tflearn中一些CNN RNN的例子

lstm.py

 

# -*- coding: utf-8 -*-
"""
Simple example using LSTM recurrent neural network to classify IMDB
sentiment dataset.
References:
    - Long Short Term Memory, Sepp Hochreiter & Jurgen Schmidhuber, Neural
    Computation 9(8): 1735-1780, 1997.
    - Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng,
    and Christopher Potts. (2011). Learning Word Vectors for Sentiment
    Analysis. The 49th Annual Meeting of the Association for Computational
    Linguistics (ACL 2011).
Links:
    - http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf
    - http://ai.stanford.edu/~amaas/data/sentiment/
"""
from __future__ import division, print_function, absolute_import

import tflearn
from tflearn.data_utils import to_categorical, pad_sequences
from tflearn.datasets import imdb

# IMDB Dataset loading
train, test, _ = imdb.load_data(path='imdb.pkl', n_words=10000,
                                valid_portion=0.1)
trainX, trainY = train
testX, testY = test

# Data preprocessing
# Sequence padding
trainX = pad_sequences(trainX, maxlen=100, value=0.)
testX = pad_sequences(testX, maxlen=100, value=0.)
# Converting labels to binary vectors
trainY = to_categorical(trainY)
testY = to_categorical(testY)

# Network building
net = tflearn.input_data([None, 100])
net = tflearn.embedding(net, input_dim=10000, output_dim=128)
net = tflearn.lstm(net, 128, dropout=0.8)
net = tflearn.fully_connected(net, 2, activation='softmax')
net = tflearn.regression(net, optimizer='adam', learning_rate=0.001,
                         loss='categorical_crossentropy')

# Training
model = tflearn.DNN(net, tensorboard_verbose=0)
model.fit(trainX, trainY, validation_set=(testX, testY), show_metric=True,
batch_size=32)

 

 

dynamic_lstm.py

# -*- coding: utf-8 -*-
"""
Simple example using a Dynamic RNN (LSTM) to classify IMDB sentiment dataset.
Dynamic computation are performed over sequences with variable length.
References:
    - Long Short Term Memory, Sepp Hochreiter & Jurgen Schmidhuber, Neural
    Computation 9(8): 1735-1780, 1997.
    - Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng,
    and Christopher Potts. (2011). Learning Word Vectors for Sentiment
    Analysis. The 49th Annual Meeting of the Association for Computational
    Linguistics (ACL 2011).
Links:
    - http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf
    - http://ai.stanford.edu/~amaas/data/sentiment/
"""
from __future__ import division, print_function, absolute_import

import tflearn
from tflearn.data_utils import to_categorical, pad_sequences
from tflearn.datasets import imdb

# IMDB Dataset loading
train, test, _ = imdb.load_data(path='imdb.pkl', n_words=10000,
                                valid_portion=0.1)
trainX, trainY = train
testX, testY = test

# Data preprocessing
# NOTE: Padding is required for dimension consistency. This will pad sequences
# with 0 at the end, until it reaches the max sequence length. 0 is used as a
# masking value by dynamic RNNs in TFLearn; a sequence length will be
# retrieved by counting non zero elements in a sequence. Then dynamic RNN step
# computation is performed according to that length.
trainX = pad_sequences(trainX, maxlen=100, value=0.)
testX = pad_sequences(testX, maxlen=100, value=0.)
# Converting labels to binary vectors
trainY = to_categorical(trainY)
testY = to_categorical(testY)

# Network building
net = tflearn.input_data([None, 100])
# Masking is not required for embedding, sequence length is computed prior to
# the embedding op and assigned as 'seq_length' attribute to the returned Tensor.
net = tflearn.embedding(net, input_dim=10000, output_dim=128)
net = tflearn.lstm(net, 128, dropout=0.8, dynamic=True)
net = tflearn.fully_connected(net, 2, activation='softmax')
net = tflearn.regression(net, optimizer='adam', learning_rate=0.001,
                         loss='categorical_crossentropy')

# Training
model = tflearn.DNN(net, tensorboard_verbose=0)
model.fit(trainX, trainY, validation_set=(testX, testY), show_metric=True,
batch_size=32)

bidirectional_lstm.py

# -*- coding: utf-8 -*-
"""
Simple example using LSTM recurrent neural network to classify IMDB
sentiment dataset.
References:
    - Long Short Term Memory, Sepp Hochreiter & Jurgen Schmidhuber, Neural
    Computation 9(8): 1735-1780, 1997.
    - Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng,
    and Christopher Potts. (2011). Learning Word Vectors for Sentiment
    Analysis. The 49th Annual Meeting of the Association for Computational
    Linguistics (ACL 2011).
Links:
    - http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf
    - http://ai.stanford.edu/~amaas/data/sentiment/
"""

from __future__ import division, print_function, absolute_import

import tflearn
from tflearn.data_utils import to_categorical, pad_sequences
from tflearn.datasets import imdb
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.embedding_ops import embedding
from tflearn.layers.recurrent import bidirectional_rnn, BasicLSTMCell
from tflearn.layers.estimator import regression

# IMDB Dataset loading
train, test, _ = imdb.load_data(path='imdb.pkl', n_words=10000,
                                valid_portion=0.1)
trainX, trainY = train
testX, testY = test

# Data preprocessing
# Sequence padding
trainX = pad_sequences(trainX, maxlen=200, value=0.)
testX = pad_sequences(testX, maxlen=200, value=0.)
# Converting labels to binary vectors
trainY = to_categorical(trainY)
testY = to_categorical(testY)

# Network building
net = input_data(shape=[None, 200])
net = embedding(net, input_dim=20000, output_dim=128)
net = bidirectional_rnn(net, BasicLSTMCell(128), BasicLSTMCell(128))
net = dropout(net, 0.5)
net = fully_connected(net, 2, activation='softmax')
net = regression(net, optimizer='adam', loss='categorical_crossentropy')

# Training
model = tflearn.DNN(net, clip_gradients=0., tensorboard_verbose=2)
model.fit(trainX, trainY, validation_set=0.1, show_metric=True, batch_size=64)

cnn_sentence_classification.py

# -*- coding: utf-8 -*-
"""
Simple example using convolutional neural network to classify IMDB
sentiment dataset.
References:
    - Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng,
    and Christopher Potts. (2011). Learning Word Vectors for Sentiment
    Analysis. The 49th Annual Meeting of the Association for Computational
    Linguistics (ACL 2011).
    - Kim Y. Convolutional Neural Networks for Sentence Classification[C]. 
    Empirical Methods in Natural Language Processing, 2014.
Links:
    - http://ai.stanford.edu/~amaas/data/sentiment/
    - http://emnlp2014.org/papers/pdf/EMNLP2014181.pdf
"""
from __future__ import division, print_function, absolute_import

import tensorflow as tf
import tflearn
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.conv import conv_1d, global_max_pool
from tflearn.layers.merge_ops import merge
from tflearn.layers.estimator import regression
from tflearn.data_utils import to_categorical, pad_sequences
from tflearn.datasets import imdb

# IMDB Dataset loading
train, test, _ = imdb.load_data(path='imdb.pkl', n_words=10000,
                                valid_portion=0.1)
trainX, trainY = train
testX, testY = test

# Data preprocessing
# Sequence padding
trainX = pad_sequences(trainX, maxlen=100, value=0.)
testX = pad_sequences(testX, maxlen=100, value=0.)
# Converting labels to binary vectors
trainY = to_categorical(trainY)
testY = to_categorical(testY)

# Building convolutional network
network = input_data(shape=[None, 100], name='input')
network = tflearn.embedding(network, input_dim=10000, output_dim=128)
branch1 = conv_1d(network, 128, 3, padding='valid', activation='relu', regularizer="L2")
branch2 = conv_1d(network, 128, 4, padding='valid', activation='relu', regularizer="L2")
branch3 = conv_1d(network, 128, 5, padding='valid', activation='relu', regularizer="L2")
network = merge([branch1, branch2, branch3], mode='concat', axis=1)
network = tf.expand_dims(network, 2)
network = global_max_pool(network)
network = dropout(network, 0.5)
network = fully_connected(network, 2, activation='softmax')
network = regression(network, optimizer='adam', learning_rate=0.001,
                     loss='categorical_crossentropy', name='target')
# Training
model = tflearn.DNN(network, tensorboard_verbose=0)
model.fit(trainX, trainY, n_epoch = 5, shuffle=True, validation_set=(testX, testY), show_metric=True, batch_size=32)