漫谈四种神经网络序列解码模型以及示例代码

continue # empty list was found

if truncating == ’pre’:

trunc = s[-maxlen:]

elif truncating == ’post’:

trunc = s[:maxlen]

else:

raise ValueError(‘Truncating type ”%s” not understood’ % truncating)

# check `trunc` has expected shape

trunc = np.asarray(trunc, dtype=dtype)

if trunc.shape[1:] != sample_shape:

raise ValueError(‘Shape of sample %s of sequence at position %s is different from expected shape %s’ %

(trunc.shape[1:], idx, sample_shape))

if padding == ’post’:

x[idx, :len(trunc)] = trunc

elif padding == ’pre’:

x[idx, -len(trunc):] = trunc

else:

raise ValueError(‘Padding type ”%s” not understood’ % padding)

return x

def vectorize_stories(input_list, tar_list, word_idx, input_maxlen, tar_maxlen, vocab_size):

x_set = []

Y = np.zeros((len(tar_list), tar_maxlen, vocab_size), dtype=np.bool)

for _sent in input_list:

x = [word_idx[w] for w in _sent]

x_set.append(x)

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