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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
from keras.models import Sequential, model_from_json
from keras.layers import LSTM, Dense, Embedding, Dropout, Bidirectional
from keras.optimizers import Adam
import cPickle as pickle
import spacy
@ -61,7 +62,7 @@ def train(train_texts, train_labels, dev_texts, dev_labels,
model = compile_lstm(embeddings, lstm_shape, lstm_settings)
train_X = get_features(nlp.pipe(train_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)
return model
@ -80,6 +81,8 @@ def compile_lstm(embeddings, shape, settings):
model.add(Bidirectional(LSTM(shape['nr_hidden'])))
model.add(Dropout(settings['dropout']))
model.add(Dense(shape['nr_class'], activation='sigmoid'))
model.compile(optimizer=Adam(lr=settings['lr']), loss='binary_crossentropy',
metrics=['accuracy'])
return model
@ -134,6 +137,7 @@ def read_data(data_dir, limit=0):
nr_hidden=("Number of hidden units", "option", "H", int),
max_length=("Maximum sentence length", "option", "L", int),
dropout=("Dropout", "option", "d", float),
learn_rate=("Learn rate", "option", "e", float),
nb_epoch=("Number of training epochs", "option", "i", int),
batch_size=("Size of minibatches for training LSTM", "option", "b", 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,
is_runtime=False,
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
model_dir = pathlib.Path(model_dir)
train_dir = pathlib.Path(train_dir)
@ -152,9 +156,11 @@ def main(model_dir, train_dir, dev_dir,
else:
train_texts, train_labels = read_data(train_dir, limit=nr_examples)
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,
{'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)
weights = lstm.get_weights()