) text = re.sub(r" ", " ", text) # Remove any extra spaces text = re.sub(r" ", " ", text) # Return a list of words return(text)
Tokenize
# Tokenize the reviews
all_reviews = train_clean + test_clean
tokenizer = Tokenizer()
tokenizer.fit_on_texts(all_reviews)
print("Fitting is complete.")
train_seq = tokenizer.texts_to_sequences(train_clean)
print("train_seq is complete.")
test_seq = tokenizer.texts_to_sequences(test_clean)
print("test_seq is complete")
word_index = tokenizer.word_index
NB: punctuation is useful!
[“The”, “cat”, “went”, “to”, “the”, “zoo”, “.”] --> [1, 2, 3, 4, 1, 5, 6]
Limiting your vocabulary
Your model should benefit from limiting your vocabulary to more common words
because it has seen each word in the text multiple times.
Reviews with the same length
I limited mine to 200 to increase the training speed of my model.
Build Graph with LSTM
def build_rnn(n_words, embed_size, batch_size, lstm_size, num_layers, dropout, learning_rate, multiple_fc, fc_units):
'''Build the Recurrent Neural Network'''
tf.reset_default_graph()
# Declare placeholders we'll feed into the graph
with tf.name_scope('inputs'):
inputs = tf.placeholder(tf.int32, [None, None], name='inputs')
with tf.name_scope('labels'):
labels = tf.placeholder(tf.int32, [None, None], name='labels')
keep_prob = tf.placeholder(tf.float32, name='keep_prob')
# Create the embeddings
with tf.name_scope("embeddings"):
embedding = tf.Variable(tf.random_uniform((n_words, embed_size), -1, 1))
embed = tf.nn.embedding_lookup(embedding, inputs)
# Build the RNN layers
with tf.name_scope("RNN_layers"):
lstm = tf.contrib.rnn.BasicLSTMCell(lstm_size)
drop = tf.contrib.rnn.DropoutWrapper(lstm, output_keep_prob=keep_prob)
cell = tf.contrib.rnn.MultiRNNCell([drop] * num_layers)
# Set the initial state
with tf.name_scope("RNN_init_state"):
initial_state = cell.zero_state(batch_size, tf.float32)
# Run the data through the RNN layers
with tf.name_scope("RNN_forward"):
outputs, final_state = tf.nn.dynamic_rnn(
cell,
embed,
initial_state=initial_state)
# Create the fully connected layers
with tf.name_scope("fully_connected"):
# Initialize the weights and biases
weights = tf.truncated_normal_initializer(stddev=0.1)
biases = tf.zeros_initializer()
dense = tf.contrib.layers.fully_connected(outputs[:, -1],
num_outputs = fc_units,
activation_fn = tf.sigmoid,
weights_initializer = weights,
biases_initializer = biases)
dense = tf.contrib.layers.dropout(dense, keep_prob)
# Depending on the iteration, use a second fully connected
layer
if multiple_fc == True:
dense = tf.contrib.layers.fully_connected(dense,
num_outputs = fc_units,
activation_fn = tf.sigmoid,
weights_initializer = weights,
biases_initializer = biases)
dense = tf.contrib.layers.dropout(dense, keep_prob)
# Make the predictions
with tf.name_scope('predictions'):
predictions = tf.contrib.layers.fully_connected(dense,
num_outputs = 1,
activation_fn=tf.sigmoid,
weights_initializer = weights,
biases_initializer = biases)
tf.summary.histogram('predictions', predictions)
# Calculate the cost
with tf.name_scope('cost'):
cost = tf.losses.mean_squared_error(labels, predictions)
tf.summary.scalar('cost', cost)
# Train the model
with tf.name_scope('train'):
optimizer =
tf.train.AdamOptimizer(learning_rate).minimize(cost)
# Determine the accuracy
with tf.name_scope("accuracy"):
correct_pred = tf.equal(tf.cast(tf.round(predictions),
tf.int32),
labels)
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
tf.summary.scalar('accuracy', accuracy)
# Merge all of the summaries
merged = tf.summary.merge_all()
# Export the nodes
export_nodes = ['inputs', 'labels', 'keep_prob','initial_state',
'final_state','accuracy', 'predictions', 'cost',
'optimizer', 'merged']
Graph = namedtuple('Graph', export_nodes)
local_dict = locals()
graph = Graph(*[local_dict[each] for each in export_nodes])
return graph
这里提到了几种思路：
Simple LSTM for Sequence Classification
|
1
2
3
4
5
6
7
|
Epoch 1/3
16750/16750 [==============================] - 107s - loss: 0.5570 - acc: 0.7149
Epoch 2/3
16750/16750 [==============================] - 107s - loss: 0.3530 - acc: 0.8577
Epoch 3/3
16750/16750 [==============================] - 107s - loss: 0.2559 - acc: 0.9019
Accuracy: 86.79%
|
LSTM For Sequence Classification With Dropout
model = Sequential() model.add(Embedding(top_words, embedding_vecor_length, input_length=max_review_length)) model.add(Dropout(0.2)) model.add(LSTM(100)) model.add(Dropout(0.2)) model.add(Dense(1, activation='sigmoid'))
|
1
2
3
4
5
6
7
|
Epoch 1/3
16750/16750 [==============================] - 108s - loss: 0.5802 - acc: 0.6898
Epoch 2/3
16750/16750 [==============================] - 108s - loss: 0.4112 - acc: 0.8232
Epoch 3/3
16750/16750 [==============================] - 108s - loss: 0.3825 - acc: 0.8365
Accuracy: 85.56%
|
