本文整理汇总了Python中data_helpers.load_data方法的典型用法代码示例。如果您正苦于以下问题：Python data_helpers.load_data方法的具体用法？Python data_helpers.load_data怎么用？Python data_helpers.load_data使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类data_helpers的用法示例。
在下文中一共展示了data_helpers.load_data方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: data_iter
# 需要导入模块: import data_helpers [as 别名]
# 或者: from data_helpers import load_data [as 别名]
def data_iter(batch_size, num_embed, pre_trained_word2vec=False):
print('Loading data...')
if pre_trained_word2vec:
word2vec = data_helpers.load_pretrained_word2vec('data/rt.vec')
x, y = data_helpers.load_data_with_word2vec(word2vec)
# reshpae for convolution input
x = np.reshape(x, (x.shape[0], 1, x.shape[1], x.shape[2]))
embed_size = x.shape[-1]
sentence_size = x.shape[2]
vocab_size = -1
else:
x, y, vocab, vocab_inv = data_helpers.load_data()
embed_size = num_embed
sentence_size = x.shape[1]
vocab_size = len(vocab)
# randomly shuffle data
np.random.seed(10)
shuffle_indices = np.random.permutation(np.arange(len(y)))
x_shuffled = x[shuffle_indices]
y_shuffled = y[shuffle_indices]
# split train/valid set
x_train, x_dev = x_shuffled[:-1000], x_shuffled[-1000:]
y_train, y_dev = y_shuffled[:-1000], y_shuffled[-1000:]
print('Train/Valid split: %d/%d' % (len(y_train), len(y_dev)))
print('train shape:', x_train.shape)
print('valid shape:', x_dev.shape)
print('sentence max words', sentence_size)
print('embedding size', embed_size)
print('vocab size', vocab_size)
train = mx.io.NDArrayIter(
x_train, y_train, batch_size, shuffle=True)
valid = mx.io.NDArrayIter(
x_dev, y_dev, batch_size)
return (train, valid, sentence_size, embed_size, vocab_size) 示例2: data_iter
# 需要导入模块: import data_helpers [as 别名]
# 或者: from data_helpers import load_data [as 别名]
def data_iter(batch_size, num_embed, pre_trained_word2vec=False):
logger.info('Loading data...')
if pre_trained_word2vec:
word2vec = data_helpers.load_pretrained_word2vec('data/rt.vec')
x, y = data_helpers.load_data_with_word2vec(word2vec)
# reshpae for convolution input
x = np.reshape(x, (x.shape[0], 1, x.shape[1], x.shape[2]))
embed_size = x.shape[-1]
sentence_size = x.shape[2]
vocab_size = -1
else:
x, y, vocab, vocab_inv = data_helpers.load_data()
embed_size = num_embed
sentence_size = x.shape[1]
vocab_size = len(vocab)
# randomly shuffle data
np.random.seed(10)
shuffle_indices = np.random.permutation(np.arange(len(y)))
x_shuffled = x[shuffle_indices]
y_shuffled = y[shuffle_indices]
# split train/valid set
x_train, x_dev = x_shuffled[:-1000], x_shuffled[-1000:]
y_train, y_dev = y_shuffled[:-1000], y_shuffled[-1000:]
logger.info('Train/Valid split: %d/%d' % (len(y_train), len(y_dev)))
logger.info('train shape: %(shape)s', {'shape': x_train.shape})
logger.info('valid shape: %(shape)s', {'shape': x_dev.shape})
logger.info('sentence max words: %(shape)s', {'shape': sentence_size})
logger.info('embedding size: %(msg)s', {'msg': embed_size})
logger.info('vocab size: %(msg)s', {'msg': vocab_size})
train = mx.io.NDArrayIter(
x_train, y_train, batch_size, shuffle=True)
valid = mx.io.NDArrayIter(
x_dev, y_dev, batch_size)
return (train, valid, sentence_size, embed_size, vocab_size) 示例3: load_data
# 需要导入模块: import data_helpers [as 别名]
# 或者: from data_helpers import load_data [as 别名]
def load_data(data_source):
assert data_source in ["keras_data_set", "local_dir"], "Unknown data source"
if data_source == "keras_data_set":
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_words, start_char=None,
oov_char=None, index_from=None)
x_train = sequence.pad_sequences(x_train, maxlen=sequence_length, padding="post", truncating="post")
x_test = sequence.pad_sequences(x_test, maxlen=sequence_length, padding="post", truncating="post")
vocabulary = imdb.get_word_index()
vocabulary_inv = dict((v, k) for k, v in vocabulary.items())
vocabulary_inv[0] = "<PAD/>"
else:
x, y, vocabulary, vocabulary_inv_list = data_helpers.load_data()
vocabulary_inv = {key: value for key, value in enumerate(vocabulary_inv_list)}
y = y.argmax(axis=1)
# Shuffle data
shuffle_indices = np.random.permutation(np.arange(len(y)))
x = x[shuffle_indices]
y = y[shuffle_indices]
train_len = int(len(x) * 0.9)
x_train = x[:train_len]
y_train = y[:train_len]
x_test = x[train_len:]
y_test = y[train_len:]
return x_train, y_train, x_test, y_test, vocabulary_inv
# Data Preparation