漫谈四种神经网络序列解码模型以及示例代码

dtype: type to cast the resulting sequence.

padding: ’pre’ or ’post’, pad either before or after each sequence.

truncating: ’pre’ or ’post’, remove values from sequences larger than

maxlen either in the beginning or in the end of the sequence

value: float, value to pad the sequences to the desired value.

# Returns

x: numpy array with dimensions (number_of_sequences, maxlen)

”’

lengths = [len(s) for s in sequences]

nb_samples = len(sequences)

if maxlen is None:

maxlen = np.max(lengths)

# take the sample shape from the first non empty sequence

# checking for consistency in the main loop below.

sample_shape = tuple()

for s in sequences:

if len(s) > 0:

sample_shape = np.asarray(s).shape[1:]

break

x = (np.ones((nb_samples, maxlen) sample_shape) * value).astype(dtype)

for idx, s in enumerate(sequences):

if len(s) == 0:

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