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Fix example

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This commit is contained in:
Matthew Honnibal 2016-10-20 03:42:34 +02:00
parent d17546681c
commit 5378949326
1 changed files with 7 additions and 3 deletions
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10
examples/deep_learning_keras.py
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@ -2,6 +2,7 @@ import plac
import collections import collections
import random import random
import pathlib
import cytoolz import cytoolz
import numpy import numpy
from keras.models import Sequential, model_from_json from keras.models import Sequential, model_from_json
@ -46,7 +47,7 @@ class SentimentAnalyser(object):
def get_features(docs, max_length): def get_features(docs, max_length):
Xs = numpy.zeros(len(docs), max_length, dtype='int32') Xs = numpy.zeros((len(list(docs)), max_length), dtype='int32')
for i, doc in enumerate(docs): for i, doc in enumerate(docs):
for j, token in enumerate(doc[:max_length]): for j, token in enumerate(doc[:max_length]):
Xs[i, j] = token.rank if token.has_vector else 0 Xs[i, j] = token.rank if token.has_vector else 0
@ -69,8 +70,8 @@ def compile_lstm(embeddings, shape, settings):
model = Sequential() model = Sequential()
model.add( model.add(
Embedding( Embedding(
embeddings.shape[1],
embeddings.shape[0], embeddings.shape[0],
embeddings.shape[1],
input_length=shape['max_length'], input_length=shape['max_length'],
trainable=False, trainable=False,
weights=[embeddings] weights=[embeddings]
@ -142,6 +143,9 @@ def main(model_dir, train_dir, dev_dir,
nr_hidden=64, max_length=100, # Shape nr_hidden=64, max_length=100, # Shape
dropout=0.5, # General NN config dropout=0.5, # 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)
train_dir = pathlib.Path(train_dir)
dev_dir = pathlib.Path(dev_dir)
if is_runtime: if is_runtime:
dev_texts, dev_labels = read_data(dev_dir) dev_texts, dev_labels = read_data(dev_dir)
demonstrate_runtime(model_dir, dev_texts) demonstrate_runtime(model_dir, dev_texts)
@ -149,7 +153,7 @@ def main(model_dir, train_dir, dev_dir,
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)
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_hidden': nr_hidden, 'max_length': max_length, 'nr_class': 1},
{'dropout': 0.5}, {'dropout': 0.5},
{}, {},
nb_epoch=nb_epoch, batch_size=batch_size) nb_epoch=nb_epoch, batch_size=batch_size)