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Fix deep learning example

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This commit is contained in:
Matthew Honnibal 2016-10-20 04:39:54 +02:00
parent 5378949326
commit 213027a1a1
1 changed files with 9 additions and 3 deletions
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12
examples/deep_learning_keras.py
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@ -7,6 +7,7 @@ import cytoolz
import numpy import numpy
from keras.models import Sequential, model_from_json from keras.models import Sequential, model_from_json
from keras.layers import LSTM, Dense, Embedding, Dropout, Bidirectional from keras.layers import LSTM, Dense, Embedding, Dropout, Bidirectional
from keras.optimizers import Adam
import cPickle as pickle import cPickle as pickle
import spacy import spacy
@ -61,7 +62,7 @@ def train(train_texts, train_labels, dev_texts, dev_labels,
model = compile_lstm(embeddings, lstm_shape, lstm_settings) model = compile_lstm(embeddings, lstm_shape, lstm_settings)
train_X = get_features(nlp.pipe(train_texts), lstm_shape['max_length']) train_X = get_features(nlp.pipe(train_texts), lstm_shape['max_length'])
dev_X = get_features(nlp.pipe(dev_texts), lstm_shape['max_length']) dev_X = get_features(nlp.pipe(dev_texts), lstm_shape['max_length'])
model.fit(train_X, train_labels, dev_X, dev_labels, model.fit(train_X, train_labels, validation_data=(dev_X, dev_labels),
nb_epoch=nb_epoch, batch_size=batch_size) nb_epoch=nb_epoch, batch_size=batch_size)
return model return model
@ -80,6 +81,8 @@ def compile_lstm(embeddings, shape, settings):
model.add(Bidirectional(LSTM(shape['nr_hidden']))) model.add(Bidirectional(LSTM(shape['nr_hidden'])))
model.add(Dropout(settings['dropout'])) model.add(Dropout(settings['dropout']))
model.add(Dense(shape['nr_class'], activation='sigmoid')) model.add(Dense(shape['nr_class'], activation='sigmoid'))
model.compile(optimizer=Adam(lr=settings['lr']), loss='binary_crossentropy',
metrics=['accuracy'])
return model return model
@ -134,6 +137,7 @@ def read_data(data_dir, limit=0):
nr_hidden=("Number of hidden units", "option", "H", int), nr_hidden=("Number of hidden units", "option", "H", int),
max_length=("Maximum sentence length", "option", "L", int), max_length=("Maximum sentence length", "option", "L", int),
dropout=("Dropout", "option", "d", float), dropout=("Dropout", "option", "d", float),
learn_rate=("Learn rate", "option", "e", float),
nb_epoch=("Number of training epochs", "option", "i", int), nb_epoch=("Number of training epochs", "option", "i", int),
batch_size=("Size of minibatches for training LSTM", "option", "b", int), batch_size=("Size of minibatches for training LSTM", "option", "b", int),
nr_examples=("Limit to N examples", "option", "n", int) nr_examples=("Limit to N examples", "option", "n", int)
@ -141,7 +145,7 @@ def read_data(data_dir, limit=0):
def main(model_dir, train_dir, dev_dir, def main(model_dir, train_dir, dev_dir,
is_runtime=False, is_runtime=False,
nr_hidden=64, max_length=100, # Shape nr_hidden=64, max_length=100, # Shape
dropout=0.5, # General NN config dropout=0.5, learn_rate=0.001, # General NN config
nb_epoch=5, batch_size=100, nr_examples=-1): # Training params nb_epoch=5, batch_size=100, nr_examples=-1): # Training params
model_dir = pathlib.Path(model_dir) model_dir = pathlib.Path(model_dir)
train_dir = pathlib.Path(train_dir) train_dir = pathlib.Path(train_dir)
@ -152,9 +156,11 @@ def main(model_dir, train_dir, dev_dir,
else: else:
train_texts, train_labels = read_data(train_dir, limit=nr_examples) train_texts, train_labels = read_data(train_dir, limit=nr_examples)
dev_texts, dev_labels = read_data(dev_dir) dev_texts, dev_labels = read_data(dev_dir)
train_labels = numpy.asarray(train_labels, dtype='int32')
dev_labels = numpy.asarray(dev_labels, dtype='int32')
lstm = train(train_texts, train_labels, dev_texts, dev_labels, lstm = train(train_texts, train_labels, dev_texts, dev_labels,
{'nr_hidden': nr_hidden, 'max_length': max_length, 'nr_class': 1}, {'nr_hidden': nr_hidden, 'max_length': max_length, 'nr_class': 1},
{'dropout': 0.5}, {'dropout': 0.5, 'lr': learn_rate},
{}, {},
nb_epoch=nb_epoch, batch_size=batch_size) nb_epoch=nb_epoch, batch_size=batch_size)
weights = lstm.get_weights() weights = lstm.get_weights()